Montenegrin Journal of Economics

Volume 14, Number 3 September 2018 Quarterly publication

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Montenegrin Journal of Economics, Vol. 14, No. 3 (September 2018)

Editor in Chief Veselin Draskovic, University of Montenegro, Maritime Faculty of Kotor, Montenegro

Co-Editors Dalia Streimikiene, Vilnius University, Kaunas Faculty of Humanities, Lithuania Radislav Jovovic, University Mediterranean, Faculty of Business Studies, Montenegro

Advisory Board Harry M. Markowitz, Nobel Laureate Rady School of Management at the University of California, USA Oliver E. Williamson, Nobel Laureate University of California, Berkeley, USA Serguei Aivazian, Laureate of the premium L.V. Kantorovich Central Economics and Mathematics Institute of the Russian Academy of Sciences / Lomonosov's Moscow State University, Russia Lloyd Blenman, University of North Carolina-Charlotte, President at Midwest Finance Education Foundation, USA Valeriy Makarov, Laureate of the premium L.V. Kantorovich Central Economics and Mathematics Institute of the Russian Academy of Sciences/ Lomonosov's Moscow State University / New Economic School, Russia Victor Polterovich, Laureate of the premium L.V. Kantorovich Central Economics and Mathematics Institute, Russian Academy of Science and Moscow School of Economics / Lomonosov's Moskow State University, Russia Yochanan Shachmurove, The City College of the City University of New York, Department of Economics and Business, USA

Technical Editors Milojko Pusica, and Nikola Draskovic Jelcic

Secretary of Editorial Boards Milica Delibasic

International Editorial Board Jaroslav Belás, Tomas Bata University in Zlín, Fakulty of management and economics, Department of Enterprise Economics, Czech Republic Tomas Balezentis, Lithuanian Institute of Agrarian Economics, Lithuania István Benczes, Corvinus University of Budapest, Faculty of Economics, Hungary Yuriy Bilan, University of Szczecin, Faculty of Economics Science and Management, Poland Bolesław Borkowski, SGGW Warsaw, Faculty of Applied Informatics and Mathematics, Department of Econometrics and Statistics, Poland Laszlo Csaba, Central European University, Department of International Relations and European Studies, Budapest / Budapest University of Eco- nomic Sciences and Public Administration, Hungary Vasile Dinu, Bucharest University of Economic Studies, Romania Oleksandr Dorokhov, Kharkiv National University of Economics, Faculty of Economic Informatics, Ukraine Fan Gang, Graduate School of Chinese Academy of Social Sciences (CASS) / China's National Economic Research Institute (NERI), China Wei Ge, Bucknell University, Department of Economics, Lewisburg, USA Wen-jen Hsieh, University Road, Tainan / Art Center National Cheng Kung University, Taiwan Borut Jereb, University of Maribor, Faculty of Logistics, Slovenia Svetlana Kirdina, Institute of Economics Russian Academy of Sciences, Russia George Kleiner, Central Economics and Mathematics Institute of the Russian Academy of Sciences, Russia

Montenegrin Journal of Economics, Vol. 14, No. 3 (September 2018)

Siu Lee Jasmine Lam, Nanyang Technological University, Singapore Ludmila Malyaretz, Simon Kuznets Kharkiv National University of Economics, Department of Higher mathematics and economic and mathematical methods, Ukraine Alojzy Nowak, University of Warsaw, Faculty of Management, Poland Jiancai Pi, School of Business, Nanjing University, China Evgeniy Popov, Institute of Economics, Urals Branch of Russian Academy of Sciences, Ekaterinburg, Russia Valdas Samonis, Royal Roads University, Canada Anna Shashkova, Moscow State Institute of International Relations, Russia Marcello Signorelli, University of Perugia, Department of Economics, Finance and Statistics, Faculty of Political Sciences, Italy Uriel Spiegel, BarIlan University, Faculty of Social Sciences, Ramat-Gan, Israel Merih Uctum, The Graduate Center City University of New York, USA João Paulo Vieito, Polytechnic Institute of Viana do Castelo, Portugal Milos Vulanovic, City University of Hong Kong Bagrat Yerznkyan, Central Economics and Mathematics Institute, Russian Academy of Science / State University of Management Moscow, Russia

Regional Editorial Board Slobodan Acimovic, University of Belgrade, Faculty of Economics, Serbia Marko Backovic, University of Belgrade, Faculty of Economics, Serbia Sanja Bauk, University of Montenegro, Faculty of Maritime Studies Kotor, Montenegro Mimo Draskovic, University of Montenegro, Maritime Faculty of Kotor, Montenegro Gordan Druzic, Croatian Academy of Sciences and Arts, Zagreb, Croatia Miomir Jaksic, University of Belgrade, Faculty of Economics, Serbia Borut Jereb, University of Maribor, Faculty of Logistics Celje, Slovenia Milan Lakicevic, University of Montenegro, Faculty of Economics Podgorica, Montenegro Andjelko Lojpur, University of Montenegro, Faculty of Economics Podgorica, Montenegro Ljubomir Madzar, Institute of strategic studies and development „Petar Karić“ of the Alfa University in Novi Beograd, Serbia Joze Mencinger, University of Ljubljana, Law School, Slovenia Romeo Mestrovic, University of Montenegro, Maritime Faculty of Kotor, Montenegro Nikola Miilovic, University of Montenegro, Faculty of Economics Podgorica, Montenegro Janez Prašnikar, University of Ljubljana, Faculty of Economics, Institute for South-East Europe, Slovenia Milivoje Radovic, University of Montenegro, Faculty of Economics Podgorica, Montenegro Ivan Ribnikar, University of Ljubljana, Faculty of Economics, Slovenia Guste Santini, University of Zagreb, Croatia Ivan Todorovic, University of Banja Luka, Faculty of Economics, Bosnia and Herzegovina

Montenegrin Journal of Economics, Vol. 14, No. 3 (September 2018)

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ISSN 1800-5845

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Montenegrin Journal of Economics

Vol. 14, No. 3 (September 2018) ‘

C O N T E N T S

Indicators of Regional Development Using Differentiation Characteristics SERGEI A. AIVAZIAN, MIKHAIL Yu. AFANASIEV and ALEXANDER V. KUDROV ...... 7

Capital Markets Integration and Economic Growth OTILIA-ROXANA OPREA and OVIDIU STOICA ...... 23

Modeling and Forecasting the Level of State Stimulation of Agricultural Production in Ukraine Based on the Theory of Fuzzy Logic SERHII KOZLOVSKYI, HENNADII MAZUR, NATALIIA VDOVENKO, TETIANA SHEPEL, and VOLODYMYR KOZLOVSKYI ...... 37

The Effects of International Tourism Receipts on Economic Growth: Evidence from the First 20 Highest Income Earning Countries from Tourism in the World (1996-2016) ÖMER YALÇINKAYA, MUHAMMET DAŞTAN and KEREM KARABULUT ...... 55

Specific Methodological Approaches to Managing State Finances ALLA KHOMUTENKO ...... 73

RITA REMEIKIENE, LIGITA GASPARENIENE and ARTIOM VOLKOV ...... 83

Monitoring of Efficiency of Russian Agricultural Enterprises Functioning and Reserves for Their Sustainable Development VLADIMIR TRUKHACHEV, IGOR SKLYAROV, YULIYA SKLYAROVA, SERGEI GORLOV and ANNA VOLKOGONOVA ...... 95

Open Economy or Protectionism: Ukraine’s Dilemma ANTON OLEINIK ...... 109

Algorithm of Forming the Category Management in the Diy Market Segment YEVGENY ROMAT and YULIIA BILIAVSKA ...... 129

Corruption Is a Problem of Political Theory and Practice ANNA VLADISLAVOVNA SHASHKOVA ...... 143

Benchmarking Intangible Assets in the Water Sector: an Evidence From Indonesia MARGA GUMELAR, SUTISNA and ALDRIN HERWANY ...... 155

Political Expenditure Cycle in V4 Countries LENKA MALICKA ...... 163

The Role of Small and Medium Entrepreneurship in the Economy of Russia IULIIA S. PINKOVETSKAIA, IRINA N. NIKITINA and TATIANA V. GROMOVA ...... 177

Examining Leadership Characteristics at International Multilaterals AGOTA GIEDRE RAISIENE, ALEKSANDRA PULOKIENE and ANDRIUS VALICKAS ...... 189

Author Guidelines ...... 199

Sergei A. Aivazian, Mikhail Yu. Afanasiev and Alexander V. Kudrov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 7-22

Montenegrin Journal of Economics

Vol. 14, No. 3 (2018), 7-22 ‘

Indicators of Regional Development Using Differentiation Characteristics

SERGEI A. AIVAZIAN1, MIKHAIL Yu. AFANASIEV2 and ALEXANDER V. KUDROV3

1 Professor, Central Economics and Mathematics Institute of the Russian Academy of Sciences, Moscow, Russia; [email protected] 2 Professor, Central Economics and Mathematics Institute of the Russian Academy of Sciences, Moscow, Russia; [email protected] 3 Professor, Central Economics and Mathematics Institute of the Russian Academy of Sciences, Moscow, Russia; [email protected]

ARTICLE INFO ABSTRACT Received June 02, 2018 On the example of two directions - "production of goods and ser- Revised from June 22, 2018 vices" and "welfare", it is developed a methodology for analyzing the Accepted August 21, 2018 interrelationship between various directions of regional develop- Available online September 15, 2018 ment. The novelty of the results is determined by the fact that the direction indicators are constructed using a common basis formed using the characteristics of regional differentiation obtained from JEL classification: theoretically based models. The index for each direction, construct- C54, E52, G18. ed in the basis, is maximally correlated with the index formed for the corresponding group of indicators. It is shown that for the con- DOI: 10.14254/1800-5845/2018.14-3.1 sidered two directions, the basis ensures higher consistency of the indices and ranks of the regions than the first principal components Keywords: constructed for each group of the indicators separately. The advantage of the approach is that the indices for different directions regional economy, based on the basis allow a general interpretation in terms of the econometric modeling, differentiation characteristics and allow one to assess the change hypothesis testing, in the level of socio-economic development of the region when indicators. these characteristics change. The presented results confirm the significance of the influence of technical efficiency on the indices of regional development and its significance in the indicator composition for the direction of "welfare".

INTRODUCTION A theoretically grounded approach to the analysis of the interrelationship between various directions of regional development is the construction of indices on the basis of a component analysis of the indicators characterizing these areas. At the regional level, the benefits associated with the use of the principal component method and its modifications are most fully disclosed in the

 The work was supported by the Russian Science Foundation (project 17-18-01080) 7

Sergei A. Aivazian, Mikhail Yu. Afanasiev and Alexander V. Kudrov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 7-22 works (Aivazyan, 2012; Makarov et al., 2014) in analyzing the quality of life. The problem of constructing indices for two directions of regional development - "production of goods and services" and "welfare" and assessing their interrelationship is discussed below. A natural way to solve this problem is the construction index of each direction using principal components analysis and comparison the regions by these calculated indices. Then this method is used for comparison analysis. The novelty of the proposed approach and the obtained results is determined by the fact that the indices are constructed on the basis of a common basis characteristics. The components of the basis are characteristics of differentiation, formed with the use of theoretically grounded models of regional development. In its composition: the scale of the economy, the technical efficiency of production, the index of industry specialization (based on the first principal component of the GRP sector-structure), the index of industrialization (based on the second principle component of the GRP sector-structure), the trend of technical efficiency. The position of the region in the basis of the characteristics of differentiation determines its economic identity. The formation of the index in the basis characteristics is carried out in such a way that it is as much as possible correlated with the set of indicators characterizing the direction in question. The advantage of the approach considered below is that the indices constructed on its basis allow us to quantify the relative change in the level of socio-economic development of the region when the characteristics of differentiation change. Analysis of the relationship between the technical efficiency of production and the level of regional development is of independent interest. In this paper, there are presented the results con- firming the significance in the composition of the basis of the technical efficiency influence on individual socio-economic indicators and its significance as part of the index in the direction of "welfare”. This can help expand the scope of application of technical efficiency not only as one of the characteristics of multifactorial productivity, but as a characteristic of regional differentiation.

1. FORMATION OF THE BASIS

12 The basis-characteristics Bltessdtet ({ iti } ,{ iti } ,{ iti } ,{ iti } ,{ iti } ) of regional differentiation on the time period tt1,  include five components: lit — the scale of the i-th region's economy at the 1 time t ; teit — a comparable estimator of the technical efficiency; sit — industry specialization index; 2 sit — index of industrialization; dteit — technical efficiency trend, dteit te it te it1 . Futher it is considered the number of economically active people as the characteristic of the economy scale. Technical efficiency comparable across all regions is considered as a characteristic of the management quality in the long run. The index of industry specialization — the first principle component of the GRP sector-structure and the index of industrialization — the second principle component of the GRP sector-structure. The trend of technical efficiency is a characteristic of the management quality in the short run. The GRP sector-structure reflects the features of the technological interconnection of resource opportunities and the results of the region's manufacturing activities. The basis-characteristics includes the first and second principal components of the GRP sector-structure. As shown in (Ayvazyan, Afanasyev and Kudrov, 2016a), the first principle component separates regions with high concentration of the mining and other regions, it is further characterized as an index of industry specialization. The second principal component separates the manufacturing regions, even- ly developed and developing regions, and is further characterized as the index of industrialization. The first two principle components account for more than 80% of the total variance in the quantitative characteristics of the GRP sector-structure, and the mutual arrangement of regions in the space of the first two principle components is stable in time. Premise. All regions can be divided into homogeneous groups, each of which is characterized by its own form of GRP dependence from the production factors. 8

Sergei A. Aivazian, Mikhail Yu. Afanasiev and Alexander V. Kudrov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 7-22

In accordance with the approach proposed by the authors (Ayvazyan, Afanasyev and Kudrov, 2016a), a homogeneous group includes regions that have a close arrangement in the space of the first two principle components of the GRP sector-structure. The homogeneity of the group is controlled by the likelihood function. The whole set of the regions in the Russian Federation is divided into five groups (Ayvazyan, Afanasyev and Kudrov, 2016b). The base group №1 consists of 38 regions with a uniformly developed industry, the group №2 includes 11 "mining" regions, the group № 3 consists of 12 "manufacturing" regions, the group № 4 includes 11 "agricultural" regions and the group № 5 consists of 8 "developing" regions. In the column (1) of Table A1, for each region it is shown the respective group. For each homogeneous group according to the data for the period 2010-2015 years, it is estimated the production function coefficients, which are assumed to be time-varying and linearly dependent on time:

lnRit00ttKtLvu  (  11  )ln it  (  22  )ln it  it it (1)

Rit — GRP of the i -th region at the time t ; Kit — the expenditures of the physical capital for the i - 2 th region at the time t , Lit — - the amount of labor for the i-th region at the time t , vit  N(0, v ) ;  2 uNit (,  u ). The random component  it  vit  uit reflects the effects of uncertainty factors and efficiency factors. In accordance with the concept of a stochastic frontier, an estimate of the technical efficiency of the i-th region production at the time t equals to the conditional mathematical expectation TEit  E(exp{uit }  it ) (Kumbhakar and Lovell, 2004). In Table. 1 it is presented the estimates for the parameters of the model (1) both separately for the regions from each of the five homogeneous groups and for the whole set of 80 regions.

Table 1. Estimates of the parameters of the model (1) for homogeneous groups and for the whole set of regions

G1 G2 G3 G4 G5 all 80 Dynamical Basic Mining Manufacturing Agricultural Developing regions  .7604*** .8154*** .3659*** .3873*** .3734*** .8590*** 1 (.0386) (.0276) (.0401) (.0760) (.0000) (.0342)  .3323*** .0981*** .6753*** .7465*** .4814*** .1751*** 2 (.0477) (.0286) (.0438) (.0817) (.0000) (.0420) .0774 1.1958*** 3.1638*** 2.1853*** 4.052*** -.1923  0 (.2858) (.2536) (.3102) (.5071) (.0000) (.2689) .0327*** .0733*** .0823*** .0473*** .1690**  0 (.0090) (.0075) (.0116) (.0000) (.0827)  -.0292*** -.0226** 1 (.0000) (.0108)  .0678*** .0255* 2 (.0000) (.0133)  -.1219 -.0807 -1.8682 -.7517 -1.9597 -.1427 2 .0002 .0704 .0025 .0008 .5428 .0003  u 2 .0453 8.61e-17 .0084 .0160 2.41e-16 .0472  v Log likelihood 29.2250 28.7609 69.7292 42.6737 23.1644 51.2145

Sergei A. Aivazian, Mikhail Yu. Afanasiev and Alexander V. Kudrov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 7-22 Premise. Glockalization creates conditions for the region's access to financial resources and knowledge created by mankind. The relative inefficiency of the region in the homogeneity group is due to the fact that it does not make full use of the available development opportunities. The technical efficiency estimates for the regions that are part of a homogeneous group are considered as indicators of the effectiveness of regional management, since the difference in the level of technical efficiency of the regions in a homogeneous group is due to the fact that the regions use in a varying degrees affordable, comparable due to homogeneity development opportunities. However, the technical efficiency estimates for regions from different groups are not comparable. To bring them to a comparable kind, the authors proposed and tested a method that allows to correct the technical efficiency estimates obtained on a common for all regions model so that their ranks correspond to the ranks of the estimates obtained from the model constructed for each homogeneous group. The description, theoretical justification, the results of approbation of the method and comparable assessments of technical efficiency are presented in the paper (Ayvazyan, Afanasyev and Kudrov, 2018).

2. THE INFLUENCE OF THE COMPONENTS OF THE BASIS ON ECONOMIC INDICATORS Based on Russian Statistical Agency data1, a set of 11 indicators has been formed, characterizing two directions of economic development: "production of goods and services" and "welfare" in the time period 2010-2015 years. The names and designations of the indicators are presented in Table 2. For each indicator from Table 2 for each year t of the considered period, it is constructed regression dependencies in which the dependent variable is the normalized indicator yit for the region i at the moment t (normalized for the whole set of regions, with zero mean and unit variance ), and the explanatory variables are the normalized values of the components of the vector basis at the moment t 1:

1 2 yit  1tlit 1  2tte2t 1  3t sit 1  4t sit 1  5t dteit   it , (2) where t — parameter vector;  it — error. Based on the estimates of the beta coefficients for the five models presented in the Appendix Table A2, for each indicator it is determined the following : the significance of the influence of each component of the basis; direction of the change in the dependent variable with the increase of the component from the basis; the time tendency of the component's influence on the value of the indicator . The results of the analysis are presented in Table. 2. In Table 2 the values for R2 are given for 2015 year. The scale of the economy has a significant impact on all indicators, except for w4 and w5. Technical efficiency affects the indicators w1, w2, w4-w6 and w9. The index of industry specialization or the index of industrialization affects all indicators, except w10. The technical efficiency trend affects w2, w3 and w5. The obtained results allow considering the basis of the considered characteristics of regional differentiation as an information basis for constructing indices of various directions of economic development of the Russian Federation regions.

 Glockalization is a combination of global and local factors in the development of territories (Kudryashova, 2008; Rob- ertson, 1992). In the context of the article, the tendency of the unification of mankind, based on the use of information technologies and new means of communication, allows almost instantaneously to receive and use for development of the region resources, created by mankind. 1 Russian Statistical Agency data: http://www.gks.ru/bgd/regl/b16_14p/Main.htm 10

Sergei A. Aivazian, Mikhail Yu. Afanasiev and Alexander V. Kudrov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 7-22

Table 2. Influence of the components of the basis on indicators2

Indicator l te s1 s2 dte R2 2015 w1: GRP per capita *** (+) ↓ ** (+) ↑ *** (+) ↑ ** (+) ↓ (+) ↑ 0,727 w2: Income per capita *** (+) ↔ ** (+) ↔ *** (+) ↔ ↔ ** (+) ↑ 0,601 w3: Population with income below the *** (-) ↔ ↔ ↔ *** (-) ↔ * (-) ↑ 0,444 subsistence level w4:Infant mortality ↔ * (+) ↑ ↔ *** (-) ↔ ↔ 0,363 rate w5: Average size of ↔ ** (+)↔ *** (+) ↔ ↔ * (+) ↑ 0.574 pensions assigned w6: Migration rate *** (+) ↔ *** (+) ↓ *** (-) ↓ *** (+) ↔ ↓ 0,448 w7: Unemployment * (-) ↑ ↔ ↔ *** (-) ↔ (-) ↑ 0,428 rate w8: Mining *** (+) ↔ ↔ *** (+) ↔ ↔ ↔ 0,437 w9: Manufacturing *** (+) ↔ * (+) ↓ ↔ *** (+) ↓ ↔ 0,911 w10: Agriculture *** (+) ↓ ↑ ↑ ↔ ↓ 0,168 w11:Electricity,gas,wat er production *** (+) ↔ ↔ *** (+) ↔ ↔ ↑ 0,902

3. FORMATION, BASED ON THE BASIS, OF AN INDEXES FOR A GROUP OF INDICATORS THAT CHARACTERIZE THE DIRECTION OF ECONOMIC DEVELOPMENT

s k Let Iy()ttkt  — a linear combination of indicators characterizing the direction S of k k social and economic development of the Russian Federation regions, where yt — vector of values k {}yit i of the indicator k of the group S for the whole set of regions i at the moment t ,  ttkk {} s 12 — vector of parameters. Let IB()t11 t l it  2 t s it 1   3 t s it 1   4 t te it  1   5 t dte it  1 — be a linear com- ** bination of the components of the vector basis. The problem is to estimate the parameters  tt, , such that I s and IBs are maximally correlated. That is ** s s (tt , ) arg maxcorr ( I , IB ) . (,)tt s * s * As a result of solving this problem, indices I () t and IB ()t for the direction S are constructed. On their basis, two groups of indices of regional development can be constructed for the direction

2 The notation used in Table. 2. *** — the significance of the beta coefficient at a level of at least 10% in four or more models; ** — the significance of the beta-coefficient at the level of not less than 10% in three models; * — the significance of the beta-coefficient at a level of at least 10% in the two models of recent years; (+) — values of the beta coefficient for each year of the period under consideration have a sign at which the growth of the component of the vector basis leads to an increase in the value of the indicator; ( - ) — values of the beta coefficient for each year of the period under consideration have a sign at which the growth of the component of the vector basis leads to a decrease in the value of the indicator ↑ — the influence of increase of the vector basis component in the direction of improving the value of the indicator rises in time; ↓ — the influence of the increase of the vector basis component in the direction of improving the value of the indicator decreases in time; ↔ — there is no tendency to change the influence of increase of the vector basis component in the direction of improving the value of the indicator.

Sergei A. Aivazian, Mikhail Yu. Afanasiev and Alexander V. Kudrov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 7-22

s * S . The first group of indices is the projection to the index I () t of the direction S indicators k {}yit k of direction indicators for each region i . The second group of indices is the projection to the s * index IB ()t of the values of the basis characteristics for each region. With a sufficiently high cor- ss** s * relation coefficient corr((),()) I tt IB  , the regional indices by IB ()t can be used as integral characteristics of the development level for the region in macro- and meso-level models, and also for building the ranking of regions by direction S . Thus, the basis vector creates a unified information basis for assessing the interrelationship of various directions of the regional socio- econom- ic development. A feature and advantage of this approach is the ability to assess the impact of relative changes in the characteristics of regional differentiation, the relative level of its socio- economic development.

()iT 1 2 Description of the method. We denote Btititititit [;ltessdte ; ; ; ] the value of the basis for the i - ()ikT 1 th region, iN 1, , at time t and Yyytitit [ ;...; ] - vector of the indicators values that characterize the direction S of social and economic development of the Russian regions, for thei -th region at time t . Then the solution of the above optimization problem under the assumption of the ()i ()i nondegeneracy of covariance matrices cov(Bt ) and cov(Yt ) has the form (for the proof see in H. Hotelling (1936), and F. Waugh, (1942):

s *Ti () 1/2 () isTii * () 1/2 () IB(tttttt ) e11 [cov( B )] B и I ( ) f [cov( Y )] Y , where e1 — the eigenvector corresponding to the maximum eigenvalue of the matrix ()iiiiiii 1/2 () () () 1 () () () 1/2 [cov(BBYYYBBttttttt )] cov( , )[cov( )] cov( , )[cov( )] , ()ii () () i 1 () ik cov(Btt ,YByBy )  cov( t , it ) cov( t , it ) — matrix with the dimension 5k , T ()ij j j 1 j 2 j j cov(Bylyteysysydteytititititititititititit , )  cov( , ); cov( , ); cov( , ); cov( , ); cov( , ) — column-сvector, T j  1, k ()ii () () ii () . Wherein cov(BYtt , )  cov( Y t , B t ) .

And f1 — eigenvector corresponding to the maximum eigenvalue of the matrix ()iiiiiii 1/2 () () () 1 () () () 1/2 [cov(YYBBBYYttttttt )] cov( , )[cov( )] cov( , )[cov( )] . Note also that the calculations ()i ()i ()ii () made under the assumption that the covariance matrices cov(Bt ) , cov(Yt ) and cov(Btt ,Y ) do not depend on i .

4. INDICATORS OF REGIONAL DEVELOPMENT AND REGIONAL RATINGS

4.1 Direction №1: "production of goods and services" Consider the direction №1 of regional development of "production of goods and services" and five indicators characterizing this direction: w1 - GRP per capita; w8 - mining; w9 - manufacturing; w10 - agricultural products; w11 - electricity, gas, water production. In the following Table. 3 it is shown the estimates of the direction indices constructed on the basis of these indicators and vector basis characteristics for the 2015 year. In the column (1) of Table. 3 – the symbols of indicators characterizing the considered direction № 1. In column (2) – the estimates of the parameters * 1* 1*  t of the index I () t . In column (3) - the correlation coefficients of the index I () t and respective indicators. In column (4) - the symbols for the vector basis characteristics. In column (5) - * 1* estimates of the parameters  t of the index IB ()t constructed on the basis of a vector basis. In column (6) - the correlation coefficients of the of the vector basis characteristics and the index 1* IB ()t . 12

Sergei A. Aivazian, Mikhail Yu. Afanasiev and Alexander V. Kudrov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 7-22

Table 3. Indices for the direction "production of goods and services"

Index of the basis- I1 Index of the indicators IB1 characteristics (1) (2) (3) (4) (5) (6) w1 -1.462e-07 0.168 l 9.626e-04 0.994 w8 -3.172e-07 0.221 te 1.940e+03 0.211 w9 7.529e-07 0.967 s1 -5.212e-03 -0.192 w10 2.912e-06 0.388 s2 6.136e-03 0.324 w11 6.291e-06 0.931 dte 3.677e+03 0.064

The correlation of indicators w9 ("manufacturing ") and w11 ( "electricity, gas, water produc- 1* tion") with the index I () t is above 0.9. Signs of the correlation coefficients of all indicators with the index in column (3) correspond to economic theory. The difference in the signs of the index coefficients in column (2) and the correlation coefficient in column (3) with indicators w1 - "GRP per capita" and w8 - "mining" is explained by the fact that in the regression of the index on indicators of the direction "production of goods and services " indicators w1 and w8 are insignificant. The 1* most significant component of the index IB ()t is the scale of the regional economy. The index of industrialization is also an important characteristic.

Figure 1. Regions in the space of indices for the direction "production of goods and services" 7 6 5 4 3 2 1 0 -2-101234567 -1 -2

In Fig. 1, the dot corresponds to the position of the region in the space of two indices. On the 1* 1* abscissa - the index values for IB ()t . On the ordinate axis - the values for I () t . The correlation coefficient is 0.982. In the upper right of the figure, there are the following regions: Moscow, Mos- cow Region, St. Petersburg, Krasnodar region. In the column (4) of Table A1 it is shown the ranks 1* of regions, based on the index I () t , in the column (5) of this table — the ranks constructed on 1* the basis of the indicator IB ()t . The Spearman coefficient of rank correlation is 0.956, which indicates a high degree of ranks consistency for the index estimated on the basis of the indicators for the direction "production of goods and services" and the corresponding the vector basis characteristics.

Sergei A. Aivazian, Mikhail Yu. Afanasiev and Alexander V. Kudrov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 7-22 4.2 Direction №2: "welfare" Consider the direction № 2 of regional development from the point of " welfare" and six indicators characterizing this direction: w2 - income per capita; w3 - population with incomes below the subsistence minimum; w4 - infant mortality rate, w5 - average size of pensions assigned, w6 – migration rate; w7 - unemployment rate. In the following Table 4 it is shown the estimates of parameters for indices of this direction, estimated on the basis of indicators and basis characteristics of differentiation according to the data for 2015 year.

Table 4. Indices for the direction "welfare"

Index of the basis- I2 Index of the indicators IB2 characteristics (1) (2) (3) (4) (5) (6) w2 0.00016 0.683 l 9.267e-04 0.956 w3 0.07638 -0.153 te 1.147e+04 0.438 w4 -0.05507 -0.293 s1 6.768e-03 0.042 w5 0.00033 0.187 s2 -6.747e-04 0.279 w6 0.00985 0.610 dte 8.758e+03 0.112 w7 -0.03792 -0.415

2* The indicators w2, w5 and w6 have a positive correlation with the index I () t , the indicators w3, w4 and w7 - negative, which corresponds to the economic theory. The difference in the signs of the coefficient in column (2) and the correlation coefficient in column (3) at w3 - " population with incomes below the subsistence level" is explained by the fact that this indicator is insignificant in the regression of the index on all indicators of the direction "welfare". In the indicator 2* 3 I () t , w2 is the most significant - income per capita and w6 - the coefficient of migration growth . 2* In the basis-characteristics index IB ()t the most significant indicators are the scale of the economy (l) and the technical efficiency (te). The index of industry specialization (s1) and the trend of technical efficiency (dte) are insignificant.

Figure 2. Regions in the space of indices for the direction " welfare"

7 6 5 4

3 2 1 0 -2-101234567 -1

-2

3 The significance of the migration growth in the index of material well-being corresponds to the concept of "voting by feet", based on the hypothesis of Ch. Tibu. 14

Sergei A. Aivazian, Mikhail Yu. Afanasiev and Alexander V. Kudrov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 7-22

2* In Fig. 2, the abscissa corresponds to the index values for IB ()t . On the ordinate axis - the 2* values of indices for I () t . The correlation coefficient is 0.839. In the upper right part of Fig. 2 the dominant position is occupied by the same regions as in Fig. 1: Moscow, Moscow Region, St. Petersburg, Krasnodar region. In the column (7) of Table A1 it is shown the ranks of regions con- 2* structed for the direction of "welfare" for six indicators based on the index I () t , in column (8) of 2* this table are the ranks estimated on the basis of the index IB ()t .

7 90

6 80

5 70 4 60 50 3 40 2 30 1 20 0 10 -2 -1 0 1 2 3 4 5 6 7 -1 0 -2 0 20 40 60 80 100

Figure 3a. Regions in the space of indices: the Figure 3b Regions in the space of ranks: the abscissa axis - "production of products and servi- abscissa - "production of products and services", the ordinate axis - " welfare" ces", the axis of ordinates - " welfare"

In Fig. 3a on the abscissa - the indices of regions according to the index IB1 of the direction "production of products and services". On the ordinate axis - the indices of regions according to the index IB2 of the direction "welfare". The correlation coefficient is 0.953. In Fig. 3b on the abscissa axis - values of ranks in the direction "production of products and services". On the y-axis - ranks in the direction of "welfare". The correlation coefficient is 0.806. The ranks of four regions in the direction of "welfare" coincide with their high ranks in the direction of "production of goods and services": Moscow - 1, Moscow region - 2, St. Petersburg - 3, Krasnodar Region - 4. In the upper right there are two regions: the Jewish Autonomous Region - 79 and the Republic of Kalmykia - 80. In the lower right part of Figure 3b, five regions are distinguished from the total population. These are the Kamchatka Territory, the Republic of Sakha (Yakutia), the Magadan Region, the Sakhalin Re- gion, and the Chukotka Autonomous District. The position of these regions in the ranking for the direction "welfare" is much higher than the position in the rating "production of goods and services". The index in the direction of "production of goods and services" gives an underestimation of the index for the main set of regions, since the industry specialization index is included in the indicator with a negative sign and the natural rent of these regions is not taken into account. In addition, in these regions, in view of climatic features, additional measures of material incentives are used. Without regard to these five regions, the coefficient of rank correlation of the indices for the considered two development directions is 0.911.

Sergei A. Aivazian, Mikhail Yu. Afanasiev and Alexander V. Kudrov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 7-22 Table 5. The first principle components in two directions

g1 The first principle component for g2 The first principle component the "production of products for the "welfare" and services" (1) (2) (3) (4) w1 0.404909 w2 0.451720 w8 0.448955 w3 -0.534579 w9 0.532441 w4 -0.337555 w10 0.112927 w5 0.306281 w11 0.581586 w6 0.205685 w7 -0.510014

For comparison, it is estimated the principal components for the two directions using the normalized values of the indicators (with an average of 0 and a standard deviation of 1). The first principal component g1 for "production of goods and services" explains 51% of the total variance. The first principle component g2 for the direction " welfare" explains 44% of the total variance. The signs at the indicators in the first principal component g1 (column (2) of Table 5) correspond to the economic theory. The most significant in the first principal component g1, as well as in the indicator I1, is w9 - the output of manufacturing and w11 - the production of electricity, gas, water. All signs with indicators in the first principal component g2 of the direction " welfare" correspond to economic theory (column (4) of Table 5). The most significant in the first principal component of g2 are the indicators w3 - the population with incomes below the subsistence level and w7 - the unemployment rate. On the level of significance indicators, the indicator I2 and the first principal component g2 are significantly different. The Pearson correlation of the indices constructed on the basis of the principal components for the two directions g1 and g2 is 0.555. Spearman's rank correlation is 0.719. For the directions of regional development considered in this work, the indices in the basis characteristics ensure a higher consistency of the indices and ranks of the regions than the first principal components of the indicator sets.

CONCLUSIONS It was formed the component composition of the basis characteristics for the formation of indices of socio-economic development of the Russian Federation regions. It includes five characteristics of regional differentiation: the scale of the economy, the technical efficiency of production, the first two main components of the GRP sector-structure (industry specialization index and industrialization index), the trend of technical efficiency. In evaluating these characteristics, theoretically based models of regional differentiation were used. On the basis of data from the Russian Statistical Agency for the period from 2010 to 2015 years, it is shown that each characteristic of regional differentiation included in the list of basis characteristics is significant in the regression models describing the dependence of the indicators of regional development on the directions "production of goods and services" and "welfare". Using the method of component analysis based on data for 2015 year, two indices for the direction "production of goods and services" have been constructed. The first is in the space of the five indicators that characterize this direction: GRP per capita; the output of mining; the output of manufacturing industries; the output of agriculture; production of electricity, gas, water. The second is based on the characteristics of differentiation. The regional indices computed from these indicators have a Pearson correlation coefficient of 0.982 and Spearman rank correlation coefficient of 0.956.

Sergei A. Aivazian, Mikhail Yu. Afanasiev and Alexander V. Kudrov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 7-22

Using the method of component analysis based on data for 2015 year, two indices for the direction "welfare" have been constructed. The first - in the space of six indicators: income per capita; population with incomes below the subsistence minimum; migration rate; unemployment rate; infant mortality rate; average size of pensions. The second is in the basis characteristics of differentiation. Pearson correlation coefficient of indices is 0.839, Spearman rank correlation coefficient is 0.611. Indices for the regions, constructed on the basis of the first principal components for the two groups of indicators, have a correlation coefficient of 0.555. Indices for the regions based on the vector of basis characteristics have the correlation coefficient of 0.953. Spearman's rank correlation coefficients are 0.719 and 0.806, respectively. Thus, for the considered directions of regional development, the vector basis ensures better consistency of the indices and ranks of the regions than the first principal components. The scale of the economy, technical efficiency and the second principal component of the GRP sector-structure - the index of industrialization - have a significant impact in the indicators of the two directions of regional development based on the basis characteristics. The importance of technical efficiency is of particular interest, as it expands the scope of its application and confirms the validity of its use as a characteristic of regional differentiation. The index of industry specialization and the trend of technical efficiency are insignificant. However, they are significant in regression models of individual indicators. Therefore, it is expedient to use these components in the composition of the basis when constructing indicators in other directions.

REFERENCES Ayvazian, S. A. (2012), Analysis of quality and mode of life of the population: econometric approach, Moscow, Nauka (in Russian). Aivazian, S., Afanasiev, M., Kudrov, A. (2016a), “Clustering methodology of the Russian Federation regions with account of sectoral structure of GRP”, Applied Econometrics, Vol. 41, pp. 24-46. Aivazian, S., Afanasiev, M., Kudrov, A. (2016b), “Models of Productive Capacity and Technological Efficiency Evaluations of Regions of the Russian Federation Concerning the Output Structure”, Economics and Mathimatical Methods, Vol. 52, No. 1, pp. 28-44 (in Russian). Aivazian, S. A., Afanasiev, M. Yu., Kudrov, A. V. (2018), “The method of comparing the regions of the Russian Federation according to the estimates of technical efficiency, taking into account the structure of production”, Economics and Mathimatical Methods, Vol. 54, No. 1, pp. 43-5’1 (in Russian). Kudriashova, A. I. (2008), Influence of global globalization on the formation of regional economic policy, The abstract of Postdoc Dissertation in Economics, M., RGTJeU (in Russian). Makarov, V., Aivazyan, S., Afanasiev, M., Bakhtizin, A., Nanavyan, A. (2014), “The Estimation Of The Regions’ Efficiency Of The Russian Federation Including The Intellectual Capital, The Charac- teristics Of Readiness For Innovation, Level Of Well-Being, And Quality Of Life”, Economica of region, No. 4, pp. 9–30. Hotelling, H. (1936), “Relationships between Two Sets of Variables”, Biometrika, Vol. 46, pp. 321– 377. Kumbhakar, S., Lovell, K. (2004), Stochastic Frontier Analysis. Cambridge U.P.. Robertson, R. (1992), Globalization: Social Theory and Global Culture, izdatel’ ???, Gorod ???. Waugh, F. W. (1942), “Regression between Sets of Variates2, Econometrica, Vol. 46, pp. 290– 310.

Sergei A. Aivazian, Mikhail Yu. Afanasiev and Alexander V. Kudrov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 7-22 Appendix

Table A1. Ranks of the Russian Federation regions in two directions of development (according to 2015 year).

№ № Region I1 IB1 g1 I2 IB2 g2 group region (1) (2) (3) (4) (5) (6) (7) (8) (9) 1 1 Belgorod region 20 30 22 16 23 10 1 2 Bryansk region 44 43 60 37 49 40 3 3 Vladimir region 33 28 46 53 37 45 4 4 Voronezh region 16 21 24 7 16 9 1 5 Ivanovo region 58 50 69 51 75 50 3 6 Kaluga region 34 42 43 32 61 27 1 7 Kostroma region 49 58 59 56 66 49 1 8 Kursk region 29 46 37 10 39 12 3 9 Lipetsk region 27 31 30 21 41 20 1 10 Moscow region 2 2 3 2 2 3 1 11 Oryol region 50 56 61 72 48 61 1 12 Ryazan region 38 44 49 38 56 32 1 13 Smolensk region 36 48 47 31 69 59 4 14 Tambov region 35 45 50 46 36 24 1 15 Tver region 30 37 38 60 53 38 3 16 Tula region 28 26 32 33 32 21 3 17 Yaroslavl region 41 34 45 22 42 18 1 18 Moscow 1 1 1 1 1 1 1 19 Republic of Karelia 66 66 58 76 50 43 2 20 Komi Republic 59 67 25 73 68 23 2 21 Arkhangelsk region 62 47 31 62 31 28 3 22 Vologda region 31 33 35 50 47 35 1 23 Kaliningrad region 39 41 44 9 38 25 1 24 Leningrad region 14 25 15 28 34 19 1 25 Murmansk region 47 61 33 59 74 14 3 26 Novgorod region 57 49 54 27 45 29 4 27 Pskov region 64 63 70 43 72 64 1 28 St. Petersburg 3 3 4 4 3 2 4 29 Republic of Adygeya 72 70 74 25 67 60 5 30 Republic of Kalmykia 73 80 78 79 80 78 4 31 Krasnodar region 5 4 10 3 4 13 1 32 Astrakhan region 61 62 52 57 59 62 1 33 Volgograd region 17 15 23 70 26 58 4 34 Rostov region 7 6 16 18 7 39 5 35 Republic of Dagestan 46 24 68 40 29 71 5 36 Republic of Ingushetia 76 73 80 69 70 80 1 37 Kabardino-Balkaria Republic 65 57 73 49 58 73 18

Sergei A. Aivazian, Mikhail Yu. Afanasiev and Alexander V. Kudrov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 7-22

1 38 Karachay-Cherkess Republic 67 74 75 78 78 77 5 39 Republic of North Ossetia-Alania 69 68 72 77 60 68 5 40 Chechen Republic 68 51 76 67 62 75 4 41 Stavropol region 19 18 29 45 24 55 3 42 Republic of Bashkortostan 9 7 11 26 8 37 1 43 Republic of Mari El 55 55 65 55 57 70 1 44 Republic of Mordovia 52 54 64 36 73 54 2 45 Republic of Tatarstan 6 8 6 12 6 8 2 46 Udmurt Republic 37 32 40 52 27 30 1 47 Chuvash Republic 48 39 62 63 65 52 1 48 Permsky Krai 15 16 14 13 20 26 1 49 Kirov region 43 35 55 65 51 34 3 50 Nizhny Novgorod region 11 10 17 15 14 17 2 51 Orenburg region 26 29 20 54 19 51 4 52 Penza region 42 36 57 44 54 33 1 53 Samara region 13 11 12 19 12 22 1 54 Saratov region 18 20 26 47 28 56 1 55 Ulyanovsk region 45 38 56 42 46 48 4 56 Kurgan region 56 59 67 74 77 69 3 57 Sverdlovsk region 4 5 7 8 9 11 2 58 Tyumen region 12 12 2 6 5 7 3 59 Chelyabinsk region 8 9 13 39 11 44 5 60 Altai Republic 74 78 77 58 76 74 4 61 The Republic of Buryatia 60 52 66 30 64 65 5 62 Republic of Tyva 75 75 79 80 63 79 1 63 Republic of Khakassia 53 69 53 64 55 63 4 64 Altai region 25 22 42 61 22 67 1 65 Trans-Baikal region 63 60 63 66 71 72 1 66 Krasnoyarsk region 10 13 9 23 10 46 1 67 Irkutsk region 22 23 19 68 15 66 2 68 Kemerovo region 23 17 18 71 17 57 1 69 Novosibirsk region 24 14 28 17 13 53 3 70 Omsk region 21 19 27 35 18 47 2 71 Tomsk region 51 53 41 48 33 42 2 72 Republic of Sakha (Yakutia) 71 64 8 34 25 31 1 73 Kamchatka region 70 71 48 41 44 15 1 74 Primorsky region 32 27 39 11 43 36 1 75 Khabarovsk region 40 40 36 14 40 16 1 76 Amur region 54 65 51 29 52 41 1 77 Magadan region 78 72 34 20 30 4 2 78 Sakhalin region 80 76 5 5 35 5 5 79 Jewish Autonomous region 77 79 71 75 79 76 2 80 Chukotka Autonomous Okrug 79 77 21 24 21 6 19

Sergei A. Aivazian, Mikhail Yu. Afanasiev and Alexander V. Kudrov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 7-22 Table A2. Estimates of parameters for the regressions of indicators on the basis characterisitics

l te s1 s2 dte R2

2015 0.239*** 0.178*** 0.760*** 0.101 0.113* 0.727 St. Err 0.064 0.066 0.064 0.064 0.064 2014 0.317*** 0.157** 0.722*** 0.120* 1.03E-01 0.681 St. Err 0.070 0.069 0.068 0.068 0.066 2013 0.355*** 0.124* 0.733*** 0.126* 3.21E-03 0.697 St. Err 0.068 0.074 0.067 0.067 0.070 2012 0.335*** 0.123 0.719*** 0.143** 1.32E-02 0.677 St. Err 0.070 0.074 0.071 0.069 0.069 2011 0.332*** 0.108 0.723*** 0.129* 4.12E-02 0.685

Grossproduct regional per capita St. Err 0.070 0.072 0.071 0.068 0.068 2015 0.443*** 0.269*** 0.449*** -5.72E-02 0.356*** 0.601 St. Err 0.077 0.080 0.077 0.078 0.077 2014 0.485*** 0.146* 0.493*** 1.59E-03 0.245*** 0.512 St. Err 0.087 0.086 0.085 0.085 0.082 2013 0.517*** 0.118 0.478*** -2.28E-02 -0.149 0.532 St. Err 0.084 0.092 0.083 0.083 0.087 2012 0.484*** 0.188** 0.456*** 1.41E-02 -8.56E-02 0.545

Income per capita St. Err 0.083 0.088 0.085 0.082 0.082 2011 0.506*** 0.219** 0.473*** -2.97E-02 1.92E-02 0.566 St. Err 0.082 0.085 0.083 0.080 0.079 2015 0.273*** -9.78E-02 -5.39E-02 0.389*** 0.314*** 0.444 St. Err 0.091 0.094 0.091 0.092 0.091 2014 0.274*** 4.95E-03 -8.11E-02 0.466*** 0.171* 0.367 St. Err 0.099 0.098 0.096 0.096 0.093 2013 0.269*** -6.18E-02 -9.40E-02 0.452*** -2.69E-02 0.354

el St. Err 0.099 0.108 0.097 0.098 0.103 2012 0.251** -0.133 -7.00E-02 0.402*** -1.07E-02 0.323 St. Err 0.101 0.108 0.103 0.100 0.100 2011 0.271** 0.191* -7.90E-02 0.325*** -6.46E-02 0.309 Population with income below the subsistence lev- subsistence below the St. Err 0.103 0.107 0.105 0.101 0.100

2015 -0.193* 0.220** -0.017 -0.464*** -0.145 0.363 St. Err 0.097 0.101 0.097 .099 0.097 2014 -0.110 0.185* 0.056 -0.477*** 0.343*** 0.415 St. Err 0.095 0.094 0.093 0.093 0.09 2013 -0.115 0.055 0.006 -0.479*** -0.214** 0.304 St. Err 0.103 0.112 0.101 0.102 0.107 2012 -0.153 0.165 -0.048 -0.497*** -0.121 0.319

Infant mortality rate rate mortality Infant St. Err 0.102 0.108 0.104 0.1 0.101 2011 -0.089 0.082 -0.047 -0.547*** -0.160 0.355

2015 -0.014 0.266*** 0.538*** -0.026 0.435*** 0.574 St. Err 0.08 0.082 0.079 0.081 0.08 2014 0.023 0.122 0.578*** 0.048 0.246*** 0.410 St. Err .095 .094 .093 .093 .090 2013 0.016 0.093 0.563*** 0.049 -0.160 0.401 St. Err .096 .104 .094 .094 .099

assigned assigned 2012 -0.026 0.170* 0.539*** 0.104 -0.108 0.428 St. Err 0.093 0.099 0.095 0.092 0.092 2011 -0.036 0.218** 0.551*** 0.090 -0.066 0.426 Averagepensions size of St. Err 0.094 0.098 0.096 0.092 0.092

Sergei A. Aivazian, Mikhail Yu. Afanasiev and Alexander V. Kudrov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 7-22

2015 -0.014 0.266*** 0.538*** -0.026 0.435*** 0.574 St. Err 0.08 0.082 0.079 0.081 0.08 2014 0.023 0.122 0.578*** 0.048 0.246*** 0.410 St. Err .095 .094 .093 .093 .090

ned 2013 0.016 0.093 0.563*** 0.049 -0.160 0.401 g St. Err .096 .104 .094 .094 .099 assi 2012 -0.026 0.170* 0.539*** 0.104 -0.108 0.428 St. Err 0.093 0.099 0.095 0.092 0.092 2011 -0.036 0.218** 0.551*** 0.090 -0.066 0.426 Averagepensions size of St. Err 0.094 0.098 0.096 0.092 0.092 2015 0.190** -2.84E-02 -3.26E-02 0.401*** 0.364*** 0.428

St. Err 0.092 0.096 0.092 0.094 0.092 2014 0.188* 8.51E-02 -5.92E-02 0.554*** -0.108 0.384 St. Err 9.80E-02 0.096 0.095 0.095 9.20E-02 2013 -0.158 0.124 -9.53E-02 0.516*** -3.74E-02 0.313 St. Err 0.102 0.111 0.101 0.101 0.106 2012 -0.164 0.116 -6.21E-02 0.502*** 0.147 0.319 St. Err 0.102 0.108 0.104 0.100 0.101 Unemployment rate 2011 -0.138 -3.47E-02 -6.39E-02 0.465*** 0.150 0.300 St. Err 0.104 0.108 0.106 0.101 0.101

2015 0.411*** 0.182* 0.315*** 0.216** -0.024 0.448 St. Err 0.091 0.094 0.090 0.092 0.091 2014 0.327*** 0.188* 0.325*** 0.219** 4.34E-02 0.379 St. Err 0.098 0.097 0.095 0.095 0.092 2013 0.414*** 0.269** 0.277*** 0.171* 5.15E-02 0.440 St. Err 0.092 0.101 0.091 0.091 0.096

population 2012 0.413*** 0.229** 0.229** 0.160* 8.62E-02 0.381 St. Err 0.097 0.103 0.099 0.095 0.096 2011 0.296*** 0.232** -0.173 0.171 0.114 0.255 Migrationper rates 10 000 St. Err 0.107 0.111 0.109 0.105 0.104 2015 0.334*** -0.112 0.629*** 8.54E-02 0.026 0.438 St. Err 0.092 0.095 0.091 0.093 0.092 2014 0.310*** -8.52E-02 0.626*** 0.101 -3.57E-02 0.440 St. Err 0.093 0.092 0.091 0.091 0.088 2013 0.329*** -9.61E-02 0.631*** 0.12165 8.54E-02 0.450 St. Err 0.092 0.100 0.090 0.090 0.095 Mining Mining 2012 0.340*** -0.167 0.624*** 0.105 3.59E-02 0.398 St. Err 0.096 0.101 0.097 0.094 0.094 2011 0.354*** 0.208** 0.649*** 9.74E-02 -6.04E-04 0.442 St. Err 0.093 0.096 0.095 0.091 0.090 2015 0.903*** 6.26E-02 -0.010 0.113*** 3.33E-02 0.912 St. Err 0.036 0.037 0.036 0.036 0.036 2014 0.887*** 0.072* -2.20E-02 0.122*** 4.64E-03 0.901 St. Err 0.039 0.038 0.038 0.038 0.037 2013 0.888*** 6.80E-02 -5.34E-03 0.133*** -3.50E-02 0.899 St. Err 0.039 0.042 0.038 0.038 0.040 2012 0.838*** 0.114** -3.34E-02 0.183*** 2.02E-02 0.876 Manufacturing Manufacturing St. Err 0.043 0.046 0.044 0.042 0.043 2011 0.834*** 0.106** -4.26E-02 0.192*** 0.073* 0.883 St. Err 0.042 0.044 0.043 0.041 0.041

Sergei A. Aivazian, Mikhail Yu. Afanasiev and Alexander V. Kudrov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 7-22

2015 0.313*** 1.20E-01 -0.14884 5.56E-02 -3.85E-02 0.168 St. Err 0.111 0.115 0.111 0.113 0.111 2014 0.333*** 0.12196 -0.110 6.07E-02 1.68E-02 0.174 St. Err 0.113 0.112 0.110 0.110 0.106 2013 0.338*** 0.140 -0.112 6.21E-02 5.48E-02 0.183 St. Err 0.112 0.122 0.110 0.110 0.116 2012 0.379*** 9.42E-02 -6.19E-02 4.67E-02 0.160 0.199 Agriculture St. Err 0.110 0.117 0.112 0.108 0.109 2011 0.421*** 4.44E-02 -7.16E-02 7.05E-02 0.044 0.220 St. Err 0.110 0.114 0.112 0.107 0.107 2015 0.952*** -0.037 0.172*** 0.019 0.073* 0.902 St. Err 0.038 0.039 0.038 0.038 0.038 2014 0.946*** 0.073* 0.183*** 0.046 0.014 0.881 St. Err 0.043 0.042 0.042 0.042 0.040 2013 0.955*** 0.080* 0.187*** 0.021 -0.025 0.885

St. Err 0.042 0.045 0.041 0.041 0.043 2012 0.954*** -0.070 0.181*** 0.021 -0.049 0.888 St. Err 0.041 0.043 0.042 0.040 0.040 2011 0.962*** -0.054 0.160*** -0.023 -0.013 0.880

Electricity,gas,water production St. Err 0.043 0.044 0.044 0.042 0.042

Otilia-Roxana Oprea and Ovidiu Stoica / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 23-35

Montenegrin Journal of Economics

Vol. 14, No. 3 (2018), 23-35 ‘

Capital Markets Integration and Economic Growth

OTILIA-ROXANA OPREA1 and OVIDIU STOICA2

1 PhD Student, Alexandru Ioan Cuza University of Iași, Faculty of Economics and Business Administration; Romania; e-mail: [email protected] 2 Professor, Alexandru Ioan Cuza University of Iași, Faculty of Economics and Business Administration, Romania; e-mail: [email protected]

ARTICLE INFO ABSTRACT Received June 07, 2018 Nowadays, the capital markets have an increasing role and weight Revised from June 20, 2018 in the modern financial systems. Economic (and financial) integra- Accepted August 11, 2018 tion should allow companies to access more sophisticated and Available online September 15, 2018 competitive capital markets for accelerating the economic development. The purpose of this paper is to investigate the impact of the capital markets’ integration on economic growth in the EU coun- JEL classification: tries and identify the main factors through which capital markets’ E44, F02, F36, G15, O47. development influences economic growth, especially in an economic (and monetary) union. In this article we had used the Autoregres- DOI: 10.14254/1800-5845/2018.14-3.2 sive Distributed Lag model for the EU countries during 2004-2016. According to the results, we can say that the integration of capital Keywords: markets has a positive impact on economic growth, and the main factors in which the capital market positively affects economic capital markets, growth are stock market capitalization, capital mobility, value trad- integration, ed, stock indices, immigrants, and, to a greater extent, small, for- economic growth, eign portfolio investment. Policymakers in this area should pay European Union, attention reducing external debt, which is a significant proportion of ARDL model. foreign capital inflows, and encouraging the foreign portfolio investments to stimulate stock market development and growth, reducing extreme stock price volatility, fostering a good correlation of savings with investment (i.e. capital mobility), boosting volume growth transactions on stock markets, they should guarantee full employment through fiscal policy, monetary policy and trade policy as stated, by counteracting private sector or trade investment volatility, and reducing inequality, and stimulating increased labor mobility from developed countries to the least developed to balance the economy.

Otilia-Roxana Oprea and Ovidiu Stoica / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 23-35 INTRODUCTION An integral part of the financial system of the economy is represented by the capital market. It promotes economic growth, investment and saving in a country. At a time when this area is ready to become more financially integrated, the current diversity of financial development in the European Union (EU) can be a great opportunity. Integration should allow companies to access more sophisticated credit and security markets to accelerate the development of the most recent financial markets. According to Modigliani (1971), rising stock prices lead to a simultaneous growth of individual holdings, resulting in greater consumption or savings. The relationship between capital markets integration and economic growth is a topic that has received great attention over the past decades, with different views on the role that financial systems in the capital market can play in economic growth. The theoretical work focuses on increasing the role of capital market intermediary in- corporate monitoring and exerting corporate control, diversifying risks, promoting liquidity, generating information for investment and capital allocation, providing vehicles for trading, and mobilizing savings.

1. PURPOSE OF THE PAPER The purpose of this paper is to examine the impact of capital market integration on economic growth in EU countries, particularly in the context of financial integration being needed to achieve economic growth; strengthening capital markets. We also investigate the main factors in which the development of the capital market influences economic growth. For this goal, we chose the most appropriate variables and models that have been used in the recent literature on the subject. Research questions:  What are the factors through which the integration of European capital markets influences the growth of the economy?  Which of these factors have a greater impact on economic growth?  What is the (positive or negative) impact of the integration of European capital markets on economic growth?

2. LITERATURE REVIEW According to a current report of the European Central Bank, the Capital Markets Union (CMU) has the potential to become a key factor in financial integration in the EU. CMU is a natural complement to the banking union that will strengthen the European Monetary Union (EMU) and deep- ens the single market. It will support a homogeneous transmission of monetary policy, strengthen funding sources and investment opportunities to contribute to financial stability by creating, inter alia, deeper, more liquid financial markets, amplifying the resilience of the banking system and the economy. The CMU will also promote a greater sharing of cross-border financial risks, supporting the functioning of EMU by balancing economic cycles. Funding via public bond and share markets is often referred to as “direct funding” as it directly transfers securities between investors and borrowers, without the need for intermediary funding. Undertakings may use organized markets to issue securities in the form of shares or bonds, or various other non-intermediate sources of finance, such as commercial credit and advances, corporate loans, family and friends loans, and equity issues own shares other than quoted shares. 24

Otilia-Roxana Oprea and Ovidiu Stoica / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 23-35

(European Financial Stability and Integration Report, 2014). With regard to direct financing through debt and the capital market, it can be argued that the main advantage of financial markets is that they allow the collection of resources from more investors. The two main instruments on these markets, namely stocks and bonds, are standardized products, which means that these secondary markets can grow to allow them to be converted into liquidity at any time. In order to start and develop a business, a series of investments are needed, which are mostly funded by external funds. This requires the existence of well-functioning financial markets in the sense that their resources are channeled from less productive firms to larger productivity firms. Limited access to finance can hinder business dynamics by inhibiting channeling of resources from less productive companies to those with higher productivity, thus reducing resource allocation efficiency. In a real financial market, the price of a foreign guarantee, the price of a foreign guarantee faced by domestic investors is the result of the local price and the exchange rate. As the exchange rate fluctuates continuously, even if the local asset is risk-free, it becomes a risky asset for domestic investors. Only after its volatility has decreased, the foreign asset can be converted to risk. The universes of investment can be represented by a risky asset (the market portfolios), and a risk-free asset. Thus the risky asset in this economy can be interpreted as an index fund. According to a study by ECFIN, in the Eurozone, more developed financial markets favored the diminishing impact of the crisis on growth in sectors that are dependent on external financing. Lack of access to finance can stop companies from realizing their growth potential, which can lead to the destruction of structurally viable companies. A lower return on banks' equity, corresponds to a greater likelihood of access to finance becoming a delicate problem for the company. The development of the capital market is considered as a factor contributing to economic growth through different channels:

 effective allocation of capital as a proportion of financial savings in all wealth;  mobilizing savings by providing attractive tools and saving vehicles;  providing vehicles for trading, pooling and diversifying risk;  reducing the costs of collecting and processing information and, consequently, improving the allocation of resources;  increasing production specialization, developing entrepreneurship and adopting new technologies (Dapeng, 2010).

A well-developed capital market facilitates the allocation of capital to an economy that is necessary for growth and economic development and provides large amounts of funding to successful entrepreneurs needed for corporate growth (McGowan, 2008). Also, diversification of market assets generates substantial profits (Guesmi et al., 2014). Moreover, the convergence of the Euro- pean economies following the European monetary union, along with the more common dynamics in the return on money and capital, suggests that capital markets are at least partially integrated (Emiris, 2002). According to a study by Komatsubar et al. (2017), stock prices in East Asia are sensitive to stocks in Europe and the US, as European and American investors are actively invest- ing in East Asian stocks. Indeed, periods reflect dramatic increases in integration, which roughly correspond to the start of Europe's intensive activity and US investment in Eastern Asia stock markets. According to a study conducted in 2017, the development of the capital market had asymmetric effects on economic growth, the development of the government bonds market being negative, but the aggregate index of other subcomponents being positively associated with economic growth. (Coskun et al., 2017). Improving cross-border capital distribution will also increase the choice for both investors and companies seeking to fund, and will lead to higher economic growth, and a promotion of diversified funding sources would reduce dependence on bank lending. (CMU report, 2015). Moreover, D. Morelli (2010), K. Phylaktis (1997), showed that European capital markets are strongly integrated.

Otilia-Roxana Oprea and Ovidiu Stoica / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 23-35 In other respects, both foreign direct investments and exports stimulate economic growth, contrary to studies that have found that FDI doesn’t generate economic growth (Milovic and Jocovic, 2017). Politically, the government could spur foreign direct investments through incentives for investors, creating a good macroeconomic environment and a careful use of weak monetary policy for growth in the economy (Sunde, 2017). According to another survey, the degree of integration in the Singapore market is satisfactorily explained by the degree of opening of trade and the first US interest rates that tended to grow, but these markets remain substantially segmented by the global market. Second, it was found that the local market risk premium explains a significant proportion of the total risk premium for the emerging market profitability (Teulon et al., 2014). Financial integration has strong implications for financial stability. On the one hand, financial integration between economies contributes to improving their ability to absorb shocks and to foster development. On the other hand, financial ties in a world of increased capital mobility can also bear the risk of cross-border financial contagion (Yu et al., 2010). From another point of view, job creation and stock market valuations are closely linked (Wu and Chen, 2017).. The source of labor market volatility and stock market volatility is represented by the time variation of risk. In times of high risk, capital market assessments are low and unemployment is rising. Between the capital market and unemployment, there is a long-term relationship in both directions, with the stock market leading to higher unemployment, and the unemployment rate can help to predict stock prices (Wachter and Kilic, 2015). Economic growth in Nigeria is consistent with job creation as growth in the economy is negative and has a significant impact on unemployment. In this respect, it is important to drive the growth of the capital market in a way to create jobs by expanding listed companies and admitting new firms to the market (Ilo, 2015). Also, investors cannot use the internal rate of unemployment to anticipate the capital market, and thus get higher profits from their investments (Tapa and Zandile, 2016). Following this review of the literature on this topic, we decided to use the variables, data, and methods presented in the following sections in this article. Thus, section 4 details the data and methods used in this study, section 5 presents the results of the empirical analysis, and section 6 discusses the implications of the research and concludes the study.

3. DATA AND METHODOLOGY The data focus on the 2004-2016 period, with an annual panel frequency, and were obtained from Eurostat, European Central Bank, Datastream, Federal Reserve Economic Data, World Bank databases and used as a sample the 28 countries of the European Union. All indicators have been transformed to provide annual data, expressed as a percentage of GDP. In terms of methodology, this study used the Autoregressive Distributed Lag (ARDL) model, Granger causality analysis, and cointegration analysis. Also, a series of tests, such as VAR, ADF, Johansen co-integration test, Granger causality test, were used to test the variables. Regressors may include delayed values of the dependent variable and the current and the delayed values of one or more explanatory variables. This model allows us to determine the effects of a change in a variable. Due to the fact that two dependent variables were used, namely GDP growth and multifactor productivity, the regression model equations were as follows: + + + + + + * Un- employment rate + * Immigrants +

+ + + + + + * Unemployment rate + * Immigrants + 26

Otilia-Roxana Oprea and Ovidiu Stoica / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 23-35

Also, the estimated equation using the lags is the following: + + + + + + + +

Where number 8 represents the number of lags according to the VAR estimate. For the equation in which multifactor productivity is used as a dependent variable.

4. RESULTS Table 1 presents the descriptive statistics for the variables used in the empirical analysis. As we can see, there are considerable variations of the variables over time. For example, capital mobility varies from a minimum of -3.54 to 15.12. Stock market capitalization also varies from a minimum of 3.73 to 326.35.

Otilia-Roxana Oprea and Ovidiu Stoica / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 23-35 Table 1 Descriptive statistics

Source: Authors’ calculations

Otilia-Roxana Oprea and Ovidiu Stoica / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 23-35

In order to estimate the ARDL, model, we need to know the appropriate number of lags. For this reason, we estimated the VAR lag order criteria. (Table no.2).

Table 2. VAR lag order selection

ENDOGENOUS VARIABLES: Capital mobility, Foreign portfolio investments, Market capitalization, GDP growth, Multifactor productivity, Value traded, Turnover ratio, Unemployment rate, Immigrants.

Exogenous variable: C Sample: 2004 2016 Included observations: 140

Lag LogL LR FPE AIC SC HQ

0 -7719.65 NA 1.21e+38 110.39 110.56 110.46 1 -6632.38 2034.74 5.45 95.77 97.28 96.39 2 -6458.16 306.14 1.14 94.20 97.05* 95.36 3 -6365.31 152.52 7.70 93.79 97.99 95.49 4 -6274.35 139.03 5.46 93.40 98.95 95.65 5 -6147.06 180.02 2.37 92.50 99.39 95.30 6 -6055.32 119.27 1.77 92.10 100.34 95.45 7 -5953.33 120.92 1.20 91.56 101.14 95.45 8 -5800.92 163.30* 4.21* 90.29* 101.22 94.73*

* indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion

Source: Authors’ calculations.

Table no. 2 shows the VAR estimation for lag selection. We can see that the LR test, the final predictive error (FPE), the Akaike information criterion (AIC) and also the Hannan-Quinn information criteria show that the correct number of lags is 8. So we estimated the Granger causality test with 8 lags. Table no. 3 presents the results of the Granger causality test. As we can see, there is a unidirectional causal relationship between turnover ratio and GDP growth (the probability is less than 0.05), between capital mobility and multifactor productivity, between capital mobility and GDP growth, between GDP growth and stock market capitalization, between immigrants and GDP growth, between multifactor productivity and unemployment rate, as well as between unemployment rate and GDP growth, which means that the change in turnover ratio influences the change in economic growth, capital mobility influences economic growth both in terms of gross domestic product and multifactor productivity, changes in gross domestic product influence changes in stock market capitalization, changes in immigrants influence the change in economic growth, changes in multifactor productivity influences the unemployment, and also changes in unemployment influences the change of economic growth. Similar results were obtained from Boubakari and Jin (2010) for stock market capitalization and GDP, J. Yu, M. Hassan and B. Sanchez (2012), M. Hoque and A. Y. Noor (2017) for stock market capitalization.

Otilia-Roxana Oprea and Ovidiu Stoica / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 23-35 Table 3. Granger Causality Test

Sample: 2004 2016 Lags: 8

Null Hypothesis: Obs. F-Statistic Prob. Capital mobility does not Granger Cause Multifactor productivity 356 3.26943 0.001 Capital mobility does not Granger Cause GDP growth 356 3.35502 0.001 GDP growth does not Granger cause market capitalization 356 2.05683 0.039 Turnover ratio does not Granger cause GDP growth 356 3.58270 0.000 Immigrants do not Granger cause GDP growth 356 2.84626 0.004 Multifactor productivity does not Granger cause unemployment rate 356 3.17709 0.001 Unemployment rate does not Granger cause GDP growth 356 2.84966 0.004

Source: Authors’ calculations.

Furthermore, to make the ARDL model, we need to check the stationary properties of the variables and also the cointegration relationship between them. So I used the Augmented Dickey- Fuller test and the Johansen Cointegration test.

Table 4. ADF unit roots test

Fisher Chi-Square Statistic Variables Fisher Chi-Square statistic at level at first difference GDP -4.04*** -10.39*** FOREIGN PORTFOLIO INVEST- -4.35*** -7.46*** MENTS TURNOVER RATIO -4.70*** -7.18*** MULTIFACTOR PRODUCTIVITY -6.54*** -8.91*** VALUE TRADED -4.58*** -6.25*** MARKET CAPITALIZATION -3.78*** -7.80*** STOCK INDICES -3.32** -9.08** CAPITAL MOBILITY -4.57*** -6.87*** UNEMPLOYMENT RATE -3.76*** -10.46*** IMMIGRANTS -4.19*** -7.77*** *** p<0.01, ** p<0.05, * p<0.1

Source: Authors’ calculations.

Table 4 shows the results of the root unit ADF test. The null hypothesis is that the data has a unit root. ***, ** means that the probability is less than the significance level, so the null hypothesis is rejected and the data has no unit root, which means that the variables are stationary, i.e. their statistical properties remain constant over time.

Otilia-Roxana Oprea and Ovidiu Stoica / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 23-35

Table 5 Johansen cointegration test

Kao Residual Cointegration Test Series: CAPITAL MOBILITY, GDP GROWTH, FOREIGN PORTFOLIO INVESTMENTS, MUL- TIFACTOR PRODUCTIVITY, TURNOVER RATIO, VALUE TRADED, MARKET CAPITALIZATION, STOCK INDICES, IMMIGRANTS, UNEMPLOYMENT RATE Sample: 2004 2016 Included observations: 364 Null Hypothesis: No cointegration. Trend assumption: No deterministic trend.

t-Statistic Prob. ADF 0.374651 0.354

Residual variance 1.868804 HAC variance 1.378236

Source: Authors’ calculations.

Table 5 presents the results of the cointegration test. As can be seen, the probability of T-test statistics is greater than the significance level (0.05), so we can accept the null hypothesis that the variables are not cointegrated.

Table 6. Estimated long-run coefficients using the ARDL approach

Dependent variable : GDP growth Variables Coefficients T statistics Turnover ratio -0.01 -1.77* Value traded 0.00 3.75*** Stock indices 3.11 2.20** Market capitalization 0.01 2.18** Capital mobility 0.62 4.04*** Foreign portfolio investments 1.65 1.66* Unemployment rate -0.71 -7.47*** Immigrants 0.68 5.25*** C 3.32 6.02*** *** p<0.01, ** p<0.05, * p<0.1

Dependent variable : Multifactor productivity Variables Coefficients T statistics Turnover ratio -2.70 -0.74 Value traded 2.93 0.57 Stock indices 7.95 0.41 Market capitalization 0.00 2.69*** Capital mobility 0.78 23.86*** Foreign portfolio investments 1.79 0.45 Unemployment rate -0.00 -6.97*** Immigrants 3.27 0.96 C 0.30 7.99** *** p<0.01, ** p<0.05, * p<0.1

Source: Authors’ calculations.

Otilia-Roxana Oprea and Ovidiu Stoica / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 23-35 In Table 6 are the results of applying the ARDL (Autoregressive Distributed Lag) model. In the first part of the table, where the GDP growth was used as dependent variable, it can be noticed that, while the value traded, stock indices, stock market capitalization, capital mobility and immigrants have a positive and statistically significant influence (p <0.01), foreign portfolio investments also have a positive influence but lower for a 10% risk), the turnover ratio and the unemployment rate have a negative influence on the economic growth, expressed by GDP growth. These results are due to the fact that the volume of transactions shows market liquidity, is an indicator used by investors to confirm a market trend, stock indices express the performance of a market for the most efficient management of investors' portfolios, market capitalization shows the size and development of the market, capital mobility expresses the correlation of savings with investments for profit, (in an integrated economic area, savings should not be strongly correlated with investments), foreign portfolio investments can offer to investors the ability to diversify its asset portfolios internationally, a high turnover ratio leads to increased fund costs and lower returns on shareholders, the number of immigrants is important because, reducing barriers to labor mobility between developing countries and developed countries would be one of the most efficient tools of poverty reduction, and the unemployment rate has a negative influence because, a rising rate is seen as a sign of weakening economy, this can help forecast stock prices, stock price stability boosting the stability of the economy. Similar results were obtained by: (Idenyi, Ifeyinwa, Samuel and Chibuzor, 2017) for the traded and stock market capitalization, M. Hoque and A. Y. Noor (2017), S. Raza and S. Jawaid (2012) for stock market capitalization Ilo (2015), M. Holmes and N. Maghrebi, (2016), W. Pan (2017) for unemployment rate. In the second part of the table, where multifactor productivity was used as dependent variable, we can observe that the variables that have a significant and positive influence are capital mobility and stock market capitalization, turnover ratio and unemployment rate have, as in the first case, a bad influence. These results are due to the fact that labor productivity, expressed through factor productivity, is influenced by stock market capitalization, because a highly developed market determines it to be more productive than a weakly developed one, and to link economies and investments with the goal of achieving productivity and profit. Turnover ratio has a negative impact because a rise in costs, a drop in shareholders' returns makes a fund unprofitable and unproductive. The unemployment rate has a negative influence because the full employment of the workforce, and thus the reduction of unemployment leads to higher productivity.

CONCLUSIONS The positive correlation between stock market development and economic growth is a well- known fact from an empirical point of view. Stock markets appear to emerge and develop only when economies reach a reasonable size and the level of capital accumulation is high. The purpose of this study was to investigate the relationship between capital market integration and economic growth in the countries of the European Union and to see what are the main factors in which the development of the capital market influences economic growth. We used data from 28 EU countries during 2004-2016. First, we analyzed the causality between capital market integration and economic growth using the Granger causality analysis. This test has shown that there is a unidirectional causal relationship between turnover and gross domestic product growth, between capital mobility and multifactor productivity, between capital mobility and gross domestic product growth, between immigrants and GDP growth, between multifactor productivity and unemployment rate, between unemployment rate and GDP growth, as well as between gross domestic product growth and stock market capitalization. This means that, taking this test into view, the main factors in which the capital market influences economic growth are stock market capitalization, unemployment rate, immi- 32

Otilia-Roxana Oprea and Ovidiu Stoica / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 23-35 grants, capital mobility and turnover. Second, we used the ARDL model to analyze the relationship between variables. This model expands the results of the Granger causality test, showing that several variables such as traded value, stock indices, stock market capitalization, capital mobility, immigrants have a positive and strongly significant influence on economic growth, the turnover ratio and the unemployment rate have a negative influence on the economic growth, expressed by GDP growth, the foreign portfolio investments also have a positive but lower influence. These results are due to the fact that the volume of transactions shows market liquidity, is an indicator used by investors to confirm a market trend, stock indices express the performance of a market for the most efficient management of investors' portfolios, capitalization shows the size and development of the market, capital mobility expresses the correlation of savings with investments for profit, (in an integrated economic area, savings should not be strongly correlated with investments), foreign portfolio investments can offer to investors the ability to diversify its asset portfolios internationally, a high turnover ratio leads to increased fund costs and lower returns on shareholders, the number of immigrants is important because, reducing barriers to labor mobility between developing countries and developed countries would be one of the most efficient tools of poverty reduction, and the unemployment rate has a negative influence because, a rising rate is seen as a sign of weakening economy, this can help forecast stock prices, stock price stability boosting the stability of the economy. Also, using multifactor productivity as an indicator of economic growth, the variables that have a significant and positive influence are capital mobility and stock market capitalization, turnover ratio and the unemployment rate, as in the first case, have a negative influence. These results are due to the fact that labor productivity, expressed through factor productivity, is influenced by stock market capitalization, because a highly developed market determines it to be more productive than a weakly developed one, and to link economies and investments with the goal of achieving productivity and profit. Turnover ratio has a negative impact because a rise in costs, a drop in shareholders' returns makes a fund unprofitable and unproductive. The unemployment rate has a negative influence because the full employment of the workforce, and thus the reduction of unemployment leads to higher productivity. According to these results, we can say that the integration of capital markets has a positive impact on economic growth, and the main factors in which the capital market positively affects economic growth are stock market capitalization, capital mobility, value traded, stock indices, immigrants, and, to a greater extent, small, foreign portfolio investment. Policymakers in this area should pay attention reducing external debt, which is a significant proportion of foreign capital inflows, due to its negative impact on economic growth and encouraging the other component of foreign capital inflows, ie foreign portfolio investments to stimulate stock market development and growth, reducing extreme stock price volatility, as the inflation rate has a negative impact on growth, fostering a good correlation of savings with investment (i.e. capital mobility), boosting volume growth transactions on stock markets, they should guarantee full employment through fiscal policy, monetary policy and trade policy as stated, by counteracting private sector or trade investment volatility, and reducing inequality, and stimulating increased labor mobility from developed countries to the least developed to balance the economy. The link between the capital market and the unemployment rate is very important and has not been discussed in many of the papers analyzed so far. Also, the immigration is an important factor which has not been used so far with reference to the integration of capital markets. Continuous development and stock market stability are essential to economic growth and can not be ignored in any economy. The integration of capital markets is nowadays essential for both market participants and policymakers. In the integrated markets, capital flows circulate freely if they generate the highest return. Integrated capital markets have easier access to foreign capital but are also more vulnerable to financial crises in other parts of the world (Büttner and Hayo, 2011). Moreover, an increase in 33

Otilia-Roxana Oprea and Ovidiu Stoica / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 23-35 the degree of global financial market integration decreases the opportunity for diversification. It is therefore essential to achieve a better understanding of the factors that lead to the integration of the financial market. An investor who owns a portfolio often tries to reduce the risk, taking into account the correlations between the component assets. If an investor opts for invest in different stock indices to give a well-diversified portfolio, the risk reduction is lost if the stock indices are cointegrated. Hence, an investor cannot spread geographical risks, as cointegration markets will act as a single market with similar risk factors. This result can influence a large proportion of investors in the European financial markets. This is due to the fact that many investors have a long-term investment horizon, with little change in the composition of the portfolio. Thus, the presence of cointegration creates greater requirements for private and professional portfolio managers because an investor cannot rely exclusively on a geographic strategy and on a risk-based correlation basis to create an efficient portfolio. In addition, the results can also be interpreted from the point of view of an investor who wants to allocate a portfolio in an efficient way. This is primarily about the long-term diversification opportunities that are affected by the identified cointegration relationship and thus imposing higher requirements on private investors and professional investors who assemble efficient portfolios.

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Montenegrin Journal of Economics

Vol. 14, No. 3 (2018), 037-053 ‘

Modeling and Forecasting the Level of State Stimulation of Agricul- tural Production in Ukraine Based on the Theory of Fuzzy Logic

SERHII KOZLOVSKYI1, HENNADII MAZUR2, NATALIIA VDOVENKO3, TETIANA SHEPEL4, and VOLODYMYR KOZLOVSKYI5

1 Professor, Vasyl’ Stus Donetsk National University, Department of Entrepreneurship, Corporate and Spatial Economics, Vinnytsia, Ukraine, e-mail: [email protected] 2 Associate Professor, Vinnytsia Regional Council, Deputy, Vinnytsia, Ukraine, e-mail: [email protected] 3 Professor, National University of Life and Environmental Sciences of Ukraine, Head of the Department of Global Economics, Kyiv, Ukraine, e-mail: [email protected] 4 Associate Professor, Lviv Polytechnic National University, Department of Accounting and Analysis, Lviv, Ukraine, e-mail: [email protected] 5 Professor, Vinnytsia National Technical University, Department of Business Economics and Production Management,Vinnytsia, Ukraine, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received June 11, 2018 Agricultural sector is a strategic sector for Ukraine; therefore, the Revised from June 20, 2018 task of developing effective incentive mechanisms for the develop- Accepted August 15, 2018 ment of agricultural production and increase of efficiency of activi- Available online September 15, 2018 ties of agricultural product manufacturers – economic entities of the agrarian market – is urgent. The conditions in which the agricultural sector of Ukraine is functioning, and research priorities of modern JEL classification: agricultural science provide an opportunity to deepen research in E62, C45, Q18. this direction and focus on justification of scientific and practical recommendations on creation of consolidated, adapted to domestic DOI: 10.14254/1800-5845/2018.14-3.3 realities tools of stimulation of development of agricultural production in the country as a whole and its individual territories. The aim Keywords: of this work is to develop economic and mathematical models of forecasting the level of economic and administrative stimulation of management, agricultural production in Ukraine based on the theory of fuzzy logic. modeling, The object of the study is the system of economic relations for stim- forecasting, ulation of agricultural production in Ukraine. The subject of the study agricultural sector, is principles of formation and development of economic and math- Ukraine. ematical models of stimulation of agricultural production in Ukraine. The agricultural sector of Ukraine's economy with all the elements and components that provide the formation of the country's food security at the national level largely depends on the functionality of the mechanism of stimulation of development of agricultural production, which is one of the important and unsolved issues for Ukraine that requires a prompt solution. The study identifies the factors influencing the level of state stimulation of agricultural production in Ukraine. Factors influencing the level of agricultural production in Ukraine are classified. Economic and mathematical model of estimation and forecasting the level of economic and administrative stimulation of agricultural production in Ukraine is developed. The level of economic and administrative stimulation of agricultural production is forecast till 2020, which will allow forming the strategy of development of Ukraine's agricultural sector. 37

Serhii Kozlovskyi, Hennadii Mazur, Nataliia Vdovenko, Tetiana Shepel, and Volodymyr Kozlovskyi / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 037-053 INTRODUCTION Deep organizational and economic transformations in the agricultural sector of the national economy over the past 25 years have created an institutional framework for the development of domestic agricultural production, and its adaptation to the market competitive business environment has led to the restructuring of this production management by the state, including the restructuring of the system of stimulation and organization, as well as implementation mechanisms of managerial influence on the actors of the agricultural sector. Ukraine, as one of the leading players on the world agricultural markets, is constantly making appropriate steps in the direction of regulation of economic relations in the agricultural sector of the economy. Prospects of development of agricultural production of Ukraine in the modern institutional environment require a higher level of competitiveness of domestic agricultural products and efficiency of activity of agricultural manufacturers. At the same time, the agro-industrial complex of Ukraine is still in a state of deep organizational and economic transformations which by their performance and functionality are not complete, which consequently makes relevant the issues of improving the incentive mechanism to promote the development of agricultural production. Stimulation of agricultural production is an important component of the state policy of formation of the national food security guarantee, and for Ukraine, it is also a toolkit to facilitate global competitiveness; therefore, there is the need to search for approaches to make predictive estimates of possible and achievable results of both the stimulation and performance of the industry. The given problem statement on the need to develop economic and mathematical models of forecasting the level of stimulation of agricultural production is associated with the fact that this sector is strategic for Ukraine, so priority is given to ensuring its strategic competitiveness. This, in turn, actualizes the issue of stimulating agricultural production and, consequently, of making creative scenarios, models and forecasts, including with respect to the level of economic and administrative encouragement of economic market players. In methodological terms, the objective is not simple, but the ability to solve it is not considered something unattainable – this issue can be solved using the theory of fuzzy logic.

1. LITERATURE REVIEW The experience of agricultural countries of the world shows that government support plays an important role in the development of agriculture. Recently, the Ministry of Agrarian Policy and Food of Ukraine has published the draft Law of Ukraine "On Stimulation of Development of Agro- industrial Complex of Ukraine" (2017), which provides for the regulation of relations connected with the implementation of the state policy on development of the agricultural sector of Ukraine, promotion of agricultural production, development of the agrarian market, creation of favorable conditions for economic entities in agriculture and food security. Currently, as noted by Vakulenko V. (2016), the state support of the agro-industrial complex should be based on protecting national interests and taking into account the assumed international obligations under the WTO. Therefore, the Government of Ukraine (Official website of the Minis- try of agrarian policy and food of Ukraine, 2018) is expected to promote the agro-industrial complex in the following directions: implementation of general measures (promotion of research and scientific-research activities, education and training of specialists, implementation of veterinary, sanitary and phyto-sanitary measures, implementation of information and consultancy work, implementation of advisory activities); activities for market development and production support (support of production of certain agricultural products, support of farmers' income, price stabilization in the agricultural market); incentives that are not related to production: regional benefits in depressed mountainous and disadvantaged regions, compensation to agricultural manufacturers of the cost of construction of social facilities in rural areas, etc.).

Serhii Kozlovskyi, Hennadii Mazur, Nataliia Vdovenko, Tetiana Shepel, and Volodymyr Kozlovskyi / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 037-053

The process of promoting the growth of agricultural production may be considered through the process of state regulation. According to Y. Krupka (2006), state regulation of the agricultural sector is a form of the state influence on the agro-industrial complex of the country by establishing and enforcing by state authorities of rules aimed at the adjustment of economic activity of agricultural business entities for agriculture to achieve maximum efficiency to meet the needs of the population in food and the needs of industry in raw materials. M. Latin (2005) considers the mechanism of state regulation of development of the agricultural sector of economy as the method of action of a regulated entity which is based on core principles and functions, providing the effective functioning of the system of state regulation to achieve the goal and to solve contradictions using certain forms, methods and means. I. Surai (2003) be- lieves that the term "state regulation of the agricultural sector of economy" should be considered as part of the state management of the agricultural sector, which should manifest itself primarily through indirect economic incentives with regulatory support of economic processes in the agricultural sector of economy. H. Pavlova (2013) considers the state regulation as "a set of methods, forms and instruments of implementation of the state development strategy related to WTO requirements at different levels of management: joint ventures, related industries, regional and national with the priority of sustainable development of the agricultural sector". Based on the above, it can be concluded that state regulation of the agricultural sector can be considered one of the most important components of the state stimulation of the agricultural sector of Ukraine. Analyzing the state and trends of development of the agricultural sector of Ukraine, it can be noted that the control of the industry has shifted to a market basis, because the agricultural sector in most of its manifestations has become a market segment with its inherent competition. Therefore, the agricultural sector requires the search for information appropriate to the requirements of business entities, analytical facts about the development of business structures, structuring of the state system of management of the agricultural industry and the like. At the same time, the management system of agricultural industry will only be functional under the condition when there is full and objective information on the state of the control object. This information may be obtained using modern economic and mathematical models, especially models those built by means of the theory of fuzzy logic. For the development of economic and mathematical models for estimating and forecasting the level of economic and administrative stimulation of the agricultural production in Ukraine, we propose the use of a modern mathematical apparatus – the theory of fuzzy logic which is successfully used in other fields of human activities T. Saati (1991), A. Rotshtein (1998 . The theory of fuzzy logic in technical systems was investigated by L. Zadeh (1976), A. Rothstein (1999), S. Shtovba (2009), O. Kozachko (2010) and others. In economic systems, the theory of fuzzy logic was used by A. Matviychuk (2007), V. Kozlovskyi (2005), O. Burlaka (2016) and others. As emphasized by K. Biliovskyi and O. Matkovska (2013) in the report "Application of fuzzy logic for the solution of economic problems," it is the uncertainty of information that makes to replace the traditional mathematical modeling methods with fuzzy logic methods. Methods of fuzzy logic allow the modeling of any socio-economic processes in the conditions of insufficient information and quantitative uncertainties of the input data. Advantages of the models based on fuzzy sets is the ability to use numerical and linguistic data, the possibility of obtaining generalized estimates in case of using mathematically unrelated input and output data, the possibility of taking into account the specifics of the studied object or process and the possibility of adjusting the model to the dynamic conditions of the economy. Therefore, for modeling and forecasting the level of economic and administrative stimulation of the agricultural production in Ukraine, it is appropriate to use a modeling method based on the theory of fuzzy logic. Exploring the theory of stimulation of agricultural production, one should note the importance of development at the state level of the agricultural policy concept, as well as the state's position on this issue, which is formalized by economic incentive institutions and implemented by the rele- 39

Serhii Kozlovskyi, Hennadii Mazur, Nataliia Vdovenko, Tetiana Shepel, and Volodymyr Kozlovskyi / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 037-053 vant state institutions. At present, the positive is the fact that the results of scientific practices developed by local experts and researchers are gradually embodied in the strategic and tactical plans, legal acts, which determine the set of incentives offered by the state to encourage the development of agricultural production. Thus, institutionalization of the agricultural production support mechanisms in Ukraine is primarily implemented in the Constitution (1996), Laws of Ukraine: "On Principles of the State Policy for the Period until 2015" (2015), "On the State Support of Agriculture of Ukraine" (2004), which define the model, schemes and procedure for the stimulation of agricultural production. Conceptu- al scientific theoretical positions and definition of the components of the agricultural production support mechanisms that have been proposed by several researchers are systematized and presented in Table. 1.

Table 1. Conceptual definitions of the components of stimulation of agricultural production

Relation to the codification Concept Author/Informal definition of the content of stimulation I. Myhasuk (2006), Set of measures of the state influence Macroeconomic and on the subjects of market exchange for the need of direct- general political ing their actions towards achieving the national goals definition of stimulated O. Kovtun (2006), Decision-making system for providing a occasions State framework regime in the economy development regulation Creation of a common institu- V. Novichkov (2001), Actions of the state against the for- of the tional base for the formation of mation of framework conditions for agricultural sector economy results of management in the development industry A. Zanchenko (2004), Principles and actions taken by the Implementation of formal rules government to resolve problem situations in agricultural of stimulating influence exchange Positioning of the general S. Mocherniy (2005), Strategic course of the state and a contours of the incentive system of measures aimed at substantial improvement of under the state doctrine of living conditions of the population and ensuring food secu- control of the agricultural sec- Agricultural rity of the country tor policy G. Kaletnik and G. Zabolotniy (2011), System of values, Approval of the institutional core imperatives around which the content and practical construction of the national effectiveness of the managerial regulation of agro- identity of incentive mecha- economic system is conceptualized. nisms V. Andriychuk (2005), National economy management sys- Functional combination of Mechanism tem through the use of economic laws, solution of contradic- leverage, tools, techniques of of state tions in the social method of production, implementation of direct implementation of state- regulation ownership, etc. market incentives

Source: compiled by the authors

2. METHODOLOGY When analyzing the current state of development of the agricultural sector of Ukraine, we can say that market reforms in the agricultural sector are not completed, the institutional reform of the agricultural production support system is delayed, and its results cannot be considered to be those that significantly stimulate the agricultural production development. Economic institutions for agricultural production support can be generally considered dysfunc- tional for the following reasons: formal and functional institutions (rules) for introduction of the 40

Serhii Kozlovskyi, Hennadii Mazur, Nataliia Vdovenko, Tetiana Shepel, and Volodymyr Kozlovskyi / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 037-053 market of agricultural lands turnover are not formed; reasons for the price disparity and nonequiva- lence of interindustry exchange are not eliminated; investment and innovative attractiveness of the agricultural sector is unstable; effective system for legal protection of producers and employees, as well as guarantees of the adequate level of wages, land rents, and property are lacking. Therefore, we can assume that market institutions in the agricultural sector of Ukraine's economy are not yet fully formed. The complexity and complexity of the problem under study does not allow the use of one indicator as a generalization criterion for assessing and forecasting the level of economic and administrative stimulation of agricultural production in Ukraine. Using the methodology of system analysis and proceeding from the need to consider the agrarian sector as a complex hierarchical structure. In order to ensure economic development of the agrarian sector of Ukraine in the conditions of globalization changes, 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.  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.

The aim of this work is to develop economic and mathematical models of forecasting the level of economic and administrative stimulation of agricultural production in Ukraine on the basis of the theory of fuzzy logic.

3. RESULTS The peculiarity of Ukraine's agricultural sector is the fact that economic institutions for agricultural production support are functioning in the market conditions close to perfect competition. At the same time, the agricultural market is a very complex institutional body and it is a set of institutions, economic relations and a set of goods and services produced in different markets: market of agricultural products, market of agricultural services; market of raw materials and food, market of material and technical resources, financial, capital, means of production market, land market, labour market, etc. Within these markets and under the influence of the results (effects) of their activities and development, appropriate mechanisms to encourage the members of agricultural production are formed and operate. The conditions of reproduction and the efficiency of agricultural production, as well as con- structiveness (rationality) of this process, are entirely dependent on the continuity of agricuural production, which, in turn, is due to the availability in agricultural producers of the necessarlty economic resources. The lack of economic resources can completely paralyze the development of agricultural production, which results in necessity for continuous diagnostics of its provision with the necessary raw materials, means of production, financial resources, etc. As the experience of developed countries shows, effective use at the micro level of the economic potential of agricultur-

Serhii Kozlovskyi, Hennadii Mazur, Nataliia Vdovenko, Tetiana Shepel, and Volodymyr Kozlovskyi / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 037-053 al product manufacturers largely depends on the quality, reliability and credibility of information received by them. The conditions in which the agricultural sector is functioning have a high level of variability and uncertainty, and this circumstance requires producers of agricultural products to find ways to obtain reliable information about the condition of the agricultural products market, organizational- functional connections between the economic market entities, prices of agricultural products, etc. One of the ways of addressing this issue is the use of economic and mathematical models of monitoring, diagnostics and forecasting built by means of the theory of fuzzy logic, which certainly will help to improve the efficiency and effectiveness of agricultural producers and ensure the development of Ukraine's agricultural sector as a whole. Modern methodological approaches to the assessment and forecasting of the level of economic and administrative stimulation of agricultural production in Ukraine will include assessment of the risk magnitude. Therefore, one of methods of assessment and forecasting the level of economic and administrative stimulation of agricultural production in Ukraine is the development of algorithms using modern information technologies that enable to significantly improve the accuracy of the assessment and improve the efficiency and quality of managerial decisions taken on the basis of this assessment. In presenting the results of research to solve the problem raised, it is proposed to use economic and mathematical methods of the new type allowing to conduct a problem-focused search, analyze information and provide the user with factual information in an accessible way (Mazur and Kozlovskyi, 2017). Fig. 1 shows a diagram of economic and mathematical models for assessment and forecast of the level of economic and administrative stimulation of agricultural production in Ukraine, which allows to implement the process described above.

Figure 1. Diagram of mathematical models of assessment and forecast of the level of economic and administrative stimulation of agricultural production in Ukraine

Initial data input module Calculation sub-system

Economic

Production and social

Sub-system for generation of report on Political assessment and forecast of the level of economic and administrative stimula-

Knowledge base tion of agricultural production in Ukraine

Source: compiled by the authors

The mathematical model shown in Fig. 1 suggests that at the state level, a responsible employee, based on the results of the accounting period (month, quarter, year), shall enter the initial data (information) which characterize specific components of agricultural production in Ukraine. The information further undergoes consolidation and analysis. That is, the responsible employee has the option, using the table and/or graphical ways to display the initial information, to compare the initial indicators with their threshold values and over any period of time in order to identify possible threats of reduction of the level of agricultural production in Ukraine. Depending on how the interaction of different levels of stimulation of the agricultural sector is organized, we can distinguish two main classes of architectures of multilevel economic and math- 42

Serhii Kozlovskyi, Hennadii Mazur, Nataliia Vdovenko, Tetiana Shepel, and Volodymyr Kozlovskyi / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 037-053 ematical models for assessing and forecasting the level of stimulation of agricultural production: horizontally organized architecture and vertically organized architecture (S. Kozlovskyi, Gera- symenko and V. Kozlovskyi, 2010 ]. In a horizontally organized architecture, all levels of economic and mathematical models are interconnected by the level of perception and action (in other words, all model levels can com- municate with each other). In a vertically organized architecture, only one of the levels is associated with the level of perception and action, and each of the other levels only interacts with a pair of adjacent levels. The main problems of implementation of horizontal architectures are due to the complexity of coordinating the work of individual levels. The disadvantage of a vertically organized architecture is considered to be congestion of the execution levels (actions).

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

Input Name of the input Range of variability Linguistic evaluation of the input parameter parameter (variable) of the input parameter parameters (terms) (variable) Low, 150-200 billion UAH (L) Gross output of agricultural 150-400 x1 Average, 200-300 billion UAH (A) production in Ukraine billion UAH. High, more than 300 billion UAH (H) Low, 100-120 billion UAH (L) Net profit of companies of the 100-200 x2 Average, 120-150 billion UAH (A) agro-industrial complex billion UAH High, 150-200 billion UAH (H) Low, 0-8 %, (L) Inflation rate in x3 0-100 % Average, 8.1-15 %, (A) Ukraine High, 15.1-100 %, (H) Level of profitability of business Low, 3-5%, (L) x4 activity of the agro-industrial 3-50 % Average, 5.1-20%, (A) complex High, 20.1-50%, (H) 50-500 Low, 50-100 million UAH, (L) Amount of subsidies in the agri- x5 million UAH Average, 101-300 million UAH, (A) cultural sector from the budget High, 301-500 million UAH, (H) Number Low 1-1.5 million units, (L) 1-5 x6 of companies of the agro- Average, 1.51-3 million units, (A) million units industrial complex High, 3-5 million units, (H) Average number Low, 3-4 million people / year, (L) 3-7 x7 of people employed in the agri- Average, 4-6 million people /year, (A) million people / year cultural sector High, 6-7 million people / year, (H) Average salary of employees of Low, 4 - 6 thousand UAH /month, (L) 4-15 x8 the agro-industrial complex of Average, 6 - 9 thousand UAH/ month, (A) thousand UAH / month Ukraine High, 9-15 thousand UAH / month, (H) Intellectual Low, 0-0.5 (L) 0-1 x9 potential (human development Average, 0.6-0.7 (A) units index) of the country High, 0.8-1 (H) Low, 0-30, (L) Level of political 0-100 x10 Average, 31-60, (A) stability in the country points High, 61-100, (H) Level of legislative support of Low, 0-30, (L) 0-100 x11 development of the agro- Average, 31-60, (A) points industrial complex of Ukraine High, 61-100, (H) International political and eco- Low, 0-30, (L) 0-100 x12 nomic influence on the agro- Average, 31-60, (A) points industrial complex of Ukraine High, 61-100, (H)

Source: compiled by the authors

Serhii Kozlovskyi, Hennadii Mazur, Nataliia Vdovenko, Tetiana Shepel, and Volodymyr Kozlovskyi / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 037-053 At the present time, these problems are solved through the application of modern methods of economic and mathematical modeling, namely fuzzy set theory (V. Kozlovskyi and S. Kozlovskyi, 2005). The main provisions of the theory of fuzzy sets and fuzzy logic that will be used later are given in L. Zadeh (1976), A. Rotshtein (1999), we will take them as a basis. Taking into account the need to respect fundamental principles for modeling the level of economic and administrative stimulation of agricultural production in Ukraine and the current conceptual apparatus of the theory of fuzzy logic, the input parameters of the model for assessment and forecast of the level of economic and administrative stimulation of agricultural production in Ukraine are given in Table 2. To establish hierarchical relationships between the factors influencing the level of economic and administrative stimulation of agricultural production in Ukraine, it is advisable to group them into the following groups (according to Table 3): economic (e) production and social (v); political (p). These groups of influence factors as an "output tree" are shown in Figure 2-4.

Figure 2. Classification of economic factors

e – economic factors Gross output of the agro-industrial production of Ukraine Scope of subsidies to the agro-industrial complex from the state budget Net profit of agro-industrial companies Level of profitability of the total activi- Level of inflation ties of agro-industrial companies in Ukraine

Source: compiled by the authors

Figure 3. Classification of production and social factors

v – production and social factors

Number of agro-industrial Intellectual potential (human deve- companies lopment index) of the country

Average number of workers employed Average salary of employees of the in the agro-industrial complex agro-industrial complex of Ukraine

Source: compiled by the authors

Figure 4. Classification of political factors

p –political factors

International political and Level of political stability economic influence on the in the country agro-industrial complex of Level of legislative support of Ukraine development of the agro-industrial complex of Ukraine

Source: compiled by the authors 44

Serhii Kozlovskyi, Hennadii Mazur, Nataliia Vdovenko, Tetiana Shepel, and Volodymyr Kozlovskyi / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 037-053

Using the diagrams shown in Fig. 2-4, we shall denote linguistic variable factors e, v, p with such relationships: e  fe x1,x2 ,x3,x4, x5 , (1) v  fv x6,x7 ,x8,x9 , (2) p  fp x10 , x11,x12 , (3) where х1  x5 – economic factors; х6  x9 – production and social factors; х10  x12 – political factors.

The initial value, i.e. the level of economic and administrative stimulation of agricultural production of Ukraine, Z can be determined by formula (4):

Z  fz e, v, p, t , (4) where e, v, p and t are linguistic variables describing the economic, production and social, political factors of influence and the forecast period, respectively. The forecast period t will be further en- coded as two characters as in the sample: (6M, 1Y, 2Y, 3Y, where the letters M and Y indicate month and year). Using the advice of experts (Official site of the Institute for Economic Research and Policy Con- sulting and Official website of the Ministry of agrarian policy and food of Ukraine ) and in accordance with the specific economic situation that has developed in the Ukrainian, the level of economic and administrative stimulation of agricultural production of Ukraine can be characterized by the following levels (on a scale from "0" to "100"):

 Z1 (85-100) – a high level of stimulation (class A or 1);  Z2 (66-84) – an average level of stimulation (class B or 2);  Z3 (51-65) – a satisfactory level of stimulation (class C or 3);  Z4 (31-50) – an unsatisfactory level of stimulation (class D or 4);  Z5 (0-30) – no stimulation (class E or 5).

Table 2 shows the universal set and the evaluation terms of influence factors х1  х12, and the generalized variables are e,v,pevaluated at a single scale with a range from "0" to "100" points (see Table 3).

Table 3. Generalized input variables and their linguistic evaluation

Input Linguistic evaluation of input Name Denomination parameters parameters (terms) Economic factors e x1 – x5 Low, 0-30, (L) Production and social v x6 – x9 Average, 30-50, (A) factors Above average, 50-75, (AA) High, 75-100, (H) Political factors p x10 – x12

State determination (or predic- t t t1=6 months t2=1 year; tion) period t3=2 years; t4=3 years

Source: developed by the authors 45

Serhii Kozlovskyi, Hennadii Mazur, Nataliia Vdovenko, Tetiana Shepel, and Volodymyr Kozlovskyi / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 037-053 The structure of the economic model for assessment and forecast of the level of economic and administrative stimulation of agricultural production in Ukraine will be presented in the form of a so-called "tree of inference". Tree of inference is a graph which shows the logical connections between the predicted value Z and factors {x1...x12} that influence this predictive value Z following the relations given in formulas (1) to (4). Structural model for assessing and forecasting the level of economic and administrative stimulation of agricultural production in Ukraine will have the form shown in Figure 5.

Figure 5. Structural model of assessing and forecasting the level of economic and administrative stimulation of agricultural production in Ukraine

х7 х6 x5 x4 x3 x2 х1 100 x 8 Z1

fe x9 fv Z2 x10 e v

x11 fp Z3 p Z

x12 fZ Z4 t

t Z5 0

Source: developed by the authors

The nodes of the "tree of inference" are interpreted as follows: the root of the tree f Z corresponds to the level of economic and administrative stimulation of agricultural production in Ukraine; the terminal nodes x1  x12are the relevant factors of influence; non-terminal nodes (double circles) are a set of partial influence factors in their totality. Terminal and non- fe , f v ,fp terminal nodes of the "tree of inference" represent linguistic variables of a universal set which are given in Tables 2-3. The structural analysis of the presented model of economic and administrative stimulation of agricultural production in Ukraine shows that this model actually consists of three interrelated models: 1) model of economic factors of stimulation of agricultural production in Ukraine; 2) model of production and social factors of stimulation of agricultural production in Ukraine; 3) models of political factors of stimulation of agricultural production of Ukraine. It is worth noting that in building the model, we simultaneously employed the input quantitative and input qualitative parameters. Input parameters {x1...x9} are quantitative, and statistical data were used for their description; parameters {x10...x12} are qualitative, therefore, rating scale from "0" to "100" points was used for their description.

Serhii Kozlovskyi, Hennadii Mazur, Nataliia Vdovenko, Tetiana Shepel, and Volodymyr Kozlovskyi / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 037-053

Since fuzzy set theory suggests the definition of levels (terms) of changes in the initial parameter, according to our model, we received three initial parameters for the assessment of which are fuzzy terms with scales given in Table 3 were used. Each term s presented as a fuzzy set with respective membership function. To describe the terms, we will use the technique given in [18]. We will be present the terms in the form of fuzzy sets using a model of membership function (MF):

T 1 , (5)  (x)  2 x  b 1   c  where b and c are parameters of the membership function (MF); b – coordinate of the function maximum; c – rate of concentration expansion.

Values of b and c for the variables x1...x12 are given in Table 4 (as an example).

Table 4. Values of b and c parameters of the membership functions of variables x1... x12 and e, v, p models

Input Name of the input Linguistic evaluation of the variables b c variable (parameter) input variables (terms)

Low, (L) 7 3 x1 Gross output of agricultural production in Ukraine Average, (A) 20 8 High, (H) 30 4 Low, (N) 5 10 Net profit of companies of the agro-industrial com- x2 Average, (A) 15 9 plex High, (H) 35 12 Low, (N) 3 8 Inflation rate in x3 Average, (A) 12 16 Ukraine High, (H) 50 35 Low, (N) 4 10 Level of profitability of business activity of the agro- x4 Average, (A) 14 32 industrial complex High, (H) 60 25 Low, (N) 75 50 Amount of subsidies in the agricultural sector from x5 Average, (A) 220 110 the budget High, (H) 400 90 Low, (L) 1 1 Number x6 Average, (A) 2 1 of companies of the agro-industrial complex High, (H) 3 1 Low, (L) 25 12 Average number x7 Average, (A) 35 9 of people employed in the agricultural sector High, (H) 45 8 Low, (L) 2 1 Average salary of employees of the agro-industrial x8 Average, (A) 3.5 2 complex of Ukraine High, (H) 5.5 3 Intellectual Low, (N) 0.2 0.5 x9 potential (human development index) of the coun- Average, (A) 0.5 0.3 try High, (H) 0.7 0.4 Level of political stability in the country Level of legislative support of development of the agro- Low, (N) 15 30 x10… x12 industrial complex of Ukraine International political Average, (A) 45 25 and economic influence on the agro-industrial High, (H) 85 38 complex of Ukraine Low, (N) 15 12 Economic factors. Average, (A) 42 15 e,v,p Production and social. Above average, (AA) 60 25 Political. High, (H) 85 20

Source: developed by the authors 47

Serhii Kozlovskyi, Hennadii Mazur, Nataliia Vdovenko, Tetiana Shepel, and Volodymyr Kozlovskyi / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 037-053 The choice of the membership function of this type (see formula 5) is associated with the fact that this function is quite flexible and simple, as it is defined by only two parameters, and is also more convenient for further arrangement of the model. Membership function for variables x1 and x2, are shown in Fig. 6 as example.

Figure 6. Membership function for x1 and x2 variables of the model of economic and administrative stimulation of agricultural production in Ukraine

Figure 6a. Membership function for x1 variable

Figure 6b. Membership function for x2 variable

Source: developed by the authors

Serhii Kozlovskyi, Hennadii Mazur, Nataliia Vdovenko, Tetiana Shepel, and Volodymyr Kozlovskyi / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 037-053

The next step of modeling the level of economic and administrative stimulation of agricultural production in Ukraine is building up a hierarchical knowledge base. To build the knowledge base, we used the information obtained from the specialists of central executive authorities of Ukraine and information obtained from the specialists of the industry. Let's consider equation (4). For assessment of the values of linguistic variables that show a causal relationship between the level of economic and administrative stimulation of agrarian production in Ukraine (Z) and the economic, production and social, political factors of influence, we will use the term-set system given in Table 3. Then the knowledge base for Z variable, which characterizes the level of economic and administrative stimulation of agrarian production in Ukraine (see equation 4), will be of the form given in Table 5.

Table 5. Knowledge base of Z variable

e v p Z w L L L Z5 w1 L C C Z5 w2 A L A Z5 w3 L A L Z4 w4 A A L Z4 w5 A L A Z4 w6 A A A Z3 w7 AA L A Z3 w8 H L H Z3 w9 A AA AA Z2 w10 AA A AA Z2 w11 H H H Z2 w12 H H H Z1 w13 H AA AA Z1 w14 AA H H Z1 w15

Source: developed by the authors

Similar to the above, knowledge bases for initial e, v, p values (not presented here) are being developed. It is known that each rule of the knowledge base represents the statement "IF- THEN". Rules that have the same initial parameter are combined in the table lines with a logical statement "OR". The weight of the w rule expresses the subjective confidence of the expert in this rule. At the stage of formation of the structure of a fuzzy model, the weights of all rules of the knowledge base are assumed to be equal to 1 (Zadeh, 1976). For Table 5, the statement "IF-THEN" is given in formula 6.

IF [e=L] and [v=L] and [p=L] OR [e=L] and [v=A] and [p=A] OR [e=A] and [v=L] and [p=A], THEN Z=Z5; IF [e=A] and [v=L] and [p=A] OR [e=A] and [v=A] and [p=L] OR [e=A] and [v=L] and [p=A], THEN Z=Z4; IF [e=A] and [v=A] and [p=A] OR [e=AA] and [v=L] and [p=A] OR [e=H] and [v=L] and [p=H], THEN Z=Z3; IF [e=A] and [v=AA] and [p=AA] OR [e=AA] and [v=A] and [p=AA] OR [e=H] and [v=H] and [p=H], THEN Z=Z2; IF [e=H] and [v=H] and [p=H] OR [e=H] and [v=AA] and [p=AA] OR [e=AA] and [v=H] and [p=H], THEN Z=Z1. (6)

For implementation of fuzzy inference, it is necessary to make the transition from logical statements to fuzzy logical equations (Zadeh, 1976). Such equations can be obtained by replacing the linguistic values with the values of the membership functions, and operations "AND" and "OR" – with fuzzy logical operations of intersection  and integration . The weight of rules in the knowledge base is taken into account by multiplying the fuzzy expression that corresponds to each row of the knowledge base by the corresponding weight value. Then, the following fuzzy logical equations will correspond to linguistic statements given in Table 5 and formula 6 (formula 7): 49

Serhii Kozlovskyi, Hennadii Mazur, Nataliia Vdovenko, Tetiana Shepel, and Volodymyr Kozlovskyi / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 037-053

Z5 (Z)  w [Н (e) Н (v)H (p)1М (t)] 1 Н С С 6М w 2 [ (e) (v) (p) (t)] C Н C 1Р w3 [ (e) (v) (p) (t)]; Z4 Н C H 1М  (Z)  w 4 [ (e) (v) (p) (t)]

C С Н 6М w5 [ (e) (v) (p) (t)] C Н C 1Р w6 [ (e) (v) (p) (t)]; Z3 С С С 1М  (Z)  w 7 [ (e)  (v)  (p)  (t)]

ВС Н С 6М w8 [ (e)  (v)  (p)  (t)] C ВС ВС 1Р w9 [ (e)  (v)  (p)  (t)]; Z2 (Z)  w [С (e)ВС (v) ВС (p) 1М (t)] 10 ВС С ВС 6М w11 [ (e)  (v)  (p)  (t)] В В В 1Р w12 [ (e)  (v)  (p)  (t)]; Z1 В В В 1М  (Z)  w13 [ (e)  (v) (p)  (t)] (7) В ВС ВС 6М w14 [ (e) (v) (p) (t)] ВC В В 1Р w15 [ (e) (v) (p) (t)].

Values of degrees of membership functions in equation (7) are determined by fuzzy knowledge bases which characterize the economic, production and social, political factors of influence. Fuzzy logical equations (7) are the mathematical implementation of the model of assessing and forecasting the economic and administrative stimulation of agricultural production in Ukraine. The procedure of dephasing is the last stage of modeling and represents the inverse transformation of the found fuzzy logic statements (conclusion) into the initial evaluating or forecasting parameter (variable) that is subject to modeling and forecasting. There are various methods of dephasing, the choice and application of which depends on the object of modeling (Kozlovskyi and Kozlovskyi, 2005). Based on the characteristics of the object of modeling and the nature of the initial parameter (variable), for the solution of logical equations we will select a method of dephasing which is called "method of center of weights, expanded" [18]. In this case, in order to determine the "center of weights", it is necessary to artificially extend the range of the initial parameter (variable). In our case, when the initial parameter (variable) has n terms, the calculation of the center of weights is reduced to the solution of equation 8:

n  Z  Z   Z  (i 1) A E Zi  E n 1  (8) Z  i1  , n Zi i1 where n – number (of discrete values) of terms of "Z" variable; ZE (ZA ) –lower (upper) boundary of the range of "Z" variable;  Zi – membership function of "Z" variable to fuzzy term "Zi". Within the mathematical package Мatlab 6.1 (Pratar, 1999), an experiment was conducted using the above technique. Fig. 7 shows the results of assessing and forecasting the level of economic and administrative stimulation of agricultural production in Ukraine until 2022. The results were obtained on the basis of analysis of values of influence factors (development) for 2012-2016.

Serhii Kozlovskyi, Hennadii Mazur, Nataliia Vdovenko, Tetiana Shepel, and Volodymyr Kozlovskyi / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 037-053

Analyzing the results of modeling the level of economic and administrative stimulation of agricultural production in Ukraine for 2018-2022, one can make a forecast: in 2020 and 2021, the level of economic and administrative stimulation of agricultural production in Ukraine will be assigned to class D – "unsatisfactory level of stimulation". In 2018-2019, the forecast level of economic and administrative stimulation of agricultural production in Ukraine will deteriorate to class E – “lack of stimulation”. In 2022, the forecast level of will improve to C level – "satisfactory level of stimulation". We emphasize again that this forecast is based on the analysis of the influence factors of 2012-2016.

Figure 7. Results of assessment and forecast of the level of economic and administrative stimulation of agricultural production in Ukraine

Source: developed by the authors

To improve the reliability of the forecast of the level of economic and administrative stimulation of agricultural production in Ukraine, it is necessary to optimize (setup) the model; this task, however, is beyond the scope of this study. As noted earlier, the advantage of economic and mathematical models constructed on the basis of fuzzy logic is the ability to use input parameters of linguistic statements (opinions) of experts, which largely compensates for the lack of analytical dependences between input and output parameters (variables) of the forecast object.

СONCLUSION Developed innovative model for predicting the level and condition of economic and administrative stimulation for the development of agricultural production in Ukraine based on the theory of fuzzy logic allows defining the condition and the level of economic and administrative stimulation of the agricultural sector with dynamic change of linguistic model parameters. This model enables to make a linguistic assessment of factors in the macro environment influencing the effectiveness of stimulation that cannot be quantified, which is particularly relevant.

Serhii Kozlovskyi, Hennadii Mazur, Nataliia Vdovenko, Tetiana Shepel, and Volodymyr Kozlovskyi / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 037-053 The use of this economic and mathematical model in practice will enable heads of government agencies, businessmen, farmers to assess and forecast the level of state stimulation of agricultural production in Ukraine. This assessment will allow taking certain managerial decisions related to business activities. It will also allow reducing business risks and carrying out effective agricultural activities in Ukraine. The developed economic and mathematical model for assessment and forecast of economic and administrative stimulation of agricultural production in Ukraine can be considered as typical for this class of objects, and the modeling methodology developed on its basis can be applied to modeling of any economic processes characterized by a fuzzy relationship between the input and output parameters, significant difficulties in formalization of impacts, ability to draw linguistic statements (opinions) of experts to build models, etc.

REFERENCES Andriychuk, V., Zubec, M., Yrchishen, V. (2005), Modern agrarian policy: problematic aspects, Agrarian science, Kiev (in Ukrainian). Biliovsky, K., Matkovska, O. (2013), „Application of fuzzy logic to solve economic problems”, avaible at: http://elartu.tntu.edu.ua/handle/123456789/19979 Burlaka, O. (2014), «Methodological approaches to the sustainable development of the agrarian sector», Agrosvit, No. 1, pp. 56-61 (in Ukrainian). Kaletnik, G. M., Zabolotnyi, G. M. Kozlovskyi, S. V. (2011), «Innovative models of strategic management economic potential within contemporary economic systems», Actual Problems of Economics, Vol, 4(118), pp. 3-11. Kozlovskyi, V., Kozlovskyi, S. (2005), Macroeconomic modeling and forecasting of the exchange rate in Ukraine, Kniga-Vega, Vinnitsa (in Ukrainian). Kozlovskyi, S. V. (2010), „Economic policy as a basic element for the mechanism of managing development factors in contemporary economic systems“, Actual Problems of Economics, Vol. 1(103), pp. 13-20. Kozlovskyi, S. V. Gerasymenko, Y. V. Kozlovskyi, V. O. (2010), „Conceptual grounds for construction of support system for investment decision-making within agroindustrial complex of Ukraine“, Actual Problems of Economics, Vol. 5(107), pp. 263-275. Kovtun, O. (2006), State regulation of economy, Noviy svit, Lviv (in Ukrainian). Krupka, Y. (2006), Agrarne Law of Ukraine, Universitet Ukraina, Kiev (in Ukrainian). Latin, M. (2007), “Improvement of the mechanism of state regulation of the agrarian sector development of the Ukrainian economy”, avaible at: http://www.kbuapa.kharkov.ua/e-book/db/ 2007-1-1/doc/2/06.pdf Law of Ukraine (2015), On Principles of the State Policy for the Period until 2015, avaible at: http://zakon5.rada.gov.ua/laws/show/2982-15 Law of Ukraine (2004), On the State Support of Agriculture of Ukraine, avaible at: http://zakon4.rada.gov.ua/laws/show/1877-15 Law of Ukraine (1996),«Constitution of Ukraine, avaible at: http://zakon3.rada.gov.ua/ laws/show/ 254к/96-вр Matviychuk, A. (2007), Modeling of economic processes using methods of fuzzy logic, KNTEU, Kiev (in Ukrainian). Mazur, G., Kozlovskyi, S. (2017), «Modeling and forecasting of the state stimulation of agro- industrial production in Ukraine on the basis of the theory of fuzzy logic», Economika ta derja- va, Vol. 9, pp. 8-15. Mihasuk, I. (2006), State regulation of economy, Magnolia Plus, Lviv (in Ukrainian). Mocherniy, S., Larina, Y., Ustimenko, S., Yriy, S. (2005), Economic encyclopedia dictionary, Svit, Lviv (in Ukrainian). Novichkov, V., Kalashnikov, I. (2001), Agrarian policy, IKC Marketing, Moscow (in Russian).

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Official website of the 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/ Official website of the Ministry of agrarian policy and food of Ukraine, avaible at: http://minagro. gov.ua/en/node/15828 Panoshichen, Y., Kozachko, O. (2010), “Fuzzy model for assessing the creditworthiness of individuals-borrowers of commercial banks”, Visnik HNU, No. 1, Т2, pp. 161-168. Pavlova, H. (2013), State regulation of the agrarian sector as a component of the system of regulation of the economy of Ukraine, avaible at: http://www.rusnauka. com/33_DWS_2013/ Economics/15_149177.doc.htm Pratar, R. (1999), Getting started with Matlab 5. A quick introduction for scientists and engineers, Oxford University Press, Oxford. Project of Law of Ukraine (2017), On stimulating the development of the agro-industrial complex of Ukraine, avaible at: http://minagro.gov.ua/node/18940 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. Rotshtein, A. (1999), Intellectual identification technologies: fuzzy logic, genetic algorithms, neural networks, Universum, Vinnitsa (in Ukrainian). Rotshtein, A., Shtovba, S. (2009), «Modeling of the Human Operator Reliability with the Aid of the Sugeno Fuzzy Knowledge Base», Automation and Remote Control, Vol. 70, pp. 163-169. Saati, T. (1991), Analytical planning, Radio, Moscow (in Russian). Surai, I. (2003), «The concept of state administration of the agrarian sector of Ukraine's economy», Scientific papers of NADU, Vol. 2, pp. 259-270 (in Ukrainian). Vakulenko, V. (2016), «Basic promotion of agricultural sector in Ukraine», Modeling and information of economic development of Ukraine, Vol. 1, pp. 13-18 (in Ukrainian). Zadeh, L. (1976), The concept of a linguistic variable and its application to making approximate decisions, World, Moscow (in Russian). Zanchenko, A., Nazarenko, V., Shaikin, A. (2004), Agrarian policy, Kolos, Moscow, (in Russian).

Ömer Yalçinkaya, Muhammet Daştan and Kerem Karabulut / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 055-071

Montenegrin Journal of Economics

Vol. 14, No. 3 (2018), 055-071 ‘

The Effects of International Tourism Receipts on Economic Growth: Evidence from the First 20 Highest Income Earning Countries from Tourism in the World (1996-2016)

ÖMER YALÇINKAYA1, MUHAMMET DAŞTAN2* and KEREM KARABULUT3

1 Department of Economics, Ibrahim Cecen University of Agri, Agri, Turkey, E-mail: [email protected] 2* Corresponding author, Department of Economics, Ibrahim Cecen University of Agri, Agri, Turkey, E-mail: [email protected], [email protected] 3 Department of Economics, Ibrahim Cecen University of Agri, Agri, Turkey, E-mail: [email protected]

ARTICLE INFO ABSTRACT Received July 05, 2018 In this study, the effects of tourism receipts on economic growth will Revised from July 20, 2018 be investigated econometrically for the top 20 countries earning Accepted August 12, 2018 most from international tourism (WTR-20) in the world for the period Available online September 15, 2018 1996-2016. From this aspect, this study aimed to empirically evaluate whether international tourism receipts have an effect on economic growth performances of the developed and developing coun- JEL classification: tries in the WTR-20 group as proposed by theoretical literature C33; E13; F14; O11. under the tourism-led growth hypothesis. To determine the effects of international tourism receipts on economic growth for WTR-20 DOI: 10.14254/1800-5845/2018.14-3.4 group countries, a model is an extended form of Cobb-Douglas type of production function, will be estimated under the second- Keywords: generation panel data analysis considering cross-sectional dependence. As a result of the study, it is determined that international Economic Growth, tourism revenues have a positive and statistically significant effect Sustainability of Economic Growth, on economic growth in the WTR-20 group countries. Also, it is found International Tourism Receipts, that there was unilateral causality running from international tour- Second Generation Panel Data Analysis. ism receipts to economic growth in the WTR-20 group countries. These findings, which are in keeping with the theoretical literature under the tourism-led growth hypothesis, indicate that international tourism receipts have a significant effect on providing economic growth and gaining sustainability in WTR-20 group countries with their current structures.

1. INTRODUCTION In its most general form, tourism, defined as a whole of the economic and socio-cultural activities to generate income by the help of attracting tourists, is touristic travel to a country with the purpose of visiting, resting, having fun, and getting to know the country, and it states in the international services of the current account on the balance of payments (İTO, 2007, p. 13). When viewed 55

Ömer Yalçinkaya, Muhammet Daştan and Kerem Karabulut / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 055-071 from this aspect, tourism, as an invisible export, provides the inflow of foreign currency to finance fixed capital investments and plays a significant role in enhancing economic growth and development through stimulating sub-sectors of an economy with direct and indirect effects (Balaguer and Cantavella-Jordá, 2002, pp. 877-878). The effects of tourism on economic growth and development are recognized due to running multiplier mechanism created by existed income-spending flow in the sector as a result of international tourism movements (Bahar and Kozak, 2013, p. 6). As a matter of fact, since tourism is a consumption activity, investment expenditures for satisfying the incremental demand of tourism in consequence of consumption expenditures made by tourists constitute incomes of productive factor owners in both the tourism sector and other sectors which promote tourism. In addition to direct income effects created by touristic consumption expenditures, expenditures for consumption and production of economic units, which acquire those touristic consumption expenditures as an income, run the multiplier mechanism by recirculation in an economy and lead to creating new incomes indirectly. In this context, tourism expenditures made by tourists in economies firstly create income effect as their magnitude, and then some part of this existing income influences creating new incomes indirectly through its transferral to various forms such as investments, savings, consumption, taxes, etc. (İTO, 2007, p. 102). Thereby, consumption-income flow exists in the tourism sector as a result of the tourism movements, which constitute more extensive income effects regarding the initial expenditures on tourism and facilitate providing economic growth and rising development levels in economies because of the multiplier effect (Çeken, 2016, p. 130-131). In addition, the tourism sector, whose role in the economic growth and development process was ignored until the 1950s, is seen as one of the most important factors of economic growth and the development of policies performed by developed and developing countries thanks to its recognized worldwide economic importance after the Second World War (Bahar and Kozak, 2010, p. 53). From this date, the ever-growing tourism sector and its economic importance came into prom- inence, and the sector has been located at the center of sustainable economic growth and development strategies in developed and, particularly, developing countries since the 1990s, when the globalization process and foreign expansion policies accelerated. From this point of view, in this study, the effects of tourism receipts on economic growth are investigated empirically within the concept of second-generation panel data analyses for the top 20 countries earning the most from international tourism (WTR-20) in the world over the period of 1996-2016. From this aspect, this study is aimed at evaluating whether international tourism receipts have an effect on economic growth performances of WTR-20 countries, which is consistent of developed and developing countries as proposed by theoretical literature under the tourism-led growth hypothesis. Findings of the study conducted concerning WTR-20 countries are thought to promote the development of empirical literature on this subject by covered countries and employed second-generation econometric methodologies. In the second part of the study, the empirical literature, which is investigating the effects of international tourism receipts on economic growth, is summarized with its main lines and the position of this study in the literature is explained. In the third part of the study, the scope of the study is explained, and the data set is established. In the fourth part of the study, the effects of tourism receipts on economic growth in WTR-20 countries are investigated econometrically through employing extended Cobb-Douglas type of production function. Finally, by presenting general assessments and policy implications, the study is concluded.

2. LITERATURE REVIEW When the related literature is viewed, it is seen that the empirical studies researching for the effects of international tourism receipts on economic growth have been progressing from the 1990s and intensifying from 2000s with tourism sector gained worldwide importance in terms of 56

Ömer Yalçinkaya, Muhammet Daştan and Kerem Karabulut / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 055-071 economics. In addition to this, it is seen that the empirical studies, researching for the effects of international tourism receipts on economic growth were not theory based until the 2000s while they have been addressed Tourism-Led Growth Hypothesis after studies of Balaguer and Canta- vella-Jorda (2002). Tourism-Led Growth Hypothesis is explained within the scope of Export-Led Growth Hypothesis assuming that economic growth not only can be provided by physical and human capital accumulation but can also be provided by expansion of export capacity (Brida et al., 2015: 646-647). In the Export-Led Growth Hypothesis based on Keynesian demand-side and Neo- classical supply-side economic growth theories, export revenues are indicated as one of the basic determinants of long-run economic growth whereas the tourism receipts are accepted as one of the main determinants of long-run economic growth in the Tourism-Led Growth Hypothesis (Balaguer and Cantavella-Jorda, 2002, pp. 877-884). In literature, it is seen that the effects of international tourism on economic growth within the concept of the Tourism-Led Growth Hypothesis are investigated in long-run relations and causality aspects with different grade estimators in empirical studies which analyze the effects (or the sign and magnitude of these effects) of international tourism receipts on economic growth for countries/country groups with different development levels by employing time series or panel data analysis. In this context, in empirical analyses, which are conducted by using a different type of estimators under the time series/panel data analysis, it is found that the effects of international tourism receipts on growth are generally positive and statistically significant (Modeste, 1995; Balaguer and Cantavella-Jorda, 2002 – Spain;, Narayan, 2004-Fiji; Durbarry, 2004-Mauritius; Martin et al., 2004; Gökovalı and Bahar, 2006; Brida et al., 2008-Mexico; Jimenez, 2008-Spain and Italy; Lee and Chang, 2008; Proença and Soukiazis, 2008; Fayissa et al., 2009; Chen and Chiou-Wei, 2009- Taiwan and Korea; Bahar and Bozkurt, 2010; Srinivasan et al., 2012- Sri Lanka; Fawaz and Rahnama, 2014; Shahbaz et al., 2015-Malaysia; Cárdenas-García et al., 2015; Chiu and Yeh, 2016). Additionally, a few studies in the same content concluded that international tourism receipts have not statistically significant, but positive long-run effects on economic growth or they have not any influence on it (Figini and Vici, 2007; Öztürk and Acaravcı, 2009-Turkey;Cárdenas- García et al., 2015). On the other hand, in some of the above studies and in other studies within the same context examining the effects of tourism receipts on economic growth are examined with causality dimension and different grade causality tests; it is found that there is either a presence of bilateral or unilateral causality or any causality relations between the variables. It is determined that there is an existence of unilateral causality running from international tourism receipts to economic growth in most of the studies in this context (Balaguer and Cantavella-Jorda, 2002-Spain; Narayan, 2004-Fiji; Durbarry, 2004-Mauritius; Dritsakis, 2004-Greece; Gündüz and Hatemi-J, 2005-Turkey; Özdemir and Öksüzler, 2006-Turkey; Lee and Chang, 2008; Belloumi, 2010-Tunisian; Akinboade and Braimoh, 2010-South Africa; Kreishan, 2011-Jordan; Dritsakis, 2012; Ridderstaat and Croes 2012-Aruba; Jalil et al., 2013-Pakistan; Chou, 2013; Hatemi-J et al., 2014; Brida et al., 2015; Alhowaish, 2016) while it is determined there is an existence of unilateral causality running from economic growth to international tourism receipts in some studies (Oh, 2005-South Korea; Lee and Chang, 2008; Payne and Mervan, 2010-Croatia; Chou, 2013; Hatemi-J et al., 2014; Alhowaish, 2016). In addition, it is concluded there is a bilateral causality among international tourism receipts and economic growth in considerable part of the studies (Lanza et al., 2003; Ongan and Demiröz, 2005-Turkey; Kim et al., 2006-Taiwan; Lee and Chien, 2008-Taiwan; Samimi et al., 2011; Çağlayan et al., 2012; Chou, 2013; Shahbaz et al., 2015-Malaysia; Seghir et al., 2015; Alhowaish, 2016; Ahad, 2016-Pakistan) while there is no causal relationship between international tourism receipts and economic growth in some of the limited number of studies (Eugenio-Martins and Morales, 2004; Yavuz, 2006-Turkey; Öztürk and Acaravcı, 2009-Turkey; Hepaktan and Çınar, 2010-Turkey; Brida et al., 2011-Brazil; Kasimati, 2011-Greece; Çağlayan et al., 2012; Chou 2013), Alhowaish, 2016)1.

Ömer Yalçinkaya, Muhammet Daştan and Kerem Karabulut / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 055-071 When studies in the literature are evaluated as a whole, it is observed that a large part of the empirical studies, which mentions the relations among international tourism receipts and economic growth within the context of Tourism-led growth hypothesis, are conducted on developing countries and carried out by using mainly time series analysis. On the other hand, the studies within the concept of panel data analysis are limited and focused on various countries (developed, developing, Latin America, Asia, Europe, Mediterranean, etc.) and specific country groups (OECD, G-7, SSA, GCC, etc.). Also, based on the literature review, the differences in the current positions of the covered countries in terms of international tourism revenues were not considered in studies conducted on various developed and developing countries/country groups within the concept of panel data analysis. When all of these studies were evaluated regarding their results, it was observed that the empirical studies, which investigated the relations between international tourism receipts and economic growth within the dimension of long-term relations and causality, generally support the tourism-led growth hypothesis. However, a limited part of these empirical studies did not reach the results that support that hypothesis. This situation shows that the obtained results for the effects of international tourism receipts on economic growth tend to be labile according to the development levels of the economic and tourism sectors of covered countries, sample periods, and differences in the econometric methodologies of studies. In this study, after the literature review, the effects of international tourism receipts on economic growth, in other words, the validity of the Tourism-Led Growth hypothesis will be analyzed within the concept of second generation panel data analysis considering cross-section dependence and within the dimension of long-term relations and causality. From this aspect, findings of this conducted study on the developed and developing countries in the WTR-20 group are considered to contribute to the empirical literature in terms of covered countries and second generation econometric methodologies used for this subject.

3. DATA AND SCOPE OF THE STUDY In this study, the effects of international tourism receipts on economic growth in WTR-20 countries for the period 1996-2016 are analyzed within the concept of second generation panel data analysis2. From this aspect, this study is aimed to examine whether international tourism receipts on economic growth performances of developed and developing countries in the WTR-20 group as predicted in theoretical literature within the concept of Tourism-Led Growth Hypothesis or not. In this study, United Nations' World Tourism Organization's (UNWTO's) ranking formed by using nominal tourism receipts (USD) in 2016 is taken as a reference for determination of the top 20 countries earning the most from international tourism. These first 20 highest income-earning countries from tourism in the world, respectively sorted by the size of their nominal tourism receipts in 2016, are the United States, Spain, Thailand, China, France, Italy, the United Kingdom, Germany, Hong Kong (China), Australia, Japan, Macao (China), India, Mexico, Austria, Turkey, Singapore, Canada, Malaysia, and the Korean Republic. Since some of the data belonging to special administrative regions (Hong Kong [China] and Macao [China]) could not provide a sufficient length to the related databases and China was included in analyses as a country, these special administrative regions were removed from the analyses, and Switzerland (21st) and Greece (22nd) were included instead of those regions.

1 In this section, highlighting the name of the country besides the year of the outlined study indicates that the related study conducted within the concept of time series analysis. In addition, studies, conducted on various countries within the concept of panel data analysis, do not include the name of the country. See related studies for detailed information about countries or country groups covered by these studies. 2 Since the data of tourism receipts have became available from 1996 in the related database, investigation period of the study begins with that time. 58

Ömer Yalçinkaya, Muhammet Daştan and Kerem Karabulut / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 055-071 In the study, table 1 shows the variables, used in models estimated for the purpose of examination of the effects of international tourism receipts on economic growth (Per Capita Real Gross Domestic Product-GDP), and their references.

Table 1. Variables Used in Models

Period: 1996-2016 Abbreviations Definition of the Variables Data Sources of the Variables RGDP Per Capita Real GDP (2011-USD). Real Fixed Capital Investments (2010- RGFCI The World Bank (WB) USD). (World Development Indicators). International Real Tourism Receipts ITR (USD). EL Employed Labor Force The Conference Board-Total Economy TFP Total Factor Productivity (USD). Database (TED May-2017).

Note: All variables described in the table are used in analyses with their annual growth rates in investigation period.

Here, the RGDP variable (constant 2011 USD) was obtained from the WB database for all of the WTR-20 countries in purchasing power parity (PPP) terms. The RGFCI variable (constant 2010 USD) was obtained from the WB database and used in per capita terms by dividing real fixed capital investments by the population taken from the same database. The EL variable was obtained for all of the WTR-20 countries by proportioning the employed labor force retrieved from the TED database to the total population in the middle of the year taken from the same database. The variable TR was taken for all of the WTR-20 countries by proportioning international tourism receipts obtained from the WB database to GDP deflators also retrieved from the same database. In this way, the TR variable was converted to a real term. TFP, which was calculated as the annual growth rate in the examination period, was taken from the TED database for all of the WTR-20 countries as a prepared variable.

4. ECONOMETRIC METHODOLOGY AND FINDINGS In this study, the econometric model, which will be estimated to determine the effects of tourism receipts on economic growth in the WTR-20 countries, was obtained by extending the Cobb- Douglas type of Neo-classical total production function. The extended CD type of production function, which is including the effects of tourism receipts and technology development level on economic growth, can be written as in the following equation.

Here the term ( ) represents the error term, ( ) represents the countries and ( ) represents the time. The term ( ) in production function indicates economic growth (per capita real GDP), the term ( ) indicates the technological development level, the term ( ) indicates physical capital accumulation (real fixed capital investments), the term ( ) indicates human capital accumulation (employed labor force), and the term ( ) indicates tourism receipts. It is admitted the level of technological development ( ) in the production function consists of total factor productivity (TPP) by considering the evaluation of economic growth theories at the point of resolving the economic growth process and the level of technological development. Likewise, it is assumed that the TFP, which composes the unexplained part of the economic growth with the changes in physical 59

Ömer Yalçinkaya, Muhammet Daştan and Kerem Karabulut / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 055-071 quantities of production factors in the form of physical and human capital accumulation, shows the increases in production provided only by technological development as Solow residual (Solow, 1956). Under these assumptions, in the CD type of production function, the level of technological development consisting of increases in TFP can be written as follows.

In this context, the CD type of model defined in equation 1, which will be estimated econometrically, can be derived as an extended form as follows: + + (3)

By expanding CD type of production function, the definition of econometric models which are including the other potential determinants of economic growth is often used in the empirical econometric literature. (See some the studies in this context: Barro (1991), Levine and Renelt (1992), Sala-i-Martin (1997), Temple (2000), Rodrik (2012)). In this study, the model defined in the equation 3 to analyze the effects of tourism receipts on economic growth is investigated through panel data methodology because of time series of WTR-20 countries are used together. The econometric model, which will be estimated to establish the effects of tourism receipts on economic growth within the second generation panel data analysis methodology considering the cross-sectional dependence, is defined in the equation 4.3 Model: + + (4)

Here (α) shows the constant parameter, (β) shows the slope parameter, (ε) is the error term, (t) indicates the time dimension, and (i) indicates the cross-section units. To avoid spurious regression in panel data analysis and to obtain more consistent results, it is necessary to examine the stationary states of the series in the model (Tatoğlu, 2013:199). At the same time, the model, which will be used to determine the stability of the series in the panel data analysis, is divided into first and second generation according to whether or not the CSD exists in the panel units. In the first generation panel unit root tests, it is assumed that a shock arisen in one of the constituent sections of the series affects whole units at an equal rate while it is expected that a shock appeared in one of the sections affects each unit at different rates. In this context, in case of an existence of CSD among the constituent units of panel, first generation panel unit root tests do not provide the consistent results (Hadri, 2000; Levin et al., 2002; Im et al., 2003; etc.) whereas second generation panel unit root tests providing more consistent results (Taylor and Sarno, 1998; Breuer et al., 2002; Pesaran, 2007; Palm et al., 2011; Hadri and Kurozumi, 2012; Pesaran et al., 2013; etc.) can be used. For this reason, it is necessary to investigate the CSD in the series / cointegration equations of models and to determine required unit root tests and sequent tests before estimating models in the panel data analysis (Menyah et al., 2014, pp. 390-391). Furthermore, it is necessary to observe time (T) and cross-section (N) of series when searching CSD. In case of T>N, using Breusch and Pagan (1980) CD-LM1 test is required while in case of T=N, employing Pesaran (2004) CD-LM2 is required. The CD-LM1 and CD-LM2 tests are based on an equation as follows:

Here the term ( ) indicates the correlation among the error series, the term ( ) shows the error series obtained section units by using least squares methodology (for t number of observa-

3 In this study, Stata 14.0, Gauss 10.0 and EViews 10.0 econometric software packages are used for estimation of the defined model. 60

Ömer Yalçinkaya, Muhammet Daştan and Kerem Karabulut / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 055-071 tion i= 1,2,…,n). C-LM1 and CD-LM2 tests, which can give biased results in cases of group mean is zero and unit mean is different from zero, are developed with CD-LMadj test as equation 6 through adding the average of cross-sections ( and variance of cross-sections ( to test statistics by Pesaran et al. (2008).

In this context, calculated CD-LMadj test statistics can give more consistent results in contrast with CD-LM1 and CD-LM1 test statistics when group mean is zero and unit mean is different than zero. This test, which is also called as the adjusted CD-LM test, can be used in all alternative cases of time and section dimensions of constituent series of the panel (Pesaran et al., 2008, pp. 105- 127). Also, in the CD-LM tests, the existence of CSD tested with the null hypothesis stating that there is no cross-sectional dependence in the series or model. In case of rejection of the null hypothesis, the existence of CSD in the series or model is accepted in the CD-LM test. The CD-LM test is assumed to have a standard normal distribution. In series in the model conducted for WTR-20 countries and in the co-integration equation, the existence of CSD was examined by CD-LM1 and CD-LMadj tests in accordance with T and N conditions. The test results are shown in Table 2.

Table 2. CD-LM Test Results

Constant + Trend CD-LM Test Statistics Variables CD-LM1 CD-LMadj L RGDP 875.82***[0.000] 150.37***[0.000] 2 RGFCI 631.30***[0.000] 96.54***[0.000] 3 EL 378.89***[0.000] 119.93***[0.000] 2 TFP 643.57***[0.000] 97.96***[0.000] 2 ITR 521.46***[0.000] 100.39***[0.000] 3 Model 766.30***[0.000] 2.22**[0.013] 3

Note The signs “***” and “**” indicates the existence of CSD in the series and model at 1% and 5% significance levels, respectively. Column “L” shows the determined optimal lag length with Schwarz information criterion for the variables. Values in the box brackets “[ ]” indicates probabilities of test statistics.

When the results in table 2 are examined, it is seen that the probability values of the CD-LM test statistics calculated in the Constant + Trend form are lower than 0.05 for all of the variables and cointegration equation in the defined model for WTR-20 countries. In this case, it is necessary to reject the null hypothesis created according to CD-LM tests for all of the variables and cointegration equation in WTR-20 countries. These results show that the cross-section units, which constitute the WTR-20 panel, are interdependent. Also, they refer that using second generation panel data test techniques which account for the existence of CSD in the later stages of analysis should be used (Baltagi, 2008, pp. 10-11). In this context, the stationary condition of series in the defined model is examined by CADF (Cross-sectional Augmented Dickey-Fuller) second generation panel unit root test developed by Pesaran (2007) considers CSD. In this test, firstly the CADF test statistics are found for all cross- sectional units in the panel then CIPS ((Cross-Sectionally Augmented IPS) statics are calculated for panel-wide by calculating arithmetic mean of these test statistics. CADF test statistics which can

Ömer Yalçinkaya, Muhammet Daştan and Kerem Karabulut / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 055-071 give consistent results by using in all of the alternative cases among T and N are calculated as equation 7: y' M y t(N,T)  i i i1 (7) 2 ' 1/ 2  yi1M i yi1 CIPS statistics values calculated by using CADF test statistics in equation 2 are obtained as equation 8: n CIPS  N 1  t(N ,T ) (8) i1

The calculated CADF and CIPS test statistics are compared with critical table values constitut- ed by Monte Carlo simulations and hypotheses are tested for stationary. In case of the computed CADF and CIPS test statistics is higher than the critical table in absolute value, the null hypothesis which claims “the series has unit root” is rejected (Pesaran, 2007, pp. 265-312). Stationarity of the variables in the defined model tested by using CADF Panel Unit Root test and the results are shown in table 3.

Table 3. CADF Panel Unit Root Test Results

Constant + Trend CIPS Test Statistics Variables Level First Difference L RGDP -2.53 -4.68*** 2 RGFCI -2.41 -3.90*** 3 EL -2.53 -2.98*** 2 TFP -1.89 -2.75** 2 ITR -2.65 -3.27*** 3 % 1 -2.92 Critical Values % 5 -2.73

Note “***” and “**” signs in the table states variables are stationary at 1% and 5% significance levels, respectively. CIPS test statistic critical table values are taken from the study of Pesaran (2007) according to T and N conditions. For information about column L, see the table 2.

When the results are examined in table 3, all of the variables in the described model on WTR- 20 countries are not stationary at levels, but they are stationary at first differences at 5% significance level. Since the calculated CIPS statistics for the variables in the Constant-Trend form at first differences are higher than 0.05 significance level in absolute value, the null hypotheses are rejected. After determining all of the variables in the model are stationary at first differences according to CADF panel unit root test, to avoid spurious unit root and to detect consistency of the results, stationary is also tested through Multifactor Panel Unit Root Test-MPURT developed by Peseran et al. (2013). MPURT test is based on the CIPS Panel Unit Root Test developed by Peseran (2007) and CSB (Simple Average of Cross-Sectional Augmented Sargan-Bhargava) test developed by SB test considering CSD. In the MPURT Panel Unit Root Test, the multifactor error structure of constituent sections of the panel includes the information of k observable factors depended on observable time series and m non-observable factors, and enables to resolve autocorrelation stemming from error structures of factors. Therefore, a stationary analysis that can be effective in macroeconomic variables such as production, interest rate, unemployment rate, etc. and series such as technological shocks and 62

Ömer Yalçinkaya, Muhammet Daştan and Kerem Karabulut / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 055-071 fiscal policies can be conducted by using created CIPSm and CSBm test statistics. Also, stationary analysis considering the effects of common factors that cause CSD can be performed by using CIPSm and CSBm test statistics. Using all cases between T and N, the CIPSm and CSBm test statistics can give consistent results. These test statistics are computed as equation 9 and 10.

Here the term ( ) shows the sample distribution of the panel. The calculated CIPSm and CSBm test statistics values as a result of the MPURT test are compared to critical table values which formed by Pesaran et al., (2013) through using stochastic simulation methodology and then hypotheses for stationarity are being tested. In case of calculated CIPSm and CSBm test statistics are higher than the table critical values, the null hypothesis referring that “there is no unit root in series for all of the constituent cross-sectional units of the panel" or “series is not cointegrated” is accepted (Pesaran et al., 2013, pp. 96-99). Stationarity conditions of the variables in the defined model are tested by using CIPSm and CSBm test statistics. Here RGDP, ITR, and TFP series, which are thought to be effective on the formation of CSD in the series, are used as a multifactor. Tests results are shown in table 4.

Table 4. MPURT Panel Unit Root Test Results

Constant + Trend MPURT Test Statistics Level Firs Difference Multi Factors Variables L L CIPSm CSBm CIPSm CSBm TFP-ITR RGDP 0.000 0.067 2 -2.96*** 0.052*** 1 RGFCI 0.000 0.060 2 -5.21*** 0.067*** 1 RGDP-ITR EL 0.000 0.084 2 -5.92*** 0.068*** 1 TFP 0.000 0.072 2 -3.94*** 0.037*** 1 RGDP-TFP ITR 0.000 0.069 2 -3.27*** 0.051*** 1 % 1 -3.43 0.036 2 -2.92 0.084 1 Critical Values % 5 -2.77 0.043 2 -2.73 0.069 1

Note “***” and “**” signs in the table states that the variables are stationary at 1% and 5% significance levels, respectively. Critical table values for two-factor CIPSm and CSBm test statistics are obtained from the study of Peseran et al., (2013) in conformity with T and N conditions. For information about column “L”, see the table 2.

When the results in the table 4 are examined, all of the variables in the defined model on WTR- 20 countries are not stationary at level but stationary ate first difference at 5% significance level. Since the calculated CIPSm and CSBm statistics for the variables in the Constant-Trend form at first differences are higher than 0.05 significance level, the null hypotheses are rejected. These results indicate that stationarities of the variables at first difference in the described model are valid even when the RGDP, ITR and TFP series, which are thought to be effective in the formation of CSD in the variables, are used as multifactor.

Ömer Yalçinkaya, Muhammet Daştan and Kerem Karabulut / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 055-071 According to CADF and MPURT Panel Unit Root Test results, taking first differences of the variables, which are not stationary at level but stationary at first difference, can remove the effects of the incidental shocks occurred in the previous period in the variables and possible cointegration relationships between series in the long run. Even if the variables are not stationary at level, it is possible that there may be a composition of these series in which the series are stationary, and cointegration tests can determine this condition (Tarı, 2010, p. 415). When there is no existence of CSD in models, first-generation panel cointegration tests can be used (Johansen 1988, Pedroni 1999, Kao 1999, etc.). In case of cross-sectional dependency, long-run relations between the series are examined by the help of the second-generation panel cointegration tests (Westerlund ve Edgerton, 2007; Westerlund, 2008; Gengenbach et al., 2016; etc.). The Westerlund and Edgerton (2007) panel cointegration test used in this study is based on the Lagrange Multiplier (LM) test developed by McCoskey and Kao (1998) and uses the bootstrap feature to allow correlation between cross-sections. In this test, which allows autocorrelation and heteroscedasticity in the cointegration equation, long-run relations are examined through normally distributed LM test statistics as follows:

Here the term( ) shows the zero mean-variance and sub-totals (which are estimated by FMOLS (Fully Modified Ordinary Least Square)) of independently-identically distributed error term. The term ( ) shows the estimated long-run variance. In case of calculated LM test statistics are higher than the table critical value (1.65), the null hypothesis referring that “there is no cointegration relation among cross-section units in the panel” is accepted at 5% significance level (Wester- lund and Edgerton, 2007, pp. 185-190). Further, homogeneity of the slope coefficients in the cointegration equation can be tested by using Slope Homogeneity Tests developed by Peseran and Yamagata (2008). In this test, whether or not the slope coefficients in the cointegration equation vary across cross-section units examined by ( adj) test statistics with the null hypothesis referring that “slope coefficients are homogeneous.”

In case of the calculated ( adj) test statistics probability value is higher than 0.05, the null hypothesis is accepted at 5% significance level, and it is decided that the cointegration coefficients are homogeneous in the constituent cross-section units of the panel (Pesaran and Yamagata, 2008:

50-93). Test results of the LM and ( adj) which examine the long-run relations among the variables and homogeneity of the coefficients in the defined model for WTR-20 countries are shown in table 5.

Table 5. Panel Cointegration and Panel AMG Test Results

Variables Coefficients Standard Error RGFCI 0.1027*** 0.0155 [0.000] EL 0.4212*** 0.0578 [0.000] TFP 0.7282*** 0.0583 [0.000] ITR 0.0053** 0.0027 [0.046] Constant Term (C) 1.8924*** 0.2280 [0.000] Test Statistics (Constant + LM 23.80º [0.937]

Trend) ( adj) -0.065ª [0.526]

Note “***” and “**” signs state that the t-statistics of the coefficients are significant at 1% and 5% significance levels, respectively. The sign “º” indicates that and existence of cointegration relation between the series in the model at 1% significance level. The sign “ª” states that slope coefficient of cointegration equa- 64

Ömer Yalçinkaya, Muhammet Daştan and Kerem Karabulut / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 055-071 tion in the model is homogeneous at 1% significance level and the probability values are shown in the box brackets “[ ].” When the LM test results are viewed in table 5, it is seen that the null hypothesis conducted according to the Constant+Trend form is accepted at %1 significance level since the calculated LM test statistics for the defined model are higher than the critical table value (2.33). That is, variables in the model are cointegrated in the long-run. Also, When the ( adj) test statistics are viewed in table 5, it is seen that the calculated probability values of the ( adj) test statistics for the defined model are higher than 0.05 and the null hypothesis is accepted. These results indicate that the constant term and slope coefficients are homogeneous in the cross-section units and the long-run cointegrating relations are valid for the panel-wide. After detecting that the variables are stationary at first difference and affecting each other, and slope coefficients are homogeneous, the long-run coefficients in the model should be estimated by appropriate methodologies. In this context, the effects of tourism receipts on economic growth in WTR-20 countries can be tested by Panel AMG (Augmented Mean Group) which consider all conditions explained above. In the Panel AMG methodology developed by Eberhart and Bond (2009), the long-run cointegration coefficients for panel-wide are calculated by weighting the arithmetic means of cointegration coefficients of cross-sections in the panel. In the Panel AMG methodology, estimation of long-run cointegration coefficients for panel-wide and cross-sections in the panel is based on following equations.

The term ( ) shows the vector of observable covariates, the terms ( ) and ( ) indicate the unobserved common factors and the term ( ) shows the factor loadings related to sections in the panel. In that sense, the long-run cointegration coefficients are estimated by considering the common factors in series and dynamic effects in the Panel AMG methodology. Also, the Panel AMG estimator is used in case of an existence of endogeneity problem based on the error term, can produce effective results for unbalanced panel data sets (Eberhardt and Bond, 2009:1-4). The conducted model was estimated by Panel AMG methodology to detect the effects of tourism receipts on economic growth for WTR-20 countries in this study. The results are shown in table 5. When the results in table 5 are viewed, the coefficients of the explanatory variables (RGFCI, EL, TFP, and ITR) in the defined model for WTR-20 countries are positive as expected and statistically significant at 1% and 5% significance levels. These results indicate that increases/developments in physical-human capital accumulation, technological development level and international tourism receipts in WTR-20 countries have a positive and statistically significant impact on economic growth in the research period. Furthermore, when the results of the model are examined with regards to coefficients of explanatory variables, it is seen that the positive and statistically significant effects of RGFCI, EL, TFP, and ITR on economic growth, respectively sorted according to their sizes, are TFP, EL, RGFCI, and ITR. These results show that the effects of the related variables on economic growth are positive and statistically significant. These results also indicate that the effect sizes of the variables are sorted from highest to lowest as technological development level, human capital accumulation, physical capital accumulation, and international tourism receipts, respectively. All of these results revealed that the economic growth performances of the WTR group countries are respectively affected by technological development level, human capital accumulation, physical capital accumulation, and tourism receipts (When the long-run determinations of economic growth are given). After detecting the long-run effects of tourism receipts on economic growth in the defined model for WTR-20 countries, the direction of these effects can be examined by causality tests. In this study, the direction of the long-run causality relationships between tourism receipts and eco- 65

Ömer Yalçinkaya, Muhammet Daştan and Kerem Karabulut / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 055-071 nomic growth variables are investigated with Panel Bootstrap Causality Tets (PBC) developed by Kónya (2006). The PBC test, which considers CSD, is based on SUR-Seemingly Unrelated Regres- sions model and it can give consistent results even when there is an existence of a simultaneous correlation between cross-section units in the panel. Also, the PBC test allows the use of extra information provided by panel data. Furthermore, since it is not necessary to determine the stationarity and cointegration conditions of the series in the PBC Test, the information losses that may occur in the series can also be avoided. In this test, the causality relationships between the two variables (y and x) are investigated through the following equations based on the SUR method:

Here the term (t) (where t=1,2,3,…., T) determines the time dimension in the panel. The term (N) (where N=i=(1,2,3,…., N) indicates the number of cross-sections in the panel and the term (l) indicates the lag lengths for the variables. Causality relations between the variables (x and y) are examined by Fisher Test statistics calculated by using bootstrap in the panel-wide (Kónya, 2006, pp. 979-981). In case of probability values of calculated Fisher Test statistics are lower than 0.05 the null hypothesis referring that “there is no causality relationship among all of the cross-sections in the panel” is rejected at 5% significance level and then it is decided that there is a causality relationship between the variables. The PBC Test results which examine the causality relationship between tourism receipts and economic growth variables are shown in table 6.

Table 6. PBC Test Results

Hypotheses Fisher Statistics RGDP does not cause TR. 33.30 [0.764] ITR does not cause RGDP. 56.47** [0.044]

Note “**” indicates the rejection of the null hypothesis at 5% significance level, % 5. Reported probability values for Fisher Test statistics are obtained from (10.000) bootstrap distribution.

When PBC test results in table 6 are examined, it is seen that there is positive unilateral causality running from tourism receipt to economic growth in the WTR-20 countries since the probability values of the calculated Fisher statistics are lower than 0.05 for ITR and GRDP variables under the related conditions. These results show that the resultant increases in international tourism receipts of WTR-20 countries cause these countries’ economic growth performances to increase. That is, these results are in keeping with the Tourism-Led growth hypothesis and they, however, indicate that the inverse implication is not valid.

CONCLUSION In this study, the effects of tourism receipts on economic growth will be investigated econometrically for the top 20 countries earning the most from international tourism (WTR-20) in the world for the period 1996-2016. From this aspect, this study aimed to empirically evaluate whether international tourism receipts have an effect on economic growth performances of the developed and developing countries in the WTR-20 group as proposed by theoretical literature under the

Ömer Yalçinkaya, Muhammet Daştan and Kerem Karabulut / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 055-071 tourism-led growth hypothesis. To determine the effects of international tourism receipts on economic growth for WTR-20 group countries, a model, is an extended form of Cobb-Douglas type of production function, will be estimated under the second-generation panel data analysis considering cross-sectional dependence. As a result of this study, the results of the defined model for WTR- 20 group countries for the period 1996-2016 are in keeping with the Tourism-Led growth hypothesis, and they can be summarized as a whole as follows. In this context, it is confirmed that all of the used representative variables for physical capital accumulation, human capital accumulation, technological development level, and international tourism receipts, without exception, have a positive and statistically significant effect on economic growth in WTR-20 countries for the research period. These results show that increases / developments existed in physical-human capital accumulation, technological development level and international tourism receipts have a positive and statistically significant effect on economic growth performances of WTR-20 countries in the research period. Moreover, it is revealed that the positive and statistically significant effects of these variables on economic growth in the long-run are respectively sorted according to their sizes as technological development level, physical-human capital accumulation, and international tourism receipts. That is, these results indicate that the physical-human capital accumulation, technological development level, and international tourism receipts have a positive and statistically significant effect on economic growth in the WTR-20 countries, and they also state that economic growth performances of the WTR group countries (When the long-run determinations of economic growth are given) are mostly affected by technological development level, human capital accumulation, physical capital accumulation, and tourism receipts, respectively. Also, the obtained results for the long-run effects of tourism receipts on economic growth are also confirmed by the direction of the causality relationship among the variables. In this context, it is detected that there is positive and unilateral causality running from international tourism receipts to economic growth in the WTR-20 group countries for the research period. These findings indicate that the resultant increases in international tourism receipts in the WTR-20 countries, as in keeping with tourism-led growth hypothesis, cause economic growth performances to increase. However, these results indicate that the inverse implication is not valid. These results obtained from defined model indicate that international tourism receipts (When the long-run determinations of economic growth are given) have a significant effect on providing economic growth and gaining sustainability in WTR-20 group countries with their current structures. In this context, it is necessary to design and implement supply-demand side policies by policymakers in developed and developing countries in the WTR-20 group to protect and strengthen the current position of the determined links between international tourism receipts and economic growth for the research period. In this direction, it is crucial to develop supply-side tourism policies aimed at diversifying existing tourism activities in the countries of the WTR-20 group and spreading them to country-wide and to all seasons. It is also crucial to develop demand-side policies aimed at increasing the quality and publicity of product-service in the tourism sector. In this way, it would be possible to get more benefit from current tourism potentials of developed and developing countries in the WTR-20 group and to be able to gain sustainability for their worldwide positions concerning international tourism receipts. Besides, sustainability of the effects of the tourism sector, which has achieved significant development trend since 1996, and international revenues from this industry on economic growth would be possible for WTR-20 group countries. Of course, all of these policy recommendations for the development of the tourism sector and increasing international tourism receipts have greater importance for developing countries in WTR-20 group as Thailand, China, India, Mexico, Turkey, and Malaysia, which have much more need of tourism-led growth strategy to finance economic growth.

Ömer Yalçinkaya, Muhammet Daştan and Kerem Karabulut / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 055-071 Besides, in studies covering tourism sector will be conducted in the near future, it is thought that investigation of the determinants of international tourism revenues, which have a significant effect on the economic growth performance of the WTR-20 countries, and establishment of the most effective factors on tourism receipts and making policy implications in this direction would provide development of the literature.

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Alla Khomutenko / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 073-081

Montenegrin Journal of Economics

Vol. 14, No. 3 (2018), 073-081 ‘

Specific Methodological Approaches to Managing State Finances

ALLA KHOMUTENKO1

1 Associate Professor, Odessa National Economic University, Ukraine, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received June 17, 2018 The article considers advantages and shortcomings of methodologi- Revised from June 26, 2018 cal approaches which are applied in public finances management, Accepted August 19, 2018 namely: synergy, structural and functional, functional and cost, and Available online September 15, 2018 socio--cultural approaches. In particular, by means of synergy approach it was defined that fluctuations which took place in Ukraine in 2014 (emergence of separatist sentiments and the Russian JEL classification: Federation`s direct invasion on the Ukraine territory) entailed a B41, Н83, F65. wave of transformations in all the public life spheres, including public finances management. Besides, influence factors on the DOI: 10.14254/1800-5845/2018.14-3.5 public finances control system were defined within such approach. Reasonably, that use of structural and functional approach will Keywords: promote to attach the state bodies with certain functional duties to the relevant public institutes. The model of such distribution is of- Public finances, fered in the article. It is noted, the effect of such management im- synergetic approach, plementation can be determined by structural and cost approach in structural andfunctional approach, public finances management, that is the price which society will pay value analysis, by for it. Based on empirical researches it is reasonable that state socio-cultural approach. managers in Ukraine without providing economic growth spend . more and more budgetary funds to maintain themselves. The research determined, it is impossible to satisfy public interests in management of public finances not using socio--cultural approach, which procedures are formed in it.

INTRODUCTION The practice of carrying out reforms in Ukraine through the change of populist slogans, political changes, unconsidered adoption of foreign experience and fragmentary improvement of legal acts without scientific rethinking of the whole system of state administration only strengthens the political crisis, leads to social disturbances and conflicts. Formation of the scientific basis for a qualitative improvement of the public administration system is impossible without synthesis of relevant

Alla Khomutenko / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 073-081 methodological approaches that will promote the use of effective methods, instruments and technologies of targeted influence on the object. The use of an expedient methodological apparatus takes on special significance when the object of management is provided by the institutions of support, in particular public finances. As it is known, public finances are economic relations that arise in connection with the formation, distribution and use of centralized money market funds at the macro level (Wu ad Chen, 2017; Aniunas et al, 2017). Thus, through finances, the compulsory alienation of a part of the social product is carried out in order to fulfill the state's functions –to ensure social and economic development, to protect the rule of law and state borders. At the same time, satisfaction and / or the protection of public and personal interests are impossible without elimination of structural distortions and deformations, imbalances and disproportions in the public financial management, which requires, first of all, the search for relevant methodological approaches to such management. Methodological approaches to the management of public finances combine a set of dialecti- cally interrelated methods of the paradigm cognition of social life as a whole and of individual spheres of society’sactivity, which are within the framework of the influence of the respective authorized state bodies and institutions. The methodological approaches to public finance management include: systematic, holistic, project-oriented, process approach, cybernetic, informational, institutional, signal approach, synergetic, structure functional approach, functional-cost approach, socio-cultural approach, etc. We note that the last four approaches mentioned above have become the subject of this study.

1. THE ANALYSIS OF RECENT RESEARCH AND PUBLICATIONS. Since the study on the methodology of public finance management is at the intersection of the public administration science, sociology, political science and financial science, the achievements of scientists in all these fields of research were subject to analysis. The methodology of public administration was the subject of research by such scientists as: G. Atamanchuk (2013), J. Buchanan (1967), H. Eliers (2014), J. Gruber (2011), P. Jain (1989), Y. Kaul', P. Kunsysan'iu (2006), R. Mus- grave (2004), V. Fedosov (2010) and other scientists have devoted their scientific works to state and public finances. The purpose of the article is to study some of the methodological approaches to public finance management.

2. UNSOLVED ISSUES THAT ARE A PART OF A COMMON PROBLEM. Paying tribute to the scientific work of these scientists, it is necessary to note the lack of a systemic conceptual and theoretical model and the developed methodology for public financial management. That is why the existing methodological approaches require a comprehensive study to synthesize the most relevant and actual ones. And these approaches will contribute to the achievement of the goal of public finance management -satisfaction, time-varying public and personal interests. The synergetic approach, unlike the system approach, which is the most common practice in management, not only explores the management system, but also takes into account the peculiarities of its development and functioning, since it is based on the possibility of self-organization of the system. The methodology of the synergetic approach implies that the periods of stable development (attractors) of the system are changed by crises, certain turning points (bifurcation points), after 74

Alla Khomutenko / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 073-081 which alternatives to social development appear. Random factors (fluctuations) at bifurcation points affect system changes. As is known, bifurcation is of two: systemic and structural.Systemic crisis of public administration in general and public finance management, in particular after overcoming a bifurcation point, lead to self-destruction of the state and the emergence of new formations. An example of a systemic bifurcation in public administration is the Yugoslav federation, which collapse is associated with certain fluctuations because of President Tito's death and the ineffective economic and national policies pursued in the country after his death. As a result, new political entities appeared on the map of the world (Shakhin, 2013). The crisis of 2014 in Ukraine is considered to be a systemic one (Elektronnyy resurs, 2014). Internal factors (fluctuations) - the emergence of separatist sentiments and external factors - the direct invasion of the Russian Federation on the territory of Ukraine has affected the division of the country and the formation of self-proclaimed new political entities. The methodology of the synergetic approach implies the need to study the parameters of the system development, the design of various scenarios for such development, risk assessment for each of the scenarios with the aim of early detection of the exogenous and endogenous factors of influence (fluctuations). Based on the above, we believe that the system of public finance management can be developed in the following scenarios:  basic scenario - legal regulation of the economic sphere and the sphere of public administration is relatively stable, the level of results achieved by managers varies within 5% (which does not exceed the threshold of materiality);  target scenario - takes into account the risks of changes in fiscal and public administration policies and their level of influence on the public financial management system;  optimistic scenario- provides for the implementation of reformation measures that will positively affect the satisfaction of the public interests and competitiveness of the country;  pessimistic scenario - a significant reduction in the efficiency of public finances management as a result of the influence of prognostic factors.

It is necessary to take into account the risks of management activities and influence factors, in order to develop the most realistic scenarios for the development of the public financial management system. The risks of a management system can be defined as the probability of occurrence of negative consequences as a result of certain events and / or making wrong management decisions. We believe that such risks can be divided into two groups: economic and functional. We propose to refer to economic risks, in particular, the risk of imbalance in the respective funds as a result of a decrease in the volume of cash inflows and / or an increase of cash outflows. In our opinion, functional risks of public finance management are the following:  the risk of abuse - the deterioration of public finances as a result of fraud, embezzlement, ex- cess of official duties, etc .;  technological risk associated with failures in the work of computer programs and equipment;  operational risk is determined by the probability of occurrence of inconsistency between expenses on activity of the management bodies and efficiency of such activity;  strategic risk - the risk of making mistakes in implementing the strategy of public financial management;  risk of distrust, loss of reputation associated with the de-legitimization of power.

In turn, the factors of influence on public financial management can be divided into: objective and subjective (depending on the degree of human participation), exogenous and endogenous (external and internal), direct and indirect influence. Thus, the main objective and subjective factors of influence are shown in figure 1. 75

Alla Khomutenko / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 073-081 Figure 1. The main objective and subjective factors of influence on the system of public financial management

Source: compiled by the author

It seems that the distribution of factors in fleecing public financial management system on exogenous and endogenous should be based on classifications under which they can be combined (Table 1).

Table 1. The main exo- and endogenous factors of influence on the public finance management system

Group of Exogenous Endogenous factors - changes in international normative legal - changes in the system Legal documents, signing of international agreements, of legal norms of the country etc. - changes in the distribution of powers within Organizational - changes in the system of public institutions the management system; -changes in office administration

Alla Khomutenko / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 073-081

- change of international standards of budget changes in methods, tools and and management accounting; Methodological levers used by management - change of scientific approaches that underpin entities economic policy and public administration; - changes in the economic situation and the main macroeconomic indicators of economic development (GDP, inflation, unemployment, - financial potential of the country; Economic budget deficits); - international financial - changes in the budget, tax, debt policy assistance; of the state; - influence of the world economy - administrative information; information on the activities Information - mass information; of management entities factors - legal information; and individuals - others - change of the socio-political system of the country; - the course of political force Political - change of the political regime; in the apparatus of government -an unstable political situation in the country

Social - adherence to the principle of social equality; dissatisfaction of managers

- social dissatisfaction and disturbance within the system

Source: compiled by the author

As shown in figure and in table the same facts can be simultaneously subjective and exogenous. At the same time, the proposed differentiation of the factors influencing the system of public finance management allows to determine more accurately the level of their influence on the system of public finance management and to neutralize the negative consequences. Since the synergetic approach involves the self-organization of society, which is not possible without such a factor as a national idea, it is considered that the effectiveness of public administration depends on the level of development of civil society (Ojomah, 2017). That is why the task for institutional development of the institute of civil society is put at the forefront. In addition, the synergetic approach also implies the availability of intellectual capacity in the field of public administration, as concepts, strategies, and programs should be developed and implemented by educated professional managers. So, according to the synergetic approach, there should be continuous self-development of the subjects of management. To this end, managers should regularly take trainings, participate in public councils, and be open to the public. Finally, it should be noted that the synergetic approach takes into account the close interrela- tionships that exist between the ideology of public administration, its organizational and regulatory mechanism and society. Failure to take into account such interconnections when setting the goals of public administration and the directions for their achievement leads to resistance, which negatively reflects both in society and in management (Atamanchuk, 2013). Therefore, we believe that laying the basis for planning and organizing public finance management of the synergetic approach will help to balance public interests and take into account the interests of the individual. The following methodological approach is structural-functional, in which the society is considered as both simply structured and difficulty-differentiated, depending on the degree of organization, social differentiation. At the same time, social facts are functional, that is useful for society 77

Alla Khomutenko / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 073-081 (Shubin, 2010). In the context of public finance management, the structural-functional approach can be used to differentiate public institutions and functional responsibilities of the relevant public administration bodies (Table 2). Since finances in general, and state in particular, is an institution for ensuring the vital functions of the state, separate administrative-territorial units, enterprises (institutions, organizations), it is obvious that such economic relations permeate all spheres of public life, and authorized public financial management entities have the opportunity to influence them. As the table shows, a large part of public institutions implies the need forthe relevant management bodies to take measures to ensure financial security. At the same time, it should be noted that the structural-functional approach, as well as the system does not provide the opportunity to determine the priority directions of the movement of financial resources. In the context of this, the necessity of using together with the structural-functional approach another one, in particular functional-cost approach, is actualized.

Table 2. Model application of a structural-functional approach in the public finance management

Social institution Main functional responsibilities of state bodies - necessary and sufficient financial provision of educational institutions; Institutions of reproduction of - creation of incentives, including financial, for young families, society's life (family, education, etc.) increase of birth rates in the country; - provision of infrastructure development; Institutions of goods production - implementation of measures to ensure the development (industry, agriculture, transport, etc.) of economic sectors; - ensuring the rule of law and religious freedom in the country; - ensuring compliance with the rights and freedoms of citizens; Institutions of regulation of legal and - assistance in the creation of civil society; social relations (law, including property - assistance in the improvement of public control over rights, morality, religion) the observance of ethical norms and rules of conduct; - ensuring the realization of the right to the property; Institutions transferring the values - financial support for the development of culture, science, sports; of the existence and development - implementation of measures for the change (transformation) of an individual's society (culture, of ideology in society, ensuring influence on subjects that science, sport, religion, ideology) form the ideology in the country (if necessary); - influence on the means (instruments) and methods of realizing Institutions of public relations certain interests to achieve the goals defined by the subject management (politics, of the political process in a particular social environment; state, authorities, etc.) - ensuring the functioning of public authorities; - formation of the government authority - sufficient financial support for health care institutions; Support institutions (health, natural - implementation of measures for rational nature management resources,nature restoration, finance) and providing restoration of natural resources; - formation of an effective financial system; - necessary and sufficient financial support for law enforcement Security and human rights institutions authorities, armed forces and judicial system (army, police, court, etc.).

Source: compiled by the author

Alla Khomutenko / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 073-081 In the functional-cost approach, not only the functional responsibilities of the management bodies are taken into account, but the cost of their implementation is estimated. As a result, the effect of the implementation of management and the price that society pays for it are compared. We believe that such an effect can be estimated, for example by comparing the growth of the ma- croindicator, which characterizes the country's economic development - GDP and budget expenditures for the national administration. Thus, over the past ten years, expenditures on general government functions in Ukraine have been growing at a faster pace than GDP (excluding 2007, 2008, 2011, 2016).The largest difference in the values of these indicators was recorded in 2015 - 30.3% and in 2014 - 23.7%, which is primarily due to an increase in the volume of public debt ser- vicing costs. The data indicates that public managers not providing growth "eat" more and more money, which form the state budget of Ukraine. From the above, it follows that the functional-cost approach, providing for cost estimating of management activities, will help to balance the costs of management activities and the effect of it, estimated in monetary terms.

Figure 2. Procedures of socio-cultural approach

Source: compiled by the author

Alla Khomutenko / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 073-081 The selection of the priority of public finance management to satisfy public interests and the provision of high-quality and valuable public goods to the population requires the use a socio- cultural approach based on the unity of culture and sociality that are created and transformed by human activity. The use of this approach in the public finance management predetermines the study of the interaction processes between authorized government bodies and the population (Se- lyutina, 2013, p. 8) and assessment through the methods of social and cultural analysis of the degree of social development. For this purpose the set of research procedures shown in Figure 2 is used. The use of the socio-cultural approach will make it possible to develop a sound strategy for the development of both the public financial management system as a whole and the management of individual components of these finances. In addition, the results of the discussions of the world's leading economists at the World Economic Forum (WEF) in Davos prove that the socio-cultural approach is the future of the economy and the management, because indicators of the country's development are offered the following ones: good work, well-being, environment, justice, health (Ka- ren Jeffrey, Juliet Michaelson). Ensuring an adequate level of such indicators is possible only with the use of socio-cultural approach in public administration. In addition, governance itself, which is often characterized by unofficial shadow forms of interaction such as clan system, corruption, growth of power with capital, clientelism, etc., will be cleared through the use of the socio-cultural approach, since the political elite of the noocrats will be formed.

CONCLUSION The results of the study proved the need to apply the following methodological approaches in public finance management:

 synergetic, which determined that the fluctuations that took place in Ukraine in 2014 (the appearance of separatist sentiments and the direct invasion of the Russian Federation on the territory of Ukraine) affected the division of the country, the formation of self-proclaimed new political entities and have caused a wave of transformations in all spheres of social life, including the public finance management. In addition, it is proposed the author's vision of scenarios for the development of the public finance management system, possible risks for each of the scenarios and exogenous and endogenous factors of influence (fluctuations);  the structural-functional approach, which allows to consolidate the relevant government bodies, endowed with certain functional responsibilities, for the relevant public institutions. This distribution model is proposed in the article;  the functional-cost approach, which will allow to determine effect of the implementation of such management and the price that society pays for it. Based on empirical research the conclusion is that public managers not providing growth "eat" more and more money, which form the state budget of Ukraine.  the socio-cultural approach, because conducting research procedures with the help of social and cultural analysis will contribute to the achievement of the main goal of public finance management – satisfaction of time-varying public interests.

To summarize, the public finance management system is integrated into the system of public administration and the financial system and has both specific and general internal and external links for such systems. In this context, the emerging scientific paradigm of social development, which must be transformed to meet the requirements and challenges of the time, plays an important role. All this leads to a continuous search for relevant methodological approaches to the management of public finances.

Alla Khomutenko / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 073-081

REFERENCES Aniunas, P., Gipiene, G., Valukonis, M., Vijunas, M. (2017), ”Liquidity Risk Management Model for Local Banks”, Transformations in Business & Economics, Vol. 16, No 1 (40), pp. 153-173. Atamanchuk, G. V. (2013), Synergetic aspects of government, available at: http://spkurdyumov. ru/globalization/sinergeticheskie-aspekty-gosudarstvennogo-upravleniya Bourdieu, P. (1993), Sociology of politics, trans. from french, commonly, Ed. andpredisl. N. A. Shmatko. Socio-Logos. Buchanan, J. M. (1967), Public Finance in Democratic Process, The University of North Carolina Press, Chapel Hill, trans. E. Sagalovich and Yu. Kuznetsov under the editorship of G. Das- hevsky, available at: http://www.strana-oz.ru/2002/4/gosudarstvennye-finansy-v-usloviyah- demokratii#_ftn3 Buchanan J., Musgrave, R. (2004), Public finances and social choice. Two opposing views of the state, trans. from english A. Yu. Ishchenko, KM Academy, Kyiv. Eliers, H. (2014), Reform strategies in the field of public finances. National programs and international requirements (IMF, EU, World Bank), available at: http://fu.minfin.gov.ua/docs/FU_14_ 07_007_uk.pdf Fedosov, V., Yuri, S. I. (2010), Theory of Finance, available at: http://westudents.com.ua/ knigi/662-teorya-fnansv-fedosov-vm.html Gruber, J. (2011), Public Finance and Public Policy, Worth Publications, New York, available at: blog.sciencenet.cn/home.php?mod=attachment&id=24268 Jain, P. C. (1989), Economics of Public Finance, Atlantic Publishers & Distributors (P) Limited, available at: https://books.google.com.ua/books?id=L2AhEMv7qeoC&pg=PA2&dq= Pub- lic+finance+definition&redir_esc=y&hl=ru#v=onepage&q=Public%20finance% 20definition&f=false Karen J., Michaelson, J. (2017), Five headline indicators of national success, available at: http://www.neweconomics.org/publications/entry/five-headline-indicators-of-national-success Kaul, I., Kunshisanyu, P. (2006), “The New Public Finance: Responding to Global Challenges”, New York - Oxward, trans. from english I. Gurovoy, available at: http://web.undp.org/ thenewpublicfinance/overview/russian.pdf Ojomah, S. O. (2017), Towards a cultural synergistic approach to globalization, Master's the sisin global studies, VID Specialized University, Stavanger, available at: https://brage.bibsys.no/ xmlui/bitstream/handle/11250/2455694/MGS-320-Ojomah.pdf?sequence=1&isAllowed=y On ways to overcome the systemic crisis and guidelines of the new government of Ukraine (2014), National Institute for Strategic Studies, available at: http://www.niss.gov.ua/public/File/ 2014 _nauk_an_rozrobku/Kriza_2014.pdf Selyutina, N. F. (2013), “Opportunities of methodology of socio-cultural approach in public administration”, Scientific works, No. 1, pp. 112-120, available at: http: //www.kbuapa.kharkov. ua/e-book/db/2013-1/doc/1/10.pdf Shakhin, Y. (2013), “Causes of the collapse of Yugoslavia in the assessment of Ukrainian historiog- raphy”, Bulletin of the Taras Shevchenko National University of Kyiv, No. 1 (114), pp. 66-69 Shubin, S. P. (2010), “Structural-functional theory of social action by T. Parsonsin a political marketing analysis”, available at: http: // irbis-nbuv.gov.ua/cgi-bin/irbis_nbuv/cgiirbis_ 64.exe?C21COM=2&I21DBN=UJRN&P21DBN=UJRN&IMAGE_FILE_DOWNLOAD=1&Image_file _name=PDF/Npchdu_2010_125_112_22.pdf. Wu, Z.-C, Chen, J.-L. (2017), “Financial Obstacles, Bank Credit, and Trade Credit: Evidence from Firm Surveys in China”, Transformations in Business & Economics, Vol. 16, No 2B (41B), pp. 787-801.

Rita Remeikiene, Ligita Gaspareniene and Artiom Volkov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 83-94

Montenegrin Journal of Economics

Vol. 14, No. 3 (2018), 083-094 ‘

Evaluation of the Influence of the Export in Agricultural Products on the Baltic States Economic Growth

RITA REMEIKIENE1, LIGITA GASPARENIENE2 and ARTIOM VOLKOV3

1 Associate Professor, Lithuanian Institute of Agrarian economics, Vilnius, Lithuania, E-mail: [email protected] 2 Associate Professor, Lithuanian Institute of Agrarian economics, Vilnius, Lithuania, E-mail: [email protected] 3 PhD student, Lithuanian Institute of Agrarian economics, Vilnius, Lithuania, E-mail: [email protected]

ARTICLE INFO ABSTRACT Received June 28, 2018 The benefits of agricultural exports’ impact to developing economies Revised from July 18, 2018 have been confirmed by most of researchers. When analyzing the Accepted August 23, 2018 Baltic States, attention should be paid firstly to the peculiarities of Available online September 15, 2018 agriculture and to relatively low subsidies of the EU funds in comparison with the old EU countries. In addition, the support of agricultural sector creates a relatively unequal conditions of competition JEL classification: towards other economic activities. As a result, it is doubtful whether Q17, F13, F63. the export of agricultural products (which was extremely funded during the year of 2002-2013) contributes to the prosperity of Baltic DOI: 10.14254/1800-5845/2018.14-3.6 states’ economies. In order to meet set goal - to research the links between the export in agricultural products and economic growth of Keywords: the Baltic States – correlation and regression analysis was used, covering the year of 2000-2016. Empirical calculations have shown Agricultural products, that the export of agricultural products (by separate sections) con- export, tributes very little to the GDP growth of the Baltic economies; how- Baltic states, ever, it negatively affects labor market indicators (self-employment, economic growth. employment in land sector, the level of labor market). The reasons . lie in rising prices of agricultural products (assessing the price index change), price indices (export growth is linked to higher agricultural prices), technological breakthrough in agriculture, which reduced the need for human capital and greater export opportunities for large farms, while mainly small size farms dominate in the Baltic States.

Rita Remeikiene, Ligita Gaspareniene and Artiom Volkov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 083-094 INTRODUCTION One of the main purposes of economics is to efficiently “allocate resources in order to ensure social welfare, including full employment and high living standards” (Latruffe, 2010, p. 5). As general economics comprises a variety of sectors, each of them can contribute to national and/or regional economic growth. In the era of industrialization (i.e. from 1800s till the 1950s (Enemark, 2001)), the role of the agricultural sector in national and/or regional economic growth was overshadowed by the development of industry and manufacture (Hydayatie, 2014). But the findings of recent studies (Henne- berry, Curry, 2010; Verter, 2015; Verter and Becvarova, 2016, etc.) show that the contribution of agriculture cannot be underestimated. Not only the development of the agricultural sector itself, but also a significant increase in the volumes of the international trade in agricultural products (in particular, agricultural export) is becoming an important catalyst of national and/or regional economic growth (Verter and Becvarova, 2014). Despite the fact that historically agricultural trade was concentrated on gaining the benefits from comparative advantage, currently it is driven by the modern theories of trade (Verter, 2015). The World Trade Organisation is making a huge effort to promote international trade through reduction of trade barriers (quotas, subsidies, direct payments, import tariffs, duties, etc.) (Verter and Osakwe, 2015). The positive effects of this effort are transmitted to all types of international trade, including the trade in agricultural products. The positive links between the international trade in agricultural products and national and/or regional economic growth were identified by numerous scientific studies (Henneberry and Curry, 2010; Sanjuan-Lopez and Dawson, 2010; Ero- khin et al., 2014; Hidayatie, 2014; Verter and Becvarova, 2014; Kang, 2015; Verter and Becva- rova, 2016, etc.). Nevertheless, the largest part of studies confirm the positive effects of the international trade in agricultural products transmitted to developing economies (Wen et al., 2013; Hydayatie, 2014; etc.), while advanced countries are found to gain more benefits from economies of scale and self-efficiency (Verter, 2015; Lescheva et al., 2018). Fast developing, but possessing the post-soviet economic heritage, three Baltic States (Lithu- ania, Latvia, Estonia) can be attributed neither to the category of emerging nor advanced economies. They are showing the trends of international trade openness (especially after accession to the EU economic community), but still maintain comparatively large shares of export to Russia, which makes the general characteristics of the changes in their economic competitiveness hard to define. Having the historical traditions of agriculture and following the European Common Agricul- tural Policy, Baltic States can expect their economies to be driven by the international trade in agricultural products. Therefore, the authors of this article find it purposeful to research the links between the export in agricultural products and the economic growth of the Baltic States. The main purpose of this article is to research the links between the export in agricultural products and economic growth of the Baltic States (Lithuania, Latvia, Estonia). The main purpose was detailed into the following objectives: a) to review the findings of the scientific literature on the impact of the export in agricultural products on economic growth; b) to select and substantiate the methodology of the research; c) to introduce the results of the empirical research on whether the export in agricultural products promotes the growth of three Baltic States – Lithuania, Latvia and Estonia. The methods of the research include systematic and comparative literature analysis, correlation and regression analysis.

Rita Remeikiene, Ligita Gaspareniene and Artiom Volkov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 83-94 1. LITERATURE REVIEW As it was noted by Verter (2015), the landscape of the international trade in agricultural products is changing, which determines growing scientific interest in this area. Some researchers provide the arguments supporting this type of trade as it is considered to ensure varieties of food, increase the food choice for population (Verter, 2015), keep a high level of commodity concentration (Karasova, 2016), maintain the stability of food demand and supply (Erokhin and Ivolga, 2013), and prompt foreign earnings and national income (Sanjuan-Lopez and Dawson, 2010; Verter and Becvarova, 2014; Verter, Bečvarova, 2016, etc.), while others criticize it for protectionism (Laborde and Martin, 2012; Markovic and Markovič, 2014, etc.), distortion of the conditions of free market competition (Josling et al., 2010; Franic and Mikus, 2013, etc.), closiness (Wen et al., 2013; Cai, Song, 2016; Viju et al., 2017, etc.), high-cost production (Wen et al., 2013; Cai, Song, 2016, etc.), inconsistence of long-term prices (Josling et al., 2010; Tothova, 2011; Roux, 2013, etc.) and incomplete international price transmission (Yang et al., 2017). The analysis of the scientific literature has allowed to review the scientific findings on the impact of the international trade in agricultural products on economic growth (see Table 1).

Table 1. Review of the scientific findings on the impact of the international trade in agricultural products on economic growth

Country/Region/ Author(s), year Research methods Findings Economy OLS regression, Agricultural exports lead to economic growth, Granger causality, Verter, Becvarova, but the relationship between the agricultural impulse response Nigeria 2016 degree of openness and economic perfor- function, variance mance is inverse decomposition International trade in agriculture is an im- Sub-Saharan Afri- portant driver of the economic growth in Descriptive analysis of can countries, developing countries where agriculture is the Verter, 2015 annual statistical data advanced econo- major product exporter; advanced countries mies gain benefits from economies of scale and self-efficiency Agricultural trade maintains stable demand Developed and EPACIS – the model of and supply that, in turn, leads to efficient Erokhin et al., 2014 developing econo- partial equilibrium exchanges and stimulates mies economic growth and development Incomplete price transmission mitigates Global trade analysis the domestic price increases as responses project with incorpora- to high international agricultural prices, Yang et al., 2017 China tion of incomplete which, in turn, leads to an increase in price transmission China’s trade deficit and prohibits net food sellers from receiving high prices Endogenous gravity, The potential for agricultural exports, in par- autoregressive distrib- ticular the one which is facilitated through Hydayatie, 2014 Indonesia uted lags, volume free trade agreements, contributes to Indo- chain-link nesian growth Countries’ rankings, provided with the aid Statistical physics, of network’s node centralities, present the graph theory, research Different countries global agricultural commodity trade as a Cai, Song, 2016 paradigm of a complex worldwide closed, imbalanced, diversified and multi- network, improved polar development hardly promoting eco- bootstrap percolation nomic growth There is an obvious corresponding relationship between the trade potential and Wen et al., 2013 Novy model China costs of agricultural products, which means that high costs lead to inadequate trade 85

Rita Remeikiene, Ligita Gaspareniene and Artiom Volkov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 083-094

Direct payments for agriculture have mixed effects on the variability of farm Balanced farm-level income, while a negative significant rela- Severini et al., 2017 panel data, non-linear Italy tionship was found on the national sam- robust regression ple; direct payments for agriculture are not effective in terms of income stabilization Statistical analysis of state regulation of Economic growth is ensured only on condition agriculture parameters Emerging countries economic potential of agriculture as of a Lescheva et al., (Producer Support in comparison to sector is exploited; growth rates of state 2018 Estimate, General selected OECD support for agriculture are synchronised with Services Support Esti- countries the growth rates of the gross product in the mate, Consumer Sup- agricultural sector port Estimate) Agricultural exports can accelerate a balanced growth in all countries if only issues Developed and Laborde, Martin, (trade restrictions and distortions) related to Survey developing coun- 2012 the world trade in primary tries agricultural trade are addressed or drastically reduced Sanjuan-Lopez, Daw- Agricultural exports Grangercause Panel cointegration 42 countries son, 2010 economic growth Henneberry, Curry, Causal relationship The relationship between agricultural exports Pakistan 2010 analysis and economic growth is positive Rice exporting Econometric ap- countries – Paki- The relationship between agricultural exports Kang, 2015 proaches stan, Vietnam, and economic growth is positive Thailand The export of particular agricultural products (coffee and oilseeds) has a significant posi- Co-integration model, tive impact on national economic growth, error correction model, while the export of some kinds of agricultural Yifru, 2015 Ethiopia Granger causality products (pulses) has a negative insignificant model and a positive insignificant (in short and long runs respectively) impact on national economic growth

Source: compiled by the authors.

As it can be seen from Table 1, the scientific findings indicate that the impact of the international trade in agricultural products on economic growth is bidirectional. On one hand, agricultural exports are often found to lead to national economic growth (Sanjuan-Lopez and Dawson, 2010; Erokhin et al., 2014, etc.), especially in developing countries where this type of export makes the major share of the total country’s export (Henneberry and Curry, 2010; Kang, 2015; Verter, 2015, etc.) and where it is facilitated through free trade agreements (Hydayatie, 2014). On the other hand, incomplete price transmission, observed in the international trade in agricultural products, mitigates a domestic price increase, which, in turn, may lead to a growth in a country’s trade deficit (Yang et al., 2017), protectionism and high-cost production typical of this type of trade makes it a closed, imbalanced, diversified and multi-polar development (Wen et al., 2013; Cai and Song, 2016), and direct payments for farmers are ineffective in terms of national income stabilisation (Severini et al., 2017). All of these factors cause the negative impact of the international trade in agricultural products on economic growth. What is more, there is little scientific evidence on the economic growth promoted by the international trade in agricultural products in developed countries (in the area under research, the latter are found to earn more gains from economies of scale and self-efficiency (Verter, 2015)). This further confirms a bidirectional causality running from the international trade in agricultural products towards national economic growth.

Rita Remeikiene, Ligita Gaspareniene and Artiom Volkov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 83-94 Summarising, previous findings of the scientific research in whether the international trade in agricultural products promotes economic growth are contradictory as they indicate bidirectional causality between the international trade in agricultural products and economic growth. The most general findings show that the international trade in agricultural products may ensure economic growth only on condition that the agricultural export accounts for the major share of the total national export (the trend mainly observed in developing economies), the full economic potential of the agricultural sector is exploited, and agricultural export is accelerated by reduction of trade restrictions and distortions. As the situation in the Baltic States, which can be considered transition economies (transferring from a command to free market economic system), thus far has been hardly investigated, the authors of this article find it purposeful to research whether the international trade in agricultural products promotes economic growth of these states.

2. METHODOLOGICAL APPROACH In scientific literature, classical assessment of the links between the phenomena under research is carried out by employing correlation analysis. In order to assess whether the Baltic countries export in agricultural products with different countries worldwide affects its economic growth, we employ the combination of Pearson correlation and multiple regression methods. Pearson correlation coefficient evaluates the strength of the linear relationship. It can be used when X and Y values of the random sizes under observation are measurable by an interval or on a relationship scale, and their two-dimensional distribution is normal. The point estimate of the population Pearson correlation coefficient (sample’s Pearson correlation coefficient) is estimated by the formula (Janilionis, 2015):

. (1) The multiple regression model refers to generalisation of a single variable linear regression model with more than one independent interval variable:

Y = â0 + â1x1 + â2x2 +...+ âkxk + ê. (2) Prognostication of the values of a dependent variable is one of regression purposes. Let us presume that the data comprises n observations in a variable set: (y1, x11, x21, ... xk1), (y2, x12, x22, ... xk2), . . . , (yn, x1n, x2n, ... xkn). The aim is to find the values a0, a1, a2,...,ak for parameters â0, â1, â2,...,  âk so that the function’s ŷ( x )=ŷ(x1,x2,...,xk) = a0 + a1x1 + a2x2 +...+ akxk estimates at the points (x1i, x2i, ... xki) would as little as possible vary from yi, i=1, 2,..., n. The above-mentioned values are selected by applying the least squares method, i.e. they are selected so that residual errors êi = yi - n  2 ŷ( x i) = yi – (a0 + a1x1i + a2x2i +...+akxki), would show the lowest square sum SSE = . This way, êi i1 function ŷ( x ), called a regression function, is developed. Prognosticated value Y is obtained by  filling function ŷ( x ) with values x1, x2, ...,xk, which fall into the data coverage area, i.e. xi (min ,max ) .  ij ij j x j x For implementation of the research purpose, time period 2000-2016 and 12 y values describing economic growth were selected (see Table 2):

Rita Remeikiene, Ligita Gaspareniene and Artiom Volkov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 083-094 Table 2: Description of dependent variables

No. Dependent variable (y) Description GDP (gross domestic product) is an indicator for a nation´s economic situation. Gross domestic product at It reflects the total value of all goods and services produced less the value of 1. market prices, million euro goods and services used for intermediate consumption in their production

(Source: Eurostat). Unemployed persons are all persons 15 to 74 years of age (16 to 74 years) who Unemployment, thousands were not employed during the reference week, had actively sought work during 2. persons the past four weeks and were ready to begin working immediately or within two weeks (Source: Eurostat). Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a "self-employment jobs." i.e. jobs where the remuneration is directly dependent Self-employed, total (% of 3. upon the profits derived from the goods and services produced. Self-employed total employment) workers include four sub-categories of employers, own-account workers, members of producers' cooperatives, and contributing family workers (Source: World Bank). Employment is defined as persons of working age who were engaged in any Employment in agriculture activity to produce goods or provide services for pay or profit, whether at work 4. (% of total employment) during the reference period or not at work due to temporary absence from a job, or to working-time arrangement (Source: World Bank). Goods and services include all government payments in exchange for goods and Goods and services ex- 5. services used for the production of market and nonmarket goods and services. pense (% of expense) Own-account capital formation is excluded (Source: World Bank). Compensation of employees consists of all payments in cash, as well as in kind Compensation of employ- (such as food and housing), to employees in return for services rendered, and 6. ees (% of expense) government contributions to social insurance schemes such as social security and pensions that provide benefits to employees (Source: World Bank). Subsidies, grants, and other social benefits include all unrequited, non- repayable transfers on current account to private and public enterprises; grants Subsidies and other trans- 7. to foreign governments, international organizations, and other government units; fers (% of expense) and social security, social assistance benefits, and employer social benefits in cash and in kind (Source: World Bank). Labour force comprises people ages 15 and older who supply labour for the production of goods and services during a specified period. It includes people 8. Labour force who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Labour force size tends to vary during the year as seasonal workers enter and leave (Source: World Bank). Tax revenue refers to compulsory transfers to the central government for public purposes. Certain compulsory transfers such as fines, penalties, and most social 9. Tax revenue (% of GDP) security contributions are excluded. Refunds and corrections of erroneously collected tax revenue are treated as negative revenue (Source: World Bank). Inflation as measured by the consumer price index reflects the annual percent- Inflation, consumer prices age change in the cost to the average consumer of acquiring a basket of goods 10. (annual %) and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used (Source: World Bank). Foreign direct investment refers to direct investment equity flows in the reporting economy. It is the sum of equity capital, reinvestment of earnings, and other Foreign direct investment, capital. Direct investment is a category of cross-border investment associated 11. net inflows (BoP, current with a resident in one economy having control or a significant degree of influ- US$) ence on the management of an enterprise that is resident in another economy (Source: World Bank). Annual percentage growth of the total governmental final consumption expenditure based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Total governmental final consumption expenditure (total govern- Total governmental final mental consumption) includes all current governmental expenditure for pur- 12. consumption expenditure chases of goods and services (including compensation of employees). It also (annual % growth) includes expenditure on national defence and security, but excludes governmental military expenditure that is part of government capital formation (Source: World Bank).

Source: prepared by the authors 88

Rita Remeikiene, Ligita Gaspareniene and Artiom Volkov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 83-94 In this study, the independent variables (x) are Lithuanian, Latvian, and Estonian exports according to sections. Complex multi-regression calculations were performed by employing modern computer software: specialised packages for statistical data analysis (SPSS, EPIINFO, SAS, MINITAB, etc.) and Ms Excel’s specialized sub-system Data Analysis (Regression module).

3. CONDUCTING RESEARCH AND RESULTS After having performed Pearson correlation calculations between exports of agricultural products from / to the rest of the world to the Baltic States and 12 indicators characterizing economic growth, statistically significant, moderate and strong correlations between the export of agricultural products from the Baltic States and these variables were received (see Table 3):

Table 3. Significant statistical relations between exports in terms of sections and economic indicators in the Baltic States

Y Lithuania Latvia Estonia

Gross domestic product rtotal IV sec- I section rtotal (0.940) I section rtotal (0.940) at market prices, mil- (0.855) tion 1,752 0,000 2,114 0,000 lion euro 0,000 1,06

rtotal (- rtotal (- IV sec- Self-employed, total (% I section rtotal (0.575) 0.828) - 0.539) tion of total employment) -2,792 0,000 0,000 0,000 0,910

Employment in agricul- rtotal (- rtotal (- III sec- I section IV section rtotal (-0.746) ture, % of total em- 0.853) 0.875) tion 3,190 4,341 0,000 ployment 0,000 0,000 0,910

rtotal (- rtotal (- IV sec- Goods and services I section I section rtotal (-0.755) 0.805) 0.662) tion expense, % of expense 0,863 3,263 0,000 0,000 0,000 2,079 Subsidies and other - r2002-2013 r2002-2013 transfers, % of ex- (0.865) (0.721) - - pense* 0,000 0,000 I section rtotal (- rtotal (- Labour force, people 0.843) 0.762) - - -0.901 0,000 0,000

* Subsidies for export of agricultural products were launched for the period 2002-2013. Therefore, correlation and regression are applied for two periods, i.e. 2002-2013 and 2014-2016

Source: own calculations

Pearson correlation coefficient values and obtained multiple regression equations allow us to formulate the following conclusions:

 Statistically significant positive and very strong correlation between the total exports of agricultural products and GDP suggests that export in Baltic States contributes to the growth of GDP, but only for a small part of it (see annexes, multiple regression equation for the GDP indicator). Export of agricultural products contributes, although to a very small extent, to the GDP growth of the Baltic States, however, the influence on the weight of sections of agricultural products on GDP in individual countries has been different. For example, the export of section IV to the world (prepared foodstuffs; beverages spirits and vinegar; tobacco and manufactured tobacco substitutes) had the biggest positive influence in Estonia during the period of 2000-2016. 89

Rita Remeikiene, Ligita Gaspareniene and Artiom Volkov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 083-094 Whereas in Lithuania and Latvia section I (live animal and animal products) the weight of export was the biggest in comparison to the export of section III in both countries (Beta Coeffi- cient 1.752 in Lithuania and Beta coefficient 2.114 in Latvia) and export of section IV production exclusively in Lithuania.  Strong negative correlation between self-employment in Lithuania and the total export of agricultural products has shown that the growth of export volumes to the world's countries has had an opposite effect on self-employment, i.e. increasing volumes of exports did not contribute to the increase level of self-employment. From 2000 until 2016, the volumes of export have increased 10 times (in eur), while the level of self-employment (% of total employment) decreased by 7.1% during the period in consideration. In Latvia, statistically weaker negative correlation was established, however, further calculations of multiple regression showed that general exports did not have statistically significant relationships with the level of self-employment in Latvia. The average, but positive relation obtained in Estonia suggests that, as export volumes grew, self-employment tended to grow (during the period of 2000 – 2016 self- employment in Estonia grew up to 1,4 percent, volume of export grew up to 5 times (in eur)). Export of section I and IV contributes not significantly to the dynamics of self-employment in Lithuania and Estonia. In Lithuania, as in the case of GDP, export volume of section I (Beta coefficient -2,792) more affected self-employed people, while in Estonia, export volume of section IV (Beta coefficient 0,910) has contributed to the growth of self-employment.  Similar conclusions can be stated on the strong statistically significant negative relationships in all Baltic States between exports and employment in agriculture, which suggest that the growth of exports has led to a decrease in employment in this sector. When assessing the impact of exporting agricultural products on employment in the agricultural sector, it can be stated, that not significant impact in Baltic States gives different effects: export of section III in Estonia (animal or vegetable fats and oils and their cleavage products; prepared and edible fats; animals or vegetable waxes) had the biggest impact on employment in agriculture (Beta coefficient 0,910), export section I in Lithuania (Beta coefficient 3,190), export section IV in Latvia (Beta coefficient 4,341). In the Baltic States there are mainly small farms that supply their products to domestic markets. Since 2004, when the Baltic States entered the EU, investment programs for agricultural development were launched for the Baltic States. The development has enabled to acquire many new and innovative techniques to meet the demand for lower human capital; the structure of capital has changed. A large number of farms have switched to plant production, so employment in the land sector has dropped dramatically as the need for human resources has decreased. Other reason of inverse connection was fixed increase in the price of agricultural products during the period considered. An analogous justification would apply to labor force, although statistically significant negative connection was obtained only in the cases of Lithuania and Latvia.  Statistically significant negative medium-strong relationships in the cases of Latvia and Esto- nia, and strong relationships in the case of Lithuania between export of agricultural products and goods and services costs suggest that the prices of agricultural products affected the costs of goods and services, i.e. increased export volumes have reduced the costs to a small extent, however this tendency occurred due to rising prices of exported production in agricultural products.  Strong relationships (the case of Lithuania) and medium-strong relationship (the case of Latvia) between subsidies and export during the period of 2002-2013 have revealed, that the export activity in subsidized agricultural products were closely interrelated, since exporters were paid export refunds. During the period of 2013-2016 the EU stopped subsidies for export, so there were no statistically significant relationships recorded during this period.

Rita Remeikiene, Ligita Gaspareniene and Artiom Volkov / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 83-94 CONCLUSION Researches at theoretical level undoubtedly justify the benefits of export to the economies of the countries, but exports of agricultural products due to their specificity and the peculiarities of subsidization lack of researches to prove these statements. The analysis of the problems in the Baltic countries of exporting agricultural products was chosen due to equal EU subsidy programs provided to these countries, similar climatic conditions, all these countries have entered the EU in 2004 and have applied general agricultural policy scheme in single payments for land. The calculations showed that exports of agricultural products insignificantly, but still contributed to the GDP of the Baltic economies. Due to EU agricultural subsidy policies and support programs, other statistically significant relationships revealed the following regularities: exports of agricultural products have a negative statistically significant relationship with self-employment, and employment in the agricultural sector in the Baltic States and labor force level in the case of Lithu- ania. Based on the general principles of increasing export volumes, the level of employment, including self-employment and employment in the agricultural sector, should increase, however while analyzing the agricultural sector, these main causes of inverse relationships are pointed out:

 Price increase (assessing the change of price indices), when exports of agricultural products are expressed in monetary terms, contributing to the formation of inverse relationships;  Launched programs since 2004, have contributed towards the introduction of technology and innovation in agriculture, which resulted in gradually moving towards lower human capital utilization;  In the Baltic States, small farms that supply their products to the domestic market prevail, and therefore large dominant farms reveal the specificity of this phenomenon, when large farms mainly export to foreign markets;  Agricultural sector is closely linked to the villages of the Baltic States, which have a population of about one third of total population, but aging population, international and internal emigra- tion, and social exclusion are secondary factors that have shown a negative relationship between export agricultural products and labor market indicators.

Empirical calculations have shown that when analyzing agricultural products in international trade it is necessary to consider periods of financing by various means and factors, such as prices of agricultural products (price indices), restrictions on international trade in key export partners, European Commission state aid schemes.

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Annexes 1.

Equation of Multiple Regression Explanation Y1 GDPEstonia = 8.070.173-2.88E-0.5*I sec- Reduced export volume by 1 unit of section I, GDPEstonia reduces tion+3.57E-05*II section+3.09E-05*IV by 2.88E-05 eur, when other conditions do not change. section Increased export volume by 1 unit of section II, GDPEstonia increas- GDPLithuania = 9.578,769+4.15E-05*I es by 3.57E-05 eur, when other conditions do not change. section+1,35E-04*III section+1.93E- Increased export volume by 1 unit of section IV, GDPEstonia in- 05*IV section creases by 3.09E-05 eur, when other conditions do not change. GDPLatvia = 9.069,836+7.55E-05*I sec- Beta coefficients show that the biggest impact to GDPEstonia tion-0,001*III section makes export of section IV (1.067). Increased export volume by 1 unit of section I, GDPLithuania increases by 4.15E-05 eur, when other conditions do not change. Increased export volume by 1 unit of section III, GDPLithuania increases by 1.35E-04 eur, when other conditions do not change. Increased export volume by 1 unit of section IV, GDPLithuania increases by 1.93E-05eur, when other conditions do not change. Beta coefficients show that the biggest impact to GDPLithuania makes export of section I (1.752). Increased export volume by 1 unit of section I, GDPLatvia increases by 7.55E-05 eur, when other conditions do not change. Reduced export volume by 1 unit of section III, GDPLatvia reduces by 0,001 show, that the biggest impact to GDPLatvia makes export of section I (2.114). Y3 Self-employedEstonia = 2.18E-08*IV sec- Increased export volume by 1 unit of section I, Self-employedEstonia tion increases by 2.18E-08 percent from all employed, when other Self-employedLithuania = 23.016-2.94E- conditions do not change. 08*I section+1.36E-08*IV section Reduced export volume by 1 unit of section I, Self- employedLithuania reduces by 2.94E-08 percent from all employed, when other conditions do not change. Increased export volume by 1 unit of section IV, Self- employedLithuania increases by 1.36E-08 percent from all employed, when other conditions do not change. Beta coefficients show that the biggest impact to Self- employedLithuania makes export of section I (-2,792) and Self- employedEstonia – makes export of section IV (0,910). Y4 Employment in agriculture Estonia = 6,559- Reduced export volume by 1 unit of section III, Employment in 5.87E-08*III section agricultureEstonia reduces by 5.87E-08 percent from all employed, Employment in agriculture Lithuania =5.35E- when other conditions do not change. 08*I section-3.11E-08*II section Increased export volume by 1 unit of section I, Employment in Employment in agriculture Latvia =-4.21E- agricultureLithuania increases by 5.35E-08 percent from all em- 08*II section-1.19E-06*IIIsection+7.26E- ployed, when other conditions do not change. 08*IV section Reduced export volume by 1 unit of section II, Employment in agricultureLithuania reduces by 3.11E-08 percent from all employed, when other conditions do not change. Reduced export volume by 1 unit of section II, Employment in agricultureLatvia reduces by 4.21E-08 percent from all employed, when other conditions do not change. 93

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Reduced export volume by 1 unit of section III, Employment in agricultureLatvia reduces by 1.19E-06 percent from all employed, when other conditions do not change. Increased export volume by 1 unit of section IV, Employment in agricultureLatvia increases by 7.26E-08 percent from all employed, when other conditions do not change. Beta coefficients show that the biggest impact to Empolyment in agricultureLithuania makes export of section I (3,190), Employed in agricultureEstonia – export of section III (0,910), Employed in agri- cultureLatvia – export of section IV (4,341). Y5 Goods and services expense (% of ex- Increased export volume by 1 unit of section I, Goods and ser- pense) Estonia = 12,904+1.98E-08*I sec- vices expenseEstonia increases by 1.98E-08 percent of total ex- tion-2.19E-08*IV section penses, when other conditions do not change. Goods and services expense (% of ex- Reduced export volume by 1 unit of section IV, Goods and ser- pense) Lithuania = 15,873-7.20E-09*I sec- vices expenseEstonia reduces by 2.19E-08 percent of total expens- tion es, when other conditions do not change. Goods and services expense (% of ex- Reduced export volume by 1 unit of section I, Goods and services pense) Latvia = 15,799+2.02E-08*IV sec- expenseLithuania reduces by 7.20E-09 percent of total expenses, tion-5.73E-08*I section when other conditions do not change. Increased export volume by 1 unit of section IV, Goods and services expenseLatvia increases by 2.02E-08 percent of total expenses, when other conditions do not change. Reduced export volume by 1 unit of section I, Goods and services expenseLatvia reduces by 5.73E-08 percent of total expenses, when other conditions do not change. Beta coefficients show that the biggest impact to Goods and services expenseLithuania makes export of section I (0,863), Goods and services expenseEstonia – export of section IV (2,079), Goods and services expenseLatvia – export of section I (3,263). Y8 Labour forceLithuania = 1.664.692,472- Reduced export volume by 1 unit of section I, Labour forceLithuania 1.79E-04*I section reduces by 1.79E-04 units, when other conditions do not change. Labour forceLatvia = -0,019*I sec- Reduced export volume by 1 unit of section I, Labour forceLatvia tion+0,032*III section+0,006*IV section reduces by 0,019 units, when other conditions do not change. Increased export volume by 1 unit of section III, Labour forceLatvia increases by 0,032 units, when other conditions do not change. Increased export volume by 1 unit of section IV, Labour forceLatvia increases by 0,006 units, when other conditions do not change. Beta coefficients show that the biggest impact to Labour forceLith- uania makes export of section I (0,901), Labour forceLatvia – export of section IV (4,114).

Vladimir Trukhachev, Igor Sklyarov, Yuliya Sklyarova, Sergei Gorlov and Anna Volkogonova / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 095-108

Montenegrin Journal of Economics

Vol. 14, No. 3 (2018), 095-108 ‘

Monitoring of Efficiency of Russian Agricultural Enterprises Functioning and Reserves for Their Sustainable Development

VLADIMIR IVANOVICH TRUKHACHEV1, IGOR YURIEVITCH SKLYAROV2, YULIYA MIKHAILOVNA SKLYAROVA3, SERGEI MIKHAILOVICH GORLOV4, and ANNA VLADIMIROVNA VOLKOGONOVA5,

1 Professor of Stavropol State Agrarian University, rector 2 Professor of Stavropol State Agrarian University [email protected] 3 Professor of Stavropol State Agrarian University [email protected] 4 Professor of North-Caucasian Federal University 5 Lecture of Stavropol State Agrarian University, Master of Economics, Master of Business Administration in Agri- business and Commerce,+7(961)4750000, [email protected]

ARTICLE INFO ABSTRACT Received June 11, 2018 The article deals with the actual problem of finding reserves and Revised from June 28, 2018 developing measures to improve the efficiency of agricultural Accepted August 25, 2018 enterprises for the Russian agrarian sector, which is in a situation of Available online September 15, 2018 shortage of financial and material resources. As the authors point out, despite the great interest in researching reserves to improve the efficiency of agricultural enterprises, there remain many JEL classification: unresolved problems of improving the management tools, O14, Q14, R11. monitoring, and diagnosing the effectiveness of the functioning of agricultural enterprises. In this regard, a study on this topic has DOI: 10.14254/1800-5845/2018.14-3.7 been conducted and its results are presented in the article. Results of an estimation of economic efficiency of agricultural production of Keywords: the Stavropol Territory have been given, the rating of an estimation of efficiency of an agricultural production has been submitted, and Efficiency, directions for improving the mechanism of state support for agriculture, agriculture has been proposed, based on the results of a agrarian sector, differentiated evaluation of the efficiency of agricultural production, agro industrial complex, adapted to modern business conditions. agricultural resources.

INTRODUCTION The economic reforms carried out in the country revealed a number of systemic problems of socio-economic, market-conjuncture, environmental-technogenic, and food, technical and technological nature. The unstable nature of the macroeconomic and microeconomic processes and the dynamics of their key indicators in the post-crisis development phase require an explanation of the reasons for the current instability, the systematization of a set of factors that 95

Vladimir Trukhachev, Igor Sklyarov, Yuliya Sklyarova, Sergei Gorlov and Anna Volkogonova / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 095-108 hinder the pace of improving the efficiency of the functioning of agricultural enterprises as the most important elements of the production sphere of the agro industrial complex. In this connection, the category “economic efficiency” should be considered in the context of the principle of “unity in diversity”, which predetermines the delineation of conceptual, methodological and applied aspects of identification, monitoring and regulation of structural components of the economic category under study. In addition to financial and economic aspects, social, environmental, production and personnel aspects of the efficiency of agricultural production are becoming increasingly important. The study of these issues in a complex allows timely and correctly respond to changes in the internal and external environment, as well as to improve the efficiency of the functioning of the agro-industrial complex of the country as a whole. Systematic study of scientific literature on selected themes has showed that in this interpretation the studies were not conducted, which explains its staging and search character, determines the relevance and timeliness not only in the scientific plan, but also in the context of practice. The questions of improving the methodological tools for monitoring and diagnosing the structural components of economic efficiency, developing recommendations for improving the state agro-food policy, taking into account the differentiation of the subjects according to this criterion, as well as the development of an effective tool for solving complex multifaceted problems of the development of the agricultural sector of the economy, which determines the relevance of the study. Despite the great interest in the problem under investigation, many issues remain to be solved, such as improving the tools for monitoring and diagnosing the performance of agricultural enterprises, which requires further theoretical and scientific methodological generalizations in order to develop practical recommendations that contribute to improving the efficiency of agricultural production in general. All this has predetermined the choice of the topic of the study. The aim of the study is to generalize and develop theoretical provisions and practical recommendations for improving the economic efficiency of agricultural enterprises. To solve the formulated goal, the following problems were set and solved in the study: to develop the foundations of the theory of the effectiveness of agricultural activities and to substantiate the need for demarcation of analytical procedures for diagnosing the effectiveness of agribusiness entities; give organizational and methodological recommendations for improving the system of state regulation of the agrarian sector of the economy. The subject of the study was instruments for monitoring and diagnosing the effectiveness of agricultural enterprises from the perspective of various categories of economic entities in the agricultural sector of the economy. The object of the study is the agricultural enterprises of the Stavropol Territory of Russia with different levels of efficiency.

1. LITERATURE REVIEW In the conditions of the market, enterprises face both the problem of increasing the efficiency of activities in general and the efficient management of certain types of resources. Their solution is impossible without the development of new theoretical provisions, methodological approaches, assessment tools and methodological support for the analysis of effectiveness. It is significant that already one of the first representatives of management theorists G. Emerson considered efficiency the main task of management So, in 1900, he published the book “Efficiency as a basis for management and remuneration of labour” and in 1912 “The Twelve Principles of Efficiency”, considered his main work in the field of management. Emerson made an extremely important contribution to the development of this concept. He saw in efficiency something that was absolutely not revealed by economists - its connection with functionality, which then began to be perceived as something self-evident (Emerson, 2010). 96

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The spread of the essence of the concept of “efficiency” to other spheres of activity was promoted by theoretical developments related to actions in general. In economic science there are the everyday and scientific notions of efficiency. In the ordinary sense, it means that “production takes place with minimal costs, effort and loss” (Dolan, 1994). In some cases, this understanding is also recognized in science. For example, it is argued that economic efficiency “characterizes the relationship between the number of rare resource units that are used in the production process and the resulting quantity of any required product” (McConnell, 1992). In microeconomics, the concepts of technical and economic efficiency are identified. Technical efficiency is understood as the possibility of achieving the set goals (output of a certain volume of products) at the lowest cost, if such factors of production as labour and capital do not limit production. Economic efficiency means choosing a combination of factors of production that are available in a limited size, which allows you to achieve results at the lowest cost. Thus, the enterprise chooses on the basis of prices input factors of production (economic efficiency) in order to achieve maximum production (technical efficiency - Cole, 1973). Later, when the pollution of the Earth’s water and air basins, and the irreplaceable consumption of natural resources, and the accelerated wear of the human body under conditions of intensification of production were forced to be attributed to costs, as well as many other factors not previously considered, then as the initial and basic category of the public production began to consider the socio-economic efficiency. The analysis of attempts to introduce criteria for socio-economic efficiency (or simply efficiency) suggests that all of them can be attributed to two main areas. In the first of them, they tried to continue to determine efficiency, based only on economic criteria, so it was not accidental that it was developed mainly by economists. Within the framework of this direction, socio-economic efficiency, as a rule, was treated as an algebraic sum of direct and indirect economic effect. Such a way out of the narrow economic scope of the concept of “efficiency” required the introduction of a new criterion basis, in the definition of which there is still no unanimity in views and approaches (Alekseeva, 2003). Representatives of the second direction proceed from the fact that in determining the efficiency should be taken into account not only and even not so much the economic aspects as socio-political, psychological, etc. This position seems more adequate, taking into account the “multidimensionality”, the multi-aspect of the concept of “efficiency”. It’s true; in this case, the authors do not have the opportunity to offer convenient and universal criteria for determining effectiveness, which reduces the practical importance of efficiency as a means of comparing ways, methods, and tools of activity (Pjerotic et al, 2017; Serban et al, 2017). In management, one of the most significant criteria of activity is success, which was philosophically justified by representatives of pragmatism. According to the position of W. James, “the truth is created by the successes of this experience” (James, 1997). Therefore, it was necessary to find and highlight that indicator of the activity that would be associated with the success. Efficiency claims the role of such an indicator. It is not by chance that Kotarbinsky T. used the concept of efficiency as one of the central. In accordance with K. R. McConnell and S.L. Bru (McConnell and Bru, 1992) investigating the problem of the effective use or management of limited production resources in order to achieve the maximum satisfaction of the people material needs: “...Economic efficiency also addresses the problem of ‘input – output’. Specifically, it characterizes the relationship between the number of rare resource units that are used in the production process and the resulting quantity of any required product. A large amount of product, obtained from this amount of costs, means an increase in efficiency. A smaller volume of product from a given amount of costs indicates a decrease in efficiency ...” 97

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The problem of efficiency attracts the attention of many academic economists. However, opinions on this issue differ significantly. For example, V. Ya. Feodoritov considers the efficiency of production as an objective category that has independent qualitative and quantitative indicators, as a category that has a historical character (in any case, when the results of work were compared with its costs, its efficiency was revealed, i.e., efficiency is the reaction of the economy to modern social needs and technological progress). It is difficult to disagree with the position of V. Ya. Feodoritov. Along with the resource concept of production efficiency, the supporter of which is V. Ya. Feodoritov (1984) have a costly concept. This point of view was shared by L. M. Konstantinova and Z. V. Sokolinsky (1987), who believed that the concept of economic efficiency should determine the degree of use of resources or costs in social production. Noting that the degree of use of productive resources depends on the economic laws in force in the society and on the ability to use them, the authors made the right conclusion, in our opinion that the study of efficiency and its dynamics falls within the scope of problems and science such as political economy. According to L. I. Abalkin (1990) the concept of resource efficiency of production is not only a comparison of costs and results. This does not provide an exhaustive description of the economic efficiency of production, since the costs do not fully represent production resources, and the cost- effectiveness factors may not coincide with the resource coefficients. It is quite legitimate for the author’s assertion that the essence of the economic efficiency of production is not only in digital relative quantities, but also determined by the production relations. Only on the basis of a steady increase in the efficiency of production is it possible to achieve the main goal of economic reform - the creation of an efficient economic system capable of ensuring the dynamic development of the economy (Atkociuniene and Kiausiene, 2017). Increasing the efficiency of production is not only the result of the achieved level of development of the productive forces, but also the indicator of their use. In order to disclose potential opportunities for increasing production efficiency, it is required to measure efficiency itself, the concept of which is closely related to the scientific justification of its criterion. Therefore, in order to properly determine the most important areas for increasing the economic efficiency of social production, it is necessary to formulate a criterion and indicators of effectiveness.

2. METHODS The methodological and theoretical basis of the study is presented by the works of domestic and foreign scientists and specialists in the field of theory and methodology of assessing and improving the economic efficiency of agricultural activities, as well as relevant legislative and regulatory acts of the Russian Federation and the Stavropol Territory. To obtain scientific results of the research, a systematic approach to the analysis of the phenomena under consideration has been used, as well as statistical and econometric methods, computational-constructive, monographic, abstract-logical and morphological analysis and expert evaluations. The information base for the study was provided by the Ministry of Economic Development, the Ministry of Agriculture of the Stavropol Territory, the Federal State Statistics Service of the region, the materials of scientific publications, expert-analytical assessments of Russian institutes and individual leading scientists in the field of agricultural sector efficiency, primary accounting and reporting data of agricultural organizations, as well as data obtained by the authors as a result of personally conducted studies.

2.1 Empirical data and analysis The conducted study and the conclusions made allow us to clarify the semantic meaning of the economic category “efficiency of agricultural production”. In our opinion, it can be defined as the ratio of the costs incurred to achieve a set of significant social, financial, economic, environmental, production and personnel results of economic activity of the subjects of agricultural activities, obtained not only in 98

Vladimir Trukhachev, Igor Sklyarov, Yuliya Sklyarova, Sergei Gorlov and Anna Volkogonova / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 095-108 the context of satisfying the interests of all subjects of the agrarian economy, but also taking into account the realization of the socially useful function of education, reproduction or preservation of agrarian socio- natural economic systems. The efficiency of agricultural production should be considered not only from the positions of a particular enterprise, but also from all subjects of the agrarian economy (Figure 1). The basis of economic relations between them is their interests. The efficiency of the functioning of any agricultural organization is determined by the degree of satisfaction of the interests of all subjects of the agrarian economy. At the same time, the subjects of each group pursue their goals and are interested in various aspects of the efficiency of the enterprise (Miroshnichenko, 2012). Specific features of agricultural production, in one way or another, in our opinion, cause problems and threats to the development of enterprises of the agro-industrial complex, which in turn inhibit the movement forward and directly affect the efficiency of their functioning. Despite the fact that in recent years, Russia’s agriculture has gradually begun to overcome the protracted crisis, primarily thanks to state support and the inclusion of agriculture in priority sectors of development in the agro-industrial complex, there are still a number of significant problems (Miroshnichenko, 2013). In our opinion, all existing problems of the agro-industrial complex are to be grouped into five main groups: macroeconomic, microeconomic, industrial-technological, social and environmental. The study has showed that in the country’s agro-industrial complex there are a large number of problems and threats to its development, this indicates the need for rational use of resources to increase the efficiency of agricultural enterprises. Consequently, the activity of any business entity, regardless of the organizational and legal form and form of ownership, in the modern national economy, should be cost-effective, with the goal of achieving an appropriate level of income and investment, ensuring independence and further development (Miroshnichenko, 2013).

2.2 Results and discussion The efficiency of agriculture as an economic category expresses not only the result of the development of the subjects of the agrarian economy, but also the qualitative characteristics of the factors that conditioned the optimization of the result obtained. The constant increase in the efficiency of agricultural production contributes to the achievement of the main goal of economic reform, which assumes the formation of an effective system of management that can provide dynamism in the development of the economy. The increase in the productivity of agricultural production is determined not only by the level of development of the productive forces achieved, but also by the indicator of their use. Thus, in order to identify possible ways to increase the efficiency of agricultural production, it is important to correctly determine the effectiveness obtained. The above statements concerning the effectiveness of agriculture are confirmed by the results of monitoring and analysis of the agricultural sector of the economy by the example of the Stavropol Territory, one of the largest agrarian regions in Russia. In general, large and medium- sized agricultural enterprises of the region in 2017 received a profit of 7607 million roubles or 2.6 times more than in 2012 (Table 1). There is a decrease in the effectiveness of entrepreneurial activity in agricultural production. The total number of large and medium-sized agricultural enterprises declined by more than 6.4% during the period under study. However, it should be noted that the net result, namely the profit from the sale of agricultural products for the period under review, increased by 2.4 times, which indicates a more efficient functioning of agricultural enterprises, as evidenced by the positive trend in reducing unprofitable organizations, the number for the study period decreased by 84%.

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Figure 1. Dialectical conditionality of interests of agrarian economy subjects and structural components of agricultural production efficiency

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Table 1. Financial performance of large and medium-sized agricultural enterprises in the Stavropol Territory

2017 in Indicators 2011 2012 2014 2015 2016 2017 %, 2012

The balanced financial result in 2.4 (profit minus the loss) of agricul- 2789 1627 6318 2886 4862 6863 times tural enterprises, million roubles.

including received: in 2.6 - profit, 2858 2421 6775 3732 5249 7607 times

- loss. 69 795 457 846 387 743 100.1

Level of profitability, % 23.1 12.4 23.7 11.5 18.1 20.9 х Number of large and medium- sized agricultural enterprises, 442 438 434 439 433 414 93.7 including: - profitable, 323 365 366 399 416 395 122.3

- loss-making. 119 73 68 40 17 19 16.0

In order to develop a holistic theoretical basis for subsequent scientific and applied research, through which the integration of efficiency assessment tools into a single system, it is necessary to form a unified concept. The developed structural-logical scheme of the rating evaluation of the efficiency of agricultural production, maximally adapted to the specific features of agro-industrial production, presupposes the successive implementation of a number of procedures that, in interrelation, make a comprehensive assessment of the category being studied. The conducted research of activity of agricultural commodity producers of various municipal areas allowed to come to a conclusion that the most optimal is the taxonometric method of rating the most important values – indicators of assessing the effectiveness of agricultural production, which allows you to combine the accounting of both internal and external factors that affect the performance indicator, as well as the ordering and classification of multidimensional objects of different nature. Further calculations assume the ordering of multidimensional units. The resulting rating places of the regions by the types of efficiency of agricultural production have been summarized. The smallest amount of rating places means the highest level of efficiency of agricultural production of the territory, the largest - the lowest level. The results of the calculations are shown in Table 2. In the study, we purposefully have presented the observation matrices and determined the rank of the municipal districts for each of the considered types of efficiency, as this gives a visual representation of the existing situation in the region in the required analytical section. It can be seen from the table that the leading position in the region is occupied by the Kochubeevsky district, which is on the first place in terms of the types of efficiency of functioning of the regional agro-industrial complex. Leading positions are also occupied by Novoaleksandrovsky, Trunovsky and Georgievsky districts. However, it should be noted that for example, Trunovsky district ranked fifth in the assessment of production efficiency, while the results of calculations of financial and economic efficiency showed that this area is on the second place. Therefore, the use of a comprehensive rating assessment allows to obtain a general characteristic of the category under 101

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Table 2. Rating of agricultural production of municipal regions of the Stavropol Territory

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The system of test indicators offered to the management and regulation bodies of the agro industrial complex is oriented to assessing the efficiency of the functioning of the agrarian sector of the regional socio-economic system as a whole. At the same time, it is important for the management bodies, along with issues of economic efficiency of agricultural enterprises, to diagnose the dynamics of the development of rural areas and in particular the state of social infrastructure in rural areas, assess the efficiency of spending budget funds, labour market dynamics and other aspects of the organization and functioning of the agricultural sector (Miroshnichenko, 2013). The system of test indicators for management bodies and regulation of the development of the agro industrial complex makes it possible to differentiate territories according to the level of development of the agricultural sector and contains the most key indicators of its effectiveness. The system of test indicators for the agricultural enterprises should be differentiated by efficiency categories, among which the key ones in our opinion are the following: personnel effectiveness - characterizing the labour potential of the enterprise and its qualitative improvement; production - consisting of indicators of production activity and sustainability; environmental - characterizing the degree of concern of the enterprise about the environment; social - characterizing the level of social responsibility of the business structure to the population of the territory; financial - consisting in the stability of financial indicators of the business entity. Thus, the totality of these evaluation criteria will fully indicate the effectiveness of the functioning of the business entity. With the purpose of more complex study of the effectiveness of the functioning of the agrarian and industrial complex of the Stavropol Territory, several typical representatives of agricultural production from 8 municipal districts of different natural and economic zones have been selected as research objects, which allowed them to extrapolate their activities to agricultural enterprises of the entire region (Table 3). As a result of the study, the following laws have been formulated with respect to test and diagnostic evaluation of the efficiency of the agro-industrial complex in the model territories of the Stavropol Territory:  only in two out of eight municipal districts the population estimates the effectiveness of the functioning of the agro-industrial complex as “average”, in none of the municipal districts has it been noted that the “high level” has been hit;  in the remaining territories the population estimates the efficiency of the AIC as low, which indicates a low level and quality of life of the population and an insufficient level of development of the social infrastructure;  the majority of municipal districts were included in the group with an “average” level of development of the AIC, while the nature of the identified state of development of the AIC in the assessment by different categories of users in them is not uniform, which implies an individual approach to solving the problems of each of the territories.

The choice of the optimal strategy for the development of production and technological processes should be based on the appropriate mechanism of information and consulting support for agribusiness, which acts as a supportive component of the mechanism of state regulation. From this perspective, the consolidation of the potential of the information and consulting services of the agro industrial complex created with our system of test and diagnostic assessment of the processes of forming the economic efficiency of the agro industrial enterprises is scientifically important; in practical terms is a significant task. Modernization from these positions of information and consulting services is a significant addition to the existing mechanism of state regulation of the development of agro economics.

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Table 3. Results of the test and diagnostic evaluation of the efficiency of the agro industrial complex of the districts of the Stavropol Territory

Ipatovsk Kursky Novoaleksandrovsk Izobilnensky Kirovsky Arzgirsky Turkmensky Predgorny Indicators y district district y district district district district district district Number of questionnaires 15 15 15 15 15 15 15 15 processed Agroclimatic zone II II III III IV I I IV The sum of points of the test and diagnostic 91 105 135 62 107 54 43 110 evaluation of the management bodies Efficiency of the 0.51 0.58 0.74 0.34 0.63 0.32 0.24 0.61 agro-industrial complex proceeding middl middl middl high low low low high from interests of e e e management bodies The sum of scores of the test and diagnostic 120 90 165 105 77 76 99 62 evaluation of the population

The effectiveness of 0.47 0.35 0.64 0.41 0.31 0.30 0.38 0.24 the AIC based on the middl interests of the low middle low low low low low population e The sum of the points of the test and diagnostic evaluation of the 405 480 542 375 327 255 235 312 employees of the agricultural enterprises Efficiency of the 0.54 0.64 0.73 0.50 0.43 0.34 0.31 0.41 agro-industrial complex proceeding middl middl from the interests of high middle low low low low e e the agricultural enterprises The average integral 0.503 0.52 0.71 0.42 0.46 0.32 0.31 0.42 level of the development middl middl middl efficiency of the high middle low low middle e e e territory’s agro industrial complex Number of parameters of inefficiency (getting into a group with a 0 1 0 2 2 3 3 2 low level in the evaluation of each user group)

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The most promising direction for increasing the efficiency of the functioning of the agro industrial complex is the process of updating production facilities on the principles of reproduction, which we understand in the agro-industrial complex as a continuous process of updating the main production resources, taking into account the rational use of morally and physically obsolete means of production, viewed as a reserve for reducing the costs of the economic entity for the acquisition of new means of production. The reproduction process is aimed at stimulating the renewal of basic production resources, the ecologization of production and the sphere of waste management of industrial and commercial activities, the activation of the sphere of production of means of production, the increase in the competitiveness of manufactured products, and the development of the business sector, incl. in the field of collection and utilization of production and processing facilities in agriculture (Feodoritov, 1984). The essence and features of the proposed system of reproduction has been revealed when we consider a set of its immanent characteristics and principles of functioning:  The proposed system is a corrective-complementary tool of the state agrarian policy, fully oriented toward increasing the technical and technological equipment of the agro industrial complex subjects;  The functioning of the reproduction system provides additional benefits from rational management of disposed means of production for all subjects of the agro industrial complex (the state, as a macro regulator, the subjects of agricultural production - as users, the population - as consumers of the final product);  The reproduction system must take into account a set of input conditions that affect the parameters of production processes, such as the overall level of capital-labour ratio, the pace of technical and technological regeneration of the industry, compared with individual parametric features of the identified components of economic efficiency of economic entities (personnel, production, environmental, financial and social efficiency);  The reproduction system should be oriented at ensuring the renewal of only those production capacities that ensure the implementation of the basic technological processes in the production sector that can produce the greatest increase in the efficiency indicators of the production and technological process, that is, have a certain selectivity and achieve maximum savings in resources and re-use them;  The proposed system is characterized not only by the initiation, implementation and control of technological renewal in the field of agricultural production, but also includes rational measures and actions for the utilization and partial processing of the means of production and its by-products, which generally fits into the paradigm of a balanced ecologically-oriented development of industries and complexes of agro industrial complex (Trukhachev et al, 2016);  The strategic goal of the reproduction system is to increase the efficiency of the functioning of the agro industrial complex subjects, which in the end improves the living standards of the rural population and the competitiveness of this sector in the context of Russia’s accession to the WTO.

Currently it is generally recognized that the search for reserves to increase the economic efficiency of the functioning of agro industrial complex subjects lies in the rational use and reproduction of financial capital, land resources, means of production, human potential. The results of the research has shown a low efficiency of using reserves in the regional economy in conditions of resource scarcity, so in the Stavropol Territory only 89.2% of agricultural lands are used, of which 61.6% are arable land. The increase in the area of crops is the main reserve for the increase in the output of agricultural products (Table 4).

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Table 4. Level of resource use in agriculture in the Stavropol Territory, %

Indicators 2013 2014 2015 2016 2017

Agricultural land in total land area 89.2 89.2 89.2 89.2 89.2

Arable land in agricultural land 61.6 61.6 61.6 61.6 61.6

Crops in arable land 70.4 70.4 70.4 70.4 70.5

Used production facilities of processing enter- 42.9 38.6 40.0 41.9 43.7 prises

Used capacity of elevators 34.8 42.2 36.1 37.1 33.9

Use of agricultural machinery in a calendar 22.1 23.9 20.1 29.0 30.3 year

Workers of agricultural enterprises in relation 14.6 14.4 13.4 13.2 13.0 to the population of working age in rural areas

In the structure of reproduction processes financing sources in the agro industrial complex, three main areas can be identified: state support funds (subsidies, subventions, and budgetary allocations), private capital and the means of the enterprise itself. Without dwelling in detail on the mechanisms of using financial resources in reproduction processes we note only that in modern conditions, the issues of scientific and methodological substantiation of approaches to the allocation of production facilities to the maximum extent become more urgent, using knowledge, innovation and technology as complementary and often the most important sources of economic growth. Their identification and also carrying out of researches on an estimation of their contribution to maintenance of industrial-technological stability can reveal required reserves the use of which will serve as an additional factor in increasing the efficiency of production activities. As an applied tool for the solution of the task, a matrix can be used to compare the results of traditional and extended (on the basis of testing) methodologies for assessing the efficiency of the functioning of the agro industrial complex allowing to allocate synthetic types of enterprises and substantiate the relevant essential features of the applied policy of saving resources and their reproduction. The proposed system of agrarian reproduction of resources has been characterized by high efficiency for each of the subjects of agrarian relations. The computational justification of the cost parameters of the benefits and losses presented is difficult, since in each subject there is a different need for updating the technical and technological sphere, fluctuating market conditions in the agro technical sphere and others. At the same time, the proposed system can realize its practical potential with due coordination at the federal level, rational performance at the regional and ubiquitous participation of representatives of municipal authorities.

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CONCLUSION As a result of the research, the following conclusions and proposals have been made. Within the framework of the consideration of conceptual and theoretical positions of the theory of the effectiveness of social production, the category in question has been identified with the process of correlating the costs carried out to achieve a set of socially significant social, financial, economic, environmental, production and personnel performance results of economic entities obtained not only in the context of satisfying their interests , but also taking into account the realization of the socially useful function of education, reproduction or conservation of agrarian socio-natural economic systems. Agrarian reproductive and economic environment is a set of synchronous-acting factors and conditions that permanently exert influence on the processes of increasing efficiency. Their integrated accounting requires the development of such an approach, which would take into account not only their origin, but also the nature of the activity. This is necessary for the development of preventive measures, response or compensating tools in the system of sectoral strategic management. In this connection, the research has suggested an approach based on taxonometry methods that allow classifying the initial infinite space of factors influencing efficiency, which complements the theory of functioning of agrarian socio-economic systems in terms of expanding the toolkit of fundamental and empirical research. On the basis of the analysis of the state of the agricultural sector in the work, it was concluded that different territories of the region are developing differently and differ in the degree of efficiency of agricultural production. In this connection, the research has suggested a method of objective evaluation of the efficiency of agricultural production. This method is based on a rating evaluation of the efficiency of agricultural production, which allowed ranking the municipal districts of the region depending on the level of their development into homogeneous typological groups, which in turn can be used to develop a mechanism for state support to agriculture based on a differentiated evaluation of efficiency. Taking into account the atypicality of certain structural components of the “efficiency” category, the idea and organizational and methodological support are proposed as a supplement to the developed structural and logical scheme of the rating evaluation of the efficiency of agricultural production. Its use in the regime of permanent diagnostics makes it possible to identify changes in hard-to-formal components of the efficiency of agricultural production in terms of environmental, social, staffing and production efficiency, as for the governing bodies, and for the population and subjects of agrarian business. In conclusion, it should be noted that the lack of resource support for agriculture minimizes the ability of the subjects of the agrarian business to move to a higher stage of the technological order, which significantly reduces opportunities to increase the level of economic efficiency and sustainable development of agriculture. In order to technically modernize the industry, it is necessary to introduce an agrarian recycling system, oriented on the re-equipment of agricultural enterprises. This makes it possible to reduce the operating costs for using the outdated technical and technological base and accelerate the formation of new ones, maintenance and preservation of existing agro-natural and socio-economic systems of agricultural type and ensure rational use of resources.

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REFERENSES Abalkin, L. I. (1990), Economic Efficiency of Agricultural Production. Production, Agropromizdat, Moscow (in Rossian). Alekseeva, A. I. (2003), The history of the present state of Russian statehood in the regions of the North Caucasus SPA, Edited by V. A. Kaznacheev, Pyatigorsk, Publishing house Pyatigorsk State Technological University, (in Rossian). Atkociuniene, V., Kiausiene, I. (2017), “Scenarios of Development of Rural Social Infrastructure: The Case of Lithuania”, Transformations in Business & Economics, Vol. 16, No 3 (42), pp. 73- 89. Cole, Ch. L. (1973), Microeconomics: A Contemporary Approach, Under the Editorship of William J. Baumol, Harcourt Brace Jovanovich, New York. Dolan, E. J., Lindsay, D. (1994), Microeconomics, translate from English, St. Petersburg. Emerson, H., Twelve principles of efficiency, Ch. I. URL: http://orel.rsl.ru/nettext/ekonomik/emerson/12pr002.htm (accessed 27 January 2010). Emerson, H., Twelve principles of efficiency, Ch. II. URL: http://orel.rsl.ru/nettext/ekonomik/emerson/12pr003.htm (accessed 27 January 2010). Emerson, H., Twelve principles of efficiency. Ch. VIII. URL: http://orel.rsl.ru/nettext/ekonomik/emerson/12pr008.htm (accessed 27 January 2010). Feodoritov, V. Ya., Brodskaya, T. G. (1984), Regional reproduction in the system of socialist James, W. (1997), Pragmatism: The Will to believe, Moscow. Konstantinova, L. M., Sokolinsky, Z. V. (1987), Economic efficiency of production: analysis of statistical indicators, Statistika, Moscow (in Rosian). McConnell, K.R. (1992), Economics: principles, problems and politics: transl. from English, 11th ed., Republic, Moscow. McConnell, K., Bru, C. (1992), Economics: Principles, problems and politics, Baku. Miroshnichenko, N. A. (2012), “Theoretical aspects of economic efficiency of agricultural production” Economics and Entrepreneurship, No. 6, pp. 169-172 Miroshnichenko, N. A. (2013), “Classification of factors affecting the efficiency of agricultural production”, Economic sciences, No. 9, pp. 27-31. Miroshnichenko, N. A. (2013a), “Increase of efficiency of functioning of agro industrial complex subjects on the basis of activation of reproductive processes in technical and technological sphere”, Economics and Entrepreneurship, No. 7, pp. 672-679. Pjerotic, L., Delibasic, M., Joksiene, I., Griesiene, I., Georgeta, C. P. (2017), “Sustainable Tourism Development in the Rural Areas”, Transformations in Business & Economics, Vol. 16, No 3 (42), pp. 21-31. Serban, A. C., Aceleanu, M. I., Saseanu, A. S. (2017), “Constraints of Transition to Ecological Agriculture in a Sustainable Development Society. Romanian Perspective”, Transformations in Business & Economics, Vol. 16, No 3 (42), pp. 56-73. Trukhachev, V., Sklyarov, I., Sklyarova, Yu. (2016), “Current Status of Resource Potential of Agriculture in the South of Russia”, Montenegrin Journal of Economics, Vol. 12, No. 3, pp. 115 – 127.

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Montenegrin Journal of Economics

Vol. 14, No. 3 (2018), 109-127 ‘

Open Economy or Protectionism: Ukraine’s Dilemma

ANTON OLEINIK1

1 Professor, Department of Sociology, Memorial University of Newfoundland and Labrador (St. John’s, NL, A1C 5S7, Canada) and Leading Research Fellow, the Central Economics and Mathematics Institute of the Russian Academy of Sciences (Moscow, Russia); [email protected]

ARTICLE INFO ABSTRACT Received June 21, 2018 Since 1991, Ukraine has had several economic crises (1991-1999, Revised from June 30, 2018 2009, ongoing since 2014) and a period of economic boom from Accepted August 20, 2018 2000 to 2008. The current crisis has external (the unfavorable Available online September 15, 2018 situation in the world commodities markets) and internal (the military conflict with neighboring Russia) dimensions. The destruction of economic infrastructure and the unpreparedness of JEL classification: Ukrainian producers to compete globally explain its depth and F52, F53, F63, O52. complexity. Radical and non-orthodox measures may be needed to help Ukraine to return to economic growth. This article discusses DOI: 10.14254/1800-5845/2018.14-3.8 pros and cons of an asymmetrical solution that involves a combination of protectionism and an outward-oriented strategy Keywords: during the recovery period. It is argued that it may be more efficient than the country’s reliance on foreign loans and aid. The analysis of Ukraine, regional-level data for 2013-2016 lends some support to this economic crisis, argument. protectionism, open economy.

INTRODUCTION The recent economic development of Ukraine is by no standard a success story. In 2014, the Ukrainian economy was still smaller than in 1990. Using data from the State Statistics Service of Ukraine (SSSU), the country’s real GDP (its physical volume) in 2014 can be estimated at 98.8% of the 1990 level.1 Nominal GDP per capita is even further from the 1990 level. “Ukraine’s GDP per capita is still only 65% of what it was on the eve of the break-up of the Soviet Union. With EUR 6,500 at PPP, it corresponds to a mere 23% of the EU average, making Ukraine the second poorest country in Europe (after Moldova and followed by Kosovo)” (Adarov et al., 2015, p. 6).2 In other

1 Calculations of A. Valchyshen, the Head of Research and a Fixed Income Analyst at Investment Capital Ukraine LLC, using the SSSU data (https://eimg.pravda.com/files/4/8/4861068-state-budget.xlsx accessed February 27, 2018). 2 This discrepancy between the nominal and real GDP can be explained by a significant drop in PPP since the early 1990s.

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Anton Oleinik / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 109-127 words, after 25 years of independence, the Ukrainian economy is still in worse shape than when it did not have an autonomous status. The military conflict with Russia started in 2014 caused an additional blow to the Ukrainian economy. In addition to the losses of infrastructure, the military confrontation undermined the trade between the two countries (Russia was Ukraine’s key trade partner from 2009 to 2012). According to one estimate, “the military conflict in Donbas reduced Ukraine’s GDP by 2.5% [in 2014], including 1.9% due to the decline in the Donetsk and Luhansk regions and another 0.6% due to contagion effects” (Adarov et al., 2015, p. 14). Loans from the international financial institutions are partly alleviating the effect of the war, but the country is still expected to bear the full costs of confronting the military aggression. The payment of the price of the war is simply post- poned to a later date and, thus, its economic effects will continue to be felt even after “freezing” the conflict. The Russian case is used as a point of comparison. In addition to being the parties of the ongoing military conflict, Ukraine and Russia have the largest economies of all the countries of the former Soviet Union. The GDP in both counties followed a somewhat similar path since 1991 even if the Russian economy performed relatively better and now exceeds its 1990 level. Before the current crisis that affected both economies, the growth of the Russian economy has been pro- pelled, on the one hand, by oil and gas rents and, on the other hand, by domestic consumption. Gaddy and Ickes (2005) noticed that oil and natural gas rents are correlated with Russia’s GDP. The World Bank defines oil and natural gas rents as the difference between the value of crude oil and natural gas production at world prices and total costs of production (World Bank, 2018). In the 2000s, the share of oil and natural gas rents in Russia’s GDP was between 10 and 15%. High oil and natural gas prices fuelled a boom in consumer spending, making domestic consumption a second source of economic growth in this country. By restricting and regulating access to two most dynamic sectors of the Russian economy, the oil and natural gas sector and the retail trade, the Russian power elite has been able to dominate businesses – national and foreign alike, by virtue of a constellation of interests in these markets (Oleinik, 2015a; 2011). In exchange for access to the markets and for the operation in the conditions of limited competition, businesses have been willing to contribute a part of the rents that they captured to group and individual endeavors of the Russian power elite. What are drivers of the Ukrainian economy? Ukraine does not possess significant oil and natural gas deposits. Consequently, oil and natural gas rents did not exceed 3% of this country’s GDP, even when world prices of these commodities were high. There is also a substantial difference in the institutional environments in which the two economies operate. Russia restricted access to the national market and extracts administrative rents (rents captured by a gate-keeper) in addition to natural resources rents. “Ukraine has a more open economy than Russia, and its trade orientation outside of the old Soviet Union is substantial” (Havrylyshyn, 2014, p. 180). This article discusses drivers, existing and potential, of the Ukrainian economy. More specifically, the goal of the article is to explore the potential of the national market as a driver of economic development in Ukraine. Since the article has an explorative character, formal hypotheses will be neither stated nor tested. A quantitative comparison of the internal and external sources of economic growth will be offered nevertheless. It is argued that the strengthening of the national market may help change the negative dynamics of Ukraine’s economy. Protectionist measures in this case have a temporary and forced character. Furthermore, the danger of the abuses of access control by the power elite has to be explicitly taken into account when adapting protectionist measures to the needs of nation-state building in Ukraine.

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1. EXTERNALLY DRIVEN ECONOMY Ukraine has a long tradition of open economy. In Kievan Rus’, the first state on the territory of today’s Ukraine, international trade provided a major source of income for its rulers and population (Hedlund, 2005). Kievan Rus’ emerged at the cross roads of trade routes connecting, on one hand, Northern and Southern Europe (“from the Varangians to the Greeks” route) and, on the other hand, Eastern Europe to Asia. Modalities of transportation and trade routes have significantly changed since the 11th-12th centuries. Nevertheless, Ukraine still enjoys a central position in some transportation networks; for instance, in the network of pipelines connecting Russian oil and natural gas fields to the European customers. The network was built in the 1970s-1980s, when all key decisions were made in Moscow. The fall of the Soviet Union led to the emergence of several decision- makers on the post-Soviet space, namely, in Moscow and in Kyiv. Some actors in Ukraine attempt- ed to use the situation of a bilateral monopoly (Russia has a monopoly over oil and natural gas fields whereas Ukraine – over the pipelines network) to their advantage, i.e. to capture a gate- keeper’s rent (see Ericson, 2009; Oleinik, 2015b for a discussion of the organization of this market). In the 1990s and the early 2000s they were rather successful. The Ukrainian government did not control the gate-keepers, allowing the significant accumulation of resources in private hands (the owners of various intermediaries from Itera to RosUkrEnergo) and thus contributing to the spread of corruption in the country (Kuzio, 2012, p. 434) and to the conservation of energy inefficient businesses (Kuzmin et al., 2016). The construction of pipelines bypassing Ukraine, the Blue Stream and the Nord Stream, significantly weakened the bargaining position of the Ukrainian gatekeepers. The gatekeepers in Ukraine never acted in concert since they were private businesses connected with competing political groups. For instance, Dmytro Firtash, who controlled RosUkrEner- go, was a creature of President Leonid Kuchma. Oleksiy Ivchenko, one of the key figures in Itera, supported Ukrainian nationalists and President Viktor Yushchenko, whose relationships with the predecessor, President Kuchma, were complex to say the least (Kuzio, 2012, p. 434). It means, first, that the rents captured by the gate-keepers have been neither controlled by the government let alone ordinary Ukrainians nor fully used in the interests of nation-state building. Second, the gatekeepers did not really restrict access to the market in Ukraine or through its territory. Since the mid-1990s, when President Kuchma implemented “shock therapy” policies, Ukraine has been among the most dependent on the international markets economies with the share of external trade (export and import combined) consistently exceeding the volume of its GDP (World Bank, 2018).3 Kuchma’s shock therapy is believed to be more radical and closer to the standards of ne- oliberalism than reforms carried out by the government of Boris Yeltsin and Yegor Gaidar in the early 1990s in Russia (Havrylyshyn, 2014, p. 175). The exposure to the world market has undeniable benefits, increasing productivity as a result of a deeper division of labor and creating incentives to modernize. Veblen considers the case the German economy that was relatively backward in the 19th century compared with the other Euro- pean economies. He notes that “it is apparently in commerce and the improvements in transportation that contact with the more advanced countries of the West first provoke movements of adjustment to the new state of things” (Veblen, 1964, p. 150). Today’s Ukrainian economy also lags behind the developed economies and badly needs “movements of adjustment”. At the same time, the high level of exposure of the Ukrainian economy to the international markets has also had

3 In 2016, the value of this World Development indicator in Ukraine was 104.8% (119.9% in 2000), which places the country 46th out of more than 200 countries included in the dataset. The world average was 56.4% (51.3% in 2000). As a comparison, the share of external trade in Russia was 46.3% (68.1% in 2000). Slovakia and the Czech Republic have more open economies than Ukraine with the shares of exports and imports 185.7% (110.7% in 2000) and 151.6% (98.2% in 2000) respectively, but in these countries the relative growth of external trade has been more steady. Also, smaller economies tend to have a higher share of external trade, all other things being equal.

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Anton Oleinik / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 109-127 three other consequences that undermine black-and-white thinking: the weakness of internal economic actors, the dependence of the situation in the Ukrainian economy on the situation in the world commodities markets, and its vulnerability to the use of the market as a weapon in a conflict situation.

1.1 External and internal actors of economic modernization The Ukrainian economy has been undergoing significant structural changes since this country declared independence in 1991. In 2001, the share of agriculture in the gross value added (GVA) was 16.1%; twelve years later – 9.3%. The share of real estate and services for businesses increased from 6.9% to 12.1% of the GVA. The share of industry in the GVA decreased from 30.2% to 25.7% (Kuzmin et al., 2012). Such changes seem to be indicative of the transition toward a service-oriented, post-industrial economy. In Ukraine, however, the process of economic modernization is totally externally driven. Internal actors, including the government and national businesses, have little say in this process, let alone control it. Two forces determine parameters of economic modernization in Ukraine: the international institutions, namely the International Monetary Fund, IMF, and the external markets – first of all, the commodities markets. The shock therapy in the mid-1990s was designed and carried out with assistance of the IMF. The IMF on more than one occasion gave conditional loans to the Ukrainian government. As of the end of February 2017, the Ukrainian government served as a guarantor of IMF’s loans totalling to 6.28 billion USD and as a direct recipient of IMF’s loans totalling to 6.88 billion USD (Ministry of Finance of Ukraine, 2017). Taken together, IMF’s loans amount to 18.35% of Ukraine’s internal and external debt or 14.5% of the country’s GDP in 2015 (90.615 billion USD; World Bank, 2018). IMF’s loans are conditional: the applicant is expected, in exchange, to implement specific policies ranging from the “Washington Consensus, as formulated by John Williamson (1990), focusing on three basic IMF requirements: a fully financed budget, a realistic exchange rate, and a recapitalization of the banking system” (Åslund, 2009, p. 377) to more far-reaching demands for structural reform. As stated in the introduction to this article, the Ukrainian economy has yet to reach its 1990 level. The next subsection contains some additional material suggesting that policies inspired by the Washington consensus did not work quite well in the Ukrainian case. The reliance on external actors as the only drivers of economic modernization may cause the weakness of the internal actors or even suppress them. Ukraine did produce quite a few business empires – all owned by so called “oligarchs”, but none of them appears to be willing and prepared to become an agens mov- ens of economic modernization and nation-state building. Åslund (2008, p. 157) rightly observes that compared to Russia, where “the Russian state under Putin has defeated the oligarchs and re- imposed a strong (albeit highly corrupt) centralized political control”, Ukraine “is a competitive oligarchy, and accordingly a much more lively and open society”. The Ukrainian oligarchs compete for influence on the government. The problem is that under the currently existing arrangement the government has a quite limited capacity for developing and implementing economic policies. It plays a mostly technical role in implementing blueprints prepared by the external actors or reacting to external shocks. Furthermore, these oligarchs have opposing interests and views on the country’s future. According to one account, an “Eastern-Ukrainian oligarchy of Russian identity, who are interested in increasing exports and would like to achieve increasingly strong positions in the international markets… The other major economic interest group is more interested in the commercial, financial and food industry sector connected with international capital. As for their identity, they are Ukrainians who – contrary to the first group – mainly support growth in imports. Their natural allies are foreign producers exporting to the Ukraine, multinational companies and the population with purchasing power to consume” (Virág, 2012, p. 627). Lastly, interests of the Ukrainian oligarchs tend to be highly circumstantial and changing in function of the situation in the

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Anton Oleinik / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 109-127 external markets (Åslund, 2009, p. 384). No oligarchic group has a vested interest in modernizing the Ukrainian economy as a whole and regardless of the global market dynamics. Neither in Russia nor in Ukraine have internal economic actors emerged as leaders in nation-state building. In the former case, they are suppressed by the all-mighty State. In the latter case, the oligarchs have more leeway but the overwhelming influence of the external actors on the country’s economic policies simply make their and the government’s input in policy making optional at best.

1.2 Dependence on the situation in the world commodities markets The dependence of Ukraine’s economy on the world market made it vulnerable to the cycles in its dynamics. The economy either tends to unexpectedly overheat, as in the mid-2000s when the GDP’s growth rates reached a two-digit level (112.1% in 2004), or suddenly collapses, as in the wake of the 2008 global financial crisis when Ukraine’s GDP contracted deeper than in most other countries of the world (85.2% in 2009). These ups and downs are unpredictable since they remain outside the control of any particular actor, which complicates the matters even further (Adarov et al., 2015, p. 33; OECD, 2014, p. 32). Åslund (2001, p. 314) admits that no one had predicted the economic boom in Ukraine in the first half of the 2000s. A few years later, it became clear that a cycle in the global steel market has to be given most credit for this boom (Åslund, 2008, p. 374). The Ukrainian export that accounts for almost a half of the country’s GDP closely follows the dynamics of the world prices for metal scrap and hot rolled steel (Dovbniak, 2009, p. 67), as Figure 1 confirms.

Figure 1.. Comparative dynamics of the value of Ukrainian exports, in hundred million US$, and hot rolled steel prices, in US$, 1996-2015

Source: Index Mundi (http://www.indexmundi.com/commodities/?commodity=cold-rolled- steel&months=300&commodity=hot-rolled-steel), the Economist Intelligence Unit (https://store.eiu.com/article.aspx?productid=960000296&articleid=1282943712) and the State Statistics Service of Ukraine

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The geographical breakdown of Ukrainian exports and imports (Figure 2) shows that the volume of trade with the EU has been exceeding that with the CIS countries since 2014, i.e. after the start of the military conflict with Russia. Ukrainian imports exceed Ukrainian imports with both of these trade partners, which suggests that Ukrainian producers are prepared to compete successfully neither with the Eastern neighbors nor with the Western neighbors. The country consumes more than it produces. A short period of time between 1999 and 2004 during which Ukraine had a positive trade balance with the EU does not change the overall pattern since it coincides with the above mentioned boom in the global steel market.

Figure 2. Comparative dynamics of Ukrainian exports and imports of goods to/from the EU and to/from the Commonwealth of Independent States, CIS, in million US$, 1996-2016.

Source: State Committee of Statistics of Ukraine, 2017b, pp. 13-14; 2014, pp. 28, 34; 2010, pp. 28, 34;, 2006, pp. 256-257 (The figures for 1996-1999 are reported by V. Dyatlova and O. Tkachenko, who also base their estimates on the SSSU data.)

It must be noted that Ukraine’s economy depends specifically on the dynamics of the world commodity markets, i.e. the markets for products with minimal value added as opposed to processed products. The exposure of the country’s economy to the world market at the early stages of economic reforms, when national producers were still in the process of adapting to a new market environment, made them uncompetitive. The competitive sectors of the Ukrainian economy do not radically differ from those that Kievan Rus’ had: agriculture (grains, honey) and basic processing of raw materials (furs and wax in the past, metals today). This specialization represents a common feature of peripheral economies where “with growing income, demand for imported machinery grows faster that the demand for exported primary materials” (Prebisch, 1959, p. 253). The structure of Ukraine’s exports to, and imports from, the European Union is revealing. In 2016, Ukraine’s exports to the EU were composed of base metals and articles thereof (22.1% of the total), vegetable products (16.1%), mineral products (12.8%) and machinery and appliances (11.5%). Ukraine imported from the EU mostly machinery and appliances (26.1%), then products of the chemical or allied industries (16.8%), transport equipment (10.7%) and plastic, rubber and articles thereof (7.1%; European Union, 2017).

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1.3 Market as a weapon in a conflict situation The dependence of a country’s economy on the international markets may become a liability in the situation of an intensive conflict or a war of aggression like the one waged against Ukraine by Russia since 2014. The existence of imbalances and asymmetries in the trade between parties in a conflict enables one of them or both to use economic levers in addition to purely military means. Before 2014, Ukraine’s dependence on imports of natural gas from Russia reached a critical level: they covered up to a half of all needs of Ukrainian businesses and households in natural gas (Zhy- lyaeva, 2010, p. 57). Russia has a record of price discrimination, i.e. charging different prices in function of the customer’s readiness to align with this country’s policies (Oleinik, 2015b). Needless to say that the price that Ukraine had recently been charged exceeded the average. The painful cuts in the consumption of natural gas and the costly re-orientation to supplies from the European common market contributed to increasing the costs of the war for Ukraine. In the context of the military confrontation, the dependence of Ukraine’s economy on the international markets also had less direct but nevertheless sensitive implications. Before 2014, Eastern Ukraine and Western Ukraine were traditionally oriented to the different external markets, Russian and European correspondingly (Adarov et al., 2015, p. 66). The conflict with Russia contributed to the aggravation of regional divides along these lines: the economy of Left-bank Ukraine suffered from the collapse of the trade with Russia particularly severely (Chuzhykov et al., 2014, p. 31). In other words, the Ukrainian experience suggests that the orientation of regions to different trading partners in an economy dependent on the world market coupled with the absence of policies of harmonization at the regional level has the potential to constitute an economic foundation of regional separatism.

2. THE NATIONAL MARKET: A SURVIVAL KIT OR AN ENGINE OF DEVELOPMENT? In 2014-2016, the negative consequences of the dependence of Ukraine’s economy on the international markets outweighed its positive effects. The world prices for hot rolled steel are in decline. The asymmetries that exist in the trade between Ukraine and Russia were used by the latter as an additional lever in the military confrontation. In response, Ukraine imposed restrictions on the trade with Russia, which in turn aggravated divides between the Western and Eastern regions of the country. In these circumstances, the national market, an institution that has been over- looked while exposing the country’s economy to international competition, plays the role of a safety net preventing its total collapse.

2.1 Sources of data and methodology Panel regional-level data inform this study, the SSSU being their key source. It is more common to use country-level data in econometric analysis. However, particularities of the current situation in Ukraine, especially the ongoing war, significantly complicate meaningful international com- parisons. Since the number of units of observation is relatively small,4 the regional-level data in Ukraine have to be used with caution. Several precedents show that the analysis of the Ukrainian regional-level data can provide valuable insights nevertheless (OECD, 2014; Masliy, 2014; Chu- zhykov et al., 2014; Nosova, 2013; Grendash, 2012). The data cover the period after the start of Russia’s military aggression, i.e. 2013 on.

4 27 before the annexation of Crimea and the city of Sebastopol by Russia in 2014, 25 currently: 24 regions and the capital city.

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The reader also has to bear in mind that the article is more oriented towards exploration than towards analysis, which involves assessing the comparative impact of multiple factors on the economic situation. The risk of running an excessive number of regressions (cf. Sala-I-Martin, 1997) was curbed with the help of shortlisting the list of predictors. They were selected in such a manner as to confront external and internal factors of economic development. A series of the linear multiple regressions, method “forward” (with the probability of F for entry set at a 0.05 level and for removal at a 0.1 level), were run in order to assess the exact contribution of the national market to the country’s economy and to compare this contribution with the effect of the other factors, namely, foreign trade and foreign direct investments (FDI). The Gross Regional Product, GRP, is used as an outcome indicator (originally in billion Ukrainian hryvnas, UAH; Lg10 transformed). Previous studies (OECD, 2014, p. 31) suggest that among key contributors to economic growth in Ukraine are domestic demand, consumption, investments and net exports. For instance, domestic demand decreased by 26.4% in 2009, which was partly offset by an 11.6% increase in net exports. In the present study, the contribution of the national market was operationalized through the following indicators: disposable income (originally in billion UAH; Lg10 transformed), average monthly salary (in UAH, Lg10 transformed in 2014), and retail trade turnover (originally in billion UAH; Lg10 transformed). The contribution of investments was operationalized through financial results of enterprises before taxation (in billion UAH; Lg10 transformed in 2014), fixed capital investments (originally in billion UAH; Lg10 transformed) and foreign direct investments, FDI (originally in billion US$; Ln transformed), that of foreign trade – through total import of goods and total export of goods (originally in thousand US$; Ln transformed), export to import ratio (export coverage), import of goods from and export of goods to Germany and Russia (originally in thousand US$; Ln transformed), import of wood lumber and import of wooden furniture (originally in thousand US$; the latter was Lg10 transformed and the former – Ln transformed; the rationale for including these measures is provided in the next section). In addition, the impact of several other variables was controlled: accumulated total population, salary arrears (originally in billion UAH; Lg10 transformed), internet penetration (% of the population who reported using the Internet past 12 months), and % of the total surface covered by forests.

2.2 On the brink of a collapse Table 1 contains results of the three regressions, for 2013, 2014 and 2015 (The 2016 GRP figures were not released yet as of the time of this writing), showing the predictors whose contribution was statistically significant only. Two variables, disposable income and fixed capital investments, were removed from the models in order to address a collinearity problem. The case of disposable income is particularly relevant. This aggregate includes salaries and various transfers with the exception of taxes and other mandatory payments. Disposable income, a proxy for the size of the national market, was highly correlated with the dependent variable, GRP (r=0.979 in 2013), and with several predictor variables. Leaving the excluded variables aside, the contribution of the national market (domestic consumption) outweighed the effect of the other predictors and con- trols. The impact of financial results of enterprises, a potential source of investment, was significant in 2013 only, before most businesses went into the red in 2014. Neither export nor import helped Ukrainian regions to cope with the economic crisis in 2013 and 2014. The contribution of export and FDI became significant in 2015 only. Assessed separately, their impact did not exceed that of domestic consumption. The national market prevented a total collapse in the country’s economy.

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Table 1. Results of linear multiple regressions to predict the Gross Regional Product (Lg10 transformed) in Ukraine, 2013-2015.

Legend: Results significant at a 0.05 level are marked by *, at a 0.01 level – by **, and at a 0.001 level – by ***. Source: State Committee of Statistics of Ukraine, 2016a, Vol.1, p. 85; Vol. 2, pp. 15, 133, 374.

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The national market plays the role of a life jacket in times of economic duress. Has it the potential to become an engine of economic development? The short answer is no. In its present conditions, the national market neither can absorb the redundant output in some sectors, for instance, the machine-building output which used to be exported to the Russian market (Adarov et al., 2015, p. 47), nor increase domestic consumption without simultaneously creating inflationary pressures (OECD, 2014, p. 33). The national market has been relegated to the back stage of economic development for too long, practically since the start of economic reforms in the early 1990s. Howev- er, it could potentially perform the role of a driver of economic development provided that the national market is first prepared for this role with the help of specifically designed and properly implemented economic policies. These policies have some roots in protectionism, but go beyond its old, and to a significant extent, discredited version. In the Ukrainian case, given the high degree of exposure of the country’s economy to the international markets and taking into account the lessons of implementing classical protectionist policies in some other countries, a new version of protectionism may be needed. On the one hand, this new version aims to protect the national market simultaneously maintaining and even increasing Ukraine’s presence in the external markets, first of all in Europe and North America. On the other hand, the protectionist policies have to be temporary. Since their very beginning the task of re-opening Ukraine’s economy in the progressive stages ought to be set. In other words, protectionism in Ukraine can work if it has a temporary character and is designed as a tool for making national producers more competitive in the external markets.

2.3 Goodwill to an asymmetrical solution Prebisch and his fellow Latin American economists associated with the Programa Regional del Empleo para América Latina y el Caribe, PREALC, made the most recent attempt to formulate a comprehensive protectionist policy in the 1960s-1970s. Namely, they promoted import substitution as a strategy for protecting the national market and for correcting disparities that exist between the less developed (peripheral) economies and the developed (central) economies. Prebisch (1959, p. 253) defined import substitution “as an increase in the proportion of goods that is supplied from domestic sources” and considered it as “the only way to correct the effects on peripheral growth of disparities in foreign trade elasticity”. The demand for imported machinery “on the periphery” increases faster than the demand for commodities exported by the peripheral economies, which creates those disparities. In the present situation, Ukraine has a peripheral economy: this country exports mainly commodities and goods with low value added and imports machinery and goods with high value added. Not surprisingly, some Ukrainian economists show interest in the issues of import substitution. For instance, Yakymenko (2015, p. 156) from Kharkiv in Eastern Ukraine calls for meeting the country’s domestic demand “mainly through the development of national production, … using protectionist policies”. The process of import substitution was actually taking place in 2015, observe Check with colleagues from Lviv in Western Ukraine (2016, pp. 89-90), pointing out to the situation in some industries oriented to the national market, namely the textile industry and the food processing industry. Import substitution may be taking place indeed, but in a spontaneous, uncontrol- lable and, hence, unpredictable manner. The other Ukrainian economists go even further, seeing in protectionist policies an element of national sovereignty understood as the government’s capacity for formulating an independent economic policy guided by national interests (Melnik, 2015, p. 147). This argument echoes a definition of sovereignty in general and economic sovereignty in particular given by John Commons (1959, p. 384): “sovereignty [is] the collective power of the concern laying down its working rules in the form of the common law, equity and administrative orders, for the purpose of better adjustment of men’s transactions in a world of relative scarcity of resources”. Seen in this perspective, 118

Anton Oleinik / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 109-127 economic sovereignty constitutes an integral element of nation-state building. This process is still on-going in Ukraine, which provides an additional reason for being highly sensitive to the issues of this country’s economic sovereignty. The national market represents an important element of a nation-state (Smith, 1991, p. 14). In open economies, the governments have few levers for protecting or promoting particular industries. Governments in countries with the established, developed market economies do not necessarily need to intervene since the national businesses can compete globally and consider an open economy as their natural environment. In contrast, governments in countries with the less developed, peripheral market economies need to intervene and to protect the national market before their national businesses get prepared for global competition. The invisible hand of the market does not suffice in these circumstances and has to be complemented by the visible hand of the government. Protectionist policies were extremely popular from the late 1960s into the 1970s. The World Bank not only tolerated them but actually “expressed considerable sympathy for the approach” (Bruton, 1998, p. 917). And this international organization had a good reason for sympathizing with protectionism since at that time it worked. Two factors help explain why protectionism started to subsequently fall out of fashion. The outward-oriented strategy of promoting export inspired by the experience of the Asian Tigers (Hong Kong, Singapore, South Korea and Taiwan) in the 1970s is one. National producers have no privilege at home. “The basic notion is to keep the domestic economy open to foreign competition and foreign capital, and to ensure that exports are in no way penalized, if not specifically encouraged” (Bruton, 1998, p. 904). The awareness of the risks of rent-seeking by the governments that heavily rely on the visible hand is the other (Rose-Ackerman, 2004). Counter-arguments can be developed with respect to both of these reasons. First, the outward- oriented strategy also fell out of fashion. It produced expected outcomes mainly in relatively small open economies (given the size of Ukraine’s population, 42.6 million as of February 2017, this country has a potentially very large internal market). Similarly to import substitution, the outward- oriented strategy does not create an environment conductive to learning. The latter strategy “fails to appreciate that learning requires conditions that are essentially internal and dependent on the basic characteristics of the society” (Bruton, 1998, p. 903). Second, rent-seeking has more to do with the lack of an adequate response to a more general problem that exists in all economies, namely, “who monitors the monitors” (Stiglitz, 1994, p. 78), than with import substitution as such. Both problems with import substitution can potentially be alleviated by imposing temporal limits on protectionism and by re-opening Ukraine’s economy in the progressive stages, after building the national market and strengthening, with its help, national producers. In parallel with policies of import substitution, efforts to promote export have to be made. The promotion of export, especially to the European Union and North America, will create incentives for learning. Is such an asymmetrical situation – a partial openness of Ukraine’s economy – defendable and practical? After all, the proposed strategy requires that the country temporarily protects its national market while still having a free access to the key prospective external markets. Several arguments can be used in defense of the asymmetrical openness. Prebisch (1959, p. 264) offers one. He argues that “protection has different meanings in the peripheral countries and in the industrial centers. In the former it is, up to a certain point, the instrument for correcting the effects of the disparity in income elasticity of demand for exports of primary commodities and for imports of industrial goods and does not hamper the rate of growth of world trade. In the industrial centers, by contrast, protection of primary production accentuates this disparity and tends to de- press peripheral development and to decrease the rate of growth of world trade. The reduction or elimination of such protection at the centers has an implicit element of reciprocity”. As paradoxical as it may seem, in a reciprocal situation there are no duties in the center, but duties and the other

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Anton Oleinik / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 109-127 protectionist measures (tariffs, exchange rates, import licenses and so forth) may be simultaneously present in the periphery as a means to alleviate the disparities between the center and the periphery. The implementation of such arrangement requires the good will of the Western partners of Ukraine, both in a literal and figurative sense of this word. The acceptance of temporary asymmetrical economic relationships with Ukraine will give the West an opportunity for expressing its soli- darity with this country, especially since no new “Marshall plan” seems to be forthcoming, as of the time of this writing. Instead of (or in addition to) giving credits and providing technical assistance, both parties, Ukraine and the West, would potentially be better off by providing Ukrainian businesses with free access to the Western markets and by accepting Ukraine’s protectionist policies during a transition period that may last from a few years to a decade or so. It terms of the costs for the West, both options may be comparable, but the acceptance of the asymmetric solution creates much more powerful incentives for learning. In a literal sense, goodwill involves restrictions voluntarily taken by a party in a transaction. Commons (1959, p. 23) defines goodwill as a negative promise: a promise not to compete. He further adds that “goodwill, being a social relation, implies reciprocity. It is the expectation of reciprocal beneficial transactions” (1959, p. 220). In the circumstances of the case, the West promises not to compete with Ukrainian businesses in the national market of this country during a certain period of time in the expectation of reciprocal beneficial transactions in the future in the free markets. In practical terms, the asymmetrical solution de facto currently exists. In spring 2015 the EU unilaterally abolished most trade barriers for imports from Ukraine, which particularly benefited agricultural products (Adarov et al., 2015, pp. 3, 10). The results of this unilateral abolition of trade barriers by the EU can be seen in Table 1: domestic consumption and exports emerged as two key drivers of Ukraine’s economy in 2015. Nothing prevents the EU from taking a step further in for- malizing the existing arrangements and appreciating that Ukraine needs to temporarily shelter the national market from the European competition. During this transition period the country will develop and implement “an industrial policy for the restructuring/conversion of sectors that are not likely to withstand competitive pressures and/or the potential loss of traditional markets also needs to be devised as an inherent part of the long-run economic development strategy” (Adarov et al., 2015, p. 52). A separate problem refers to the fact that the asymmetrical arrangement discussed in this section conflicts with requirements of the IMF Extended Fund Facility (EFF) package and obligations that Ukraine assumed as a member of the World Trade Organization, WTO, since 2009 (Ada- rov et al., 2015, p. 50). However, it is a question of negotiation and the Ukrainian government’s capacity to persuade these international organizations that special circumstances exist in the case at hand. The absence of organized groups or political forces making the strengthening of the national market their top economic priority represents a far more serious problem. Ukrainian wooden furniture producers make an exception. They lobbied a law that can be considered as a rare and rather successful example of protectionist policies in Ukraine.

3. CASE OF THE TIMBER INDUSTRY: TENSIONS BETWEEN OPEN ECONOMY AND THE NATIONAL MARKET Before discussing some particularities of this law and its implementation, background information on the timber industry will help the reader to better understand the relevant context. For- ests cover 17.6% of the Ukrainian lands: 10633.1 Ha of 60354.9 Ha (Derzhavna Sluzhba Stat- ystyky Ukrainy, 2016b, p. 188). To compare: forest areas represent almost a half of Russia’s territory. At the same time, forest rents extracted in Ukraine actually exceed those in Russia: 0.397% of 120

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GDP against 0.346% of GDP in 2015 respectively (World Bank, 2018). The World Bank defines forest rents as roundwood harvest times the product of average prices and a region-specific rental rate. Forests rents in Ukraine have significantly increased since 2011, currently reaching a historic high (Figure 3). This reflects the intensification of the exploitation of forests, a scarce resource in Ukraine. The exploitation of forests tends to be particularly high during the periods of low prices of the other important export commodity, steel: before 2002 and after 2011 (see Figure 1). In a sense, lumber substitutes steel in the Ukrainian exports and vice versa.

Figure 3. “Forest rents (% of GDP) in Ukraine and Russia, 1991-2015”

Source: World Bank, 2018

The intensive exploitation of forest resources in the country in which they do not abound attracted policy-makers’ attention well before the current crisis and the rise in forest rents resulting from attempts to substitute lumber for the other export commodities. A first version of the Law of Ukraine “On particularities of government regulation of businesses specializing in sale and export of wood lumber (lisomaterialiv)” was promulgated in September 2005 under President Yushchen- ko.5 The most recent additions to the Law were initiated by a group of MPs representing the entre political spectrum, from Samopomich to Radikal’na Partiia Lyashka, from the West (5MPs from Lviv and one from Ivano-Frankivsk) as well as from the East (2MPs from Dnipro) and the Center (3MPs from Kyiv), in October 2015. They involve a 10 year ban on the export of lumber wood of all

5 http://zakon2.rada.gov.ua/laws/show/2860-15.

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6 233 votes ‘for’, no abstentions, no vote ‘against’, 80 MPs did not take part in the voting, see http://w1.c1.rada.gov.ua/pls/zweb2/webproc4_1?pf3511=52828. 7 Article 246 of the Criminal Code of Ukraine criminalizes logging without permit without imposing heavy penalties nevertheless. 8 The head of the State Forest Resources Agency of Ukraine estimates the volume of criminal logging at 24 thousand cubic meters in 2015, which represents approximately 0.1% of the legal logging (http://ukurier.gov.ua/uk/articles/hristina-yushkevich-shob-zupiniti-chornih-lisorubi/). A government sponsored study conducted with the help of GIS technologies in Zakarpattia, one of the most affected by criminal logging regions, suggests a higher figure: the total surface of areas of criminal logging is 2.3% of that of the legal logging (www.kmu.gov.ua/document/249450793/lis.pdf, see also an on-line map at http://texty.org.ua/d/deforestation/). 122

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Table 2. Results of linear multiple regressions to predict the merchantable wood volume (Lg10 transformed) in Ukraine, 2013-2016.

Legend: Results significant at a 0.05 level are marked by *, at a 0.01 level – by **, and at a 0.001 level – by ***. Source: Derzhavna Sluzhba Statystyky Ukrainy, 2017a, Vol. 2, pp. 264-265, 396-397, 399-476; Derzhavna Sluzhba Statystyky Ukrainy, 2016a, Vol. 1, p. 261; Vol. 2, pp. 250, 319-320, 357-359, 426-594;

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In spite of the government’s lack of resolution and unwillingness to produce and implement a comprehensive set of policies to strengthen the national market, the ban on the export of lumber wood seems to be working, at least so far. The linear regression analysis of the region-level data, method “forward” (with the probability of F for entry set at a 0.05 level and for removal at a 0.1 level), shows that in 2013 and 2014 the logging was driven by the demands of export and import. In 2015 their impact became insignificant (Table 2). The dependent variable, the merchantable wood volume (originally in thousand cubic meters; Lg10 transformed), was predicted from a series of the independent variables, namely, forest area (% of the territory covered by forests), forest restoration (the area of new forests in Ha), lumber wood production (wood ripped or cut, divided into parts or peeled, 6mm plus thick, originally in thousand square meters; Lg10 transformed), total import of goods and total export of goods (originally in thousand US$; Ln transformed), export of wood (thousand US$), export of wooden furniture (thousand US$), import of wooden furniture (originally in thousand US$; Ln transformed) and total wooden furniture production (office, kitchen, living and dining room furniture, originally in units; Lg10 transformed). The production of wood with low value added (lumber wood) had a positive and significant impact on the merchantable wood volume in 2013 and 2014 but not in 2015. The volume of total exports during the same period had a negative and significant impact, which may indicate that the lumber wood production tends to substitute the other commodities and products that Ukraine exports. The value of imports of furniture positively influenced the dependent variable in 2014 that immediately preceded the imposition of the ban. Ukraine produces and exports lumber wood im- porting at the same time wooden furniture. This situation is typical for peripheral economies. The 2015 pattern is different. The contribution of exports and lumber wood production became insignificant. At the same time, forest restoration positively contributed to the inter-regional variation in the merchantable wood volume. The impact of forest restoration remained positive and significant in 2016. At the same time, the impact of the volume of lumber wood became significant again suggesting that the policy has to be more carefully designed and implemented. There was no association between the merchantable wood volume and the volume of total exports in 2016 nevertheless. Does it mean that the ban helped make progress both in solving ecological problems and in protecting the national producers of furniture? The tentative answer is yes, but the consideration of a larger period is needed to further confirm this assumption.

CONCLUSION Ukraine currently lives in a period of economic duress. An unfavorable situation on the world commodities markets negatively affected the country’s economy. The national processing industries are unable to compete globally and even in the national market, as the case of furniture production suggests. More substantially, the country bears all the costs of the undeclared war waged by Russia. A part of the industrial, agricultural and transportation infrastructure has been either destroyed (in Donbass) or lost (in Crimea). The military expenditures have significantly increased – at the expense of investments and consumption. The military conflict that still remains without solution in sight negatively influences expectations of businesses and households, which further undermines prospects of economic development. In these circumstances, economic recovery will require massive public investment. “Since the cash-stripped Ukrainian government will hardly be able to come up with adequate funds on its own, the EU could potentially play a crucial role here – ideally by designing a sort of ‘Marshall Plan’ for Ukraine; similar plans have been advocated recently e.g. by G. Soros and Dmytro Firtash” (Adarov et al., 2015, p. 21). No new Marshall plan seems to be forthcoming, however.

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As a better alternative to credits and loans that simply postpone the payment of the costs caused by the current crisis, this article proposes the asymmetrical arrangement that involves a combination of temporary protectionism and the free access of Ukrainian producers to the Western markets. Being temporary protected at home, Ukrainian producers will have more resources for competing globally. The unrestricted access to the Western market will provide them with powerful incentives to learn. In other words, Ukraine may need a combination of protectionism and the outward-oriented strategy. At least three obstacles prevent Ukraine from moving in this direction. First, Ukraine has obligations to keep its economy open. Long and difficult negotiations will be needed to persuade the international organizations, first of all the WTO and the EU, that this country’s special circumstances require non-orthodox, exceptional measures. Goodwill of the international organizations will help accept the asymmetrical solution and enforce its implementation (so, for example, no unilateral extension of protectionist policies is possible). Second, Ukrainian businesses and political parties prefer to wait and see if the current crisis may end as miraculously as the crisis in the 1990s instead of acting proactively and promoting at least a discussion of alternative economic policies for this country. Ukrainian furniture producers make an exception that confirms the rule. Without healthy economic debates existing economic policies, neoliberal or protectionist, will not be adapted to the particularities of the economic and institutional environment in this country and, thus, will unlikely lead to the desired outcomes. Third, protectionist policies create a risk of rent seeking and government representatives’ op- portunism in the other forms. Without solving the problem of incentives, not only for businesses but also for the government, any form of protectionism is doomed. “Issues of incentives are at the core of economics: Some economists have gone so far to suggest that they are the economic problem” (Stiglitz, 1994, p. 47). The volunteer movement that emerged and strengthened in Ukraine from 2013-2016 provides some ground for optimism in this respect. This movement is not satisfied with the role of a service provider progressively assuming the function of an advocate and a watchdog controlling the government on a daily basis. The civil society’s control over the government potentially creates much needed incentives for its representatives and serves as a guarantee against the appropriation of benefits of the gate-keeping by them, as it has been occurring in Russia. The situation with the draft law tentatively titled “Buy Ukrainian, Pay Ukrainians” (Kupui Ukrain’ske, platy Ukraintsiam)9 offers a good opportunity for comparing risks and benefits of protectionist policies, the Ukrainian style. The Law aims to make Ukrainian producers more competitive when bidding for government and municipal contracts. The draft was originally submitted in October-November 2017 and is scheduled for a second hearing, as of the time of this writing. The first draft was supported by MPs representing several political forces, from the centrist parties, such as Petro Poroshenko Bloc and People’s Front to the opposition, Fatherland and Opposition Bloc. The Law, if it eventually enters into force, has several features that potentially allow the civil society to exercise some control over the process of awarding contracts and, hence, to “monitor the monitors”. Legislators have no intention to organize a separate competition for the Ukrainian bidders. All bidders will be taking part in the same tenders organized on Prozorro (literally – Trans- parent),, an on-line platform created in 2014 by a group of volunteers in an effort to increase the level of transparency in the operation of the Ukrainian government.10 At the same time, price proposals from the national producers are expected to be corrected using a formula defined in the

9 http://w1.c1.rada.gov.ua/pls/zweb2/webproc4_1?pf3511=62736. 10 https://tender.uub.com.ua/.

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Law: the more one uses local resources, including financial, the more his or her price bid is adjusted downward. The bids, formula for calculating the adjusted price and results are transparent and accessible on-line for everyone interested. However, Section 7 of the draft law says that only the government, a public body or a municipality may initiate the procedure for checking the validity of the information supplied by the bidders and entered in calculations. Without allowing the civil society to access and verify this information, the Law may well contribute to the continuous reproduction of rent-seeking behavior instead of helping to find a way out for the national economy.

REFERENCES Adarov, A. et al. (2015), How to Stabilise the Economy of Ukraine: background study, The Vienna Institute for International Economic Studies, Vienna. Åslund, A. (2009), “Ukraine’s Financial Crisis, 2009”, Eurasian Geography and Economics, Vol. 50, No. 4, pp. 371-386. Åslund, A. (2008), “Reflections on Ukraine’s Current Economic Dilemma”, Eurasian Geography and Economics, Vol. 49, No. 2, pp. 152-159. Åslund, A. (2001), “Ukraine’s Return to Economic Growth”, Post-Soviet Geography and Economics, Vol. 42, No. 5, pp. 313-328. Bruton, H. J. (1998), “A Reconsideration of Import Substitution”, Journal of Economic Literature, Vol. 36, No. 2, pp. 903-936. Chekh, M. M., Vasylytsya, O. B., Polovska, V. T. (2016), “Import Substitution and Foreign Direct Investment Promotion in Ukraine”, Actual Problems of Economics, Vol. 4, No. 178, pp. 87-96 (in Ukrainian). Chuzhykov, V., Fedirko, O., Chuzhykov, A. (2014),. “Methodological Background of Post-Soviet Re- gionalism: The Case of Ukraine. Baltic Journal of European Studies, Vol. 4,No. 1, pp. 20-33. Commons, J. R. (1959 [1924]), Legal Foundations of Capitalism, University of Wisconsin Press, Madison. De Micco, P. (2015), In-depth analysis: Ukraine’s will to liberalise: Tested on many fronts, EU Policy Department, Directorate-General for External policies. http://www.3dcftas.eu/system/tdf/ EX- PO_IDA%282015%29549072_EN.pdf?file=1&type=node&id=111 accessed Febr. 27, 2018. State Committee of Statistics of Ukraine (2017a), Regions of Ukraine in 2017, 2 volumes, Kyiv (in Ukrainian). State Committee of Statistics of Ukraine (2017b), Ukraine’s foreign trade in 2016, Kyiv (in Ukraini- an). State Committee of Statistics of Ukraine (2016a), Regions of Ukraine in 2016, 2 volumes. Kyiv (in Ukrainian). State Committee of Statistics of Ukraine (2016b), Statistical Yearbook of Ukraine 2015, Kyiv (in Ukrainian). State Committee of Statistics of Ukraine (2014), Ukraine’s foreign trade, Kyiv (in Ukrainian). State Committee of Statistics of Ukraine (2010), Ukraine’s foreign trade, Kyiv (in Ukrainian). State Committee of Statistics of Ukraine (2006), Ukraine’s Statistical Yearbook 2005, Kyiv (in Ukrainian). Dovbniak, T.F. (2009), “Ukrainian Economy Cycling in 2000–2008”, Actual Problems of Econom- ics, Vol. 1, No. 91, pp. 65-72 (in Ukrainian). Ericson, R. E. (2009), “Eurasian Natural Gas Pipelines: The Political Economy of Network Interde- pendence”, Eurasian Geography and Economics, Vol. 50, No. 1, pp. 28-57. European Union, Directorate General for Trade (2017), Trade in goods with Ukraine, http://trade.ec.europa.eu/doclib/docs/2006/september/tradoc_113459.pdf accessed Feb- ruary 27, 2018. Gaddy, C., Ickes, B. (2005), “Resource Rents in the Russian Economy”, Eurasian Geography and Economics, Vol. 46, No. 8, pp. 563-567.

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Global Wood Market Info (2016), Rising EU imports of lower value Eastern European hardwood, June 16. https://www.globalwoodmarketsinfo.com/rising-eu-imports-of-lower-value-eastern- european-hardwood/ accessed February 27, 2018. Grendash, T. (2012), “Regional Disparities in Ukraine”, Scientific Proceedings of the Black Sea State University named after Petr Mogila, Ekonomika, Vol. 177(189), pp. 116-120 (in Ukraini- an). Havrylyshyn, O. (2014), “Ukraine: Greatest Hopes, Greatest Disappointments”, in: Åslund, A. and Djankov, S., (Eds.), The great rebirth: lessons from the victory of capitalism over communism (pp. 165-184). Washington, US: Peterson Institute for International Economics. Hedlund, S. (2005), Russian Path Dependence, Routledge, London and New York. Kuzio, T. (2012), “Twenty years as an independent state: Ukraine’s ten logical inconsistencies”, Communist and Post-Communist Studies, Vol. 45, No. 3-4, pp. 429-438. Kuzmin, O., Shpak, N., Pyrog, O. (2016), “Model of sustainable development of the national economy of Ukraine: assessment of current state and prospects of development”, Econtechmod: An International Quarterly Journal, Vol. 5, No. 1, pp. 43-50. Masliy, V. V. (2014), “Analysis Methods of Territorial Distribution of Foreign Direct Investments in Ukraine”, Actual Problems of Economics, Vol. 7, No. 157, pp. 454-462 (in Ukrainian). Melnik, T., (2015)., “The economic sovereignty support under the conditions of economy’s openness”, Marketing and management of innovations, Vol. 1, pp. 147-157. Ministry of Finance of Ukraine (2017), Sovereign and state guaranteed debt on February 28, 2017, http://www.minfin.gov.ua/uploads/redactor/files/28.02.2017%20%D0%B1%D0%BE%D1%80 %D0%B3.xlsm accessed February 27, 2018. Nosova, O. (2013), “The Innovation Development in Ukraine: Problems and Development Perspec- tives”, International Journal of Innovation and Business Strategy, Vol. 2, pp. 1-13. OECD (2014), OECD Territorial Reviews: Ukraine 2013, OECD Publishing. Oleinik, A. (2015a), “Benefits of entry control: the Russian case”, Post-Communist Economies, Vol. 27, No. 2, pp. 216-232. Oleinik, A. (2015b), “Price of opulence: on a constellation of interests in the European market for natural gas”, Baltic Worlds, Vol. 8, No. 3-4, pp. 51-61. Oleinik, A. (2011), Market as a Weapon: the Socio-Economic Machinery of Dominance in Russia, Routledge, London. Prebisch, R. (1959), “Commercial Policy in the Underdeveloped Countries”, The American Econom- ic Review, Vol. 49, No. 2, pp. 251-273. Rose-Ackerman, S. (2004), “Governance and Corruption” in: Lomborg, B. (Ed.), Global Crises, Global Solutions (pp. 301-344). Cambridge University Press, Cambridge. Sala-I-Martin, X. X. (1997), “I Just Ran Two Million Regressions”, The American Economic Review, Vol. 87, No. 2, pp. 178-183. Smith, A. D. (1991), National Identity, Penguin Books, London. Stiglitz, J. E. (1994), Whither Socialism? The MIT Press, Cambridge, MA. Yakymenko, N. V. (2015), “Mechanism of Strategic Import Substitution in Ukraine’s Economy”, Actual Problems of Economics, Vol. 10(, No. 72, pp. 154-159. Veblen, T. (1964 [1939]), Imperial Germany and Industrial Revolution, Augustus M. Kelley, New York. Virág, A. (2012), “The Cultural and Geopolitical Dimensions of Nation-Building in the Ukraine”, So- ciety and Economy, Vol. 34, No. 4, pp. 619-641. World Bank (2018), Open Data, http://data.worldbank.org/ accessed February 27, 2018. Zhylyaeva, N. M. (2010), “Prospects of Competitiveness Increase for Ukrainian Economy: Sectoral and Regional Priorities”, Actual Problems of Economics, Vol. 4, No. 106, pp. 51-59.

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Montenegrin Journal of Economics

Vol. 14, No. 3 (2018), 129-142 ‘

Algorithm of Forming the Category Management in the Diy Market Segment

YEVGENY ROMAT1 and YULIIA BILIAVSKA2

1 Professor, Kyiv national university of trade and economics, Kyiv, Ukraine, E-mail: [email protected] 2 Associate Professor, Kyiv national university of trade and economics, Kyiv, Ukraine, E-mail: [email protected]

ARTICLE INFO ABSTRACT Received June 27, 2018 The aim of the paper is to formulate a scientifically grounded and Revised from July 14, 2018 expedient for use in practice algorithm of the formation of category Accepted August 22, 2018 management in the DIY segment. The subject of research is not only Available online September 15, 2018 this technology but also its commercial exploitation. The basic hypothesis is that category management which is understood as the formation and implementation of the technology management of JEL classification: the product range by using information and innovation support, М 12, М 31, М 39 strategy generation and methodical use of management tools for making the key competence and ensuring long-term competitive- DOI: 10.14254/1800-5845/2018.14-3.9 ness of the enterprise. Category management is developing fast in Ukraine and provides a balanced work of the company in any seg- Keywords: ment of the market that is connected with the retailer. The growing domestic market is attracted by the world's foremost «Do It Yourself» category management, (the “DIY”) trading format. This tendency is stipulated by an increase marketing, in consumer requirements, not only in the quality of goods, but also role of product categories, in the technology of the process of making a purchase. The imple- goods category, mentation of category management program is not always per- assortment management ceived by the staff as it is a relatively new area of work. The point is that the introduction of category management is most likely connected with a change in the structure of purchases and sales. The practical significance of the research results is related to the possibility of using them directly by entrepreneurs when assessing the implementation of category management.

INTRODUCTION The deceleration of the growth rate of domestic market, decrease of consumers' purchasing activity cause aggravation of systemic contradictions in the activity of enterprises. Misleading orientation of enterprise management for the use of outdated management tools leads to the decrease in the efficiency of activities, curtailment of programs of socio-economic development, the destabilization of internal processes and the destruction of the hierarchy of organizational values.

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In difficult economic conditions, the key issue for enterprise management is the search for new sources of competitive advantage, where the formation of commodity category roles can be a factor that will positively influence their implementation into activities. Category management is developing fast in Ukraine and provides a balanced work of the company in any segment of the market that is connected with the retailer. The growing domestic market is attracted by the world's foremost «Do It Yourself» (the “DIY”) trading format. This tendency is stipulated by an increase in consumer requirements, not only in the quality of goods, but also in the technology of the process of making a purchase. Supermarkets and hypermarkets have more attractive terms of trade, a wider range of products and additional services. These factors are decisive in the process of allocation of the Ukrainian retail market and, most of all, they determine the consumer loyalty. Unlike many other segments, for the DIY market the crisis has become not only the cause of the downturn, but also the motivation for managerial change and business process optimization. Networks reviewed the product range, supplemented it by groups of goods corresponding to the changed price request of buyers, held measures for price containment and began to actively develop the franchise. With the help of marketing, merchandising and assortment management techniques, we can conclude that it is appropriate to combine them in certain categories with common features to optimize product performance. The main task of the retailer is to allow consumers to choose the desired product under the stock, quality and price. If the range of goods is significant, and there is a severe competition, the traditional functionally-specialized organization of retail management leads to a decrease in the overall efficiency of the business as a whole. The purpose of category management is to maximally meet the needs of consumers and to increase the efficient interaction between the supplier and the seller. The implementation of category management program is not always perceived by the staff as it is a relatively new area of work. The point is that the introduction of category management is most likely connected with a change in the structure of purchases and sales. This strategy is directed primarily at the realization of those programs and tasks that are followed by discrete companies and retail networks, meeting the needs of the consumer, therefore the main instrument of effective enterprise management is not the production process or the implementation process, but the effective category management, including self-skillful formation of commodity category roles.

1. LITERATURE REVIEW Brian Harris, the founder of the consulting firm «The Partnering Group, TPG», has formalized category management more than 29 years ago, and his Haris model still remains the basis of classical science of category management. Category management was born in the supermarket trade format, when the owners of one of the stores found that they can group goods in unusual way for them and evaluate the range not as a set of individual goods, but as a product mix taking into account the key views of the consumer. The first publications on category management belong to foreign authors, in particular, G. J. Verra (1994), M. Durban (2007), M. G. Zenor (1994), F. Speer (1994) and others. The writings of these scholars reveal the specific provisions of the new management concept and describe their implementation at Western businesses. For Ukraine, the research of category management is a relatively new direction. In our opinion, the most interesting are the works of such authors as O. N. Mirgorodckaya (2014), Y. V. Biliavska (2018), C. V Balakirev (2006), O. Kreschenko (2013), S. V. Sysoieva (2010), N. K. Moiceeva et al. (2005). The most recent publications by scholars and practitioners are mainly focused on introduction of category management in the practice of business

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Yevgeny Romat and Yuliia Biliavska / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 129-142 management, as well as on the implementation of certain of its functions and tasks (Abdoli et al, 2017; Moraru et al, 2017). Despite of the fact that scientific research has been completed, the problem still remains not researched enough, and it is quite obvious that in the fullest possible sense the formation of the tendencies of category management of trade in enterprises is uncov- ered, yet there is no universally accepted definition of this concept in the economic literature.

2. THE AIM AND OBJECTIVES OF THE STUDY The aim of the research is to formulate a scientifically grounded and expedient for use in practice algorithm of the formation of category management in the DIY segment. According to this goal, the following tasks are:

 to identify the main premises that should be used as the basis for such algorithm;  to offer an algorithm of the methodical approach to the formation of balanced category management of the enterprise;  to develop economic-mathematical model of formation and development of category management of the enterprise in the segment DIY;  to show the possibilities of improving the results of control of category management by constructing a map of consumer perceptions of product categories roles.

3. CONDUCTING RESEARCH AND RESULTS The most common is the definition belongs to N. B. Gurova (2018): "Category management is a process of managing the range in which each category product is considered as an independent business unit." The main objective of category management is the wide reach of customers, maximum satisfaction of their needs and simultaneous increase of the efficiency of interaction between the supplier and the consumer, increase productivity by reducing costs. It is difficult to disagree with the opinion of O. Kreschenko (2018), who considers the category product as an independent business unit within the strategic business unit, because “category business unit should develop an independent strategy of behavior in the market (even radically different from the overall strategy). However, this strategy must necessarily be an integral part of the overall strategic portfolio and coordinated in the main positions (finance, trade, pricing and other types of policies)”. We should also consider the idea of V. Zateykin (2003), who, views the category management as “... the process of management of an item in which each category of goods is considered as a domestic business unit, and aims at maximally meeting the needs of the consumer, on the one hand, and increasing the efficiency of work between the producer and retailer, from another”. The theoretical aspects of category management are described in the works of O. Voitzekhov- skiy (2018) as “... a process that takes place between the blocks of the logical chain, where the categories are controlled as strategic twin-units and provide improved financial results due to the need to satisfy the needs of the consumer”. The term "category management" given by N. K. Moiceeva, T. N. Golikov and Y. C. Dolgachev (2015) is interpreted as “... a process of management of trade activity, when each category of goods is considered as a separate business unit, where the main goal is the maximum satisfaction of the consumer’s needs and the enhancement of efficient cooperation between producer and retailer”. A. Vellhoff and J. E. Mason (2017, p. 39) determine the category management differently: “This is a process, in which the unit of management is a product category, not a single brand”.

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As S. Balakirev (2006) defines, the category management is “... the system of management of an assortment, in which the distinction is made in the structure of the product categories, followed by their management as independent business units”. Ukrainian and foreign scholars identified a number of issues related to the new management concept - category management. Often in scientific works, the research of category management comes to general description of its character and future benefits from the introduction into the business activity. However, there is a lack of attention towards the research of the object of management in the category management, therefore, in the context of solving problems of implementing category management into the domestic retail, the search for object of management is crucial. As a result of the conducted research of scientific sources, practices of the companies involved in trade and using the target, system and process approaches, it is offered a corrected definition of the concept of “category management”, which is understood as the formation and implementation of the technology management of the product range by using information and innovation support, strategy generation and methodical use of management tools for making the key competence and ensuring long-term competitiveness of the enterprise.

3.1. The problem of formation of category management The theoretical and methodological foundations of the study are classical and cognitive theories of management, the base of the systematic, evolutionary, situational, synergetic and proce- dural approaches, legislative and regulatory documents governing activities related to the management of the roles of a product category. While researching, the following set of general scientific and special methods was used: analytical, historical and logical generalizations, induction and deduction, citation, concretization, analysis and synthesis. It was used to clarify the conceptual apparatus and to identify the key features of individual definitions, analysis of approaches to cu- mulative category management, and the role of product categories; economical and practical methods (selective observation, comparative and techno-economic analysis, grouping on the precise use of software MS EXCEL, STATISTICA) - for the purpose of visual representation of the results of the researches, the updating of the dynamics of changes in the economic indicators of the active enterprises in the DIY segment for the investigation of the process of formation the product category roles; graphic analysis (Microsoft office) - for the visual display of analytical research materials; marketing and sociological research (surveys), as well as expert evaluations - for building a consumer perception map of product categories. The number of product categories depends on the specificity of the enterprise. The format in the DIY segment can have 400 categories / subcategories. Each of them plays an important, but variable role in achieving goals. The management of the company (retailer, producer) which is involved in the control of product range should form a completed list (classifier) of the categories that will be used by the entire enterprise in the DIY segment and probably by its business partners. Despite the market format, category management of the enterprise will always be based on the key premises given by B. Haris. By adapting them to the conditions of domestic business and real- time mode we provide the following Figure 1. While forming a list, irrespective of its place in the chain «manufacturer - retailer» an enterprise should be guided by the basic principles. Roles of the product category are intended to allow better use for the benefit of the consumer behavior that makes purchases in different categories. The process of the role of product categories can be divided into the following stages: clarification of the role of a specific product for the enterprise, which will be applied within the existing assortment matrix of the enterprise; indication of roles for each category of goods; division of resources among the prevailing categories based on these roles. The general scheme of the process of management of the commodity category at the pre-trading level is pre-set at the Figure 2 [2, p. 87].

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Figure 1. Premises of category management

Source: it is worked out by authors

During category management development it is necessary to identify the main development zones, a perspective direction for the revival and improvement of the assortment of the enterprise. The most successful is the second stage of implementation of category management - placing the roles for product categories. Determination of the list of roles to be used within the range of matrix adopted by the company in the DIY segment. The role of the product category is very important stage for cooperation between the consumer and the seller. Depending on the relevance of the product and its popularity, the stores allocate the necessary square, shelves, apply different approaches from the side of advertising, develop a system of discounts, and this is all to ensure that the consumer in any case noticed and drew attention to this product. Therefore, the main purpose of the category management is in the maximum satisfaction of the needs of the buyers on the one hand, and in the promotion of efficient cooperation between the producer (the supplier) and the networks of retail trade, on the other. Thus, a category manager is a person with a higher qualification. He is responsible for collecting work on purchasing and procuring goods from a definite, well-formed group, maintaining business ties with suppliers or retail operators, seeking ways to optimize the costs of delivery and logistics, organizes and super- vises promotional activities.

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Figure 2. The scheme of control of the product category at the enterprise in the DIY segment

Source: it is worked out by authors

3.2 Algorithm for the formation of category management Investigating the algorithm for forming the roles of a product category in the DIY segment, an analysis of scientific and methodological literature on the subject of research has shown that, as a rule, all approaches to evaluation are based on such methods of scientific research as comparative, statistical, factor analysis, method of expert assessments. We believe that these methods are not isolated from each other, but they are connected and complement each other. Each of the proposed stages of the process of forming the roles of the product category involves implementation of specific works to the category management. When forming the list of categories, management should consider that the category management primarily contributes to the management of enterprise revenue. For this reason, while exploring the rationalization of the development of category management of trade enterprises, the focus should be given on those factors that help category managers to work with the maximum efficiency and benefit, rather than using strict rules and norms of behavior and result orientation. Thus, we offer an algorithm for the formation of balanced category management of the enterprise, the main stages of which are shown in Figure 3.

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Figure 3. The algorithm of the methodical approach to the formation of balanced category management of the enterprise in the DIY segment

The application of this approach allows to ensure the harmonization of existing category management with the objectives, strategy and resource capabilities of the enterprise. The next stage is the mathematical analysis of the model. At this stage of the economic-mathematical modeling, the mathematical measures of the study are used. In particular, the important point is to prove there is a solution to the set problem. In the process of forming a role of product category of enterprise in the DIY segment, the preparation of the outgoing information is the most intensive phase of economic and mathematical modeling, since it is not limited by passive data collection. Mathematical modeling imposes strict requirements for the information system. At the same time, it is necessary to consider not only the possibility of preparing the information on the level expected, but also the cost for preparation of the information arrays. During the preparation of information, the methods of probability theory, mathematical statistics are used for the organiza-

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Yevgeny Romat and Yuliia Biliavska / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 128-142 tion of sample surveys, evaluation of the reliability of data. For systemic economic and mathematical modeling of forming the roles of product category, the results of operation of some models are the source information for others. The numeral solution of the stage of economic-mathematical modeling includes the development of algorithms for the formation of roles of product category of enterprise, the preparation of programs on the PC and the direct conducting of calculations. At the same time, some difficulties are caused by the large dimensionality of economic problems. Usually calculations based on the economic-mathematical model have a diverse nature. Numeral solution significantly complements the results of analytical research, and it is the only option for many models. At the stage of analyzing numeral results and their use of economic-mathematical modeling, the most important question regarding the correctness and completeness of the modeling results and their applicability both in practice and in order to improve the model is solved. The use of numeral modeling results in economy is directed to solve the practical problems (analysis of economic objects, economic forecasting of practical and social development, and making control decisions at all levels of the economic hierarchy). The determined stages of the economic-mathematical modeling of the algorithm for the formation of the roles of product category in the DIY segment are closely interrelated, in particular, there might be reversed ties of the stages. Thus, at the stage of constructing the model, it may become clear that the problem statement is either controversial or leads to complex mathematical model; in this case, the initial setting of the task must be adjusted. Most often, the necessity of return to the previous stages of economic-mathematical modeling occurs at the stage of preparation of the outgoing information. If the necessary information is missing or the costs for its preparation are too large, it is necessary to return to the stages of the task statement and its formalization to adapt to the information available to the researcher. Economic- mathematical modeling has a cyclic nature. Disadvantages that cannot be corrected at the certain stages of the simulation are eliminated in subsequent cycles. However, the results of each cycle of economic-mathematical modeling have a completely independent value. If we start a research from constructing a simple model, we can get useful results, and then continue creating more complex and advanced model, which includes new conditions and more accurate mathematical dependencies. The model of cyclical development is characterized by historicity and inevitability of processes associated with changes in economic power, economic and social potential of an enterprise due to the passage of particular phases of life cycle of the roles of product category in the DIY segment. In order to optimize the final results, the profile of product category roles should be adapted to the changing factors of the internal and external environment of management. For example, at the stage of fading development of an enterprise it is necessary to make changes to transfer the roles of the product category of the enterprise to the market type. Based on the above definition, we will build an economic and mathematical model that will allow us to make optimal decisions in process of forming category management in the DIY segment, regardless of its size, specialization, while ensuring the uniqueness of each enterprise. By formaliz- ing the target installations and limiting the management of category management in the DIY segment by linear programming, we have the following:

, (1)

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, (2)

Where i - is the index of category management in the DIY segment; I - the number of components that can form (be part of) category management; Хi - variables characterizing the i component (component or constituent) of category management; ri - change in the level of category management due to the formation or improvement of the i component of category management; j - index of the resource necessary for the formation / improvement of components of category management; J - the number of names of resources necessary for the formation / improvement of the components of category management; aij - volume of j resource necessary for the formation or improvement of i component of category management; Аj - the boundary (maximum possible) volumes of the j resource that an enterprise has (can spend) on the development of its category management; m - index of the method (or a set of management measures) for the formation / improvement of the components of category management; M - number of methods (or sets of management measures) for the formation / improvement of components of category management; bim - the magnitude of the risk of loss due to the formation or improvement of the i component of category management in the m way (or subject to the implementation of the m-set of management measures); Вm - the maximum acceptable for the enterprise value of the risk of losses due to the formation / improvement of category management in the m way (or subject to the implementation of the m totality of management measures).

Note that Хi can be represented both in points and in the form of coefficients when specific quantitative indicators are used for evaluation. With the use of scores, the variable cannot exceed the upper limit of the scoring scale, it means that Хi will be limited both from the bottom and from above: where is the maximum possible score for the selected scale. A simplex-method was used to the developed economic-mathematical model for determining the density of connection or establish the fact of multicollinearity and the sequential exclusion of random components of the model. Thus, a system of constraints is formed, and it consists of the groups of components of category management: definition of a category is construction of product classifier; the role of category which is accepted to divide according to the following principle: basic, seasonal, target, comfortable; rating category is to analyze information on the market share of each category or individual product groups; the purpose by category indicators is to fix goals that the company seeks to achieve; category strategy; action plan: category tactics is to determine the number of manufacturers in the category, conduct an ABC analysis; project implementation; evaluation of results are usually based on the results of quarterly reports.

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In order to determine the susceptibility to the formation of category management, it is necessary to conduct a questionnaire of the heads of the enterprises’ departments in the DIY segment. This will reveal the state of preparation of the enterprise to change. Before introducing changes in the enterprise, it is necessary to determine the level of employee's susceptibility to innovation. To this end, we propose the following methodology for assessing the effectiveness of forming product category roles with the help of questionnaires. To determine the importance of the statement given in the questionnaire, the employees are asked to use five possible answers: disagree (1 point), rather disagree (2 points), not sure (3 points), most likely agree (4 points), totally agree (5 points). From each statement it is necessary to select a point, which evaluates the most positions, the value of the estimates obtained (Ki). Each statement is assigned a degree of significance for the trading company on a tribal scale, where 1 point is the least significant statement, and 5 points is the most important statement. We will note the received subjective point as Ki. An empirical assessment is a derivative of the value of a factor as to its importance:

P  K N emp i i , (3)

where P emp – is empirical assessment; Ki – the point; Ni – the importance of the factor. There can be any number of evaluated statements (criteria), but the susceptibility of staff to innovation is determined only by the ratio of the points of ideal and real assessments which is the level of improved category management. However, the decisions cannot be based only on the sum of points of empirical assessment. It is necessary to determine the ratio of the empirical assessment obtained by the survey to ideal assessment, which is defined as the product of the importance of the factor to the maximum possible score (the maximum possible score is 5).

M  N Z ideal i ideal, (4)

where M ideal - is perfect assessment; Ni - importance of the factor; Z ideal - the maximum possible score. The obtained ratio is an index of category management (an index of preparation for the adoption of innovation strategy). The index of category management, which shows how empirical evaluation differs from the ideal, is calculated as the ratio of the sum of estimates and characterizes employee readiness for change:

I  P / M km  emp  ideal , (5)

where I іkm - is an index of category management; Р emp - empirical evaluation; M ideal - the maximum possible score. In order to achieve the maximum effect from the introduction of category management, the most relevant is the use of action in relation to the groups of goods and increase the scope of services. If we want to create a constant demand for products, we should invoke an interest of a consumer in the fact that our product is the best, the price is acceptable and service remains in per-

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Yevgeny Romat and Yuliia Biliavska / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 129-142 son’s memory as pleasant. Therefore, as category management is a system of assortment management, which is based on marketing principles (targeting the target audience) and aims to maximize company profits, then both the manufacturer and the distributor, and retailers must do this. The value of the index of category management in the DIY segment should be at least 0.5, the lower index values indicate that improvements will not be perceived, and category management is not innovative and relevant in the enterprise. However, the high value of the index does not mean that the company is actively introducing innovations. The creative and innovative abilities of category managers, their awareness of the need to implement improvements only create a favorable basis for change. The ability to realize the innovative potential of employees and the company directly depends on the general state of the market.

3.3. The results of application of the algorithm offered We should consider the results of use of the proposed algorithm on the example of the leading enterprises of the DIY market in Ukraine. The effectiveness of the economic and mathematical model has been tested at the enterprises and found that they need to establish public relations, improve marketing and advertising and systematic work on formation and building the organizational capacity. Economic-mathematical modeling has made it possible to identify the relationship between the effectiveness of category management and its financial and economic indicators. The dependence is based on the increase of expenses for elimination the defects of identified groups of category management's components. Thus, optimization of category management’s components minimizes the loss of financial resources in the structure of total costs of the enterprise. The calculation of the index of category management according to the established method for enterprises in the DIY segment is presented in the Table 1.

Table 1. Index of category management according to the established method for enterprises in the DIY segment («Epicenter К Ltd»)

The im- The point Empirical Ideal point The maxi- portance Ki* evaluation of mum possi- No Factor of the fac- of Р emp Мideal «Epi- ble point tor «Epicenter center К 1 2 3 4 Z ideal Ni К Ltd» Ltd»

Definition of the market 1 0,07 5 5 3 4 0,35 5 0,35 segment

Define the enterprise 2 0,09 5 5 4 5 0,45 5 0,45 format

Formation of assortment 3 0,08 3 4 2 2 0,24 5 0,4 by categories

Completing market analy- 4 sis and marketing re- 0,09 3 4 3 3 0,27 5 0,45 search

Research of particular 5 0,05 3 3 2 3 0,15 5 0,25 product categories

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The establishment of the 6 0,08 3 3 3 3 0,24 5 0,4 role of product categories

Formation of the needs of 7. 0,09 4 4 3 4 0,36 5 0,45 the consumer

Systematization of prod- 8 0,08 4 4 3 4 0,32 5 0,4 uct groups

Determination of the price 9 0,1 3 3 2 3 0,3 5 0,5 segment

Construction of business 10 0,08 3 3 2 3 0,24 5 0,4 processes

Communication process 11 0,09 3 3 3 3 0,27 5 0,45 «retailer-consumer"

Improving of the product 12 0,1 4 4 4 4 0,4 5 0,5 category

Total 1 - - - - 3,59 - -

Index of category management (∑Р emp / ∑Мideal) 0,72

Note: Ki* indicators are summarized as a result of questionnaire survey of category managers (5 people) selected for research: 1. Epicenter K Ltd.; 2. LLC "Leroy Merlin"; 3. LLC "33 square. meter "; 4. LLC "New Line". The maximum Z ideal point for all businesses is 5. The empirical P emp and the ideal Mideal scores are calculated according to the formulas presented in the algorithm by text.

Source: it is formed by authors

According to the results of the approbation of the economic-mathematical model and the definition of category management’s index of enterprises in the DIY segment in Figure 4, we offer a map of consumer perceptions of product category roles, which consists of four segments, each of which has certain limits. The ideal among all of the enterprises is LLC Epicenter K, which occupies a leading position and more closely approximates to the ideal situation. Also, LLC "New Line", which declared itself as an enterprise with a strong resource potential, a strong market position, high prospects and a mighty variety of product categories, became one of the leaders in the ideals. Unfortunately, none of the enterprises of our research was in the "Magnat" segment (such enterprises have large resource opportunities, which should be directed to the right course for the transition to a group of enterprises - ideals). Enterprises which belong to the «Leader» group have more limited financial, labor, informational and technical capabilities. Despite this, the level of category management is high enough, which is a positive phenomenon and can facilitate the transition of enterprises to the "Ideal" group. This requires investments in enterprise development, the search for cheap sources of resources, and so on. And, the last group - "Outsiders", which have very weak positions in the market. This happens due to inadequate funding, development and awareness. To survive, they need capital investment and a change in development strategy or liq- uidation. Thus, we can observe a fairly stable state of category management in the DIY segment in Ukraine.

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Figure 4. A map of consumer perceptions of product categories roles

Source: it is formed by authors

As a result of answering to the set goal we have revealed that according to the priority of implementation the main measures defined by the actual values of the hierarchical system of indicators are: zooming of enterprises’ trading due to the expansion and deepening of the range, the efficiency of cooperation and responsibility of all participants in the trade and processing chain in the management material, financial, organizational and information resources; use of modern management techniques in the range of unquestioning balancing assortment and pricing and con- form to international standards of quality and safety; increasing customer loyalty; increasing the interest of partners in cooperation with enterprises and trade through the introduction of new programs of integration and the creation of vertically integrated industrial and commercial complexes; increase investment in environmental programs, creating the image of a socially responsible company, increased motivation and effectiveness of the employers, because of the clear division of functional responsibilities, the introduction of tangible and intangible incentives, as well as new training programs with the category management.

CONCLUSION Within the above-mentioned postulates and the established requirements for a category manager, it should be noted that the key point is that the retailer works together not only with the consumer, but also with the manufacturer. So, the retailer provides sales data for categories on their network, defining the role of the category it plays. The manufacturer, who has an expertise in the category, purchasing behavior studies, and sales data from the entire market, gives recommendations to the retailer about the strategy and tactics of category development. In the development of retail it became clear that offering the same brands on shelves, they risk losing uniqueness in the eyes of the consumer. With the growth of competition among retailers, they need to find their own distinctive feature to differ themselves from competitors. In order

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Yevgeny Romat and Yuliia Biliavska / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 128-142 to do this, they need to have a clear strategy for each category, which includes defining its role, ways of promoting products, pricing, principles of merchandising. In a harsh competitive struggle, using the consumer satisfaction index, trade managers will be able to focus their attention on the critical success factors of the category management in the enterprise, which are associated with increased customer satisfaction.

REFERENCES Abdoli, M., Rostamzadeh, R., Feizi, J., Joksiene, I. (2017), “Impact of Perceived Value and Satisfac- tion on Customer Loyalty in Banking Industry”, Transformations in Business & Economics, Vol. 16, No 2A (41A), pp. 421-441. Balakirev, C. V. (2006), “Category management as a modern approach to managing the commodity assortment”, Management in Russia and abroad, Vol. 5, pp. 87-99 (in Russian). Biliavska, Y. V. (2017), “Features and trends of categorical management of trade enterprises”, Collection of scientific works "Problems of the system approach in economy", Vol. 4 (60), pp. 81–88 (in Ukrainian). Biliavska, Y. V. (2018), “Category management: present, postulates and philosophy”, International scientific and practical journal "Logistics problems and solutions“, Vol. 2 (75), pp. 16–23 (in Ukrainian). Durban, M. (2007), Driving Sustainable Growth Through Category Management, www.kamcity.com. Kreschenko, O. (2013), “Methodological principles of the concept of categorical management“, East. Economy, Vol. 4 (124), pp. 39-44 (in Ukrainian). Mirgorodckaya, O. N. (2014), “Category management as a modern marketing technology for managing the commodity assortment”, Scientific and methodical electronic magazine "Concept", Vol. 17, pp. 36–40 (in Russian). Moiceeva, N. K., Golikov, T. N., Dolgacheva, Yu. C. (2005), “Category management and merchandising in retail”, Marketing, Vol. 2(81), pp.101–108 (in Russian). Moraru, A.-D., Ilie, M., Sorici, C. O. (2017), “The Mediating Role of Market Orientation in the Rela- tionship between Entrepreneurial Orientation and SMEs Business Performance. Evidence from Romania”, Transformations in Business & Economics, Vol. 16, No 2A (41A), pp. 386-401 Romat, Ye. V. (2016), “System of brand marketing communications”, International Scientific and Practical Journal "Goods and Markets", Vol. 1(21), pp. 16-24 (in Ukrainian). Speer, F. (1994), “Kompetenz als Erfolgsstrategie – Warengruppen management: Kooperation zwischen Handel und Industrie unerlasslich”, DIH, Vol. 38(6), pp. 12–16. Sysoeva, S. V., Buzukova, E. A. (2010), Assortment management in retail. Category management, Piter, St. Petersburg (in Russian). Thayer, W. A. (1994), “A Category Management Rx from Doctor Harris”, Frozen Food Age, Vol. 42(10), pp. 1–24. Verra, G. J. (1997), Category Management: a matter of joint optimization, Nyenrode Research Pa- pers Series, Universiteit Nyenrode Research Centers of Universiteit Nyenrode, The Netherlands Business School, www.kamcity.com. Zateykin, V. (2003), Managing an inventory by categorical principle, Ctudtsentr, Harkov (in Ukrain- ian). Zenor, M. J. (1994), The Profit Benefits of Category Management. Journal of Marketing Research, Vol. 5, pp. 202–231.

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Vol. 14, No. 3 (2018), 143-154 ‘

Corruption Is a Problem of Political Theory and Practice

ANNA VLADISLAVOVNA SHASHKOVA1

1 Associate Professor, Department of Constitutional Law, Moscow State Institute of International Relations (University) Moscow, Russia, [email protected]

ARTICLE INFO ABSTRACT Received June 25, 2018 The present article is dedicated to the analysis of “corruption” from Revised from June 29, 2018 the viewpoint of political practice and theory. Hypotheses of the Accepted August 25, 2018 research is that corruption is not a recent phenomenon, it has been Available online September 15, 2018 following humanity from ancient times causing great damages. The present article studies historical examples of corruption: corruption during the era of Alexander the Great, Carthage and the Roman JEL classification: Republic. The goal of the research is understanding of results of the D53. corruption, e.g., the link between the deaths of great empires and corruption. The article gives the evolution of the term “corruption”, DOI: 10.14254/1800-5845/2018.14-3.10 pointing out current aspects of the term. The methodological ground of the present article represents the dialectic scientific Keywords: method of the socio-political, legal and organizational processes with the principles of development, integrity, consistency, etc. The globalization, definitions of corruption given in the present article make the au- consequences of corruption, thor resume that corruption is connected with persons endowed political structure, with some form of power. The results of the article give an under- Alexander the Great, standing of effects of corruption, which are generally negative but in corporate corruption. certain narrow instances may have positive temporary character as well: at the very short perspective it makes people work harder and think twice. The present article analyzes the results of corruption: economical, political and social. The present article studies most important political consequences of corruption. The author makes a general conclusion that the globalization process increases corruption and corporate corruption comes to the front row.

INTRODUCTION Corruption has been accompanying humanity since ancient times. This phenomenon was not called “corruption” then, but there are many historical examples when acts of corruption (as it is called today), in fact, took place. Different historical periods pass and political ideas, values and concepts change (Vancea et al, 2017). Some things which were appreciated before may have a negative connotation today. Any phenomenon has its advantages and disadvantages. Even in corruption cases, one can find not only negative effects but certain positive effects as well (Hersh, 143

Anna Vladislavovna Shashkova / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 143-154 2018). If a person is being offered a bribe, that means that he has some clout in the society. No one will bribe just anybody. A person has to attain a certain position in order to be offered a bribe. This assumes competition. Even speaking about nepotism, competition still take place. And competition is a positive phenomenon: it makes people work harder and think twice (Milovic and Jocovic, 2017). There are many forms of corruption: bribery, extortion, illegal distribution and redistribution of public resources and funds, protectionism, appropriation of public resources for personal purposes, illegal privatization, lobbying, illegal support and financing of political structures (parties, etc.), the famous Russian “blat” (the use of personal contacts to gain access to public resources – goods, services, sources of income, privileges, rendering public services to relatives, friends, acquaintances (Ledeneva, 1998), etc. Corruption is a global problem. From a historical point of view, corruption [lat. corruptio] means spoilage, decay, and damage (Shashkova, Rakittskaya and Pavlov, 2017). The contemporary meaning of corruption is bribery: bribery and corruption of public and political figures, government officials and officials. The facts of corruption are present in all countries of the world. Some countries like New Zealand or Denmark have very low levels of corruption (see Table 1), especially when compared with such countries as Syria, South Sudan and Somali. But the corruption perception index never shows 100 out of 100. The maximum result ever shown was Denmark in 2014 – 92 out of 100. That means that even when a country is the best compared to the rest of the world some level of corruption still exists. Thus, what matters is not the corruption itself but the level of corruption, its scale, regularity, disclosure and sanctions.

Table 1. Corruption Perceptions Index 2017

2017 2016 2015 2014 2013 2012 Rank Country Region Score Score Score Score Score Score New Zea- 1 89 90 91 91 91 90 Asia Pacific land Europe and Central 2 Denmark 88 90 91 92 91 90 Asia Europe and Central 3 Finland 85 89 90 89 89 90 Asia Europe and Central 3 Norway 85 85 88 86 86 85 Asia Europe and Central 3 Switzerland 85 86 86 86 85 86 Asia 6 Singapore 84 84 85 84 86 87 Asia Pacific Europe and Central 6 Sweden 84 88 89 87 89 88 Asia 8 Canada 82 82 83 81 81 84 Americas Europe and Central 8 Luxembourg 82 81 85 82 80 80 Asia Europe and Central 8 Netherlands 82 83 84 83 83 84 Asia United Europe and Central 8 82 81 81 78 76 74 Kingdom Asia Europe and Central 12 Germany 81 81 81 79 78 79 Asia Middle East and North 169 Iraq 18 17 16 16 16 18 Africa 169 Venezuela 18 17 17 19 20 19 Americas Equatorial 171 17 N/A N/A N/A N/A N/A Sub Saharan Africa Guinea Guinea- 171 17 16 17 19 19 25 Sub Saharan Africa Bissau 144

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Korea, 171 17 12 8 8 8 8 Asia Pacific North Middle East and North 171 Libya 17 14 16 18 15 21 Africa Middle East and North 175 Sudan 16 14 12 11 11 13 Africa Middle East and North 175 Yemen 16 14 18 19 18 23 Africa 177 Afghanistan 15 15 11 12 8 8 Asia Pacific Middle East and North 178 Syria 14 13 18 20 17 26 Africa South Su- 179 12 11 15 15 14 N/A Sub Saharan Africa dan 180 Somalia 9 10 8 8 8 8 Sub Saharan Africa

Source: : https://www.transparency.org/news/feature/corruption_perceptions_index_2017#table. Accessed 15.07.2018

Different studies are conducted on understanding the phenomenon of corruption and resolving such a problem. Works of political scientists, lawyers, economists and anthropologists are dedicated to the matter. Most researchers believe that the main cause of corruption is the imperfection of political institutions that can provide internal and external containment of corruption. A professor of the University of Bergamo, Italy, D. Torsello (2012, p. 241) in his study of the impact of culture on corruption shows that, for example, in Italy, the exchange of services is an important cultural practice. In this case, the exchange of gifts will be regarded by the state as a factor of corruption. However, in most Asian and African countries, small gifts to officials or employees of commercial enterprises are not considered bribes, as they represent established traditions. Studies are carried out to understand the relationship between the concepts of “corruption” and “bribery”. Is bribery a particular case of corruption or its tool? Is it necessary to dwell exclusively on the aspect of bribery to determine the level of corruption in the state? For example, in the UK, the Bribery Act of 2010 virtually replaced all other previously existing anti-corruption tools. Despite the existence of the 1889 Act on the Corporal Bodies of Corrupt Practices Act, the Prevention of Corruption Act of 1906 and the Anti-Terrorism and Security Act (Crime and Security Act) of 2001, criminal responsibility for corruption crimes arises precisely on the basis of the Act on Combating Bribery1.

1. CORRUPTION IN ANCIENT TIMES It is necessary to consider and analyze historical examples of corruption in the world. The historian Ronald Kroese explains that an analysis of the history of corruption helps to understand better modern corruption and methods of combating it (Kroeze, Kerkhoff and Corni, 2013). In the times of Alexander the Great in the 320s BC, Cleomenes, the Greek governor of Egypt, used his position to manipulate the supply of grains from Egypt to Greece (Kuzovkov, 2010, p. 5). At that time there was a shortage of grains in Greece and it was necessary to import them from Egypt. Cleomenes created artificial obstacles on the way of such supplies, thereby earning himself a huge fortune. As a result of such an operation, a grain deficit was created in Greece and Epirus, grain

1 United Kingdom Review of Implementation of the Convention and 1997 Recommendation Phase I Bis Report. URL: http://www.oecd.org/unitedkingdom/2498215.pdf; Anti-Terrorism, Crime and Security Act 2001. URL: http://www.legislation.gov.uk/ukpga/2001/24/section/109/ni/2001-12-20; Mendelson Mark. The Anti-bribery and Corruption Review. UK> Law Business Research Ltd. 2016. P. 323. URL: https://thelawreviews.co.uk//digital_ assets/83d607ac-3535-41e0-9f83-0c61377f5f09/TLR---Anti-Bribery-and-Anti-Corruption-5.pdf. Accessed 16.07.2018. 145

Anna Vladislavovna Shashkova / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 143-154 prices increased many times, the starvation lasted for five years, and the population came to the verge of extinction. Analyzing this situation, it should be noted that the power was in the hands of a few people: Alexander the Great and his deputies. This model is called the oligarchy. This small group has practically unlimited power, as a result of which it gets access to all the riches of the country. The population was on the verge of starving to death. Such facts increased social tension in the society. The most typical way out of this situation is a change of power. Change of power can occur forcibly, for example, by popular revolt or voluntary displacement. In this particular case, Alexander the Great, indifferent to wealth, after learning about what happened, ordered the execution of Cleomenes by way of boiling him in oil. Such example says that during the era of Alexander the Great political values did not accept corruption at the central level. The presence of corruption at the regional level led to the punishment of the guilty regional official by the central authorities. A particular instance of corruption in the ancient world is Carthage in III c. BC. Carthage was richer than Rome, but corruption here had a greater presence. According to the testimony of the Greek historian Polybius in Carthage, nothing that led to profit was considered shameful. Candidates for government posts received these state posts by open payment of bribes (Polibij, 2004). The party, which was in power, according to the German historian T. Mommsen (1997, p. 399) supported the interests of Rome (with whom Carthage was at war) rather than those of Carthage. Consequently, the great Carthaginian commander and head of the People's Party Hannibal practically did not receive reinforcements from Carthage for many years while conducting a military campaign in Italy. Lack of the necessary support ultimately led to his defeat. In this case of Carthage, the governing elite acted in the interests of the enemy in the war against its own country and people. This instance is a vivid manifestation of corruption. The oligarchy acted in its own interests from the restoration of the trade relations with Rome to the defeat of the people's party led by Hannibal. The main incentive for corruption – the possibility of obtaining the economic rent – is present in this particular case. In this instance corruption corresponded to the values maintained in Carthage. The extreme form of such corruption is the support of the enemy. It led to the destruction of Carthage, which shows the danger of corruption. Thus, the author concludes that, even when corruption is, in fact, maintained at the state level and possesses a character recognized by the state, its destructive power can entail the downfall of such a state. The oligarchy of Carthage did not think about it. In this case, the characteristic pattern of corruption, which is a conflict between the actions of the oligarchy and the interests of the state and society as a whole, is present. The state should react promptly to existing corruption. Delays in the reaction of the state can lead to disastrous consequences. In the Roman Republic the state legislatively tried to tackle the existing corruption by imposing a ban on senators engaging in maritime trade, financial transactions and government contracts (Ljapustin, 1991, p. 54). In addition, laws on luxury were introduced for the entire population of Rome as were restrictions on land ownership. The laws on luxury limited the expenses on feasts and dinnerware. The laws also limited the daily rate of consumption of certain products, for example, dried and canned meat (Fellmeth, 2001). However, these laws were easily circumvented; for example, the ban of fattening hens for feasts made it pretty legal to replace hens with roosters (Rat-Veg, 1996). There were certain endeavours to combat manifestations of corruption. The attempt of Tiberius Gracchus in accordance with the old law of 367 BC adopted through the popular vote in 133 BC to stop corruption and to achieve an equitable distribution of state lands led to the fact that a group of senators killed Tiberius Gracchus, a new chairman of the commission for the redistribution of lands Publius Crassus Mutsiana, and Tiberius’ younger brother Gaius Gracchus. For several centuries similar confrontations continued. Only in the year 43 BC did Octavian Augustus manage to redistribute public lands in favour of 146

Anna Vladislavovna Shashkova / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 143-154 hundreds of thousands of ordinary Roman citizens, almost completely destroying the opposing oligarchy. This was achieved at the cost of one of the longest civil wars, lasting at least 50 years (Egorov, 2013, p. 356). Millions of people took part in these wars. These civil wars were caused by social tensions in the society, a huge gap between the poor population and the rich individuals. Because of the widespread use of slave labour for the cultivation of land, the Roman peasants were driven from their lands and reduced to beggars. This was because the labour of slaves did not cost anything whereas the labour of the peasants had to be paid for. Roman senators invented a lot of ways to save the land from the peasants. Another way to eliminate the peasants from the land was to issue loans to them at very high interest rates, which was even increased if the loan was not repaid on time. Thus there were legitimate reasons to drive the peasant families off the land. And it was the Roman peasants who formed the basis of the economy, the basis of the middle class. But the senators did not care for the preservation of such middle class. Thus, step by step bribes started being considered as a normal phenomenon. All Romans, including senators, believed that bribes were legal and discussed them openly. In case of violation of such an accepted order, initiatives became punishable. Mark Livy Drusus – the people's tribune – asked for a senatorial investigation into the bribes. The result of this step was his prompt murder. The same ended the effective struggle against corruption by Julius Caesar. Civil servants and governors in the provinces were not satisfied with receiving only salaries from their posts. They believed that, like before, the post should give them unlimited powers and unlimited wealth. The termination of bribes and kick-offs from the population made their work a heavy and thankless state duty, since the intention of corrupt officials assumed obtaining a feeder post and not only a respected one. In the above example, the political values of the Roman state by virtue of the law (the restriction on the ownership of public land by 500 yugurs, the ban on bribery) came into conflict with the greed of the Roman Senate oligarchy in power. This led to civil wars, the destruction of the middle class, subsequent weakening of the state and eventual death of the Roman Republic. Also, the Roman senators, starting from II century BC, did not seek to develop Rome's trade infrastructure and did not take effective measures against piracy. They should have done it as an elementary defence of the state against an external enemy. And all these despite the substantial population growth and the financial possibilities of the Roman Republic. This situation was also characterized by the manifestation of corruption, although it did not immediately strike the eye. However, if one digs deeper, it can be noted that the oligarchy represented by the Roman senators was mainly represented by large latifundists. This circle of governors was not interested in the uninterrupted supply of bread. Prices for bread, when there were crises with supplies, increased, which led to more enrichment of landowners. The author notes another manifestation of corruption. The personal greed of the senators led to the impoverishment of the Roman population, the weakening of the Roman ports, and the paralysis of activities to expand the commercial infrastructure. Such examples reflect corruption, affecting virtually all areas of activity and public life of the Roman Republic. The fact should be noted that the public and the state interests were ignored by the oligarchic elite in favour of the interests of individuals. Even in the absence of an immediate negative effect of corruption on the economy of the Republic of Rome, from the historical perspective, there were catastrophically delayed consequences. From the above examples, one can come to the conclusion that the oligarchy is always connected to corruption. The interests of the oligarchy are contrary to the interests of the state and the society, so the oligarchy either does not make any attempts to eliminate crime and corruption or does everything to strengthen them. Oligarchy, however, does not mean all the rich people because among the rich people there are also those who are interested in the public and state welfare. Similarly, not all poor people oppose the oligarchy, as some poor persons are interested in the oligarchy, are dependent on it, or support it. Moreover, the poor are usually illiterate and do not 147

Anna Vladislavovna Shashkova / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 143-154 see, and cannot see actually, the prospects for the development of the state (Barkemeyer, Preuss and Ohana, 2018).

In addition, it shall be noted that the interests of the oligarchy are associated with persons directly or indirectly related to anarchy, for example, kidnappers, bandits and pirates. With the help of such people, the oligarchy deals with persons who disagree and try to stop the oligarchy’s self- enrichment. The above historical examples reflect today's reality. It is important to note the link between the deaths of great empires and corruption. If the greed of individual people turns against the state and society as a whole, the state, which allows itself such a shaky position from the political point of view, will be wrecked.

2. UNDERSTANDING OF THE MEANING OF “CORRUPTION” Nowadays the world lives in a consumer society: in a society where the purchase and sale of goods, services, education, work experience and other benefits are the most important social and economic activities. In such a society, wealth is the measure of success. So, having achieved a certain position in the state, public or commercial service, why not take advantage of those benefits that come with this position? That is why it is necessary to define the term "corruption" to understand which social layer shall be included in this term and who can be affected by such an issue in particular. There are many definitions of the term "corruption". Corruption is defined as the moral disintegration of officials and politicians, which is expressed in illicit enrichment, theft, bribery and intergrowth with mafia (criminal) structures (Ozhegov and Shvedova, 1998). This definition, given with the explanatory dictionary of Ozhegov and Shvedova, concerns only officials and politicians. Outside the scope of this definition, there are both relatives of officials and managers of commercial organizations. Also, in this definition only illegal – criminal – acts are considered. Therefore, to apply this definition, the state must clearly define what is legal and what is not. When the matter concerns corruption, the legality or illegality of the acts is not so obvious. The author concludes that such a definition does not correspond to today's realities. Corruption can also be defined as a socially dangerous phenomenon in the field of public administration or politics, which is expressed in the deliberate use by officials of their official status to obtain property and non-property benefits unlawfully in any form, as well as bribing those persons. Such a definition, given in the legal dictionary, though in more detail, also leaves commercial structures out of business. Corruption is the abuse of state power to obtain benefits for personal gain (The background Document of the Anti-corruption Fight of the ONU, 1995). This definition was also worked out in the 90's of the XX century. It contains personal goals and personal enrichment, which is very important for the interpretation of the concept of corruption. Corruption includes not just illegal actions but actions aimed at extracting personal benefits. The narrowness of this definition of the term "corruption" is the regulation of the matter solely to state power. Later, the interpretation of the concept of corruption develops: this term begins to include crimes and minor offences not only at the level of officials but also at the level of individuals and organizations. Corruption embodies bribery and any other behaviour of a person who is entrusted with performing certain duties in the public or private sector, which leads to a violation of the duties entrusted to that person by the status of a public official, employee of a private company, independent agent or other and is aimed at obtaining any personal benefits for themselves and others (Luneev, 1999). This definition was developed by the Council of Europe's Multidisciplinary Group on Corruption (GMC). It is more detailed and includes not only the state but also the private sector; it includes bribery but is not limited to bribery. The definition of corruption in the Russian Federal Law "On Combating Corruption" is quite 148

Anna Vladislavovna Shashkova / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 143-154 consistent with the international standards. Corruption is interpreted as an abuse of power, giving a bribe, accepting of a bribe, commercial bribery, abuse of authority or other illegal use by an individual of his or her official position in violation of the legitimate interests of the state and society to obtain personal benefits in the form of money, valuables, other property or services of a property nature for themselves or other persons or illegal provision of such benefits to other individuals, as well as the commission of such acts on behalf of or for the benefit of a legal entity (The Federal Law of Counterfighting Corruption, 2008). The Oxford dictionary defines corruption as a fraudulent or dishonest behaviour of a person who is endowed with power, usually using bribery. Such definition provides an interpretation of corruption in general terms. It is important to note that in this definition there is no indication of a functionary or other official. The importance of such a definition is that it concerns power at any level, including within a commercial organization. The vocabulary of business terminology develops the concept of corruption. It defines corruption as the improper conduct of an official or a person endowed with power, using illegal, immoral means or means incompatible with ethical standards. This definition is more elaborate because it includes not only issues of political corruption but also the issues of corporate corruption discussed below. Such transformation of a definition of “corruption” corresponds to political and economic realities. The world has entered the era of globalization, and transnational corporations have gained real political influence. The interests of an individual state and those of individual companies are often in direct contradiction. Globalization has become another problem against which corruption is developing well. First of all, it should be noted that globalization is a market, trade, and speculation is always present in trade. Due to market conditions, it is attractive to purchase the cheapest imported goods due to which, quite often, national production is declining. Globalization implies low duties for both imports and exports of goods, which often makes home production less lucrative than buying imports. Looking back to the examples of ancient civilizations, including the Roman Republic, this correlation has already been analyzed. What is common in all definitions of corruption given in the present article is that corruption is connected with persons endowed with some form of power. And, if at the initial stages of the definition of the concept of “corruption” the issue concerned only the state, political power, and later with the development of globalization trends, all persons endowed with power began to be included in the concept of “corruption”. Globalization leads to the creation of a market economic system, thereby contributing to the development of corruption. In addition, the change in political systems which took place in recent decades also led to economic and social changes. Not every state is ready for this. In non-market relations, it is extremely difficult to take advantage of the use of material goods. The historian Wallerstein pointed out that, unlike protectionism, which plays an important role in achieving the state's long-term advantages, free trade promotes short-term profit for the class of traders and financiers (Wallerstein, 1974). The author speaks about the class of oligarchy and analyzes the examples of antiquity. Thus one can conclude that the most corrupt states are losing the benefits of globalization. There is a vicious circle: globalization intensifies corruption, and corruption does not allow globalization to show the best side of itself. And, of course, corruption develops more when there is access to financial means. The possibility of remote asset management leads to the fact that it becomes more difficult to monitor the cash flows circulating in the virtual world; it is increasingly difficult to distinguish legal money flows from illegal cash flows. On the other hand, globalization leads to the possibility of mutual influence of the states on each other: the government of Switzerland, for example, was compelled, under pressure from the governments of other countries, to amend its banking secrecy legislation to prevent the use of Swiss banks for laundering of illegal proceeds (Shashkova, 2013).

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3. DISCUSSION Examples of modern states indicate that corruption is a satellite of weak economies and poor countries (Kulshreshtha, 2007; Draskovic et al., 2017; Peković, 2017). With all the negative consequences of corruption, it is entirely possible that the value orientations of corrupt authorities are in accordance with the laws adopted in the given country. There are examples where corruption has positive, not negative, consequences. The question is the long-term nature of such positive consequences. What are the risks of political practice while indulging in corruption? There is a theory of functionalism, according to which corruption dies by itself as the opposition of political systems weakens when one elite replaces another (Latov, 1999). Is this phenomenon always observed in political practice? Is it possible to overcome corruption in a particular region and not in the whole state? What is corruption: is it a game with pre-established rules or consequences that can be unpredictable? Are there any positive examples of state corruption? The example of Iraq under the leadership of Saddam Hussein testifies that the population was provided with good medical aid and education. All education and medicine, including expensive drugs, were free (Kuz'min, 2013). At the same time, Saddam Hussein got rich by 10-40 billion US dollars (various sources give different figures). At present, the population of Iraq receives neither medical assistance nor education, and there is a civil war in the country.

Figure 1. GDP per capita (USD) / Corruption Perception Index

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Source: http://www.profile.ru/economics/item/112816-vorovat-nado-menshe. Accessed 13.07.2018. During the thirty years that Muhammad Suharto was in power in Indonesia, the standard of living of the population increased ten-fold: the average per capita income rose from $ 50 in 1966 to $ 1000 at the end of Muhammad Suharto's governance. At the time as of the beginning of the May Revolution of 1998, the fortune of Muhammad Suharto was estimated at 35 billion US dollars. It is to be researched whether in these examples the growth of the welfare of the nation simply coincides with huge rampant corruption. A study conducted by Transparency International on perceptions of corruption, even with a cursory glance on a map of the world, makes it possible to establish that states with a low level of perception of corruption are more successful from the economic and political points of view (see Figure 1). The list of consequences of corruption is wide. One normally distinguishes several groups of consequences: economic, political, social. The Swedish economist G. Myrdal, the founder of economic studies of corruption, summarized the experience of modernization of the Third World countries in the 1960s. He defined corruption as one of the main factors hindering economic development (Latov, 1999). The author can distinguish the following economic consequences of corruption:

 The shadow economy is growing. The result of such shadow economy growth is a reduction in tax revenues and a weakening of the state budget. As a consequence, the state loses the levers of economic management, and due to non-fulfilment of budget obligations, social problems are aggravated. Scientists at Harvard University calculated that lowering the country's corruption from the Mexican level to the level of Singapore produces an economic effect equivalent to an increase in the collection of taxes by 20% (Kuznecov, Silinskiy and Homutova, 2016).  Competitive market mechanisms are violated. The winner of state tenders is often not the one who is competitive but the one who has managed to gain advantages with bribes. This entails a decline in market efficiency and discredits the ideas of market competition. An example of this is that after operation “Clean Hands” in Italy, government spending on road construction reduced by 20%.  It often happens that funds accumulated through bribes are moved out from the active economic turnover and are settled in the form of property or valuables.  Efficient private owners decline in number, primarily due to violations during privatization.  Budgetary funds are used inefficiently, e.g. in the case of the allocation of government orders and loans. In future it complicates the budget problems of the state.  Prices rise due to corruption “overhead costs”. As a result, the burden of costs falls on the consumer. Bribes turn into a kind of additional taxation.  Market participants lose confidence in the authorities' ability to establish and observe honest 151

Anna Vladislavovna Shashkova / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 143-154 rules of the market game. The investment climate is deteriorating and, therefore, the problems of overcoming the decline in production and renewal of fixed assets cannot be resolved.  The scale of corruption is aggravated in non-governmental organizations (at private firms, enterprises, in public organizations). This leads to a decrease in the effectiveness of their work; therefore, the effectiveness of the economy of the country as a whole is reduced.

Among the social consequences of corruption one shall distinguish the following:

 Corruption involves a significant gap between declared and real values and forms a "double standard" of morality and behaviour among members of society. This leads to the fact that money becomes the single measure of all values in society; the significance of a person is determined by the size of his personal state, regardless of the ways of acquiring it. The devaluation and destruction of civilized social regulators of people's behaviour take place, e.g. in morals, religion, public opinion, etc.  The diversion of colossal funds from the goals of social and humanitarian development happens. Thus, the budget crisis is aggravated and the government is unable to solve social problems.  The property inequality and poverty of the majority of the population sharply increases. Corruption spurs unjust and unrighteous redistribution of funds in favour of narrow oligarchic groups at the expense of the most vulnerable segments of the population (Policardo and Carrera, 2018; Burns and DeVillé, 2017). Corruption in the system of tax collection allows the rich to evade taxes by shifting the burden of paying taxes to the shoulders of poor citizens.  Law as the main instrument for regulating the life of the state and society loses its value and is discredited. In the public consciousness, an idea is formed about the defencelessness of citizens and against crime, and in the face of power, active civil society disappears.  Corruption of law enforcement bodies helps to strengthen organized crime. Crime, merging with corrupt groups of officials and entrepreneurs, is further enhanced by access to political power, thereby gaining additional opportunities for money laundering.  Social tensions are intensified, hitting the economy and threatening the political stability of the country.

Among the political consequences of corruption, one can distinguish the following:

 The political goals change from national development to ensuring the governing of oligarchic groups.  There is a decrease in trust in power, and the alienation of power from society is growing. Thus, any good beginnings of power are put in jeopardy. As can be seen from the historical examples given, corrupt regimes are usually politically unstable.  The prestige of the country on the international level falls, the threat of its economic and political isolation grows.  Anti-corruption personnel are not prepared to forgo personal interests for public and state purposes.  Qualified personnel are dismissed from the civil service when they do not accept the system of corruption.  Political competition declines. The population is disappointed in the values of democracy (Constitutional Control in Foreign Countries, 2017). There is a threat of the disintegration of democratic institutions. The slogan of fighting corruption is capable of turning the state into a dictatorship. The risk of eradicating democracy increases under the prevailing scenario of the arrival of the dictatorship under the pretext of fighting corruption.

Depending on the perception of corruption by one or another state, it is possible to analyze the economic indicators of the state and the indicators of the gross domestic product (GDP) per capita (see Figure 1). When considering these graphs, one can draw a conclusion about the direct dependence of the state's economic welfare and its negative attitude to corruption. 152

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Even with a cursory glance at such negative consequences of corruption, the struggle against it becomes the necessity of a modern civilized state that strives to survive and successfully exist. A question arises if it is necessary, from the point of view of political practice, to drive corruption into the civilized framework of lobbying or to eliminate it by radical means. Each specific state shall develop its most optimal approach to fighting corruption. The methods that successfully work in one state may not yield any positive results in another state. According to the Marxist view, political practice, like law, the state and other political phenomena and institutions, is an objective phenomenon. At the same time, all these phenomena and institutions are not a constant but are in a constant process of change and movement. Consequently, the political theory, values, and ideological principles of society can influence political practice. Thus, the study of corruption as a problem of political theory simultaneously influences this problem from the point of view of political practice. Economically successful international corporations can influence political processes. As a result, the problem of investigating corruption as a phenomenon of political theory and practice descends to the level of such corporations.

CONCLUSIONS Taking into account all of the above, the author comes to the following conclusions:

 Corruption has been with humanity since ancient times. It is necessary to consider historical examples of corruption and the experience of combating it to develop a successful independent experience in the fight against corruption.

 In all definitions of the term “corruption”, the sound “a person endowed with power” exists. As the research on corruption expands, the sphere of application of such power widens: gradually, private corporations and individuals are covered.

 The process of globalization of the world leads to increased corruption. With the increase in the globalization process, the role of corporations also increase, and corporate corruption comes to the forefront.

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Benchmarking Intangible Assets in the Water Sector: an Evidence from Indonesia

MARGA GUMELAR (Corresponding Author)1, SUTISNA2, and ALDRIN HERWANY3

1 Faculty of Economics and Business, Universitas Padjadjaran, Bandung, Indonesia, E-mail: [email protected] 2 Faculty of Economics and Business, Universitas Padjadjaran, Bandung, Indonesia, E-mail: [email protected] 3 Faculty of Economics and Business, Universitas Padjadjaran, Bandung, Indonesia, E-mail: [email protected]

ARTICLE INFO ABSTRACT Received June 05, 2018 This article aims to provide an empirical evidence of the value of Revised from June 18, 2018 intellectual capital, as a proxy of intangible assets, in the water Accepted August 05, 2018 sector. It is very interesting considering there are only a few Available online September 15, 2018 researches that explore the value of the intellectual capital in this sector and is expected to fill the gap of the literature review. This study presumed that the value of intellectual capital in the water JEL classification: sector is low in view of the fact that it is a capital-intensive sector. It E24, L95, J24. adopted the value added intellectual coefficient (VAICTM) as the instrument to measure the value of intellectual capital. The data DOI: 10.14254/1800-5845/2018.14-3.11 were collected during the year of 2013-2015 from 253 municipal water utilities (MWUs) in Indonesia consisting of 83 MWUs in 2013, Keywords: 79 MWUs in 2014, and 91 MWUs in 2015. It was found that the average VAICTM value was 2.082 and the range values were Intangible assets, between 1.157 and 4.298. This wide range indicates a gap between intellectual capital, MWUs. Even another result performs that half of the MWUs have municipal water utilities, sustainable criteria. Finally, this research recommends MWUs VAICTM. management to re-evaluate human capital policies due to the low value of intellectual capital, so that the bigger amount of investment in human capital would not be wasted.

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Marga Gumelar, Sutisna, and Aldrin Herwany / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 155-161 INTRODUCTION In the current economy, the tasks of physical assets, such as factories and machineries, are taken over by intangible assets. Intangible assets are non-physical assets that generate value- added to the company in the future. They are dynamic by nature and built on knowledge and competence, such as organisational structure, skills, R&D innovation, and intellectual capital (Tsai et al., 2012; Dženopoljac et al., 2016; Maji and Goswami, 2016; Deep and Narwal, 2013). Until recently, many concepts about intellectual capital have been revealed. Intellectual capital is an intangible asset that accomplishes the company's success yet cannot be accurately measured and represented on the company's balance sheet (Chan, 2009; Chen et al., 2005; Kujansivu and Lönnqvist, 2007; Berzkalne and Zelgalve, 2014; Mondal and Ghosh, 2012). It is also defined as a knowledge-based achievement of value-added creation for the company. Intellectual capital in the water sector is rarely observed. Interesting findings were revealed from the study of Kujansivu and Lönnqvist (2007). They demonstrated no difference in the average of total efficiency and in the average of intellectual capital efficiency among industries. Moreover, the intellectual capital efficiency in the electricity, gas, and water sector performed better and at the same time, the intellectual capital value experienced lower than the other sectors. The reason was the fact that the sector was based on tangible assets and considered less oriented to the utilization of knowledge than other sectors, i.e. financial and banking industry, pharmaceuticals, and information technology (Kujansivu and Lönnqvist, 2007; Dženopoljac et al., 2016). This study aims to examine the status of intellectual capital, as a proxy of intangible assets, in the water sector. It utilizes value added intellectual coefficient (VAICTM) as the approach to achieve intellectual capital measurement. This article is expected to fill the gap in the academic literature on research of intellectual capital in the water sector. The results will provide a basis for taking and evaluating policies related to intellectual capital by municipal water utilities (MWUs) management in developing the strategy for sustainable operations. .

1. LITERATURE REVIEW Intellectual capital is an intangible asset that cannot be accurately measured and has an impact on the company's success and performance, yet this factor is not represented on the company's balance sheet (Kujansivu and Lönnqvist, 2007; Berzkalne and Zelgalve, 2014; Mondal and Ghosh, 2012). The issues occurred in intellectual capital are the measurement and recording because of its nature. Furthermore, Pulic (1998) created an intellectual capital measurement method called value added intellectual coefficient (VAICTM). It points out the decent system for monitoring the efficiency of the business activities. It is also an alternative method to measure intellectual capital to the private company. The VAICTM assumes that the use of physical and intellectual capital forms the basis for the creation of value-added that is linked to the overall efficiency of the enterprise (Ståhle et al., 2011). The measurement of VAICTM is constructed of three components, namely human capital (HC), structural capital (SC), and capital employed (CE) (Pulic, 1998, 2000, 2004, and 2008). The creation of value added by the company (value added or VA) which is the difference between income or output and expenditure or input, is the ground of VAICTM calculation. This formula excludes employee expenses which are considered an investment. Human capital (HC) is all expenses incurred for employee expenses. The creation of value added from every investment in the human capital is called human capital efficiency (HCE). Structural capital (SC) is the difference between VA and employee expenses (HC). The higher the HC value, the lower the SC value. Meanwhile, the amount of SC used to reach the VA is called 156

Marga Gumelar, Sutisna, and Aldrin Herwany / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 155-161 structural capital efficiency (SCE). Capital employed (CE) is the physical and financial capital that shapes the company. The relationship that shows how the company's physical and financial capital creates value added is called capital employed efficiency (CEE).

2. METHODS Based on the available data, the sample taken in this study during the year of 2013-2015 was 253 MWUs in Indonesia consisting of 83 MWUs in 2013, 79 MWUs in 2014, and 91 MWUs in 2015. The data were collected from MWUs performance reports issued by the Implementation and Improvement National Agency for Water Supply System. The value of VAICTM was measured using several stages of calculation (Pulic, 1998, 2000, 2004, and 2008; Chen et al., 2005) as follows: The first is to calculate the creation of value added by the company (value added or VA) which is the difference between income or output and expenditure or input. VA = Output – Input (1)

VA : Value-added of the company Output : Total revenue of the company Input : Total expenses of the company

Chen et al. (2005) formulated another VA formula as follows:

VA = S - B - DP = W + I + T + NI (2)

VA : Value-added of the company S : Sales revenue B : Costs of goods sold DP : Depreciation W : Wages (employee expenses) I : Interests T : Taxes NI : Net income

The next is to measure the relationship between VA and human capital (HC) which represents how value added is created from every investment in the human capital where the value of HC is all expenses incurred for employee expenses. This relationship is called human capital efficiency (HCE).

HCE = (3)

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Marga Gumelar, Sutisna, and Aldrin Herwany / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 155-161 HCE : Human capital efficiency VA : Value-added of the company HC : Employee expenses

The third is to calculate the amount of structural capital (SC) spent to reach the VA, where SC is the difference between the VA and the employee expenses (HC). This variable is called structural capital efficiency (SCE).

SCE = (4)

SCE : Structural capital efficiency VA : Value-added of the company SC : The difference between the VA and the employee expenses

The fourth is to assess how the company's physical and financial capital creates added value. The company's physical and financial capital is CE. This variable is known as capital employed efficiency (CEE).

CEE = (5)

CEE : Capital employed efficiency VA : Value-added of the company CE : The company's physical and financial capital

The last is to measure the value of VAICTM coefficient, which is the sum of HCE, SCE, and CEE.

VAIC = SCE + HCE + CEE (6)

3. RESULTS AND DISCUSSION The calculation of the intellectual capital value which is the proxy of the intangible assets through the VAICTM approach shows the average coefficient value of 2.082 with the range of values between 1.157 and 4.298. There are 46 MWUs, or 18.18 percent, which have a coefficient of 2.500 and above. Meanwhile, the MWUs have a coefficient value of less than 1.750 as many as 125 MWUs or nearly half of the samples.

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Table 1. Descriptive Statistics of VAICTM Calculations

SCE HCE CEE VAIC

Mean 0.277 1.457 0.348 2.082 Median 0.276 1.381 0.298 2.005 Maximum 0.697 3.302 1.202 4.298 Minimum 0.001 1.001 0.036 1.157 Std. Dev. 0.149 0.385 0.192 0.555 Obs. 253 253 253 253

The empirical results perform that half of the MWUs have sustainable criteria according to Pullic (2008). Sustainable criteria are the condition where the company has a VAICTM coefficient value above 2.000 with which it is considered capable of spending business investment for the development of the company after covering all operational expenses. In both categories, they are able to move investment in human capital becomes more productive and impact on the company's financial performance. MWUs that are already in these stages generally have a smooth operation. They do not experience an over cost that causes less balanced income. Large revenues have equality between tariffs and large numbers of the customer. It concludes that the MWUs serve and operate mostly in big cities and areas with adequate infrastructure.

Table 2. The Level of Intellectual Capital Efficiency

Description of Number of Efficiency efficiency levels MWUs 2.500 (Or more) is a sign of a very successful business perfor- 46 mance. 2.000 This is a minimum for efficient business performance in most sectors. Enough is left for intensive investment in 82 development. 1.750 Business is in relatively good shape but does not guarantee long term safety. All liabilities are liquidated, however, 57 there is not enough for business investments and therefore future business success is uncertain. 1.250 Worrying – survival of company is endangered – not 63 enough value is created to ensure business development. 1.000 Much worrying, on the edge of the survival – OUTPUT is insufficient for covering all inputs necessary for operation- 5 al business

At the lowest level, there are five units of observation for the category of companies with VAICTM coefficient values between 1.000 to 1.249 and 63 units of observation for the category of companies with VAICTM coefficient values between 1.250 to 1.749. The two bottom categories are considered much worrying and worrying, respectively, because the resulting value added is very small and cannot be used for intensive investment for the company’s development. 159

Marga Gumelar, Sutisna, and Aldrin Herwany / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 155-161 In order to improve the investment returns on human capital, MWUs must first improve their operational performance. As its nature, the water sector is a physical capital-intensive sector where a disturbance in operations will reduce financial performance. Human capital is a support in generating profits. Appropriate policies in human capital are needed by MWUs in these categories.

CONCLUSION The average VAICTM value is 2.082 and the range values are between 1.157 and 4.298. This wide range indicates a gap between MWUs. Even another result performs that half of the MWUs have sustainable criteria. In other words, the overall value of intellectual capital in the water sector in Indonesia is still low. MWUs at a sustainable level are capable to generate advantageous from the human capital investment. For those to become at this stage, it is necessary to make their operations steadily. Factors affecting MWUs performance are tariff rates and a number of customers. The fact is, most MWUs on sustainable level operating in big cities and areas with adequate infrastructure. The limitation of the method used in this paper, which is VAICTM, is the over-simplification nature in measuring intellectual capital and its incapability to measure the company in the condition of losses or low productivity. Better yet, the advantages of this instrument are the method can be used for comparison between companies in large samples that make it appropriate for the purpose of this research. Further research can observe deeply into the character of MWUs for each level of efficiency. The last, this study recommends MWUs management to re-evaluate human capital policies due to the low value of intellectual capital, so that the bigger amount of investment in human capital would not be wasted. Moreover, the better value of intellectual capital performs better company's performance towards its sustainability.

REFERENCES Berzkalne, I., Zelgalve, E. (2014), “Intellectual capital and company value”, Procedia-Social and Behavioral Sciences, Vol. 110, pp. 887-896. Chan, K. H. (2009), “Impact of intellectual capital on organisational performance: An empirical study of companies in the Hang Seng Index (Part 1)”, The Learning Organization, Vol. 16, No. 1, pp. 4-21. Chen, M. C., Cheng, S. J., Hwang, Y. (2005), “An empirical investigation of the relationship between intellectual capital and firms’ market value and financial performance”, Journal of intellectual capital, Vol. 6, No. 2, pp. 159-176. Deep, R., Pal Narwal, K. (2014), “Intellectual capital and its association with financial performance: A study of Indian textile sector”, International Journal of Management and Business Research, Vol. 4, No. 1, pp. 43-54. Dženopoljac, V., Janoševic, S., Bontis, N. (2016), “Intellectual capital and financial performance in the Serbian ICT industry”, Journal of Intellectual Capital, Vol. 17, No. 2, pp. 373-396. Fijalkowska, J. (2014), “Value Added Intellectual Coefficient (VAIC™) as a Tool of Performance Measurement”, Entrepreneurship and Management, Vol. 15, No. 1, pp. 129-140 (in Poland). Firer, S., Williams, S. M. (2003), “Intellectual capital and traditional measures of corporate performance”, Journal of intellectual capital, Vol. 4, No. 3, pp. 348-360. Joshi, M., Cahill, D., Sidhu, J., Kansal, M. (2013), “Intellectual capital and financial performance: an evaluation of the Australian financial sector”, Journal of Intellectual Capital, Vol. 14, No. 2, pp. 264-285. Kujansivu, P., Lönnqvist, A. (2007), “Investigating the value and efficiency of intellectual capital”, Journal of Intellectual Capital, Vol. 8, No. 2, pp. 272-287. Maji, S. G., Goswami, M. (2016), “Intellectual capital and firm performance in emerging economies: the case of India”, Review of International Business and Strategy, Vol. 26, No. 3, 160

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pp. 410-430. Mondal, A., Ghosh, S. K. (2012), “Intellectual capital and financial performance of Indian banks”, Journal of Intellectual Capital, Vol. 13, No. 4, pp. 515-530. Pulic, A. (1998, January), “Measuring the performance of intellectual potential in knowledge economy”, In 2nd McMaster World Congress on Measuring and Managing Intellectual Capital by the Austrian Team for Intellectual Potential. Pulic, A. (2000), “VAIC™–an accounting tool for IC management”, International Journal of Technology Management, Vol. 20, No. 5-8, pp. 702-714. Pulic, A. (2004), “Intellectual capital–does it create or destroy value?”, Measuring business excellence, Vol. 8, No. 1, pp. 62-68. Pulic, A. (2008), The principles of intellectual capital efficiency - A brief description, Croatian Intellectual Capital Center, Zagreb. Ståhle, P., Ståhle, S., Aho, S. (2011) “Value added intellectual coefficient (VAIC): a critical analysis”, Journal of Intellectual Capital, Vol. 12, No. 4, pp. 531-551. Tsai, C. F., Lu, Y. H., Yen, D. C. (2012), “Determinants of intangible assets value: The data mining approach”, Knowledge-Based Systems, Vol. 31, pp. 67-77.

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Lenka Malicka / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 163-176

Montenegrin Journal of Economics

Vol. 14, No. 3 (2018), 163-176 ‘

Political Expenditure Cycle in V4 Countries

LENKA MALICKA1

1 Assistant Professor, Technical University of Kosice, Faculty of Economics, Department of Finance, Kosice, Slovakia, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received June 15, 2018 Public expenditure subjects to government and its volume and Revised from June 27, 2018 structure is influenced by decisions of government according to Accepted August 25, 2018 activities and fiscal policies realized. Political expenditure cycles Available online September 15, 2018 could also act as one of potential public expenditure determinant and could influence the public expenditure volume and structure. The paper examines whether the political expenditure cycle is pre- JEL classification: sent in V4 countries at the central level of government considering H72, D72. the opportunistic approach. Variance of central government expenditure indicates the presence of changes in the central govern- DOI: 10.14254/1800-5845/2018.14-3.12 ment total expenditure and in its sub-categories defined according to government functions, while the analysis of their dynamics in Keywords: connection with parliamentary election brings only vague results. After, the OLS model is estimated for each V4 country for the period public expenditure, 1995-2015. Results of estimations present certain statistically central government, significant impact of election period on the central government total political expenditure cycle, expenditure. As the monitored time-series cover the period of the opportunistic behaviour financial crisis, models were tested for the structural break. Aug- . mented regressions were re-estimated. Results point to cases, when election variables meet the hypothesis about the opportunistic behaviour of central government. Additionally, prevalent part of results shows opposite behaviour of the central government before and after the financial crisis. However, observed results do not allow generalizing the opportunistic model to all V4 post-communist countries, because statistically significant opposite effects of tested hypothesis are also observed.

INTRODUCTION Revising the political cycle literature, authors distinguish among opportunistic and partisan approach (see, e.g. Alesina and Roubini, 1990) introduced to the economy in the 1970s. Oppor- tunistic behaviour of political incumbent is explained by the interest in re-election, which motivate it to change the volume and structure of public expenditure in election years (Delgado et al. 2011). As Alesina and Roubini (1990, p.1) mention, “… the policymakers maximize their popularity or their probability of re-election… “. Partisan approach resists in following different party interests distin- guishing among left wing and right wing parties. Opportunistic government activities in the period 163

Lenka Malicka / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 163-176 of election might be connected with two types of political cycle. In the case of political business cycles, there are political cycles in economic activity (Drazen 2010, p.462). In the case of political budget cycle, the political incumbent is engaged into the manipulation with the volume of public expenditure and provides the expansionary fiscal policy, or, if the voter is conservative, the incumbent changes the structure of the public expenditure in favour of visible and citizen friendly areas as housing or social services (Drazen and Eslava, 2010). In the empirical research, there is a broad investigation of the political business cycle presence in the case of OECD countries. Alesina and Roubini (1990, 1992) examining 18 OECD countries, search for answers on two research questions in their paper. One of them is to elicit “… whether or not the dynamic behaviour of GNP growth, unemployment and inflation is systematically affected by the timing of elections and of changes of governments …” (Alesina and Roubini, 1990, p.2). Their main findings point to the rejection of the both opportunistic and partisan theories. Gärtner (1994) also seeks for the political cycles in OECD countries. He is reflecting on the parti- sanship and he considers his empirical findings as inconsistent with the rational political business cycle model (investigated also by Alesina and Roubini, 1990, 1992). Clark et al. (1998) at the same sample revise the hypothesis of manipulating the macroeconomy at the base of opportunistic motivations. Their pooled OLS time-series show constraining effect of the government action in the field of exchange regime in the highly mobile capital conditions and of the above-average independence of central bank on the political cycle appearance. Potrafke (2012) investigates the economic performance in connection with political cycle at the sample of OECD countries in period 1951-2006. According to him, in two-party systems political cycles are present more frequently. He argues that voters can clearly punish or reward political parties for governmental performance. In the field of political budget (or expenditure) cycle, a survey of the empirical literature is provided by Lici and Dika (2016). Reviewing also earlier studies, the most frequently mentioned are works of Rogoff (1990), Alessina and Roubini (1990 and 1992), Persson and Tabellini (2003), Drazen and Eslava (2005 and 2010) or Efthyvoulou (2012). From the current research, Enkelmann and Leibrecht (2013) provide a panel analysis of political expenditure cycle on a sample of 32 Eu- ropean countries. In a part of their research they distinguish among western and eastern countries. Results point to more obvious existence of political expenditure cycles in eastern European countries. Despite of the manipulation with public expenditure (or with its sub-categories) present in both western and eastern democracies, according to these authors, electorally motivated changes in public expenditure are ineffective instrument to win the elections. Klomp and Haan (2013) estimate a panel data model of 65 democratic countries in period 1975-2005. Their main findings are about a strong influence of election period on fiscal policy arrangements. Brender and Drazen (2013) stress the relationship between elections and changes in government expenditure structure in a dataset of 71 democracies over 1972–2009 using a panel data model. Further division of countries into developed and undeveloped lead them i.a. to findings, that older democracies have larger changes in the expenditure structure than newly established ones. Manjhi and Mehra (2015) use an optimal control method and mention, that incumbent behaving in the line with the opportunist model is able to mobilize votes at the much higher cost of budgetary deficit to the economy. The connection of terms of deficit and political budget cycle is investigated also in the research of Sáez (2016). His main findings are about significant increases in expenditures on the debt occur the year in which a state assembly election is held in India. The aim of the paper is to capture changes in the public expenditure volume and partially in its structure in V4 countries in connection with the period of elections - verifying the presence of the political expenditure cycle in the case of V4 countries central government level. The basic motivation was given by the paper of Enkelmann and Leibrecht (2013). The choice of countries relies on results of a wide empirical research, which point to opportunistic electoral motivation in developing post-communist countries formerly including also Eastern European countries (also V4 countries) as mention Brender and Drazen (2005), Vergne (2009), Enkelmann and Leibrecht (2013) and also Castro and Martins (2015).

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The paper is organized in a following way. After the Introduction involving also the literature review, the chapter of Methods and Data is presented. Here the methodology, data sources and basic assumptions of the research are explained. Next chapter of Results and Discussion provides a quantification of the variance of central government total expenditure and a regression analysis to describe the relationship between central government total expenditure and parliamentary elections. Conclusion, list of References and Appendices close the paper.

1. METHODS AND DATA Data are collected from the Eurostat database on annual base predominantly from the Gov- ernment Finance Statistics part (Eurostat, 2018b). The paper interest is straitened to the central government expenditure activities (according to Eurostat labelled as S1311 subsector). Partial interest consists in the public expenditure items. They emulate the government functions and they are developed in ten divisions (known as COFOG) as follows: 01 expenditure on general public services, 02 defence, 03 public order and safety, 04 economic affairs, 05 environmental protection, 06 housing and community amenities, 07 health, 08 recreation, culture and religion, 09 education and 10 social protection (Eurostat, 2018a). The revision of expenditure items according to COFOG is provided also by Enkelmann and Leibrecht (2013), Brender and Drazen (2013) or Castro and Martins (2015). Data focusing on parliamentary elections are collected from national web sites as Czech Republic Government (2018), The Chancellery of the Prime Minister (2018), Statistical Of- fice of the Slovak Republic (2018) or from related informative web sites as Norsk Senter for For- skningsdata (hereinafter NSD) (2018). Period covered by data is 1995-2015 (in case of Poland 2002-2015 for the COFOG divisions). To capture changes in structure of public expenditure provided on central government level of each V4 country, the variance of expenditure according to COFOG divisions is computed. It is followed by monitoring the dynamics of the total central expenditure growth and growth of expenditures according to COFOG divisions (listed in Appendices) in context with parliamentary elections in each country. After, the regression analysis is made on the basis of OLS linear regression for each country separately. The dependent variable is expressed as central government total expenditure. Ex- planatory variable is expressed as dummy variable achieving 1 in the period of elections, otherwise 0. Alternative explanatory variable is captured to the analysis to cover the potential manipulation of public expenditure in a one year period before election. This dummy variable achieves 1 in the period one year before elections, otherwise 0. To control for the public expenditure, the set of control variables is also involved to the estimation. Its choice is inspired by the related literature. The basic expectation focuses on the positive relationship between election variable and central government total expenditure in the sense of the opportunistic approach is defined in the In- troduction. Additionally, the control variables are involved to the estimations. In the case of public deficit, both negative and positive impact might be observed. While the negative impact of the public deficit is expected due to requirement of public expenditure reduction to decrease the public deficit, positive impact of public deficit on public expenditure might be obtained due to increasing requirements on returnable resources (Sáez, 2016). Central government revenue might influence the central government expenditure in the way of determining the total disposal of the government. If the central government revenue increases, the central government expenditure might increase, too. More resources are created, more is spent. As mention e.g. Zhu and Krug, 2005 or Lamartina and Zaghini, 2008, expected relate of GDP to public expenditure is positive in developed countries (according to the Wagner´s Law). This tendency might change in industrialized or developed countries. However, countries in the sample belonged to the developing ones in the beginning of monitored period; their economic development from transition to market-oriented countries is finished, yet. The GDP variable is similarly involved to the estimations provided by Enkelmann and Leibrecht (2013) or Brender and Drazen (2013). The inflation rate variable is also considered. Its increase is often connected with the worsening of the economic situation in a country. In the field of inflation 165

Lenka Malicka / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 163-176 rate, the activity of central bank is primarily desired. The role of central government is supplemen- tary but also important. Brender and Drazen (2013) found its positive but insignificant impact in developed countries. OLS estimations are attended by a Heteroskedasticity and Autocorrelation Consistent (HAC) Covariance Matrix to avoid heteroskedasticity and autocorrelation. Additionally, models are tested for heteroskedasticity (Breusch-Pagan test, BPtest, under the null hypothesis: heteroskedasticity not present), autocorrelation (Durbin-Watson test, DWtest, values might meet the value around 2) and collinearity (Variance Inflation Factor test, VIF test, values higher than 10 may indicate problems with collinearity). To test the regression for the structural break, which might be present in the covered period (1995-2015), the Chow test is realized under the null hypothesis of no structural break (H0: no significant improvement in fit from running two regressions). As a splitting variable the financial crisis variable is used. Dependent variable (centTE) is expressed as the central government (subsector S1311) total expenditure-to-GDP, growth of the HDP per capita (gGDPpc) is based on GDP at market prices and country total population on 1st January, inflation rate (infl) is expressed using the all items HICP annual average rate of change (2015=100), central government total revenue (centrTR) is expressed as central government (subsector S1311) total revenue-to-GDP. Election variables are dummy variables scored 1 in election year (El) or pre-election year (PreEl), otherwise 0. Financial crisis variable is a dummy variable score by 1 in the period when the financial crisis influences the economic development, otherwise 0.

2. RESULTS AND DISCUSSION

2.1 Variance and Dynamics of Central Government Expenditure Figure 1 presents variance of public expenditure provided by central government as total amount and according to COFOG divisions for each V4 country. Figure 2 presents the evolution of the central government total expenditure in the monitored period. During the monitored period the variance of central government total expenditure (centTE) is highest in case of Slovakia (thereinafter SK, see Figure 1). The volume of central government total expenditure reached its maximum in 2000 (38.4% of GDP), when certain reforming steps in the public sector and public finance were prepared by the central government. It decreased dramatically in 2001 (see Figure 2), when the process of fiscal decentralization started and regional level of self-government was established by law. In the period of financial crisis (2009), when the central government activities in the field of macroeconomic stabilisation and revenue redistribution were desired, it increased to the value of 29% of GDP. The variance of central government total expenditure in Czech Republic (thereinafter CZ) is influenced by the massive decrease of central government total expenditure in period of 1995–1996 (deep economic recession in all post-communist V4 countries in transition), and by excessing public deficit in 2003 (Trend 2003). The variance of the COFOG 04 division emulates the variance of central government total expenditure. It might be explained by realizing required public finance reforms in terms of excessive public deficit. In Hungary (thereinafter HU), the central government total expenditure also decreases in period 1995-2000 (see Figure 2), which covered both the economic recession in 1995-1996 in transitive economics of central and eastern Europe and the period of implementation of stabilization programmes to revive the economic growth (Kornai 1996 and 2000, OECD 2000). In 2006 its increase might be connected with the fortified activity of central government in the period of excessive public deficit and public debt (Orosz, Biederman 2015). It mirrors the variance of the general public services division (see Figure 1), which includes public deficit and public debt transactions. Evident is increase of central government total expenditure in 2013, when central government had to reduce local government indebtedness (Reuters 2012), which was highest in the EU. In Poland (thereinafter PL), central government total expenditure decreases in period 1995-2000 (see Figure 2), despite of the fact that Polish economy growth 166

Lenka Malicka / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 163-176 much faster than other central and eastern European countries in transition (Gomulka, 2016). It also decreases since 2010 (see Figure 2), when the boom-bust in public investments decreased (more drastically in period of 2011-2012 (OECD 2014)). However, the variance of particular expenditure items (total and according to COFOG) is low, in comparison with other V4 countries.

Figure 1. Changes in structure of public expenditure provided on central government level – variance of total central government expenditure and central government expenditure according to COFOG divisions

Notes: Variance of central government expenditure (centTE) in Slovakia (SK) is more than 10. Remind on COFOG divisions: 01 general public services, 02 defence, 03 public order and safety, 04 economic affairs, 05 environmental protection, 06 housing and community amenities, 07 health, 08 recreation, culture and religion, 09 education, 10 social protection. Monitored period of Poland is 2002-2015 due to missing data in Eurostat database.

Source: own computation

Summarizing the variance of COFOG divisions in V4 countries, the highest variance is observable in the field of economic affairs, general public services and social protection (ranked from the highest value - CZ: 04 economic affairs, 10 social protection, 07 health, 01 general public services; HU: 01 general public services, 04 economic affairs, 07 health, 10 social protection; PL: 10 social protection, 09 education, 01 general public services, 04 economic affairs; SK: 04 economic affairs, 01 general public services, 10 social protection, 03 public order and safety). That implies that changes in the central government expenditure are present. 167

Lenka Malicka / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 163-176 Figure 2. Evolution of the central government total expenditure of V4 countries in 1995 – 2015.

CZ HU PL SK

Source: own computation

Table 1 Dynamics of the central government total expenditure growth in context with parliamentary elections in v4 countries

CZ HU PL SK gE E gE E gE E gE E 1996 -0.25 -10.50 -0.07 -2.50 0.19 5.90 0.10 3.40 1997 -0.07 -2.40 -0.08 -2.70 -0.19 -7.10 -0.10 -3.50 1998 0.04 1.10 0.01 0.30 -0.06 -1.70 -0.05 -1.70 1999 -0.03 -1.00 -0.04 -1.20 -0.15 -4.40 0.06 2.00 2000 -0.02 -0.60 -0.06 -1.90 -0.02 -0.40 0.16 5.20 2001 0.09 2.50 0.04 1.10 0.10 2.30 -0.19 -7.40 2002 0.02 0.70 0.07 2.30 0.02 0.40 0.00 0.10 2003 0.11 3.70 -0.06 -2.00 0.04 1.20 -0.18 -5.60 2004 -0.16 -5.80 0.01 0.40 -0.07 -2.00 -0.02 -0.40 2005 -0.02 -0.60 0.03 0.90 -0.02 -0.40 0.02 0.50 2006 -0.03 -1.00 0.11 3.50 0.02 0.40 0.01 0.30 2007 -0.01 -0.30 -0.08 -3.00 -0.01 -0.20 -0.10 -2.50 2008 -0.01 -0.20 -0.01 -0.30 0.02 0.50 -0.01 -0.20 2009 0.07 2.00 0.03 1.10 -0.01 -0.20 0.25 5.70 2010 -0.01 -0.40 -0.01 -0.40 0.06 1.50 -0.04 -1.30 2011 0.05 1,40 0.02 0.70 -0.06 -1.60 -0.01 -0.40 2012 0.05 1.60 -0.01 -0.50 -0.04 -1.10 -0.01 -0.40 2013 -0.06 -1.90 0.11 3.70 -0.03 -0.80 -0.02 -0.60 2014 -0.02 -0.50 -0.04 -1.40 -0.05 -1.20 0.01 0.30 2015 0.00 0.10 -0.04 -1.30 0.00 0.10 0.12 3.20

Note: Periods of parliamentary elections are grey shaded, the particular expenditure (E) growth rate (g) is computed Et  Et1 as gE  , first difference E is computed as E  Et  Et1 , monitored period 1995-2015. E is expressed as Et1 % of GDP. Source: own computation

As Table 1 shows, the growth of central government expenditure in connection with the period of parliamentary elections is not evident in V4 countries. There is not observable any regularity 168

Lenka Malicka / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 163-176 in the evolution of the central government total expenditure growth in the election year or in the pre-election year. This indicates low (or none) statistically significant relation between election variables and central government total expenditure in V4 countries, what mirrors the results obtained in estimations presented in Table 2. Dynamics of the growth of central government expenditure according to COFOG divisions in context with parliamentary elections in v4 countries is presented in Tables listed in Appendices.

2.2 Regression Analysis of Central Government Total Expenditure Determinants with Accent on Election Results of regression analysis presented in Table 2 show existent relation between political cycle and volume of central government total expenditure in case of CZ, PL and SK. However, all this cases are different. In case of CZ, the election year variable is statistically significant and its relation to the central government total expenditure is positive. It might indicate the existence of opportunistic behaviour of the central government seeking the re-election. Regarding the results of election of the lower house of CR parliament since 1996, almost same parties won the election; differences are observable in the coalition construction. The case of the certain party re-election and repeated involvement to the coalition is observable in the period of 1998, 2002 and 2010 (Czech Republic Government, 2018). In HU, the impact of the election period on central government total expenditure is not observed. However, according to the parliamentary election results, the case of re-election and repeated involvement to the coalition is present in the period of 2006 and 2014 (NSD, 2018). In PL, the election year variable is not significant. Although, the positive relation between pre-election year and central government total expenditure is observable. It might also indicate the manipulation with the public expenditure volume before election in the sense of the opportunistic approach. In PL, since 2005 a case of a political party re-election and its membership in the coalition is observable, additionally in 2005 and 2015 the one-party government (coalition) was established (The Chancellery of the Prime Minister, 2018). Different (opposite) are result of the SK estimation. Here the negative statistically significant relation between election year variable and central government total expenditure is presented, contrary to given hypothesis. In SK, the current turbulent social and political process might result in preterm elections. Since 1998, alternately two main political parties were leading the coalition. In the brief history of this republic, there is an example of re-election and repeated coalition membership in the period of 2002 and 2016 (Statistical Office of the Slovak Republic, 2018). Contrary to the other V4 countries, the central government activity (in financial expression) is decreasing in the election period. Beside the election variables also certain control variables are statistically significant. In all cases the relationship between public deficit and central government total expenditure is negative. The increase in public deficit causes decrease of central government total expenditure. The effect of central government total revenue on its total expenditure is also similar in all V4 countries. The increase of revenue causes the increase of expenditure. The additional financial resources are spent regarding the actual demand for public goods. In case of SK, also one year lagged central government total revenue variable is significant and its influence on dependent variable is positive in accordance with expectation given hereinbefore. In case of CZ model, the GDP per capita growth variable is statistically significant and in negative relationship with central government total expenditure. The increase of the GDP per capita growth induces the decrease of central government total expenditure. It might refer on the Wagner Law´s interpretation that in developed countries is this relation positive, while the CZ has overcame the transition process and became a marked-oriented (developed) country. Statistically significant in case of CZ model is also the variable of inflation rate. Its positive relationship with central government total expenditure is in accordance with the expectation of realizing certain activities on central government level to ame- liorate the economic situation in the country.

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Lenka Malicka / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 163-176 Table 2 Regression analysis; relation between central government expenditure volume and defined election variables in V4 countries

CZ HU PL SK Variable Coeff. Signif. Coeff. Signif. Coeff. Signif. Coeff. Signif. intercept −8.361 ** −5.787 * −1.570 1.023 gGDPpc −5.309 ** Def −0.520 *** −1.010 *** −0.768 *** −0.947 *** infl 0.144 ** centTR 1.290 *** 1.206 *** 1.091 *** 0.603 *** centTRt-1 0.370 *** El 0.545 * 0.367 * −0.670 ** PreEl

AdjR2 0.841 0.807 0.967 0.971 BP p-value 0.211 0.238 0.166 0.940 DW 2.206 1.686 1.750 1.468 VIF gGDPpc 1.315 Def 2.678 Def 1.059 centTR 4.754 Def 1.318 centTR 2.406 centTR 1.019 centTRt-1 5.763 infl 1.361 El 1.250 Def El 1.654 centTR 1.318 1.257 El 1.384 Note: OLS using HAC, dependent variable is Total expenditure of central government according to Eurostat S1311. Mod- els do not suffer from heteroskedasticity (BPtest), autocorrelation (DW test) and collinearity (VIF test). *** denotes significance at 0.01, ** at 0.05 and * at 0.1 significance level. Source: own computation

As the financial crisis variable (dummy variable) might influence the central government activity (in the field of redistribution and stabilization), the regression stability test, known as Chow test (Chow, 1960) was realized. Results of Chow test indicate the presence of structural break in case of all V4 countries. Results of augmented regressions for Chow test are shown in Table 3.

Table 3 Split of the samples in case of HU and SK (according to the regression stability test)

CZ HU PL SK Variable Coeff. Signif. Coeff. Signif. Coeff. Signif. Coeff. Signif. intercept -3.091 -10.283 *** -12.624 ** 4.075 ** gGDPpc Def -0.546 *** -0.967 *** -0.905 *** -1.081 *** infl centTR 1.112 *** 1.380 *** 1.580 *** 0.620 *** centTR_1 El 0.580 * 0.647 ** -0.498 ** PreEl 0.685 * crisis 1.757 ** 37.830 -1.277 18.787 Cr_gGDPpc 12.423 ** cr_Def 0.556 ** 1.226 ** 0.319 ** 0.646 *** cr_centTR omitted 0.165 * 0.350 * cr_centTR_1 -1.135 ** cr_infl omitted cr_El -1.034 ** -1.388 *** 2.514 *** cr_PreEl 1.952 *** omitted AdjR2 0.806 0.773 0.893 0.975 DW test 2.268 2.124 1.641 1.591 Note: Augmented regression for Chow test, OLS, using observations 1996-2015 (T = 20), Dependent variable: centTE, HAC standard errors, bandwidth 2 (Bartlett kernel), marked variables are omitted due to exact collinearity. Chow test p- value < 0.000 for all models, financial crisis dummy variable is used. *** denotes significance at 0.01, ** at 0.05 and * at 0.1 significance level. Abbreviation “cr” states for dummy variable “crisis”. Coefficients of statistically insignificant variables are not displayed in the Table, except of the splitting variable and intercept coefficients. Source: own computation 170

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As the results presented in Table 3 show, in the case of CZ, election variables before the financial crisis are not statistically significant. After it, the results of augmented regression show significant effects of both variables on central government total expenditure. However, their coefficients are opposite to each other and only the pre-election year variable is in line with the hypothesis about the opportunistic government behaviour. In case of HU augmented regression, the election variable constructed as election year and pre-election year are statistically significant in the period before the financial crisis. Their coefficients are positive, what meets the hypothesis of political expenditure cycle and opportunistic approach. After the financial crisis these variables became insignificant. In the case of PL model, the election year variable is significant before and also after the financial crisis. It behaves in accordance with tested hypothesis in the period before the financial crisis. After the financial crisis the relation between election year variable and central government total expenditure is negative and indicates reduction of public expenditure provided by central level of government in the period of election. In the case of SK augmented regression, the split of the sample by the financial crisis variable also brings different results for the election period impact on the central government total expenditure. Before and also after the financial crisis, the election year variable is the only one statistically significant among election variables. Its coefficient is opposite in these two cases. Before financial crisis the negative relationship between election year and central government total expenditure indicates the absence of the manipulation with public expenditure at the level of central government. The situation changes after the financial crisis, where the coefficient turns to positive and the opportunistic approach is here admissible. Control variable public deficit changes its coefficient after the financial crisis from negative to positive in both models. While in the period before the crisis the relationship between public deficit and central government total expenditure was negative, after the financial crisis it is positive. Obvi- ously, the need of providing additional public goods and services by central government mirrors in the volume of the public debt. If the public debt increases the central government total expenditure increases, too. Impact of central government total revenue on its expenditure also changes after the financial crisis. While in the case of HU is this change present in current period, in case of SK, is negative effect observed in one year lag of central government total revenue. This inverse relationship might be explained by the central government financial situation in the period of financial crisis (after 2009), when its revenue decreased, but it did not induce correspondent reduction in central government spending. The need of macroeconomic stabilization and fortification of redistribution activities made on central government level was required in mentioned period. In case of CZ model strong influence of GDP per capita growth on the central government total expenditure after the financial crisis is observable.

CONCLUSION Public expenditure seems to be an important macroeconomic instrument. Decisions in the field of public expenditure pertain to the government. Its activities might refer to the actual needs and preferences of the country. However, government as a political incumbent implements its political ambitions and one of them might be a re-election in the next period. In the aim of raising the probability of re-election, government might manipulate with the volume of public expenditure to achieve temporary enhancement of economic conditions in the country. If the voter is conservative, the political incumbent might manipulate with the public expenditure structure and promote expenditure categories with citizen friendly impact on economy as housing amenities or social services. Such a model of political incumbent is known as opportunistic behaviour in the literature.

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Lenka Malicka / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 163-176 In this paper, searching for opportunistic behaviour of the central government in V4 countries is realized. First, the evolution of V4 countries´ central government total expenditure is analysed and variance of the central government expenditure and its sub-items is computed to recognize conditions in each of V4 countries. In SK and CZ the highest variances of monitored categories are observable. Summarizing the variance of COFOG divisions in V4 countries, the highest variance is observable in the field of economic affairs, general public services and social protection. Unfortu- nately, the dynamics of the total expenditure and expenditure sub-items in connection with the period of parliamentary elections does not reveal clear regularities of their growth in the election or pre-election year. Contrary, results of regression analysis give some results favourable to the tested hypothesis of the opportunistic behaviour of the government at the central level, but they should not be generalized. More precise results are given by considering the presence of the structural break (financial crisis) in the period covered by data. Augmented regressions show different behaviour of the central government in the field of public expenditure in almost all V4 countries. The tested hypothesis is satisfied by results in case of HU and PL in the period before the financial crisis and in case of SK in the period after the financial crisis. In the CZ model, the expectation is satisfied in case of pre-election year (contrary to election year) in the period after the financial crisis.

ACKNOWLEDGEMENTS The paper is supported by grant VEGA 1/0559/16.

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APPENDICES

Table A. Dynamics of central government expenditure according to COFOG divisions in context with parliamentary elections in CZ

g01 g02 g03 g04 g05 g06 g07 g08 g09 g10 1996 0.11 0.00 -0.06 -0.61 -0.29 0.38 0.21 0.20 0.13 -0.10 1997 -0.18 0.06 0.06 -0.11 -0.20 -0.55 -0.24 -0.33 -0.34 0.19 1998 -0.09 -0.17 -0.11 0.17 0.00 0.20 0.15 0.00 0.17 -0.02 1999 0.00 0.13 0.13 -0.18 -0.25 -0.17 0.00 0.00 -0.03 0.03 2000 -0.03 0.00 0.00 -0.15 0.00 0.20 -0.07 0.00 0.06 0.02 2001 0.18 0.00 0.00 0.37 0.00 0.00 0.07 0.00 0.03 -0.01 2002 0.03 0.06 0.06 -0.03 0.00 -0.50 -0.07 0.00 0.03 0.06 2003 0.29 0.06 0.00 0.35 0.33 -0.33 0.14 0.00 0.11 -0.02 2004 0.02 -0.32 -0.05 -0.45 -0.25 3.00 -0.06 0.00 -0.07 -0.08 2005 0.09 0.15 0.06 -0.18 0.00 -0.13 -0.13 0.00 0.00 -0.02 2006 -0.14 -0.27 0.00 -0.02 0.00 0.00 0.15 0.00 0.00 0.00 2007 -0.14 0.00 -0.11 0.02 -0.33 -0.14 0.00 0.00 -0.03 0.06 2008 -0.06 -0.09 0.00 0.07 0.00 -0.17 -0.07 0.00 0.00 0.00 2009 0.12 0.00 0.06 0.09 -1.50 0.20 0.07 0.25 0.05 0.11 2010 0.00 0.00 0.00 -0.12 -2.00 0.00 0.07 -0.20 0.00 0.00 2011 -0.03 -0.10 -0.11 0.00 3.00 -0.17 0.94 0.50 -0.05 -0.01 2012 0.49 -0.11 -0.06 -0.05 0.00 -0.40 0.00 0.00 0.05 0.01 2013 -0.33 -0.13 0.07 -0.02 -0.50 0.00 0.00 0.00 0.05 0.02 2014 0.00 0.00 -0.06 0.00 0.00 0.00 0.00 0.00 -0.05 -0.03 2015 -0.08 0.29 0.07 0.18 -0.50 -0.33 0.03 0.00 -0.03 -0.03

Note: Periods of parliamentary elections in CZ are grey shaded, positive values above 0.2 are bold, the particular expenditure (E) growth rate (g) is computed as g=(Et-Et-1)/Et-1, monitored period 1995-2015. Source: own computation

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Table B. Dynamics of central government expenditure according to COFOG divisions in context with parliamentary elections in HU

g01 g02 g03 g04 g05 g06 g07 g08 g09 g10 1996 0.00 0.10 0.00 -0.30 0.50 -0.67 0.47 -0.25 0.13 -0.14 1997 -0.12 0.27 0.00 0.00 0.33 1.00 -0.44 -0.25 -0.03 -0.03 1998 -0.07 -0.14 0.12 0.41 0.00 0.00 0.14 0.11 0.00 -0.16 1999 -0.01 0.08 -0.05 -0.23 -0.25 -0.50 0.06 0.00 0.00 0.09 2000 -0.19 -0.08 0.11 0.09 0.00 0.00 0.18 -0.10 0.06 -0.15 2001 0.13 0.00 -0.05 -0.05 0.00 0.00 0.00 0.22 0.00 0.06 2002 -0.11 0.17 0.16 0.27 0.00 0.00 0.10 0.18 0.00 0.15 2003 -0.13 -0.07 -0.09 -0.23 0.00 0.00 -0.05 -0.08 0.22 0.07 2004 0.08 0.00 -0.10 -0.07 -0.33 0.00 0.29 0.08 -0.11 0.06 2005 -0.02 0.00 0.00 0.02 0.00 0.00 0.04 -0.15 0.00 0.14 2006 0.08 0.08 0.11 0.12 0.00 0.00 0.46 0.00 -0.03 0.13 2007 -0.02 -0.07 -0.10 0.11 0.50 -1.00 -0.34 -0.09 -0.03 -0.19 2008 0.17 -0.23 0.11 -0.14 0.00 0.00 -0.22 0.00 -0.05 0.01 2009 0.01 -0.10 -0.05 -0.04 0.00 0.00 0.10 0.00 0.03 0.14 2010 -0.07 0.33 -0.05 0.00 -0.33 1.00 0.48 0.10 -0.05 -0.08 2011 0.05 -0.08 0.06 0.31 0.50 1.00 0.00 0.00 -0.06 -0.20 2012 0.00 -0.36 0.00 -0.15 0.00 0.00 0.32 0.27 -0.03 -0.10 2013 0.20 0.00 0.05 0.07 0.00 -0.50 0.33 -0.07 0.28 -0.11 2014 -0.12 -0.14 -0.05 0.05 0.67 0.50 -0.07 0.15 0.10 -0.12 2015 -0.25 -0.17 0.05 0.25 0.40 0.00 -0.13 0.07 0.00 0.00

Note: Periods of parliamentary elections in HU are grey shaded, positive values above 0.2 are bold, the particular expenditure (E) growth rate (g) is computed as g=(Et-Et-1)/Et-1, monitored period 1995-2015. Source: own computation

Table C. Dynamics of central government expenditure according to COFOG divisions in context with parliamentary elections in PL

g01 g02 g03 cg04 g05 g06 g07 g08 g09 g10 2003 0.12 0.00 0.11 0.05 0.00 0.33 -0.08 0.33 0.04 0.00 2004 -0.08 0.00 -0.05 0.05 0.00 -0.25 -0.09 0.00 -0.07 -0.10 2005 -0.10 0.00 0.05 0.09 0.00 -0.33 0.20 -0.25 0.00 -0.01 2006 -0.04 0.06 0.00 0.16 0.00 0.50 0.00 0.33 0.02 -0.04 2007 -0.08 0.18 0.05 0.14 0.00 -0.33 -0.08 0.00 -0,06 -0.01 2008 -0.04 0.00 0.00 0.06 1.00 0.00 0.09 0.00 0.00 0.06 2009 0.16 -0.25 0.00 0.00 -0.50 0.00 -0.08 -0.25 -0.18 0.10 2010 0.02 0.07 0.00 0.03 1.00 -0.50 0.09 0.33 0.02 0.13 2011 -0.04 0.00 -0.05 -0.03 0.00 2.00 -0.08 -0.25 0.02 -0.15 2012 0.02 -0.06 0.00 -0.09 0.00 -0.33 0.09 0.00 -0.02 -0.08

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2013 0.02 0.13 0.00 -0.28 0.00 0.00 0.00 0.00 -0.02 -0.01 2014 -0.13 -0.12 0.00 0.26 0.00 0.00 0.00 0.00 0.00 -0.15 2015 -0.04 0.07 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.05

Note: Periods of parliamentary elections in PL are grey shaded, positive values above 0.2 are bold, the particular expenditure (E) growth rate (g) is computed as g=(Et-Et-1)/Et-1, monitored period 2002-2015 due to missing data. Source: own computation

Table D. Dynamics of central government expenditure according to COFOG divisions in context with parliamentary elections in SK

g01 g02 g03 g04 g05 g06 g07 g08 g09 g10 1996 0.22 0.07 0.06 0.15 0.20 1.00 0.07 0.13 0.04 -0.06 1997 -0.06 -0.13 -0.06 -0.10 -0.25 -0.67 -0.10 -0.11 0,00 -0.10 1998 0.04 0.00 0.00 -0.16 -0.22 0.50 -0.04 0.00 -0.13 -0.02 1999 0.14 -0.07 -0.09 0.03 0.29 0.67 -0.12 -0.13 0.29 0.09 2000 0.39 0.00 0.00 0.22 0.33 0.60 0.09 0.14 0.07 -0.10 2001 -0.32 0.00 0.00 -0.33 -0.50 -0.63 -0.04 -0.13 -0.10 0.15 2002 0.07 0.00 0.00 -0.03 0.00 0.33 0.00 0.00 0.04 -0.03 2003 -0.05 -0.31 -0.32 -0.26 -0.17 -0.50 -0.29 -0.29 -0.04 -0.16 2004 -0.08 -0.11 -0.05 -0.02 0.00 0.00 -0.06 0.00 -0.04 0.14 2005 0.03 0.00 -0.10 -0.11 -0.20 0.50 -0.06 0.00 0.08 0.14 2006 -0.04 0.13 0.11 0.05 0.00 -0.33 0.07 0.00 0.07 -0.02 2007 -0.10 -0.11 -0.10 -0.12 -0.25 0.00 -0.06 0.00 -0.10 -0.06 2008 -0.03 0.00 0.00 0.11 0.33 0.00 -0.07 0.00 -0.04 -0.05 2009 0.28 0.13 0.22 0.20 0.25 0.50 0.21 0.20 0.28 0.25 2010 -0.16 0.00 -0.05 -0.14 -0.20 -0.33 0.00 0.00 -0.06 0.16 2011 0.00 0.00 0.00 -0.02 -0.25 0.00 0.00 0.00 0.03 -0.06 2012 0.02 0.00 -0.05 -0.05 0.33 0.00 -0.06 -0.17 -0.03 0.00 2013 0.06 0.00 0.05 0.03 0.00 0.00 0.06 0.00 -0.03 -0.14 2014 0.01 0.00 0.05 0.02 0.00 0.00 0.00 0.00 0.03 -0.02 2015 0.14 0.22 0.05 0.34 0.50 0.50 0.06 0.20 0.03 0.00

Note: Periods of parliamentary elections in SK are grey shaded, positive values above 0.2 are bold, the particular expenditure (E) growth rate (g) is computed as g=(Et-Et-1)/Et-1, monitored period 1995-2015, The next parliamentary election were held in 2016, that is why the situation in 2015 is evaluated. Source: own computation

A, page 163_there is a needless dot after Keywords? B, page 165_there is a needless dot in a blanc space after the Introduction C, page 172_there is a missing dot after the sentence in the Acknowledgement

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Montenegrin Journal of Economics

Vol. 14, No. 3 (2018), 177-188 ‘

The Role of Small and Medium Entrepreneurship in the Economy of Russia

IULIIA S. PINKOVETSKAIA1, IRINA N. NIKITINA2 and TATIANA V. GROMOVA3

1 Associate Professor of the Economic analysis and state management department, Ulyanovsk State University, Russia e-mail: [email protected] 2 Associate Professor of the Linguistics and foreign business communication department, Samara State University of Economics, Russia, e-mail: [email protected] 3 Professor of the Linguistics and foreign business communication department, Samara State University of Economics, Russia, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received June 19, 2018 Small and medium entrepreneurship is an important factor in the devel- Revised from June 30, 2018 opment and transformation of the Russian economy. Taking this into Accepted August 20, 2018 account, the study of the current employment structure in small and Available online September 15, 2018 medium enterprises (SMEs) is topical. The subject of study is the role of SMEs in the modern Russian economy and the potential for its in-

crease. The aim of the research was to assess the patterns existing JEL classification: sectoral and regional structure of employment in SMEs including indi- L26, O13, O25, P25. vidual entrepreneurs. The study was based on official statistical data all SMEs in Russia for 2015. For modeling we used functions of normal DOI: 10.14254/1800-5845/2018.14-3.13 distribution, quality of approximation empirical data by these functions was analyzed by tests Kolmogorov-Smirnov, Pearson, and Shapiro- Keywords: Vilk. The hypothesis of research - presence significant differentiation of indicators characterizing the share of SMEs employees by regions in small and medium enterprises, the total number of employed population, as well for types of economic share of SMEs employees in activity. Based on the results of the research hypothesis was confirmed. total employment, Trade, manufacturing and real estate operations accounted for the type of economic activity, largest share in the total number of SMEs employees. SMEs did not regions play significant role in fishing and fish farming, mining, education, . healthcare, production and distribution of electricity, gas and water, as

well as financial sectors. The presence of SMEs development significant reserves in Russia is proved. Identified regions with high and low entrepreneurial climate. The methodology and tools that were used in the research can be applied to similar studies for countries with a significant number of territorial (administrative) units. Government, regional and municipal authorities may use the research results in the practice of formation and implementation of entrepreneurship development projects and programs, including those with the aim to increase its role in the regions and municipalities where SMEs is not sufficiently developed.

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INTRODUCTION Small and medium entrepreneurship is a complex system, including a large number of independent economic entities. Small and medium enterprises (SMEs) in Russia involve both legal entities (LE) and individual entrepreneurs (IE). The current criteria for classifying economic entities as small and medium enterprises were established by Federal Law No. 209-FZ of July 24, 2007 "On small and medium business development in the Russian Federation". The main criterion is the number of employees, which for a small enterprise should not exceed 100 people, and for a medium enterprise is in the range to 250 from 101 people. The second criterion is share state and municipal authority in the total capital of enterprises (less than 25%), and also turnover and balance price of assets. Maximum values of the last two parameters are set by the government and adjusted annually if necessary. SMEs is an important factor in the economic development of many countries, including those in conditions of the economic crisis (Acs et al., 2008; Baumol, 2004; Decker, et al., 2014; Simon- Moya, et al., 2016). Today there are 5.6 million SMEs with 18 million employees in Russia. SMEs produce 20% of Russia's gross domestic product (GDP). The share of Russian SMEs in GDP and employment is twice lower than the corresponding figures for the countries of the European Union (The development of small and medium-sized businesses, 2015). To increase the role and number of SMEs, as well as the volume of goods, works and services produced by them, the state strategy for the SME development until 2030 was adopted (Strategy of SMEs development in the Russian Federation for the period up to 2030, 2016). The strategy provides for doubling SME share in GDP (up to 40%) and increasing the share of SMEs employees to 35% of the total employed population of the country. Experience of other countries shows (Sollner, 2014) that these goals are real. The implementation of the strategy involves the formation of medium and long term plans for SMEs sector. The development of these plans should be based on the determination role of SMEs in Russian economy. Therefore, an important research problem is to evaluate the share of SMEs in the total indicators on all enterprises and firms in Russia. Such indicators can provide federal and regional authorities with the data needed to identify reserves increasing quantity of employees in SMEs. The main attention should be paid to SMEs saturation of those sectors of the national economy and regions of the country where the role of SMEs is not yet great. Foreign experience (Choi, Choi, 2015) shows, that this approach is very efficient. Therefore, a topical problem is to assess the current structure of employment in SMEs and their contribution to total employment of the country.

1. METHODOLOGY OF RESEARCH To assess the role of SMEs by type of economic activity, we consider it expedient to use employment indicator (the number of employees). The choice of this indicator is caused by the fact that it is less dependent on SMEs specialization, socio-economic features and geographical situation of the regions where they operate. The aim of this study is to determine the structure of SMEs employment in different types of economic activity, as well as the share of SMEs employees in the total employment of the country. The object of the study is the number of employees of small and medium enterprises located in each of Russia's regions and engaged in different economic activities. SMEs related both to legal entities and individual entrepreneurs are considered. To ensure comparability of indicators by types of activity and regions, the calculations are made on the basis of relative indicators. Two groups of indicators are considered. The first group includes the shares of SMEs employees relating to different types of economic activity in the total number of employees of all SMEs in the country. The second group of indicators includes the 178

Iuliia S. Pinkovetskaia, Irina N. Nikitina and Tatiana V. Gromova / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 175-186 shares of SMEs employees in the total number of employees of all enterprises and organizations relating to different types of economic activity. The indicators of the second group are determined only for those activities that are significant enough for SMEs. The hypothesis tested in the research is as follows: the indicators of the second group have a significant differentiation by the regions of Russia. To test this hypothesis, we modelled the distribution of the shares of SMEs employees in the total number of employees across all regions of the country. Normal distribution functions were used for modeling. These functions are widely used in modern economic research. For example, Allanson P. (Al- lanson, 1992) presented a distribution function analysis of the evolution of the size of agricultural land, including small farming. R. Vince (1992) considered the application of normal distribution function to characterize trading activity, profits and losses in particular. S. Filatov (2008) focused on a comprehensive methodology for assessing the financial status of a group of enterprises. Ma- rek L. and Vrabec M. (2013) discussed the possibility of forecasting the trend of wage distribution based on the functions of normal distribution. I. Pinkovetskaya (2015) considered the possibility of using density functions of normal distribution to describe the relative indicators of SMEs performance in her pilot work. Each SME acts as an independent entity, determines its goals and objectives based on a specific situation, and conducts risky economic activity. The number of such enterprises in the regions of Russia is very large. Accordingly, a group of SMEs located in each of the regions and formed according to the above-mentioned criteria includes a significant number of enterprises or entrepreneurs. Economic, historical, climatic, demographic, educational and other features of the development of a particular region have a significant impact on the SME sector indicators. Each SME operates independently, so we can assume a probabilistic (stochastic) distribution of indicators values. According to Chebyshev theorem (Kramer, 1962), the values of individual random variables can have a significant spread while their arithmetic average is relatively stable. A similar conclusion follows from the Central Limit Theorem (Jenish and Prucha, 2009), which states that the arithmetic average of a sufficiently large number of independent random variables loses the character of a random variable. Thus, the shares of SME employment in the total employment of the region are random variables. Their values may vary considerably, but we can anticipate their arithmetic average. G. Kramer (1962) in his study also indicated that some random variables can have a significant spread, but their arithmetic average is stable. It should be noted that, in accordance with Lya- punov's theorem, the distribution of average values of independent random variables approaches normal distribution if the following conditions are met: all values have finite expectations and variance, none of the values differs greatly from the others. The above conditions correspond to the values of the shares of SME employees by regions. V. Gmurman (2003) noted that distribution of average values of independent random variables quickly approaches normal distribution (starting from ten variables already). The number of SMEs located in each region and relating to specific size type and specific types of economic activity varies from hundreds to tens of thousands, which far exceed Gmurman’s criterion. Thus, there are theoretical prerequisites for using the functions of normal distribution to describe the distribution of the shares of SMEs employees in the total number of employees of all enterprises and organizations in the regions of Russia. SMEs operate in all types of economic activity, except for public administration, social insurance and military security. The source of the data used in this study is the official information of the Federal State Statis- tics Service of the Russian Federation collected during the so-called total (general) monitoring of SMEs. Data from such monitoring, conducted every five years, provide more accurate information than sample surveys conducted annually. Data for 2015 and 2010 were used (Federal State Sta- tistics Service, 2018) in the research. Statistical monitoring of SMEs activities was carried out by 179

Iuliia S. Pinkovetskaia, Irina N. Nikitina and Tatiana V. Gromova / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 177-188 14 types of economic activity: agriculture; fishing and fish farming; mining; manufacturing; production and distribution of electricity, gas and water; construction; wholesale and retail trade; transport and communication; hospitality (hotels and restaurants); real estate operations; financial operations; education; healthcare; provision of communal, social and personal services. SMEs are located in all regions of Russia without any exception, therefore, the data used in the study describe the number of SMEs employees located in 82 regions of Russia, including 22 republics, 9 territories, 46 oblasts, 1 autonomous oblast, 1 autonomous district and 3 federal cities. Besides, the authors used the official statistics for 2015 for the number of employees of all enterprises and organizations (including large enterprises, as well as state and municipal enterprises and organizations) located in each of the regions (Federal State Statistics Service, 2018). The number of observations (empirical data) is important in the development of normal distribution functions. Corresponding justifications are presented in the work by Heinhold, Gaede (Hein- hold and Gaede, 1964). According to this research, the number of observations should be not less than 40. The total number of observations in our study is greater: it is 82 (corresponding to the number of Russian regions). Quality of the developed normal distribution functions can be tested with the help of relevant criteria (tests). The analysis of literature (Bolshev and Smirnov, 1983; Hollender and Wulf, 1983; Pearson, D'Agostino and Bowmann, 1977; Shapiro and Francia, 1972) shows that Kolmogorov- Smirnov, Pearson and Shapiro-Fork tests are most frequently used in modern studies.

2. SECTORAL STRUCTURE OF SME EMPLOYMENT Table 1 presents the structure of SMEs employment. The table describes the number of employees of legal entities and individual entrepreneurs relating to 14 types of economic activity. Col- umn 4 of Table 1 shows the total number of employees for both types of SMEs. Column 5 shows the share of SMEs employees in each of the activities, that is, the sectoral structure of employment in the entrepreneurial sector of the Russian economy. To analyze the dynamics of indicators for five years, the corresponding values of total statistical observation for 2010 are given in parenthe- ses. According to the data presented in Table 1, the number of SMEs employees in Russia in 2015 amounted to 18.45 million people. The total number of employees of IE was 2.7 times less than that of LE. Moreover, the prevalence of the number of employees in LE was typical for all types of economic activity. In 2015, the largest number of SMEs employees was in wholesale and retail trade (almost 5.9 million people). Their share in the total number of SMEs employees reached almost 32%, that is, every third SMEs employee was engaged in trade. A relatively high proportion of SMEs employees were engaged in real estate operations (over 18%) and manufacturing sector (15%). Construction, transport and communication, agriculture accounted for 5% of SMEs employees. More than 500 thousand employees were engaged in hospitality and provision of communal, social and personal services sectors. The smallest number of SME employees (up to 45 thousand people) was registered in fishing and fish farming and education. The largest number of SMEs employees (45%) in 2015 were registered in legal entities related to wholesale and retail trade and real estate operations. While LE of manufacturing and construction sectors accounted for 17% and 12% of SMEs employees respectively. Among individual entrepreneurs, there was an absolute predominance of the number of employees engaged in wholesale and retail trade (more than 51%). While IP related to real estate operations, transport and communication accounted only for 10% of SME employees.

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Table 1. SMEs employment by types of economic activity for 2015

Number of employees, thousands of people Share in the total num- Type of economic activity ber of SMEs employees, LE IE SMEs % 1 2 3 4 5 722.1 278.1 1000.2 5.42 agriculture (989.5) (399.6) (1389.1) (7.28) 35.9 5.2 41.1 0.22 fishing and fish farming (32.2) (6.3) (38.5) (0.49) 100.8 1.5 102.3 0.55 mining; (91.8) (0.8) (9.,6) (0.49) 2335.9 432.1 2768 15.00 manufacturing (2259.2) (470.6) (2729.8) (14.31) production and distribution 213.9 3.2 217.1 1.18 of electricity, gas and water (220.1) (3.6) (223.7) (1.17) 1637.2 145.6 1782,8 9.66 construction (1748.7) (129.0) (1877,7) (9.82) 3341.7 2536.4 5878.1 31.86 wholesale and retail trade (3445.0) (3032.0) (6477.0) (33.49) 512.5 206.6 719.1 3.90 hospitality (525.1) (161.1) (686.2) (3.60) transport and communica- 851.8 476.3 1328.1 7.20 tion (828.4) (470.8) (1299.2) (6.81) 129.3 24.8 154.1 0.84 financial operations (127.4) (14.5) (141.9) (0,74) 2920.8 52.,6 3445.4 18.67 real estate operations (2870.9) (390.6) (3261.5) (17.12) 23.2 21.4 44.6 0.24 education (21.4) (15.7) (37.1) (0.17) 342.9 26.8 369.7 2.00 healthcare (227.8) (20.5) (248.3) (1.30) provision of communal, 349.4 249.7 599.1 3.25 social and personal services (343.3) (238.5) (581.8) (3.05) 13517.3 4932.3 18449.6 100.00 Total (13731.9) (5350.1) (19082.0) (100.00) Most significant types of 12671.4 4849.4 17520.8 94.96 economic activity

Source: federal statistics and own calculations.

Comparative analysis of changes in the number of SMEs employees for the period from 2010 to 2015 showed an employment decrease for legal entities by almost 215 thousand people, and for individual entrepreneurs - by 418 thousand people. The decrease in the number of SMEs employees was registered in trade, agriculture and construction. However, in most of the activities an employment growth was marked for this period. Thus, the number of SMEs employees increased in such sectors as fishing and fish farming, mining, manufacturing, hospitality, transport and communication, financial operations, real estate operations, education, healthcare, and provision of communal, social and personal services. Analysis of SMEs employment by types of economic activity enabled us to identify eight most important activities for SMEs: wholesale and retail trade, construction, manufacturing, real estate operations, transportation and communication, agriculture, hospitality, provision of communal,

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Iuliia S. Pinkovetskaia, Irina N. Nikitina and Tatiana V. Gromova / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 177-188 social and personal services. Each of them now employs more than 500 thousand people. In 2015 these activities accounted for slightly less than 95% of the total number of SMEs employees in Russia (last line of Table 1). SME role in other activities (mining, production and distribution of electricity, gas and water, education, fishing and fish farming, healthcare, financial operations) was not great. Moreover, a significant increase of SMEs role in these activities in the short term is not expected for a number of objective and subjective reasons. We are going to consider them in detail. The mining of miner- als by SMEs is mainly related to the development of stone quarries, the extraction of gravel, sand and clay. A significant increase of SME role in this activity seems unlikely in view of the lack of a growing demand for relevant products. Production, transmission and distribution of steam and hot water dominates in production and distribution of electricity, gas and water. This type of SMEs activity is concentrated in some regions of Siberia and the Far East of the country. In the vast majority of other regions, large corporations (mainly municipal and regional enterprises) are involved in production and distribution of steam and hot water. There are no economic and organizational prerequisites for increasing of SMEs role in relevant markets. Financial activity of SMEs is limited to financial intermediation, which involves the use of temporarily idle funds and their provision for temporary use. Lately there has been many claims to this type of activity and now its volumes are decreasing. In the field of education SMEs participation is limited to provision of educational services for adults. Absolute majority of educational services in Russia are concentrated in state, regional and municipal institutions, and this trend has been increasing in recent years. The main reason for SMEs poor development in fishing, fish farming and healthcare is significant costs for organization and management of these activities. Since it is quite difficult for SMEs to get cheap loans, in most cases, entrepreneurs use their own funds and money borrowed from relatives. Such approach to investment financing is quite inefficient for these two types of activity. Therefore, despite the significant demand for fish products and health services in the country, there are no opportunities for increasing the role of SMEs in these activities.

3. SHARE OF SME EMPLOYEES IN THE TOTAL NUMBER OF EMPLOYEES BY TYPE OF ACTIVITY Share of SMEs employees in the total number of employees by type of economic activity was conducted in accordance with the above methodology procedure. In this case, the contribution made by eight most significant activities was considered. Calculations were based on the share of SMEs employees (legal entities and individual entrepreneurs) in the total number of employees of all enterprises and organizations in each of the regions of Russia. These indicators were determined in the process of computational experiment based on the statistics by each of Russian regions. Values differentiation modeling of the share of SME employment was based on the development of normal distribution density functions. These normal distribution density functions ( y ) describe the share of SMEs employees ( x ), engaged in a certain type of economic activity by the region in the total number of employees in the corresponding type of economic activity by the same region. These functions are given below:

 the share of SMEs employees engaged in agriculture, hunting and forestry

2 ( x117) 465 266 y1 (x1 )  e ; (1) 6 2

 the share of SMEs employees engaged in manufacturing

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2 ( x2 29) 703 299 y2 (x2 )  e ; (2) 9 2  the share of SMEs employees engaged in construction

2 (x3 31) 820 299 y3 (x3 )  e ; (3) 9 2

 the share of SMEs employees engaged in wholesale and retail trade

2 ( x4 50) 1128 21212 y4 (x4 )  e ; (4) 12 2

 the share of SME employees engaged in hospitality

(x 57)2  5 1025 21616 y5 (x5 )  e ; (5) 16 2

 the share of SMEs employees engaged in transport and communication

(x 23)2  6 615 277 y6 (x6 )  e ; (6) 7 2

 the share of SMEs employees engaged in real estate operations

( x 56)2  6 820 21212 y7 (x7 )  e ; (7) 12  2

 the share of SMEs employees engaged in provision of communal, social and personal services

2 (x8 22) 400 266 y8 (x8 )  e . (8) 6 2

In addition, the distribution of the shares of SMEs employees in the total number of employees of all enterprises and organizations by regions was estimated:

2 287.00 (x925.01) y (x )  e 24.304.30 . (9) 9 9 4.30 2

Table 2. Estimated values of statistics

Estimated value by quality Function number Kolmogorov-Smirnov Pearson Shapiro-Vilk 1 2 3 4 (1) 0.06 2.68 0.96 (2) 0.03 0.43 0.98 (3) 0.05 1,78 0.97 (4) 0.05 1.57 0.97 (5) 0.08 4.38 0.95 (6) 0.04 1.30 0.96 (7) 0.01 0.34 0.98 (8) 0.03 1.83 0.97 183

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(9) 0.05 3.44 0.95

Source: Own calculations According to the theory of mathematical statistics the verification of normal distribution functions is based on application of Kolmogorov-Smirnov, Pearson, and Shapiro-Vilk tests. The tests allow comparing the empirical distribution of the studied parameters with theoretically described functions of normal distribution. The tests also demonstrate the deviation of empirical data from these functions. The methodology for using the tests is detailed in the literature given in references (Bolshev and Smirnov, 1983; Hollender and Wulf, 1983; Pearson, D'Agostino and Bowmann, 1977; Shapiro and Francia, 1972). Table 2 shows the actual values of statistics from the results of the computational experiment. Analysis of the data shown in Table 2 is provided below. The estimated values by Kolmogorov- Smirnov test (column 2 of Table 2) are to 0.08 from 0.01. It is less than the tabulated value, which is 0.152 (with a significance level of 0.05). Similarly, the estimated values by Pearson test (column 3 of Table 2) range to 4.38 from 0.34, which is less than the tabulated value of 9.49. The estimated values by Shapiro-Fork test (column 4 of Table 2) range to 0.98 from 0.95. These values are greater than the tabulated value of 0.93 (with a significance level of 0.01). Thus, all designed functions (1) - (9) have high quality in all tests and well describe approximated data.

4. ANALYSIS RESULTS OF MODELING Normal density function allows determining average values of the current shares of SME employees relating to specific type of economic activity. The corresponding indicators are given in Table 3. The table shows the change intervals of the indicators under consideration (column 3), which are typical for the majority (68%) of the country's regions. The intervals are estimated on the basis of average values of indicators and standard deviation values. To estimate the interval limits, the specified deviation is added to or subtracted from the average value of the indicator, respectively. The average values and the change intervals of the indicators in the table correspond to the density functions of normal distribution (1)-(9).

Table 3. Characteristics of the share of SME employees in the total number of employees by type of activity, %

Type of economic average standard change activity value deviation interval agriculture 17 6 11-23 manufacturing 29 9 20-38 construction 31 9 22-40

wholesale and retail trade 50 12 38-62

hospitality 57 16 41-73 transport and 23 7 16-30 communication real estate operations 56 12 44-68 provision of communal, social and 22 6 16-28 personal services

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All types of economic activity 25 4 21-29

Source: Own calculations Analysis of the data presented in Table 3 shows that the average values of the share of SMEs employees in the total employment differ significantly for each type of economic activity. The highest values are recorded in such types of activity as hospitality and real estate operations. The share of SMEs employees engaged in trade in total employment of all trade enterprises is slightly lower. These three activities account for more than 50% of all SMEs employees. In manufacturing and construction the share of SME employees is about a third. The share of SMEs employees in the number of employed in such sectors as agriculture, provision of communal, social and personal services, transport and communication is quite large (between 17% and 23%). Thus, the number of employees engaged in eight types of SME activities, which we consider to be the most significant, is not only high in absolute value, the share of SMEs employees in the total employment of these sectors is also quite substantial. In general, SMEs contribution to the total employment in the country (last line of Table 3) reaches 25%. That is, every fourth economically active resident works in one of the SMEs. It is of some interest to compare this indicator with the same indicators in the foreign countries that had similar starting conditions for the formation of a market economy: Latvia (79%), Estonia (78%), Lithuania (76%), Bulgaria (75% %), Slovenia (72%), Slovakia (70%), the Czech Republic (70%), Po- land (69%), Croatia (68%), Romania (66%), Georgia (44%), Armenia (42%) , Belarus (28%) [Statis- tics Explained. Eurostat, 2018; Shmavonyan, 2015]. These data confirm the assumption that there are large reserves for SME development in Russia. As mentioned earlier, the values of SMEs contributions to total employment by regions of the country are well described using the obtained density functions of normal distribution (1)-(9). There can be a significant differentiation of these contributions by specific regions of the country. This conclusion stems from the meaning of normal distribution. The change intervals for the values of SMEs contributions to total employment, typical for most of the country's regions, are given in column 3 of Table 3. The hypothesis of a significant differentiation of indicators characterizing the share of SMEs employees by regions in the total number of employed population in these regions, as well as distribution of similar indicators by the most important for SMEs types of economic activity has been confirmed. Of particular interest is the identification of the regions of the country with the indicators under consideration, the values of which go beyond lower and upper bounds of the intervals. Such information can provide federal and regional authorities with data on business climate, as well as the weak and strong development of SMEs by regions and sectors. The results of this analysis with examples of SMEs contribution to total employment in all activities and in trade sector particularly are presented below. The values of the shares of SMEs employees in the total regional employment went beyond the lower bound (21%) in 2015 in the following regions of the country: the republics of North Ossetia- Alania, Karachaevo-Cherkessia, Tyva, Dagestan, Kabardino-Balkaria, Kalmykia and Chechnya; Murmansk, Orenburg, Volgograd Oblasts and Trans-Baikal Territory. SMEs contributions to the total regional employment went beyond the upper bound (29%) in the following regions: the Republic of Adygea, Krasnodar Territory, Sverdlovsk, Kirov, Ivanovo, No- vosibirsk, Ryazan, Voronezh, Kaliningrad, Kostroma Oblasts and the city of St. Petersburg. As for trade sector, these indicators went beyond the lower bound (38%) in 2015 in the following regions of the country: the republics of North Ossetia - Alania, Dagestan, Kabardino-Balkaria; Murmansk, Kursk, Leningrad, Tula, Moscow, Bryansk, Amur, Volgograd Oblasts and the city of Moscow.

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Iuliia S. Pinkovetskaia, Irina N. Nikitina and Tatiana V. Gromova / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 177-188 SMEs contributions to the total number of employees of trade enterprises went beyond the upper bound (62%) in the following regions: the republics of Sakha (Yakutia), Khakassia, Komi and Chechnya; Voronezh, Belgorod, Ryazan, Kostroma Oblasts and Chukotka Autonomous District. This situation is caused by peculiarities of SMEs activity in the respective regions. It should be taken into account in the formation of business development programs in these regions. Russian government should create a system of incentives for SME development in the regions where the role of entrepreneurship is low.

CONCLUSIONS The results of the study have significant novelty and originality. They allow us to draw the following conclusions:

 In 2015 the total number of SME employees in Russia was just under 18.5 million people. At the same time, legal entities employed 12.6 million people, and individual entrepreneurs - 4.8 million people.  Wholesale and retail trade accounted for the largest share in the total number of SME employees (almost 5.9 million people).  More than 500,000 people are employed in SMEs which specialize in each of the eight types of activities: wholesale and retail trade, real estate operations, manufacturing, hospitality, transport and communication, construction, agriculture, as well as the provision of communal, social and personal services. These activities accounted for 95% of the total number of SMEs employees.  SMEs did not play significant role in fishing and fish farming, mining, education, healthcare, production and distribution of electricity, gas and water, as well as financial sectors.  The density functions of standard distribution are developed, which describe the share of SMEs employees in the total number of employees of all enterprises and organizations in the following sectors: agriculture, manufacturing, construction, wholesale and retail trade, hospitality, transport and communication, real estate operations, the provision of communal, social and personal services. The developed functions have a high quality of approximation of empirical data.  Using the developed density functions of normal distribution, the average values of SMEs contributions to total employment and by types of activity are determined.  Differences in values of SMEs contributions to total employment by the regions of the country are confirmed. Regions with high and low values of these indicators on the examples of all SMEs and those engaged in trade sector were determined.

The results of the research can be used in scientific studies related to entrepreneurial sector development. The developed functions of normal distribution can be used to justify the concepts, plans and programs for SME development in the regions and municipalities. The methodology and tools that are used in the research can be applied to similar studies for countries with a significant number of territorial (administrative) units. The government, regional and municipal authorities may use the research results in the practice of formation and implementation of entrepreneurship development projects and programs, including those with the aim to increase its role in the regions and municipalities where SMEs is not sufficiently developed. The study is able to provide authorities with information on potential opportunities to increase SMEs contribution to the national economy in accordance with the Federal Strategy for SMEs De- velopment (Federal Strategy for Development of Small and Medium Entrepreneurship in the Rus- sian Federation for the period up to 2030, 2016). In addition, the results of the work can be used in the current activity of state, municipal and 186

Iuliia S. Pinkovetskaia, Irina N. Nikitina and Tatiana V. Gromova / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 175-186 public organizations related to SMEs regulation and support in solving the problems of monitoring, assessing the current level and determining ways to enhance the role of SMEs. Further research may relate to the assessment of the role of SMEs in individual municipalities. REFERENCES Acs, Z., Desai, S., Hessels, J. (2008), “Entrepreneurship, economic development and institutions”, Small Business Economics, Vol. 31, No. 3, pp. 219-234. Allanson, P. (1992), “Farm size structure in England and Wales 1939–89”, Journal of Agricul- tural Economics, , Vol. 43 , No. 2, pp. 137–148. Balaev, A. I. (2014), “Modelling Financial Returns and Portfolio Construction for the Russian Stock Market”, International Journal of Computational Economics and Econometrics., Vol. 4, No. 1- 2, pp. 32–81. Baumol, W. J. (2004), “2Entrepreneurial enterprises, large established firms and other components of the free-market growth machine”, Small Business Economics, Vol. 23, Issue 1, pp. 9- 21. Bolshev, L. N., Smirnov N. V. (1983), Tables of mathematical statistic, Science, Moscow (in Rus- sian). Choi, K-S., Choi, J. (2015), “Small and Medium Business and Investment Decision”, Indian Journal of Science and Technology, Vol. 24, No. 8, pp. 1-6. Decker, R., Haltiwanger, J., Jarmin, R., Miranda, J. (2014), “The Role of Entrepreneurship in US Job Creation and Economic Dynamism”, Journal of Economic Perspectives, Vol. 28, No. 3, pp. 3– 24. Federal State Statistics Service. Continuous observation of activity of small and medium business. Available at: http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/ statistics/enterprise/reform/ (accessed 20.06.2018). Filatov, S. V. (2008), “Some questions of perfection methods of complex evaluation of condition enterprise”, Economy, statistic and informatics, Journal UMO, MESI, No. 3, pp. 56-62 (in Rus- sian). Gmurman, V. Е. (2003), Theory of probability and mathematical statistic, High school, Мoscow, Heinhold I., Gaede K.W. Ingenieur statistic. München, Wien, Springler Verlag. 1964, 352 p. Hollender, М., Wulf, D. (1983), Nonparametric methods of statistic, Finance and statistic, Мoscow (in Russian). Jenish, N., Prucha, I. R. (2009), “Central limit theorems and uniform laws of large numbers for arrays of random fields”, Journal of Econometrics, Vol. 150, No. 1, pp. 86-98. Kramer, G. (1962), Mathematical methods of statistic, Princeton University Press, Princeton. Marek, L., Vrabec, M. (2013), “Model wage distribution - mixture density functions”, International Journal of Economics and Statistics, , Vol. 1, No. 3, pp. 113-121. Pearson, E. S., D’Agostino, R. B. (1977), “Bowmann K.O. Test for departure from normality: Com- parison of powers”, Biometrika, , No. 64, pp. 231-246. Pinkovetskaia, I. S. (2015), “Methodology of research indicators of work entrepreneurial structures”, Works of Carel science centre RAN, No. 3, pp. 83–92 (in Russian). Shapiro, S. S., Francia, R. S. (1972), “An approximate analysis of variance test for normality”, Journal of the American Statistical Association, Vol. 67 (337), pp. 215-216. Shapkin, А. S. (2003), Economic and financial risks. Estimation, management, investment portfolio, Dashkov and К., Moscow (in Russian). Shmavonyan, G. D. (2015), “Improvement of the tax policy in the sphere of small business and ensuring economic growth (on the example of Armenia)”, Bulletin of the Financial University, No. 5, pp. 116-126 (inRussian).

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Montenegrin Journal of Economics

Vol. 14, No. 3 (2018), 189-198 ‘

Examining Leadership Characteristics at International Multilaterals

AGOTA GIEDRĖ RAISIENĖ1, ALEKSANDRA PULOKIENĖ2 and ANDRIUS VALICKAS3

1 Professor, Management Institute, Mykolas Romeris University, Lithuania, e-mail: [email protected] 2 MA, Head of Centre of Culture of Zarasai Municipality, Lithuania, e-mail: [email protected] 3 Professor, Management Institute, Mykolas Romeris University, Lithuania, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received June 19, 2018 The paper discusses leadership characteristics in international Revised from June 30, 2018 projects that require cooperation. The influence of the leader's traits Accepted August 20, 2018 and qualities on the implementation of the multilateral project was Available online September 15, 2018 studied under the framework of the Latvia-Lithuania-Belarus Trans- Border Cooperation Program. A multistage survey in different target groups was used for the research. The research revealed that there JEL classification: is no discrepancy between the effective leadership at multilaterals L14; L38; M12. and effective leadership concepts in general. Nevertheless, some specific leader’s characteristics and qualities can be underlined. DOI: 10.14254/1800-5845/2018.14-3.14 Multilateral projects require leader to be competitive, self-confident, visionary and supportive at first. Herewith, the leader must have Keywords: good knowledge on the project technical requirements, an ability to consult project team on performance, a capability to coordinate and leadership, to control project processes, a capacity to effectively manage pro- leadership traits, ject documentation, a competence of mentoring and employee leadership characteristics, engagement, an ability to meet project team needs, and perfect multilateral collaboration, skills of project internal and external communication. The research project management also revealed that cultural differences of team members can deter- . mine the attitude towards leadership.

INTRODUCTION Public administration organizations invoke interinstitutional cooperation, international experience sharing, etc. when developing their practices by standing ahead of challenges. The article analyzes one of them that is the way leadership influences the cooperation within international projects. In particular, the leadership traits and behavior are investigated in a project which was implemented under Latvia-Lithuania-Belarus border cooperation program (ENPI1) which was dedicated to strengthen the border regions of the countries.

1 ENPI - European Neighbourhood and Partnership Instrument, official website on the internet: http://www.enpi- cbc.eu/go.php/lit/IMG. 189

Agota Giedre Raisiene, Aleksandra Pulokiene and Andrius Valickas / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 189-198 In modern society, people with leadership qualities tend to be valued more in institutions and companies as superior employees, nevertheless leadership is treated differently in different cul- tures. S. Nauman et al. (2010) claims that it might be impossible to define the only one model of leadership in intercultural context, and the behavior of the leader which occurs when facing the challenges of management becomes a field requiring more and more attention. The purpose of the article is, on the basis of case analysis, to explore how the features of leadership differentiate between neighbor countries in the projects that require cooperation. Theories and concepts of leadership are shortly reviewed in a theoretical part of the article. Results of quantitative and qualitative research are discussed in the research results section. At the end of the article, model based on the empirical research is offered, which lets the executives of the projects to improve the performance of international projects.

1. THEORETICAL BACKGROUND Leadership is perceived as interdisciplinary phenomenon. Researchers and practitioners attention is focused on every component of this phenomenon and the most important – their interaction: for the leader as an individual, his influenced group and the overall results of the group, which, in turn, determine the changes in the environment. Therefore, leadership should be understood as a set of various combinations of variables (Bahreinian et al., 2012; Porvazník, 2011; Valickas et al., 2017). Research of leadership can conditionally be divided into two time periods (Vries et al., 2010): in the period from 1953 to the middle of 1980 the definition of leadership and the initial structures were considered; after 80’s, the resolution and the effect of the leadership was started to be analyzed. In this period, there appeared new directions of the approach to leadership. These are trans- formational leadership, transactional leadership, situational leadership, visionary leadership, supportive leadership (Bhatti et al., 2012; Skakona et al., 2010; Morhart et al. 2011; Elenkov et al., 2005; Naqvi et al., 2011; Chaudhry and Javed, 2012; Borkowski et al., 2015; Jankurova et al., 2017; Pucetaite et al., 2015), etc. Furthermore, the behavior of the leader is often analyzed within four styles of leader’s behavior: directive leadership, supportive leadership, achievement-oriented leadership and participative leadership (Bhatti et al., 2012, p. 745). J. Iqbal et al. (2012), after the research of scientific articles, has noticed that publications which analyze the phenomenon of the leadership can be divided into two groups. The first one was assigned with articles which claim that the main task of the head of the company is to control the employees, because a person by nature avoids working. The second group is assigned with research which findings claim that head of the company reaches the best results when motivating the employees. This article is based on the latter approach. Research is based on the modern paradigm of the leadership, i. e. collaborative leadership. It claims that the specialists, the followers of the leader, knows everyday problems and processes the best, which is why they have to contribute to decision making (Raisienė, 2012; ). Several theories are attributed to the paradigm of collaborative leadership: facilitative leadership (e.g. Wald al., 2017), coaching (e.g. DiazGranados, 2017) and servant leadership (e.g. Roberts, 2014) are among those. Leaders who go by cooperative leadership behavior models tend to support employees in case of an emerged problem instead of punishing them. These theories are characterized by a communications relationship. Management as a leadership goes useless when comparing to the behavior of the manager when he relies on other’s followership (Raisienė, 2012; 2014). According to Z. Bhatti et al. (2012, p. 750), „There is no assurance that any one leadership behavior will all the time be effective. But it must be agreed that any leadership behavior used by the leader while managing the affairs of his office is possible to have an influence on organization performance, be it positive or negative.“

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Leader’s influence is the biggest when assuring the group’s productivity and employees’ personal effectiveness environment. If advice and inspiration is expected from the leader in the personal level, in the organization level leader is responsible for institution or company’s culture, changes and innovation decisions (Skakona et al., 2010; Mishchuk et al., 2018). It is also worth to mention that the behavior of the leader that was effective in one environment could work absolutely different in the other circumstances (Elenkov et al., 2005, p. 679). By analyzing the social environment in which leader has to reach for the managing results, leader can increase the effectiveness of the actions by focusing to the behavior that leads to the best results. It is especially relevant in the intercultural environment, and in the context of this article – when carrying out international projects. When talking about multilateral international projects, leader’s behavior is well explained by a 3D model in which figures three dimensions of leader’s behavior. It is orientation towards the tasks, relationship and the effectiveness of the general activity (Limbare, 2012, p. 173). These three directions of the leader’s attention describe leaders’ and their followership and give leaders a voice in the vertical of the management of organization. Meanwhile, in the horizontal level of the cooperation, when including stakeholders of the project, leader’s role gets another character. Leader is not isolated at the top of the hierarchical pyramid. Here he is an element of the network, and the success of his work depends not on the individual behavior but on the cooperation (Maak and Pless, 2006). When talking about leadership it is worth to notice that from 1990’s, definition of the project management has changed a lot. More and more attention was focused on organizational and human resource aspects for the implementation of the projects. Meanwhile, the technical aspects were more highlighted before. It was found that in order to reach higher results of technical activity, very important are the leadership competences. Thanks to them, human issues are being addressed (Thompson, 2010; Flannes, 2004). Among the most effective leadership principles that make an impact on project’s management are: promotion of decision-making with participation; exercise of open communication ensuring availability of information about the project; support, representation and empowerment in the distribution of powers; conflict resolution in the team; education and training of employees, etc. (Nauman at al., 2010). In terms of competences the following classical leadership competences in project management are underlined: definition of roles and responsibilities, communication of expectations and clarity in communication, employment of consistent processes, establishment of trust (Ahmed and Anantatmula, 2017; Bileviciutė et al., 2016). However multilateral projects are complex projects involving at least several stakeholders. The scientific literature on leadership characteristics in multilaterals is fragmented and insufficient. However this problem finds its conceptualization in closely related areas. Complexity leadership theory developed by: M. Uhl-Bien et al. (2007), propose leadership in complex systems as a set of emergent, interactive network dynamics. Within complex systems it is difficult to attribute change to individual leaders: leadership acts emerge in different networks each at their own level. Local acts can produce small or bigger changes in other aspects of the system or in the system as a whole (Uhl-Bien et al., 2007, Nooteboom, Termeer, 2013). Complexity leadership theory proposes three leadership functions: administrative, adaptive, and enabling. Multiple actors can exhibit any or all three of these leadership functions. Administrative leadership is grounded in traditional bureaucratic notions of hierarchy, alignment and control. Adaptive leadership produces new ideas, innovations, adaptability and change. It originates in fight among agents over conflicting needs, ideas, or preferences and results in movements, change alliances, or cooperative efforts. Enabling leadership maneuvers and protects the conditions in which adaptive leadership can flourish, and it allows for emerging innovations (Uhl-Bien et al., 2007, Noote- boom and Termeer, 2013).

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Agota Giedre Raisiene, Aleksandra Pulokiene and Andrius Valickas / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 189-198 The authors investigating leadership competencies in complex healthcare research projects found the implementation of a private, public, and academic partnership, where scientific merit of methods and results to be a critical foundation to the development of public policy. The following key essential leadership competencies were identified in such multilateral projects: clear and transparent communication, ability to provide support, empowerment and ability to build relevant capacity, systematical and critical thinking, consensus building, mobilization, negotiations and me- diation skills, evidence-based decision making (Strudsholm et al, 2016). Based on focused group discussions and a survey of Indian Administrative Service officers, V. Gupta, S. Chopra and R. K. Kakani (2018) identified the following leadership competencies relevant for complex public sector projects, namely considering people first, leading others, integrity, decision-making, planning, coordination and implementation, problem-solving, self-awareness and self-control and innovative thinking. The eight competencies were further clubbed under four meta- competencies, namely stakeholder analysis and decision-making, managing change and innovation, team building and positive administrator personality (Gupta et al, 2018). Observing contemporary leadership capacity in involving network state D. Pedersen and C. Tangkjær (2013) identify several essential competences of leaders dealing with complex projects in public sector institutions. The first of them is analytical thinking. Public managers play an important role not just as neutral bureaucrats, but they also have a decisive role and a heavy responsibility to create public value. Leaders should be able to understand institutional and discursive complexities and inconsistencies in a multi-contextual and multi-layered public sector (Ibid.). Communityship, the second competence. The term was coined by H. Mintzberg. It is basically about how managers are responsible for their own institutions, i.e. municipalities, as a community driven by engagements and strong ethical beliefs and standards, it is a matter of sustainability of public services and organisation, rather than short-sighted beliefs in profit. Authority and legitimacy in the involving municipality are no longer distributed through hierarchies and formal organisational fo- rums, but are to a much higher degree a result of capability to act, create and relate own organisational effort to complex contexts of politics, strategies, ambiguities and counterproductive expectations from politicians, citizens, employees, partners, enterprises, medias, and so on (Pedersen and Tangkjar, 2013). The third competence is critical reflexivity. It is more than a matter of technical skills regarding organisation and its management. Critical reflexivity means raising social, political and cultural issues, questioning purposes and intentions and, if necessary, challenging the assumptions and taken for-granted-ness on which organizational policies and practices are based (Reynolds, 2011). The last competence, which is very important in the contemporary institutional contexts, is managination. This means that public strategic leadership is a creative practice that brings forth an action world for public value creation using public, action and transformation imag- es (Thygesen and Tangkjær, 2005). In general, summarizing the insights of J. Kodjababian and J. Petty (2007), M. Uhl-Bien et al. (2007), K. Thompson (2010), K. Yen-Lin (2009), M. Bahreinian et al. (2012), S. Nooteboom and C. Termeer, (2013), S. Nauman et al. (2010), D. Pedersen and C. angkjar (2013), T. Strudsholm et al. (2016), R. Ahmed and V. Anantatmula (2017), V. Gupta, et al. (2018) and other researchers, we can say that an effective project leader must make decisive decisions that are in line with the interests of all stakeholders, ensure an open information and communication circulation, build good relationships in the working group, address emerging conflicts, anticipate operational and collaborative threats, ensure teamwork as well as to take care of technical project indicators and to keep in mind the vision of the organization and the general group activity and the objectives of the project. To ensure operational efficiency, productivity and maintain good relationships with the project team - these two areas need to be combined. And even if the manager succeeds in solving this challenge, combines human and technical factors, there are still many environmental factors that also determine the performance of the organization or the project being carried out. It is clear that effective leadership and project management involve a huge amount of competencies and requirements.

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2. METHODS The influence of the leader's traits and qualities on the implementation of the multilateral project was studied under the framework of the Latvia-Lithuania-Belarus Trans-border Cooperation Program (under the European Neighborhood and Partnership Instrument 2007-2013). The purpose of the research was to delineate a model of the leadership in multilateral projects on gath- ered data from particular international project. A multistage survey in different target groups was used for the research. In the study there were interviewed ENPI Program Coordinators-experts (3 persons), project leaders (10 people) as well as the interviewees of the ENPI private Facebook group followers - project professionals (60 people). Coordinators of the ENPI program with codes K1, K2, K3 given when generalizing interview data were involved in the first phase of the study. The survey involved 2 Lithuanian and 1 Latvian citizen. Respondents' experience in the field of multilateral projects in various positions, e.g. project management, coordinator, monitoring maker, trainer, etc. was 3 and more years. The number of projects in ENPI program coordinated by experts was 10 to 30. This demonstrates a great deal of respondents' experience and knowledge in the field of similar type of activity. In the second phase of the study, 10 project leaders from the ENPI program participated. The survey was attended by Lithuanians, Latvians and Belarusian citizen living in Lithuania and Latvia and Belarus. 7/10 respondents' experience in the field of projects was over 5 years, 2 respondents - from 3 to 5, and 1 respondent from 1 to 3 years. Half of the respondents executed 1 ENPI project each, and the other - 2 to 3 projects. 6 of interviewees were project managers, and 4 - coordinators. Types of projects carried out by leaders: infrastructural, public service, cultural or socio- cultural. A semi-structured interview questionnaire for coordinators of the ENPI program consisted of 14 questions, and the one for leaders consisted of 15 questions. Questions were asked directly to respondents. Their responses were captured and transcribed and analyzed by grouping similar content and concepts after. During the third phase of the survey, when interviewing the project professionals, the link to the survey at www.manoapklausa.lt was placed in a private ENPI Facebook group. No demographic data of the participants of the study was asked. With the use of open questions, respondents were asked to describe characteristics of the program's project leaders and their behavioral peculiarities, as well as to indicate which leadership characteristics would positively influence the effectiveness of the projects that were being implemented.

3. RESEARCH RESULTS AND DISCUSSION Summing up the results of research Phase I, it can be stated that interviewees highly value the influence of leadership on the success of ENPI program projects and consider leadership competencies as crucial. Speaking about leader’s knowledge, competencies and behavior, respondents highlighted: i) the specific knowledge and ability to meet project technical requirements, ii) the knowledge of team members needs and ability to manage them successfully, and iii) maintaining team spirit and teamwork capability. Summarizing answers, it turned out that respondents identified the leadership with the ability to meet the twofold challenges of project management: technical expertise and managerial expertise. According to interviewees, the leader in the ENPI program projects must firstly understand the specifics of the program. The second most important factor in project implementation efficiency is the ability of leaders to bring partners into a united team. The factor of internationality of team is a significant challenge requiring specific competencies for the leader. 193

Agota Giedre Raisiene, Aleksandra Pulokiene and Andrius Valickas / Montenegrin Journal of Economics, Vol. 14, No. 3 (2018), 189-198 During the interview, program coordinators were also asked about the differences between the leaders of the three program countries. Respondents were asked to describe the leaders of Latvia, Lithuania and Belarus with one of the most characteristic adjectives and one of the verbs. Latvia's leaders were described as experienced and proactive, Lithuania’s – as more balanced and practical, and Belarus’s - as authoritative and influential. As could be seen from a Figure 1, leaders of Lithuania and Latvia were also described with one mutual attribute. It is effectiveness. However, all the other features that are common to the leaders of different countries differ substantially.

Figure 1. Characteristics of leadership in ENPI program

Source: research done by authors.

As well as ENPI program coordinators, project managers emphasized the importance of leadership for the effectiveness of the projects, too. However, leadership here relates exclusively to the technical part of the project. According to project managers, a leader is a good guide to ensure that the project is implemented in a timely manner, according to a strict plan, budget, etc. The motivation of the project team, the rewards to the team members, inspiration and support are not considered important. Finally, results of the last stage of the research (answers of project professionals to the questionnaire) has shown that for them, the most important attributes of the leader was to be an organ- izer/coordinator, a team coach and a general visionary.

4. MODEL OF LEADERSHIP AT A MULTILATERAL PROJECT Based on results of the three research phases, it can be said that the effective leadership in multilateral projects includes such components: i) knowledge on the project technical requirements and ability to consult project team on the project performance as well as capability to coordinate and to control project processes; ii) competences of team building, covering mentoring, engagement and support; iii) ability to meet project members’ needs related with the project activities; iv) capability to integrate a vision, long-term goals and everyday activities of the project; v) capability to responsibly control the project documentation; vi) capability to secure well-timed, open, and adequate communication inside and outside the project team. As noted above, the leadership differs in the context of different countries. Communication between partners helps to minimize the differences, and as the research shows, leader is also responsible for it.

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Combining the results of all three research phases, the theoretical model is proposed which can be used by the project managers of similar multilateral projects so they can achieve the intended results.

Table 1. Leadership characteristics and qualities at multilaterals

Traits and characteristics Competitive Self-confident Visionary Supportive

LEADER AT MULTILATERALS

Skills and qualities Knowledge Ability to Capability Capability to Competences Ability to Perfect on the pro- consult to coordi- manage proof team meet communication ject technical project team nate and ject documen- building cov- project skills requirements on perfor- to control tation ering mentor- members’ mance project ing and en- needs processes gagement related with the project activities

Source: authors.

The research-based model consists of two parts: leader’s personal characteristics, and his skills and qualities (Table 1).

CONCLUSIONS The study showed that cultural differences can determine the attitude towards leadership. Alt- hough the focus of our research was leaders of jointly implemented projects from neighboring countries - Latvia, Lithuania and Belarus, it turned out that the leaders of each country are characterized by certain characteristics. The research also revealed that there is no discrepancy between the effective leadership at multilaterals and effective leadership concepts in general. Nevertheless, some specific leader’s characteristics and qualities can be underlined. Joint, multilateral projects require leader to be competitive, self-confident, visionary and supportive at first. Equally, leader must have good knowledge on the project technical requirements, an ability to consult project team on performance, a capability to coordinate and to control project processes, a capacity to effectively manage project documentation, a competence of mentoring and employee engagement, an ability to meet project team needs, and perfect skills of project internal and external communication.

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