Montenegrin Journal of Economics

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Montenegrin Journal of Economics, Vol. 14, No. 2 (June 2018)

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

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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

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Montenegrin Journal of Economics, Vol. 14, No. 2 (June 2018)

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Montenegrin Journal of Economics, Vol. 14, No. 2 (June 2018)

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ISSN 1800-5845

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Montenegrin Journal of Economics

Vol. 14, No. 2 (June 2018) ‘

C O N T E N T S

Impact of the Main Currencies Exchange Rates on the Romanian Economic Policy Transformation JEAN ANDREI, MARIAN ZAHARIA, and and MIHAELA CRISTINA DRĂGOI ...... 7

Risk Management of Dollarization in Banking: Case of Post-Soviet Countries ANDRII KAMINSKYI, and NATALIIA VERSAL ...... 21

Some Aspects of Actual CBI and Inflation in the Countries of Southeast Europe DAMIR PIPLICA, IVO SPERANDA, and ZVONIMIR JOSIP PERKOVIC ...... 41

Social Aspect of Sustainable Development: Issues of Poverty and Food Shortage ASTA MIKALAUSKIENE, RAMINTA NARUTAVICIUTE-CIKANAUSKE, INGRIDA SARKIUNAITE; DALIA STREIMIKIENE, and RUMYANA ZLATEVA ...... 59

An Estimation of the Logistics Potential of Enterprises in the Region’s Management VOLODYMYR GOVORUKHA, and OLGA KUCHKOVA ...... 79

Activation of the Economic Security of Ukraine in Terms of the European Integration ANDRII KUBAIENKO ...... 91

Organizational Agility Conceptual Model RIMA ŽITKIENĖ, and MINDAUGAS DEKSNYS ...... 115

Implementation of Du Pont Model in Non-Financial Corporations SYLVIA JENCOVA, EVA LITAVCOVA, and PETRA VASANICOVA ...... 131

Resource Misallocation and Rice Productivity in Thailand SIWAPONG DHEERA – AUMPON ...... 143

Span of Control in Teamwork and Organization Structure KATARINA REMENOVA, ZUZANA SKORKOVA and NADEZDA JANKELOVA ...... 155

Network Topology of Renewable Energy Sector in Stock Exchange MANSOOREH KAZEMILARI, ALI MOHAMADI, ABBAS MARDANI, and DALIA STREIMIKIENE ...... 167

The Effect of Word of Mouth Marketıng on the Purchase Behavıor Vıa Brand Image and Perceıved Qualıty ZUHREM YAMAN ...... 175

Author Guidelines ...... 183

Jean Andrei, Marian Zaharia and Mihaela Cristina Dragoi / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 7-19

Montenegrin Journal of Economics

Vol. 14, No. 2 (2018), 7-19 ‘

Impact of the Main Currencies Exchange Rates on the Romanian Economic Policy Transformation

JEAN ANDREI (Corresponding Author)1, MARIAN ZAHARIA2 and MIHAELA CRISTINA DRAGOI3

1 Associate Professor, Habil., Petroleum - Gas University of Ploiesti, Faculty of Economic Sciences, 39 Bucuresti Blvd., 100680, Ploiesti, Prahova, Romania, E-mail: [email protected] Director of Research Network on Resources Economics and Bioeconomy http://rebresnet.eu/ 2 Full Professor, Petroleum-Gas University of Ploieşti, Faculty of Economic Sciences, 39 Bucuresti Blvd., 100680, Ploiesti, Prahova, Romania, e-mail: [email protected] 3 Associate Professor, The Bucharest University of Economic Studies, Faculty of International Business and Econom- ics, 6 Piata Romana, 1st district, 010374, Bucharest, Romania, email: [email protected]

ARTICLE INFO ABSTRACT Received March 24, 2018 Evolutions of the exchange rates in a contemporary competitive Revised from March 29, 2018 economy represent an influential marker in evaluating the effects of Accepted May 23, 2018 the governmental policies in field of monetary policy and marks Available online June 15, 2018 future directions in developing the specific policies in the field. This study investigates how the exchange rates of EUR, USD, GBP and CHF were influenced by disturbing factors and the existence of JEL classification: potential quantitative correlations and dependencies among the C54, E52, G18. four exchange rates in terms of uncertainty. In this context the starting premise of the research took into account the exchange rate DOI: 10.14254/1800-5845/2018.14-2.1 developments for these four currencies for the period January 2008 – October 2015, period marked by two major disruptive events, Keywords: which highly influenced the exchange rates of the main currencies on the Romanian market, such as the financial crisis started in late exchange rate, 2008 and CHF exchange rate’s significant variation at the beginning quantitative correlation, of 2015 that triggered a deep debt crisis in the case of mortgage disruptive events, loans in Romania. The research results reveal particular evolution currency, patterns in the case of the exchange rates of the four currencies uncertainty. taken into analysis, confirming the existence of disturbing factors and the correlations and quantitative dependencies between them.

INTRODUCTION Recent developments in the European economy have shown its vulnerability to a range of internal and external factors that determined both an increase in exchange rates volatility and also the instability of national monetary policies in the case of countries that are not yet part of the Eu- ropean Monetary Union. From this perspective, Romania's integration into the European economic and social space has brought not only the need for compliance with requirements of the Communi- ty model but also for implementing monetary policy measures which would facilitate adopting the Euro.

Jean Andrei, Marian Zaharia and Mihaela Cristina Dragoi / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 7-19 The analysis of the relationship between exchange rate volatility of various currencies and the national one constitutes a defining element in understanding the behavior in terms of monetary policy and to outline intervention measures and instruments in order to counteract its effects’ propagation within the national economy. The available literature points out various opinions regarding the characteristics of exchange rates and the determinants of their evolution. Such studies as Chinn and Meese (1995) and Mark (1995) argue that the evolution of the exchange rate can be predicted and somehow managed for long periods of time, while other views (Della Corte and Tsiakas, 2012; Verdelhan, 2013) highlight the randomness of the exchange rate evolution and the impossibility of predicting it. Ahmed, Liu and Valente (2016) analyzing the predictability of the exchange rates using linear factor models in the case of three currency-based risk factors reach the same conclusions expressed in previous studies (Meese and Rogoff, 1983), according to largely fail to outperform the benchmark random walk. On the other hand, Ahmed and Straetmans (2015) try to predict the cyclical behavior of exchange rates by using five risk factors, namely violations of uncovered interest rate parity (UIP), relative purchasing power parity (RPPP), pseudo-parity for equity returns, relative (cross-country) TED spreads and relative term spreads. Their conclusion highlights the usefulness of using financial variables and exchange rates as warning financial indicators for investors and especially for policy makers. On one hand, the exchange rate evolution reflects to a high extent both the effects of monetary policy promoted by the National Bank and the result of government measures taken in order to strengthen fiscal strategy applied in the case of excised products, which depend on the exchange rate, particularly against the EUR, and on the other hand, major implications are also received from main retailers which are forced to make transnational payments in order to pay off capitalized goods within the retail chain. The existence of substantial economic imbalances in the national economy correlated with enhanced effects of the CHF crisis on an important part of bank loans has led to persistent disparities regarding the evolution of the main currencies in the forex market. This situation derives from a multitude of factors that influence aggregate currency demand and supply tending to propagate negative effects on the entire economy. Between January 2008 and October 2015, the exchange rates of EUR, USD, GBP and CHF against RON pointed out developments with alternating increases and decreases spread around upward trends which characterized the period under review taken as a whole. Most empirical studies conducted in this field reveal the existence of diffusion (at least indirect and incomplete) of exchange rate fluctuations within the price mechanism in the national economy. In this context, understanding the trends of the exchange rates of the four currencies taken into analysis – EUR, USD, GBP and CHF – and the factors that influenced their evolution, such as identifying and highlighting possible correlations and quantitative dependencies between the four exchange rates constitute the central objective of this article. The models developed in this paper complement previous research conducted in this field (Berg and Miao, 2010; Ghiba, 2010; Fidrmuc and Horváth, 2008; Nuti 1996 or Babetskii et al., 2004), thus contributing to outspreading this knowledge area by highlighting both the characteristics of the evolution of the four main currencies considered for this analysis and the implication of any factors and quantitative dependencies in determining exchange rates. We contribute to the empirical literature by identifying, building and testing various models type AR (p), MA (q) and ARI- MA (p, i, q). We have organized the paper in four sections. The following section summarizes the methodology employed in the study. Third section called Results and discussions contains the analysis of the characteristics of the trend of exchange rates and the correlations between exchange rate developments as well as a time series analysis. The empirical results are also reported in this section. The last section of the paper is represented by the conclusions. 8

Jean Andrei, Marian Zaharia and Mihaela Cristina Dragoi / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 7-19

1. METHODOLOGY In order to determine the factors that influenced the evolution of exchange rates for the four currencies under analysis and to build econometric models the ANOVA methodology was used (Montgomery, 2001, Moore and McCabe 2003). We have considered it more suitable for this case, although specialized studies on models for determining exchange rates tend to use more frequently the so-called Taylor rule models deriving from Engel and West (2005 and 2006) previous works, which are very popular in this field. The period January 2008 – October 2015 was marked by at least two events that influenced the exchange rates of the main currencies on the Romanian market: the financial and economic crisis that started in late 2008 and CHF exchange rate significant alteration in early 2015. Based on these findings, the empirical model employed in this study aimed at proposing and testing three hypotheses, as follows:  The first hypothesis is based on the argument that rates have been significantly influenced by disturbing factors;  The second hypothesis envisages a development that is not significantly different in the case of the four currencies EUR, USD, GBP and CHF;  The third and final tested hypothesis assumes the existence of significant correlations between EUR, USD, GBP and CHF respectively.

In order to determine and analyze the statistical significance of the trend for the case of all 4 currencies (ER) addressed in this paper, several models were developed under the (1):

2 ER  f t , t  1,1976,  ~ N 0,  . (1) while for emphasizing the quantitative relation between EUR, USD, GBP and CHF, a model has been tested using (2): EUR  f USD,GBP,CHF   (2)

Determining the parameters of models (1) and (2) has been conducted using the least squares method, ANOVA methodology (Montgomery 2001, Moore and McCabe 2003) was used for testing the validity of the acquired models, while the validation of their parameters was achieved using t- Test (2-tailed). For primary regression testing, we employed the Durbin-Watson test (Durbin and Watson, 1951), using the following equation:

n ˆ ˆ 2   t  t1 d  i2 (3) n ˆ 2  t i1 and to eliminate autocorrelation of order p, values  p were identified by applying the least squares method on the following model: p ˆ ˆ  t  c   i  ti  et (4) i1

where et are not auto correlated (Andrei et al. 2008, Săvoiu and Necşulescu 2009). The series EUR, USD, GBP and CHF were analyzed as time series. To test their stationarity Augmented Dickey-Fuller was used with Null Hypothesis: The series has a unit root. For obtaining stationary series the first difference was employed (5):

Jean Andrei, Marian Zaharia and Mihaela Cristina Dragoi / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 7-19

y  yt  yt1  1 Lyt (5)

k In (5), L is backward shift operator in order to achieve L yt  ytk .

For all 4 time series we have tested AR(p), MA(q) and ARIMA(p,i,q). A stationary series yt tZ follows a process AR( p) if the following condition is met (Zaharia and Gogonea, 2009):

p yt  k ytk   t , t  Z (6) k 1

2 2 2 where  t ~ N0,   stationary series, M  t   0 , M  t   and cov( t , i )  0 t  i .

2 p Using lag operator L and denoting by  p L  11L 2 L  p L , formula (6) can be re- written under the form  p Lyt   t , whose characteristic polynomial is:

p p1 p2 P()    1  2     p (7)

The stationary series condition is i  1 i  1, p .

In the case of moving average processes MA(q), the stationary series yt tZ can be represented by (8): 2 yt   t 1 t1  q tq   q L t where t ~ N0,  . (8) In order to process the evolution of the 4 analyzed currencies, after developing stationary data series for EUR, USD, GBP and CHF, ARMA (p, q) models were tested whose general form is (Zaharia and Gogonea, 2009): p q yt  0  i yti   t  i ti (9) i11i In carrying out the analysis contained herein, we have used initial EUR, USD, GBP and CHF data series taken from the database of The National Bank of Romania (The National Bank of Roma- nia, 2015).

2. RESULTS AND DISCUSSIONS

2.1 Characteristics of the exchange rates’ trend

Within the analyzed timeframe the largest fluctuations were recorded by the GBP (Figure 1). On the opposite side in terms of fluctuations’ amplitude was EUR. Regarding the relationship between the exchange rates of the four currencies, from the start of the timeframe up to September 2010, GBP ranks first, with quotations from 4.0777 RON/GBP (30th December 2008, the absolute minimum for GBP) to 5.3722 RON/GBP (29th June 2010), followed by EUR with quotations from 3.4719 RON/EUR (6th August 2008, the absolute minimum for the EUR) to 4.3688 RON/EUR (30th June 2010), USD with quotations from 2.2319 RON/USD (23rd April 2008, the absolute minimum for the USD) to 3.5697 RON/USD (29th June 2010), respectively CHF quotations from 2.1256 RON/CHF (7th August 2008, the absolute minimum for the USD) to 3.3089 RON/CHF (1st July 2010). From October 2010 until the end of the analyzed period, CHF exchange rate outran the USD, evolving between 3.1046 RON/CHF (1st November 2010) and 4.5817 RON/CHF (23rd January 2015, the absolute maximum for CHF), while the USD exchange rate evolved between 2.7408 10

Jean Andrei, Marian Zaharia and Mihaela Cristina Dragoi / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 7-19

RON/USD (29th May 2011) and 4.2107 RON/USD (16th March 2015, the absolute maximum USD). Within the same time frame, GBP quotations evolved between 3.2107 RON/GBP (4th May 2011) and 6.3547 RON/GBP (29th January 2015, the absolute maximum for GBP) while EUR quotations evolved between 4.0735 RON/EUR (24th April 2011) and 4.6481 RON/EUR (3rd August 2012, the absolute maximum for EUR).

Figure 1. Evolution trends of main currencies during 03.01.2008-12.10.2015

6.5 6.0 5.5 5.0 4.5

RON 4.0 3.5 3.0 2.5 2.0 Jan. 2008 Jan. 2009 Jan. 2010 Jan. 2011 Jan. 2012 Jan. 2013 Jan. 2014 Jan. 2015 Jan. Sep. 2008 Sep. 2009 Sep. 2010 Sep. 2011 Sep. 2012 Sep. 2013 Sep. 2014 Sep. 2015 Sep. May. 2008 May. 2009 May. 2010 May. 2011 May. 2012 May. 2013 May. 2014 May. 2015 May. EUR CHF GBP USD

Source: authors based on The National Bank of Romania, 2015

Table 1. Testing the influence of random factors on exchange rate fluctuations

Null Hypothesis (H0): The exchange rate was significantly influenced by random factors Model type: f(t)=a0+a1t Period: 3 January 2008 – 12 October 2015. Number of observations: 1976

Model Coefficients Sign. Lower Upper R2 F Acc Value P-value Acc F 95% 95%

a0 3.91185 0.000 3.89821 3.92549 H1 EUR 0.6286 3341.67 0.000 H1 a1 0.00035 0.000 0.00034 0.00036 H1

a0 2.43029 0.000 2.41238 2.44820 H1 CHF 0.8648 12625.8 0.000 H1 a1 0.00089 0.000 0.00088 0.00092 H1

a0 4.45521 0.000 4.43444 4.47599 H1 GBP 0.7494 5904.41 0.000 H1 a1 0.00071 0.000 0.00069 0.00073 H1

a0 2.64685 0.000 2.62591 2.66779 H1 USD 0.6590 3815.37 0.000 H1 a1 0.00058 0.000 0.00056 0.00059 H1

Source: authors’ own calculation

Jean Andrei, Marian Zaharia and Mihaela Cristina Dragoi / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 7-19 A first examination aimed at emphasizing the characteristics of exchange rates’ developments, expressed in RON for the entire period of time. The null hypothesis was tested regarding the randomness of these developments. In order to test the null hypothesis several type (1) models were settled, whose statistical significance was tested by F test (Table 1). Given that for all 4 currencies under investigation F  F0.05;1;1974  4.84 , respectively Sign.F  0.000    0.05 , the null hypothesis is rejected and the alternative hypothesis (H1) is accepted: the exchange rate was not significantly influenced by random factors. Particularly, trends in exchange rates of EUR, CHF, GBP and USD during the time frame January 2008 – October 2015 turned out to be linear.

Regarding the coefficients a0 and a1 for the 4 models, they are statistically significant (for all P- value=0.00<=0.05). By analyzing the values of the regressors (a1), the CHF recorded the most pronounced evolution (an increase of 0.00089 RON per trading day, in a confidence interval between 0.00088 and 0.00092 RON per day), followed by GBP with a daily increase of 0.00071 RON (the confidence interval being comprised between 0.00069 and 0.00073 RON per day). At the opposite side we find EUR whose average daily increase was 0.00035 RON (2.54 times lower than CHF and 2.03 times lower than GBP), the confidence interval being between 0.00034 and 0.00036 RON. During the same period, USD recorded an average daily increase of 0.00056 RON (1.69 times higher than the EUR, but 1.2 times lower than the GBP and 1.53 times lower than CHF), the confidence interval being situated between 0.00056 and 0.00059 RON . For testing the hypothesis of no significant differences between the EUR, USD, GBP and CHF exchange rates’ evolution during the time frame under review, the dispersion analysis of the 4 data series was conducted, determining variances between groups and with groups, and thus applying the F test. The resulting information is comprised in Table 2, where SS – Sum of Squares, df – De- grees of Freedom, MS – Mean Square, P-value – probability corresponding to the value of F and F crit – critical value for F;k;nk1 .

Table 2. ANOVA table for testing Null Hypothesis: There are no significant differences between the evolutions of EUR, USD, GBP and CHF exchange rates within the period under review

Source of Variation SS df MS F P-value F crit Between Groups 4916.7801 3 1638.9267 8686.7231 0.0000 2.6060 Within Groups 1490.4954 7900 0.1887 Total 6407.2755 7903

Source: authors’ own calculation

Given that the for the chosen significance threshold =0.05, the value of F statistics is much higher than Fcrit  F0.05;3;7900  2.6060 , the null hypothesis (H0) is rejected and the alternative hypothesis (H1) is accepted: there are significant differences between the data series corresponding to analyzed exchange rates. In conclusion, although the exchange rates for the 4 currencies recorded an upward trend, the influence of random factors proving to be insignificant, during January 2008 – October 2015 the evolution of the exchange rates for EUR, USD, GBP and CHF differ significantly.

2.2 Correlations between exchange rates’ fluctuations Based on the series of data containing the exchange rates of EUR, GBP, CHF and USD against RON, over the time interval January 2008 – October 2015, table 3 shows the Pearson correlation coefficients corresponding to exchange rates’ fluctuations. 12

Jean Andrei, Marian Zaharia and Mihaela Cristina Dragoi / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 7-19

A first observation relates to the fact that by testing their statistical significance (t-Test 2-tailed) for the significance threshold =0.005, it results that all are statistically significant (therefore null hypothesis regarding their insignificance was rejected). A second observation relates to their values (r). Except for the relationship between the EUR and GBP for which r=0.702 shows a direct medium correlation, in all other cases the values of correlation coefficients highlight direct correlation of strong intensity. The strongest direct correlation was determined between USD and CHF (r=0.879), which emphasizes that the modification of one’s exchange rate will determine, with 95% probability, the modification in the same direction of the other. Moreover, for the time frame subject to analysis, strong correlations have been determined between CHF and EUR (r=0.870), USD and GBP (r=0.867), GBP and CHF (r=0.857) and also between USD and EUR (r=0.826).

Table 3. Pearson Correlation Coefficients corresponding to the evolution trends of the main currencies during January 2008 – October 2015 and the results of their statistical significance testing

EUR CHF GBP USD EUR Correlation Coefficient 1 Sig. (2-tailed) - CHF Correlation Coefficient 0.870 1 Sig. (2-tailed) 0.000 - GBP Correlation Coefficient 0.702 0.857 1 Sig. (2-tailed) 0.000 0.000 - USD Correlation Coefficient 0.826 0.879 0.867 1 Sig. (2-tailed) 0.000 0.000 0.000 -

Source: authors’ own calculation

To conclude, both the values of correlation coefficients between EUR, CHF, GBP and USD exchange rates (Table 3) and the results of the statistical significance testing confirmed the hypothesis concerning their interactions for the period January 2008 – October 2015. In order to highlight the connection between EUR the other 3 currencies, a linear model was tested using (10):

EUR  c  a1 CHF  a2 GBP  a3 USD   (10) The results obtained by applying the least squares method for determining the parameters (c,a1,a2,a3) and the ANOVA method for validating model (10) are shown in table 4.

Table 4. Results of parameters determination and of linear model (10) testing

Dependent Variable: EUR Method: Least Squares Included observations: 1976 Variable Coefficient Std. Error t-Statistic Prob. C 3.158838 0.032778 96.37084 0.0000 CHF 0.357469 0.010570 33.81916 0.0000 GBP -0.192746 0.011901 -16.19644 0.0000 USD 0.282568 0.014880 18.98968 0.0000 R-squared 0.799894 F-statistic 2627.584 Durbin-Watson stat 0.015848 Prob(F-statistic) 0.000000

Source: authors’ own calculation 13

Jean Andrei, Marian Zaharia and Mihaela Cristina Dragoi / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 7-19 Although both the model (10) and coefficients thereof are statistically valid (Prob(F- statistic)=0.000<=0.05, respectively Prob.=0.000 for all coefficients), the value d=0.015848 (Durbin-Watson test) indicates a strong positive autocorrelation of residual values and therefore the model (10) cannot be used.

Table 5. AR(2) model of error autocorrelation

Dependent Variable: RESID01 Method: Least Squares Included observations: 1974 after adjusting endpoints Variable Coefficient Std. Error t-Statistic Prob. C -0.000998 0.035321 -0.028257 0.9775 AR(1) 1.136575 0.022284 51.00324 0.0000 AR(2) -0.145567 0.022288 -6.531286 0.0000 R-squared 0.984534 F-statistic 62733.18 Durbin-Watson stat 1.987151 Prob(F-statistic) 0.000000

Source: authors’ own calculation

An AR(2) model was used in order to remove autocorrelation, and parameter values

1  1.136575 and  2  0.145587 (Table 5), given that et errors are not auto correlated. Taking this into account and based on data series corresponding to the evolution trends of the EUR, CHF, GBP and USD exchange rates, the series MEUR, MCHF, MGBP and MUSD were generated by applying transformations as (11):

yt  xt  1.13658 xt1  0.145567  xt2  (11) Under these circumstances, with 95% probability, the linear regression model which describes the quantitative dependencies between all 4 currencies taken into analysis (Table 6) is: MEUR  0.023471 0.195492 MCHF  0.128190 MGBP 0.198096 MUSD  (12) Bearing in mind the value of Prob(F-statistic)=0.000<=0.05 it turns out that model (12) is statistically valid. Likewise, the value of Prob=0.000 indicates that all model’s parameters are statistically significant. Parameter values a1=0.195492, b=-0.02758 and c=0,403236 point out which was, over the observed time frame, the effect of modification by 1 RON of the exchange rate of CHF, GBP and USD respectively upon the exchange rate of EUR.

Table 6. The characteristics of the quantitative dependencies’ model between EUR, CHF, GBP and USD exchange rates during January 2008 – October 2015

Dependent Variable: MEUR Method: Least Squares Included observations: 1974 after adjusting endpoints Variable Coefficient Std. Error t-Statistic Prob. C 0.023471 0.000446 52.58829 0.0000 MCHF 0.195492 0.010743 18.19705 0.0000 MGBP 0.128190 0.009024 14.20561 0.0000 MUSD 0.108096 0.012427 8.698562 0.0000 R-squared 0.461440 F-statistic 562.6343 Durbin-Watson stat 1.973466 Prob(F-statistic) 0.000000

Source: authors’ own calculation 14

Jean Andrei, Marian Zaharia and Mihaela Cristina Dragoi / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 7-19

2.3 A time series analysis For obtaining more information regarding the evolution trends of the EUR, CHF, GBP and USD exchange rates in Romania, for the period January 2008 – October 2015, the quotations of the 4 currencies were analyzed as time series. Testing of the stationarity hypothesis has been conducted using Augmented Dickey-Fuller test. For the chosen significance threshold   0.05 , both Prob. values (table) which are strictly higher than 0.05 and the fact that all ADF values are higher than the critical value (-2.862803) lead to accepting the null hypothesis for all 4 data series. Subse- quently, all series are not stationary. Still, we need to make the observation that for the significance threshold   0.1 (Confidence Level 90%), for the EUR data series the stationarity hypothesis was not accepted.

Table 7. The results of applying Augmented Dickey-Fuller test for the EUR, CHF, GBP and USD data series

Null Hypothesis: The series has a unit root Test critical value Series ADF test statistic Prob. Conclusions (5% level) EUR -2.862803 -2.574052 0.0986 EUR has a unit root CHF -2.862803 -1.659365 0.4519 CHF has a unit root GBP -2.862803 -1.434326 0.5668 GBP has a unit root USD -2.862803 -1.628341 0.4678 USD has a unit root

Source: authors’ own calculation

For obtaining stationary series, the transformation provided by equation (5) was used, based on which the series DEUR, DCHF, DGBP and DUSD were generated. Testing their stationarity led to rejecting the null hypothesis (The series has a unit root) and to accepting alternate hypothesis. Therefore, DEUR, DCHF, DGBP and DUSD series are stationary and can be employed in identifying autoregressive and moving average processes which describe the fluctuations of the 4 currencies. Following the testing of several types of models and the analysis of their quality, the AR- MA(3,4) models have been selected for DEUR series. For this particular series we identified an autoregressive and moving average type ARMA(3,4) model. Except for constant C (Table 8) for which Pr ob  0.2328    0.05 and thus the null hypothesis is accepted: constant C is not significantly different than zero, for all other variables the coefficients’ values are statistically significant for the confidence level of 95% (coefficients of AR(3) and MA(1) are statistically significant for a confidence level of 99%).

Table 8. Characteristics of ARMA(3,4) model Dependent Variable: DEUR Method: Least Squares Included observations: 1972 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob. C 0.000417 0.000349 1.193589 0.2328 AR(3) -0.063642 0.022489 -2.829902 0.0047 MA(1) 0.183513 0.022122 8.295312 0.0000 MA(4) -0.053883 0.022120 -2.435971 0.0149 Durbin-Watson stat: 2.007027 F-statistic: 27.07592 Prob(F-statistic): 0.0000 Breusch-Godfrey Serial Corr. LM Test: F-statistic: 0.808657 Probability: 0.4456

Source: authors’ own calculation 15

Jean Andrei, Marian Zaharia and Mihaela Cristina Dragoi / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 7-19 Also, the statistical value DW=2.007027 shows that residual values are not auto correlated, Probability=0.4456 being higher than the significance threshold corresponding to the statistical value Breusch-Godfrey Serial Corr. LM Test emphasizes the absence of serial correlations. Taking all these aspects into consideration, the equation for DEUR model becomes (13):

EURt  0.000417  0.063642  EURt3   t  0.183513 t1  0.053883 t4 for t  5 (13) The moment in time t=5 corresponds to the date of 9th of January 2008.

Based on (13) and on the fact that EURt  EURt  EURt1 , the model which corresponds to RON/EUR exchange rate evolution is ARIMA(3,1,4). The explicit equation of the model for the EUR fluctuations during 9th of January 2008 – 12th of October 2015 is: EUR  0.000417  EUR  0.063642  EUR  0.063642  EUR  t t1 t3 t4 (14)   t  0.183513 t1  0.053883 t4 , for t  5 On the other hand, for the DCHF series the autoregressive and moving average model is type ARMA(1,3). In this case too, the constant C (Table 9) is not statistically significant for a confidence level of 95% ( Pr ob  0.1102    0.05 ). The coefficients of AR(1) and MA(3) variables are statistically significant for a confidence level higher than 99%).

Table 9. Characteristics of ARMA(1,3) model Dependent Variable: DCHF Method: Least Squares Included observations: 1974 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob. C 0.000935 0.000585 1.598203 0.1102 AR(1) 0.117577 0.022379 5.253981 0.0000 MA(3) -0.091962 0.022444 -4.097419 0.0000 Durbin-Watson stat: 2.003628 F-statistic: 21.66278 Prob(F-statistic): 0.0000 Breusch-Godfrey Serial Corr. LM Test: F-statistic: 0.260936 Probability: 0.7704

Source: authors’ own calculation

Therefore, for the DCHF data series the actual equation of the autoregressive and moving average model is:

CHFt  0.000935  0.117577  CHFt1   t  0.091962  t3 for t  4 (15) The moment in time t=4 corresponds to the date of 8th of January 2008. Starting from equation (14) the model which describes the evolution of the RON/CHF exchange rate for the time frame between 8th of January 2008 – 12th of October 2015 is:

CHFt  0.000935 1.117577  CHFt1  0.117577  CHFt2   t  0.091962  t3 for t  4 (16) The autoregressive and moving average model assigned to the evolution of DGBP is type AR- MA(3,1). Again, except for constant C (Table 10) which is not statistically significant for a confidence level of 95% ( Pr ob  0.4669    0.05 ), the coefficients of variables AR(1) and MA(1) are statistically significant for confidence level above 99%, while the coefficient of variable AR(3) is statistically significant for a confidence level of 95%.

Jean Andrei, Marian Zaharia and Mihaela Cristina Dragoi / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 7-19

Table 10. Characteristics of ARMA(3,1) model Dependent Variable: DGBP Method: Least Squares Included observations: 1972 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob. C 0.000575 0.000790 0.727640 0.4669 AR(1) -0.754125 0.182975 -4.121465 0.0000 AR(3) -0.048732 0.020200 -2.412479 0.0159 MA(1) 0.813871 0.177620 4.582088 0.0000 Durbin-Watson stat: 1.981455 F-statistic: 4.4716278 Prob(F-statistic): 0.0028 Breusch-Godfrey Serial Corr. LM Test: F-statistic: 0.437954 Probability: 0.6454

Source: authors’ own calculation

Subsequently, for the DGBP data series, the ARMA(3,1) autoregressive and moving average model becomes (16): GBP  0.000575  0.754125  CHF  0.048732  CHF  t t1 t3 (16)   t  0.813871 t1 , for t  5 The moment t=4 corresponds to the date of 9th of January 2008. Since the model best describing the evolution of the RON/GBP exchange rate is type ARIMA(3,1,1), its explicit form turns out: GBP  0.000575  0.245875  GBP  0.754125  CHF  0.048732  CHF  t t t2 t3 (17)  0.048732  CHFt4   t  0.813871 t1 , for t  5

Table 11. Characteristics of ARMA(10,10) model Dependent Variable: DUSD Method: Least Squares Included observations: 1965 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob. C 0.000690 0.000591 1.167796 0.2430 AR(10) -0.970097 0.009642 -100.6097 0.0000 MA(10) 0.974898 0.011470 84.99207 0.0000 Durbin-Watson stat: 1.945866 F-statistic: 17.49418 Prob(F-statistic): 0.0000 Breusch-Godfrey Serial Corr. LM Test: F-statistic: 1.313398 Probability: 0.2685

Source: authors’ own calculation

Finally, for the RON/USD exchange rate, the data series DUSD is a type ARMA(10,10) autoregressive and moving average model, whose characteristics are presented in table 11. The equation describing this model is:

USDt  0.00069  0.970097  CHFt10   t  0.974898  t10 for t  11 (18) The moment t=11 corresponds to the date of 17th of January 2008. The model characterizing the fluctuation of the RON/USD exchange rate is ARIMA(10,1,10) and for the period 17th of Janu- ary 2008 – 12th of October 2015, the appropriate equation is:

Jean Andrei, Marian Zaharia and Mihaela Cristina Dragoi / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 7-19 USD  0.00069  USD  0.970097  CHF  0.970097  CHF  t t1 t10 t11 (19)   t  0.974898  t10 for t  11 Unlike the autoregressive and moving average processes corresponding to the other 3 currencies, in which cases the impact of previous trends became significant after 1 trading day for CHF and after 3 trading days for EUR and GBP, for the RON/USD exchange rate the time frame is wider (10 trading days). Also, while the time interval taken into calculation for the moving averages was comprised between 1 day (in the case of GBP) and 4 days (for EUR), the case of USD showed a 10 day-interval.

CONCLUSIONS During 2008-2015, economic developments in most EU countries and Romania as well were significantly influenced by the economic crisis which started in the beginning of the proposed period. In Romania, even though at the beginning of 2008 the RON has appreciated against the other currencies subject to analysis, both during the crisis and afterwards the exchange rates presented an increasing trend, thus determining (between July 2008 – May 2015) an almost doubled RON/CHF exchange rate after just an average increase of 0.089 RON per trading day. During the same period, the price of USD had increased by 79%, and GBP and EUR were more expensive by 38% and 25% respectively. Despite all economic oscillations, in Romania, their influence upon the exchange rates of main currencies did not create severe turbulences, the fluctuations associated with ascending trends of the main currencies being insignificant. Even though the RON/CHF exchange rate had 2 sudden increases in August 2011 (when the exchange rate exceeded 4 RON/1 CHF) and in January 2015 (when CHF exceeded 4.5 RON), these did not influence significantly the linear increasing trend observed for the whole period of time. Another characteristic of the evolution of exchange rates in Romania is that although exchange rates for the four currencies recorded an increasing trend, and the correlations between them are significant, the correlation coefficients’ values range from 0.702 (between EUR and GBP) and 0.879 (between USD and CHF), the dispersion analysis pointing out significant differences between their evolutions. This aspect is also underlined by the Durbin-Watson statistical value, corresponding to the first regression model showing the dependence between EUR and the other three currencies (Table 11), highlighting a strong autocorrelation of residual values and the influence of a factor not considered in the analysis carried out on EUR. Eliminating its influence has yielded a valid regression model which emphasizes that for a significance level of 95% the highest pressure on EUR was in fact exercised by CHF (a modification of one monetary unit leading to a change in the same direction of the EUR exchange rate of approximately 0.19 monetary units), followed by GBP and USD. The particularities of the four currencies’ evolution are also stressed by their evolution models as time series. Consequently, the RON/EUR exchange rate developed an ARIMA(3,1,4) model, while the GBP exchange rate an ARIMA(3,1,1) model pointing out that the influence of previous exchange rates occurs after approximately 3 trading days. In the case of CHF, the influence mani- fests much rapidly, the model employed being type ARIMA(1,1,3). Unlike these, the autoregressive and moving average model – ARIMA(10,1,10) – which corresponds to the RON/USD exchange rate entails considering a much wider time frame.

Jean Andrei, Marian Zaharia and Mihaela Cristina Dragoi / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 7-19

REFERENCES Ahmed, J., Straetmans, S. (2015), ”Predicting exchange rate cycles utilizing risk factors”, Journal of Empirical Finance, No. 34, pp. 112–130. Ahmed, S., Liu, X., Valente, G. (2016), “Can currency-based risk factors help forecast exchange rates?,” International Journal of Forecasting, Vol. 32, No. 1, pp. 75–97. Andrei, T., Stancu, S., Iacob, A. I., Tuşa, E. (2008), Introduction to Econometrics Using Eviews, Eco- nomic Publishing House, Bucharest, (in Romanian). Babetskii, I., Boone, L. Maurel, M. (2004), “Exchange rate regimes and shocks asymmetry: the case of the accession countries,” Journal of Comparative Economics, Vol. 32, No. 2, pp. 212– 229. Berg, A., Miao Y. (2010), “The Real Exchange Rate and Growth Revisited: The Washington Consen- sus Strikes Back?”, IMF Working Paper, WP/10/58, Washington, D.C. Chinn, M. D., Meese, R. A. (1995), ”Banking on currency forecasts: How predictable is change in money?”, Journal of International Economics, Vol. 38, No. 1-2, pp. 161–178. Della Corte, P., Tsiakas, I. (2012), ”Statistical and economic methods for evaluating exchange rate predictability” in J. James, I. W. Marsh & L. Sarno (Eds.), Handbook of exchange rates, John Wiley & Sons, Hoboken, pp. 221-264. Durbin, J., Watson, G. S. (1951), ”Testing for Serial Correlation in Least Squares Regression”, II. Biometrika, Vol. 38, No. 1–2, pp. 159–179. Engel, C., West, K., (2005), ”Exchange rates and fundamentals”, Journal of Political Economy, Vol. 113, No. 3, pp. 485-517. Engel, C., West, K. D.(2006), “Taylor rules and the deutschmark–dollar real exchange rate”, Jour- nal of Money, Credit, Bank, No. 38, pp. 1175–1194. Fidrmuc, J., Horváth, R. (2008), ”Volatility of exchange rates in selected new EU members: Evi- dence from daily data”, Economic Systems, Vol. 32, No. 1, pp. 103-118. Ghiba, N. (2010). ”Implications of exchange rate volatility on international trade (The case of Ro- mania)”, Munich Personal RePec Archive. Available at: http://mpra.ub.uni-muenchen.de/ 28453/ (Accessed: February 10, 2017). Mark, N.C. (1995), ”Exchange rates and fundamentals: Evidence on long-horizon predictability”, American Economic Review, Vol. 85, No. 1, pp. 201–218. Meese, R. A., Rogoff, K. (1983), ”Empirical exchange rate models of the seventies: Do they fit out of sample?”, Journal of International Economics, No. 14, pp. 3–24. Montgomery, D.C. (2001), Design and Analysis of Experiments, 5th ed., John Wiley & Sons, New York. Moore, D. S., McCabe, G. P. (2003), Introduction to the Practice of Statistics, 4th ed., W. H. Free- man & Co, New York. Nuti, D. M. (1996), ”Inflation, interest and exchange rates in the transition”, Economics of Transi- tion, Vol. 4, No. 1, pp. 137-158. Săvoiu, Gh., Necşulescu, C. (2009), Econometrics, University Publishing House, Bucharest (in Ro- manian). The National Bank of Romania (2016), “Exchange rates”. Available at: http://www.bnr.ro/ Ex- change- rates- 1224.aspx (Accessed: October 12, 2017). Verdelhan, A. (2013), ”The share of systematic variation in bilateral exchange rates”. Working paper, MIT Sloan School of Management. Available at: https://www.ecb.europa.eu/events/ pdf/conferences/130627/2.1a_A.Verdelhan_Paper.pdf?69013638c4b84e5f3c7e364fef9f3f 65 (Accessed: December 19, 2017). Zaharia, M., Gogonea, R.-M. (2009), “Econometrics. Fundamental elements”, University Publishing House, Bucharest (in Romanian).

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40

Montenegrin Journal of Economics

Vol. 14, No. 2 (2018), 21-40 ‘

Risk Management of Dollarization in Banking: Case of Post-Soviet Countries

ANDRII KAMINSKYI1, and NATALIIA VERSAL2

1 Professor, Faculty of Economics, Department of Economic Cybernetics, Taras Shevchenko National University of Kyiv, Ukraine, Email: [email protected] 2 Associate Professor, Faculty of Economics, Department of Insurance, Banking and Risk-Management, Taras Shevchenko National University of Kyiv, Ukraine, Email: [email protected]

ARTICLE INFO ABSTRACT Received February 07, 2018 Dollarization of bank assets and liabilities is a typical phenomenon Revised from March 19, 2018 with multi-faceted effects. Primarily, this phenomenon raises risks of Accepted May 23, 2018 imbalance in assets and liabilities following a sharp local currency Available online June 15, 2018 devaluation. However, dollarization may deliver additional returns. This calls for development and implementation of strategies for dollarization risk management. This study aims at designing risk JEL classification: management strategies for ensuring optimal currency structure of F31, G01, G21. loan and deposit portfolio under an extremely high level of devaluation risk in six post-Soviet countries. The study is based on risk DOI: 10.14254/1800-5845/2018.14-2.2 modeling methods and asset liability management techniques. The value-at-risk methodology is applied to measure and identify main Keywords: risks banks face under dollarization. Two types of risk are identified: the strategic risk measured as a ratio of deposits in foreign curren- financial dollarization, cy; and the tactical risk measured as a ratio of foreign currency banking, deposits transformed into local currency loans. Suggested effective post-Soviet countries, risk management strategies are based on the optimization of these currency shocks. two types of risks. The first strategy deals with deposit de- dollarization and non-transformation of foreign currency deposits into local currency loans. Under the second strategy, increased deposit dollarization is complemented with non-transformation of foreign currency deposits into local currency loans. The third strategy involves deposit de-dollarization and conducting transformation of foreign currency deposits into local currency loans. Each strategy entails maximization of return with subsequent minimization of risk. The study concludes that the first strategy is appropriate for Ukraine and Belarus, the second one suits Azerbaijan and Moldova, and the third one fits Armenia and Georgia.

INTRODUCTION Financial dollarization in transition economies is quite an ambiguous matter. The issue of financial dollarization is a complex one since there is no clear answer whether this phenomenon is positive or negative and whether it is a source of problems in the economy or a solution to those.

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40 Under this phenomenon, a significant part of the liabilities and assets of financial institutions is presented in dollars (or, generally, in any stable currency). The existence and growth of dollarization phenomenon result from the lack of confidence in local currency due to risks of devaluation and inflation. Economic agents are afraid to keep savings in local currency because of possible loss of value. This leads to the trend of presenting a part of liabilities of financial institutions in foreign currency. These may include foreign currency deposits in banks, investment fund portfolios, etc. Moreover, insurance and pension savings in foreign currency contribute to financial institution’s liabilities presented in foreign currency. Hence, financial intermediaries face the dollarized liabilities (Ize and Levy Yeyati, 1998, 2003). Economic logic also implies the appropriateness of forming the assets of financial intermediaries in foreign currency. Inflation and devaluation risks, as well as dollarized liabilities stimulate this tendency. Bank’s loan portfolios in local currency may be unprofitable because the profit can be "eaten up" by inflation. Nevertheless, for financial intermediaries having assets in local currency has some benefits from the return point of view (Kutan, Ozsoz, and Rengifo, 2012). Return on local currency assets includes, as a rule, high premiums for inflation and devaluation risks. It significantly exceeds the return in dollars, sometimes by several times. For example, а bank forms a significant part of its deposit portfolio in dollars, attracted at a relatively low-interest rate. Then, transforming the raised funds into local currency, it issues loans in local currency at an extremely high- interest rate. If marginal revenue exceeds losses from possible devaluation/inflation, then it becomes a profitable business for a bank. Thus, dollarization raises risk-return correspondence for financial institutions. Consequently, the question about risk management naturally appears. How can one build effective risk management for a financial institution in conditions of high dollarization? What is the optimal ratio of liabilities in different currencies and, in turn, assets in different currencies? This article discusses these questions. The goal of our study is to highlight the possibility of using of CVaR modelling for optimal currency structuring of a bank’s loan and deposit portfolio under devaluation risk in transition economies. We focus mainly on the bank’s risk management. The reason is that dollarization is the pre- rogative of financial systems in emerging markets. In these markets, banking segment essentially dominates the structure of financial systems (Cihak et al., 2012). We believe that putting up an effective risk management system with regard to dollarization is an urgent task for banks operating in countries where its level is high. Conceptually, we suggest an approach to risk management built around its classical interpretation: risk identification, risk analysis, its measurement, decision justification, and monitoring. At the same time, for each of these components, we introduce specific elements associated with dollarization. Thus, within risk identification, we distinguish two types of risks - strategic and tactical. Strategic risk is related to the bank's strategy in attracting dollar deposits. The tactical risk stems from the bank’s decision on converting dollar deposits into loans in local currency. In our view, these two risks form the bank's general dollarization risk, formally expressed as the risk of total return of bank. Such returns have specific probability distributions characterized by long and heavy tails. Therefore, we propose to use Conditional Value-at-Risk (CVaR) as a risk measure in our model. The risk-return correspondence analysis leads to the optimal solution. The model is tested out in six transition countries (Armenia, Azerbaijan, Belarus, Georgia, Moldova, and Ukraine). The analysis encompasses the period from 2008 to 2017. These countries are chosen due to four main reasons. Firstly, they underwent a severe devaluation and it was one of the main shocks in these countries during the analyzed period. These countries faced two waves of currency devaluation during the analyzed period: the first resulting from the global financial crisis and the second resulting from the economic and political crises in the post-Soviet area (Figure 1).

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40

Figure 1. Devaluation/appreciation of local currencies in selected countries, %

Source: Authors’ calculation based on the official exchange rate (LCU per US$, period average) according to data gathered from the World Bank.

Secondly, these countries are characterized by high level of financial dollarization. We can observe a drastic increase in deposit (foreign currency deposits as a percent of total deposits - DDI) and loan (foreign currency loans as a percent of total loans - LDI) dollarization indices in the aftermath of the global financial crisis and economic and political problems of 2014-2016 years in selected countries. Both surges in financial dollarization were provoked by the high level of local currency devaluation shown in Figure 2. It had a serious impact on the structure of the bank balance sheet and resulted in the appearance of currency mismatches.

Figure 2. Trends in financial dollarization in selected countries

Trends of DDI, LDI, and ER, Ukraine Trends of DDI, LDI, and ER, Armenia

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40 Trends of DDI, LDI, and ER, Georgia Trends of DDI, LDI, and ER, Azerbaijan

Trends of DDI, LDI, and ER, Belarus Trends of DDI, LDI, and ER, Moldova

Source: Authors’ calculation according to data gathered from the statistical bulletins of central banks in selected countries

Thirdly, the consequences of dollarization for banking system were not discussed under the issue of risk-return correspondence in case of a mismatch of the loan and deposit dollarization in these countries. Finally, they represent the post-Soviet countries from Eastern Europe and the Caucasus and the research of financial dollarization in the countries of this region has been limited over recent years. Our findings are structuring bank’s behaviour into three strategies based on the proposed model. The first strategy concentrates on the deposit de-dollarization and decreasing (not conducting) transformation of foreign currency deposits into local currency loans. Under the second strategy, we consider the idea based on the increase of deposit dollarization and also decreasing (not conducting) transformation of foreign currency deposits into local currency loans. The third strategy entails the deposit de-dollarization and conducting transformation of foreign currency deposits into local currency loans. The application of the proposed model, which is based on data on the interest rates on deposits and loans in local and foreign currency, as well as the foreign exchange rates, allowed us to identify the appropriate strategies for the banks in analysed countries. Based on the data of deposit dollarization as of the end of 2016, the model shows that the first strategy could be used by banks in Ukraine and Belarus, the second strategy – by banks in Azerbaijan and Moldova, 24

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40 and the third strategy – by banks in Armenia and Georgia. The specificity of balancing of strategic and tactical risks is determined by input data to the model. The structure of this paper is as follows. Section 2 provides a literature review. Section 3 describes the analysed data and considers the methodological approaches to research. Section 4 presents analysis of the suggested model. Section 5 deals with the findings of the study on the case of selected countries. Section 6 is devoted to further considerations and discussion. Section 7 provides some general conclusions.

1. LITERATURE REVIEW Traditionally, when we mention financial dollarization we should distinguish between two of its components: deposit and loan dollarization. When looking into deposit dollarization, there is an implication of supply of deposits owners. Economic agents consider foreign currency to be a reliable asset worth of investing money in (Duffy, Nikitin and Smith, 2006). Main reasons for deposit dollarization in emerging markets can be explained by hysteresis or ratchet effect due to high inflation, exchange rate volatility, interest rates volatility, etc. J. Mongardini and J. Mueller (1999) analyse ratchet effects in currency substitution model on the example of the Kyrgyz Republic during 1993 – 1998. They conclude that the devaluation of local currency is the main factor in the growth of the consumer substitution ratio and that the interest rates on the deposits in local currencies are not high enough to ensure better profitability than the deposits in foreign currency. M. Brown and H. Stix (2015) deepen understanding of the issue of deposit dollarization by means of analysing households’ behaviour under inflation expectations, devaluation expectations and negative experience with the local currencies in the past. They underline that experience of Eastern Europe countries shows the inevitability of deposit dollarization. P. Honohan and A. Shi (2002) explore the links between deposit dollarization and the pass-through of exchange rate changes, the ratchet effect, the volume and currency structure of bank lending in 58 emerging countries during 1990 – 2000. Nevertheless, some of these issues remain ongoing. For example, authors underline, that the links between the growth of dollarization and profitability of banks remain unclear, especially under existing of indirect currency risk for lenders. Also, banks in emerging markets do not have instruments for foreign exchange rate risk hedging. It means that the bank balance structuring is one of the very few instruments of protection against dramatic devaluation of local currencies. The other factors affecting deposit dollarization are a currency risk premium, a money flow from abroad (foreign direct investments, remittances etc.), currency competition in value storing etc. Thus, dollarization of deposits inevitably develops in banking systems of emerging markets. Moreover, according to Honohan and Shi (2002) and G. De Nicolo et al. (2005), it leads to specific risks that require clear identification and appropriate analysis, evaluation and managing. In turn, loan dollarization occurs as the result of deposit dollarization (Luca and Petrova, 2008), high inflation expectations under low volatility of exchange rate (Luca and Petrova, 2008; Brown and De Haas, 2012), currency competition in interest rates, etc. It should be pointed out, that loan dollarization is rarely explored as an autonomous phenomenon. It is especially applicable to banking activity, because loan dollarization in banks’ balance sheet is closely linked to deposit dollarization, and usually this linkage brings to currency mismatch. This raises the question of optimal bank balance sheet structuring in terms of risk-return correspondence, especially in the case of sharp devaluation of local currencies under a high level of financial dollarization. It is important to note that currency shock for the emerging markets is particularly painful because banks get used to working under a fixed exchange rate for a relatively long time. Moreover, on the eve of the crisis, the analysis of macroeconomic indicators showed a high probability of devaluation, but banks failed to predict its level. In fact, W. Choi and D. Cook (2004) note that under a fixed exchange rate, bank balance sheets are more stable in the conditions of dollarization. They have been right and that has become especially obvious when we observe the situation in coun- 25

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40 tries we analysed. H. Basso, O. Calvo-Gonzalez, and M. Jurgilas (2011) provide an analysis of deposit and loan dollarization in transition countries during 2000 - 2006 and conclude that the indices of deposit and loan dollarization do not always closely correlate, and that interest rates play a special role in decisions on the formation and allocation of funds in local or foreign currency. K. Neanidis and C. Savva, (2009) note, that deposit and loan dollarization is a way to minimize portfolio risk and analyse the problem in terms of both depositors and banks in the short term period. Research by A. Ize and E. Levy Yeyati (2003) proposes a portfolio model, which represents the idea of currency composition in assets and liabilities of banks. Such considerations have formed one of the pillars of our study. Also, A. Armas, A. Ize and E. Levy Yeyati (2006) mention return volatility – credit risk correspondence depending on currency resilience. We attempt to use this concept in developing dollarization risk management tools for the banking sector. Also, I. Marcelin and I. Mathur (2016) examine effects of dollarization in countries with high dollarization level and conclude that currency risk devaluation is crucial in terms of risk-return correspondence in banks, especially under assets and liabilities currency mismatch of banks customers. The consequences of financial dollarization need to be evaluated, that is why we consider the risk-return correspondence in the situation of high volatility of foreign exchange rates in selected highly dollarized post-Soviet countries. It has transpired that there have been only a few country-specific research papers that describe the trends in the financial dollarization on the eve and in the aftermath of the global financial crisis, and also provide the solution to the currency mismatch issues. N. Versal and A. Stavyt- skyy (2015) conduct analysis of deposit and loan dollarization in Ukraine during 2005 – 2014 and propose the model, which explains household decision making on deposit placing in the banks under the high level of dollarization, inflation expectations, and foreign exchange vulnerabilities. A. Skrypnyk and M. Nehrey (2015) propose a model of optimal currency structure of deposit portfolio under exchange rate instability. They verify this model on historical data of Ukraine in 2002 – 2007. O. Loiseau-Aslanidi (2012) suggests using a money-in-utility function model for dollarization evaluating and approves it on monthly data from Georgia during 1996 – 2007. The results show an important role of the exchange rate, interest rates on foreign and domestic currencies time deposits, and domestic and foreign inflation. The review of the literature and the results obtained by the scholars prompted us to conduct a study that would clarify some aspects of currency structure of bank’s loan and deposit portfolio under high-level dollarization, sharp devaluation of local currencies and existing interest rates in six post-Soviet countries from Eastern Europe and the Caucasus. This means that banks should find possibilities to respond to challenges of deposit as well as loan dollarization under poor access to hedging instruments. We would like to test two main hypotheses: i. The risk-return correspondence of dollarization phenomenon can be effectively represented by the application of two criteria to the general model of a bank’s expected profit/losses distribution. One criterion is the minimisation of risk measured by CVaR. The risk measure CVaR is significant. Different aspects of this measure are analysed in G. Scego (2004). Another criterion is the maximization of expected return. ii. The essential grounding of risk-management of dollarization lies in considering strategic risk (presented by a ratio of deposits in foreign currency) and tactical risk (identified through a ratio of foreign currency deposits transformed into local currency loans and vice versa).

2. DATA AND METHODOLOGY

2.1 Data In the study, we use data from six countries. They include three parameters that are included in the proposed risk management system. 26

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40

The first parameter – the deposit dollarization data (the percentage of deposits attracted by banks in foreign currency). Data show that the deposit portfolio of a bank contains a significant part in foreign currency. This is shown in Figure 2. Apparently, in four countries, the level of dollarization at the end of 2016 exceeds 60%, and in two countries, it is within the range of 45-55%. The second parameter – the loan interest rates data. These rates determine the profitability of lending activities of banks in national and foreign currencies, as well as the yield from the transformation of deposits in one currency into loans in another currency. We used data from the statistical bulletins of central banks of selected countries. The completeness of these data is the subject of discussion. One should note that in reality, lending has a number of special features in selected countries. First, the conditions of loans are not transparent due to latent interests, fees, insuranc- es, etc. That is why the interest rates data from central banks are not fully representative because they do not include all these payments. Second, interest rates vary for different types of loans. In particular, interest rates on loans to corporations and to households can differ significantly. With regard to deposits, it should be stressed that the income from them, as a rule, is subject to taxa- tion and therefore the real interest rate may be lower. Nevertheless, we chose data from the statistical bulletins of central banks of the selected countries because of their comparability. The third parameter – the foreign exchange rates data. We used exchange rates for the period from January 2008 to December 2016 (96 months). The ratio formes the basis for our analysis. These calculations indicate probability distribution functions (PDF) which are asymmetric and have right-skewed tails. Figure 3 displays PDF values for monthly average data. It is obvious that all graphs illustrate the long tails of PDF. This explains the logic for introducing CvaR (Conditional Value-at-Risk) as a quantitative measure of risk.

Figure 3. Empirical PDF for currencies ratios in selected countries

Empirical PDF for Armenian Currency Empirical PDF for Azerbaijani Currency

Empirical PDF for Belarusian Currency Empirical PDF for Georgian Currency

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40 Empirical PDF for Moldovan Currency Empirical PDF for Ukrainian Currency

Source: Authors’ calculation according to data gathered from the statistical bulletins of central banks in selected countries

2.2 Methodology Bank attracts deposits in local and foreign (dominated by US dollars) currencies and issues loans in local and foreign currencies. Bank’s depositors use potentials of both currencies. Deposits in local currency are characterized by high deposit rates and, simultaneously, high risk of possible depreciation. Contrariwise, deposits in dollars are characterized by low deposit rates and re- sistance to depreciation risk. Simultaneously, bank issues loans in both currencies. Granting loans in local currency is attractive because loan rate is high whereas loans in dollars have a lower rate. Therefore, banks need to consider the issue of ensuring an optimal mix of currencies on both sides of the balance sheet. We propose the following formalization of this problem and management approach. Let us consider one-time interval, for instance, of one year. At the beginning of interval , bank attracts deposits in both currencies and immediately issues loans, also, in both currencies. Currencies exchange rate is . At the end of period , bank plans to receive payments from borrowers and pay back deposit payments. The exchange rate at period will be , which is a random variable with some probability distribution. Assumption 1. Bank’s goal is to maximize total return on local currency. Assumption 2. Bank wants to measure risk in an adequate way. Assumption 3. Optimisation should be considered from the “risk-return correspondence” standpoint. Additional assumptions have technical character. Reserve ratio is 0%. All interest payment will be paid at period . There are no restrictions on loan granting. There are no fees or commissions either. We denote volumes of attracted deposits in local and foreign currencies as and . De- posit interest rates are denoted as and . Naturally, .

Volumes of loans granted are denoted as (in the local currency) and (in the foreign currency). Credit interest rate in local currency is higher than the similar rate in the foreign currency .

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40

Our first suggestion for the model to be constructed concerns attracted money in local currency. It is logical to transform all volume into the loans in local currency taking into account that and . Moreover, as long as a bank does so, the dollarization risk is not present. The second suggestion is to divide attracted deposits in foreign currency according to a certain proportion where indicates part of to be transformed into loans in foreign currency, and indicates part of to be converted (by exchange rate into the local currency and transformed into loans in local currency. Differences in these two approaches constitute a basic case of risk-return correspondence. Indeed, is low return and no direct currency exchange risk, while is high return but highly prone to currency exchange rate. Let us analyse risks and returns at abovementioned three parts. Net profit from borrowing and lending in local currency at the time will be:

where is the bad rate for loans in local currency. The bad rate is defined as the annual percentage of expected losses for a diversified credit portfolio (it is a little different from classical bad rate presented in percentage of defaulted loans). Recovery rate is not included in the . According to our first suggestion , and we can rewrite formula 1 as:

where . It is useful to note that depends on the currency exchange rate. Since a drop in exchange rate usually reflects some problems in economy, the may increase. Assume that ( ) is a part of attracted deposits in foreign currency transformed in- to loans in foreign currency. The formula for profit in foreign currency is the following: , (3)

where is the bad rate for loans in foreign currency. It is useful to note that also depends on the currency exchange rate. Part of borrowers receive their incomes in local currency and depreciation of local currency increases debt pressure and raises non-performance. Return for deposit-lending activity in foreign currency is as follows: (4) Expression of (4) in local currency will essentially depend on currency exchange rate at period :

(5)

Therefore, a higher exchange rate has two implications: higher return under depreciation and lower return under increased bad rate. The remainder of deposits in foreign currency is transformed into loans in local currency at : ( ) where the interest rate is . The main economic logic for this is the higher level of than . 29

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40 Net profit (expressed in local currency) from transforming deposits in foreign currency into loans in local currency ( , is:

(6)

Therefore, the return from this operation will be:

(7)

This expression is crucial for understanding risk-return correspondence under dollarization. If , then profit may be high because of . By the other way, if , then losses may be high. Aggregated financial results, expressed in local currency, equal to: (8) Deeper insight into “generating return” implies decomposing return into three generators: ; and . The weights of each amount in the structure of bank’s attracted money are equal:

(9)

(10)

(11)

Consequently, the total return on local currency will be: . , are random variables.

2.3 Risk management model The variability of in our risk management model is identified as the risk of dollarization. The risk escalates under the local currency devaluation and is dependent on deposit dollarization level in a bank. Indeed, the values of depend on the percentage of dollar deposits in total deposits (denote it d) and on the percentage of their transformation into the local currency ( ). The level d will be called a strategic risk, and – a tactical risk. The risk management of dollarization is carried out on the base of the bank's choice of d-values and -values. Also, we assume that the bad rate is zero. This assumption is used to focus on the risk associated with the transformation of foreign deposits into local loans. As a measure of risk, we propose Conditional Value at Risk for variable C . – the period of modelling (1 year), – confidence level (95%). Details about CVaR are presented in G. Scego (2004), as an example. There are several reasons for our choice of such measure of a risk. Firstly, it is logical to use the Value-at-Risk methodology, which is widely used for modelling marketing risks in the banking. This measure is proposed by Basel Committee on Banking Supervision. Secondly, the nature of dollarization risks in emerging markets is characterized by a long and heavy right-skewed tail of probability distributions of exchange rates

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40

. These risk characteristics are effectively measured by Value-at-Risk and Conditional Value-at- Risk. For this reason, we choose these measures for Assumption 2. The risk management model implies a restriction on C : C and maximization of expected value under this condition. Reduced restriction on C generates the area of and , which outline set for maximization of return. This area is pictured in grey on the picure OAF (Figure 4).

Figure 4. Risk estimation in model of risk management

Strategic risk

Tactical risk

The maximum value of profitability is determined by the ratio between interest rates and expected exchange rates. Optimal value by strategic risk is connected with correspondence between

and . If the first value is greater, it is logical to reduce the level of dollarization, if the second one is greater, then the level of dollarization should be increased. So, it will tend to the 0% or to the 100%. An optimal level of tactical risk is defined by the correspondence between which denotes expected return of dollarization procedure and

. If the first term is higher, it will stimulate transformation of dollar deposits to local currency loans. If the second term is higher, it will lead to loan granting in dollars. The widespread case is characterized by some (approximately stable) level of dollarization of deposits . The optimization, in this case, is characterized by segment . The optimal solution

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40 will be point or point . The points are defined by correspondence between abovementioned terms. Therefore, the risk management model based on CVaR measure provides optimization problem in the grey area. The optimal solution is attained by a combination of strategic and tactical risks .

3. APPLICATION OF THE MODEL TO POST-SOVIET COUNTRIES We applied the constructed model of risk management to six post-Soviet economies: Armenia, Azerbaijan, Belarus, Georgia, Moldova, and Ukraine. In these countries, the high level of dollarization and the issues with managing it are relevant both for bank management and for regulators. We used the data of the central banks in these countries from 2010 to 2017. As the parameter for the exchange rate, we used the ratio , where the exchange rate of the local currency against the foreign currency for a certain day from the specified interval, and - the rate one year after the .

The ratio is close to 1 when the exchange rate is stable and higher than 1 if local currency edges lower. The base period is the period "0", i.e. calculations were made "ahead". This approach is explained by the logic of the model, which is the assessment of profitability and risk for the year ahead. Table 1 illustrates basic statistical indicators of historical values of in considered countries.

Table 1. Statistical indicators for exchange rates for currencies in selected countries

Armenia Azerbaijan Belarus Georgia Moldova Ukraine Range 0,334 1,0932 1,8994 0,48319 0,4975 1,839 Mean 1,0615 1,1045 1,3734 1,065 1,0907 1,2604 Variance 0,00765 0,04909 0,19412 0,01244 0,01362 0,12656 Std. Deviation 0,08744 0,22156 0,44059 0,11155 0,11669 0,35575 Coef. of Variation 0,08237 0,2006 0,32081 0,10475 0,10698 0,28225 Std. Error 0,00892 0,02261 0,04497 0,01139 0,01191 0,03631 Skewness 0,81165 1,9882 2,0977 1,0772 1,0157 1,6185 Excess Kurtosis -0,41506 3,6543 4,139 0,70942 0,67313 3,2163

Source: Authors’ calculation according to data gathered from the statistical bulletins of central banks of the selected countries

As one can see, distributions of have positive skewness and excessive kurtosis (except for Armenia). Positive skewness is the indicator of a right tail and positive excess kurtosis is the indicator of “heavy” tail. Values of VaR, CVaR and correspondence between them based on historical values for the analysed period are presented in Figure 5. The analysis shows two countries with the essentially high difference in these values, namely Belarus and Ukraine. Ratios CVaR/VaR for them are 1,45 and 1,18 correspondingly. This is an additional indicator, characterized by heavy tails for probability distribution functions (PDF).

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40

Figure 5. VaR and CvaR for

Source: Authors’ calculation according to data gathered from the statistical bulletins of central banks in selected countries

Our calculations for strategic and tactical risks following considered model are based on indicators of deposit interest rates and loan interest rates in both currencies. We use deposit interest rates and loan interest rates as the average values in the 2016 year. values are estimated on monthly data for period Jan 2008 – Dec 2016 ( runs from Jan 2008 – Dec 2015, runs from Jan 2009 – Dec 2016). The results are shown in Table 2.

Table 2. Strategic and tactical risks and dollarization management

Strategic risk Tactical risk

Country Dollarizati Transformation on dollar deposits

into local currency loans Armenia 0,0574 0,039 Decrease 1,076 1,062 Increase Azerbaijan 0,0498 0,0525 Increase 1,019 1,105 Decrease Belarus 0,0945 0,0781 Decrease 1,147 1,373 Decrease Georgia 0,102 0,0605 Decrease 1,093 1,065 Increase Moldova 0,0358 0,0415 Increase 1,079 1,091 Decrease Ukraine 0,077 0,0506 Decrease 1,097 1,260 Decrease

Source: Authors’ calculation according to data gathered from the statistical bulletins of central banks in selected countries

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40 Following the results of the model application, it is reasonable to divide markets into two groups according to the management of strategic risk (Table 2). Countries from the first group (Ar- menia, Belarus, Georgia, and Ukraine) should decrease deposit and loan dollarisation because the interest margin in local currency (the difference between interest received on local currency loans and interest paid on local currency deposits) is higher than the interest margin in foreign currency (the difference between interest received on foreign currency loans and interest paid on foreign currency deposits) corrected on the expected value of and denominated in local currency. Countries from the second group (Azerbaijan and Moldova) should, on the contrary, increase the deposit and loan dollarization due to higher interest margin in foreign currency. Also, the risk premium in lending interest rates in local currency does not cover the devaluation risk. Based on the tactical risk, we can also divide countries into two groups. The first group (Azer- baijan, Belarus, Moldova, and Ukraine) is characterized by a decrease in transformation of foreign currency deposits into local currency loans. The explanation is based on the fact that expected value of is higher than the ratio of lending rates in local currency. The second group (Armenia and Georgia) is characterized by the logic of increasing transformation of foreign currency deposits into local currency loans. Thus, we see that, based on a combination of strategic and tactical risk, the banks of the analysed countries can choose the following strategies. The first strategy, which involves reducing the dollarization of deposits and reducing (not conducting) the transformation of deposits in foreign currency into loans in national currency, is appropriate for Belarus and Ukraine. These countries are characterized by a decrease of both indicators – the decrease of deposit dollarization and the decrease of the transformation of foreign currency deposits into local currency loans. This can be explained by two factors. The first factor is the high expected value of - 1,3734 for Belarus and 1,2604 for Ukraine. This is a reason for excessive risk of transforming dollar deposits into loans in local currency. The second factor is connected with relatively high returns on local currency loans due to strong demand for such loans and a risk premium for the devaluation of the local currency. Thus, these countries have unique opportunities for using lending in local currency due to high- interest rates on such loans. The second strategy, which implies an increase in the dollarization of deposits and the reduction (not conducting) of the transformation of deposits in foreign currency into loans in local currency, is suitable for banks in Azerbaijan and Moldova. The interest margin in foreign currency corrected on the expected value of is higher than the interest margin in local currency, or, more simply, it is better to do banking in foreign currency because the devaluation of local currency provides a better return for this case. The third strategy involves reducing the deposit dollarization and at the same time increasing (conducting) the transformation of deposits in foreign currency into loans in local currency. This strategy is suitable for banks in Armenia and Georgia. This is possible due to high-interest rates on loans in local currency, which cover the risk of its devaluation, and low-interest rates on deposits in foreign currency. The difference between strategic and tactical risks is essential. Strategic risks are connected with the strategy of attracting deposits in foreign currency. Hence, there may be some contradic- tion with the tendency of supplying deposits. In turn, the tactical risk is determined exclusively by risk-return correspondence. Taking into account this distinction, we can assume that ratio of deposits in foreign currency is determined by “external” factors. Thus, we have a predetermined dollarization level. The case with predetermined dollarization level is shown in Table 3 and Table 4, which provide the values of and , calculated for the existing levels of dollarization in the selected countries. Graphical interpretation is also given below (Figure 6). 34

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40

Table 3. for the existing levels of dollarization in selected countries

Expected Dollarization Tactical risk ( ) Return level ) 0% 20% 40% 60% 80% 100%

Armenia 60,30% 4,64% 4,86% 5,08% 5,30% 5,51% 5,73% Azerbaijan 70,70% 5,17% 4,34% 3,51% 2,67% 1,84% 1,01% Belarus 70,10% 8,30% 5,29% 2,28% -0,73% -3,74% -6,75% Georgia 72,50% 7,20% 7,81% 8,43% 9,05% 9,67% 10,29% Moldova 51,30% 3,86% 3,69% 3,52% 3,35% 3,19% 3,02% Ukraine 46,30% 6,48% 4,79% 3,10% 1,41% -0,28% -1,97%

Source: Authors’ calculation according to data gathered from the statistical bulletins of central banks in selected countries

The illustration of finding the optimal level of transformation of dollar deposits into local currency loans can be effectively seen in Tables 3 and 4. Indeed, the countries form group 1 according to tactical risk (Azerbaijan, Belarus, Moldova, and Ukraine) indicate minimization of such transformation. The banks from countries presented in group 2 according to tactical risk (Armenia and Georgia) should choose an acceptable level of risk and find point (see Fig.5). Therefore, for example, if we choose CvaR with 0%, the solution (point C at the Fig.4) will be 40% for Armenia and Georgia. If we choose CVaR -6% it will be 100% for Armenia and 80% for Georgia.

Table 4. for the existing levels of dollarization in selected countries

Dollarization Tactical risk ( ) Risk level ) 0% 20% 40% 60% 80% 100%

Armenia 60,30% 4,39% 2,70% 0,31% -2,08% -4,47% -6,86% Azerbaijan 70,70% 4,77% -3,43% -14,24% -25,06% -35,87% -46,68% Belarus 70,10% 6,82% -8,40% -30,09% -51,79% -73,48% -95,18% Georgia 72,50% 6,58% 4,70% 1,10% -2,51% -6,11% -9,71% Moldova 51,30% 3,52% 1,19% -2,01% -5,22% -8,43% -11,63% Ukraine 46,30% 6,46% -5,13% -18,96% -32,80% -46,64% -60,48%

Source: Authors’ calculation according to data gathered from the statistical bulletins of central banks in selected countries

Graphs in Figure 6 also illustrate differences in rates of decreasing CVaR and rates of decreasing/increasing expected return. These are the essential components of risk-return correspondence for markets from group 2. Bank management should compare the rate of increased expected value with the ratio of decreased CVaR.

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40 Figure 6. Dependency expected return and CVaR from tactical risk in selected countries

Expected Return: Dependency from Tactical Risk Conditional Var: Dependency from Tactical Risk

Source: Authors’ calculation according to data gathered from the statistical bulletins of central banks in selected countries

4. FURTHER CONSIDERATIONS AND DISCUSSION The first discussion issue is the choice of the model distribution of exchange rates. In the calculations presented in the previous paragraph, we used the distribution of annual returns based on the data of seven years (2010-2016). For each currency, this period includes both stable periods and periods of sharp devaluation. In stable periods, it is logical to use one distribution and another one in the period of falling. A generalization in this direction can be the use of shorter time intervals and modelling them using a certain family of probability distributions. The family of distributions is characterized by certain parameters that vary depending on the trend of the exchange rate. Each trend corresponds to certain values of the parameters. When identifying a trend, distribution functions with appropriate parameters can be applied. Considering the fact that the real data show distributions with heavy right tails, it makes sense to use distribution families that would model well such characteristics. Such families are, for example, Gumbel, Freshet, Weibull, Generalized Extreme Value (GEV) distribution and others, as it is pointed out in Coles (2001). Based on the "EasyFit 5.6 professional" software, we obtained fitted distributions. One of the adequate families was Generalized Extreme Value distribution (for all analyzed countries except Azerbaijan). This family of distribution includes three parameters: a location parameter m, a scale parameter σ, and a shape parameter ξ, the values of which are given in Table 5.

Table 5. Parameters of fitted GEV distribution

Armenia Azerbaijan Belarus Georgia Moldova Ukraine Shape 0,10 0,57 0,37 0,10 0,05 0,34 parameter

Source: Authors’ calculation according to data gathered from the statistical bulletins of central banks in selected countries

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Development of such approach entails structuring time intervals. Modelling distribution from GEV is the next step. Then, it is necessary to analyse the behaviour of parameters through the time. Let us illustrate this for the Georgian foreign currency exchange rate.

Table 6. Dynamics of parameters of GEV distribution: Georgia

Shape Time period Std. Deviation Mean parameter Jan 2010 –Dec 2010 0,06905 0,03668 0,92325 July 2010- June 2011 -0,20048 0,02912 0,92862 Jan 2011 –Dec 2011 -0,91997 0,0267 0,97913 July 2011- June 2012 -0,22298 0,00809 0,99805 Jan 2012 –Dec 2012 -0,0103 0,00814 1,0029 July 2012- June 2013 -0,22906 0,02718 1,0251 Jan 2013 –Dec 2013 0,10785 0,01148 1,0535 July 2013- June 2014 0,02565 0,08146 1,1009 Jan 2014 –Dec 2014 -0,70788 0,08216 1,2754 July 2014- June 2015 -0,65608 0,17403 1,1773 Jan 2015 –Dec 2015 0,14202 0,064 0,99763

Source: Authors’ calculation according to data gathered from the statistical bulletins of central bank of Georgia

Dynamics of parameter ξ has cyclical character, which is connected with currency exchange rate behaviour. Indeed, growth tends to growth ξ and reverse. In the period of the falling exchange rate, the estimated ξ is high, but after the fall we get the stabilization of exchange rate and we may suppose that ξ should be low, instead this parameter is high. That is why it is possible to use in the model a supposed (estimated) ξ. Another important question is linked to the dependency of the bad rate on currency devaluation. Bad rates for loans in foreign currency, as a rule, react stronger to sharp devaluation in case when borrowers get incomes in local currency. Under such conditions, sharp devaluation eats up income when calculated in foreign currency and increases the probability of borrower’s default. Different types of lending may also become an important factor for the proposed model. First of all, there may be a different configuration of “loan interest rates - bad rates”. For example, consumer lending vs corporates lending. The foreign exchange restrictions introduced by central banks may also affect the model application. Central banks may introduce some restrictions on the transformation of attracted deposits into loans in different currencies or may restrict consumer lending in foreign currency. For instance, in some countries (Ukraine, Belarus) loans to households, who do not have earnings in foreign currencies, were banned after the Global financial crisis.

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40 CONCLUSIONS This paper presents findings of deposit and loan dollarization management in banking for six post-Soviet countries: Armenia, Azerbaijan, Belarus, Georgia, Moldova, and Ukraine. The level of dollarization in banks in these countries is high and ranges from 45% to 75%. This naturally raises questions about an optimal economic strategy for banks under these conditions. The proposed model of risk management enables to identify a strategy for resolving the dilemma of how to balance loan and deposit dollarization. The model is based on analysis of risk-return correspondence for a total return of bank that tries to balance loan and deposit dollarization. Effective strategy under dollarization should be built around two factors. One is linked with an opportunity of getting high returns from converting attracted deposits in dollars into loans in local currency. However, this will only be justified in case of relatively stable exchange rates. Another is risk burden of such con- version. The study proves that risk management of dollarization is based on the combination of two main types of risks. The first is the strategic risk of deposit structure presented by the ratio between attracted deposits in foreign currency and local currency. The second is tactical risk presented by the ratio of dollar deposits transformed into local currency loans. Risk measure in this model is introduced as Conditional Value-at-Risk. Such approach is justified by a possibility of an extreme devaluation of local currency presented in the long tail of probability distribution function. Our model provides an effective tool for the choice of an adequate strategy. The first strategy concentrates on the deposit de-dollarization and not conducting transformation of foreign currency deposits into local currency loans. The second strategy implies, on the contrary, the increase of deposit dollarization, but at the same time also not conducting transformation of foreign currency deposits into local currency loans. The third strategy entails the deposit de-dollarization and conducting transformation of foreign currency deposits into local currency loans. According to our calculations, the banks of four countries can conduct deposit de-dollarization, using the first (Ukraine, Belarus) and the third (Armenia and Georgia) strategies. Banks of the two countries (Azerbaijan and Moldova) can be guided by the second strategy, which involves an increase in the dollarization of deposits. In the latter case, proceeding from the fact that the level of deposit dollarization in these countries is quite high, it is also necessary to take into account other factors, in particular, currency restrictions. Further model development entails taking into account the rate of non- performing loans in foreign and local currency. Also, country-specific regulatory restrictions on currency structure of deposits and loans could be considered.

REFERENCES Armas, A., Ize, A., Levy Yeyati, E. (2006), Financial Dollarization: An Overview, Palgrave Macmillan, London. Basso, H. S., Calvo-Gonzalez, O., Jurgilas, M. (2011), “Financial dollarization: The role of foreign- owned banks and interest rates”, Journal of Banking & Finance, No. 35, pp. 794-806. Broda, C., Levy Yeyati, E. (2006), “Endogenous deposit dollarization”, Journal of Money, Credit and Banking, No. 38, pp. 963-988. Brown, M. Stix, H. (2015), “The Euroization of Bank Deposits in Eastern Europe”, Economic Policy, Vol. 30, Issue 81, pp. 95–139. Brown, M., De Haas, R. (2012), “Foreign currency lending in emerging Europe: Bank-level evidence”, Economic Policy, No. 27, pp. 57–98. Choi, W. G., Cook, D. (2004), “Liability dollarization and the bank balance sheet channel”, Journal of International Economics, No. 64, pp. 247-275. Coles, S.G. (2001). An Introduction to Statistical Modeling of Extreme Values, Springer-Verlag, London. De Nicolo, G., Honohan P., Ize, A. (2005), “Dollarization of bank deposits: Causes and consequences”, Journal of Banking & Finance, No. 29, pp. 1697-1727. Duffy, J., Nikitin, M., Smith, R.T. (2006), “Dollarization traps”, Journal of Money, Credit and Bank- 38

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40

ing, No. 38, pp. 2073-2097. Eichengreen, B. (2001), “What problems can dollarization solve?”, Journal of Policy Modeling, No. 23, pp. 267-277. Feridun, M. (2012), “Liability dollarization, exchange market pressure and fear of floating: empirical evidence for Turkey”, Applied Economics, No. 44, pp. 1041-1056. Fidrmuc, J., Hake, M., Stix H. (2013), “Households' foreign currency borrowing in Central and East- ern Europe”, Journal of Banking & Finance, No. 37, pp. 1880-1897. Gale, D., Vives, X. (2002), “Dollarization, bailouts, and the stability of the banking system”, Journal of Economics, No. 117, pp. 467-502. Guégan, D., Hassani, B.K. (2014), “A mathematical resurgence of risk management: An extreme modelling of expert opinions”, Frontiers in Finance and Economics, No. 11 (1), pp. 25–45. Honohan, P., Shi, A. (2002), “Deposit dollarization and the financial sector in emerging economies”, World Bank Working Paper, No. 2748. Ize, A., Levy Yeyati, E. (1998), “Dollarization of Financial Intermediation: Causes and Policy Implica- tions”, IMF Working Paper, No. 98/28. Ize, A., Levy Yeyati, E. (2003), “Financial dollarization”, Journal of International Economics, No. 59, pp. 323-347. Kaminskyi, A. (2006), Modelling of Financial Risks, Kyiv National University Pbl, Kyiv. Kutan, A., Ozsoz, E., Rengifo, E. (2012), “Cross-sectional determinants of bank performance under deposit dollarization in emerging markets”, Emerging Markets Review, No. 13, pp. 478-492. Levy Yeyati, E. (2006), “Financial dollarization: evaluating the consequences”, Economic Policy, Vol. 21, pp. 61-118. Liubkina, O., Borovikova, M. (2013), “Imbalances in the financial system of Ukraine and their trans- mission to the systematic risks in the monetary stability”, Bulletin of Taras Shevchenko Na- tional University of Kyiv, No. 152, pp. 57–62. Loiseau-Aslanidi, O. (2012), “Dollarization in Emerging Markets: Evidence from Georgia”, Emerging Markets Finance and Trade, Vol. 48, Issue 3, pp. 70-84. Luca, A., Petrova, I. (2008), “What drives credit dollarization in transition economies?”, Journal of Banking & Finance, No. 32, pp. 858-869. Marcelin, I., Mathur. I. (2016), “Financial sector development and dollarization in emerging economies”, International Review of Financial Analysis, Vol. 46, pp. 20-32. Mecagni, M. et al. (2015), Dollarization in Sub-Saharan Africa: experience and lessons, Interna- tional Monetary Fund, Washington, D.C. Mendoza, E. G. (2001), “The benefits of dollarization when stabilization policy lacks credibility and financial markets are imperfect”, Journal of Money, Credit and Banking, No. 33, pp. 440-474. Mongardini, J., Mueller, J. (1999), “Ratchet Effects in Currency Substitution: An Application to the Kyrgyz Republic”, IMF Working Paper, No. WP/99/102 Naceur, S.B., Hosny, A., Hajdian, G. (2015), “How to De-Dollarize Financial Systems in the Cauca- sus and Central Asia?”, IMF Working Paper, No. WP/15/203, available at https://www imf.org/external/pubs/ft/wp/2015/wp15203.pdf (accessed 18 November 2017). Neanidis, K. C. (2010), “Financial Dollarization and European Union Membership”, International Finance, No. 13(2), pp. 257–282. Neanidis, K. C., Savva, C. S. (2009), “Financial dollarization: Short-run determinants in transition economies”, Journal of Banking & Finance, No. 33, pp. 1860-1873. Ozsoz, E. (2009), “Evaluating the Effects of Deposit Dollarization in Financial Intermediation in Transition Economies”, Eastern European Economics, Vol. 47, No. 4, 5-24. Palley, T.I. (2003), “The economics of exchange rates and the dollarization debate: the case against extremes”, International Journal of Political Economy, Vol. 33, No. 1, pp. 61-82. Reinhart, C. M., Rogoff, K. S., Savastano, M. A. (2014), “Addicted to Dollars”, Annals of Economics and Finance, No. 15, pp. 1-51. Szegö, G. (2004), Risk Measures for the 21st Century, Wiley Finance, West Sussex England. Skrypnyk, A., Nehrey, M. (2015), “The Formation of the Deposit Portfolio in Macroeconomic Insta-

Andrii Kaminskyi, and Nataliia Versal / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 21-40 bility”, in ICT in Education, Research and Industrial Applications: Integration, Harmonization and Knowledge Transfer proceedings of ICTERI 2015 in Lviv, Ukraine, pp. 225-235, available at, URL: http://ceur-ws.org/Vol-1356/ICTERI-2015-CEUR-WS-Volume.pdf (accessed 21 No- vember 2017). Versal, N., Stavytskyy, A. (2015), “Financial Dollarization: Trojan Horse for Ukraine?”, Ekonomika, No. 94 (3), pp. 21 – 45. Vieira, F. A. C., Holland, M, Resende, M. F. (2012), “Financial dollarization and systemic risks: New empirical evidence”, Journal of International Money and Finance, No. 31, pp. 1695-1714.

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57

Montenegrin Journal of Economics

Vol. 14, No. 2 (2018), 41-57 ‘

Some Aspects of Actual CBI and Inflation in the Countries of Southeast Europe

DAMIR PIPLICA1, IVO SPERANDA2, and ZVONIMIR JOSIP PERKOVIC3

1 Assist. Professor, University Department of Professional Studies Split, Croatia, e-mail: [email protected] 2 Assist. Professor, University of Dubrovnik, Department of Economics and Business Economics, Dubrovnik, Croatia, e-mail: [email protected] 3 student, University Department of Professional Studies Split, Croatia, [email protected]

ARTICLE INFO ABSTRACT Received February 02, 2018 Although the countries of Southeast Europe are connected in many Revised from March 09, 2018 ways, there are a lot differences among them with reference to de- Accepted May 20, 2018 velopment of the market economy, and especially in the way of con- Available online June 15, 2018 ducting of the monetary policy and achieving of the price stability. The subject of this article's research is the actual central bank independence and its impact on monetary stability in the specific envi- JEL classification: ronment of Southeast European countries. Therefore, we applied it E52, E58. TOR as a research method because shorter average duration of the mandate of a central bank governor can be an obstacle for conduct- DOI: 10.14254/1800-5845/2018.14-2.3 ing of the monetary policy in the long run, as in such a case central bank would be less interested in obtaining its primary goal – keep- Keywords: ing of monetary stability. The main hypothesis in this study is that there was a significant influence of the actual central bank indepen- inflation, dence to monetary stability, regardless of different implementations monetary policy, of the monetary policies of the central banks of the observed coun- monetary stability, tries. We have used statistical methods to prove the hypotheses and actual CBI, then we gave an adequate explanation of the research results. The TOR. result of our research has shown that in the period 2000-2016, despite their differences, actual independence of the respective central banks was strengthening, while the inflation rate in the countries of the Southeast Europe was decreasing, but the connection between the two was weak. However, we have established that in the South- east European countries in the end relatively higher degree of the actual independence of their central banks has been obtained, as well as lower inflation rate in 2016, while their negative correlation has become very strong. The observed countries can have obtained their monetary stability greatly thanks to the higher degree of the actual independence of their respective central banks, but at the same the independence by itself is not enough to keep the inflation rate at the desired rate, like the one requested by Maastricht's criteria. In modern circumstances lower inflation rate can depend on some other factors, such as political lobbies, mutual adjustment of fiscal and monetary policies, imperfection of the labour market, national culture of the inflation, etc.

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57 INTRODUCTION Only a few decades back most of the central banks in the world were under a strong political influence of the government and they had no independence, or the independence was of a very low level in conducting of the measures of the monetary policy. The growth of the central bank independence have substantially changed definition and understanding of the creators of the monetary policy, as well as responsibility for the goals achieved in its conduction. A very strong support of the central bank independence were the results achieved in conducting of the monetary policy of the German central bank (Deutsche Bundesbank), which was considered the most independent central bank in the world, until it transferred its independence to the European Central Bank. It was breaking of the link between government of a country and its authority in conduction of monetary policy that influenced such authorities to be transferred to an independent and competent institution that would not depend on any political pressures. Thus, government has lost its discretion ability to trick other economic participants by its acts, on the contrary it has been put into 'more equal position' with other economic participants, whereas monetary authority has been given to central bank exclusively, whose credibility became very important for all other economic participant's behaviour. Considering the undisputable importance of establishing the rate of the central bank independence, ways and methods of its measuring became very important in order to for this measuring to be more credible and calculation more precise. Measuring of the central bank independence that uses legal indicators can show substantially different results compared to measurement of the so called actual central bank independence, due to its flaws. Therefore actual central bank independence became an issue of the research of more and more authors. Namely, it is difficult to measure certain factors that can be important for establishing of the central bank independence, such as the Governor’s or other bank leaders' personalities, factors that arise from the tradition, etc. At the same time it is possible to apply certain legislative regulations differently in practice, although they may be expressed in the same way in the legislation regulating the work of the central bank. Hence, it is often due to various types of pressure that the mandate of a Governor of the central bank can last shorter than his term, in order to elect the Governor of the central bank who would be more convenient for conducting of such monetary policy that would suit to certain political, interest and other groups. Although the countries of Southeast Europe have common similarities, and are additionally connected by common regional area, at the same time it is obvious that there are differences in their political and economic development, achieved transition results, etc., while in the context of our research we especially point out differences in conducting of monetary policy. Time that has passed from the beginning of the transition, as well as a degree of total liberalization of the economies have created basis for a quality research of the influence of the actual central bank independence to the monetary stability in the countries of Southeast Europe, and also possibility to properly observe mutual correlation of the researched phenomena, and consequently, the possibility of making adequate conclusions.

1. THEORETIC CONSIDERATIONS AND RESEARCHES OF THE CENTRAL BANK INDEPENDENCE AND THE INFLATION Broad consensus of economists, politicians and public has influenced the decision to direct monetary policy to keep monetary stability as a long-term goal. Such a position of the central bank enabled it to be free of the obligation of some other macro-economic goals that have remained as an obligation of the governments. Tinbergen (1954) is considered to be modern creator of an idea that central banks should be independent, because he thought that if there were more economic goals which were in conflict, each of them should have been solved by an independent specialized institution. However the very idea of central bank independence is based on time-inconsistency of 42

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57 the monetary policy that was explained by Kydland and Prescott (1977). The above authors relate negatively to implementing of economic policy on the basis of the discretion and they support its implementing on the basis of the set rules („rules against discretion“). The very conducting of the monetary policy by the rules can efficiently solve the problem of the inflation. Economic participants would deem conducting of such an economic policy credible, unless there are inflation sur- prises. Therefore, nowadays, solving of the problem of time-inconsistency of the monetary policy is in the very scope of operation of central banks. On the other hand, Buchanan and Wagner (1977) have clearly pointed out that only an independent central bank can be resistant to political pressure that would result in inflation tendency. Bade and Parkin (1982) have conducted their research of 12 OECD countries and they have calculated the level of the independence of their central banks for the period 1951-1975. They have established that the central bank independence have negative influence to the inflation rate. While researching elements of the central bank independence, Rogoff (1985) has established that its Governor has conservative approach to the monetary policy, i.e. more important meaning in its conduction should be directed to the price stability instead some other goals of the economic policy, such as increasing of the employment rate, economic growth, etc. Several authors, such as Alesina (1988), Burdekin and Willet (1991), Cukierman (1992), Cukierman, Webb and Neyapti (1991), Eijffinger and Schaling (1993) have pointed out that the central bank independence is institutionally essential for obtaining of the monetary stability. Alesi- na (1988) has also used Bade-Parkins index in his researches and has established negative correlation of the central bank independence and the inflation. Furthermore, Alesina (1989) has additionally concluded that the independent central bank can reduce fluctuation in monetary policy during election cycles. Neumann's (1991) research has shown clear reasons for entrusting central bank to independently implement monetary policy, but also certain elements that could be used for creating of different models for measuring of the central bank independence. Grilli, Mascianda- ro and Tabellini (1991) have constructed (GMT) index for measuring of political and economic central bank independence. By its application in the research the authors have established negative correlation between central bank independence and the inflation in developed western countries. On the other hand Lohmann (1992) has been researching optimum design of the institution of the central banking. While researching the influence of the central bank on the monetary stability, Cukierman and the others (1992), and later Cukierman again (1994) have established that the legal central bank independence is more convenient to be measured in developed countries, where it is connected to the lower inflation rate. On the other hand, turnover rate of governors (TOR) is more convenient for researching of the central bank independence in developing countries, where it is connected to the lower inflation rate. Furthermore, Alesina (1993) has established negative correlation between inflation rate and central bank independence for a group of developed western countries, where he used and upgraded model made by Bade and Parkin. Capie, Mills and Wood (1993) came to the conclusion that in order to keep the stability of prices, central bank independence is not the only necessary condition, but also some other conditions are needed. Fischer (1994), and again De- belle and Fischer (1995) attributed larger importance to the economic central bank independence and not political independence, while Walsh (1995) researched central bank as government's mediator that tried to maximize goal function. A unique historic period, such as transition of socialist economies into modern market economies created at the same time new opportunities for researching of certain social and especially economic phenomena together with their specific characteristics. These circumstances differed greatly when compared to conditions where monetary policy was conducted by central banks of developed countries. Radzyner and Reisinger (1997) were researching central banking of Czech, Hungary, Poland, Slovakia and Slovenia. However, the authors have established, as a negative side, that there was still certain direct crediting of the governments from the central bank. Loungani and Sheets (1997) have established that the central bank independence in transition 43

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57 conditions negatively influences inflation rate, which has also been established by Cukierman (1998), whereas he pointed out necessity for economic liberalization of adequate level. Lybek (1999) has researched connection of the central bank independence and certain macro-economic indicators in former Soviet countries, whereas he included elements of the accountability of the central banks into the model designed for measuring of the independence. His model is more rig- orous than Cukiermans's or GMT model, in order to be less influenced by subjective elements when applied. Lybek has established that the countries with higher level of de jure independence and accountability of their central banks have lower annual inflation rate for the period 1995 – 1997. Within our research context it is important to point out that Lybek was researching the influence of the actual central bank independence on the inflation rate, but due to the short period of the research, he was not able to establish significant correlation. Maliszewski (2000) was researching independence of central banks in 20 transition countries, where he used GMT model as a research basis. The model that Maliszewski used differed from the original model because he presumed that central bank has higher level of political independence if countries' Governor could only be relieved by non-political factors and that crediting of the government by the central bank is less harmful if the whole direct credit is securitized. Maliszewski has concluded that there is significant influence of the central bank independence to the inflation rate in the observed countries, but that there was no such an important correlation with the economic independence of the central banks. Ilieva et al. (2001) have constructed index of the central bank independence that included legislative and behavioural aspects of its independence and they have established that the central bank independence is higher in transition countries that are in the process of being admitted into EU than in other transition countries. Cukierman et al. (2002) have researched influence of the central bank independence to certain macro-economic elements in 26 transition countries, whereas they have used two Cukierman's measures of the independence of the Central bank: LVAW, made out of 16 indicators and its modified version LVAU. The authors have established that following elements have the most important influence on the level of the central bank independence: allocation of the authorities in conducting of the monetary policy, procedure of resolving the issues between the government and the central bank and attributing the importance to the price stability within the legislation of the central banks. Freytag (2003) has researched legal central bank independence in some of transition countries, whereas he has created monetary commitment index and has established its very high independence. Dvorski (2004) has used index of frequency of changing the Governors of the central banks and has researched legislative regulations in the work of central banks of Southeast Europe, whereas she has included Maastricht Criteria requirements. The author has concluded that Maas- tricht Criteria have mostly been implemented into legislations, but that the central banks are not completely free from political influence in practice. Piplica (2012) has researched actual central bank independence and the inflation in Croatia and transition countries EU members and has concluded that there is relatively higher level of the actual central bank independence and lower inflation rate for the period 1998-2010. The author has also concluded that the higher level of the actual central bank independence is no more correlated to the lower inflation rate once monetary stability is already obtained. Furthermore, Piplica (2015) has also transformed and upgraded GMT model and has researched the influence of the central bank independence to the monetary stability in transition countries EU members in early and in later phase of the transition process. The author has concluded that in the early and in the whole transition period there is a significant negative influence of the legal central bank independence to the inflation, but that this is not so obvious in the latter phase of the transition. Bogoev and Petrevski (2015) have analysed political and economic arguments for establishing of the independent central banks, and have especially criti- cally valued different element for quantifying of the legal and actual central bank independence. The authors have also given a review of the evolution of the central bank independence in the transition countries of the Central and Eastern Europe. Angelovska-Bezhoska, A. (2017) has oriented to researching of the legal regulations that define the independence of the National Bank of the Republic of Macedonia based on the index of Cukierman et al. (1992) and the index of Jacome 44

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57 and Vasquez (2005), important for keeping the stability of the prices. The author has established the growth of the independence of the National Bank of the Republic of Macedonia, whereas an improvement in the area of expressing of the monetary policy is essential, as well as the process of appointment of the non-executive members of the council of the National Bank of the Republic of Macedonia. Of course, there are also the authors that in their researches of the central bank independence have come to the opposite results with reference to the above mentioned. Thus for example, de Haan and Sierman (1994) and Cargill (1995) have established that there is no significant correlation between the central bank independence and the inflation rate. Also, Eijffinger and Van Kuelen (1995) in their research used models designed by Alesina, as well as Bade and Parkin model, and Eijffinger and Schaling model, and finally model designed by Grilli, Masciandaro and Tabelllin, and they have also concluded that there is no significant influence of the central bank independence to the inflation. Cargill (2016) thinks that the central bank independence is only a myth and he states that: „The conventional wisdom so widely accepted in the academic literature is based on a confused perception of independence that fails to distinguish between legal (de jure) and actual (de facto) independence.“ Many other authors have researched central bank independence and its influence to the monetary stability, but we have pointed out only authors important to the context of this conducted research.

2. APPLIED MODEL AND MEASUREMENT OF THE ACTUAL CENTRAL BANK INDEPENDENCE The researches have often shown that the actual central bank independence differs from its legal independence, whereas lower level of actual independence was shown. Actual central bank independence has become research subject of greater number of the authors, because it is not possible to get some answers by applying of the measures of legal central bank independence. Prevailing theoretical opinion of large number of the authors considers that monetary policy in the long run has a neutral character and that government must not gain from direct measures of the monetary policy in the short run, because it can result in unwanted effects. That is why shorter mandate of the Governor of the central bank can be an obstacle for credible conduction of the monetary policy in the long run. It is not possible to precisely measure the actual central bank independence, because it is very difficult to measure all the elements that in fact actually impact its independence. One of the means of measuring of the actual central bank independence is the grade (index) of frequency of the changing of the Governors of the central bank and we will apply it in our research. The central bank independence is often measured by questioner (questionnaire based index), whereas monetary experts from the observed countries fill in the questioner. We are of the opinion that such questionnaires are subject to certain subjective feeling of the questioned persons, hence they are less credible. Regardless of the fact that they are monetary experts, they can enter a certain subjectivity that will be expressed in positive or negative perception of some measuring elements that can result in a distorted picture of reality. The rate of frequency of replacement of the Governor of the central bank is, in its nature, a very simple indicator, based on the fact that the Governor of the central bank is the most important person in conducting of the monetary policy, and if often replaced it is a sign of political (or some other) influence in restraining of the central bank independence. Moreover, this index is not so subject to subjective approach as questionnaire based index, hence it is more appropriate for measuring of the actual central bank independence. Because all mentioned above, and some other circumstances, it is presumed that the term of mandate of the Governor of the central bank is an assurance for stability of the conducting of the monetary policy, whose primary goal will be obtaining and maintaining the price stability. Turn-over rate of the Governor of the central bank (TOR)

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57 represents average term of mandate, and it is obtained as a relation of the number of the Gover- nors of the central bank in the period of time and the length of the observed period. Thus,

Turn-over Rate of the Governor of the Central bank (TOR)

It is obvious that lower result at the same time means higher level of the actual central bank independence. Considering the length of the term of mandate of the Governor of the central bank, that mostly lasts 4-5 years (somewhere even longer), as well as the length of duration of the election cycles in democratic countries, Cukierman et al. deem it is not desirable for the upper level of turnover rate of the Governors to be above 0,2 or 0,25. Of course, low level of TOR does not auto- matically mean high level of the central bank independence and this way of measurement should not be taken as exact way. We cannot exclude the fact that the Governor of the central bank is subject to government pressures, in order to prolong his mandate as a head of the central bank. Therefore, sometimes there are situations when the Governors of the central banks for certain reasons were connected to the governments, such as in Romania, Czech Republic, etc. where they later became members of the governments in these countries.

3. INFLUENCE OF THE ACTUAL CENTRAL BANK INDEPENDENCE TO THE INFLATION IN THE COUNTRIES OF SOUTHEAST EUROPE The research has taken place in the countries of Southeast Europe: Albania, Croatia, Romania, Bulgaria, Serbia, Bosnia and Herzegovina, Montenegro, Macedonia, Kosovo and Greece, which although connected geographically, historically, economically, culturally, nationally and in many other ways, at the same time show significant differences in forming of the states, democratic tradition, structures of the national economies, (non)membership of the EU, conducting of the monetary policy, etc. Therefore, each of the central banks conducts monetary policy in somehow different political and economic surrounding, which can influence the success in obtaining the goals of the monetary policy. Monetary policy of the Southeast European countries has many differences, but primary goal of all the central banks was obtaining and maintaining monetary stability. Thus for example, the National Bank of Romania conducts direct inflation targeting as of 2005. Inflation goals are established at the level of yearly changes of consumer price index, for precisely established percentage points. Similar to the above, Serbia is conducting inflation targeting since 2009, whereas such monetary strategy has been gradually introduced since 2006. National Bank of Serbia conducts independent monetary policy with floating exchange rate of its national currency. Inflation targeting is also conducted in Albania in order to modify and anticipate inflation. On the other hand national currencies of Bosnia and Herzegovina and Bulgaria function in the currency board system. The Croatian National Bank keeps the stability of the exchange rate of its national currency according to euro as so called nominal anchor of monetary policy in order to stabilize inflation expectations. The stability of exchange rate regime is kept by currency interventions (according to euro). In 2009 IMF considered Croatia as a regime of directed floating currency exchange. Also, the National Bank of the Republic of Macedonia uses direction of nominal exchange rate since 1995, first according to Deutsche Mark and since 2002 according to euro as a nominal anchor. Greece is the only observed country of the Southeast Europe that is a member of Euro zone, and accordingly it has transferred its responsibility for monetary policy to ECB during 2001. On the other hand, Montene- gro and Kosovo have done one-sided euroisation (so called dollarization) of their national currencies. Such a rigid form of the regime, where a country uses other countries' currency instead of its own, was present in Montenegro first as unofficial euroisation, than as partly official euroisation, and in the end Montenegro became officially euroised economy by introducing firstly Deutsche Mark and later euro as an only legal means of payment. Similar as Montenegro, Kosovo has also simply adopted Deutshe Mark and later euro as a means of payment with no agreements whatso- 46

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57 ever with the central bank of Germany (Deutsche Bundesbank), or later ECB. It is total of the mentioned similarities and differences that has created a unique surrounding, and an opportunity to explore the influence of the central bank independence to the inflation in different circumstances, than for example in developed western countries, or elsewhere.

3.1. ACTUAL CENTRAL BANK INDEPENDENCE IN THE COUNTRIES OF SOUTH EAST EUROPE – APPLICATION OF TOR ANALYSES We have started the research of the average term of mandate of the governors of the central banks of the Southeast Europe with different periods, because there are differences in some countries in forming of their central banks, etc. The data for Romania, Croatia, Bulgaria, and Greece have been researched since 1990; data for Serbia have partly been taken from former Yugoslavia, data for Bosnia and Herzegovina dates back to 1997, data for Montenegro since 2001, data for Macedonia and Albania since 1992, while data for Kosovo are from 2008. Term of their Governors’ mandates is presented in Table No. 1.

Table 1. Term of Mandate of the Governors of the Central Banks in the Countries of South East Europe

Central Bank Governor Term in Office Ante Čičin-Šain VIII.1990. V.1992. Pero Jurković VI.1992. II.1996. Marko Škreb III.1996. VII.2000. Croatia Željko Rohatinski VII.2000. VII.2006. Željko Rohatinski VII.2006. VII.2012. Boris Vujčić VII.2012. incumbent Mugur Constantin Isărescu IX.1990 XII.1999 Romania Eugen I. Ghizari1 (interim) XII.1999 XII.2000 Mugur Constantin Isărescu XII.2000 incumbent Ivan Dragnevski XII.1989. I.1991. Todor Valchev I.1991. I.1996. Lyubomir Filipov I.1996. VI.1997. Bulgaria Svetoslav Gavriiski VI.1997. X.2003. Ivan Iskrov X.2003. VII.2015. Dimitar Radev VII.2015 incumbent SFRJ Dušan Vlatković VI.1986 VI.1992. Vuk Ognjanović VII.1992 VII.1993. Borisav Atanacković VII.1993 X.1993. FR Yugoslavia Dragoslav Avramović III.1994 V.1996. Dušan Vlatković VI.1997 XI.2000. Mlađan Dinkić XI.2000 II.2003. Mlađan Dinkić II.2003 VII.2003 Kori Udovički VII.2003 II.2004. Serbia Radovan Jelašić II.2004 VII.2010. Dejan Šoškić VII.2010 VIII.2012. Jorgovanka Tabaković VII.2012 incumbent Peter Nicholl VI.1997. XII.2004. Bosnia and Her- Kemal Kozarić I.2005. VIII.2015. zegovina Senad Softić VIII.2015. incumbent Ljubiša Krgović III.2001. X.2010 Radoje Žugić X.2010. XII.2012. Montenegro Milojica Dakić I.2013. X.2016. Radoje Žugić X.2016. incumbent Albania Ilir Hoti, V 1992 IX 1993. 47

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57

Dylber Vrioni, IX 1993 XII 1994 Kristaq Luniku, XII 1994 IV 1997 Qamil Tusha, IV 1997 VIII 1997. Shkelqim Cani, VIII 1997 IX 2004. Ardian Fullani, X 2004 IX 2014 Elisabeta Gjoni, IX 2014- II 2015 Gent Sejko, II.2015 incumbent Borko Stanoevski IV 1992 V 1997 Ljube Trpeski V 1997 V 2004. Macedonia Petar Gosev V 2004 V 2011. Dimitar Bogov V 2011 incumbent Hashim Rexhepi III 2008 VII 2010 Kosovo Gani Gërguri VII 2010 III 2013 Bedri Hamza III 2013 incumbent Dimitrios Chalikias II. 1984 II. 1992 Efthymios Christodoulou II. 1992 XII 1993 Ioannis Boutos I. 1993 X. 1994 Greece Lucas Papademos X. 1994 VI. 2002 Nikolaos Garganas VI. 2002 VI. 2008 Georgios Provopoulos VI. 2008 VI. 2014 Yannis Stournaras VI. 2014 incumbent

Source: Central banks of the mentioned countries

The reasons that have caused shorter term of mandate of some Governors of the central banks are very important, because it is not the same if mandate was terminated due to some mis- fortune, unlawful activities or because of the political pressure of the government. In that sense, we point out that Croatian Governor Pero Jurković has not completed his term of mandate for medical reasons. On the other hand, National Bank of Romania had only one Governor, Mugur Isăres- cua, however, inflation rate in Romania was not the lowest of the observed countries. It is also interesting that Romanian Governor has also been president of the Romanian government in De- cember 1999 and during 2000, while he was not relieved from the function of the Governor, but his authorities were temporarily transferred to his deputy. Also, the Governor of the Central bank of the Republic of Kosovo, Hashim Rexhepi, was arrested mid-2010, under suspicion for corruption and money laundering by EULEX, but he was released at beginning of 2012, as innocent.

Table 2. Actual CBI and Inflation 2016

Predicted & Residual Values - inflation 2016 Observ. Countries TOR Value Pred. Stand. Stand. Std.Err. Mahal. Delet. Cook's Inflat. Resid. Inflat. Value Pred. v. Resid. Pred.Val Distan. Resid. Distan. (π /1+ π)

Greece 0.26 0.29 0,003 0,068 -0,065 -0,356 -0,398 0,012 0,127 -0,065 0,000 Albania 0.32 2.18 0,021 0,084 -0,063 -0,142 -0,386 0,011 0,020 -0,063 0,000 Romania 0.04 -0.53 -0,005 0,009 -0,014 -1,138 -0,087 0,017 1,295 -0,014 0,000 Bulgaria 0.23 -0.50 -0,005 0,060 -0,065 -0,462 -0,398 0,012 0,214 -0,065 0,000 Serbia 0.37 1.53 0,015 0,097 -0,082 0,035 -0,505 0,011 0,001 -0,082 0,001 Bos.and Her. 0.15 -0.29 -0,003 0,038 -0,041 -0,747 -0,255 0,014 0,558 -0,042 0,000 Montenegro 0.25 1.00 0,010 0,065 -0,055 -0,391 -0,339 0,012 0,153 -0,055 0,000 Croatia 0.19 0.20 0,002 0,049 -0,047 -0,605 -0,290 0,013 0,366 -0,047 0,000 Macedonia 0.16 -0.29 -0,003 0,041 -0,044 -0,711 -0,271 0,014 0,506 -0,044 0,000 Kosovo 0.34 1.29 0,013 0,089 -0,076 -0,071 -0,468 0,011 0,005 -0,076 0,001

Source: own calculation 48

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57

In our research we have set a thesis that in the observed period a significant actual independence of the respective central banks of the countries of Southeast countries of Europe measured by TOR was achieved. In calculation of the actual central bank independence measured by the index of frequency of changing of the Governors of the central banks, we have taken only the Gov- ernors of the central bank and not the persons that substituted them in certain periods. If some of the Governors were re-elected to the position of the head of the central bank, we took the whole period of his function as one mandate, but if in the meantime another Governor was elected, than we deem that such a change in the implementation of the monetary policy can occur, so we en- tered such a change in the calculation. It is obvious that most of the central banks have had TOR values that are lower of around limits of election cycles in the observed countries, which means that they have expressed high or higher level of the central bank independence. It is also obvious that in several of the observed countries Governors were often replaced before they have completed their mandates. Furthermore, we can see that the inflation rate in all the countries of Southeast Europe was at low level and within Maastricht Criteria.

3.2 Influence of the Actual Central Bank Independence to Inflation in the Countries of Southeast Europe Considering all the circumstances of the conducted research, we have observed TOR values since 2000, when cumulative liberalization index (CLI) was on a higher level in all of the observed countries, i.e. when there was a certain time lapse from the very beginnings of the transition and when it was possible to count some average time of the Governors' terms of mandate. In our research we have set the thesis that there was a significant influence of the actual central bank independence to monetary stability, regardless of different implementations of the monetary policies of the central banks of the countries of Southeast Europe. Central bank independence of the countries of Southeast Europe expressed in TOR values would be set into relation to the inflation expressed in index of depreciation of the actual value of money  whereas π presents inflation rate. We'll observe inflation for years when TOR values 1   of the independence of the central banks were observed. The inflation rate π is expressed by the index of consumer’s prices at the end of the year. Our research comprises period of 2000-2016. The above is clearly visible from the following graph. In our research we have taken total of 161 indicators, whereas it is obvious that the average change of the Governors of the central banks in the period 2000-2016 was 0,28, meaning that in the observed countries’ Governors are often replaced before completing their terms of mandate. However, we should point out that in the mentioned period, TOR value was continuously lowering and actual central bank independence was growing. At the same time inflation rate was on average slightly higher than the one requested by Maastricht Criteria, but by the end of our time research was within requested limits.

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57 Graph 1. TOR and Inflation 2000-2016

Source: own calculation

Table 3. TOR and Inflation (π /1+ π) 2000-2016

Test of means against reference constant (value) Reference Mean Std.Dv. N Std.Err. t-value df p Variable Constant

TOR 0,283106 0,175482 161 0,013830 0,00 20,47048 160 0,000000

Inflation 0,039443 0,059264 161 0,004671 0,00 8,44475 160 0,000000

Own calculation

Regression line is positive (Y=0,2585 + 0,6233 X) whereas graph visually shows that there is a very weak correlation between frequency of the turnover of the Governors of the central banks in the observed transition countries (TOR) and the inflation measured by the depreciation of the actual value of money index. It is also obvious that in the period 2000-2016, for which this research was done, we had relatively low inflation rates, regardless of the frequency of the changes of the Governors of the central banks of the countries of Southeast Europe. It is interesting that Romania shows high level of the actual independence of its central bank, but in some years still had high inflation rates. In that sense, expressed TOR value for 2000 is 0,10, but the inflation rate was 40,71%, in 2001 expressed TOR value was lowered to 0,09, but the inflation rate was still high 30,19%, in 2002 TOR value was 0,08 and inflation rate was 17,5%. In later years of the research Romanian inflation rate is within limits of Maastricht Criteria, and even showing deflation character. At the same time, it is interesting that Greece for a sequence of years had inflation rate above Maastricht Criteria requests, although it has euro as payment means, so in 2010 it amounted 5,16%, but in the latter years of our research it is however low, even expressing deflation. Similar situation is in Kosovo and Montenegro (in 2012 inflation rate was 5,12%). 50

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57

Graph 2. Inflation in the Countries of Southeast Europe

Source: WEO database 2016, own calculation 51

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57 Our research comprises total of 161 cases, for the period between 2000 and 2016, for which period data was available. Standard error of evaluation is 0,058, determination index of 0,044 is low, while the level of the correlation is positive, amounting 0,211, and such correlation can be considered as almost insignificant. Since lower level of TOR values means higher level of actual central bank independence, it is obvious that the actual CBI and the inflation have a weak negative correlation. The regression results are shown in the following table:

Table 4. Regression Results 2000 – 2016

Dependent: Stand. err of est: R2= 0,04431609 No. of cases: 161 Inflation 0,058118306 adjusted R2= R = 0,21051387 df = 1,159 p = 0,007353 0,03830550 Std.Error: F = 7,373001 t(159) = 2,2167 p = 0,0281 0,0087135 Intercept:0,019315342 Inflation b*= 0,211

Source: own calculation

Graph 3. TOR and Inflation 2016.

Source: own calculation

On the other hand, actual central bank independence of the countries of Southeast Europe in later years of our research had common influence to monetary stability of the mentioned countries. Although in Serbia, Kosovo and Albania, expressed TOR values are still significantly higher than 0,25, i.e. they show that the Governors are frequently changing before completing their mandates, 52

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57 price stability have been obtained and inflation rate is below 2% in last years. The last year of our research shows that there was a very strong mutual connection between the frequency of changing the Governors of central banks of the observed countries (TOR) and inflation, measured by depreciation of the actual value of money index, i.e. that actual CBI strongly influences lowering of prices, which is visible from the graph below. Despite small number of the observed cases, graph No. 3 clearly visually shows that in 2016 there was a very strong positive connection between frequency of the turnover of the Governors of central banks of the observed transition countries (TOR) and inflation measured by the index of depreciation of the actual value of money. Linear regression equation is positive (Y=.1883 + 8.8875 X), just like correlation index which is 0,83, while determination index is 0,69, of course, is high, meaning that at the same time there was a strong negative correlation between actual CBI and inflation for 2016. Therefore, it is obvious that we have substantial change of the influence of actual CBI at the end of the observed period, regarding the whole period of the research.

Table 5. Regression Results TOR and Inflation 2016

Dependent: Stand. err. of est: R2= 0,68878174 No. of cases: 10 Inflation 0,005528258 R = adjusted R2= df = 1,8 p = 0,002965 0,82992876 0,64987946 Std.Error: F = 17,70543 t(8) = -2,849 p = 0,0215 0,0045998 Intercept: -0,013102564 Inflation b*= 0,830

Source: own calculation

Our research has confirmed our thesis that in the observed specific surrounding of the countries of Southeast Europe relatively high (higher) actual central bank independence was achieved, influencing lower inflation rate.

4. FACTORS THAT ENDANGER ACTUAL CENTRAL BANK INDEPENDENCE OF SOUTHEAST EUROPE AND REFLECTION TO MAINTAINING OF THE MONETARY STABILITY All economic participants in the observed countries have accepted point of view that monetary stability is long-term goal of implementing of monetary policy and that inflation results in numerous harmful consequences for their economies. Obtaining of high (higher) level of the central bank independence, and lower inflation rate represent at the same time a task for maintaining of such values, at the same or better level. However, countries of Southeast Europe are in their regulative have differently incorporated regulations that leading persons of their respective central banks should fulfil, which is not in accordance with theoretical considerations regarding the central bank independence. (Neumann, J.M.M., 1991). There are various factors that endanger preserving of the central bank independence, as well as monetary stability, while political pressure (of the government) is always continuous and can hardly ever be stopped. Namely, political interests of the leading parties are always aimed to beau- tify reality in the eyes of the public, in order to gain trust from voters for a new mandate. Therefore, they often try to use the influence of monetary policy in order to obtain certain economic goals that were not realized by other measures of economic policy (especially by fiscal measures). On the 53

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57 other hand, insufficiently restructured economies of the observed countries still have economic subjects that used continuous depreciation of domestic currency in comparison to the foreign one, and the stability of the prices and exchange rates does not suit them, as they could lose their preferential position. Furthermore, there are continuous pressures of the workers unions for irrational recovering of some poor economic subjects that represent a huge pressure to the government, requesting deficit financing, in order to keep social peace of the citizens. Underdeveloped labour market and necessary restructuring of the economy has resulted in surplus of the labourers, and higher unemployment rate in the observed countries. That, again, has created social pressure to the governments to increase employment, economic growth, and the like, making these issues more important than obtaining monetary stability. A special danger for preserving of the monetary stability represents lack of coordination in conducting measures of monetary and fiscal policies. Inefficient fiscal policy, which was conducted in a part of the observed countries, resulted in higher budget deficit than desirable, lacks on non- budget accounts, insolvency, etc. At the same time, significant fiscal evasion and still very strong grey economy resulted in inability to finance budget expenditures which further creates pressure for such expenditures to be financed by a new money emission. Coordination of the monetary and fiscal authorities should be reached at the moment of establishing of macro-economic goals it the process of parliamentary acceptance. Of course, unpredicted situations should always be kept in mind, such as big natural or social catastrophes, that can create disturbances in the economy, and that can reflect to the activity of the central bank and monetary stability of a country. On the other hand, monetary sovereignty of several countries of Southeast Europe has been transferred to ECB and euro has been introduced as a means of payment, which has strengthen monetary stability from abroad. It, however, means that certain inner (or outer) factors could not endanger price stability. A strong factor that supports monetary stability in the countries of South- east Europe is financial sector, since it is a significant source of the funds and financial institutions get short-term funds, while they grant the long–term. Bank system of the countries of Southeast Europe is largely owned by foreign bank houses, that prefer monetary stability and an independent institution whose primary goal is maintaining price stability. However, it is not rear that financial institutions inspire irrational strengthening of domestic consumption above the abilities of the economic participants. Therefore, the central banks of the observed countries have to evaluate properly future expectations of the economic participants, but also foreign influences, in order to conduct an adequate strategy of measures of monetary policy. A very specific situation developed in the countries that used exchange rate as a nominal anchor (and other reasons as well) that resulted in the fact that many domestic prices were indexed in foreign currency, firstly in Deutsche Mark, and later in euro, which has supported monetary stability of these countries. Furthermore, a large part of the companies' deposits, and citizens' deposits too, was expressed in the foreign currency, and the loans were granted in foreign currency, and all of this resulted with stabilization of the financial sector. Some of the observed countries have applied monetary policy where their central bank targeted inflation in order to strengthen the credibility of their central banks towards the domestic public. Thus, all economic participants can adjust their activities toward inflation expectations, without fear that they would be deceived by discretion measures of the monetary policy. Strengthening of the central bank independence of the observed countries was enlarged by higher level of transparency in conducting measures of the monetary policy as well as legislative regulations that have established mandate of the leaders of central bank to be conditioned by achieving of the set goals, i.e. preserving the monetary stability, while they could be relieved only by non-political reasons. At the same time in all of the Southeast countries in the observed period, national culture of inflation has grown. Large number of citizens of the mentioned countries work in developed western countries, where public is aversive to the inflation, and such citizens present power that had pressured political structures of their countries to reach for values existing in the countries where they have worked. Strengthening of the level of democratic standards has at the 54

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57 same time strengthened the predispositions for the level of the actual independence of the central bank to be increased, as it cannot exist in non-democratic systems. A part of the observed countries are fully equal members of EU and they are obliged to fulfil Maastricht Criteria, while the other part of the countries are in the process of being accessed to the EU membership, and accordingly they will have to implement the same standards.

CONCLUSION The research of the influence of the actual central bank independence to the monetary stability was conducted in the countries of Southeast Europe: Albania, Croatia, Romania, Bulgaria, Ser- bia, Bosnia and Herzegovina, Montenegro, Macedonia, Kosovo and Greece, that are significantly connected, but at the same time there are differences in formatting of certain state communities, democratic tradition, structures of the national economies, (non)membership of EU, conducting of the monetary policy, etc. Although each of the respective central banks conducts monetary policy in somewhat different political and economic surrounding, that can influence the success in obtaining the goals of the monetary policy; primary goal of all the central banks was achieving and maintaining of monetary stability. In our research we have used the rate of the frequency of turnover of the Governor of the central bank, considering the Governor of the central bank to be the most important person in conducting the measures of the monetary policy, and that frequent changes of the Governor show political (or some other) influence that limit the independence of the central bank. The research has shown continuous strengthening of the actual central bank independence and lowering of the inflation rate in the observed countries in the period 2000 – 2016. However, in the whole observed period there is weaker influence of the actual central bank independence to the lowering of the inflation. But at the same time, we have established that at the end of the observed period in the countries of Southeast Europe relatively high or higher level of the independence of the respective central banks have been reached, and lower inflation rate as well, in 2016, whereas the influence of the actual central bank independence to the lowering of the inflation rate became very strong. There is a large number of factors that can result in damaging consequences at preserving of high level of the central bank independence and monetary stability in the countries of Southeast Europe, such as inconsistency in monetary and fiscal policies, continuous pressure for depreciation of the national currency in relation to foreign currencies, increasing of the debit side of the state budget, and deficit financing of the necessities, underdeveloped labour market, etc. Howev- er, recent law regulations have made an important step towards higher accountability of the central bank in conducting the measures of the monetary policy, and transparency of its work to all of the economic participants, that have a positive influence in maintaining of monetary stability. Membership, i.e. accession of the observed countries to EU has resulted in significant support to monetary stability of the observed countries.

REFERENCES Alesina, A. (1988), “Macroeconomic and politics”, NBER Macroeconomic Annual 1988, Cambridge University Press, Cambridge, pp. 17-52 Alesina, A. (1989), „Politics and business cycles in industrial democracies“, Economic Policy, Vol. 8, No. 4, pp.55-98. Alesina, A., Summers, L. (1993), “Central Bank Independence and Macroeconomic Performance: Some Comparative Evidence” Journal of Money Credit and Banking, No. 25, pp. 151-162. Angelovska Bezhoska, A. (2017), “Central Bank Independence - the Case of the National Bank of Republic of Macedonia”, Journal of Central Banking Theory and Practice, No. 3, pp. 35-65. 55

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57 Bade, R., Parkin, M. (1982), Central Bank Laws and Monetary Policy, University of Western Ontario, unpublished manuscript Bogoev, J., Petrevski, G. (2015), Central Bank Independence in Transition Economies, Palgrave Dictionary of Emerging Markets and Transition Economics, pp 5-27 Buchanan, J. M., Wagner, R.E. (1977), “Democracy in Deficit: The Political Legacy of Lord Keynes”, In: Brennan HG, Kliemt H, Tollison RD (eds) The collected works of J.M. Buchanan Liberty Fund, Vol. 8, Indianapolis. Burdekin, R., Willett, T. (1991), “Central Bank Reform: The Federal Reserve in International Per- spective”, Public Budgeting and Financial Management, Vol. 3, No. 3, pp. 531-551. Capie F, Mills, T., Wood, G. (1993), Central Bank Independence: What Is It and What Will It Do For Us?, Institute of Economic Affairs, London. Cargill, T. (1995), “The Statistical Association Between Central Bank Independence and Inflation2, Banca Nazionale del Lavoro Quarterly Review, XLVIII (193), pp. 159-172, Cargill, T., (2016), “The Myth of Central Bank Independence”, Mercatus Working Paper, Mercatus Center at George Mason University, Arlington, VA. Cukierman, A. (1992), Central Bank Strategy, Credibility and Independence: Theory and Evidence, The MIT Press, Cambridge, MA. Cukierman, A. (1994), „Commitment through Delegation, Political Influence and Central Bank In- dependence“ in: J. de Beaufort Wijnholds, S. Eijffnger and L. Hoogduin (eds.), A Framework for Monetary Stability, Kluwer Academic Publishers, Dordrecht/Boston/London, pp. 55-74. Cukierman, A., (1998), “The Economics of Central Banking” in: Wolf, H. (Ed.), Contemporary Eco- nomic Issues: Macroeconomics and Finance (IEA Series), The Macmillan Press, Houndmills, Basingstoke, UK. Cukierman, A., Webb, S. B., Neyapti, B. (1992), “Measuring the Independence of Central Banks and Its Effect on Policy Outcomes”, The World Bank Economic Review, Vol. 6, No. 3, pp. 353- 98. Cukierman, A., Miller, G. P., Neyapti, B. (2002), “Central Bank Reform, Liberalization and Inflation in Transition Economies – An International Perspective”, Journal of Monetary Economics, No. 49, pp. 237-264. Debelle, G., Fischer, S. (1995), “How Independent Should a Central Bank Be?” in: J. C. Fuhrer (ed.), Goals, Guidelines and Constraints Facing Monetary Policymakers, Federal Reserve Bank of Boston, Conference Series, No. 38, Boston, pp. 195-221. De Haan, J., Siermann, C. L. J. (1994), “Saving, Investment, and Capital Mobility Comment”, Open Economies Review, Vol. 5, No. 1, pp. 5-17. Dvorsky, S. (2004), “Central Bank Independence in Southeastern Europe with a View to Future EU Accession”, Discussion Paper, BOFIT. Eijffinger, S.C.W., Schaling, E. (1993), “Central Bank Independence: Theory and Evidence”, Discus- sion Paper, No. 9325, Center for Economic Research, Tilburg University. Eijffinger, S. C. W., Van Kuelen, M. (1995), “Central Bank Independence in Another Eleven Coun- tries”, Banca Nazionale del Lavoro Quarterly Review, 48(192), pp. 39-83. Fischer, S., (1994), “The Costs and Benefits of Disinflation” in J. A. H. de Beaufort Wijnholds, S. C. W. Eijffinger and L. H. Hoogduin (eds.), A Framework for Monetary Stability,: Kluwer Academic Publishers, Dordrecht /Boston/ London, pp. 31-42. Freytag, A. (2003), “Central Bank Independence in Central and Eastern Europe on the Eve of EU- Enlargement”, Occasional Paper, No. 4/2003, Institute for Economic Research (IER), Ljublja- na. Grilli, V., Masciandaro, D., Tabellini, G. (1991), “Political and Monetary Institutions and Public Fi- nancial Policies in the Industrial Countries”, Economic Policy, No. 13, pp. 341-392. Ilieva, J., Healey, N. M. (2001), “Measures and Patterns of Central Bank Independence in Transi- tion Economies”, Statistics in Transition, Vol. 5, No. 1, pp. 151-166 Jacome, L., and Vasquez, F. (2005), Any Link Between Legal Central Bank Independence and Infla- tion? Evidence from Latin America and the Caribbean, IMF WP, No. 05/75.

Damir Piplica, Ivo Speranda, and Zvonimir Josip Perkovic / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 41-57

Kydland, F. E., Prescott, E. C. (1977), “Rules Rather than Discretion: The Inconsistency of Optimal Plans”, Journal of Political Economy, Vol. 85, No. 3, pp. 473-491. Lohmann, S., (1992), “Optimal Commitment in Monetary Policy: Credibility versus Flexibility”, The American Economic Review, Vol. 82, Issue 1, 273-86. Lybek, T., (1999), “Central Bank Autonomy, and Inflation and Output Performance in the Baltic State, Russia, and Other Countries of the Former Soviet Union, 1995-97”, IMF Working Paper. Loungani, P., Sheets, N. (1997), “Central Bank Independence, Inflation and Growth in Transition Economies”, Journal of Money, Credit and Banking, Vol. 29, No. 3, pp. 381–399. Maliszewski, W. S., (2000), “Central Bank Independence in Transition Economies”, Economies of Transition, Vol. 8, pp. 749-789. Neumann, J. M. M. (1991), “Precommitment by Central Bank Independence”, Open Economies Review, No. 2, pp. 95-112 Piplica, D. (2012), “Term of Office of the Governor of the Central Bank as a Guarantor of Maintain- ing Monetary Stability in Croatia”, Oeconomica Jadertina, Vol. 1, No. 2, pp. 4-22 (in Croatian). Piplica, D., (2015), “Legal central bank independence and inflation in various phases of transition”, Economic Research, Vol. 28, No. 1, pp. 167-186. Radzyner, O., Riesinger, S. (1997), “Central Bank Independence in Transition: Legislation and Real- ity in Central and Eastern Europe”, Focus on Transition, Vol. 2, No. 1, pp. 55–90. Rogoff, K. (1985), “The Optimal Degree of Commitment to a Monetary Target”, Quarterly Journal of Economics, No. 10, pp. 1160–1190. Tinbergen, J. (1954), Centralization and Decentralization in Economic Policy, North-Holland Pub- lishing Co., Amsterdam Walsh, C. E. (1995), “Optimal Contracts for Central Bankers”, The American Economic Review, Vol. 85, Issue 1, pp. 150-167.

Web pages http://old.hnb.hr/o-hnb/ho-hnb.htm, accessed 19.10.2017. http://www.bnr.ro/NBR-Governor-5933.aspx, accessed 19.10.2017. http://www.bnb.bg/AboutUs/AUHistory/AUHBNBGovernors/index.htm accessed 19.10.2017. https://www.nbs.rs/internet/latinica/10/10_8/10_8_2.html, accessed 19.10.2017. http://www.cbbh.ba/ accessed 20.10.2017. http://www.cb-cg.org/eng/, accessed 24.10.2017. https://www.bankofalbania.org/web/BoA_Governor_1760_2.php, accessed 24.10.2017. http://www.nbrm.mk/za_nbrm-en.nspx, accessed 24.10.2017. http://www.bqk-kos.org/, accessed 16.09.2017. https://www.nbg.gr/en, accessed 16.09.2017.

Asta Mikalauskiene, Raminta Narutaviciute-Cikanauske, Ingrida Sarkiunaite, Dalia Streimikiene, and Rumyana Zlateva / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 59-78

Montenegrin Journal of Economics

Vol. 14, No. 2 (2018), 59-78 ‘

Social Aspect of Sustainable Development: Issues of Poverty and Food Shortage

ASTA MIKALAUSKIENE1, RAMINTA NARUTAVICIUTE-CIKANAUSKE2, INGRIDA SARKIUNAITE3; DALIA STREIMIKIENE4, and RUMYANA ZLATEVA5

1 Vilnius University, Faculty of Communication, Lithuania, e-mail: [email protected] 2 Vilnius University, Faculty of Communication, Lithuania, e-mail: [email protected] 3 Vilnius University, Kaunas Faculty, Lithuania, e-mail: [email protected] 4 Vilnius University, Kaunas Faculty, Lithuania, e-mail: [email protected] 5 Shumen University, Bulgaria, e-mail: [email protected]

ARTICLE INFO ABSTRACT

Received February 12, 2018 Social aspect in the context of Sustainable development became Revised from March 22, 2018 more important, when it was realized that the level of poverty in the Accepted May 21, 2018 world is not decreasing. There is a need to investigate poverty con- Available online June 15, 2018 ception as a separate element. It is important to understand the transformation road of poverty concept from original thought – lack of income for fulfilling basic physical needs, to the emphasizing on JEL classification: needs of individuals and life quality. Poverty is a multifaceted phe- Q01 ; I32 ; Q18. nomenon, which can be caused by a variety of different reasons; it is also constantly changing, depending on how the countries and DOI: 10.14254/1800-5845/2018.14-2.4 their societies are developing. Persons living in poverty are at the group of risk; individuals are more vulnerable, often have more Keywords: frequent health problems, their resources and quality of life are generally lower than the national average. Object – social dimension sustainable development, of sustainabale development. The aim – to analyze the shift of poverty, sustainable development and poverty concepts, and to analyze dif- food shortage, ferent problematic aspects of poverty and food shortage. Selection sustainable development indicators. of scientific sources used, analysis, methods of generalization of information, selection of statistical data, processing both quantitative and qualitative methods, classification of data. Social aspect in the context of Sustainable development became more important, when it was realized that the level of poverty in the world is not decreasing. There is a need to investigate poverty conception as a separate element. It is important to understand the transformation road of poverty concept from original thought – lack of income for fulfilling basic physical needs, to the emphasizing on needs of individuals and life quality. Poverty is a multifaceted phenomenon, which can be caused by a variety of different reasons; it is also constantly changing, depending on how the countries and their societies are developing. Persons living in poverty are at the group of risk; individuals are more vulnerable, often have more frequent health problems, their resources and quality of life are generally lower than the national average.

Asta Mikalauskiene, Raminta Narutaviciute-Cikanauske, Ingrida Sarkiunaite, Dalia Streimikiene, and Rumyana Zlateva / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 59-78

INTRODUCTION The relevance of the topic. Sustainable development is the global development strategy of the entire mankind. Civilization is facing great global problems in the environmental, social and economic fields. Differences between regions, between the developed and underdeveloped countries, are escalating. Current events of the world, the economic crisis and the consequences of natural disasters have resulted in the scarcity of the resources that would enable balanced and sustainable nourishment of the entire humankind and prevent severe poverty. This is particularly significant because, based on the projections by the UN, the current population will increase by a billion over the next 12 years, and in 2050 it will reach 9.6 billion. The principal growth is expected to occur in the developing countries, half of which are in the African region. Even though the number of children born in countries such as India, China, or Brazil has decreased, it is anticipated, for instance, that the population will continue to rapidly grow in Nigeria, Ethiopia, Uganda, and Afghanistan, where it is common for families to have 5 children (UN, 2013). The UN report also highlights the European region and claims that the population of the continent will fall by 14 percent over the next decades and the issues of an aging population that are evident already will only worsen. Thus, it can be predicted that the impoverished elderly population will rise. The World Food Summit organized by the Food and Agriculture Organization (FAO) in 1996 set the goal to halve the population suffering from food shortages. Later, in 2000, this aim was also included in the Millennium Development Goals. It was emphasised in the Rio+20 summit that the main goals of sustainable development are reduction of poverty, replacement of unsustainable consumption and production models with the sustainable ones, and the protection and management of natural resources balanced with economic and social development (FAO, 2012). This vision, which was presented in the summit, cannot be implemented before the issue of food shortage is solved. Taking into account the extent of various health issues, the World Food Programme (WFP) claims that hunger and malnutrition is a much greater threat than AIDS, malaria and tuberculosis combined. However, the problem of hunger is easier to solve than difficult illnesses and the threat of their outbreak. The world has enough food to feed all who suffer from hunger (Gilbert, 2004). Unfortunately, this requires the appropriate political decisions, the balancing of the extraction and production of food resources, and the adjustment of the society’s food consumption habits. According to R. Conger and M. Donnellan (2007), sustainable human development can be influenced by every separate element of the socio-economic status. Taking into consideration the fact that the assurance of the necessary amount of nutrients is one of the main conditions for the support of human life and health, it is possible to analyse in detail why families are incapable of providing a sufficient amount of food for themselves. The object of the research is the issues of poverty and food shortage. The goal of the research is to research the issues of poverty and food shortage in the context of sustainable development. The tasks of the research:  To analyse the assessment of the progress of sustainable development using indicators.  To examine the understanding, causes, and concepts of poverty.

To analyse problematic aspects of food shortage.

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1. ASSESSMENT OF SUSTAINABLE DEVELOPMENT PROGRESS USING INDICATORS The importance and character of the indicators was the subject of much debate in the late 20th century. The tackled issues included the selection of indicators and the setting of goals with the knowledge that sustainable development does not have the ultimate goal and is a permanent process. P. Hardi and T. Zdan (1997) emphasised that “indicators are the main tool of the assessment of the efficiency of sustainable development strategies. They can be quantitative and qualitative. They have to ensure accurate and reliable information on environmental changes, social and economic conditions, and a response to the goals of the strategies. Assessment indicators should determine the trends and changes, to measure the progress and determine potential future development” (Lyytimaki, 2012). Harger and Mayer (1996) distinguished the characteristics that the indicators chosen for assessment should have ideally: “simplicity, wide applicability, quantitative expression, they can be used to formulate trends, sensitive to changes, make it possible to observe a change in a specific period” (Ness, Urbel-Piirsalu, Anderberg and Olsson, 2007). According to J. Lyytimaki (2012), indicators and more complicated indexes are a tool that is traditionally used in order to objectively measure the progress made towards the pursued objectives. He also notes that such a method has a drawback – subjectivity and the broad- ness/vagueness of the sustainable development strategy goals. Nevertheless, the necessity of the indicators is indisputable. When integrating strategies at Eu- ropean level and defining the pursued goals, methods are required that could determine whether a change has indeed been achieved. Indicators can also indicate problems in certain areas and give hints that significant effort in one or another field and, perhaps, a transformation of certain established goals are required. On the other hand, indicators are important not only in order to evaluate the goals established in the strategy and the made progress, but also to formulate the strategy itself. Selection of a unified sustainable development assessment system for one region is a complicated challenge, primarily because the member states and the candidate states are very different. The region contains both some of the richest countries in the world as well as very poor ones. An- other problem is the infrastructure of government institutions, the shortage of capabilities, insufficient experience in environmental areas etc. In order to select the appropriate measurement indicators, it is necessary to analyse the sustainable development strategy and to find the links between the goals and the expressions that would make it possible to observe the changes being made (Streimikienė, Mikalauskiene and Barakauskaite-Jakubauskiene, 2011). The indications should be clearly understandable not only to specialists but also to other members of society, because a specifically named change motivates individuals more than abstract theses. Many scientists and organizations of various fields have proposed collections of sustainable development indicators. They are constantly reviewed and updated whenever necessary and in the interests of efficiency. Some countries manage the sustainable development indicators by using small collections of indicators, e.g. France uses 12, Germany 28, while other countries, such as Denmark, Italy, or Switzerland, use collections of indicators containing over 100 components. On the other hand, large collections of indicators may raise difficulties in the communication of general results and trends of sustainable development achievements. Some EU member states use integrated indicators, such as the human development index (HDI) or the concept of ecological footprint, for evaluation.

Asta Mikalauskiene, Raminta Narutaviciute-Cikanauske, Ingrida Sarkiunaite, Dalia Streimikiene, and Rumyana Zlateva / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 59-78

There are many means and methods to evaluate sustainable development. Ness, Urbel- Piirsalu, Anderberg, and Olsson (2007) proposed a classification of assessment methodologies into groups: rates and indicators, and measures of sustainability assessment at the levels of product projects and nationally. The majority of research related to sustainable development in the EU region is based on the data in the Eurostat database. The indicators in the database were chosen after analysing the plans of regional strategy and comparing them with the indicators mentioned in the national strategy documents. Additionally, qualitative content analysis of other documents that are significant to the field was conducted. As R. Steurer and M. Hametner (2010) proposed, the sustainable development indicators determined by the EU were compared with the national indicators and a point of reference was defined which makes it possible to compare the data and observe the changes of trends. The indicators are also classified according to importance into the top and high priority ones and the main questions. A hierarchical system is created in this way (Ibid.). Often the statistical data of the Euro- pean region is classified according to sub-regions: Eastern, Western, Central, and Northern Europe. The headline indicators of sustainable development specified in the EUROSTAT database are presented in Table 1.

Table 1. The headline indicators of the EU Sustainable Development Strategy

Theme Headline indicator Socio-economic development GDP per-capita growth rate. Sustainable consumption and Resource productivity. production Social inclusion Proportion of people at risk of poverty and social exclusion (percentage of entire population). Demographic changes Employment rate of older workers. Public health Likely life expectancy. Climate change and energy Greenhouse gas emissions. Share of renewable energy in gross final energy consumption. Primary energy consumption. Sustainable transport Energy consumption of transport relative to GDP. Natural resources Common bird index. Fishing yields outside the determined permitted biological norms. Global partnership Official development assistance as share of gross national income. Good governance. –

Source: EUROSTAT. Sustainable development Indicators, 2013.

The indicators presented in Table 1 are identified as the headline ones because their purpose is to demonstrate the general progress made by the European Union’s member states. There are over a 100 of indicators in the statistical database that can be used in a more thorough data analysis. In accordance with the Lithuanian National Strategy for Sustainable Development, a list of indicators was prepared in which 27 separate indicators were created for the measuring of social field. The data for these indicators is provided by various institutions in Lithuania, such as the Po- lice Department, Centre for Communicable Diseases, Ministry of Education and Science, State Centre for Environmental Health, and Statistics Lithuania. The latter institution presented the Re- port on Sustainable Development Indicators, in which headline indicators were selected to define 62the social status in Lithuania (Table 2).

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Table 2. Sustainable development indicators presented by Statistics Lithuania

Element of the social aspect Indicator  Population density in municipalities; Population and demographic trends  Population by main age groups.  Likely average life expectancy; Likely average life expectancy  Likely average life expectancy of a healthy person. Mortality, causes of death  Mortality by causes of death. Employment of residents  Involvement of population.  Population unemployment rate; Unemployment  Unemployment rate in districts. Poverty and social exclusion  At-risk-of-poverty rate.  Learning inclusion rate; Education  Lifelong learning rate of population aged 25-64.

Source: Sustainable development indicators, Statistics Lithuania, 2010.

Statistics Lithuania indicates that the aspiration of the social field is for the entire population of the country to have the conditions to earn and thus support the adequate standard of living and quality of life. The indicators specified in Table 2 can help determine whether the goal has been achieved and can single out the problematic elements of the country’s social elements as well as the geographic regions where the difficult situation is the most pronounced. Evidently, the most appropriate statistical indicators for the evaluation of progress are in most cases quantitative. They should have the following characteristics: wide applicability, simplicity, comprehensibility, objectivity, and possibility of measuring in various intervals. Because of these features, indicators have an important role in the field of educating the public as it makes it possible to more precisely explain to the public the processes of sustainable development. The indicators enable one to determine a point of reference and observe the change patterns. This avoids long descriptions and textual analyses and the data is typically presented visually, for the conven- ience, clarity and possibility to interpret it more easily. There are many dozens and hundreds of indicators dedicated to the description of sustainable development. However, for the sake of con- venience, they are grouped into collections which provide a clearer structure. The EU stands out as the leading region in the structure of sustainable development evaluation. This is primarily because the national sustainable development strategies and indicators of the member states are coordinated with the regional indicators, thus ensuring the possibility to evaluate the progress and compare the countries.

2. UNDERSTANDING, CAUSES, AND CONCEPTS OF POVERTY Development of the understanding of poverty. There is no unequivocal concept of poverty in the contemporary global world. Even though the world agrees that we have to aim to diminish poverty, it is difficult to attain a uniform definition of the term. Poverty may depend on various factors, such as the standards of living in a specific country, which are regarded differently in different countries. Examinations of poverty as a phenomenon first emerged in the late 18th century. English economist A. Smith defined poverty in 1776 as missing “not only the commodities which are indis- pensably necessary for the support of life, but whatever the custom of the country renders it inde- cent for creditable people, even of the lowest order, to be without” (Townsend, 1962). This defini-

Asta Mikalauskiene, Raminta Narutaviciute-Cikanauske, Ingrida Sarkiunaite, Dalia Streimikiene, and Rumyana Zlateva / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 59-78 tion is a clear reflection of Adam Smith’s economist mindset. The emphasis is also placed on the fact that the poverty standards are determined in accordance with the standards present in society. This idea results in further extensions of the terms and the arguments that, perhaps, uniform understanding of poverty in the entire world would not be favourable as the societies, as well as their deepest social problems, differ. Over a hundred years later, Alfred Marshal explored the field of poverty in 1890 and stated that the differences of climate and customs in various countries determine that a certain object is necessary in one country and completely unessential in another. However, each country has a specific threshold for the income which is necessary in order to support the existence of an individual in a society (Townsend, 2010). This definition of the concept mentions a new aspect, which had not been mentioned by researchers before, that poverty may be determined by the country’s geographic status, climate conditions, and cultural features. Another English scientist, B. S. Rowntree, presented the following definition of poverty in 1901: “poverty is insufficient income for the acquisition of the minimal amount of necessities (food, household, clothing etc.) that are required in order to support a person’s physical capacity” (Town- send, 2010). This definition refers to the importance of a person’s capability, as only by being capable/active an individual is able to perform various activities. It could be claimed that a person receives the energy required for physical activity from food, thus, according to Abraham Maslow, the acquisition of food as a fundamental human physiological need is necessary. Inability to ensure the amount of food necessary for the support of physical activity may mean that an individual is experiencing poverty. Due to the narrow nature of these concepts of poverty, as they emphasise its economic aspect, the definitions were criticised in the second half of the 20th century and proposals emerged of alternatives or updates of the concept. An economist and explorer of the concept of poverty, P. Townsend (2010), claimed that “poverty is a dynamic, shifting concept, as a human himself is a social being who exists in the society, therefore the poor can experience not only monetary but also social poverty. They are eliminated from the life of the society”. This definition is important because it focuses on the non-static nature of the phenomenon and notes that poverty can be not just economic (monetary) but also social. The scientists started looking at human needs somewhat more broadly. It was previously claimed that good quality of human life requires assurance of economic goods, but the term’s understanding has shifted: it now includes satisfaction of individuals’ social and spiritual needs. Harold Watts observed that the phenomenon of poverty should be explored from two vantage points. Poverty should be divided into economic (monetary) and non-monetary. Economic (monetary) poverty is the insufficiency of economic resources possessed by a group of the population for the purchase of economic goods and performance of economic transactions. Economic poverty describes the status of the individual. Contrary to monetary poverty, the definition of non-monetary poverty is the characteristics of the individual’s internal state of mind, the nature of his behaviour, in other words, it is the social discomfort that is being experienced (Watts, 1964). In this definition, poverty is separated from the hindrance of needs’ satisfaction resultant from only economic / financial restrictions. Economic poverty is based on quantitative characteristics of poverty measurement, while non-monetary poverty is defined by various aspects of social, demographic and personal characteristics, and consequently is more difficult to measure. Thus, the latter definition reveals that today, as we consider the definitions of poverty by early researchers Smith and Marshal, they could be assigned to the category of economic poverty. A. Sen (1987) underlines that income of an individual is “as significant as the degree to which they increase a person’s possibilities and provide the opportunity to take part in the social life”. Sen takes into account that a human is a social being and “functions” in a group, community, soci- 64ety. As an evaluation of the researchers’ definitions of the concepts, it could be claimed that

Asta Mikalauskiene, Raminta Narutaviciute-Cikanauske, Ingrida Sarkiunaite, Dalia Streimikiene, and Rumyana Zlateva / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 59-78 they all agree that a certain threshold (which may differ in different societies) exists for income which is necessary in order to satisfy the main/minimal needs necessary for human existence. However, it is noteworthy that minimal satisfaction of various human needs cannot ensure his effective development and improvement. The concept of poverty has been defined not only by individual researchers but also various international, cross-border organizations. The European Commission, the executive organ of the economic and political community of the European Union, adopted a ruling in 1984 that people experiencing poverty are defined as individuals, families or groups of individuals whose resources (material, cultural, and social) are insufficient for the assurance of the minimal lifestyle prevalent in their country (Forster, Tarcali and Till, 2004). Evidently, this definition was prepared after taking into consideration the criticism of the previous definitions of the phenomenon. Since the European Union consists of many countries of Europe (and their number keeps increasing as it is joined by new member states), the definition of the European Commission emphasises that different countries have different subsistence levels. The United Nations, an organization which tackles various economic, social, and humanitarian problems, declares that in essence poverty is the inability to have possibilities and choice, a violation of human dignity. In other words, it is the lack of basic possibilities to effectively take part in the society, absence of sufficient food and clothing for oneself and family members, unavailability of school or medical centre one could attend, lack of land where one could grow oneself food, absence of a job which would earn one money, and inability to receive credit. This signifies insecurity, powerlessness, and exclusion of individuals, households, and communities. The situation of poverty increases sensitivity to violence and is usually characterised by people living in a vulnerable environment which potentially lacks access to clean water and the appropriate sanitary conditions (Gordon, 2005). The UN’s concept of poverty is much wider, i.e. more detailed, than the previously analysed definitions. This is because the UN has founded numerous specialized agencies (FAO, WHO, UNDP etc.) whose goals are associated with the tackling of poverty and its consequences, and the presented definition simultaneously enables a better understanding of the UN’s areas of activity. A wider definition gives a clearer picture of the potential consequences of poverty and demonstrates marked distinctions between countries and regions. Access to clean water or assurance of sanitary conditions and stable supply of electricity may not be among the problematic areas in developed countries, whereas in the developing world it is a daily issue which is still difficult to solve. World Health Organization, a subdivision of the UN, describes poverty as violation of fundamental human attributes, including health. People who face poverty are unable to adequately feed themselves, and the poor receive less information and find it harder to reach the centres that provide medical services, as a result facing greater threat of contracting various diseases. The diseases result in the diminishment of income and savings, affect their productivity, and the quality of life in general suffers even more, increasing the level of experienced poverty (WHO, 2005). Clearly, this definition of poverty has been determined by the nature of the organization’s activities and focuses more on the consequences of poverty to the human health. This description, like several others, underscores that people who experience poverty cannot feed themselves adequately. Significantly, the usage of the word “adequately” may be understood at several levels, in terms of both quantity and quality. It is important that the food consumed by the person was not only adequate in quantity but also appropriate in the extent of its energetic impact and characteristics of the microelements. Typically the individuals who have low financial funds to spend on food resources cannot choose high-quality food products.

In Lithuania, people who experience poverty are defined as those whose income and other resources (material, cultural, and social) are so scarce that they do not ensure the standards of living that are common to Lithuanian society. Due to insufficient income or other resources, these people 65

Asta Mikalauskiene, Raminta Narutaviciute-Cikanauske, Ingrida Sarkiunaite, Dalia Streimikiene, and Rumyana Zlateva / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 59-78 cannot take part in the fields of activities that are considered commonplace to other members of society (Poverty Reduction Strategy, 2000). This definition is presented in the strategy of poverty reduction, which was published in 2000 and has remained unchanged until now. The definition given in this strategy is identical to the one proposed by the European Commission. As explained below, the EU member states use identical indicators to describe poverty. This makes it possible to compare the situation in different countries, to present the general statistics of the region, and to objectively present the extent of poverty. Thus, naturally, the definition of the term is identical as well. After exploring the trends of the changes in the concept of poverty over the recent decades, a shift was noted from the focus on insufficient funds for the satisfaction of fundamental physical needs to the analysis of the concepts of individual quality of life, equal opportunity, and the freedom of choice and possibility. There is also a heavy emphasis on the general prevalent living conditions in the country. Importantly, the variation between the definitions of the concepts has been determined by the differing societies from which the field’s researchers hail, or the varying interests of the organizations, e.g. the World Health Organization sees poverty through its impact on individual health, whereas economists and organizations promoting economic growth tend to describe poverty only in terms of insufficient financial resources. Usually the poor are defined as people whose resources and quality of life are lower than the average standard in the country. It is possible to claim that the concept is multifaceted and constantly shifting, depending on how the society and the phenomenon of poverty itself are developing. In order to continue the research of the issue and the consequences of poverty, especially in different regions or countries, it is necessary to consider how poverty is perceived in that country and what the minimum resources required to satisfy the people’s needs are. In one country they may be considered poor while in the other they may be named average income recipients. In Lith- uania, the definition of poverty includes physical as well as social and cultural aspects. This leads to the conclusion that the definition of poverty in Lithuania is similar to the definitions of poverty in other more developed countries. The causes and concepts of poverty. There are several different theories on the causes of poverty prevalent in scientific literature foreseeing certain actions in order to minimize or eliminate those causes. Economist R. Kersiene (2011) named 5 factors that cause poverty:  Poverty is caused by individual shortcomings. Individuals are responsible for their poverty situation. It is assumed that they would avoid this problem by working more and harder. Often attempts are made to explain this poverty-causing factor by certain predetermined genetic characteristics (e.g. intellectual aptitude). Based on this perspective, various anti-poverty programs are implemented whose aim is to employ the poor.  Poverty is caused by cultural beliefs. Poverty is created by a multitude of prevalent beliefs, values, and skills that are transferred from generation to generation. It is noted that people are not to blame for being simply a part of a certain culture or subculture. Simply put, individuals are impacted by the environment in which they function. This perspective differs from the first one, which was about individual shortcomings, in that it establishes that in the case of cultural beliefs there is no appeal to the person’s abilities. Poverty culture is actually the culture of poor people who live in isolated communities or poor regions, or it is the social conditions in which they create their own rules, lifestyle norms, and values that are usually clearly different from those of the remaining majority of the society.  Poverty is caused by economic, political, and social deformations or discriminations. This perspective emphasises that the cause of poverty is an economic, political and/or social system. It claims that the imperfections and weaknesses of the individuals result in limited alternatives and resources for them that could ensure the creation of welfare. It is considered that, because of the “faults” of the system and regardless of their competences, the disadvantaged lag behind the people with average income. The benefits provided by the social protection, includ- 66

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ing healthcare and health promotion, are also insufficient for the low-skilled workers. Obstruc- tions of elimination of poverty exist together with the political system in which the interests and participation of the poor are hardly possible, or their interests are represented wrongly and poorly. This is because the poor are less interested in political matters and rarely take part in discussions, and thus the representation of their interests is poor or lacks strong support.  Poverty is caused by geographic differences. The poverty of rural areas and the slowly developing countries reveals that poverty can be caused by an unfavourable geographic location. In certain regions, the resources necessary for the assurance of the population’s well-being are not as easily accessible as in other countries. These regions are not as competitive and struggle to attract investments that could reduce the risk of poverty. This perspective, which names geographic differences as the cause of poverty, proposes that the problem could be solved by focusing on resources and processes that could make the region independent. It proposes promotion of the competitiveness of local businesses, the creativity of the members of society, the investments into development of various infrastructures etc.  Poverty is caused by accumulated and cyclical inter-dependencies. This perspective on the emergence of poverty combines the components of the previous perspectives. It examines separate members of the society and the people in their surroundings that have found themselves in a circle of opportunities and obstacles which becomes cyclical. To put it simply, each action has a reaction and, as a result, a chain reaction is created. For instance, at the state level, as the number of the poor increases, the portion of the budget allocated for the social benefits also increases, the amount of taxes collected by the state decreases, less funds are allocated for the promotion of business etc. At the individual level, insufficient resources for learning result in lower qualification, which may affect employability and financial stability, decline of personal motivation etc. In order to combat the poverty that arises from these issues, socialization, creativity, and relationships with the people in the surroundings have to be en- couraged, leadership qualities have to be developed, skills have to be improved, and an attractive environment has to be built (Kersiene, 2011; McKernan and Ratcliffe, 2005). All of the described causes of the emergence of poverty delve deeper than just the economic nature of poverty. Here it is seen as just one of the components of the process. Evidently, poverty is multidimensional. The analysed causes may arise as a combination of several of them in one place simultaneously, thus making the phenomenon of poverty even more complicated. In the analysis of the understanding of poverty, two main concepts of poverty can be distinguished: absolute poverty and relative poverty (Sileika and Zabarauskiaite, 2006). These two concepts are fundamental and very significant in the attempts to measure the boundaries of poverty and other indicators of poverty. These concepts diverge in their perspectives on people’s needs depending on the level of their well-being in the country they live in. In the concept of absolute poverty, poverty is associated with the minimum level of satisfaction of personal needs (which has a tendency to increase as the production and the progress of the entire society increase). In other words, it is the minimum standard of consumption. Based on this concept, a certain quantity of economic resources is determined which ensures the minimum satisfaction of the personal needs of the residents. Individuals whose income is lower than this predetermined amount (i.e. the threshold of poverty) are defined as poor (Bidani, Datt and Lanjouw, 2001; Bellu and Liberati, 2005). Generally, absolute poverty is the condition when the family income is lower than the predetermined official threshold of poverty. In Lithuania, the equivalent of this threshold of poverty is the basic social benefit (previously called MGL, the minimum standard of living). Notably, the concept of absolute poverty is more widely applied in the analysis of poverty issues in the less developed countries where the individuals suffering from poverty have insufficient financial resources to satisfy their minimum essential needs (Sileika and Zabarauskaite, 2006).

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Another widely popular concept in the analysis of poverty issues is the concept of relative poverty. The essence of this concept is the following: it is based on the measurement of the individual standard of living, which is defined in relation to the distribution of the economic resources available to other residents, or, in other words, to the average standard of living in the country. In the case of the concept of relative poverty, the poor are defined as the individuals whose economic (financial) resources are much lower than the average of the country (Bidani, Datt and Lanjouw, 2001). It can be said that the concept of relative poverty interprets poverty as the phenomenon of economic inequality between individuals. Analysis of the concept of relative poverty distinguishes the relative threshold of poverty, which is related to the average indicators of income/expenses: the mean and the median. It is precisely the relative threshold of poverty that is completely dependent on the distribution of income/expenses in the society, it shifts as the distribution of individuals by the standard of living is shifting, i.e. as the average standard of living in the country is increasing or decreasing. The relative threshold of poverty rises as the income/expenses rise as well. As a result, the relative threshold of poverty is higher in the richer regions. The relative level of poverty is typically measured in the developed countries (Sileika and Zabarauskaite, 2006). The developed countries focus more on the social and cultural rather than physical features of poverty. Since the relative threshold of poverty varies from region to region, this concept is not very appropriate for comparisons at the global level. Furthermore, there are many cases when the country has great extremities: its population includes both very rich and very poor residents. In such a case, the relative threshold of poverty is distorted and does not represent the actual situation. Based on scientific literature, the concept of relative poverty is used in the more developed countries, as the income is much higher, therefore, in order to measure poverty, both the physical and the social-cultural aspects of poverty are used (of course, taking into account the culture and traditions of the specific country). That is, if the individual even has sufficient financial resources to satisfy the essential physical needs, he may consider himself poor because his income and standard of living is much lower than the average level in the country he lives. In the analysis of scientific articles (Bidani, Datt and Lanjouw, 2001; Ravallion, 2008), another concept can be distinguished: that of the subjective poverty. The concept of subjective poverty demonstrates the assessments of the individuals themselves, their opinion of their quality of life and satisfaction of personal needs. Based on the concept of subjective poverty, the minimum amount of resources, i.e. the acceptable standard of living, is determined by the country’s residents themselves. The subjective threshold of poverty is determined by inquiring the respondents on what amount of money they would find ample to satisfy their minimum needs. Typically the subjective threshold of poverty is much higher than the threshold of poverty calculated using other methods, as it is established by researching individuals who receive varying amounts of income, and the minimum needs of the rich are much higher than those of the poor. This concept is not used, because usually the aim is to measure and evaluate the poverty level in the country as objectively as possible, and this method is not objective. The diversity of the concepts of poverty makes it possible to better understand the issue and provides the opportunity to measure it in different ways. The absolute threshold of poverty and the relative threshold of poverty are representative indicators that can be utilised to evaluate the situation, to observe a shift in a specific interval, or to compare regions. Subjective concept of poverty is not as representative, but it can be helpful in exposing the views of the public and the dominant perspective on the issues of income, expenses, and prices. It can also help determine how accurately the public perceives the issues of poverty present in the country.

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2. THE ISSUE OF FOOD SHORTAGE Relations between poverty and food shortage. The socio-economic status of the family (financial resources, human and social capital) affects the entire family and the development of the children. Adults and children who experience deprivation are at higher risk and can be more vulnerable both physically and emotionally and have more problems with health and behaviour (Conger and Donnellan, 2007). Children aged 5 or less are particularly vulnerable to malnutrition, which is mainly a consequence of poverty. Hunger may not be experienced but the quality of food and the amount of microelements necessary to the organism are very important. Shortage of necessary substances raises the threat of anaemia, hindered mental development, absence of immune system, and blindness (Gilbert, 2004). It may be concluded that hunger and malnutrition are the main risk to health. If countries are unable to ensure that their residents are adequately fed, i.e. that there are sufficient food resources, then the poverty level of the country will not be reduced and the state’s economic development will be in disarray. Usually, as a result of complicated financial status, families are at risk of poverty and social exclusion. Hunger is typically perceived as the condition caused by the shortage of food. It is the desire to eat and the deficiency of energetic food substances in the organism. The individual senses inexo- rable urge to seek food. In the analysed sources of information, hunger is described as the sense of discomfort caused by the sensations of the body due to the desire to receive more food. All people experience this sensation at some point or another. However, to the people in the developed countries, this feeling is usually short-lived, until the time when another dose of food is received, according to the predetermined normal daily diet (The hunger project, 2008). When the sense of hunger continues for a longer time period, and the individual does not receive food, this signals an issue that can have grave consequences. The commonly used terms are food shortage, undernourishment, and malnutrition. The first two terms mostly reflect the quantitative perspective on food, which can be determined by various factors. The term malnutrition describes qualitative aspects of food and refers to the energetic value of food, the shortage of mineral elements that are necessary for a balanced development of a human organism. Many people in the world are unable to properly feed themselves because they lack enough funds to acquire food, much less better quality food which would have more of the necessary microelements. Food shortage is also characterised by individuals lacking funds to acquire the substances required to grow food. In the analysis of the relations between poverty and food shortage issues, it is necessary to emphasise that not every poor person experiences hunger, but the people who face food shortage are almost always poor. Taking into consideration the consequences caused by the phenomenon of poverty, hunger and the even more horrible stage of food shortage, starvation, are assigned to the category of absolute poverty. It is often mentioned that this is an instance of extreme poverty. As stated by numerous researchers (Townsend, 1985; Gilbert, 2004; Sileika and Zaba- rauskaite, 2006; IFPRI, 2013 et al.), the poor experience hunger because of various reasons: high prices, global economic recession, diseases, exclusion and discrimination, inadequate government decisions and ruling, unfavourable country climate, natural disasters. Notably, the group of the hungry also contains highly vulnerable groups that require greater attention to nutritional substances, namely people who have various diseases such as AIDS or tuberculosis, pregnant and breastfeeding mothers, small children. The solution to the issue of food shortage has to be immediate, before the risks and challenges arising from the problem can no longer be theoretically and practically managed. If no actions are taken, the consequences can be tragic and even with the forces of the global community and organizations, the problem will not be solvable. 69

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According to the data of the European Commission (2012), 40 million of the 116 million EU population experience great material deprivation and poverty, while one of the greatest manifestations of deprivation is food shortage, “individuals cannot receive food in sufficient amount and quality” (European Commission, 2012). Children facing poverty are an acute problem, as they are one of the most vulnerable risk groups. In the EU, 25.4 million children are threatened by poverty and social exclusion, which can result in adverse health effects, decline in learning performance etc. The Europe 2020 Strategy intends to reduce the number of the poor and individuals in the risk group by 20 million until 2020. The aims of the reduction of poverty and hunger issues are named as the first item in the Mil- lennium Development Goals. Thus there can be no doubt that this is a priority area not only in the developing countries but also elsewhere in the world. Historical development and statistics of food shortage. Food shortage and hunger result in a much more complex socio-economic phenomenon: starvation. Starvation may be treated as the consequence of poverty. It may also arise due to various natural disasters (e.g. earthquakes, floods, droughts), military conflicts (Afghanistan, Balkan countries during the Spring of Hungry Na- tions), extreme country policies (North Korea), or the country’s geographic location in infertile lands (the Southern part of the Sahara desert in Africa). The threat of starvation also increases as the population rapidly rises and the food production cannot be increased accordingly. The issue of starvation and food shortage has been recorded in historical sources as early as 2500 B.C. in an- cient Egypt. In the European history, large scale food shortage has been mentioned since 9th century, usually as a result of infertile years, drought, wars, or economic crisis. Russia, Ireland, France and Great Britain are countries where the issue of food shortage was frequent and pronounced during the Middle Ages (5th – 15th c.) and the modern times (15th c. – 1918). In a large number of countries, food shortage and eventually starvation was a major problem both during and after the years of World War I and World War II. In the Nazi and Soviet totalitarian systems, starvation was used as a measure of genocide (Jonuškienė, 2002).

Table 3. The prevalence of undernourishment (population size and percentage of prevalence rate)

1990-1992 1999-2001 2004-2006 2007-2009 2010-2012 1000 million 919 million 898 million 867 million 868 million Global total 18.6 % 15 % 13.8 % 12.9 % 12.5 % 20 million 18 million 13 million 15 million 16 million Developed regions 1.9 % 1.6 % 1.2 % 1.3 % 1.4 % 980 million 901 million 885 million 852 million 852 million Developing regions 23.2 % 18.3 % 16.8 % 15.5 % 14.9 % 175 million 205 million 210 million 220 million 239 million Africa 27.3% 25.3% 23.1% 22.6% 22.9% 739 million 634 million 620 million 581 million 563 million Asia 23.7% 17.7% 16.3% 14.8% 13.9% Latin America and the 65 million 60 million 54 million 50 million 49 million Caribbean 14.6% 11.6% 9.7% 8.7% 8.3% 1 million 1 million 1 million 1 million 1 million Oceania 13.6% 15.5% 13.7% 11.9% 12.1%

Source: FAO (Food and Agriculture Organization of United Nations). The State of Food Insecurity in the World (2012m.)

Today the subject of food shortage is very acute in the countries of Africa and Southern Asia. Numerous globally functioning charity organizations have been founded in order to fight this 70

Asta Mikalauskiene, Raminta Narutaviciute-Cikanauske, Ingrida Sarkiunaite, Dalia Streimikiene, and Rumyana Zlateva / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 59-78 global problem, e.g. Food Bank, Caritas, Order of Malta. These organizations function as represen- tations in various countries. In 1961 the United Nations founded the World Food Program, which is dedicated to providing food support during and after emergencies, helping the communities recov- er. In order to tackle the starvation problem globally both in developed and developing countries, the UN Food and Agriculture Organization was established in 1945. This organization seeks to increase the agricultural productivity, to raise the quality of living conditions in remote regions, and to contribute to improved nutrition opportunities and economic growth. The organization prepares reports of statistical data on the size of the population suffering from malnutrition (The State of Food Insecurity in the World). In the report for 2012, the organization analyses how economic growth can contribute to the reduction of the food shortage issue and also presents the data of the last 20 years (Table 3). The data presented in Table 3 demonstrates that the greatest extent of insufficient nutrition is in the developing regions, where the undernourishment affects about 852 million residents (in 2012). The major progress was achieved before 2007, and since then the reduction of global starvation has remained almost invariably stable. The cause of this is thought to be the increase of food prices and the global economic crisis. Taking into account the scale of the growth of GDP and GDP per capita in various regions, it is worth noting that the economy has been growing steadily over the last decade (even though a decline is evident when the global economic crisis struck in 2008, after which the GDP growth slowed down). It could be claimed that this contributed to the reduction of the issue of food shortage. According to the FAO (2012), in order to strengthen the influence of economic growth on the diminishment of the problem, it must be ensured that:  The growth would reach and include the poor. It is likely that the increased employment opportunities would result in increased income to the unemployed poor people. Whether or not the consequences of economic growth will contribute to the reduction of poverty will largely depend on the level of inequality in the country. The greater the inequality between the social groups in terms of access to various resources (land, water, education, health), the harder the poor will find it to join the growth process. Because of high inequality, especially in education, the poor people will struggle to find a job with better pay. However, it is likely that economic growth in the agricultural sector can assist more in the decrease of poverty and its consequences, as the agricultural growth has a direct positive influence on the remote locations where the impoverished people reside as well.  The poor would use the extra income on the improvement of food quantity and quality, health, and sanitary conditions. A problem is encountered as even though the higher income is spent on food, its energetic value is not considered, instead giving the priority to taste and smell. In other words, individuals wish to consume more valuable food but, primarily, tastier. The Food and Agriculture Organization also notes that women who have more influence on the management of household income spend the extra funds on the food that is healthier and has greater energetic value.  The government would accumulate resources for public products and services (education, public health services etc.) that would benefit the poor and the hungry. There is encouragement of the reforms and programs of various institutions and investments into remote areas in order to improve the lives of the poor. According to the organization, a large portion of public revenue should be allocated to the education on the issues of finances, skill development, and public health measures.

Considering these three aims, it is clear that a structure and purposeful political decisions are necessary. The problem of hunger also needs to be given a priority. A correlation is required between the political decisions and actions as well as the institutional transparency and performance of obligations. In order to ensure that consumption habits change in a positive way as the income increases, information campaigns are necessary for the target groups on the energetic value of 71

Asta Mikalauskiene, Raminta Narutaviciute-Cikanauske, Ingrida Sarkiunaite, Dalia Streimikiene, and Rumyana Zlateva / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 59-78 food and the possibilities of choice and alternatives. On the other hand, a lot of benefit can be provided by including the target vulnerable group in the process of decision-making. Global Hunger Index. When analysing the projections of population growth, the feeding / support of the entire humankind becomes relevant. For this reason, there is an aim towards such a production of goods/services that would make it possible to provide more and ensure the necessary standard of living while using less resources. In pursuit of this goal and based on Maslow’s hierarchy of needs, in which the need of food is the main one, the problem of global hunger has to be solved. The solution of the problem of food shortage has been rather slow in the last several decades and the Millennium Development Goal to “halve poverty and hunger in the world” will not be fully achieved before 2015. The International Food Policy Research Institute (IFPRI), founded in 1975, proposed the Glob- al Hunger Index in 2006 as a method to measure the problem of hunger in the world. This collection of multidimensional statistical data, which is updated annually, demonstrates the country’s status in terms of hunger. The Global Hunger Index is used as measurement in 120 developing and transitional countries. Utilising this index, a change can be observed not just inside the country but also in the entire region and the world, thus a sufficiently objective comparison of the situation is possible. Food shortage and hunger can be measured in various different ways, using diverse statistical quantitative and qualitative indicators. The use of the latter would provide a large degree of subjectivity if there was a need to compare the countries’ food shortage situation. Therefore typically statistical data is used and certain indicators are selected based on it that are collected in the researched countries during a determined time period. Since the issue of hunger is multifaceted, expressing it in one indicator would have little purpose. If there is a unified and sufficiently representative index, it is likely that the problem will be solved more effectively and in a more balanced manner. The examined multidimensional Global Hunger Index consists of three indicators of equal weight that are expressed in percentages (IFPRI, 2013):  Undernourishment. The indicator indicates proportion expressed in percentage of the country’s society characterised by undernourishment, i.e. receiving insufficient amount of necessary mineral materials.  Child underweight. The indicator indicates the proportion of children aged younger than 5 whose weight is too low taking into consideration their age. It reflects the children’s exhaustion and normal pace of growth.  Child mortality. This indicator indicates the mortality of children aged younger than 5. The indicator partly reflects the consequence of insufficient nourishment and inadequate, unhealthy environment.

Evidently, this index the most clearly reveals the risk level of the most vulnerable group, children, to be categorized together with the hungry. In spite of the distinction of the separate population group (children), the index also reflects the remaining portion of the society which is unable to feed themselves adequately. After managing the data methodically, the index is presented on the scale from 0 (countries that do not face hunger) to 100 (extreme hunger situation in the country). Notably, the index’s extremities of 0 and 100 do not exist in practice. After preparation of the index, countries are assigned to one or another group based on the degree of the problem:

 0–4,9 low  5–9,9 moderate  10–19,9 serious  20–29,9 alarming  30–100 extremely alarming

The data presented in Figure 1 clearly reflects the inequality between different regions of the world. The differences are particularly extreme when comparing the region of Eastern Europe 72

Asta Mikalauskiene, Raminta Narutaviciute-Cikanauske, Ingrida Sarkiunaite, Dalia Streimikiene, and Rumyana Zlateva / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 59-78 and CIS countries with the region of South Asia. Even though in the latter region the value of the index declined the most – by 10.8 (from 31.5 to 20.7) during the analysed period of over 20 years, it still remained the highest in comparison with other regions in 2013. Taking into consideration the Millennium Development Goals, it is foreseen that the goal will not be completely achieved until the intended date. However, when we evaluate regions separately, it can be observed that in the regions of Southeast and East Asia, Latin America and the Carib- bean, and Eastern Europe and CIS countries, the hunger problem has halved over the analysed period from 1990 until 2013 (respectively, 15,9–7,6; 9,5–4,8; 5,3–2,7). Still, the regions that raise greater concerns, South Asia and South Africa, affect the overall statistics.

Figure 1. Regional distribution of Global Hunger Indicator’s (GHI) components in 1990, 1995, 2000, 2005, 2013.

Source: IFPRI. Global Hunger Index 2013.

Analysing the index’s components separately, we note that the child mortality issue is the most evident in the region of South Africa. Significantly, the mortality indicator is highly affected not just by food shortage and undernourishment, but also various diseases, such as malaria, tuberculosis, and AIDS. The children’s low weight issue decreased the most rapidly in the region of Latin America and the Caribbean. IFPRI organisation underlines that due to the shortage of timely data from certain countries (e.g. Afghanistan, Myanmar, Somali), it is impossible to calculate the precise and objective index for some countries and therefore the overall data of the regions may be distorted because of this. Analysing the Global Hunger Index separately in the countries of Eastern Europe and the CIS countries, it has been noted that in most countries, including Lithuania, throughout the entire researched period the index is expressed as <5 (lower than 5) and is categorized as low degree of the problem. In order to analyse the transitional countries, this expression of the index would not be objective or informative, thus a separate look at each component of the index is necessary. Even though the Global Hunger Index proposed by the IFPRI is probably the most popular, the most frequently mentioned in literature, and combines three different aspects of hunger issues, 73

Asta Mikalauskiene, Raminta Narutaviciute-Cikanauske, Ingrida Sarkiunaite, Dalia Streimikiene, and Rumyana Zlateva / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 59-78 nevertheless the method has also received criticism. According to Masset (2011), all three indicators used in order to calculate the index correlate highly between themselves and thus duplication is unavoidable during calculation. Doubts are expressed in the decision to add up the percentages of indicators without introducing a coefficient. The researcher also claims that the index is not very sensitive to changes, especially short-term ones. Food system. When discussing the reduction of food shortage, also frequently mentioned are reforms of the area’s management and efficient policymaking in order to ensure balanced distribution of food and other resources required for the solution of the problem. For this purpose, food system and its models are used. World Food System is a non-linear system composed of various processes, infrastructures, expenses and production which is dedicated to the solution of the issue of population feeding (Grant, 2012). The system makes it possible to rationally review the elements and their unions. Food systems provide support to the solution of the issue of hunger by considering the population’s well-being and health, the environmental and social surroundings, and the economic context. P. J. Ericksen (2008) claims that food system is an interaction between the biological, geological, physical, and human environment. This interaction determines various activities (from production to consumption) that in turn determine results (social security, environmental safety etc.). Evaluating the definitions of the concept, we observe that food system is dependent on the social, environmental, and economic status: all elements of sustainable development are included. Naturally, the entire environment is changing and developing, the extent of urbanization is shifting, and technological progress is achieved, therefore the system itself has to change and adapt to today’s conditions (Table 4).

Table 4. Comparison of the features of traditional and modern food system

“Traditional” food system “Modern” food system Employment of people in food production. Employment of people in food processing, packag- ing, and retail. Supply chain is short, local. Supply chain is long. Food production is diverse, of varied intensity. Food production variety is low, but of high intensity. Typical farm is small to moderate. Typical farm is industrial, large. Typical food consumed is basic staples. Typical food consumed is processed. Food is purchased in local shops or markets. Food is purchased in supermarkets. Nutritional concerns include under-nutrition. Insuf- Nutritional concerns include overconsumption of ficient quantity of calories and necessary micro- fat, sugar, and salt. Chronic diseases resultant from elements. dietary habits. The main threats to the system are unfavourable The main threats to the system are international weather and production disturbances. price and trade problems. The main environmental threats include soil degra- The main environmental threats include chemical dation, land clearing, and pesticide pollution. runoff, increasing water demand, and harmful gas emissions. Influential scale spans from local to national level. Influential scale spans from national to global level.

Source: adapted from Maxwell, S. and Slater, R. Food policy Old and New (2003).

When analysing the changing elements of the food system it becomes clear that industrializa- tion has a very high influence. The change from buying products in local shops or markets to purchasing them in supermarkets is yet another change which at first glance would seem to have a negative connotation, as there is frequent encouragement to support local business. However, from a wider perspective, supermarkets are often a part of global corporations and can offer a 74

Asta Mikalauskiene, Raminta Narutaviciute-Cikanauske, Ingrida Sarkiunaite, Dalia Streimikiene, and Rumyana Zlateva / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 59-78 wider selection of products and higher quality goods and also ensure delivery of large amounts of food at the required/predetermined time. Obviously, the new food system does not necessarily have to be seen as universally better and more innovative. This statement is confirmed by the aspect of environmental threats, which remain or in certain cases even increase in a modern food system. These environmental threats may be caused by the same new technologies that promote the changes. Additionally, today’s threats include GMOs (genetically modified organisms), and chronic diseases such as obesity, diabetes, etc. that are caused by inadequate nutritional habits. Naturally, the latter issues are much more relevant in the developed regions. Another important change is the shift towards commercial growers and sellers, and most people work not directly with food and growth but in other areas of the food industry. A systematic view makes it possible to understand the fundamental factors and the results of their interaction that determine the actions of the stakeholders. The Food and Agriculture Organi- zation, working together with scientists (Kennedy, Nantel and Shetty, 2004), presents a conceptual model of the food system. They (Ibid.) points out that a conceptual model is more applicable in the developing countries. The following elements are distinguished as the most important in causing changes in the system:  Economic factors.  Urbanisations, market liberalisation, direct foreign investments, income increase.  Food product supply.  Manufacturing of food products based on intensive agriculture, long shelf life of product, replacement of food markets with supermarkets, food availability in all seasons.  Social factors.  Migration of rural and urban populations, increasing women’s employment, sedentary lifestyle.  Nutrition habits.  Blending of different dietary habits, food product selection is based on accessibility, increased amount of consumed fat, sugar, and salt.

It is evident that, in the analysis of the food system, a holistic, all-encompassing approach has to be applied. The elements are related to one another. For instance, looking at the factors of the food product supply – supply through supermarkets and accessibility in all seasons determine a factor of nutrition habits: the wider selection of food products. Another example: a social factor – migration of rural and urban populations – affects a factor of nutritional habits – the blend of different dietary habits etc. It is worth emphasising that in order for this system to work, supervision of various institutions and their mediation in the coordination of economic and social factors are required. In conclusion, it can be claimed that the model of food system reflects the undergoing changes in the society, the economy, and, of course, in food production, distribution, and consumption. A clear system is required in order to ensure food security, a solution to the issue of food shortage has to be developed in a sustainable way and the sustainable development strategy has to be implemented. The aim is to combine various systems related to food production, delivery/distribution and waste management, and to improve both the economic and the social situation. The combination of the systems enables assurance of a more efficient use of various types of resources.

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CONCLUSIONS The implementation of sustainable development is based on strategies, and even though they are not legally binding documents, it is customary that regional strategy plans are ratified and adapted by countries at national level. Each country has different problematic areas and their governments determine which areas is high priority. National governments have to ensure transparency of the implemented projects, financial investment opportunities, and distribution of responsibili- ties among institutions that have to be renewed as well. The progress is best measured by utilising indicators. They make it possible to evaluate separate phenomena, fields, cities, and regions. In the context of sustainable development, the social field has become more relevant, as it is observed that poverty level is not decreasing in the world. A demand emerged to explore the content of poverty as a separate element. In the understanding of poverty, there was a gradual shift from the initial idea – the emphasis on insufficient income for the satisfaction of the primary physical needs, to the analysis of individual quality of life and the concept of the possibilities of choice. The phenomenon of poverty is multifaceted and constantly shifting depending on the development of the country and the entire society, and consequently scientific literature distinguishes the following causes of poverty: individual personal shortcomings; cultural beliefs; faults of the economic, political, and social system; geographic differences; accumulated and cyclical inter-dependencies. It was determined that generally people become poor due to the lack of financial resources and inability to receive income, while the chief manifestations of poverty include unemployment, emigration, and food shortage. People who experience poverty are at risk, individuals are more vulnerable and face more severe/frequent health problems, while their resources and general quality of life are lower than the average level in their country. For the purpose of nurturing a stronger, healthier society and reducing the problem of hunger more rapidly, a structured social security system is required. It is highly important to deepen our understanding of the issue of poverty, to seek methods of reducing the problem, and to purposefully direct economic growth toward the decrease of the poor. This is significant because individuals who suffer from poverty have no possibilities to effectively take part in the social life, which not only reduces their possibilities to be represented in the respective institutions and in the policymaking, but also results in the disturbance of the opportunity to realise their potential, damage of their health, unbalancing of society, negative impact on the environment, and violation of the concept of sustainable development.

REFERENCES Bellu, L. G., Liberati, P. (2005), Impacts of Policies on Poverty: the definition of povertylapkritis, EASYPol, available at: www.fao.org/docs/up/easypol/312/povanlys_defpov_004en.pdf Bidani, B., Datt, G., Lanjouw, J. O. (2001), Specifying Poverty Lines: How and Why. In Asian Devel- opment Bank, available at: citeseerx.ist.psu.edu/viewdoc/download? doi=10.1.195.2504&rep=1&type=pdf Conger, R. D., Donnellan, M. B. (2007), “An Interactionist Perspective on the Socioeconomic Con- text of Human Development”, Annual Review of Psychology, Vol. 58, available at: http:// www.annualreviews.org/doi/abs/10.1146/annurev.psych.58.110405.085551 Ericksen, P. J. (2008), “Conceptualizing food systems for global environment change research”, Global Environmental Change, Vol. 18, No. 1, available at: www.sciencedirect.com/science/ article/pii/S0959378007000659# European Commission (2012), Poverty. The Commission proposes a new European Fund for Aid for the Most Disadvantaged, (In Lithuanian), available at: http://europa.eu/rapid/press- release_IP-12-1141_lt.htm 76

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Eurostat (2013), Sustainable development indicators, available at: http://epp.eurostat.ec. europa.eu/portal/page/portal/sdi/indicators FAO (2012), Toward the future we want [Interactive]. available at: www.fao.org/docrep/015/ an894e/an894e00.pdf Forster, M. F., Tarcali, G., Till, M. (2004), Income and non-income poverty i Europe: What is the minimum acceptable standart in an enlarged European Union?, available at: http://pdc. ceu.hu/archive/00004295/01/1135243684_51096.pdf Gilbert, G. (2004), World poverty, ABC-CLIO-JAV, p. 12. Gordon, D. (2005), Indicators of poverty & hunger, available at: http://www.un.org/esa/socdev/ unyin/documents/ydiDavidGordon_poverty.pdf Grant, M. (2012), World Food System Center Strategy Overview 2012-2014, available at: www. worldfoodsystem.ethz.ch Hardi, P., Zdan, T. (1997), „Assessing Sustainable Development: Principles in Practice“, The International Institute for Sustainable Development, p. 175. Harger, Meyer (1996), "Definition of indicators for environmentally sustainable development", Chemosphere. Vol. 33, No. 9, pp. 1749-1775. IFPRI (2013), Global Hunger Index: The challenge of hunger. Building resilience to achieve food and nutrition security, available at: www.ifpri.org/publication/2013-global-hunger-index Jonuskiene, Z. (2002), Universal Lithuanian Encyclopedia, Institute of Science and Encyclopaedia Publishing. Famine. (In Lithuanian). Kennedy, G., Nantel, G., Shetty, P. (2004), “Globalization of food systems in developing countries: a synthesis of country case study” in Globalization of food systems in developing countries:impact on food security and nutrition, FAO, Rome. Kersiene, R. (2011), "Poverty and its Causes in Lithuania", Economics and Management, No. 16, pp. 535-542 (In Lithuanian), available at: www.ktu.lt/lt/mokslas/zurnalai/ekovad/16/1822- 6515-2011-0535.pdf Lyytimaki, J. (2012), “Evaluation of sustainable development strategies and policies: The need for more timely indicators”, Natural Resources Forum, Vol. 36, No. 2, available at: http:// onlinelibrary.wiley.com/doi/10.1111/j.1477-8947.2012.01447.x/pdf Masset, E. (2011), “A review of hunger indices and methods to monitor country commitment to fighting hunger”, Food Policy, Vol. 36, Sup. 1, available at: http://www.sciencedirect.com/ science/ article/pii/S0306919210001211# Maxwell, S., Slater, R. (2003), “Food policy Old and New”, Development Policy Review, Vol. 21, No. 5-6, available at: http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8659.2003.00222.x/pdf McKernan, S., Ratcliffe, C. (2005), “Events that trigger poverty entries and exits”, Social Science Quarterly, Vol. 86, Sup. 1, pp. 1146–1169. Ness, B., Urbel-Piirsalu, E., Anderberg, S., Olsson, L. (2007), “Categorising tools for sustainability assessment”, Ecological Economics, Vol. 60, No. 3, available at: http://www. sciencedirect.com/science/article/pii/S0921800906003636# Ravallion, M. (2008), Poverty lines, available at: www.sow.vu.nl/pdf/FPS/Ravallion _povertylines_PALGRAVE.pdf Sen, A. (1987), “The Living Standart”, Oxford Economic Papers, Vol. 36, available at: http://www. jstor.org/stable/2662838 Sileika, A. and Zabarauskaite, R. (2006), "Poverty, its Measurement and Trends in Lithuania", Eco- nomics, Vol. 74, (In Lithuanian) available at: http://www.leidykla.eu/fileadmin/ Ekonomi- ka/74/Algis_Sileika__Rasa_Zabarauskaite.pdf Streimikiene, D., Mikalauskiene, A., Barakauskaite-Jakubauskiene, N. (2011). "Sustainability assessment of policy scenarios", Transformations in business and economics, Vol. 10, No. 2 (23), pp. 168-184. Poverty Reduction Strategy (2000), (In Lithuanian), available at: www.socmin.lt/index. php?- 906224221 77

Asta Mikalauskiene, Raminta Narutaviciute-Cikanauske, Ingrida Sarkiunaite, Dalia Streimikiene, and Rumyana Zlateva / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 59-78

Steurer, R., Hametner, M. (2010), “Objectives and indicators in sustainable development strategies: similarities and variances across Europe”, Sustainable, Development, Vol. 21, No. 4, pp. 224-241. available at: http://onlinelibrary.wiley.com/doi/10.1002/sd.501/pdf The hunger project (2008), Hunger and Poverty: definitions and distinctions, available at: www.thp.org/files/Hunger%20and%20Poverty.pdf Townsend, P. (2010), “The meaning of poverty [1962]”, British journal of sociology, Vol. 61, Sup. 1, available at: www.jstor.org/stable/587266 UN (1992), Earth Summit. UN Conference on Environment and Development, available at: www.un.org/geninfo/bp/enviro/html UN (2013), La crisis económica y alimentaria amenaza los recientes adelantos hacia la elimi- nación del hambre y la pobreza, según un informe de las Naciones Unidas, available at: www.unic.org.ar/prensa/archivos/PR_Global_MDG09_SP.pdf Watts, H. W. (2005), An economic definition of poverty, available at: www.ssc.wisc.edu/ ir- pweb/publications/dps/pdfs/dp568.pdf WHO (2005), Poverty, available at: www.who.int/topics/poverty/en

Volodymyr Govorukha, and Olga Kuchkova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 79-89

Montenegrin Journal of Economics

Vol. 14, No. 2 (2018), 79-89 ‘

An Estimation of the Logistics Potential of Enterprises in the Region’s Management

VOLODYMYR GOVORUKHA1, and OLGA KUCHKOVA2

1 Professor, Head of the Department of Mathematics and Physics, Dnipropetrovsk State Agrarian and Economic University, Dnipro, Ukraine, e-mail: [email protected] 2 Senior Lecturer in Marketing, Department of Marketing, Ukrainian State University of Chemical Technology, Dnipro, Ukraine, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received December 22, 2017 The article is devoted to the problem of researching the logistic Revised from February 27, 2018 potential of the region. The logistics capacity of the region is defin- Accepted March 23 2018 ing characteristic for the development level of regional logistics Available online June 15, 2018 system. Accordingly, the definition of its components and their estimation are important for drafting the strategic guidelines of its development. The purpose of the study is to investigate the main JEL classification: factors necessary for assessing the current logistics potential of the F63, О18, R11. region and planning for its further development. The increase of economic performance of the region is directly related to the for- DOI: 10.14254/1800-5845/2018.14-2.5 mation and development of regional logistics capacity, its components and methods of assessment. The article studies the method Keywords: of assessment of logistical capacity of the region. As the main method of research, qualitative and quantitative analysis of statisti- logistical capacity, cal data is used. This method involves the calculation of an inte- management, grated index of logistic potential in the region on the basis of geo- innovation, graphic, socio-economic, transport, infrastructure, and institutional strategy, components. For testing the methodology the logistics potential of economic development. Dnipropetrovsk region was analyzed. This region was compared to the Kiev and Odessa regions. These regions were chosen as regions with optimal location of logistics centers. It is assumed that if the proposed measures to improve the development of the regions are implemented, the logistics potential will increase, which will lead to an increase in the level of logistic support of the regions. Based on the results of the research components of the region's logistics potential, the strategy of regional development was proposed with the aim of increasing the program for the development of logistic potential. The developed strategies for the development of the regional logistics system allow to form a logistical potential, to ensure a high level of development of the regional economy. As a consequence, this approach will contribute to the development of the regional logistics system as a whole. A synergistic effect will arise in this case if a new quality of economic processes is formed both at the level of a single region and at the national level.

Volodymyr Govorukha, and Olga Kuchkova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 79-89 INTRODUCTION New economic conditions in Ukraine caused the transformation of scientific-innovative sphereand necessity of its adaptation to radical changes in the economy. Smart government innovation policies could lead the economy out of the crisis, as evidenced by the experience of many developed countries. Therefore, Ukraine needs to establish appropriate mechanisms to stimulate investments in scientific-innovative processes, in the recovery of domestic productionthat is capable producing the competitive products. It is possible only if production intensifies by bringing new advanced technologies, retaining highly qualified scientific and innovative potential. Management of innovative activity in the state, regions and enterprises should be based on the study of the basic problems of innovative activitysuch as organizational and economic relations in scientific-innovative sphere, methods of increased innovative activity, preservation of personnel potential and the competitiveness of enterprises and the development of new forms of innovative entrepreneurship (Zelazny and Pietrucha, 2017; Zygmunt, 2017). Dynamics of world scientific and technological progress contributes to a significant change in the economy of all countries. The passive reactions of the state authorities, slow response of managers in enterprises of all forms of ownership to new requirements for activities and manufacturing products, belated adaptation to the new conditions entail negative economic consequences, as well as a sharp decline in the profitability of their own products compared to similar competitive (Becerra-Alonso et al., 2016). Most of the large industrial enterprises and organizations in Ukraine realized the need for innovative policy and tend to strengthen their innovation activity, particularly in the fields of product and process innovation, despite its dire economic situation, the imperfect legislative base of the state, the braking of reforms, uncertainty in the structural transformation of the economy in modern conditions. The development process is one of the most important subjects in management and the management system. The lack of attention of managers to the development of regions and ignoring the necessity of choice plan for the development of logistics infrastructure leads to the deterioration of the investment attractiveness not only in the regions but also companies in the area, as well as macroeconomic indicators. It involves the use of the logistics principles in a regions management. Logistics is now seen as an innovative approach that ensures the optimization of flow processes in economic activities through the use of reserves for organizational improvement. Logistics has both strategic and dynamic. A strategic option can be determined material stock and dynamic of the material flow. Dynamics in logistics represents the development, or change of a phenomenon or process in support of entrepreneurial activities (Velychko, 2013; Kovács and Kot, 2017).

1. INTERNATIONAL LOGISTICS RATING According to research by the World Bank, the level of development of the logistics industry (Logistics Performance Index (LPI)) of Ukraine in 2016, it is ranked 80 out of 160 countries (World Bank, 2016). First place logistics rating was occupied by Germany, Luxembourg, Sweden and the Nether- lands. Besides these, ten indicators make up the LPI, Singapore, Belgium, Austria, UK, Hong Kong and the United States. China was on the 27th place (1 tier higher than the rating of 2014). India (fastest growing economy) were not included in the top 30, but was on 35. This is 19 places higher than 2014's (Kuchkova and Arkhireiska, 2017). The ranking of countries according to the LPI for 2012 -2016 shows that Ukraine's place in world offset deteriorating (Table 1).

Volodymyr Govorukha, and Olga Kuchkova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 79-89

Table 1. Logistics Performance Index (LPI) of countries

Deviation Country 2012 2014 2016 2016/2012 Germany 4 1 1 +3 Poland 30 31 33 -3 Romania 54 40 60 -6 Russian Federation 95 90 99 -4 Ukraine 66 61 80 -14 Moldova 132 94 93 +39 Hungary 40 33 31 +9 Slovakia 51 43 41 +10 Belarus 91 99 120 -29

Source: compiled according to World Bank, 2016.

In 2016 Ukraine dropped from 61 to 80th position. This is due to the fact that other countries develop their logistics system more rapidly than Ukraine. Another reason for the downgrade is a complex socio-economic and political situation in the country. In 2012, Germany ranked 4th place in the rankings, and 2016 has become a role model. Poland in 2016 have reduced their positions and was on 33. Romania is in 2016, 60, Russian Federation – 99, Hungary – 31, Slovakia – 41. The worst situation is in Belarus, which in 2016 was 120 seats, and in 2012 was at 91 positionі. Rapidly developing logistics in Moldova. For a short period of time, it has increased its level of 39 positions (2012 – 132 place, 2016 – 93). Complete the list: Equatorial Guinea, Mauritania, Soma- lia, Haiti and Syria (World Bank, 2016). From the analysed data it is clear that for the development of logistics in Ukraine lags far behind the leading countries and neighboring countries. Therefore there is an urgent need to restruc- ture the logistics system of Ukraine (Kuchkova and Arkhireiska, 2017; O. Velychko and L. Velychko, 2017). The purpose of the article is a scientific rationale and development of theoretical and methodological foundations and practical guidance on the effective management of the logistic capacityin region. To achieve this goal following tasks are solved in the work: – to analyze the theoretical and methodological approaches to the management of logistics potential in the region; – to clarify the concept of "logistic capacity of the region"; – to explore the structure of the logistics capacityin the regions; – to assess the efficiency of management mechanism of logistic capacityin the regions.

2. METHODOLOGICAL FOUNDATIONS OF LOGISTIC POTENTIAL In the current circumstances the regional development policy of territories should be based on internal capacity and trends of territorial development, form competitive advantage. All this is the reason for new approaches to local economic issues. So the forming and assessment the current and prospective capabilities are the top of priority to date for the region development, that is, an analysis of the potential of the region. The insufficient research the potential of the region requires the additional theoretical and practical substan- tiation at the regional level.

Volodymyr Govorukha, and Olga Kuchkova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 79-89 Sustainable development of regional economies provides the positive dynamics of satisfaction. And it depends on the economic potential that focused on this area. Improvement of the current forms and methods of trade logistics in economic relations at the regional level allows for the resolution of key issues of regional governance (Belyakova, 2008), including: – formulation of the best possible strategy in regulating the structural proportions and material flows of social production; – optimization of financial and related service, financial, information streams of the region. Practical implementation of mechanisms of logistic management is related to the forming of regional logistics system. The last one in its nature is an organizational-economic mechanism for coordination of functions of management of flow processes (Oklander, 2008). Thus, the level of development of regional logistics system defines the level of development in the region. We suggest understand the logistic potential of the region as a set of optimal parameters of economic flows in space and time through the application of logistic management methods (Ko- blianska et al., 2015).

2.1 Assessment of logistics potential It is necessary to make an assessment of the resources in the region to achieve the effective management of its logistic capacity. The assessment of the recourses is needed to ensure better use of resources and sustainablecapacity increase. Effective formation and the choice of directions of development of logistics capacity of the region are impossible without the analytical lines of research on their condition. The analysis of the level of development of logistics potential in the region allows determining the features and level of development of the logistics system. Such analysis allows highlighting the strengths and weaknesses of the development of logistics potential incomparison with other regions. It is also determines the optimality extend of proceeding of material flow from producer to consumer. Methodical approach to estimation of logistic potential of the region involves the implementation of the comparative analysis. The purpose of the analysis and estimation of logistic potential of the region is a comprehensive evaluation of the level of logistics potential and developing on this basis the strategic guidelines for the growth in the region. The main objectives of evaluation are: – the choice of a system of partial indicatorsfor assessment of logistical capacity in the region; – qualitative and quantitative analysis of selected indicators; – integrated estimation of logistic potential of the region. To assess the components of the logistics potential we use the quantitative method. An integrated assessment should be undertaken by combining various indicators of the development level of individual components of the logistic potential of the region. Evaluation of the potential of the regions will carry out according to the method proposed by I. Kobylecko and N. Rybalko (Koblian- ska et al., 2015). This method involves the calculation of an integrated index of logistic potential of the region on the basis of five components: – geographical (proximity to the capital, the length of the border, the area of the region); – socio-economic (the volume of industrial production sold, freight traffic, passenger traffic, exports and imports of goods and services, retail trade turnover, average monthly wage per employee); – transport (the number of checkpoints of vehicles, the number of rail nodes, density of roads 82

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and railways, the volume of cargo transportation by road, transport of goods by rail); – infrastructure (density of retailers, the density of the enterprises of wholesale trade and mediation, the density of enterprises of transport and communication, the density of enterprises financial activities); – institutional (the number of industrial enterprises, number of wholesale and retail trades, the number of enterprises providing services of repair of motor vehicles and motorcycles,the number of enterprises in transport, storage, postal and courier activities, the number of enterprises in the field of insurance and financial activities). The assessment study of logistic capacity in the region is carried out by calculating the above indicators in selected areas of evaluation. In this case we use the following formulas to calculate (i) partial and cumulative indexes ( X ij ) of the development level of logistic infrastructure of the region (Oklander, 2008).

(i) X ij X ij  , (1) X m1 j

(i) X m1 j X ij  , (2) X ij where i  1, 2, ..., m ; j  1, 2, ..., n ; X m1 j – j-th index of the reference region (the reference value is the region with the best indicator value for the studied set). It is important to know that the formula (1) is used when the reference value of the index has a maximum value, and the formula (2) – when the reference value of the index has minimal one.

2.2 Comparative characteristic of logistic potential of regions of Ukraine For testing the methodology we analyze the logistics potential of Dnipropetrovsk region by comparing it to Kiev and Odessa regions. These regions were chosen as regions with optimal location of logistics centers. Information sources for monitoring the development of the regions are the State Statistics Service of Ukraine (2017), Main Department of Statistics in Kyiv Region (2017), Main Department of Statistics in Dnipropetrovsk Region (2017) and Main Department of Statistics in Odessa Region (2017). The results are shown in Table 2.

Table 2. Comparative assessment of the components of the logistic capacity in the region for 2014–2016

Components of integral Years Regions index 2014 2015 2016 Kyiv 0.46 0.46 0.46 Geographic component Odessa 0.58 0.58 0.58 Dnipropetrovsk 0.66 0.66 0.66 Kyiv 0.61 0.56 0.54 Socio-economic com- Odessa 0.60 0.57 0.56 ponent Dnipropetrovsk 0.85 0.81 0.59 Kyiv 0.61 0.62 0.6 The transport compo- Odessa 0.68 0.69 0.67 nent Dnipropetrovsk 0.91 0.88 0.85 Infrastructural compo- Kyiv 0.33 0.34 0.38 83

Volodymyr Govorukha, and Olga Kuchkova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 79-89 nent Odessa 0.59 0.59 0.60 Dnipropetrovsk 0.94 0.95 0.96 Kyiv 0.58 0.59 0.52 The institutional com- Odessa 0.76 0.79 0.77 ponent Dnipropetrovsk 0.91 0.91 0.92 Kyiv 0.53 0.52 0.51 Cumulative index of Odessa 0.65 0.65 0.64 logistics capacity Dnipropetrovsk 0.86 0.85 0.81

Source: compiled by authors based on the data of State Statistics Service of Ukraine, 2017.

An integrated assessment should be carried out by constructing analytical indicators of development for separate components of the logistics capacity. The integral index of development of logistics capacity in the region (S) determined by the formula:

2 2 2 2 2 Eg  Es  Et  Ep  Eo I  , (3) S where Eg , Es , Et , Ep , Eo are the corresponding calculated values of the individual partial indexes for the components of the logistics capacity – geographical, socio-economic, transport, infrastructure, and institutional, respectively (Kovalska and Savka, 2012). The calculation results indicate that the integrated index of logistic potential in the Kyiv region over the last three years decreased from 0.53 to 0.51. Institutional and socio-economic components had significant influence on the calculated value of this indicator. They are decreased by 10 % and 12 % respectively. Positive developments are observed in the infrastructure component of the region. This indicator increased by 15 % (Fig. 1).

Figure 1. Diagram of the integral indicator of the logistics capacity in Kyiv region

Geographic component 0,7 0,6 0,5 0,4 0,3 Socio-economic Institutional component 0,2 component 0,1 0

Infrastructure Transport component component

2014 2015 2016

Source: compiled by authors based on the data of Main Department of Statistics in Kyiv Region, 2017.

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The strategy of building up the logistic potential of the region should be aimed at formation and development of logistics infrastructure in the region. In this regard, an important place is occupied by investors. They can direct their investments on the construction of logistics centers and the development of logistics infrastructure. The last one must satisfy the needs of most demanding customers. For instance, building up the logistic capacity of the region implies: – the presence of specialized storage facilities; – their high level of security equipment and relevant standards for storage; – sorting and transportation of goods depending on the specialization of the region. The objective necessity for a specially equipped places to store exists at all stages of the flow – from the primary source of raw materials to the final consumer. And the problem of transportation plays an important role. This problem requires the development of regional logistics centers or logistics hubs. They are sort of business centers, which play an essential role in managing trade flows as hubs of international trade. There are following main events in delivery of goods (stage in this process):  identification of need for the goods;  the choice of sources of provenance of goods and their suppliers;  establishing rational schemes of supply;  contracting for the supply of goods (supply agreements, purchase and sale of goods);  the operative monitoring of the implementation of delivery contracts;  the choice of forms for delivery of goods;  finding the rational delivery frequency and optimal batch sizes and delivery of goods;  arranging delivery of goods to the stores;  the acceptance of the goods and its documentation (Apopii et al., 2005). The level of development in the theory and practice of logistic systems formation that has achieved proves: to optimize the movement of goods flows in the system should efforts to stream- line the system of movement of commodity flows be intensified. This also applies to their concentration and transformation in logistics centers (terminals), the effective management of inventory management and orders, maintain retail enterprises in terms of calendaring in delivery of goods and optimization of its routes. This strategy thus holds an important place in the regional logistics system. At the regional level the formation of the logistics system within the framework of this strategy should serve as a means of overcoming the crisis phenomena in the economy of the region characteristic of the current stage of development. It also promote creation of appropriate infrastructure, development of interregional, intersectoral and international relations, rational use of financial, material and information flows. Analysis of the Odessa region showed (Fig. 2) its infrastructure component is expanding mod- erately. However, attention should be paid to the insufficient level of socio-economic component: in the analyzed period it increased by 7 %. For a more rapid development of the region strategies for regional logistics system formation must be implementing. The main goal is to create the precondi- tions for the development of regional logistics system and ensuring logistical economy of the region. This is achieved by formation of system for management of material and information flows, which would ensure bringing the right product of the required quality in the required quantity at the stipulated place and time with minimal costs. The basis for the formation of the logistics system must become logistics centers. They should have sufficient material and technical base, experience of delivery of goods, qualified commercial machine, using the possibility of reducing the total logistics costs, obtaining the appropriate discounts and a relative reduction in transport costs per unit.

Volodymyr Govorukha, and Olga Kuchkova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 79-89 Figure 2. Diagram of the integral indicator of the logistics capacity in the Odessa region

Source: compiled by authors based on the data of Main Department of Statistics in Odessa Region, 2017.

Implementation of this strategy will involve the following activities:  to find sources to procure the goods and choose the best reliable suppliers who can offer competitive goods (for price, quality and other parameters), to ensure their supply and payment on favorable terms, to provide a variety of services that increase the value of the goods;  to establish economic relations with suppliers of goods and documented their issue through the conclusion of supply contracts;  to determine the optimal batch sizes of delivery of the goods, frequency and methods of delivery, to choose the most effective type (types) of transport for the carriage of goods, to calculate the need for it, to develop the route of delivery of goods etc.;  to establish operational monitoring of the implementation of supply contracts, availability of stock and turnover, which will give the ability to react and make changes in delivery of goods;  to create optimal conditions for the accumulation and storage necessary to ensure uninter- rupted commodity supplies;  to ensure proper receipt and preparation of goods for sale. Among the instruments of implementation for strategies suggested above it is appropriate to identified such as:  tariff and non-tariff regulation;  financial support;  fiscal adjustment;  pricing;  licensing and certification of logistics services;  the competitive allocation of orders and so on. Logistics potential of Dnipropetrovsk region (Fig. 3) is growing fast, but the integrated index of logistic capacity is far from an ideal reference. Significant influence on the calculated value of this index is carried out for high values of the partial indicators of the institutional component (0.92), transport (0.8) and the infrastructure component (0.96). However, it is worth noting that develop- 86

Volodymyr Govorukha, and Olga Kuchkova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 79-89 ment of socio-economic (0.56) and geographical (0.66) components are insufficient.

Figure 3. Diagram of the integral indicator of the logistics capacity in Dnipropetrovsk region

Source: compiled by authors based on the data of Main Department of Statistics in Dnipropetrovsk Region, 2017.

Each of the estimated components indicates the level of development of the region in a particular industry. It enables to identify directions of strategy of development in the region and strengthen its transport and logistics capacity. The results of the evaluation of logistic capacity allow state and regional authorities, local authorities and businesses make tactical and strategic decisions in the field of investment and development of not only the logistics infrastructure in the region, but also companies with regard to their effectiveness. It is also possible to make a conclusion about the state of the logistics system in the region and identify potential improvement criteria, cost optimization, risk minimization, timely delivery. Based on the results of the assessment of the level of logistic capacity in the region, we believe that to build logistics capacity of the region as a development programme should take the strategy of innovative logistics development, which includes:  improving transport accessibility within the region:  development of innovative and competitive industrial region;  stimulation of investment activity;  promoting the development of the business environment on the principles of state-private partnership;  encourage the development of small and medium business;  ensuring the effective specialization of regions, with a priority on using its own resource potential;  achieving a uniform and balanced development of territories, development of inter-regional cooperation, prevention of deepening socio-economic disparities through the formation of “growth points”. The main activities under this strategy are:  formation of transport-logistical cluster, the use of an integrated system of logistics potential;

Volodymyr Govorukha, and Olga Kuchkova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 79-89  definition of directions for improving the system of management of enterprises to increase efficiency through innovation and logistics simulation by combining the logistics and innovative aspects of development. In our opinion, the implementation of these measures will facilitate  greater specialization of the region,  improvement of trade services (increasing the range of products, lower prices by reducing costs for managing material flow),  development of the service sector,  growth of employment in the region,  increase tax revenues to budgets of all levels. And all this will contribute to achieving the goal of sustainable development of the region.

CONCLUSION Conducted a regional analysis reflects an analysis of the state of the logistics system in the regions. Thus, a careful analysis gave the opportunity to some shortcomings of this system and makes certain ways to improve the strategy of logistics management in the regions. The calculated values of individual components and the integral index of the level of development of the Kiev region were given the opportunity to determine strategy to expand logistics potential. For its implementation it is necessary to develop logistics infrastructure, an optimized structure of the regional economy, in order to ensure the specialization and the introduction of modern logistics technologies of management of regional logistics system. Analysis of logistic potential of the Odessa region showed that it is necessary to apply the strategy of formation of regional logistics system for the region. The main purpose is formation of system of management of material and information flows. Study of logistics potential of Dnipropetrovsk region showed the necessity of a strategy to increase the transport and logistical capacity. The basic goal of this strategy is to improve transport accessibility within the region, the development of innovative and competitive industrial region, to stimulate investment activity. Developed strategy of development of regional logistics system give the possibility to form the logistic potential of the region, to ensure the highest level of logistical the region's economy. As a consequence, this approach will facilitate the development of regional logistics system as a whole. Synergies will arise in this case, will form a new quality of economic processes at the level of individual region and at the national level. The strategies of development of regional logistics system described above give the possibility to form the logistic potential of the region, to ensure the highest level of logistical the region's economy. As a consequence, this approach will facilitate the development of regional logistics system as a whole. Synergies that it would entail will form a new quality of economic processes at the level of individual region and at the national level. In light of the above the promising direction for further research is to identify opportunities for interregional cooperation for the creation of logistics systems for over-region level.

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REFERENCES Apopii, V. V., Mishchuk, I. P., Rebytskyi, V. M., Rudnytskyi, S. I., Khomiak, Yu. M. (2005), Organizati- on of trade, Tsentr uchbovoi literatury, Kyiv (in Ukrainian). Belyakova, E. V. (2008), „Innovative management in regional logistics systems”, Problems of Mod- ern Economics, No. 2(26), retrieved January 10, 2018, from http://www.m-economy.ru/art. php?nArtId=2039 (in Russian). Becerra-Alonso, D., Androniceanu, A., Georgescu, I., (2016), “Sensitivity and vulnerability of Euro- pean countries in time of crisis based on a new approach to data clustering and curvilinear analysis”, Administratie si Management Public, No. 27, pp. 46-61. Koblianska, I. I., Rybalko, N. O., Mishchenko, O. V. (2015), „Logistic potential of a region: essence and methodological approach to its evaluation”, Bulletin of Sumy State University, Series: Eco- nomics, No. 2, pp. 23–30 (in Ukrainian). Kovacs, G., Kot, S. (2017), “Economic and social effects of novel supply chain concepts and virtual enterprises”, Journal of International Studies, Vol. 10, No. 1, pp. 237-254. doi:10.14254/ 2071-8330.2017/10-1/17 Kovalska, L. L., Savka, B. R. (2012), „Formation and development of logistics infrastructure in the region”, Bulletin of the Lviv Polytechnic National University, Series: Logistics, No. 749, pp. 410- 416 (in Ukrainian). Kuchkova, O. V., Arkhireiska, N. V. (2017), „The Logistics Potential of Ukraine in the International Ranking”, Business Inform, No. 1, pp. 39-43 (in Ukrainian). Kuchkova, O. V., Dotsenko, G. E., Kozlov, J. M. (2017), „Rationale the theoretical basis of the logistic potential of the region”, Economic Bulletin of Ukrainian State University of Chemical Technology, No. 1(5), pp. 68-70 (in Ukrainian). Main Department of Statistics in Dnipropetrovsk region (2017), retrieved January 10, 2018, from http://www.dneprstat.gov.ua (in Ukrainian). Main Department of Statistics in Kyiv region (2017), retrieved January 10, 2018, from http://kyivobl.ukrstat.gov.ua (in Ukrainian). Main Department of Statistics in Odesa region (2017), retrieved January 10, 2018, from http://www.od.ukrstat.gov.ua (in Ukrainian). Oklander, M. A. (2008), Logistics, Tsentr uchbovoi literatury, Kyiv (in Ukrainian). State Statistics Service of Ukraine (2017), retrieved January 10, 2018, from http://www.ukrstat. gov.ua (in Ukrainian)]. Velychko, O. P. (2013), „Methodology for estimation of enterprise logistics development”, Actual Problems of Economics, No. 8, pp. 45-54 (in Ukrainian). Velychko, O., Velychko, L. (2017), “Logistical modelling of managerial decisions in social and marketing business systems”, Journal of International Studies, Vol. 10, No. 3, pp. 206-219. doi:10.14254/2071-8330.2017/10-3/15 World Bank. (2016), Doing Business: Understanding Regulations for Small and Medium-Size En- terprises, World Bank Group, Washington, DC, retrieved January 10, 2018, from http://lpi.worldbank.org/ Zelazny, R., Pietrucha, J. (2017), “Measuring innovation and institution: the creative economy index”, Equilibrium. Quarterly Journal of Economics and Economic Policy, Vol. 12, No. 1, pp. 43- 62. doi: 10.24136/eq.v12i1.3 Zygmunt, A. (2017), “Innovation activities of Polish firms. Multivariate analysis of the moderate innovator countries”, Oeconomia Copernicana, Vol. 8, No. 4, pp. 505-521. doi: 10.24136/oc.v8i4.31.

Andrii Kubaienko / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 91-114

Montenegrin Journal of Economics

Vol. 14, No. 2 (2018), 91-114 ‘

Activation of the Economic Security of Ukraine in Terms of the European Integration

ANDRII KUBAIENKO1

1 Associate Professor, Department of Administrative activities and economic security, Odessa State University of Internal Affairs, Odessa, Ukraine; e-mail: [email protected]

ARTICLE INFO ABSTRACT Received March 22, 2017 The full-fledged involvement of Ukraine into the European associa- Revised from April 29, 2018 tion stipulates the creation and adherence to the terms so to rein- Accepted May 30 2018 force the economic and commercial relations promoting the gradual Available online June 15, 2018 integration of the national economy into the European market. The presence of significant regional disproportions in the social and economic growth in Ukraine requires differentiated approaches in JEL classification: working out measures of regional development designated to assist E01; H56; O18; O21; R11. in creating a free trade area and reinforcing the economic and sectoral collaboration of Ukraine and the EU. So, there arises a need of DOI: 10.14254/1800-5845/2018.14-2.6 monitoring and estimation of the economic reform progress in the regions of Ukraine in terms of the European Integration processes Keywords: aimed to establish strong and weak sides, to substantiate priorities and measures of their achievement and an opportunity of their due Economic security, correction in order to increase the ability of a region to work in the index of European integration, area of the European Integration. In order to implement the purpose directions development, of the research, it is reasonable above all to measure the integral consequences of euro-integration, value – European Integration Progress Index (Ір). From the most scenarios of changes. general standpoint, the index is a relative value designated to play the role of a generalised measure of a particular phenomenon formed under the influence of various components that cannot be directly summarised. Then Ір can be defined as a relative value measured in terms of the aggregation of single indexes showing the efficiency of achieving the main goals of the economic integration with the EU at the regional level. Applying the integral approach will make it possible not just to determine and actually assess an achieved progress in making shifts in the trading field and matters, which are related to it, of the economic and sectoral collaboration, but to highly likely forecast its future dynamics as well. Overall regional rating. Odessa Region took the 8th place among all the regions of Ukraine according to the measured European Integration Progress Index (index value – 0.01357) after the western regions of Ukraine, which quite lively build the foreign economic ties with the EU countries in general and cross-border cooperation in particular, and after Kyiv Region, where the majority of national enterprises engaged in the foreign economic activity is traditionally founded.

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The given scenarios of changes in the national economy as the result of the European Integration effect may be interesting for study. It is especially important to study the problem of influence on the economic security, health of companies.

INTRODUCTION One of objective tendencies in the modern economics of Ukraine is the European Integration, i.e. formation and further strengthening of relationships with the European countries at all the levels – transnational, interregional, between particular economic entities. Nowadays, the European Integration is a strategic course of Ukraine, its speed grows. It obliges to pay attention to the effects of the European Integration, in particular, on the stability of the Ukrainian enterprises. To strengthen the national sovereignty of Ukraine in the realm of foreign policy means to inte- grally represent the state in the global civilisation space as an active geopolitical entity. It is possible in the event of the dynamic dialogue of Ukraine with other countries, which will be based on adhering to the standard and principles of international law, beneficial cooperation, safety and understanding by our country of the essence of its national interests. The topical matter is to establish friendly, partnership relations of the EU and Ukraine. The Eu- ropean Union is an active actor in the contemporary geopolitical space. To unite Europe is one of the most significant geopolitical events of XX c. As a result of the extensive and deep integration, the European Union has become a powerful geopolitical centre. The geopolitical future of the Euro- pean project and its role in the global policy primarily depends on the efficient collaboration with countries and regions that are direct neighbours of the European Union. So, the collaboration of Ukraine and the EU has a great significance for both the sides. The main task of Ukraine is to go back to the European civilisation space. Such a specific geopolitical position and history of the continuous cooperation with the European countries cause the interest of Ukraine to actively participate in the integration processes within the European continent. The strategic interests of Ukraine in Europe comprise the necessity of technological modernisation of domestic production, opportunity to master scientific technologies, substitution of the inertial industrial progress for the innovative one. For Ukraine, moving in a direction of the Europe- an Integration is a matter of its efficient inclusion into the present system of allocation of functions and roles in the contemporary global economic and political system. The activation of the Europe- an Integration processes also means more intensive inclusion of Ukraine to the international collaboration in settling conflicts and resisting new threats to the international security. The economic security is an integral part of the national safety, its ground and material basis. It organically fits into the national safety system with such its components as provision of the national defence capability, support of the social peace in the society, protection from environmental disasters and so forth. Military security is impossible when economy is weak, and efficient economy is impossible in terms of social conflicts.

1. METHODOLOGY OF RESEARCH In studying the notion “economic security”, the contemporary basic category is “national security” (introduction of this term to the standard turn is dated 1934 and associated with the adoption of the US Law “National Security”), and more deeply – safety in general. These notions have a specific historical content and are closely interrelated with all the elements of the interaction system “personality – society – state”. On grounds of the modern concept of safety in the scientific literature, the following definition is presented: “Safety is absence of threats, integrity, reliability, i.e. 92

Andrii Kubaienko / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 91-114 absence of any threats to a person, society and state.” (Avanasova, 2016 рp. 74-82; Hluschenko and Tuleninova, 2016 p.112-119). Safety is a state of absence of danger, which is one of the most important needs of entities [2]. This philosophical category, which is not only general scientific, but pretty much specific, clear and exact, is not anything substantive, material, but it serves as an abstract form to express sustainability ad resilience of certain entities (objects), their relation to the internal and external environments. The category of economic security is considered from different sides (Ivankiv, 2015 pp. 109-112; European integration, 2011) . Philosophers, legal experts, economists, politicians and scientists – representatives of a specific subject area – differently interpret the complex notion “safety”, trying to adapt it to their domains. In a psychological interpretation, it is a feeling, perception and experience of need to protect basic interests of an entity; in a legal interpretation, it is a system of legal guarantees of safety set by laws, support of living, rights and freedoms of a person or a society; in a philosophical and sociological interpretation, it is a state of growth and terms of living of an entity (object), its structures, when the preservation of their quality distinctness, optimal balance of freedom and necessity is enforced. Approaches to define the notion of safety are based on interpreting safety as an ability to resist a destructive influence or as an absence of threats to values and interests to be protected (Yl'iashenko, 2015, pp. 162-168; Zubok, 2012 p. 27-38). The full-fledged involvement of Ukraine into the European association stipulates the creation and adherence to the terms so to reinforce the economic and commercial relations promoting the gradual integration of the national economy into the European market. The presence of significant regional disproportions in the social and economic growth in Ukraine requires differentiated approaches in working out measures of regional development designated to assist in creating a free trade area and reinforcing the economic and sectoral collaboration of Ukraine and the EU. So, there arises a need of monitoring and estimation of the economic reform progress in the regions of Ukraine in terms of the European Integration processes aimed to establish strong and weak sides, to substantiate priorities and measures of their achievement and an opportunity of their due correction in order to increase the ability of a region to work in the area of the European Integration (Contract - legal cooperation Ukraine – EU, 2013). The modern science offers a great number of tools to establish indicators of a state and dynamics of various processes happening in the life of society. But studies on measuring the progressive shifts in the economic realm of regions in the view of achieving the main goals of the Ukraine-European Union Association Agreement (The Association Agenda, 16.03.2015) are absent now. Considering the great importance of the problem related to the implementation of the main provisions of the Ukraine-European Union Association Agreement (Association Agreement 27.06.2014) and Association Agenda, there is a need to work out methods which make it possible to quantitatively assess the progress in carrying out the relevant economic reforms in the regions of Ukraine (The EU-Ukraine Association Agenda, 16.03.2015). The main tool of this research shall perform the following tasks:  to assess a current state and a relative dynamics of the economic reforms of the Ukrainian regions in future in terms of the provisions of the Ukraine-European Union Association Agree- ment;  to provide the analytical support of the central government in determining problems that slow- down the implementation of economic reforms at the regional level;  to provide the analytical support of the local government in identifying factors that influence the progressive shifts for achieving the European Integration goals in comparison with other regions, the activity of which should be increased or minimized. Ideally, the methods for measuring the European Integration Progress Index shall meet the following requirements (Derij and Zosymenko, 2016, pp. 17-19): 93

Andrii Kubaienko / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 91-114  adequacy: the index computation methods shall be appropriate for the goals and tasks set;  comparability: opportunity to regularly repeat the index computation and get results that can be used for defining trends;  index objectiveness: reasonableness of final and intermediate results for the economic interpretation;  information richness: output data for computation shall be easy to get or be measured by means of easy mathematical operations. In order to implement the purpose of the research, it is reasonable above all to measure the integral value – European Integration Progress Index (Ір). From the most general standpoint, the index is a relative value designated to play the role of a generalised measure of a particular phenomenon formed under the influence of various components that cannot be directly summarised. Then Ір can be defined as a relative value measured in terms of the aggregation of single indexes showing the efficiency of achieving the main goals of the economic integration with the EU at the regional level. Applying the integral approach will make it possible not just to determine and actually assess an achieved progress in making shifts in the trading field and matters, which are related to it, of the economic and sectoral collaboration, but to highly likely forecast its future dynamics as well. Most often, the integral index is built in an additive form. Its sense is to determine this integral index by summarising its actual values of the absolute measures. But, by applying the sum method, it is necessary to remember about its main disadvantages. In particular, the measuring scale high limit will have more than 1 limit that will make it impossible to fix a benchmark index. Furthermore, this method of measurement stipulates that inserting all the indexes selected for Ір to be measured should be deemed as coequal. It is also necessary to consider such a fact when one of indexes affecting the regional European Integration progress gets a zero value while using such mathematical formulae as product, geometrical mean, weighted geometrical mean will result in getting a zero value of the integral index (Korobov, 2016 pp. 22-26; Pryimak, 2009).

From the above, it is necessary to stop with the method of Ір measurement that:  makes it possible to come to the scale with the range of -1 to 1 or 0 to 1 as it limits the maximum level of the European Integration Progress of the region for a certain period, which enables its using as a benchmark for determining positions of one region in comparison with other regions of Ukraine. From this perspective, such methods as geometrical mean, weighted geometrical mean are included as the integral index will get a value lower than 0.5 or lower than - 0.5. In fact, measuring by the geometrical mean method becomes impossible if a value of standardised partial coefficients fluctuates in the interval [-1; 1];  stipulates the opportunity to use weighting factors that show an action of each components, which represents progress in achieving the main goals of the economic integration with the EU at the regional level;

 makes it possible to measure Ір even if one of its component indices gets a zero value.

From the above, we deem as most reasonable to use the weighted geometrical mean for measuring the European Integration Progress Index. In building the index, such a structural and logical scheme is offered (Figure 1 - Korobov, 2016, pp. 22-26; Pogorelov and Adamenko, 2015, pp. 173-181).

Andrii Kubaienko / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 91-114

Figure 1. Structural and logical scheme on building and using the European Integration Index

Source: compiled by the author

As a subject of this research is to assess the state and dynamics of the economic reforms of the regions in the European Integration area in order to establish courses of the activation of economic security facilities, to measure Ір it is necessary to select indicators, which can be divided into two main aspects according to the Ukraine-European Union Association Agreement: I. Trade and trade-related matters; II. Economic and sectoral collaboration. Each of them will include the groups of indicators showing the prioritised trends of the reform (Figure 2 - Koriavets', 2017).

Andrii Kubaienko / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 91-114 Figure 2. Trends in the economic development determined in the Ukraine-European Union Associa- tion Agreement

Economic reform trends

I.Trade and trade- II. Economic and related matters sectoral collaboration

1.1 Commodity trade 2.1. Collaboration in the energy area

1.2. Sanitary and phy- 2.2. State internal tosanitary measures financial control

1.3. Trade in services 2.3. Transport and investments

1.4. Intellectual 2.4. Environment property

1.5. Simplification of customs 2.5. Industrial policy procedures and trade promotion

1.6. Regulation 2.6. Financial services transparency

2.7. Information society

2.8. Tourism

2.9. Agriculture

2.10. Science, technologies and innovations

2.11. Collaboration in the matter of civil society

Source: compiled by the author

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For their description, public statistical indicators, open access information should be chosen: 1.1. Commodity trade. As one of the terms of functioning of the free trade area between Ukraine and the EU is to provide free access for goods of the Ukrainian origin to the European Union countries and vice versa, the following was selected as quantitative indicators of this process:  export ratio of goods to the EU countries in bulk, %;  import ratio of goods from the EU countries in bulk, %. 1.2. Sanitary and phytosanitary measures. To the number of indicators that directly represent the economic outcomes of the Ukrainian legislation proximity to the EU legislation in the matter of sanitary and phytosanitary measures, we included the indicators showing a number of entities entitled by the State Veterinary and Phytosanitary Service to export food (dairy products, meat, eggs and egg products, honey and hive products, mayonnaise, mayonnaise sauces and margarine) to the EU countries. 1.3. Trade in services and investments. An important trend of the public policy in the field of ini- tiation of the entrepreneurial activity, trade in services and electronic commerce should be namely simplification of foundation and functioning of companies, branches and representative offices, protection of investors` rights due to the further proximity of the legislation in these areas to the laws, standards and practices of the EU. The principle indicators of such indexes growing are as follows: share of export of services to the EU countries, %; share of import of services from the EU countries, %; total share of direct investments of non-residents from the EU in a region, %. 1.4. Intellectual property. One of the prioritised trends of the Ukrainian reforms in a climate of the European Integration is to improve the national legislation considering the best world practice. As a result, the intellectual property is to be reorganised into the strategic resource in the system of formation of national wealth and increase of competitiveness of the Ukrainian economy, accel- eration of the innovation development and integration of Ukraine in the international economic area. Correspondingly, we suggest including such indicators as a number of applications for an invention, a number of applications for utility models from national applicants to the target statistical indicators representing the effectiveness of mechanisms in achieving the legal protection in the field of intellectual property. 1.5. Simplification of customs procedures and trade promotion. As the reformation of customs affairs is performed on the basis of a new revised version of the Customs Code of Ukraine, which is designed in reliance on the provisions of the International Convention on the Simplification and Harmonisation of Customs Procedures, the Convention on Temporary Admission, the EU Customs Code, human factor remains to be a single factor that may negatively influence customs procedures. Presentation of the similar negative influence implicitly at the regional level may be done by analysing the quantitative indicators of applications of individuals and legal entities in the context of the customs activity zones. Another important task in terms of the progress of Ukraine in the European Integration is to reduce the number of common points between the business and state, what is reasonable to represent as an indicator of a number of inspections of economic entities carried out by the Department of Tax and Customs Audit in relation to a number of economic entities. 1.6. Regulation transparency. The index, which, in our opinion, shows the regulation transparency, is a number of violations abated as anticompetitive practices of the government in the context of a region. 2.1. Collaboration in the energy area. Considering the fact that the main purpose of the collaboration of Ukraine with the EU countries is to increase the security of energy supply, competitiveness and stability; in this bloc, the statistical indicator – disincentive: loss of energy-related materials and petroleum refinery products in the course of distribution, transportation, total volume ratio, is

Andrii Kubaienko / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 91-114 chosen as a measure of efficiency. Conversely, the collaboration in the energy area is aimed at providing the reasonable energy sources for consumers, so the other selected indicator that has disincentive influence on the index is the level of indebtedness for public utility charges, %. 2.2. State internal financial control. The collaboration in the field of public finance management is aimed at ensuring the development of the budget policy and reliable internal control and external audit systems that are based on the international standards and comply with the basic principles of accountability, transparency, cost effectiveness, efficiency and performance. So, the main feature of the progress in this field should be the reduction of such indicators: illegal and undesig- nated expenditures, shortages (plundering) of funds and valuables, TUAH; volume of lost financial sources, TUAH. 2.3. Transport. As, in particular, the efficient and safe transportations and reinforcement of the principal transport linkages of countries belong to the basic aims of the collaboration in the field of transport, we included into the list of indexes in this bloc of reforms such statistical indicators: export ratio of transport services in the total volume of export of services to the EU countries, %; import ratio of transport services in the total volume of import of services from the EU countries, %. The quantitative indicator of road traffic accidents on roads and streets of the region versus the overland transport passenger traffic (unit/million passengers/km) is chosen in consideration of the importance to perform the task on increasing the efficiency and safety of transportations. 2.4. Environment. Implementing the long-term goals of the stable development and green economy stipulates the available proper investment support of the greening processes in the Ukrainian regions. So, the share index of capital investments for conservation of natural resources and their rational use in bulk, % is selected as an indicator in this bloc. 2.5. Industrial policy. In the field of industry and entrepreneurship, a prioritised trend of the collaboration of Ukraine and the EU is to improve conditions for the entrepreneurial activity. Thus, positive achievements shall come with the increase of such indicators:  share of direct foreign investments in the industry in bulk, %;  export ratio of material resource processing services in the total volume of export of services, %;  import ratio of material resource processing services in the total volume of export of services, %;  export share of industrial products in the total volume of export of goods to the EU countries, %;  import share of industrial products in the total volume of import of goods from the EU countries, %. Modernisation shifts shall be represented in a gradual reduction of indicators:  portion of dilapidated and hazardous water supply lines expressed as percentage of the general stretch;  portion of dilapidated and hazardous heating and steam networks expressed as percentage of the general stretch. 2.6. Financial services. Considering the importance of the financial services development for establishing the constant market environment to assess the efficiency of reforms in this bloc, it is reasonable to use the following indicators:  export ratio of financial services, in the overall volume of export of services, %;  portion of enterprises providing financial and insurance services, in the overall number of enterprises, %. 2.7. Information society. The collaboration in this area particularly includes the furtherance of broadband access, improvement of security networks and wide use of computer and information technologies by private parties, business and administrative authorities as a result of the 98

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Internet local resources development and incorporation of online services. So, we selected the following statistical indicators:  portion of enterprises that used computers, in the overall number of enterprises, %;  portion of employees used computers, in the overall number of employees, %;  portion of enterprises had access to the Internet, in the overall number of enterprises used computers, %;  portion of enterprises had a website or homepage, in the overall number of enterprises with the Internet access, %;  portion of enterprises carrying out the automated data exchange, in the overall number of enterprises used computers, %;  portion of the Internet users, in the overall number of population, %;  portion of mobile communication subscribers, in the total population, %;  RITS Level Index for the IT invasion of the life of society. 2.8. Tourism. Economic reforms in this bloc should be aimed at promoting and developing the tourism products and markets, infrastructure, human resources and institutional structures, growing the potential capacity in tourism for the purpose of increasing the tourism service quality standards. We included the following to the list of indicators that allow assaying the efficiency of tourism reforms: portion of tourism facilities, in the overall number of economic entities, %; number of tourists served through tourism facilities; export ratio of travelling services, in the overall services export volume, %; import ratio of travelling services, in the overall import services volume, %. 2.9. Agriculture. Reforms in this bloc are aimed at improving the agricultural competitiveness and markets efficiency and transparency, including investment conditions, at sharing the best practices as for policy support mechanisms in the field of agriculture and rural development. The following indicators serve as statistical indicators of the reforms efficiency:  production of agricultural products per 1 person, UAH;  labour efficiency in agricultural enterprises for 1 employed, UAH;  profitability level of the agricultural industry in agricultural enterprises, %;  portion of small entities and medium-sized enterprises expressed as percentage in the overall number of agricultural enterprises;  share of direct foreign investments in agriculture in total, %. 2.10. Science, technologies and innovations. The collaboration in this field shall assist Ukraine in supporting the reformation and reorganisation of the academician field management system and research institutions (particularly in developing its potential capacity as regards science and technologies development) for the purpose of furthering the development of competitiveness of economy and society based on knowledge. From this perspective, we selected the following indicators of the reforms efficiency in view of the influence on the creation of a favourable environment for economic entities to develop, cope with and commerce innovations:  portion of organisations that performed research and scientific and technological works in the overall number of enterprises, %;  portion of industrial enterprises engaged in the innovation activity as % in the overall number of industrial enterprises;  share of financing of research and scientific and technological works at the expense of foreign states, %;  number of new applied engineering procedures at industrial enterprises;  number of mastered innovative types of products at industrial enterprises; 2.11. Collaboration in the matter of civil society. As reforms in this bloc are aimed at promoting the process of institutional development and consolidation of civil society organisations as a quan-

Andrii Kubaienko / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 91-114 titative indicator of progressive changes, we chose the indicator of a number of legalised civil organisations in relation to the population size of a region. So, in general the formula for measuring the European Integration Progress Index takes the following form (1) (Pryimak, 2009; Korobov, 2016, pp. 22-26): NwtNwe** І  te (1) p 2

Ір – index of the progress in the European Integration of the region;

Nt – indicator characterising the reform trend “Trade and trade-related matters”;

Nе – indicator characterising the reform trend “Economic and sectoral collaboration”; Wt, we – weighting coefficients, levers of directions that are appropriate to be formed by means of Delphi method.

It is clear that the integral index measurement may be only in the event when values of every primary indicator will be equal. To do so, it is necessary to normalise them. Among normalising methods, we chose Min-Max method, which allows avoiding an unfavourable effect of high extreme values and putting all data in one range from 0 to 1. To normalise the indicators-incentives, it is necessary to use formula (2), and for indicators-disincentives – formula (3).

xxij imin yij  (2) xiimax x min

yij – dimensionless (normalised) value of I statistical indicator in J region;

xij – value of I statistical indicator in J region; x i min and xmax – minimum and maximum value of I statistical indicator.

xiijmin  x yij  (3) xiimax x min

Even if this method somehow complicates the calculation process in real time, it makes it possible to reflect a range of relevant values. Furthermore, Min-Max method makes it possible to convert data better in those cases when statistical data values are close to one another in regions.

2. RETRIEVAL As a result of the performed measurement and studies, the integral index of the progress in the European Integration is obtained, which consists of two interim indexes of the reform areas, which conversely consist of indexes in each particular bloc of reforms within a relevant area (Koriavets', 2017; Korobov, 2016, pp. 22-26; Kozachenko and Adamenko, 2015, pp. 90-95).

100

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Figure 3. Index of the economic progress in the European Integration of the regions of Ukraine for 2016.

Source: compiled by the author with Koriavets', 2017.

The studies performed within the project “Assistance of V4 countries in assessing the economic reforms of the regions of Ukraine in the conditions of the EU Integration” carried out by the Polish Fund of International and Regional Studies (Chernihiv City), Association of Regional Analyti- cal Centres supported by the International Visegrad Fund (Slovakia), Research Centre of the Slovak Foreign Policy Association (Slovakia), International Centre for Democratic Transition ICDT (Hunga- ry), Polish-Ukrainian Cooperation Foundation PAUCI (Poland), Association of Analytical Centres for an Open Society PASOS (the Czech Republic) and Chernihiv National University of Technology (Koriavets' M. , 2016; Honcharenko, Bohatyrenko, Bakal, Vynokurova, Halimuk and Hejderova, 2017).

Table 1. Index of the economic progress in the European Integration of the regions of Ukraine for 2016.

Region Rating Region Rating 1 Chernivtsi Region 0,4960 13 Zaporizhia Region 0,3298 2 Volyn Region 0,4760 14 Cherkasy Region 0,3292 3 Ternopil Region 0,4649 15 Khmelnytsk Region 0,3288 4 Lviv Region 0,4518 16 Vinnytsia Region 0,3114 101

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5 Ivano-Frankivsk 0,4369 17 Dnipropetrovsk Re- 0,3063 Region gion 6 Zakarpattia Re- 0,4341 18 Odessa Region 0,3045 gion 7 Chernihiv Region 0,3983 19 Poltava Region 0,3045 8 Rovno Region 0,3952 20 Kharkiv Region 0,2916 9 Sumy Region 0,3932 21 Lugansk Region 0,2862 10 Kyiv Region 0,3684 22 Mykolaiv Region 0,2699 11 Zhytomyr Region 0,3604 23 Kirovograd Region 0,2028 12 Kherson Region 03546 24 Donetsk Region 0,1520

Source: compiled by the author with Koriavets', 2017.

Overall regional rating. Odessa Region took the 8th place among all the regions of Ukraine according to the measured European Integration Progress Index (index value – 0.01357) after the western regions of Ukraine, which quite lively build the foreign economic ties with the EU countries in general and cross-border cooperation in particular, and after Kyiv Region, where the majority of national enterprises engaged in the foreign economic activity is traditionally founded. The regional rating upon the indicator characterising the reform aspect “Trade and trade- related matters”. It should be mentioned that Odessa Region has the 11th place under the reform direction “Trade and Trade-Related Matters”, which is lower than the overall rating giving way to the central regions of Ukraine (Vinnytsia, Kirovograd), except the western regions, and to such a powerful economic regional centre of the country as Kharkiv Region. The index value in this direction is 0.05105. The regional rating upon the indicator characterising the reform aspect “Economic and sectoral collaboration”. Odessa Region has the 6th place with the relevant index value of 0.01428. In general, the lower ratings of the western regions upon the integral index allowed Odessa Region to take the eighth place in the overall rating. Advantages and obstacles of reforming in the directions “Trade and trade-related matters” and “Economic and sectoral collaboration”. If to elaborate advantages and obstacles to the reforms in the directions “Trade and trade-related matters” and “Economic and sectoral collaboration” specific namely for Odessa Region determining a current state of the relevant reforms in the region, special mention should go to: Advantages:  strategic location of Odessa Region (international highways through the territory of the region, extensive network of sea and river ports, presence of the common borders with the EU countries);  available highly skilled workforce, a considerable proportion of which is implemented at the enterprises engaged in the foreign economic activity;  maturity of small entities and middle-sized enterprises.

Obstacles:  underperformance of using the potential capacity of the strategic location of Odessa Region, including the high level of corruptness of the customs affairs;

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 considerable proportion of the shadow economy in small entities and middle-sized enterprises;  relatively insufficient development of the cross-border cooperation with the EU countries;  relatively low awareness of the general public about the opportunities of the economic and sectoral collaboration with the EU, insufficient quantity and quality of the present competences for carrying out this collaboration;  absence of a system approach to the development of relations between Odessa Region and the EU countries.

Ratings of the region upon the indicators characterising the reform blocs. Proof of the said advantages and obstacles to the reforms in the selected areas can be found in performing a detailed gradual analysis of certain reform blocs. The following reform blocs within the reform area “Trade and trade-related matters” are considered: Reform bloc “Commodity trade”. Having almost the greatest opportunities for developing the international commodity trading, Odessa takes the 18th of 24 possible places with the relevant index value of 0.10416 in this reform bloc. In fact, the ratio of the EU countries in the value exports and imports of Odessa Region is lower than, for instance, in the eastern regions. It should be pointed out that a great number of products of metallurgy and related sectors was exported from Odessa Region to the CIS and other countries in the world during a long period of time; concurrent- ly, light industrial goods are intensively imported to Odessa from China, Turkey. So, the low level of commodity and, correspondingly, geographic diversification of export and import of Odessa Region is noticed. Reform bloc “Sanitary and phytosanitary measures”. In the meantime, slightly better is the situation of awarding permits to enterprises of Odessa Region for food export, what made it possible for Odessa Region to take the 12th place with the relevant index value of 0.03619 in the bloc “San- itary and phytosanitary measures”. Indeed, a minor difference in the number of enterprises obtained this permit let Odessa Region be qualitatively different from the other half of regions of Ukraine. Reform bloc “Trade in services and investments”. The state of service trade, free founding and investments is beneficially different too. In this bloc, Odessa Region has the 14th place with the relevant index value of 0.19459. This indicator could be higher providing a lower development level of the shadow economy in the region. Reform bloc “Intellectual property”. It is a real breakthrough of Odessa Region. It is the 4th place with the relevant index value of 0.18121 in the bloc “Intellectual property”. And it is not sur- prising as Odessa Region is a scientific centre of the south of the country. The majority of state- owned classic higher education institutions with a wide network of branches is located right here. Such high values are an absolute guarantee of the improvement of the indicators in the previous reform blocs. Reform bloc “Simplification of customs procedures and trade promotion”. In this reform bloc “Simplification of customs procedures and trade promotion”, Odessa Region has the 11th place with the relevant index value of 0.90785 due to the relatively high number of inspections of economic entities to their total number: this situation has incidentally been mentioned while finding obstacles to the European Integration reforms in the region. Reform bloc “Regulation transparency”. In the reform bloc “Regulation transparency”, the region has the 12th place with the relevant index value of 0.75936 due to a large number of violations abated as anticompetitive practices of the government, what counts again in favour of a relatively high level of corruptness of the business activity regulation in the region.

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Andrii Kubaienko / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 91-114 In terms of the reform area “Economic and sectoral collaboration”, the following reform blocs are considered. Reform bloc “Collaboration in the energy area”: Odessa Region has the 10th place with the relevant index value of 0.29779 in the bloc “Collaboration in the energy area”. This place could be different providing a higher level of public utility charges paid by the population. However, the latter depends more on increasing the standard of living of the resident population of the region. Reform bloc “State internal financial control”. The level of state internal financial control is also low. In this bloc, Odessa Region takes nearly next to the last, 22nd place of the relevant index value of 0.24498. Usually such results dispute the success of any efforts for the European Integra- tion. Reform bloc “Transport”. In the reform bloc “Transport”, Odessa Region has the first place with the relevant index value of 0.30426 due to the fact that the EU countries are principal partners in the field of transport services trade. First of all, it is so because Odessa Region is “national sea gate”, through which export and import of goods are performed in favour of many other regions of the country. Reform bloc “Environment”. Unfortunately, having the high recreation potential capacity, the regional rating upon the indicator “Environment” is critical (23rd place). The cause of it is low volumes of investments in the natural environment protection, what is likely associated with a small number of corresponding programs in the region, which could be adopted and supported by the government. Reform bloc “Industrial policy”. The underrun is noted in the reform bloc “Industrial policy” either – the 22nd place with the relevant index value of 0.06266, what occurred, first of all, because little attention is paid to the export of processing services at sites and a large number of hazardous water supply lines either in Odessa or other localities of the region. But, with a due administrative approach, the status of these realms can be improved. Reform bloc “Financial services”. The relatively small export and import ratios of financial services to/from the EU countries in the total volume of services export and import were compensated with a relatively large number of financial enterprises, the growth of which is firstly associated with the progress in the residential and non-residential construction works in the region. Consequently, Odessa Region has the 13th place in the rating in the reform bloc “Financial services”. Reform bloc “Information society”. Considering the fact that in Odessa Region the financial services, IT and other sectors, the operations of which are impossible without using the Internet or cellular communication services became well-developed, Odessa Region comes first with the relevant index value of 0.08342 in the reform bloc “Information society”. Reform bloc “Tourism”. In the reform bloc “Tourism”, where Odessa Region seemed to be able to take one of leading places, it gives way to all the regions of Western Ukraine, including Kharkiv, Dnipropetrovsk, Vinnytsia regions. The cause of it is an extremely inefficient activity due to a large number of travel companies operating in the territory of Odessa Region in relation to the EU countries – corresponding relative export and import ratios of travelling services are lower than in the aforesaid regions. The majority of tourists coming to the region is students or temporary migrants from the Middle East and Asia. Most often, tourists travel to Turkey and Egypt. In this bloc, Odessa Region takes the 12th place with the relevant index value of 0.06781. Reform bloc “Agriculture”. Odessa Region is one of leading agricultural regions of Ukraine, its breadbasket, a powerful wine growing and wine making centre, but the volume of foreign investments in this area is relatively low. The indicator of cost effectiveness of agricultural enterprises is also low. The average are indicators of labour efficiency and gross output per unit of workforce. It is highly likely associated with a low level of transparency in carrying out a corresponding activity, and that is why with an incomplete representation of its results in the official statistics. The place of the region in this bloc is 23rd. The relevant index value is 0.05253. 104

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Reform bloc “Science, technologies and innovations”. In the bloc “Science, technologies and innovations”, Odessa Region takes the 23rd place with the relevant index value of 0.01647. It should be pointed out that despite a relatively small number of enterprises engaged in the innovation activity the number of new applied technologies, mastered types of innovative products, etc., the costs of sales of goods of a corresponding activity are of the highest in Ukraine. It evidences that Odessa Regions implements large, but single innovation projects. While measuring the outcomes of this activity, it is actually difficult to only rely on the quantitative indicators. Reform bloc “Collaboration in the matters of civil society”. In the reform bloc “Collaboration in the matters of civil society”, Odessa Region takes the honourable second place with the relevant index value of 0.58607 and is after Kyiv Region primarily due to a large number of legalised NGO per one person of the present population – about 7 organisations. So, the performance of this activity should be studied. Recommendations for increasing the rating of the European Integration Progress Index of Odessa Region. The carried out analysis only confirms the general conclusions about the status of the European Integration Progress reforms of Odessa Region, and the presence of certain disproportions and inconsistences in the region development becomes more evident. From this perspective, the following lines of activity may be proposed as recommendations for increasing the regional rating of the European Integration Progress Index (Derij Zhand Zosymenko, 2016, pp. 9-17; Kaynak, 2011, pp. 31-49; Tarasiuk, 2013, pp. 19-26):  Diversification of the international commodity and service trading.  Pulling travel, agricultural, IT companies activity and so forth out of the shadow.  Creation of favourable conditions for the innovation activity growth of servicing and manufacturing enterprises.  Use of NGO efforts to grapple with the inadequate performance of the government, abate governmental financial misconducts.  Infrastructure renovation for carrying out the entrepreneurial activity, investment in the industrial re-equipment of production, repairs of general service circuits (water supply systems, gas, etc.)  Running of appropriate environmental practices of business operations.  Strengthening of the public awareness campaigns as for Ukraine-EU free trade area and the role of all the economic entities, NGOs and government authorities in its implementation.

Study of the European Integration Progress Index of Odessa Region in 2016 (Koriavets', 2017). In 2016, Odessa Region took the 18th place in the national rating, having moved by 7 positions down for two years. The most significant contribution to the worsening of the general situation in the region was made by the reduction of the number of indicators of the trade and economic relations strength. The stably low institutional support level had also a negative impact on the integral index of the progress in the European Integration. The best regional indicators were a volume of money remittances of individuals from the EU to the region, share of direct investments coming from the region to the economy of the EU countries, number of economic entities gained a right for export- ing food livestock products to the EU countries, number of events on the economic matters of implementation of the Association Agreement. General results. Odessa Region was gradually losing its positions in the rating of the European Integration Progress during 2014-2016 in view of the general growth of the European Integration Progress Index: in 2014 – 11th place (index value – 0.2884), in 2015 – 14th place (0.3214), in 2016 – 18th place (0.3045). The latter shows a low progress of the European Integration in comparison with the speed of a corresponding progress in other regions of Ukraine.

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Andrii Kubaienko / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 91-114 The most significant contribution to the worsening of the general situation in the region was made by the Sub-Index of trade and economic relations strength (7th place in the rating in 2014 and 11th – in 2016) in view of a relatively steadfast position of the sub-index of institutional support of the progress in the European Integration. But it should be pointed out that according to this indicator Odessa Region had almost the lowest rating position (between the 22nd and 23rd) among all the regions. In other words, the general worsening was exclusively caused by lowering the trade and economic relations strength. It is conceivable that, in addition to the really “arithmetic” effect of the stably low level of institutional support, its absence had the greatest impact on the general economic situation, i.e. the first integral index component. Strength of trade and economic relations. The best indicators in 2016 were: 1.16. Indicator of money remittances of individuals from the EU to the region” (1st place with the value of 18.37 %), 1.14. Share of direct investments coming from the region to the economy of the EU countries in the general volume of investments made from the region” (2nd place, 94.92 %) an 1.11 concerning the right of export of food livestock products to the EU countries that was obtained by 4 food, beverage and tobacco production enterprises. The worst indicators in 2016 were: 1.12. Number of approved (entitled) exporters in relation to the overall number of exporters of goods” – the region took the 23rdd place. The region took the 22nd place under the following indicators: “1.1 Export ratio of goods to the EU countries in the total volume of export of goods from the region” (25.33 %), “1.2 Import ratio of goods from the EU countries in the total volume of import of goods to the region” (27.29 %) and “1.6 Annual average speed of growth of import to the region from the EU countries (for the last three years)” (72.17 %). The region had the small volumes of goods export to the EU counties in relation to the overall volumes. The import volumes were also low amid the relatively moderate speed of the latter growing. It can be explained by the worsening of the general economic situation due to the annexation of Crimea and military conflict in Donbas, which had the devastating consequences for the maritime trade (namely for ferrous export, raw materials import for corresponding industries). Institutional support. The best indicator of the region in 2016 was “2.7 Number of measures in the economic matters of implementation of the Ukraine-European Union Association Agreement, which were arranged and carried out by the government authorities in relation to the number of positions of public officers of the public administration” (3rd place; number of measures made 13). The following indicators had a zero value: 2.6. Specific volume of financing in the frame of the EU technical assistance in the region; 2.9. Number of concluded agreements for bilateral cooperation in the matter of developing the rural green tourism between the region and corresponding administrative territorial units of states- members of the EU in relation to the number of the present region population; 2.10. Number of training trips concerning the rural green tourism to the states-members of the EU, which were arranged by the regional public administrations, in relation to the number of the present population; 2.11. Number of trainings, corresponding conferences, round tables, seminars for the rural population with the assistance of the EU experts, which were arranged and carried out by the Re- gional Public Administration, in relation to the number of the present rural population of the region;

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2.13. Number of presentations of the economic and investment potential of the region held at the EU agencies and structures, in relation to the number of enterprises in the region; 2.5. Number of projects as a part of the European neighbourhood instrument and other EU programs available for Ukraine, implemented in the region in relation to the number of population of the region.

Assessment of actions of the regional government authorities on implementing Decree No 847-p of the Committee of Ministers of Ukraine dated 17.09.2014. As for the general assessment of actions of the regional government authorities on implementing “economic” points of Decree No 847-p of the Committee of Ministers of Ukraine dated 17.09.2014 “On implementation of the Ukraine-European Union Association Agreement” in 2014, 2015 and 2016, this activity can generally be assessed as quite average. First of all, there is a lack of any system reflected in the absence of an Action Plan for implementing specific provisions of the Agreement at the local level (the corresponding information has been absent on the website of the Regional Public Administration since 2015). The action plan of the Chief of the Regional Public Administration has recently been published, and plans of social and economic development of the region comprise the particular provisions about the stimulation of the European Integration of Odessa Region by intensifying the trade, developing a relevant transport and another infrastructure, assisting in the cross-border collaboration. But, the said documents do not contain any direct references to the text of the Ukraine-European Union Association Agreement. Correspondingly, it is embodied in quite sporadic efforts in all the directions of collaboration. The performed analysis shows the insufficient use of the potential of the European Integration of Odessa Region and many existing disproportions in the substantial components of this system process. Among the most important disproportions is the absence of institutional support of the European Integration that negatively influences either SIP, which is quintessence and generalisa- tion of this support, or efficiency according to the true economic components of the Sub-Index of trade and economic relations strength. The second disproportion is financialisation of the economic relationships of Odessa Region and the EU as well as import substitution. Additional asymmetry of the relevant flows is clearly shown through the example of the following indicators: number of remittances from the EU, percentage of cumulative investments of non-residents in the region and so forth. The performed analysis just confirms the general conclusions about the state of the reforms in the area of the Odessa Region Progress in the European Integration. From this perspective, the following lines of activity may be suggested as recommendations for increasing the regional rating according to the European Integration Progress Index.

3. RESULTS AND DISCUSSION The progress in the integration of Ukraine to the European association goes on. This process cannot be studied in a simplified manner, i.e. in one plane (economic, political or cultural). It is complex in itself, and its consequences have a varied nature. These consequences may have either a positive or negative nature. By applying the method of scenario analysis in the most general terms, the European Integration effects for the economy of Ukraine is generally shown in Figure. 4. (Tarasiuk, 2013, pp. 19-26; Marmazov and Pilyayev, 2013, pp. 36-41; Draskovic, V. and Draskovic, M., 2012, p. 119-136).

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Andrii Kubaienko / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 91-114 Figure 4. Determination of the European Integration effects for the Ukrainian economy

Effects of the European Integration for Ukraine

Growth of the European market publicity Switch of enterprises to new markets

Probable rise in unemployment levels Simplification of the access to financing

due to the implementation of new technologies Structural deformations in the national Activation of the tourism and education economy sectors

Probability of activation of capital out- Activation of investment processes flows

Competitiveness activation in various Increase of the Ukrainian reliance on foreign suppliers and investors sectors of economy

Growth of influence of transnational Participation of Ukraine in transnational companies on the economy of Ukraine transport corridors

Souce: compiled by the author

The influence of the European Integration process on the economic security of Ukraine should be studied by stages. At the first stage, it is necessary to study how the European Integration will influence the national economy, its state and processes, which significantly affect the economic health of national enterprises. For analysing the European Integration influence on the economic security of Ukraine, we should apply the scenario method that makes it possible to determine the most probable scenarios. The economic security is considered within the protecting approach. So, at the second stage, one should study what threats to the economic securiy each of the Ukrainian economy changes scenarios has as a result of the European Integration processes. Formation of such scenarios is possible (Table 1 - Three scenarios for the development of Ukraine-EU relations after the elections, 2012; Yermolaiev et al., 2012, pp. 111-117; Elements of the EU strategy for Ukraine (Czech view: Policy statement), 2013 pp. 67-74).

The scenario names definitely show their content. Indeed, the given scenarios do not cover all the possible points, and the given list of scenarios cannot be deemed as closed, in other words, as finally-formed. The given list of scenarios likely shows capabilities of the scenario method to determine changes in the Ukrainian economy due to the European Integration processes.

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Table 2. Ukrainian economy changes scenarios as the result of the European Integration effect

Scenario name Preview 1 Takeover of the Transnational companies have a leading position in the Ukrainian economy national economy by thanks to merge and acquisition operations. The most attractive economy sec- transnational compa- tors are beyond the government control and pass to the possession of transna- nies tional companies on the right of ownership. All the segments of the national economy significantly grow qualitatively and 2. National economy quantitatively due to the inflow of investments, new technologies, coming to development new markets. GNP increases. Large volumes of foreign investments have a revitalising effect on the national 3. Sea of investments economy, which allows improving its condition through the openness of new

markets and access to new resources. Competitive intervention of foreign manufacturers goes at the Ukrainian mar- 4. Intervention ket, as a result of which is reduction of the share of national manufacturers. At and allocation the same time, the access of the Ukrainian manufacturers to foreign markets is limited to the allocation mechanism application.

Just certain segments and sectors of the national economy will be able to gain traction due to the international specialization of labour and available competi- 5. Specialisation tive advantages. Stagnation will be in other segments due to the domination of foreign manufacturers. Prices of a large number of goods increase due to the price equalization in the Ukrainian and foreign markets. Commodities, prices of which are higher in 6. Tariff deficit overseas markets than in the Ukrainian market, are exported, which may cause the commodity shortage in the national market. 7. Agrarian develop- Serious development due to new technologies and publicity of new markets of ment of the national the agrarian sector of the Ukrainian economy. Other segments of the national economy economy grow slowly and gradually stagnate. 8. Skilled personnel A large number of skilled professionals leaves the Ukrainian labour market due deficiency to the simplified employment abroad, as a result of which national enterprises start feeling the skilled personnel deficiency. Serious changes will never happen in the Ukrainian economy on the institu- 9. No significant tional grounds despite the access to new resources and opportunities. Cases of change successful entrance of national companies to the overseas markets and successful partnership with foreign manufacturers stay single. (source: compiled by the author)

Each scenario provided in Table 3 shall get a qualitative assessment. It is proposed to give it according to the following criteria:  scenario desirability (on a scale from one to five, where 1 is exceptional undesirability and threat to the national economy, and 5 is a positive nature and desirability of a scenario);  probability of the scenario implementation (on a three-point quality scale: “high”, “average”, “low”);  time of the scenario implementation.

The last criterion is chosen due to the fact that it takes some time to see the European Inte- gration effects. So it is reasonable to study two timeframes – medium-term and long-term. The description of the national economy changes scenarios according to the influence outcomes of the European Integration processes is presented in Table 2.

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Andrii Kubaienko / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 91-114 It is obvious that the scenarios shown in Table 3 cannot be implemented simultaneously. Di- viding the scenarios according to the criteria “scenario implementation time” and “probability of the scenario implementation” makes it possible to build a map of scenarios by applying the arc method (Figure. 5).

Table 3. Assessment of the Ukrainian economy changes scenarios as a result of the processes of the European Integration

Probability of the Scenario Scenario imple- Scenario name scenario imple- desirability mentation time mentation 1. Takeover of the national economy by 2 Average Long-term transnational companies 2. National economy prosperity 5 Low Long-term 3. Sea of investments 4 Average Medium-term 4. Intervention and allocation 1 Average Medium-term 5. Specialisation 3 Average Long-term 6. Commodity shortage 2 Low Medium-term 7. Agrarian development of the national 2 Average Long-term economy 8. Skilled personnel deficiency 1 Low Medium-term 9. No significant change 2 High Long-term

Figure 5 represents two timeframes: medium-term and long-term. The arrow type shows the probability of moving from a present time point or scenario to another scenario: a full line shows a high probability of moving; stipples show an average probability; dots show a low probability. The scenarios numeration corresponds to Table 2 and 3.

Figure. 5. Scenario timeframes of the Ukrainian economic development as the result of the Euro- pean Integration effect

Present time

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Figure 5 graphically represents the connection of the national economy change scenarios as the result of the European Integration effect. It affords ground for the following conclusions.  In the medium-term period, just one scenario should be considered of four studied – Sea of investments: it will be favourable for national companies. Other scenarios, though some of them are hardly probable, can bring challenges to national companies, including the economic security assurance.  In the long-term period, despite the positive consequences for the national economy as a result of the European Integration, the scenario of takeover of the national economy by foreign transnational companies is quite probable. This scenario is ominous for the economic security of enterprises that become uninteresting for transnational companies.  The scenario of national economy prosperity is only possible providing the prior implementation of the scenario Sea of Investments, which stipulates a large number of foreign investments coming to Ukraine. But foreign investments are always targeted, i.e. funds are contributed to the enterprises selected according to the certain criteria by investors. If an enterprise does not comply with criteria of foreign investors, then it will not get such investments, what negatively affects its economic health.  Other medium-term scenarios do not make it possible to get on the path of stable development of the national economy and economic security.  The long-term period has got the scenario uncertainty as the connection between the scenarios with the long-term and medium-term implementation time is very close and completely uncer- tain at once.  Among all the scenarios, the scenario of national economy specialisation due to the international specialisation of labour is probable. A kind of this scenario is the Ukrainian economy agrarian development, which is to influence negatively the economic health of enterprises with other types of economic activity, which are not directly or indirectly related to agriculture.  The scenario of no significant change in the national economy is probable despite all the processes of the European Integration of Ukraine. The framework of this scenario is established nowadays in some ways by the government actions – absence of real reforms, effective fight against corruption and political stability.

The given scenarios of changes in the national economy as the result of the European Integra- tion effect may be interesting for study. It is especially important to study the problem of influence on the economic security, health of companies.

CONCLUSIONS The carried out research allows us to choose tools that can be applied for measuring the economic reforms progress in the context of the European Integration and determining trends of the national economic security activation in general and separately in the regions. For measuring the integral index (European Integration Progress Index), it is suggested applying the geometrical mean method. The efficiency of the regional progress in the European Integration depends on the efficiency of the reforms in trend I: Trade and trade-related matters, from one side, and on the efficiency of the reforms in trend II: Economic and sectoral collaboration, from the other side. Each of them comprises corresponding groups of indicators showing the prioritised reform areas. For their description, there were some statistical indicators selected, the information of which is public. For trend I, it was possible to make an assessment in 6 groups, among which is as follows: 1.1. Com- modity trade; 1.2. Sanitary and phytosanitary measures; 1.3. Trade in services and investments; 1.4. Intellectual property; 1.5. Simplification of customs procedures and trade promotion; 1.6. Regulation transparency. For trend II, the available information allowed distinguishing 11 reform blocs, among which is as follows: 2.1. Collaboration in the energy area; 2.2. State internal financial control; 2.3. Transport; 2.4. Environment; 2.5. Industrial policy; 2.6. Financial services; 2.7. Infor-

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Andrii Kubaienko / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 91-114 mation society; 2.8. Tourism; 2.9. Agriculture; 2.10. Science, technologies and innovations; 2.11. Collaboration in the matter of civil society. The promising area of further studies is comparative analysis of integral indexes of the progress of regions in the European Integration, values of their interim indexes in the main reform areas. From this perspective, the following conclusions can be made:  the European Integration is an objective process going at the national economy level, and particular enterprises cannot have a significant influence on it;  the European Integration as an objective process has got a number of consequences for all the spheres of life in Ukraine;  the European Integration effects form either opportunities or threats to national companies;  coping with or neutralising threats to the economic security of national enterprises due to the European Integration opens new opportunities for us, reinforces their positions (also in markets), and threat activities conversely weaken them;  the European Integration processes for the Ukrainian economy may develop under the given interrelated scenarios as assessed according to the suggested criteria;  for each of the given scenarios of changes in the national economy as the result of the Europe- an Integration effect, the consequences of implementation are determined, which may affect the economic security of Ukraine. The results of analysing every national economy changes scenario as the result of the Europe- an Integration effect are interesting either in terms of establishing perspectives of the national economy while coming from one scenario to another one, or in terms of the economic security assurance for national enterprises according to the results of identifying a practically actualised scenario. The areas of further studies of the European Integration influence on the economic security is quantification of this influence, assessment of probable change of certain scenarios, study of triggers of this change and adaptation of economic agents of various levels, either the national economy in general, or particular enterprises before the effects of the European Integration. According to the results of measurement of the European Integration Progress Index of Odessa Region, the following suggestions are made. To the Head of the Odessa Public Administration: to initiate the process of designing a regional plan of measures for implementing the Ukraine-European Union Association Agreement, which takes into account the regional specifics and needs, and to insert therein measures targeted at the institutional support of the European Economic Integration, namely: collaboration in the matter of development of the rural green tourism between the region and relevant administrative territorial units of states-members of the EU, training trips concerning the rural green tourism to the states- members of the EU, presentations of the economic and investment potential of the region held at the EU agencies and structures, events for the rural population with the involvement of the EU experts. To the Department of Economic Policy and Strategic Planning, and Department of Invest- ments, International and Interregional Collaboration of the Odessa Regional Public Administration: to initiate consultations with business representatives about possible directions of diversification of the international commodity and services trading for the purpose of overcoming negative consequences of the worsened general economic situation in the country and destruction of sea export- import channels; to design a system strategy of presentation of the Odessa regional economic potential capacity at the EU structures and at relevant Internet portals. To the Department of Economic Policy and Strategic Planning of the Odessa Regional Public Administration: to take measures on stimulating the small and medium-sized businesses, inclu- sively due to the money transfers and collaboration development in the Black Sea region. To the Department of Investments, International and Interregional Collaboration of the Odessa Regional Public Administration: to activate operations on joining the programs financed by the EU, stimulat- 112

Andrii Kubaienko / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 91-114 ing the cross-border cooperation in the real sector, holding information campaign in this regard. To the NGOs of Odessa Region: to place greater focus on fighting against the government inadequate performance as regards adapting an action plan on implementing the Ukraine-European Union Association Agreement at the regional level. In a climate of the European Integration, the economic security system shall protect from a negative influence of external and internal threats, destabilising factors, and assist in pursuing interests of owners, employees, partners of companies and other parties concerned.

REFERENCES Avanasova, N. E. (2016), “Economic security of Ukraine in the system of economic security of the European Union“, Bulletin of the Economy of Transport and Industry, No. 56, pp. 74-82 (in Ukrainian). Contract - legal cooperation Ukraine – EU (2013), The Delegation of Ukraine to the European Union, the Verkhovna Rada of Ukraine, Committee on European Integration, 27.02.2013. Re- trieved from: http://comeuroint.rada.gov.ua/komevroint/control/ uk/publish/article;jsessionid =653A3AA11F0E0A3ED760591CBC45D1A8?art _id=55176&cat_id=47123. (in Ukrainian). Draskovic, V., Draskovic, M. (2012), “Institutional Nihilism as a Basis for Anti-Development Policy”, Montenegrin Journal of Economics , Vol. 8, No. 1, pp. 119-136. Derij, Zh., Zosymenko, T. (2016), “Methodology for Assessing the Progress of Economic Reforms: Regional Dimension of European Integration”, Problemy i perspektyvy ekonomiky ta upravlin- nia [Problems and prospects of economy and management], Vol. 2, No. 6,pp. 9-17 (in Ukraini- an). European integration (2011), Business literature, Moscow (in Russian). Elements of the EU strategy for Ukraine (Czech view: Policy statement) (2013), “Institutional Re- forms in the EU”, Analytical Quarterly, No. 4, pp. 67-74, (in Ukrainian). Hluschenko, O. O, Tuleninova, D. M. (2016), “Influence of Eurointegration on the economic security of domestic enterprises. Scientific herald of Mukachevo State University”, Series Economics, No. 195, pp. 112-119 Retrieved from: http://nbuv.gov.ua/UJRN/nvmdue_2016_1_20 (in Ukrainian). Honcharenko, O. M, Bohatyrenko, A. O. Bakal, K. V., Vynokurova, A. I., Halimuk, B. O., Hejderova, K. T. (2017), Problems and prospects of European integration of Ukraine. Retrieved from; http://jrnl.nau.edu.ua/index.php/IMV/article/viewFile/5915/6665 (in Ukrainian). Ivankiv, O. Ya. (2015), “The essence of the system of economic security of the enterprise and the conditions of European integration”, Scientific Herald of the International Humanitarian Uni- versity Series: Economics and Management, No. 11, pp. 109-112 (in Ukrainian). Kaynak, M. (2011), Economic Growth Theories, Gazi Publishing, Ankara (in Turkey). Koriavets', M. (2017), Index of European Integration Economic Advancement in Ukraine, Report. Chernihiv: Polissya Foundation for International and Regional Studies, Retrieved from: http://pfirs.org/produkti/book/59-indeks-evrointegratsijnogo-ekonomichnogo-postupu- regioniv-ukrajini/3-produkti.html (in Ukrainian). Kozachenko, H., Adamenko, T. (2015), “Economic security of an enterprise: an analysis of existing definitions”, Scientific Herald Poltava University of Economics and Trade, Vol. 1, No. 69, pp. 90-95 (in Ukrainian). Korobov, V. K. (2016), „The Index of Eurointegration Economic Progress of the Regions of Ukraine in the Course of Regional Studies”, Scientific Bulletin of the Kherson State University. Series geographic sciences, No. 4, pp. 22-26 (in Ukrainian). Marmazov, V. ,Pilyayev, I. (2013), “With a road map on the way to the EU”, Politics and time, No. 11, pp. 36-41 (in Ukrainian).

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Andrii Kubaienko / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 91-114 Pogorelov, Yu. S., Adamenko, T. (2015), “Problems of forming a methodological basis of the science of economic security of the enterprise”, New Economy, Vol. 2, No. 66, pp. 173-181 (in Rus- sian). Association Agreements between Ukraine and the EU to prepare and facilitate the implementation of the Association Agreement dated March 16, 2015. [Web-portal of Ukrainian Government]. Retrieved from http://www.kmu.gov.ua/kmu/control/en/publish/article?art_id= 248542569 &cat_id=248542039. Pryimak, V. I. (2009), Mathematical methods of economic analysis, Center for Study Literature, Kyev (in Ukrainian). Tarasiuk, B. (2013), “Ukraine-EU: Priorities, Problems, Prospects. EU-Ukraine Cooperation“, Analytical Quarterly, No. 3, pp. 19-26 (in Ukrainian). Three scenarios for the development of Ukraine-EU relations after the elections (2012), Retrieved from: http://iwp.org.ua/img/tri_czenari_ukr.pdf (in Ukrainian). The Association Agenda between Ukraine and the EU to prepare and facilitate the implementation of the Association Agreement of 16.03.2015. Retrieved from: http://www.kmu.gov.ua/ control/uk/publish/article%3fshowHidden=1&art_id=243281941&cat_id=223345338& ctime=1266423569791. (in Ukrainian). Association Agreement between the European Union its Member States, of the one part, and Ukraine, of the other part 27.06.2014. Official web portal of Verkhovna Rada Ukraine. Retrieved from http://zakon4.rada.gov.ua/laws/show/984_011/paran2820#n2820. (in Ukrainian). Venice Commission to publish conclusions on changes to the Constitution in the part of the system of judicial system in June 921.05.2013. Retrieved from: http://www.ukrinform.ua/ukr/news/ venetsianska_komisiya_oprilyudnit_visnovki_shchodo_zmin_do_konstitutsiii_v_chastini sistemi_sudoustroyu_u_chervni_182875 (in Ukrainian). Yl'iashenko, E. V. (2015), 2Economic security of the enterprise: the content of the concept”, L'As- socТatТon 1901 «SEPIKE», No. 11, pp. 162-168 (in Ukrainian). Yermolaiev, A. V , Parakhons'kyj, B. O., Yavors'ka, H. M., Reznikova, O. O. [ta in.] (2012), Europe- an project and Ukraine, NISD, Kiev (in Ukrainian). Zubok, M. I. (2012), Economic security of entrepreneurs: a manual, Beam, Kiev (in Ukrainian).

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Montenegrin Journal of Economics

Vol. 14, No. 2 (2018), 115-129 ‘

Organizational Agility Conceptual Model

RIMA ŽITKIENĖ1 and MINDAUGAS DEKSNYS2

1 Professor dr. Mykolas Romeris University, Institute of Economics, Vilnius, Lithuania, e-mail: [email protected] 2 PhD student, Mykolas Romeris University, Institute of Economics ,Vilnius, Lithuania; e-mail: [email protected]

ARTICLE INFO ABSTRACT Received March 29, 2018 Organizational agility is a complex and multidimensional concept. Revised from April 30, 2018 One of the challenges in researching organizational agility is its Accepted May 25, 2018 unified definition and concept. Literature analysis reveals various Available online June 15, 2018 dimensions and frameworks are used to analyze organization agility. Many of them focus on narrow industry or only approach the organizational agility concept from limited perspective. This article JEL classification: attempts to combine different approaches and angles to organiza- M1, M14, M2, M21. tional agility to a more cohesive and encompassing conceptual model that is applicable to variety industries and organizations. DOI: 10.14254/1800-5845/2018.14-2.7 The variety and combination of attributes, characteristics, capabilities, and practices make the measurement of organizational agility Keywords: are analyzed. Building on research main frameworks for analyzing organizational agility concept are identified. Conceptual organiza- Organization agility, tional agility model based on organizational agility attributes, capa- business, bilities and practices framework is presented. This article contrib- conceptual model, utes to research by providing more unified concept, which can be market environment, adapted in studying organizational agility in a wide and global range organizational framework. of organizations, regardless of the industry they operate in.

INTRODUCTION Organizations are constantly facing change. Globalization, fast technological advances, competition, disruptive business models, emerging new markets, constantly evolving consumer preferences – are daily challenges for most big and small organizations. Combined with more traditional risks of business and economy lifecycles, these everchanging challenges force organizations to become more efficient and agile in order to survive. Accoding Kazlauskienė, Bartusevičienė, Tamu- lienė (2017) a lot of discussions on the variety and identification of individual abilities and/or general competences arise. There is a lack of unanimous approach among scholars. Authors determined the potential of leveraging abilities to increase income of country population by distinguishing abilities in the context of intangible assets definition and evaluation. Organizational agility is increasingly growing in significance as one of the main tools for gaining and maintaining a competitive advantage in the fast-changing market environment. Agility is be- 115

Rima Žitkienė and Mindaugas Deksnys / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 115-129 coming one of the key organizational characteristics that business practitioners seek in order to stay adaptive and competitive in turbulent environments. Due to increasing popularity of e-commerce, even small-size businesses are able to compete on a global scale. These developments further increase the need for organizational agility in order to adapt and compete outside home markets. To take advantage of emerged global opportunities, companies have to have efficient and agile business processes, flexible organizational structure, open-to-change workforce, agile networks and partners, and easily adaptable technology. Consumer habits and needs constantly change as well. The current age of information abun- dance and speed provides new levels of choice for the customers. Customers are becoming more knowledgeable. Product and service reviews, user ratings are available instantly for the judgment and choice by consumers. Ability to select the best service provider or business partner at moment’s notice gives new meaning for a competitive marketplace. In order to stay competitive companies should not only provide great product or service but also be ready to change and customize them to accommodate the ever-changing customer tastes and expectations. Agility allows organizations to adapt to the changing market situation, customer expectations and plays a major role in organizational survival in the consumer-driven environment. In order to effectively compete in changing market environment, organizations have to be proactive and anticipate change. To achieve that, organizational structures should allow for greater agility, through flexibility and response. Švagždienė, Jasinskas, Simanavičius (2017) sugested that even though views of the various interested groups on the preferred business practices are quite different, however, many of them have a common denominator and can be harmonized. The success of learning organization depends on the values conveyed. Practitioners need new organizational solutions, forms, and tools to embrace the changing environment and capture new opportunities. Successful adaptation to external forces requires agile organizational enablers, abilities, and practices. In order to control and improve agility level, organizations need to be able to understand agility and identify which internal organizational factors affect it. The aim of the article is to combine different approaches and angles to organizational agility to a more cohesive and encompassing conceptual model that is applicable to variety industries and business organizations. The methods of research include the following: analysis of scientific literature, the conceptual organizational agility model is a basis for further studies, research, practical applicability and measurements of agility level.

1. THE CONCEPT OF ORGANIZATION AGILITY While organizational flexibility has been studied for the last few decades and many attempts have been made to define agility in the business organizations. However, most definitions focused on separate functional areas of the businesses. By Wendler (2013), only recently organizational agility - as entire enterprise phenomena, gained more interest from researchers. Organizational agility is a multidimensional and complex topic and is approached by many researchers from different perspectives. One camp of the researchers Alberts & Hayes, (2003); Bottani ( 2010); Cai (2013); Charbonnier-Voirin (2011); Eshlaghy et al. (2010); Giachetti et al. (2003); Jackson & Johansson (2003); Lin et al.(2006); Ren et al.(2009); Sharifi & Zhang (2001); Yusuf et al. (1999) study agility from the perspective of enablers and capabilities which help organizations to achieve agility. The second group of researchers Charbonnier-Voirin (2011); Gehani (2010); Goldman et al.(1995); Sherehiy et al. (2007); Vázquez-Bustelo et al.(2007) identify main practices that agile organizations use in their daily operations. Third group of researchers Dove (2005); Dyer & Shafer(2003); Holsapple & Li(2008); Lu & Ramamurthy( 2011a); Nijssen & Paauwe (2012); Sambamurthy et al.(2003); Singh & Sharma(2013); Wright & Snell (1998) approaches agility from the perspective of how organizations interact with changing environment through sense-response dimension. 116

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Most of the earlier research is concentrated only on specific industry of organizations, in particular – manufacturing sector, where researchers analyze what manufacturing organization can do to enhance their agility (Ganguly et al., 2009; Jackson & Johansson, 2003; Yang & Li, 2002; Yauch, 2011; Yusuf et al., 1999). Others evaluate agility in a narrow context of business process or area – e.g. supply chain agility (Ren et al., 2009; Sharifi & Zhang, 2001; Van Hoek, 2001), human resource agility (Breu et al., 2002; Shafer, 1997), knowledge management and IT capabilities (Cai, 2013; Kassim & Zain, 2004; Lu & Ramamurthy, 2011b; Sarker & Sarker, 2009; Singh et al., 2013), business processes (Arteta & Giachetti, 2004), strategic alignment (Tallon & Pinsonneault, 2011), market orientation (Grewal & Tansuhaj, 2001). Lin et al., (2010) focuses on agility properties in organization networks. Recently, researchers have increased focus on IT, learning and innovations effect on agility level in an organization and its performance (Cegarra-Navarro et al., 2016; Khoshlahn & Ardabili, 2016; Ravichandran, 2017). Few of the researchers analyze how organizational agility influences competitive advantage (Côrte-Real et al., 2017; Mikalef & Pateli, 2017). Other analyses focus on individual factors and their influence on organizational agility. For example, Panda & Rath (2017), Mikalef & Pateli (2017), Felipe et al. (2016), Yeganegi & Azar (2012) study how information technology capabilities affect agility level in the organization. Their empirical research shows the direct positive correlation between IT capabilities and agility level in the organization, i.e. the better IT capabilities organization has, the more agile it is. As literature review reveals, organizational agility has its roots in manufacturing context. It was defined as a manufacturing system which is able to meet the needs of a changing marketplace, shifts quickly between products, in real time in order to adapt to changing customer needs. Early agility research (Goldman et al., 1995; Sharifi & Zhang, 2001; Yusuf et al., 1999) characterized agility as an ability to reconfigure manufacturing system in order to respond to unpredictable changes in the market. The ability to reconfigure entails utilization of structural and infrastructural elements, which adds to the position that agility is a more encompassing capability compared to flexibility (Attafar et al., 2012). By synthesizing existing technologies and production methods (Goldman et al., 1995), combining managerial and manufacturing tools (Sharifi & Zhang, 2001) with the help of people and processes organizations are able to reach agility. Literature often con- fuses definitions of organizational agility and manufacturing agility, due to mixing performance outcomes and manufacturing processes (Narasimhan et al., 2006). Researchers conceptually dif- ferentiate organizational agility – a performance capability, from agile manufacturing systems – cluster of related practices (Attafar et al., 2012). Goldman et al. (1995) introduced the concept of agile enterprise strategy and vision, by defining the agile organization as one which is profitable in continuously changing environment and is able to adapt to unpredictable consumer habits. Dove (1996) proposed that organizational agility level depends on a balance of its four dimensions: cost, time, quality and scope. Yusuf et al. (1999) argued that organizational agility level is influenced by aligning “competitive bases” (speed, flexibility, innovation proactivity, quality, and profitability), reconfigurable resources and knowledge. In order to improve organizational agility level, companies have to combine these enablers and adapt to consumer needs and changing marketplace. Referring back to the discussion of the differences between organizational flexibility and agility, from Yusuf et al. (1999) definition possible distinction between these two terms can be identified, with the flexibility being an inclusive enabler for organizational agility and emphasis on speed. The concepts of speed and innovation as key properties of organizational agility was brought up by (Lu & Ramamurthy, 2011a). They define agility as an organizational capability to deal with unexpected changes in the environment via rapid and innovative responses, which help to take advantage of those changes. Speed is one of the most important requirements for agility in terms of response and implementation, while innovativeness refers to the quality and substance of response (e.g. strategic orientation, product development, decision-making) (Cai, 2013).

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Rima Žitkienė and Mindaugas Deksnys / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 115-129 Review of various organizational agility definitions in the scientific literature allows identifying common themes and building blocks of organizational agility. In the simplest form, enterprise agility can be defined as organization’s ability to identify changes in the environment and respond accordingly. Ability to recognize the changes in the environment refers to the know-how, experience, and knowledge of the organization and its decision makers. Dove (1999) refers to this ability as ‘knowledge management’. Environmental change is also present in other definitions of organizational agility as ‘competitive market opportunities’ (Sambamurthy et al., 2003), ‘dynamic and continuous change’ (Sarkis, 2001); and referred as changes arising from competitor’s actions, consumer preferences, regulatory or legal changes, economic shifts, technological advancements etc. (Overby et al., 2005). Ability to respond or ‘seize’ (Sambamurthy et al., 2003), ‘reconfigure’ (Sharifi & Zhang, 2001) is an ability to act in response to the changes and in the situation dictated by the environment and internal resources and abilities. Identification of abilities that help organizations to respond to the environmental changes and their evaluation is one of the purposes of this article. Overby et al., (2005) adds a strategic element to the process of sensing the changes and responding to them. Response to the changes has to be appropriate and factor in the quality and cost of the actions. The response has to support organizational goal (e.g. market share increase, international expansion, strengthening competitive position, etc.) and adjust to the change so that it helps in advancing towards these goals. On the other hand, the pursuit of short-term market opportunities may distract the organization from long-term strategic focus. Such short-term market opportunities can be risky, capital-intensive, or unprofitable. Putting together all these elements of organizational agility allows to generate the following definition: organizational agility is an organizational ability to recognize unexpected changes in the environment and appropriately respond in a swift and efficient manner, by utilizing and reconfiguring internal resources, thus gaining competitive advantage in the process.

2. ORGANIZATIONAL AGILITY FRAMEWORKS Many frameworks and models analyze agility and its characteristics in a different context, therefore they differ in content and structure, which leads to organizational agility definition issues. Some of the frameworks and definitions do not apply to the whole organization, others are too narrow and cover specific industry. Literature review reveals that there is no consensus about the construct of agility, which makes the empirical research difficult (Wendler, 2013). In order to build the basis for empirical research, further distilling of the agility theory is needed and agility domain with applicable frameworks has to be selected. Based on Wendler (2013) and literature review, different agility frameworks can be categorized under four domains: agile manufacturing, agile software, agile workforce and agile organization/enterprise. For most of the domains, agility frameworks are interrelated and have similar themes: organizational culture, workforce, customers, organizational abilities, technology, which are represented in each separate domain with an emphasis on that particular domain (Wendler, 2013). For example, studies in both agile manufacturing and agile software development domains analyze the effect of the agile workforce, however, with former, the emphasis is on manufacturing organization employees, while the latter focuses on software developers. Therefore, it is important to distinguish domains and discuss them as separate areas of research. It allows to simplify and focus already the multidimensional concept of agility and act as a development process which grows into encompassing organizational agility domain. Main agility domains are well researched; however, they mostly focus on their own respective field. A large number of publications cover agile manufacturing, agile software development, and agile workforce perspectives. However, there is a lack of studies addressing the conceptualization and development of integrated and holistic agile enterprise concept (Sherehiy et al., 2007). Alt- hough enterprise agility concept started together with agile manufacturing idea development, the 118

Kubaienko Andrii / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 115-129 most interest organizational agility gained only recently – after the increased attention to the agile software development concept. This shows the increased interest in the effects of agility on the entire organization, not only on separate functional or structural areas (Wendler, 2013). Development of organizational framework, which is unified and applicable to different organizations is a difficult challenge. Organizational agility frameworks are not yet clearly defined and conceptualized. According to Sherehiy et al. (2007), some of the approaches to organizational agility are too broad and imprecise, that involves all definitions, practices, and technologies used in the industry over the last two decades. This approach syntheses agile manufacturing, flexible production technologies, Just-in-Time production, Total Quality Management, lean manufacturing, workforce empowerment under the same coverage of agile organization concept. The idea is that only agile organization is able to utilize various new methods and technologies and take the most advantage of it. Based on this approach it is clear why organizational agility concept was and is somewhat confusing and difficult to classify. On the other hand, the second approach to organizational agility is much more narrow and focused and distinguishes it from all other organizational methods. An agile organization is different from lean manufacturing, TQM, JIT or even agile manufacturing concepts, as it covers the whole organization, while these concepts are only a collection of operational techniques and methods that mostly apply to manufacturing organizations. The agile organization extends the manufacturing concept and encompasses different organizational elements, goals, and objectives, allowing them to adapt to unexpected and fast changes in a dynamic business environment (Sherehiy et al., 2007). Literature analysis shows that most of the organizational agility research could be divided into 3 main approaches or frameworks, based on the dimensions and focus they use. Each framework has a similar approach to agility in organization and concepts to describe it. One camp of the researchers (Alberts & Hayes, 2003; Bottani, 2010; Cai, 2013; Charbonnier-Voirin, 2011; Dyer & Shafer, 1998; Eshlaghy et al., 2010; Giachetti et al., 2003; Jackson & Johansson, 2003; Lin et al., 2006; Ren et al., 2009; Sharifi & Zhang, 2001; Yusuf et al., 1999) use the framework of agility enablers and capabilities which help organizations to achieve agility. The second group of researchers (Charbonnier-Voirin, 2011; Gehani, 2010; Goldman et al., 1995; Sherehiy et al., 2007; Vázquez-Bustelo et al., 2007) try to identify main practices that agile organizations use in their daily operations. Third group of researchers (Dove, 2005; Holsapple & Li, 2008; Lu & Ramamurthy, 2011a; Nijssen & Paauwe, 2012; Sambamurthy et al., 2003; Singh & Sharma, 2013; Wright & Snell, 1998) developed the framework based on sense-response dimensions. Sense-response framework sees organizational agility through the two perspectives: abilities to identify opportunities and ability to act upon them in an efficient manner. All three approaches are discussed in more detail in the following sections. Discussion of each approach helps to construct a more holistic and inclusive model of organizational agility in further sections of this article.

2.1. Enabler-capability framework The main purpose of agility in an organization is to better adjust to change and gain competitive advantage and to take opportunities from changes in the environment and thrive in uncertainty and unpredictability. Therefore, agile enterprises need a set of capabilities and enablers to respond to such change. The framework of enablers and capabilities is based on the premise that agile organization can achieve competitive advantage in changing the environment. This framework can approach organizational agility subject from two dimensions: static and dynamic. Static dimension refers to the question “What organizational has that makes it agile?” and focuses on the structural aspects of the organization. While dynamic dimension attempts to answer the question “What organization is able to do, in order to be more agile?” and focuses on abilities of the organization. Both dimensions are mutually dependable on each other. The organization cannot be agile if it only relies on the structural element. Having newest technology or organizational 119

Rima Žitkienė and Mindaugas Deksnys / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 115-129 structure will not provide the benefit if it is not properly utilized in order to respond to changing the environment. While having knowledgeable employees and ability to be agile will be ineffective if organizational structure is not flexible enough and restricting. Therefore, both dimensions are important and should be used and developed together when aiming for higher organizational agility level. Enablers and capabilities framework has roots in agile manufacturing, where established processes and activities have to be changed often to adjust to the changes in customer demand or market conditions, therefore e.g. flexibility of the processes is one of the enablers that organization has. Different researchers use different terms to identify the features of the agile organization has and call them enablers, attributes, providers, levers etc. Authors of this article use term ‘enabler’, as it more accurately describes the context (according to Cambridge Dictionary (2016) “enabler, noun. – Something or someone that makes it possible for a particular thing to happen or be done”) Yusuf et al. (1999) bases essential organizational agility enablers on main competitive foundations of agility: speed, flexibility, innovation, proactivity, quality, and profitability. Agility enablers are used as leverage to achieve agile capabilities (Bottani, 2010; Eshlaghy et al., 2010; Gunasekaran, 1999). Gunasekaran (1999), discusses seven enablers of agile manufacturing: virtual enterprise formation tools and metrics, physically distributed teams and manufacturing, rapid partnership formation tools and metrics, concurrent engineering, integrated product, production, business information system, rapid prototyping tools, and electronic commerce. Agile capabilities refer to whether the organization is able and can do what is necessary in order to be more agile. Agile capabilities allow organizations to respond effectively to change and reach competitive advantage. Various researchers use different terms to name those capabilities: responsiveness, quickness, innovation, knowledge management, learning (Charbonnier-Voirin, 2011; Dove, 1999; Sharifi & Zhang, 2001). Charbonnier-Voirin (2011) groups different aptitudes into three key organizational agility capabilities. First one - the ability to mobilize a rapid response to change – is based on reactive flexibility and refers to an ability to organize existing resources. Second capability – aptitude to read the market - refers to the organizational ability to see the changes in the market and identify the opportunities. The third capability in Charbonnier-Voirin (2011) framework – aptitude to integrate organizational learning, refers to the organizational capacity to align employee skills and experience with those of organization Other authors identify similar organizational capabilities that help organizations to deal with challenges arising from environmental change, and summarize them according to four principles: responsiveness, competency, flexibility/adaptability, and quickness/speed (Eshlaghy et al., 2010; Giachetti et al., 2003; Lin et al., 2006; Ren et al., 2009; Sharifi & Zhang, 2001; Yusuf et al., 1999). Macro perspective on the organizational capabilities can identify organizational agility as a higher- order dynamic capability itself, which can enhance performance by effectively adjusting organization to environmental changes (Cai, 2013).

2.2 Organizational agility practices and processes This approach towards organizational agility asks a question “What organization does to be agile”. Not exactly a framework in the sense of structured approach to study agility, however an important dimension and focus, as it emphasizes the utilization of enablers and capabilities in improving organizational agility. The focus lies on the practices and processes of the organization aiming towards agility, although there is a lack of consensus in the literature. For example, Yusuf et al. (1999) use enablers and practices as synonyms. They offer a general list of enablers under ten domains: the introduction of new products, the formation of partnerships, continuous improvement, short conception/production of deadlines, decentralized decision-making, response to market requirements, etc. These enablers can be turned into actions or practices, e.g. formation of partnerships is not a static ability, but rather an action which utilized people and networking skills to form successful partnerships. 120

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It is important to distinguish the framework of enablers and framework of practices. Enablers or characteristics is what abilities, features, and capabilities organization has. However, it does not mean that organization is actually using those agility enablers in order to become agile and achieve competitive advantage while adapting to the fast-changing environment and seizing market opportunities. The action or practice is what distinguishes agile organizations from the rest, as it utilizes those enablers and characteristics that organization has. Agile abilities and enablers can be wasted if not properly used. Or they can be too costly or time-consuming. At the same time, an organization might not have any of the necessary agility enablers and still be able to aim towards and improve organizational agility level by practice alone. Therefore, constant practicing of agility is much more important than just mentioning or identifying it as another organizational enabler. Goldman et al. (1995) identify four dimensions that organizations should aim towards in order to stay competitive and achieve agility. The dimensions can be interpreted as activities or practices that organization should perform continuously: enrich the customer, cooperate, organize for change, and leverage the impact of people and information. Similarly to Goldman et al. (1995), Charbonnier-Voirin (2011) identifies practices that are important in the agile organization and structure them into four main categories: practices directed towards mastering change, practices promoting the value of human resources, cooperative practices, and practices to create value for customers. In order to implement the agility-based strategy, Gehani (2010) recommends to use the following actions and implement practices based on them: front-line decision-making empowerment, cross-functional team sharing, modular integration of available technologies, delayed design specification, product succession planning, enterprise-wide integration of learning. Empowerment of employees allows the organization to shorten the decision-making time, reduces delays, improves response and delivery times. In process employees are more involved and motivated, the organization is more agile in responding to changes and customers are satisfied due to improved service. Organizations may not have necessary enablers or capabilities to become agile by simply activating them, Organizational agility is a process and needs to be changed constantly in order to adapt to constantly changing the environment. Thus high organizational agility level becomes a goal and organizations set different strategies in achieving these goals in a most efficient and effective way. By acting towards these goals, organizations start to utilize, develop or invent different enablers and capabilities necessary for organizational agility.

2.3 Sense-response framework of organizational agility The overview of both: enablers-capabilities framework, and practices approach, reveals some commonalities. Most of the researchers, discussing organizational agility using these frameworks rely on two main dimensions: sensing dimension and response dimension. The process of change is highly influenced by these dimensions. A catalyst for change, or organizational agility driver, usually occurs externally. Whether it is a change in customer tastes, competitor behavior or industry changes, they all influence organization from outside. In order to take advantage of these changes and utilize them as opportunities, first organizations must be able to recognize them and acknowledge. This ability or act of recognition by itself highly depends on the organizational abilities and people skills, experience and know-how. Organizational agility within sense-response framework (Figure 1) can be described as a process of events, with sensing ability as a first step (sub-process). Once external threats or opportunities are identified, the organization has to identify whether it can cope with these changes: are external changes applicable to the organizational existing status and future strategic goals; are these changes potentially benefit an organization; can organizational actually do something about 121

Rima Žitkienė and Mindaugas Deksnys / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 115-129 those changes. The ability to utilize internal and external organizational resources (capabilities and enablers) in response to environmental changes is a second sub-process in the sense-response framework. The organizational response is highly dependent on its capabilities and enablers. One the decision to act towards an external change is made, the organization uses its capabilities and enablers to respond to the opportunity or threat. This complex process of evaluation and effective action to external stimuli is the basis for successful agile organization, based on the sense- response framework.

Figure 1. Sense-response framework

Organization

Sensing (capability to identify the external Capabilities that are used to change) Sense/respond (e.g. experience, External change insight, awareness, competence, (opportunity, motivation, etc.) threat)

Enablers that are used to Response (Action to- Sense/respond (e.g. skilled work-

wards the external) force, capital, equipment, etc.) change)

Source: Authors‘ estimation.

Sense-response framework stands out of other frameworks (enabler-capability and practice) as it encompasses both of the previously discussed frameworks into a cohesive structure, based on constant process and action. According to Singh & Sharma (2013), sense-response framework can be explained by the concept of the magnitude of variety change (flexibility) and rate of generating a variety of change (speed). The flexibility component represents a structural dimension of organizational change and shows the degree to which organization is able to respond by changing its practices, products, services or processes. While second concept – speed, represents how fast organization can sense the market changes and adapt to them (Singh & Sharma, 2013). Sensing dimension in sense-response network focuses on the abilities to see the external changes. It is directed outwards of the organization. This dimension is highly dependant on experience and abilities of decision-makers in the organization and their personal abilities. Lu & Ramamurthy (2011a) refer to this dimension of organizational agility as market capitalizing agility, as it focuses not only on collecting and processing external and internal information but also on the internal abilities. The organization reaches agility when it can effectively match external changes in the market and customer needs with internal abilities to meet those changes and needs. To achieve high organizational agility level just sensing abilities are not enough. Once organization realizes that it has to change and adapt to the new environment using sensing abilities, it has to have abilities that empower the internal change. These abilities can vary from one organization to another, however, some commonalities exist. Response dimension in a sense- response framework focuses on the internal abilities of the organization to respond to the environmental changes. Lu & Ramamurthy (2011b) call it operational adjustment agility as it focuses on an internal maneuvering to provide fast response to changes and is reactive in nature.

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One of the purposes of this article is to identify the common response abilities that successful organizations share and enable them to reach agility. Response-ability involves various dimensions within the organization and with its partners and is complex in nature, as it involves various levels and dimensions of the organization. Organizations should have a flexible structure, which allows shifting resources easily. Organizational culture should encourage change and empower employees to adapt to the new strategy. The external network should also support organizational agility. Suppliers, outsourced services, partners and other members of the external organizational network should be able to adapt to the changing demands and promote agility. Responsive capabilities refer to the ability to select available actions and enable those actions. When the change occurs in the environment or organization pro-actively identifies opportunities it has to select the appropriate action to take from the alternative ones. Decision makers have to evaluate available opportunities, coordinate and integrate with the rest of the functional areas and outside partners, learn from the experience and reconfigure available resources if necessary (Holsapple & Li, 2008). Although this dimension is not singled out in organizational agility research, many of the agility definitions have one or few elements that emphasize the importance of qualitative elements of organizational agility. Organizational agility on its own will not be of value to the company if it is too expensive and costs exceed the benefits of being agile. Effectiveness dimension gives a framework to achieve a high level of organizational agility practicality. Organizational agility will not be effective and practical if processes take too long to implement compared to market conditions, which change faster than organization is able to adapt. Therefore, such organizational agility effectiveness measures as cost, speed, quality, and scope, must be considered when establishing organizational agility framework (Dove, 2006). Organizational agility can be identified as an encompassing perspective for both sense and response dimensions. Without the qualitative dimension, sense and response abilities lose its usefulness and applicability in business situations. An organization with a high level of agility, that fail at this dimension eventually may fail competitive race in a fast-changing environment. On the other hand, organizational agility based on only one qualitative dimension can act as a competitive strategy. For example, agility to adapt to the fast-changing market environment based on only cost criteria can be effective even when other elements such as speed, quality are not reached to their potential. Authors of this article believe that all three organizational agility frameworks (enabler- capabilities, practices, and sense-response) are not separate from each other. Each of them depends on the core abilities and enablers organization has. For example, organizational agility practices cannot be performed without proper infrastructures (enablers) and know-how (capabilities). In the same way, sense-response framework is in essence shifting focus towards organizational capabilities. Capability to identify shifts and changes in the environment, and capability to respond to these changes by utilizing applicable enablers which organization has. The three organizational agility frameworks are interdependent. Therefore, instead of selecting the preferred framework to approach organizational agility, authors of this article propose to combine them and offers a unified conceptual model.

3. ORGANIZATIONAL AGILITY CONCEPTUAL MODEL The development of a unified and holistic organizational agility framework is a challenging task. There are several issues with current organizational agility frameworks. As discussed in preceding sections, the first problem arises from definition and conceptualization of the agility itself. Then issues with different domains, approaches, and frameworks complicate this task even further. Each different domain identifies agility concept differently, use enablers, characteristics, capabilities, practices, that are important for that particular industry or domain. Although preceding discussion of different organizational agility frameworks reveals some similarities. One of the goals of this article is to build upon those similarities between domains, frameworks and their parts and 123

Rima Žitkienė and Mindaugas Deksnys / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 115-129 attempt to construct a more unified and universal framework that can be applied to different types of organizations, regardless of their industry or business model. As a first step of the conceptual model - causes and reasons for organizational agility should be established. For the purposes of simplifying and making model applicable for a wider range of organizations, agility drivers can be narrowed down to five main categories: changes in the market, competition, customers, technologies and social factors (Eshlaghy et al., 2010; Sharifi & Zhang, 2001). These agility drivers pressure organizations to adapt to the changing environment thus maintaining or gaining competitive advantage. There is no single set of organizational agility enablers that will fit every organization. However, based on the literature review we can identify common organizational enablers that can be applied universally. Enablers play important role in development of organizational agility (Charbonnier-Voirin, 2011). Enterprise infrastructure consists of adjustable levers used in implementing successful agile practices and can be deployed when the environment changes: structure and organization; processes; technology, human resources and network (Charbonnier- Voirin, 2011; Dyer & Shafer, 2003; Sharifi & Zhang, 2001; Yusuf et al., 1999). These enablers work together with organizational agility capabilities, by enhancing each other and compensating the weaker or less agile components of the organization. All five enablers of the enterprise should have certain characteristics that make those enablers agile. Foremost, structure and organization should be flexible and open to change. Components of the organizational structure should be easily adaptable to the external and internal agility drivers. Agile structure and organization should have informal, flat, horizontal; with goal- oriented leadership, decentralized knowledge and control, which allows to accept risks and concentrate on teamwork (Eshlaghy et al., 2010; Sherehiy et al., 2007). Processes in the agile organization should be flexible, consist of few rules, procedures and have adaptable role definitions (Sherehiy et al., 2007). Processes should concentrate and sense external environmental developments and promote adaptation to these changes. According to Sherehiy et al. (2007), human resource agile enabler of the organization should be proactive, adaptable, resilient, be able to collaborate, take personal initiative and responsibility, cope well with stress and unexpected changes. To develop these skills, organizations should promote employee empowerment and involvement, job rotation and enrichment (Sherehiy et al., 2007). Agile organizations should maintain flexible and adaptable networks, both internal and external. Internal networks should rely on teamwork, information and knowledge sharing. External partners, such as suppliers, contractors, distributors, etc., should be able to adapt to the changes in the business environment together with the agile organization. The fifth enabler of agile organizations – technology, should have similar flexibility characteristics as the rest of enablers. In addition, in order to adapt to the fast-changing environment, technologies should be modular, easily scalable and downgradable and have efficient cost structure. The third component of the conceptual organization agility model consists of organizational capabilities that are interdependent with organizational enablers. Interdependency of organization agility enablers and capabilities are represented in the form of enhancing or compensating each other. For example, organization lacking some equipment or technology (enabler) to adapt to changing business environment, can compensate for the abilities of its employees (capabilities). In the same way, organizational agility enablers can be enhanced by organizational capabilities – e.g. technology combined with capable workforce can provide a very strong competitive advantage for the organization and ability to adapt to changing the environment. Agile organization capabilities can be summarised into two main groups, based on the sense- response framework: sensing capabilities and response capabilities. Sensing capabilities refer to organizational abilities to sense external environment for threats and opportunities. One of sensing capability is awareness, which allows the organization to notice and anticipate changes in the environment. Awareness capability should also concentrate on the internal organizational ability to 124

Kubaienko Andrii / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 115-129 address the external changes. This second sensing capability – competence, relies on organizational experience and knowledge. It allows the organization to evaluate internal abilities to cope or take advantage of external changes. For example, the organization sees the change in consumer tastes (awareness) and needs to adjust its product line (response), however, its current equipment and experience make it impossible to act on these changes (competence). The second group of organizational agility abilities is response capabilities. Response capabilities refer to the organizational ability to act (respond) to the changes in the environment. Authors of this article use three capabilities based on dynamic capabilities theory (Sambamurthy et al., 2003; Teece, 2010): reconfiguration, learning, coordination and cooperation capabilities. According to Sambamurthy et al. (2003), response capabilities to opportunities and threats arising from changes in environment depend on the organizational ability to select appropriate actions and ability to enable those actions. Reconfiguration capability refers to organizational ability to adjust workforce, recourses and assets, partners, and processes in order to change and adapt to changing the environment, both internally and externally. Learning capability refers to organizational ability to learn from its experience, accumulate and share know-how and develop employee skills in order to face the changes in the market. Learning capability also covers internal know-how and ability to utilize external knowledge, which is an important factor in the innovation process. Last two capabilities – coordination and cooperation are similar in scope. Former one applies to coordination of internal resources – ability to motivate employees, mobilize for change, develop and foster change culture. Cooperation capability searcher for the support from the external organizational network during the change process. It involves coordinating partners, suppliers, distributors, etc. for the objective to improve organizational agility and its ability to adapt to changing the environment. Agile organization enablers and capabilities work together via compensation and enhancement to move the organization forward. This process leads to actions that agile organization takes in order to efficiently change and adapt to the environment. These individual actions grow into agile practices. Agile organizations take the organizational assets (enablers and capabilities) and combine them into effective and meaningful activities and practices, that increase organizational agility level. Based on literature analysis (Charbonnier-Voirin, 2011; Gehani, 2010; Goldman et al., 1995; Sherehiy et al., 2007; Vázquez-Bustelo et al., 2007), main organizational practices focus on four dimensions: organization itself, employees, customers, and partners. Organizational practices aim at the development and increasing flexibility and adaptability of organizational structure, processes, and technology. If certain organizational enablers lack agility, organizational practices should be focused on improving the characteristics of those enablers or developing capabilities required for organizational agility. In Figure 2 presented conceptual model can be applied as a process roadmap or a flow for decision making. Organizational adaptation starts as with the change in the environment through agility drivers. Once the change is sensed and recognized, decision-makers should take inventory of current situation in the organization by answering the questions ‘what resources we have to address the changes in the environment and do we have necessary abilities to utilize those resources and adapt to the changes’. Once the inventory is established, organization decision makers should respond to the environment drivers by utilizing these of enablers and capabilities. The response is represented by action or practice, which leads to an outcome, such as the development of new product, or change in procedures. This process of adaptation to the environmental changes increases organizational agility level further through experience sharing. As business environment is constantly changing, the agile organization is also constantly applying its enablers, capabilities, and practices to adapt to these changes.

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Rima Žitkienė and Mindaugas Deksnys / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 115-129 Figure 2. Organizational agility conceptual model

Organization Agile Enablers What organization has to address these changes? Agile Practices  Structure and organization What organization does?  Processes  Technology  Organizational prac-  Human resources tices (structure, processes, resource, Agility Drivers  Network etc.) What forces organiza-  Employee empower- tion to become agile? Sense Changes in: ment practices (skill sharing, learning, mo-  Market tivation, etc.)  Competition  Customer enrichment  Customer Enhancing/ Re- practices (value preferences Compensating spond Acting proposition, competi-  Technology tive advantage, prod-  Social factors Agile Capabilities uct flexibility and fea- Can organization adapt to tures, etc.) changes?  Cooperation practices  Sensing capabilities (supply chain speed, (Awareness, competence) outsource flexibility,  Response capabilities distribution, etc.) (Reconfiguration, learning, coordination, cooperation)

Source: Authors‘ estimation.

CONCLUSIONS The analysis of the literature on the organizational agility made in this article has revealed the need for unified conceptual model of organizational agility. Definitions of organizational agility in the scientific literature vary. Many researchers, however, agree that the organizational agility is a multidimensional factor the definition of which requires focusing on the country, market, and industry in which the company operates. The notion of organizational agility has been developed on completion of a comparative analysis of the literature. In this article, organizational agility is defined as organizational ability to recognize unexpected changes in the environment and appropriately respond in a swift and efficient manner, by utilizing and reconfiguring internal resources, thus gaining competitive advantage in the process. The following organizational agility drivers, enablers, capabilities and practices have been identified on completion of the analysis and systematization of the organizational agility literature. Agility drivers which force organizations to stay or become agile: changes in market; changes in competition; changes in customer preferences; changes in technology; changes in social and economic factors. Agility enablers refer to what resources organizations have that help them to adapt and be agile. After literature research and systematization, authors of this article identified the following agility enablers: structure and organization; processes; technology; human resources, network. Agile capabilities refer to organizations’ ability to identify the external changes and act on them. They can be divided into two groups: sensing capabilities and response capabilities. Authors 126

Kubaienko Andrii / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 115-129 of the article identified the following organizational agility practices that refer to the actions and activities agile organizations perform before or during change periods: organizational practices, employee empowerment practices and customer enrichment practices. A theoretical model for evaluation of organizational agility level has been developed. Organizational adaptation starts as with the change in the environment through agility drivers. Once the change is sensed and recognized, decision-makers should take inventory of current situation. Once the inventory is established, organization decision makers should respond to the environment drivers by utilizing these of enablers and capabilities. The response is represented by action or practice, which leads to an outcome, such as the development of new product, or change in procedures. This process of adaptation to the environmental changes increases organizational agility level further through experience sharing. As business environment is constantly changing, the agile organization is also constantly applying its enablers, capabilities, and practices to adapt to these changes.

REFERENCES Alberts, D. S., Hayes, R. E. (2003), Power to the edge: Command and Control in the Information Age, Command & Control Research Program, Washington. Arteta, B. M., Giachetti, R. E. (2004), "A measure of agility as the complexity of the enterprise system", Robotics and Computer-Integrated Manufacturing, Vol. 20, No. 6, pp. 495–503. Attafar, A., Ghandehari, M., Momeni, G. (2012), "Study of Required Organizational Base for Implementation of Agility Strategy in Organizations", Journal of Contemporary Research in Business, Vol. 3, No. 11, pp. 141–150. Bottani, E. (2010), "Profile and enablers of agile companies: An empirical investigation", International Journal of Production Economics, Vol. 125 No. 2, pp. 251–261. Breu, K., Hemingway, C. J., Strathern, M., Bridger, D. (2002), "Workforce agility: The new employee strategy for the knowledge economy", Journal of Information Technology, Vol. 17, No. 1, pp. 21-31. Cai, Z. (2013), "Developing organizational agility throught IT capability and KM capability : the moderating effects of organizational climate", Proceedings of the 17th Pacific Asia Confer- ence on Information Systems (PACIS), Jeju Island, Korea, 18–22, June, pp. 1–19. Cambridge Dictionary, C. (2016), Cambridge Dictionary, Meaning (entry 124), pp. 138–138. Cegarra-Navarro, J.-G., Soto-Acosta, P., Wensley, A. K. P. (2016), "Structured knowledge processes and firm performance: The role of organizational agility", Journal of Business Research, Vol. 69, No. 5, pp. 1544–1549. Charbonnier-Voirin, A. (2011), "The development and partial testing of the psychometric properties of a measurement scale of organizational agility", Management, Vol. 14, No. 2, pp. 119–156. Côrte-Real, N., Oliveira, T., Ruivo, P. (2017), "Assessing business value of Big Data Analytics in European firms", Journal of Business Research, No.70, pp. 379–390. Dove, R. (1996), "Tools for Analyzing and Constructing Agility", Agility Forum, Vol. 70, Issue 5649, pp. 1–13. Dove, R. (1999), "Knowledge management, response ability, and the agile enterprise", Journal of Knowledge Management, Vol. 3, No. 1, pp. 18–35. Dove, R. (2005), "Agile Enterprise Cornerstones: Knowledge, Values & Response Ability", Business Agility and Information Technology Diffusion, No. 180, pp. 313–330. Dove, R. (2006), "Engineering Agile Systems : Creative-Guidance Frameworks for Requirements and Design", Systems Engineering, Proceedings of 4th annual conference on systems engineering research (CSER). Los Angeles, CA, pp. 1–10. Dyer, L., Shafer, R. a. (2003), "Dynamic organizations: Achieving marketplace and organizational agility with people", CAHRS Working Paper (03-04), pp. 1–39. Eshlaghy, A. T., Mashayekhi, A. N., Rajabzadeh, A., Razavian, M. M. (2010), "Applying path analysis 127

Rima Žitkienė and Mindaugas Deksnys / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 115-129 method in defining effective factors in organisation agility", International Journal of Production Research, Vol. 48, No. 6, pp. 1765–1786. Felipe, C. M., Roldán, J. L., Leal-Rodríguez, A. L. (2016), "An explanatory and predictive model for organizational agility", Journal of Business Research, Vol. 69, No. 10, pp. 4624–4631. Ganguly, A., Nilchiani, R., Farr, J. V. (2009), "Evaluating agility in corporate enterprises", International Journal of Production Economics, Vol. 118, No. 2, pp. 410–423. Gehani, R. R. (2010), "Time-based management strategic roles", International Journal of Operational and Production Management, Vol 15, pp. 19-35 Giachetti, R. E., Martinez, L. D., Sáenz, O. a., Chen, C.-S. (2003), Analysis of the structural measures of flexibility and agility using a measurement theoretical framework, International Journal of Production Economics, Vol. 86, No. 1, pp. 47–62. Goldman, S. L., Nagel, R. N., Preiss, K. (1995), "Agile Competitors and Virtual Organizations: Strategies for Enriching the Customer", Long Range Planning, Vol. 29, pp. 131-135. Grewal, R., Tansuhaj, P. (2001). Building Organizational Capabilities for Managing Economic Crisis: The Role of Market Orientation and Strategic Flexibility, Journal of Marketing, Vol. 65, No.2, pp. 67 -80. Gunasekaran, A. (1999), "Agile manufacturing: a framework for research and development", International Journal of Production Economics, Vol. 62, No. 1, pp. 87–105. Holsapple, C. W., Li, X. (2008), "Understanding Organizational Agility : A Work-Design Perspective Understanding" In Proceedings of the 13th ICCRTS, pp. 1–25. Jackson, M., Johansson, C. (2003), "An agility analysis from a production system perspective", Integrated Manufacturing Systems, Vol. 14, No. 6, pp. 482–488. Kassim, N. M., Zain, M. (2004), "Assessing the Measurement of Organizational Agility", Journal of American Academy of Business, Cambridge, Vol. 4, No. 1-2, pp. 174–177. Kazlauskienė E., Bartusevicienė I., Tamulienė V. (2017), "The potential/opportunities for leveraging competences: the intangible assets dimension", Montenegrin Journal of Economics, Vol. 13, No. 1, pp. 51-62 Khoshlahn, M., Ardabili, F. S. (2016), "The Role of Organizational Agility and Transformational Leadership in Service Recovery Prediction", Procedia - Social and Behavioral Sciences, No. 230, pp.142–149. Lin, C. T., Chiu, H., Chu, P. Y. (2006), "Agility index in the supply chain", International Journal of Production Economics, Vol. 100, No. 2, pp. 285–299. Lin, Y., Desouza, K. C., Roy, S. (2010), "Measuring agility of networked organizational structures via network entropy and mutual information", Applied Mathematics and Computation, Vol. 216, No. 10, pp. 2824–2836. Lu, Y., Ramamurthy, K. R. (2011a), "The Link Between IT Capability & Organizational Agility Introduction", MIS Quarterly, Vol. 35, No. 4, pp.931–954. Lu, Y., Ramamurthy, K. R. (2011b), "Understanding the Link Between Information Technology Capability and Organizational Agility : an Empirical Examination", MIS Quarterly, Vol. 35, No. 4, pp. 931–954. Mikalef, P., Pateli, A. (2017), "Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA", Journal of Business Research, No. 70, pp. 1–16. Narasimhan, R., Swink, M., Kim, S. W. (2006)", "Disentangling leanness and agility: An empirical investigation", Journal of Operations Management, Vol. 24, No. 5, pp. 440–457. Nijssen, M., Paauwe, J. (2012), "HRM in turbulent times: how to achieve organizational agility?", The International Journal of Human Resource Management, Vol. 23, No. 16, pp. 3315–3335. Overby, E., Bharadwaj, A., Sambamurthy, V. (2005), "Business Agility and Information Technology Diffusion", international conference proceedings: A Framework for Enterprise Agility and the Enabling Role of Digital Options, Atlanta: Georgia pp. 295–312. Panda, S., Rath, S. K. (2017), "The effect of human IT capability on organizational agility: an empirical analysis", Management Research Review, 40, No. 7, pp. 800–820. Ravichandran, T. (2017), "Exploring the relationships between IT competence, innovation capacity 128

Kubaienko Andrii / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 115-129

and organizational agility", The Journal of Strategic Information Systems, Vol. 27, No. 1, pp. 22- 42. Ren, J., Yusuf, Y. Y., Burns, N. D. (2009), "A decision-support framework for agile enterprise partnering", International Journal of Advanced Manufacturing Technology, Vol. 41(1–2), pp. 180– 192. Sambamurthy, V., Bharadwaj, A., Grover, V. (2003), "Shaping Agility through Digital Options: Reconceptualizing the Role of Information Technology in Contemporary Firms", MIS Quarterly, Vol. 27, No. 2, pp. 237–263. Sarker, S., Sarker, S. (2009), "Exploring agility in distributed information systems development teams: An interpretive study in an offshoring context", Information Systems Research, Vol. 20, No. 3, pp. 440–461. Sarkis, J. (2001), "Benchmarking for agility", Benchmarking: An International Journal, Vol. 8, No. 2, pp. 88–107. Shafer, R. A. (1997), "Creating organizational agility: The human resource dimension", Dissertation Abstracts International Section A: Humanities and Social Sciences, Gornell University Sharifi, H., Zhang, Z. (2001), "Agile manufacturing in practice - Application of a methodology", International Journal of Operations & Production Management, Vol. 21, No. 5-6, pp.772–794. Sherehiy, B., Karwowski, W., Layer, J. K. (2007), "A review of enterprise agility: Concepts, frameworks, and attributes", International Journal of Industrial Ergonomics, Vol. 37, No. 5, pp. 445–460. Singh, D., Oberoi, J. S., Ahuja, I. S. (2013), "An empirical investigation of dynamic capabilities in managing strategic flexibility in manufacturing organizations", Management Decision, Vol. 51, No. 7, pp. 1442–1461. Singh, J., Sharma, G. (2013), "Organizational agility: What it is, what it is not, and why it matters", Academy of Management Proceedings, Vol. 2013, No. 1, pp. 1–40. Švagždienė B., Jasinskas E., Simanavičius A. The success of learning organisation: values contextualization dimension. Montenegrin Journal of Economics, Vol. 13, No. 4, pp. 101-108. Tallon, P. P., Pinsonneault, A. (2011), "Competing Perspectives on the Link Between Strategic Information Technology Alignment and Organizational Agility: Insights from a Mediation Model.", MIS Quarterly, Vol. 35, No. 2, pp.463–486. Teece, D. J. (2010), "Business Models, Business Strategy and Innovation", Long Range Planning, Vol. 43, No. 2–3, pp. 172–194. Van Hoek, R. I. (2001), "Rediscovery of postponement a literature review and directions for research", Journal of Operations Management, Vol. 19, No. 2, pp. 161–184. Vázquez-Bustelo, D., Avella, L., Fernández, E. (2007), "Agility drivers, enablers and outcomes: Empirical test of an integrated agile manufacturing model", International Journal of Operations & Production Management, Vol. 27, pp. 1303 - 1332. Wendler, R. (2013), "The Structure of Agility from Different Perspectives", Computer Science and Information Systems (FedCSIS), pp. 1165–1172. Wright, P. M., Snell, S. A. (1998), "Toward a unifying framework for exploring fit and flexibility in strategic human resource management", Academy of Management Review, Vol. 23, No. 4, pp.756–772. Yang, S. L., Li, T. F. (2002), "Agility evaluation of mass customization product manufacturing", Journal of Materials Processing Technology, Vol. 129, No. 1–3, pp. 640–644. Yauch, C. a. (2011), "Measuring agility as a performance outcome", Journal of Manufacturing Technology Management, Vol. 22, No. 3, pp. 384–404. Yeganegi, K., Azar, M. S. Z. A. (2012), "The Effect of IT on Organizational Agility", Proceedings of the 2012 International Conference on Industrial Engineering and Operations Management Istanbul, Turkey, July 3 – 6, pp. 2537–2544. Yusuf, Y. Y., Sarhadi, M., Gunasekaran, A. (1999), "Agile manufacturing: the drivers, concepts and attributes", International Journal of Production Economics, Vol. 62, No. 1, pp. 33–43.

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Montenegrin Journal of Economics

Vol. 14, No. 2 (2018), 131-141 ‘

Implementation of Du Pont Model in Non-Financial Corporations

SYLVIA JENCOVA1, EVA LITAVCOVA2, and PETRA VASANICOVA3

1 Associate Professor, University of Presov, Faculty of Management, Slovakia, e-mail: [email protected] 2 Associate Professor, University of Presov, Faculty of Management, Slovakia, e-mail: [email protected] 3 PhD. Student, University of Presov, Faculty of Management, Slovakia, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received April 20, 2018 In this paper, the practical application of the dynamic decomposi- Revised from May 20, 2018 tion of the return on equity is used on the basis of the ratio indica- Accepted May 30, 2018 tors. These indicators are determined according to the absolute Available online June 15, 2018 items taken from the Register of Financial Statements of the Slovak Republic and are obtained for individual non-financial corporations according to SK NACE 26 and SK NACE 27 for the period 2013- JEL classification: 2016. Du Pont decomposition of the return on equity allowed quan- G3, O16. tifying the impact of the financial indicators such as return on sales, total assets turnover ratio, financial leverage, and interest and tax DOI: 10.14254/1800-5845/2018.14-2.8 profit reduction, which were linked by a multiplicative product interaction. Quantification of the factors impact was applied to a sample Keywords: of 138 non-financial corporations using the equations valid for the application of the functional method. By applying the exploration Du Pont, data analysis, the impact of the factors is plotted graphically. The model, box plot shows that the obtained values are very varied and it fol- profitability, lows that no clear conclusion can be deduced. There is potential for return on equity, further exploration of the dynamic model. corporation.

INTRODUCTION Slovakia was, is and will be an industrialized state. According to the statistics of the European Union, the Slovak Republic is even the most industrialized state in the European area. The electrical engineering industry is one of the largest industries in the world. In Slovakia, electrical engineering and engineering sector are the main pillars of industry and are therefore backbone of the Slovak economy. Here, the electrical engineering industry had the fastest growth rate among all manufacturing industries, and it was one of the most attractive sectors for foreign investors. The electrical engineering industry has a long-standing tradition in Slovakia; it is the third strongest manufacturing sector just behind the engineering and automotive industries.

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Sylvia Jencova, Eva Litavcova, and Petra Vasanicova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 131-141 Slovakia is an industrial country, and forecasts showing that the future of the industry is not threatened, but one threat results from the lack of qualified labour. Identification, analyzation, comparation, and evaluation of the electrical engineering industry among the self-governing regions of Slovakia was provided by Midler and Dubcova (2014). „Financial analysis involves comparing the firm’s performance to that of other firms in the same industry field and evaluating trends in the firm’s financial position over time“ (Vitkova and Semenova, 2015, p. 744). Non-financial corporations from electrical engineering industry were studied in research of Jencova and Litavcova (2013), Jencova, Litavcova and Vasanicova (2016), Litavcova, Jencova and Vasanicova (2017), Jencova et al. (2017). Taking into account the volume of sales, these companies represent the entire manufacturing electrical engineering industry of the Slovak Republic. Jencová and Litavcova (2013), in their monograph, provided financial and economic analysis of the selected company from electrical engineering industry, since 2008, and applied mathematical and statistical methods. In this paper, we provide the practical application of the dynamic decomposition of the return on equity on the basis of the ratio indicators. On a sample of 138 non-financial corporations from electrical engineering industry, we quantify the impact of the financial indicators such as return on sales, total assets turnover ratio, financial leverage, and interest and tax profit reduction, on the return of equity.

1. DU PONT MODEL „Financial ratio analysis is a process of determining and interpreting relationships between the items of financial statements to provide a meaningful understanding of the performance and financial position of a company“ (Babalola and Abiola, 2013, p. 132; Vitková and Semenova, 2015, p. 744). Financial analysis of the company can be performed by various methods. The methodology should include indicators, among which there are simple and understandable relations and causal connections. One of the appropriate methods is the pyramid system of indicators, which is a logical-deductive indicator system composed by the synthetic indicator that is decomposed to other partial indicators that are in the position of causal factors (Gurcík and Jancíkova, 2002). Pyramidal models form a set of financial metrics that are linked by causal and comple- mentary links, in order to provide the greatest possible value for financial management (Jencova, 2016). Figure 1 illustrates a simplified pyramidal model. For the field of financial analyzes, the chosen indicator should have the highest reporting value (base period, actual period, index, growth rate, absolute impact, relative impact, comparison of more methods of quantifying the influence of determining factors). Peak indicator, i.e. the synthetic indicator is the indicator of the zero level; the indicators 1, 2 are the indicators of the first level, and they are gradually divided to the further indicators of the lower level. The intent of the pyramidal systems is to explain the behavior of the peak indicator and then quantify the influence of the partial indicators to convert this synthetic indicator. The degree of decomposition quantifies the weight and aggregate of the indicators. Indicators are linked by vertical links that have a causal character, and by horizontal links that are com- plementary. Using these linkages, we perform quantification of the individual analytical indicators to change the synthetic indicator. Factor analysis is actually an analysis that allows quantifying causal factors to the peak factor.

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Figure 1. Simplified pyramidal model

Source: own processing according to Jenčová (2016)

Boda and Uradnicek (2016, p. 73) defined the static pyramidal decomposition as ”a decomposition of the peak, synthetic indicator into a series of partial factors, between which there are precise mathematical-logical and economic-causal relations. This requirement implies that the change of each partial factor at the higher decomposition stage affects the change of all other analytical factors in the appropriate decomposition branch upwards. Then it also affects the change of the peak synthetic indicator assuming ceteris paribus”. Mentioned authors also pointed out that for the purpose of further exploring linkages between factors, it is appropriate to analyze static pyramidal decompositions in a certain chronological sequence. Then the pyramidal decomposition becomes to a certain extent more dynamic. In the valuable papers of Boda (2014) and Uradnicek (2014) is pointed to the inclusion of weights express subjective importance into the dynamic multiplier pyramidal decomposition of the financial metrics. The first pyramidal model, known as the Du Pont decomposition, was applied to the chemical company Du Pont de Nemours. The term Du Pont refers to the company E. I. du Pont de Nemours and Company that was established by Éleuthère Irénée du Pont de Nemours, in 1802. The author of this model was Frank Donaldson Brown (Marek, 2009) who created a model of return on assets (ROA) decomposition in 1912. The ROA indicator is not ideal for investor decision making as it does not distinguish between capital appreciation for shareholders and creditors. This role is better performed by the return on equity (ROE) indicator that Brown created in 1919. The original Du Pont decomposition of ROA is illustrated in Figure 2. On the first level of decomposition is applied the multiplicative product linkage between the indicators of profitability, activity, indebtedness, and ratio of the various profits. The profitability indicator is represented by the operating profit margin; the activity indicator is represented by the use of assets; indebtedness indicator is represented by the leverage factor. Profit ratio is given by the ratio of net profit for the accounting period and the operating earnings before interest and taxes for the accounting period. Models of Du Pont decomposition are specified in Mitchell, Mitchell and Cai (2013). For ROE decomposition is appropriate disaggregation by using the eight indicators. The ROE indicator is decomposed into two branches and three rows. The left branch quantifies seven 133

Sylvia Jencova, Eva Litavcova, and Petra Vasanicova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 131-141 metrics of ROA as basic earning power, return on sales (ROS), total assets turnover ratio, earnings after taxes (EAT), sales, and assets. The right branch represents the ratio of total assets, or the total invested capital on equity, i.e. the indicator of financial leverage that is the reciprocal value of equity ratio (Jencova, 2016).

Figure 2. Original Du Pont decomposition

Source: own processing according to Marek (2009)

Du Pont model in the manufacturing industries was used in many foreign publications, e.g., Vasiu, Baltes and Ciudin (2012), Lubinski, Fear and Perez (2013), Mihola and Kotesovcova (2015), Vitkova and Semenova (2015), Rudrajeet and Aneja (2017), Carvalho et al. (2017). Pyramidal models of financial corporations were studied by authors Zhang, Han and Zhang (2016). Profitabil- ity behavior within manufacturing industry by using data of return of assets that is one of the components of Du Pont decomposition was studied in Pruziak (2017).

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2. THE METHODS OF QUANTIFYING THE INFLUENCE OF DETERMINING FACTORS The profitability indicator of ROE is one of the most widely used financial evaluators the economic situation of the company in terms of profitability (Paulik, Sobekova Majkova, Tykva and Cer- vinka, 2015). To quantify the impact of analytical factors on the return on equity, in this paper, we apply the logarithmic and functional method within the multiplicative linkage. In the pyramidal system, using appropriate methods it is possible to quantify the intensity of the influence of the individual sub-indicators on the peak indicator and thus explain the development of the financial situation of the company between selected periods. In addition, it is possible to evaluate differences between the real and planned value of the peak indicator, to compare the company’s performance with competitors, to monitor the differences between company’s performance and performance of the whole industry or the best companies in the given industry, to predict future development resulting from the causal links between indicators (Sedlacek, 2007; Jencova, 2016). In additive linkages between the indicators, the influence is quantified by the elementary method, using the standard shape, using the ratio of the change and the corresponding overall change multiplied by the impact of the corresponding peak financial indicator. The implementation of the logarithmic method in the analyzed company is based on the indices of differences of the individual analytical indicators, which are interconnected by multiplicative product and quotient interactions and acquire the values which are valid for applying the logarithmic method (Kucharcikova et al., 2011). As is stated in Zmeskal, Dluhosova and Tichy (2013), and in Dluhosova (2008), the logarithmic method is given by the formulas (1), (2), (3), (4), (5):

(1)

(2)

(3)

(4)

(5)

where x0 is the basic value of analyzed indicator x, x1 is the current value of analyzed indicator x, ai are analytical factors, y is immediately previous synthetic factor, and I denotes index. Using functional method one can determine discrete revenue (DV, Rx). Taking into account four indicators, calculation is given by equations (8), (9), (10), (11). Functional method, in which are applied two indicators, is given by equations (6), (7), (12), (13):

(6)

(7)

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(8)

(9)

(10)

(11)

where X is the synthetic indicator (in this paper ROE), X0 is the basic value of analyzed indicator x, DV means discrete revenue, and a, b, c, d are analytical factors. Functional method, in which are applied two indicators, is given by equations (12), (13), (14). According to Zmeškal, Dluhošová and Tichý (2013), discrete revenue is denoted as Raj =Δaj/aj0 a Rx=Δx/x0. This method removes the problem of negative indexes of the indicators.

(12)

(13)

(14)

3. RESULTS AND DISCUSSION In the following text, on the basis of a detailed analysis, the impact of analytical indicators on the synthetic indicator of return on equity is quantified. Here are calculated impacts for 138 companies which taking into account the volume of their sales represent almost the entire manufacturing electrical engineering industry. In the period 2013-2014, the company Visteon Electronics Slovakia, Ltd, Namestovo (established 3rd May 2014) was not included in the analysis; in the period 2014-2016, the company Panasonic AVC Networks Slovakia, Ltd, Krompachy was not included in the analysis due to the zero net turnover; in the period 2013-2016 was not included in the analysis the company Bizlink Tech- nology, Ltd. (established 4th December 2015). In 2016, 98 electrical engineering companies have profit for the accounting period and 40 have loss. For comparison, in the period 2014-2015, 106 non-financial corporations of electrical engineering industry were in profit; in 2014, 31 companies were in loss, in 2015, 32 non-financial corporations of electrical engineering industry had loss for the accounting period. From the per- 136

Sylvia Jencova, Eva Litavcova, and Petra Vasanicova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 131-141 formed analysis resulted that in the period 2013-2014, the decline in return on equity occurred in 71 companies (52.2%); the decline in return of sales occurred in 61 companies (44.85%), and increase occurred in 75 companies (55.14%). The intensity of asset utilization positively determined 77 companies (56.6%). The increase in financial leverage influenced the ROE in 66 companies; the decrease in financial leverage negatively influenced the ROE in 70 companies. The decrease in the tax reduction of profit negatively affected 70 non-financial corporations. In the period 2014-2015, the decline in ROE indicator has occurred in 76 companies (55.74%). In the period 2014-2015, from the analyzed group of companies, the decrease in ROS occurred in 78 companies (56.93%) and increase in 59 companies. The intensity of asset utilization positively determined 62 companies and negatively influenced 75 companies. The increase in financial leverage increased the ROE indicator in 64 companies (46.71%). Change in the ratio of the profit/loss for the accounting period and operating earnings before interest and taxes had a positive influence on 80 companies. In the period 2015-2016, negative change in ROE indicator occurred in 84 companies (60.86%), increase in return on sales ROS occurred in 66 companies, decrease was in 70 companies. The intensity of asset utilization positively determined 66 companies (47.82%). The growth of the financial leverage caused the growth of ROE in 59 companies (42.75%). Change in the ratio of the profit/loss for the accounting period and operating earnings before interest and taxes had a positive influence in 69 companies. Within the analyzed group of companies, in the period 2015-2016, in 44 companies (33.82%), the return on sales ROS mostly contributed to the change in return on equity ROE; in 21 companies (15.44%) ranked second; on the third place ROS influenced 23 companies (16.91%); and on the fourth place was indicated in 48 companies (35.29%). The impact of total asset turnover TAT most determining 25 companies (18.38%), this indicator occurred second place within 45 companies (33.08%), third place within 40 companies (29.41%), and fourth place within 26 companies (19.11%). The financial leverage FL mostly contributed to the change in ROE in 30 companies (22.05%); in 33 companies (24.26%) was on the second place; as the third mostly determined factor was identified in 41 companies (30.14%) and as the fourth in 32 companies (23.52%). Table 1 lists the order of importance of the individual impacts of the particular analytical indicators on the overall ROE change over three consecutive periods.

Table 1. Ranking of the impact of factors on ROE change in the corporations from the electrical engineering industry

Period / Rank / 2013-2014 2014-2015 2015-2016 Factor 1 2 3 4 1 2 3 4 1 2 3 4 ROS 47 23 24 42 58 13 13 53 44 21 23 48 TAT 34 37 40 25 25 39 47 26 25 45 40 26 FL 25 40 36 35 23 39 46 29 30 33 41 32 EAT/EBIT 30 36 36 34 31 46 31 29 37 37 32 30

Source: own compilation

If we would assume that the development of change, or the growth rate of return on equity, as well as the partial impacts of analytical indicators, will be similar within the context of dynamic decomposition, the assumption would not be confirmed. The limitation is the length of the time series. There is potential for further exploration of the dynamic model. Using the exploration data 137

Sylvia Jencova, Eva Litavcova, and Petra Vasanicova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 131-141 analysis, we graphically visualized the box plots that show the impact of factors (Figure 1-3). The box plot shows the minimum value, maximum value, lower quartile, upper quartile, and median. The graph points to outliers or extreme values. The box plot shows that values are very varied, therefore no clear conclusion can be drawn.

Graph 1. Box Plot of the impact of factors on ROE in the period 2013-2014

Source: own processing

Graph 2. Box Plot of the impact of factors on ROE in the period 2014-2015

Source: own processing 138

Sylvia Jencova, Eva Litavcova, and Petra Vasanicova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 131-141

Graph 3. Box Plot of the impact of factors on ROE in the period 2015-2016

Source: own processing

CONCLUSION In order to quantify the impact of the individual components of the financial equilibrium, we have applied methods for additive, multiplicative and combined linkages between financial indicators. Research suggests that it is still appropriate to implement a functional method that elimi- nates the disadvantages of other methods; i.e., the logarithmic method may have a problem with negative indexes. The financial aspects are key factors in a process of company’s development (Stefko, Ga- vurova, Korony, 2016). Managers are still confronted with the decision on how to allocate limited company resources in a challenging and highly competitive environment (Miron, Petrache, 2012). For professionals, accountants, or financial managers, the implementation of the system of indicators is of great importance. Financial metrics systems help financial managers to generate the concept of development, to choose the right strategy, as well as to plan all financial aspects in the short or long term. Therefore, the company's management should emphasize and increasingly implement financial models in its financial and economic analyzes. Du Pont decomposition of the return on equity allowed quantifying the impact of the four financial indicators such as return on sales, total assets turnover ratio, financial leverage, and interest and tax profit reduction, which were linked by a multiplicative product interaction. Quantifica- tion was applied on a sample of 138 non-financial corporations of the Slovak electrical engineering industry using the methods of quantifying the influence of determining factors. It was found that the return on equity is mostly determined by profitability and indebtedness. The box plot shows that the obtained values are very varied and it follows that no clear conclusion can be deduced. There is potential for further exploration of the dynamic model. In future research, the authors will use mentioned model on a sample of non-financial corporations from the tourism sector.

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Sylvia Jencova, Eva Litavcova, and Petra Vasanicova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 131-141 ACKNOWLEDGEMENT This contribution was supported by research grant VEGA No. 1/0470/18 “Economic activity of tourism in the European area” and by research grant VEGA No. 1/0945/17 “Economic research on quantification of marketing processes improving values for patients, multidimensional analysis of the marketing mix of healthcare facilities and quantification of their importance in the process of establishing a system to measure the quality and efficiency of the Slovak healthcare system”.

REFERENCES

Babalola, Y. A., Abiola, F. R. (2013), „Financial ratio analysis of firms: A tool for decision making“, International journal of management sciences, Vol. 1 No. 4, pp. 132-137. Boda, M. (2014), „A note on weighted dynamic decomposition of a financial indicator based on a multiplicative relationship“, Forum Statisticum Slovacum, Vol. 4, pp. 18-23 (in Slovakian). Boda, M., Uradnicek, V. (2016), „Inclusion of Weights and their Uncertainty into Quantification within a Pyramid Decomposition of a Financial Indicator“, Ekonomický časopis, Vol. 64 No. 1, pp. 70-92. Carvalho, F. P., Maia, V. M., Louzada, L. C., Goncalves, M. A. (2017), „Sectorial performance of Brazilian companies: a study from the standpoint of ROE, Tobin's Q ratio and Market to Book“, Finanças e Contabilidade, Vol. 7 No. 1, pp. 149-163. Dluhosova, D. (2008), Financial management and decision-making in the company, Ekopress, Praha (in Czech). Gurcik, L., Jancikova, V. (2002), Factor analysis of owners equity effectiveness, Agricultural Economics - UZPI (Czech Republic). Jencova, S. (2016), Financial and economic analysis of business entities, Bookman, Prešov (in Slovakian). Jencova, S., Litavcova, E. (2013), Implementation of the financial and statistical methods in the company under the conditions of the 21st, Tribun, Brno (in Slovakian). Jencova, S., Litavcova, E., Vasanicova, P. (2016), „Implementation of financial and statistical models to study the Electrical industry of the Slovak republic“, in 3rd International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM 2016, Bulgaria, Albena, pp. 329-336. Jencova, S., Litavcova, E., Vasanicova, P., Kosíkova, M. (2017), „Implementation of multidimensional analytic methods to compare performance between corporations“ in Nemec, R., and Chytilova, L., Strategic management and its support by information systems, Proceedings of the 12th International Conference, SMSIS, VSB - Technical university of Ostrava, Ostrava, pp. 21-29. Kucharcikova, A. et al. (2011), Effective production, Computer Press, Brno (in Czech). Litavcova, E., Jencova, S., Vasanicova, P. (2017), „On Modelling the Evolution of Financial Metrics in Decision Making Unit in the Electronics Industries“, in Prazak, P., MME 2017. 35th Inter- national Conference on Mathematical Methods in Economics, University of Hradec Kralove, Hradec Kralova, Czech Republic, pp. 408-413. Lubinski, C., Fear, J., Perez, P.F. (2013), Family Multinationals: Entrepreneurship, Governance, and Pathways to Internationalization, Routledge, New York. Marek, P. (2009), „Not very well-known creator of Du Pont Model – Frank Donaldson Brown (1885 – 1965)“, Český finanční a účetní časopis, Vol. 4, No. 2, pp. 70-75 (in Czech). Midler, M., Dubcova, A. (2014), „Current Condition of Electrical Engineering Industry in Individual Self-Governing Regions in Slovakia“, in Proceeding of International PhD Students Conference 2014, pp. 354-360. Mihola, J., Kotesovcova, J. (2015), „The intensity of development and making of Du Pont's model“, in 10th International Scientific Conference on Financial Management of Firms and Financial Institutions, Ostrava, Czech republic, pp. 799-806. 140

Sylvia Jencova, Eva Litavcova, and Petra Vasanicova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 131-141

Miron, D., Petrache, A. M. (2012), „The linkage between corporate social performance and the corporate financial performance in the information and communication technology multinational companies of Romania“, Economics & Sociology, Vol. 5 No. 2A, pp. 86-99. Mitchell, T., Mitchell, S., Cai, C. (2013), „Using The DuPont Decomposing Process To Create A Mar- keting Model“, Journal of Business & Economics Research, Vol. 11 No. 11, pp. 485-496. Paulik, J., Majkova, M. S., Tykva, T., Cervinka, M. (2015). „Application of the CSR measuring model in commercial bank in relation to their financial performance“, Economics & Sociology, Vol. 8 No. 4, pp. 65-81. Puziak, M. (2017), „The Persistence of Abnormal Returns: Analysis of Polish Manufacturing In- dustry“, Economics & Sociology, Vol. 10 No. 1, pp. 48-60. Rudrajeet, P., Aneja, A. P. (2017), „Ambidexterity drivers of value-creation and appropriation in business models An explorative study from Du Pont“, Research Journal of textile and apparel, Vol. 21 No. 1, pp. 2-26. Sedlacek, J. (2007), Financial analysis of the company, Computer Press, Brno (in Czech). Stefko, R., Gavurova, B., Korony, S. (2016), „Efficiency measurement in healthcare work management using Malmquist indices“, Polish Journal of Management Studies, Vol. 13, pp. 168-180. Uradnicek, V. (2014), „Dynamic pyramidal decomposition of a financial indicator“, Forum Statisti- cum Slovacum, Vol. 4, pp. 184-189 (in Slovakian). Vasiu, D., Baltes, N., Ciudin, A. (2012), „Study regarding financial performance from Du Pont analysis perspective“ in 18TH International conference - The knowledge-based organization: economic, social and administrative approaches to the knowledge-based organization, Confe- rence proceedings 2 Knowledge Based Organization International Conference, Sibiu, Romania, pp. 360-365. Vitkova, E., Semanova, T. (2015), „The Impact of Key Parameters Change on Economic Develop- ment of the Company“ in Conference on ENTERprise Information Systems (CENTERIS), International Conference on Project MANagement (ProjMAN), International Conference on Health and Social Care Information Systems and Technologies (HCist), Vilamoura, Portugal, pp. 744-749. Zhang, F., Han, L., Zhang, J., (2016), „The Analysis of Property Insurance Company's Profitability and its Impact Factor“ in 7th China International Conference on Insurance and Risk Manage- ment (CICIRM), Xian Univ Finance & Econ, Sch Econ, Xian, China, pp. 235-248. Zmeskal, Z., Dluhosova, D., Tichy, T. (2013), Financial models. Concepts, methods, applications, Ekopress, Praha (in Czech).

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Vol. 14, No. 2 (2018), 143-153 ‘

Resource Misallocation and Rice Productivity in Thailand

SIWAPONG DHEERA – AUMPON1

1 Assistant Professor, Department of Economics, Faculty of Economics, Kasetsart University, Bangkok, Thailand e-mail: [email protected], [email protected]

ARTICLE INFO ABSTRACT Received April 15, 2018 Thailand’s manufacturing sector is characterised by considerable Revised from May 12, 2018 resource misallocation compared with this sector in other countries, Accepted May 30, 2018 and the problem may extend to its agricultural sector as well. Using Available online June 15, 2018 detailed household-level data on rice production from the 2013 Agricultural Census, this paper examines resource misallocation across farms in Thailand and its effect on the country’s aggregate JEL classification: productivity in rice farming. I find that the marginal products of land O1; O4. and capital were largely dispersed, which is an indication of significant resource misallocation. I further estimate that reallocation of DOI: 10.14254/1800-5845/2018.14-2.9 resources could increase aggregate output and productivity by approximately a factor of 1.67. This potential gain is not small, but it Keywords: is smaller than that predicted in other studies for the Thai manufacturing sector and the Malawian agricultural sector, a result suggest- allocation, ing that the Thai rice farming sector is relatively less plagued by efficiency, resource misallocation. Other developing countries may encounter output, similar degrees of misallocation in their agricultural sectors. I also rice, find that an effective reallocation policy cannot involve simply reduc- Thailand. ing the landholdings of large landholders but rather supports high- productivity farmers to have more land and capital.

INTRODUCTION It is widely accepted that differences in income across countries are for the most part attribut- able to differences in productivity, and recent literature suggests that the misallocation of resources or factors of production plays an important role in this variation. Thus Restuccia and Rog- erson (2008) showed that resource misallocation can lower a country’s aggregate productivity by as much as 50%, and Hsieh and Klenow (2009) found high levels of resource misallocation in the Chinese and Indian manufacturing sectors and predicted that reallocation of resources could increase aggregate manufacturing productivity by 87-115% in China and 100-128% in India. A number of studies have followed in the path of the pioneering empirical work by Hsieh and Klenow (2009). Bellone and Mallen-Pisano (2013) and Camacho and Conover (2010), for instance, found that the misallocation of resources in the manufacturing sectors of France and Co- lombia, respectively, was no more extensive than in the United States. On the other hand, Calligaris (2015), Ha et al. (2016), Ryzhenkov (2016), Neumeyer and Sandleris (2010), and Busso et al. 143

Siwapong Dheera – Aumpon / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 143-153 (2013) found that the manufacturing sectors of Italy, Vietnam, Ukraine, and several other Latin American countries, again respectively, were characterised by higher levels of resource misallocation than was the case in the United States. In the specific case of Thailand, Dheera-aumpon (2014) and Paweenawat et al. (2017) found that the manufacturing sector performed more poorly in terms of misallocation than in China, India, or the United States. In some countries, the agricultural sector may likewise suffer from a resource misallocation problem. Thus Restuccia and Santaeulalia-Llopis (2017) in the study just mentioned reported severe resource misallocation of resources in the agricultural sector in Malawi and an associated adverse effect on aggregate output and productivity; by their calculations, reallocation of resources could increase aggregate agricultural output by as much as 260%. In Thailand, the agricultural sector may be compromised in like manner as the manufacturing sector. This study accordingly explores resource misallocation and its effect on aggregate productivity in the Thai agricultural sector. The 2013 Agricultural Census conducted by the National Statistical Office of Thailand provides detailed household-level data both for inputs and for output relating to rice production. This paper applies the method used by Restuccia and Santaeulalia-Llopis (2017) in their study of Malawi to data relating to rice farmer households in Thailand in order to assess the extent of resource misallocation and its effect on the aggregate productivity of the country’s rice farming industry. I find that the marginal products of land and capital are broadly dispersed across farmers, a situation that is indicative of extensive resource misallocation. According to my findings, efficient allocation of resources could increase the aggregate output and productivity of the Thai rice industry by approximately 67%. This gain is not small, though it is less than those estimated for the Thai manufacturing sector by Dheera-aumpon (2014) or for the Malawian agricultural sector by Restuccia and Santaeulalia-Llopis (2017). Thus Thai rice farming, while impeded by resource misallocation, is nevertheless more efficient in this regard than Thai manufacturing or the Malawian agriculture sector. Other developing countries outside Africa may find similar degrees of resource misallocation in their agricultural sectors. In what follows, Section 2 describes the data used in the analysis and Section 3 the model and method used to derive the results, which are presented and discussed in Section 4. Section 5 describes robustness checks, Section 6 discusses the policy implications, and conclusions are offered in Section 7.

1. DATA I used household-level data from Thailand's 2013 Agricultural Census, which was conducted by the National Statistical Office of Thailand. To be specific, the data set consisted of a sample of households based on the Census that was released by the National Statistical Office. The Census provides information on the characteristics of household-farms over a period of 12 months ending May 1, 2013. The original sample represented 62,984 households. The Census was relatively comprehensive in terms of the collection of the output of rice as compared with other crops produced by households, though only the data concerning the quantity of rice harvested by each was available. As a result, it was only possible to calculate the value of rice production. Omitting households in which no rice was produced, the sample was reduced to 38,850 rice-producing households. I created from the Census data variables for the quantity of rice production (including both rice and sticky rice and both in- and off-season), the amount of chemical fertiliser used, the extent of land holding for rice cultivation, and the uses of agricultural equipment. To construct the value added, I subtracted the cost of chemical fertilisers, which was calculated using the average annual price from May 2012 to April 2013, from the value of rice production, which was calculated using the average price of paddy rice over the same period; the relevant information was obtained from 144

Siwapong Dheera – Aumpon / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 143-153 the Office of Agricultural Economics. It is important to note that the information was available only for chemical fertilisers (i.e. not for such other intermediate inputs as herbicides, pesticides, and seeds) and that subtracting the common average cost of rice production from the value of rice production did not alter the results. In terms of capital, the Census reports information on agricultural equipment, including both implements (such as sprayers, weeders, and planters) and machinery (such as tractors, motorised pumps, harvesters, and threshers). As a proxy for capital, I constructed an asset index for capital following the procedures of Filmer and Pritchett (2001), McKenzie (2005), and Restuccia and San- taeulalia-Llopis (2017). The index was based on dummy variables pertaining to the uses of agricultural equipment. I used a set of 17 relevant pieces of agricultural equipment from the Census to calculate a score from the first principal component. To control for land quality, I constructed an index from data on physical appropriateness for rice cultivation obtained from the Land Development Department of the Ministry of Agriculture and Cooperatives. This index was based on the plots of land classified as particularly suitable for rice cultivation. To control for temporary output shocks, I used the total average rainfall in millimetres for 2012 and 2013. It must be observed that the National Statistical Office data only include the region in which a household is located and not the province or the district. The index of land quality and the total rainfall accordingly had to be assessed in terms of the weighted average over a given region based on the amount of land holding for rice cultivation. After excluding households that did not provide information regarding chemical fertilisers and agricultural equipment, the sample consisted of 31,801 households.

2. METHOD To assess resource misallocation across farms and its effect on the aggregate productivity of rice farming in Thailand, I used the method developed by Restuccia and Santaeulalia-Llopis (2017). To measure productivity at the farm level, I relied on the household-level data from the 2013 Agricultural Census as described in the previous section. Farm-level total factor productivity

(TFP) si was defined as the residual from the following farm-level production function:

 k l yiiiiii sk  ql , kl 1, where yi is the value added, ki is capital, li is the amount of land holding for rice cultivation,  i is a rain shock, qi is land quality, and k and l are the input elasticities. For comparability with other studies, I chose k = 0.36 and l = 0.18, again following Restuccia and Santaeulalia-Llopis (2017). To obtain the efficient allocation to serve as a benchmark, a planner was assumed to allocate capital and land across a given set of heterogeneous farmers whose productivity si differed in order to maximise output given fixed total amounts of capital K and land L . The benevolent social planner thus solved the following problem:

e kl Yskl max ii i , kl,  ii i subject to

Kk  i , Ll  i . i i Efficient allocation principally equates marginal products of capital and land across farmers, taking the following simple form:

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Siwapong Dheera – Aumpon / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 143-153 z z kKe  i , lLe  i  zi  zi i i

11 kl  where zsii . To calculate the efficient allocation, I used farm-level productivity si derived from the farm-level TFP calculated previously and the total amounts of capital and land from the data. To quantify the effect of resource misallocation on aggregate productivity, I calculated the output gain, defined as the ratio of efficient aggregate output to actual aggregate output: ye Y e   i , Yyaa i

e eeekl a where Y is aggregate efficient output, yskl ii   i is farm-level efficient output, Y is

a kl aggregate actual output, and yskl ii i is actual output. The calculation of actual output abstracts from rain and land quality was done in order to make it them comparable to the efficient output. Of note here is the fact that the output gain was also a TFP gain because the total amounts of capital and land and the number of farmers were fixed quantities.

3. RESULTS

Figure 1 plots the distribution of the natural logarithm of farm-level TFP ln si , showing clearly a large productivity dispersion and a long left tail. This result indicates that the productivity of some farmers fell considerably below the average.

Figure 1. Distribution of Farm Productivity si

3000 2000 Frequency 1000 0 -2 0 2 4 6 8 Farm Productivity

Note: Farm-level productivity or TFP has been naturally logged. 146

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Table 1 reports various measures of dispersion of the natural logarithm of farm-level TFP ln si , including the standard deviation, the interquartile range, and the interdecile range. The dispersion of farm productivity was less than that of manufacturing plant productivity in Thailand but was comparable to levels in Malawi and the United States.

Table 1. Dispersion of Farm Productivity and Manufacturing Plant Productivity

Farms Manufacturing Plants Thailand Malawi USA Thailand (2013) (2010/11) (1990) (2006) Standard deviation 1.03 1.19 0.80 1.59 75th – 25th percentile 1.33 1.15 1.97 2.18 90th – 10th percentile 2.63 2.38 2.50 4.12 Observations 31,239 7,157 - 49,547

Note: Statistics are for the natural logarithm of productivity ln(si). The second, third, and fourth columns report statistics obtained from Restuccia and Santaeulalia-Llopis (2017), Adamopoulos and Restuccia (2014), and Dheera-aumpon (2014), respectively.

Table 2 reports the variance decomposition of farm-level output using the assumed production function. The key determinant of output variation across farms was farm-level productivity si , followed by the inputs of capital and land while rain and land quality, though these determinants played minor roles. Specifically, the variation in farm-level TFP explains approximately 73% of the total variation of output and the variation of inputs, including capital and quality-adjusted land, about 10%. When rain and land quality are assumed constant across farms, the variances and the contribution of farm-level TFP and inputs to the total output variation remain practically unchanged. So also in the case of Malawi investigated by Restuccia and Santaeulalia-Llopis (2017), rain and land quality played only small roles in output variation across farms.

Table 2. Variance Decomposition of Farm Output

Benchmark  ii1,q  1 Level % Level % var  y 1.462 100.0 1.462 100.0 var s 1.063 72.7 1.039 71.0   var  0.0069 0.5 - -   varf kql , 0.147 10.1 0.147 10.0    2covs , -0.028 -1.9 - -  2covsf , kql , 0.288 19.7 0.139 9.5    2cov ,f kql , -0.0152 -1.0 - -  

Note: All variables have been naturally logged. The first two columns report results from the benchmark specification, in which rain and land quality were controlled for. The last two columns report the results abstract- ing from rain and land quality. In each case, the ‘Level’ column reports the variance and the ‘%’ column the contribution to the total variance. 147

Siwapong Dheera – Aumpon / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 143-153 Figure 2. MPL, MPK and Farm Productivity

(a) MPL and Farm Productivity

(b) MPK and Farm Productivity

Note: Panel (a) reports marginal product of land (MPL) with respect to farm productivity. Panel (b) reports marginal product of capital (MPK) with respect to farm productivity. All variables have been naturally logged.

Figure 2 plots the natural logarithms of the marginal product of land (MPL) and the marginal product of capital (MPK) against the natural logarithm of farm-level TFP. Both MPL and MPK exhibit strong positive associations with farm-level TFP, with correlation coefficients of 0.71 and 0.88, respectively. This result is in contrast with efficient allocation, which is characterised by equalisa- tion of both MPL and MPL across farms, and is thus indicative of extensive resource misallocation in rice farming in Thailand. To illustrate the extent of resource misallocation more clearly, Figure 3 contrasts the actual allocation of land and capital with the efficient allocation of these resources, plotting both against farm-level TFP. All variables are presented in their natural logarithm form. When allocation is efficient, land size and capital strongly increase with farm productivity. In this case, the actual allocation of both land and capital contrasted strikingly with efficient allocation; thus the fitted lines of 148

Siwapong Dheera – Aumpon / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 143-153 the actual allocations have flatter slopes and relatively looser mapping compared with those of efficient allocations. This finding confirms the magnitude of resource misallocation in Thai rice farming.

Table 3. Output Gain YYea

(a) Main Results Bootstrap Simulations Full Sample Median 5th percentile 95th percentile Output Gain 1.67 1.67 1.64 1.70

(b) Within Productivity- si Variation

Removing Within- si Variation Benchmark 5% 10% 20% 100% Output Gain 1.67 1.63 1.61 1.58 1.43

Note: Entries are YYea. In Panel (a), bootstraps median and confidence intervals were computed based on 500 simulations obtained from random draws with replacement. In Panel (b), within productivity- si were removed by regressing land and capital on farm productivity and using the estimated relationships to construct measures of factor inputs from which residual variations have been partially or fully removed. Panel (c) reports the output gains that occurred within regions.

Table 3 reports the ratio of efficient aggregate output to actual aggregate output. The efficient aggregate output is equivalent to the aggregate output when marginal products are equalised across farms or in the absence of resource misallocation. Panel (a) reports the main results using the full sample: the output gain was 1.67-fold, or 67%, meaning that, were resource misallocation eliminated from Thai rice farming, output and productivity would increase by this factor. As alluded to above, a 67% gain would be considerable, though far less than the 148% gain in the Thai manufacturing sector reported by Dheera-aumpon (2014) or the 260% gain in the Malawian agricultural sector reported by Restuccia and Santaeulalia-Llopis (2017). The clear implication is that the extent to which resources were being misallocated in Thai rice production was significantly less than in either of these other sectors. Panel (a) also reports the results from 500 bootstrap simulations obtained from random draws with replacement. The 95% confidence interval of the output gain is (1.61, 1.70), indicating that this quantity was tightly estimated and confirming that extensive resource misallocation had a significant adverse effect on the aggregate output and productivity of Thai rice production. As can be seen in Figure 3, resources were misallocated across farmers with various levels of productivity and were dispersed among those with similar levels of productivity. The former relationship is indicated by the fact that the fitted line of the actual allocation displays a flatter slope than that of efficient allocation and is vertically dispersed. Once more following Restuccia and San- taeulalia-Llopis (2017), I assessed the extent of the output gain associated with within-productivity misallocation by removing within-productivity si variation from factor inputs and, in particular, regressing the natural logarithms of land size and capital separately on the natural logarithm of farm

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Siwapong Dheera – Aumpon / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 143-153 productivity. The estimated regression equations were then used to construct measures of factor inputs from which residual variations were partially or fully removed.

Panel (b) of Table 3 reports the results when 5, 10, 20, or 100% of within-productivity si variation has been removed. As can be seen, the output gain decreased as this percentage increased. In the absence of within-productivity variation, resources can be seen to have been misallocated only across farmers with various levels of productivity. In this extreme case, the output gain was still 1.43-fold, or 43%, meaning that a large portion of the output gain can be explained in terms of resource misallocation across these farmers. Since a reallocation of land across farmers nationwide seems impractical, I also report the output gain at the regional level, in Panel (c) of Table 3. The output gains were considerable in all regions except Bangkok, ranging from 1.41-fold for the central region to 1.68-fold for the southern. This result suggests that resource misallocation was most extensive in the southern region, followed by the north-eastern region. In sum, the results indicate that resources were extensively misallocated in Thai rice farming over the period studied and that this misallocation had a significant negative effect on the aggregate productivity of this sector. The magnitude of the impact of misallocation was, however, less than that on Thai manufacturing sector.

4. ROBUSTNESS Here I present robustness checks for the calculations of output gain. Specifically, I report the output gain at the group level classified in terms of education and membership in agricultural organisations in Table 4. The output gains are in all cases considerable. Thus, reallocation of resources among household-farms led by individuals with comparable levels of education resulted in gains ranging from 1.53- to 1.91-fold. Likewise, reallocation among farmers with similar levels of membership in agricultural organisations resulted in gains ranging from 1.64- to 1.73-fold.

Table 4. Output Gain YYea– Robustness

(a) By Education Level No Educa- Lower than Higher tion Elementary Elementary Secondary Vocational Education Others Mean Output Gain 1.62 1.64 1.68 1.67 1.54 1.53 1.91 1.66

(b) By Membership of Agricultural Organisations Non-Member Member Mean Output Gain 1.73 1.64 1.69

Note: Entries are YYea. Panel (a) reports the output gains that occurred within groups classified by the educational level of the heads of households. Panel (b) reports the output gains that occurred within groups classified by membership of agricultural organisations.

Table 5 reports the output gains based on alternative measures of land size and output. Spe- cifically, instead of the amount of land holdings for rice farming, I used the amount of land cultivated for rice farming to obtain an output gain of 1.55-fold. Also, rather than total rice output I used either in-season rice output or off-season rice output separately, with output gains of 1.51- and 1.47-fold, respectively. The fact that these output gains were in all cases considerable again con-

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Siwapong Dheera – Aumpon / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 143-153 firmed the existence of extensive resource misallocation and its significant adverse impact on the aggregate productivity of the Thai rice farming sector.

Table 5. Output Gain YYea– Alternative Measures

Benchmark Cultivated Land Size In-Season Rice Off-Season Rice Output Gain 1.67 1.55 1.51 1.47

Note: Entries are YYea. The first column reports the result from the benchmark specification using land holding for rice farming and total rice output. The second column reports the results using cultivated land size instead of land holding. The third and the forth columns report the results using in-season and off- season rice outputs instead of total rice output, respectively.

5. DISCUSSION As Figure 3 shows, under a more efficient allocation of resources, high-productivity farmers would receive more land and capital than low-productivity farmers. Effective land reform policy, therefore, involves not merely reallocating land to reduce the landholdings of large landholders; and indeed such a policy would lead to a decrease in aggregate output and productivity. Thus an experiment reallocating land from farmers with holdings greater than 5 hectares to those with holdings below the median size indicated a resulting decrease in aggregate productivity of 2.7%. This result is consistent with that of a study by Adamopoulos and Restuccia (2014) on the imposi- tion of a ceiling of 5 hectares on land holdings in the Philippines.

Figure 3. Land Size, Capital and Farm Productivity: Actual and Efficient Allocations

(a) Land Size and Farm Productivity

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(b) Capital and Farm Productivity

Note: Panel (a) reports actual and efficient land size of farms with respect to farm productivity. Panel (b) reports actual and efficient capital of farms with respect to farm productivity. All variables have been naturally logged.

An effective allocation policy, then, would support high-productivity farmers with land and capital. Governments may not find it easy, however, to identify such farmers. The findings presented here do suggest one possible method. Specifically, regarding the relative productivity of farmers in relation to their membership in agricultural organisations, the differential between 4.06 for those who belonged and 3.81 for those who did not was found to be statistically significant at the 1% level. Governments could, therefore, target to support farmers who participate in such organisations.

CONCLUSIONS To study resource misallocation and its effect on the aggregate productivity of rice farming in Thailand, I applied the method used by Restuccia and Santaeulalia-Llopis (2017) to data for some 31,000 Thai household-farms obtained from the country’s 2013 Agricultural Census. I found that a reallocation of resources, in particular land and capital, could increase the aggregate output and the productivity of Thai rice farming by approximately 1.67-fold, or 67%. While such a potential gain is not small, it is less than those predicted in other studies for either the Thai manufacturing sector or the Malawian agricultural sector. The implication is that resources have been misallocated in Thai rice production, but that the problem is not as severe as it can be in other contexts. Contrasting actual with efficient allocation, I found the distribution of land and capital for rice farming in Thailand to be sub-optimal, with high-productivity farmers receiving too little and low- productivity farmers receiving too much of these resources. A policy designed to allocate resources more efficiently would favour the former. A further finding presented here is that farmers who belonged to agricultural organisations tended to be more productive than those who did not, the implication being that members of such organisations could be targeted for preferential allocation of land and equipment in governmental policies in the furtherance of efficient resource allocation. 152

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I conclude by stressing that an effective policy in this regard cannot involve simply reducing the landholdings of large landholders, for doing so would lower the aggregate output and thus decrease aggregate productivity.

ACKNOWLEDGEMENTS The author is grateful to the reviewers and the editor for their helpful comments and suggestions. The author also would like to thank the National Statistical Office of Thailand for providing data from the 2013 Agricultural Census.

REFERENCES Adamopoulos, T., Restuccia, D. (2014), “The size distribution of farms and international productivity differences”, American Economic Review, Vol. 104, No. 6, pp. 1667-1697. Adamopoulos, T., Restuccia, D. (2015), “Land reform and productivity: A quantitative analysis with micro data”, Working Paper tecipa-540, University of Toronto, Department of Economics, To- ronto, Ontario. Bellone, F., Mallen-Pisano, J. (2013), “Is misallocation higher in France than in the United States?”, GREDEG Working Paper No. 2013-38, Groupe de Recherche en Droit, Economie, Gestion (GREDEG CNRS), University of Nice Sophia Antipolis, Valbonne, France. Busso, M., Madrigal, L., Pages, C. (2013), “Productivity and resource misallocation in Latin Ameri- ca”, The B. E. Journal of Macroeconomics, Vol. 13, No. 1, pp. 1-30. Calligaris, S. (2015), “Misallocation and total factor productivity in Italy: Evidence from firm-level data”, Labour, Vol. 29, No. 4, pp. 367-393. Camacho, A., Conover, E. (2010), “Misallocation and Productivity in Colombia’s Manufacturing In- dustries”, IDB Working Paper Series No. IDB-WP-123, Inter-American Development Bank, Washington, DC. Dheera-aumpon S. (2014), “Misallocation and manufacturing TFP in Thailand”, Asian-Pacific Eco- nomic Literature, Vol. 28, No. 2, pp. 63-76. Filmer, D., Pritchett, L. H. (2001), “Estimating wealth effects without expenditure data—or tears: An application to educational enrollments in states of India”, Demography, Vol. 38, No. 1, pp. 115-135. Ha, D. T. T., Kiyota, K., Yamanouchi, K. (2016), “Misallocation and Productivity: The Case of Vietna- mese Manufacturing”, Asian Development Review, Vol. 33, No. 2, pp. 94-118. Hsieh, C.-T., Klenow, P. J. (2009), “Misallocation and manufacturing TFP in China and India”, The Quarterly Journal of Economics, Vol. 124, No. 4, pp. 1403-1448. McKenzie, D. J. (2005), “Measuring inequality with asset indicators”, Journal of Population Econo- mics, Vol. 18, No. 2, pp. 229-260. Neumeyer, P. A., Sandleris, G. (2010), “Understanding productivity during the Argentine crisis”, Bu- siness School Working Paper 04/2010, Universidad Torcuato Di Tella, Buenos Aires, Argenti- na. Paweenawat, A., Chucherd, T., Amarase, N. (2017), “Uncovering productivity puzzles in Thailand: Lessons from microdata”, PIER Discussion Paper No. 73, Puey Ungphakorn Institute for Eco- nomic Research, Bangkok, Thailand. Restuccia, D., Rogerson, R. (2008), “Policy distortions and aggregate productivity with heterogeneous establishments”, Review of Economic Dynamics, Vol. 11, No. 4, pp. 707-720. Restuccia, D., Santaeulalia-Llopis, R. (2017), “Land misallocation and productivity”, NBER Working Paper 23128, National Bureau of Economic Research, Cambridge, MA. Ryzhenkov, M. (2016), “Resource misallocation and manufacturing productivity: The case of Ukraine”, Journal of Comparative Economics, Vol. 44, No. 1, pp. 41-55.

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Montenegrin Journal of Economics

Vol. 14, No. 2 (2018), 155-165 ‘

Span of Control in Teamwork and Organization Structure

KATARINA REMENOVA1, ZUZANA SKORKOVA2 and NADEZDA JANKELOVA3

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

ARTICLE INFO ABSTRACT Received April 15, 2018 The span of control expresses how many subordinates correspond Revised from May 16, 2018 to one manager, while determined by many factors and unable to Accepted May 29, 2018 be expanded indefinitely. The study monitors the impact of de- Available online June 15, 2018 terminants, like the functional area of control, the level of management, the duration of managerial position, the age of the manager, the size and composition of the team. The results of JEL classification: non-parametric testing (Chi-Square Test of Independence) and M12, M54. rates of association between variables (using Cramér’s V and Phi coefficient and for ordinal variables, Kendall’s tau-b, Goodman- DOI: 10.14254/1800-5845/2018.14-2.10 Kruskal’s Gamma, Kendall’s tau-c and Somers’ d) indicate that the team size is not related to the level of management, which Keywords: does not confirm the assumption that the largest teams work at the lowest management level and vice versa, that the team leader span of control, at the top level is in charge of the smallest number of people. teamwork, Instead the size of the team depends on the functional area of team leader, control. The assumption that increasing age, and so increasing functional area of control, experience also increases the size of the team, has not been management level confirmed. However, the fact that the manager works longer in . the leading position has been confirmed. With the current manner of managing an organization, the age of the manager is the weak indicator of the duration of the managerial position.

INTRODUCTION The management and development of qualified teams is one of the challenging tasks of modern management, mainly in building team intelligence and team dynamic, which applies to operational forces within all team members (Eubanks et al., 2016, Klug – Bagrow, 2016). A team’s own dynamic dominates within each one, but varying in intensity for different teams. In the case of team building and development, it is essential to think of a personal question that relates to the personality characteristics of the members and the team leader, as well as the scope of the team and the competence of the leader. Before the team is set up, a team analysis must be performed

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Katarina Remenova, Zuzana Skorkova and Nadezda Jankelova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 155-165 to determine the team-building parameter. Without analysis, we cannot identify the necessary control span and the synergistic effect of the team (Creasy, Carnes, 2017; Gelbarda, Carmeli, 2009).

1. LITERATURE REVIEW The span of control expresses how many employees are directly subordinated to one supervisor (Urwick 1974; Ouchi and Dowling, 1974). The span of control can be different in the same enterprise at different hierarchical levels, but can also be different at the same level of control (McMullen and Nethersell, 2009). We distinguish actual span of control – which gives a realistic picture of how many subordinates correspond to one manager, from an optimal span of control, which presents the number of subordinates one manager can effectively manage in a given post. This means the maximum number of subordinates the team leader can effectively manage in terms of his/her limited physical and mental capacity. Enterprises focusing on knowledge management and offering progressive training for talented employees can create more qualified teams. Teams with a higher span of control permit better selection of managers, higher likelihood of self-presentation, as well as faster results in the early stages of their career (Nikolowa, 2015). The performance of the teams and the overall effectivity of the organization can be estimated through the span of control. The maximum span of control is determined as the product of two independent variables – the individual index of company administration and how many managers manage one operation individually (Bagautdinova and Validova, 2014).

1.1 Span of control – determinants and span size Till now implemented research studies have demonstrated that determining a specific span influences a variety of organizational parameters, with the most commonly used analytical approach. The analytical approach to the determination of the optimal span of control is represented by Grai- cunas’ theory, based on the assumption that the head employee must manage the relationships that arise between him/her and his/her subordinates but also between his/her subordinates themselves (Urwick, 1974; Hopej and Martan, 2006). Graicunas concludes that if we arithmetically increase the number of subordinates, the relationships that the manager has to manage increase in geometric order, accelerating when they cross the border of 5-6 subordinates (Bedeian, 2017). Therefore it is insisted that the number of subordinates should not exceed the limit of 6 people. Many American companies today consider the number of 5-7 subordinates to be optimal. V. A. Graicunas recommended a span of control of 5 employees, but of course depending on the level of management (it is different when 5 workers are subordinated to 1 supervisor or 5 divi- sions are subordinated directly to the general director), while the span must have its limitations (Urwick, 1974). H. Fayol argued that the number of subordinates at the lowest level of management should be 10 to 30, 15 on average, and 2 to 5 at the highest level of management. However, there is no exact rule in determining the exact number of subordinates per one supe- rior (Delbecq, 1968; Staats et al., 2012; Davison, 2003; House and Miner, 1969; Nasrallah, 2015; Pendharkar et al., 2009; Holm-Petersen et al., 2017; Wallin et al., 2014; Walter and Zimmermann, 2016) because the size of the span influences several determinants that deviate from industry, enterprise size, type of organizational structure, performance of the organization (Staats et al., 2012). Zagoršek (2014) is also dedicated to a team’s performance in terms of individual performance. There are also the competence of the head employee, the personality of the subordinates and their experience, knowledge (Schyns et al., 2010), motivation and degree of engagement, innovation in the team (Peltokorpi and Merv, 2014).

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Another factor is spatial organization. If the subordinates are concentrated in one place, it creates a lesser burden on the time and communication of the head employee. Conversely, if his/her subordinates are situated in different locations or even in different time zones, it places a heavy burden on the communication skills and time management of the head employee. As well as the quality of mutual relations in all directions (horizontal, vertical and diagonal) and the existence and efficiency of staff departments are important. The level of process organization and standardiza- tion– if the level of organization is optimal and the individual processes are standardized, appropriate conditions are created to increase the span of control (Udell, 1967).

2. METHODOLOGICAL APPROACH Numerous research studies have so far well elaborated on factors influencing the span of control, such as the geographic interface, the competence of the team leader, as well as its individual members, the scope, similarity and volume of tasks in the team. The objective of our exploratory study is to identify the scope of influence of the individual parameters of the managerial work (size and composition of teams, functional area of activity, level of management) and the characteristics of the manager (age of the manager, owning the current managerial position) on the span of control within the organizational structure and the organization of team work. The statistical sample (N = 268 respondents) is comprised of managers operating in enterprises in the Slovak Republic in the management positions of operational, tactical and top management. This category includes general directors, managing directors and business owners, directors and managers. The sample was created in PSPP by random selection. The aim of the study is to clarify the impact of these parameters at all levels of management in Slovak enterprises. The reason why we focused on this theme was that there are few scientific studies addressing the influence of selected elements of the organization and the characteristics of a manager and the nature of his/her work. The research was implemented using a questionnaire on the teamwork of managers, the scope of leadership and their position in the organizational structure. The data obtained through the questionnaire method present a nominal and ordinal variable

3. CONDUCTING RESEARCH AND RESULTS

3.1 Data analysis The data obtained through the questionnaire method are of a nominal (level of management, functional area of control) and ordinal variable (the number of team members, the composition of the team members – men, women, both; age of the manager, owning the current management position - number of years). Thus the type of the variable also made the selection of statistical methods conditional. Two-dimensional inductive statistics methods were used to test the dependence of the nominal variables by a non-parametric test - The Chi-Square Test of Independence, and the coefficients for determining the dependence of the individual variables for the nominal data were Cramer’s V, Lambda, and the Phi coefficient. The Cohen scale was used to interpret the value of coefficients (Cohen, 1988). To determine the dependence of ordinal variables, the Kendall’s tau-b, Goodman-Kruskal Gamma, Kendall’s tau- c coefficients were used, and to determine the association in the dependent and independent variable, the Somer’s d coefficient (Hanak, 2016) was used. The data were analysed in PSPP statistical software. Hypotheses were tested at a significance level of p ≤ 0.05; while maintaining the primary rule of the Chi-Square Test of Independence, where the theoretical frequencies did not fall

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Katarina Remenova, Zuzana Skorkova and Nadezda Jankelova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 155-165 below a value of 5 in 80%, and for other values X > 1 applied. Null and alternative hypotheses were tested, which we present in individual results. The mapped development of organizational structures over the last 20 years has revealed two organizational trends. New managing directors begin their function with a wider span of control, trying to comprehensively understand the enterprise and all its activities. However, over time, once a steady state is reached, the span of control is gradually decreasing to a level consistent with the original enterprise norm (Neilson and Wulf, 2012). The second trend is that the new general director increasingly appears in the organizational structure without an official representative. The positions of Chief Operating Officers (COO) gradually disappear in all sectors. The association between team size, the dominance of the general manager, and organization performance is significant in an environment that allows top managers broad discretion in strategic decisions, but is insignificant in environments with limited freedom (Haleblian and Finkelstein, 1993). The organizational structure (flat or high in relation to the span of control) can also affect the degree to which the manager is satisfied with his/her work in terms of how well he/she can achieve success with his/her team. In a flat organizational structure, managers perceive greater satisfaction than in a high organizational structure (Ghiselli and Johnson, 1970). Of the N = 268 enterprises researched, up to 35% of them consists of teams of 6 to 12 members, 29.85% of enterprises consists of teams of up to 5 members, teams of more than 20 members are represented by 25% of enterprises, and the least used are teams of 13 to 20 people (10.07%). We present the summary results of the relationship of each variable in Table 1.

Table 1. Summary results table for association between variables

Variables/ 1 2 3 4 5 6 association 1 No. of team members - V=.07 V=.33 G=.04 V=.28 p = .843 =.23 Tau b=.03 =.18 p =.02 p =.000 2 Management level - 3 Functional area of - V=.16 control p =.150 4 Age - G=.18 Tau b=.13 p = .012 5 Owning position in years - 6 Gender -

Source: own compilation

3.2. The team size that the manager leads and the level of management The impact of the organizational structure on the span of control is evident, so we study the relationship between the management level, the functional areas of control and the size of the team. We assume that the largest teams are working at the lowest level of management, and vice versa, that top-level teams have the smallest span of control. We tested the following hypotheses: H1: The size of the team that the manager leads directly depends on the level of management (operational, tactical, top management level) H0: There is no statistically significant association between team size and management level

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Table 2. Strength of Association for Management Level and Team Size

Category Statistic Value Asymp. Std. Error Approx. T Approx. Sig. Nominal by Nominal Phi .10 Cramer's V .07 N of Valid Cases 134

Source: own processing with PSPP

We reject alternative hypothesis H1 at the significance level of p >.05, and accept null hypothesis H0. There is no statistically significant dependence between the researched variables (p = .843, ChiSQ =1.41, df = 4). Based on the results, we can state that different team sizes are active at all levels of management, and for a particular level of management, the team size is not specific. The result has refuted the assumption that the largest teams operate at the lowest level of management, and conversely, that top-level teams have the smallest number of members.

3.3 Team size and functional area of control Based on the results of the research study on the impact of the functional area of control and the team size (Campion et al., 1993), the authors recommended that it is desirable to have 10 or less team members to ensure the effectiveness of a team. However, this recommendation is general and may not be applicable, for example, to producing conditions. A 12-member team is suitable for the functional area ,,producing” (Hirschfeld et al., 2006, Stewart, 2006). On a sample of Slovak companies, we observed the dependence of the functional area of control and the team size. We monitored if in functional areas such as producing, sales and logistics work significantly larger teams than in the functional areas of IT, marketing and PR, and human resources. We tested the following hypotheses: H1: The size of the team that the manager leads depends directly on the functional area of control H0: There is no significant dependence between team size and the functional area of control

Table 3. Strenght of Association for Functional area of control and No. of team members

Category Statistic Value Asymp. Std. Error Approx. T Approx. Sig. Nominal by Nominal Phi .47 Cramer's V .33 N of Valid Cases 144

Table 4. Goodman – Kruskal’s lambda

Asymp. Std. Approx. Approx. Category Statistic Type Value Error T Sig. Nominal by Lambda Symmetric .12 .06 2.09 .037 Nominal Functional area of con- .02 .05 .45 .654 trol Dependent NO. of members De- .23 .09 2.35 .019 pendent Goodman and Functional area of con- .04 . Kruskal tau trol Dependent NO. of members De- .10 . pendent

Source: own processing with PSPP 159

Katarina Remenova, Zuzana Skorkova and Nadezda Jankelova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 155-165 The H0 hypothesis is rejected at a significance level of p ≤ .05, and an alternative hypothesis H1 is accepted, because there is a strong evidence of association among the researched variables (p-value = .02, ChiSQ =31.36, df = 12). The dependence was confirmed by strength (V = .33) between the two variables, which is a moderate dependence. The Goodman-Kruskal’s lambda coefficient was used to detect the error reduction for a dependent and independent variable. The dependence of the team’s size on the functional area of control is = .23 (we reduced the error by 23% when predicting the dependent variable) and it is statistically significant because p = .019. There is a moderate dependence between the functional area of management and the size of the team that the manager leads, the size of the team depending on the functional area. Teams consisting of 6 to 12 people (57% of enterprises) are typical for the IT area, with only 14% of the other sizes presented. For the area of finance there are teams consisting of up to 5 people (42% of enterprises) and 6 to 12 people in 38% of enterprises. Other sizes are reported by only 9% of enterprises on average. In manufacturing, up to 43% of enterprises declare teams consisting of over 20 people, from 6 to 12 people in 32% of enterprises. In sales and logistics, there are teams of up to 5 people (50% of enterprises) and only 18% of enterprises have teams of more than 20 people. Based on the data analysis, we can evaluate the team size from 13 to 20 people as an atypical number, in only 10% of all enterprises. Most often enterprises consist of teams ranging from 6 to 12 people.

3.4 Team composition, functional area of control and team size If the team size is characteristic for the functional area of control, is the composition of the team in terms of gender typical for it? We assume that the enterprise’s organizational structure contains functional areas such as marketing and PR, human resources, where women predomi- nate in the teams. Producing, IT, sales and logistics have many male and mixed teams. We tested the following hypotheses: H1: There is a dependency between the functional area of control and the composition of the team (in terms of gender) H0: The composition of the team is not typical (in terms of gender members) for any functional area of control

Table 5. Strength of Association for Functional area of control and Composition of the team

Category Statistic Value Asymp. Std. Error Approx. T Approx. Sig. Nominal by Nominal Phi .22 Cramer's V .16 N of Valid Cases 136

Source: own processing with PSPP

We rejected alternative hypothesis H1 at the significance level of p >.05, and accept null hypothesis. There is no statistically significant dependence among the researched variables (p = .150, ChiSQ =6.75, df = 4). Based on the results, we can say that there are teams with different gender structure in all the functional areas of control. There are no purely female teams in IT, and in the functional areas of sales and logistics. Only 22% of managers stated that there is a purely female collective in the functional area of finance and 17% in human resources. No purely male teams are in human resources. The most frequent male collectives are presented by companies in manufacturing (34%).

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We also monitored the dependence between the size of the team and its composition (men, women, mixed). We tested the following hypotheses: H1 = The number of team members directly depends on the composition of the team (in terms of gender members) H0 = There is no dependence between the number of members of the team and the composition of the team (in terms of gender).

Table 6. Strength of Association for No. of team members and Composition of the team

Category Statistic Value Asymp. Std. Error Approx. T Approx. Sig. Nominal by Nominal Phi .40 Cramer's V .28 N of Valid Cases 151

Table 7. Goodman – Kruskal’s lambda

Asymp. Std. Approx. Approx. Category Statistic Type Value Error T Sig. Nominal by Lambda Symmetric .13 .04 3.27 .001 Nominal NO. of members .18 .05 3.27 .001 Dependent Gender in team .00 .00 NaN NaN Dependent Goodman and NO. of members .08 . Kruskal tau Dependent Gender in team .10 . Dependent

Source: own processing with PSPP

The H0 hypothesis is refuted at a significance level of p ≤ .05, and an alternative H1 hypothesis is accepted, because there is a strong evidence of dependence (p-value = 0.00, ChiSQ = 24.30, df = 4), but this dependence is weak (V = .28). Goodman-Kruskal’s lambda coefficient was used to determine the strength of dependence of the dependent variable of the team’s size and the independent variable composition of the team. The dependence of the team size on its composition (only men, only women, mixed team) is = 18 (we reduced the error by 18% when predicting the dependent variable) and is statistically significant because p = .001. However, the strength of the dependence is weak. Only 27% of enterprises reports the team of 5 members consisting only of men. The least purely male teams are in a size of 6 to 12 people (10.41%). Most women’s teams consist of up to 5 people (20.8%), and the least over 20 people (1.82%). The densest representation was mixed teams with a size of 6 to 12 people. Enterprises presented the smallest mixed teams at a size of up to 5 people (16.5%).

3.5 Team size, manager age and the number of years in the current position The assumption for success in leadership positions is competence and experience, which increase with age. The question is whether the age of the manager is an indicator of the owning lead- 161

Katarina Remenova, Zuzana Skorkova and Nadezda Jankelova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 155-165 ing position. We monitored the current position, but we did not monitor the time span between recruitment and accepting a leading position, or leadership even after changing the employer, or other department. We tested the following hypotheses: H1: The number of years in the current management position depends on the manager’s age. H0: There is no dependence between the manager’s age and the number of years in the current managerial position.

Table 8. Strength of Association for Manager’s age and Number of years in the current managerial position

Category Statistic Value Asymp. Std. Error Approx. T Approx. Sig. Ordinal by Ordinal Kendall's tau-b .16 .07 2.29 Kendall's tau-c .16 .07 2.29 Gamma .20 .09 2.29 N of Valid Cases 143

Table 9. Somer’s d for Manager’s age and Number of years in the current managerial position

asymp. std. approx. Category Statistic type value approx. t error sig. Ordinal by Ordinal Somers' d symmetric .16 2.29 .022 age_interval de- .16 .07 2.29 .022 pendent owning of current .17 .07 2.29 .022 position dependent Source: own processing with PSPP

We rejected null hypothesis H0 at the significance level of p <.05, and accept alternative hypothesis H1. There is a statistically significant dependence between the research variables (p = .012), but its intensity is very weak. There is a weak dependence (tau – b = .16) between the manager’s age and the number of years in the current management position, although the duration of acting in the current position presents a dependent variable (d = .16). The assumption of increasing age, and thus of increasing experience (independent variable), also increases the duration of owning the leading position (dependent variable). Somer’s d = .17 confirmed a weak dependence, which is statistically significant at p = .022. We assume that with the manager’s increasing age, the experience he/she uses to manage the team will also grow. The question remains whether there is a dependency between the growing number of team members and the age of the manager. We tested the following hypotheses: H1 = There is a dependence between the team size and the age of the manager. H0 = There is no dependency between the team size and manager’s age.

Table 10. Strength of Association for Team size and Manager’s age

Category Statistic Value Asymp. Std. Error Approx. T Approx. Sig. Ordinal by Ordinal Kendall's tau-b .03 .08 .32 Kendall's tau-c .03 .09 .32 Gamma .04 .12 .32 N of Valid Cases 128 Source: own processing with PSPP 162

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We dismissed alternative hypothesis H1 at the significance level of p >.05, and accept hypothesis H0. There is no statistically significant association among the researched variables (p = .107, tau – b = .03, G = .04). There is no interdependence between the size of the team and the age of the manager. We assumed that with the increasing age, and thus with increasing experience (independent variable), the team size (dependent variable) the manager can manage with increasing experience also grows. However, Somer’s d = .03 and p = .752 confirmed the non-existence of a relationship as well as association of dependent and independent variables (p = .107, tau – b = .03, G = .04).

CONCLUSION The span of control can be different in the same enterprise at different hierarchical levels and may be determined by a number of factors verified by several research studies. The article is focused on little-known determinants of the span, such as the age of the manager, the level of management, the functional area of management, and the gender of the members and their representation on a team. Based on the results, we can state that different team sizes are active at all levels of management, and there is no characteristic team size for a particular level of management. The result has refuted the assumption that the largest teams operate at the lowest level of management, and conversely, that top-level teams have the smallest number of members. The Goodman-Kruskal’s lambda coefficient was used to detect the error reduction for a dependent and independent variable. The dependence of the team’s size on the functional area of management is  = .23 (we reduced the error by 23% when predicting the dependent variable) and it is statistically significant because p = .019. There is a low dependence between the functional area of management and the size of the team the manager manages, and the size of the team depends on the functional area. The same results were obtained by the authors of the span of control study in manufacturing functional areas (Campion et al., 1993), the result confirmed. The composition of the teams in terms of the gender of the individual members determines the manner of their management. We monitored their composition according to functional areas of control. Teams with different gender structures are involved in all functional areas of control. There are no purely female teams in IT, and in the functional areas of sales and logistics. Only 22% of managers stated that there is a purely female collective in the functional area of finance and 17% in human resources. No purely male teams are in human resources. The most frequent male collectives are presented by companies in manufacturing (34%). The Goodman-Kruskal’s lambda coefficient was used to detect the error reduction for a dependent and independent variable. The dependence of the team size on the functional area of control is statistically significant, because p = .001, but with a low dependence of = .18. Only 27% of enterprises reports the team of 5 members consisting only of men. The least purely male teams are in a size of 6 to 12 people (10.41%). Most women’s teams consist of up to 5 people (20.8%), and the least of over 20 people (1.82%). The densest representation was mixed teams with a size of 6 to 12 people. Enterprises presented the smallest mixed teams at a size of up to 5 people (16.5%). In relation to the age of the manager and his/her leading position we confirmed the assumption that with increasing age, and thus with increasing experience (independent variable), the duration of acting in a leading position (dependent variable) is also increasing. Somer’s d = .17 confirmed a weak dependence, which is statistically significant at p = .022. With the current manner of managing an organization, the age of the manager is the weak indicator of the duration of the managerial position. We also monitored the relationship between the manager’s age and size of 163

Katarina Remenova, Zuzana Skorkova and Nadezda Jankelova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 155-165 the team. We expected that also the size of the team (independent variable) grows with increasing age and with increasing experience (dependent variable). However, Somer’s d = .03 and p = .752 confirmed the non-existence of a relationship as well as association of dependent and independent variables (p = .107, tau – b = .03, G = .04).

ACKNOWLEDGEMENT The authors are thankful to VEGA No.: 1/0109/17 The innovative approaches to management and their influence on the competitiveness and the successfulness of the companies within the conditions of the global economy for financial support to carry out this research.

REFERENCES Bagautdinova, N., Validova, A. (2014), “Defining Optimal Span of Control for an Enterprise”, Proce- dia Economics and Finance, Vol. 14, pp. 30-34. Bedeian, A. G. (1974), “Vytautus Andrius Graicunas: A biographical note”, Academy of Manage- ment Journal, Vol. 17, Issue 2, pp. 347-348. Campion, M. A., Papper, E. M., Medsker, G. J. (1996), “Relations between work team characteristics and effectiveness: a replication and extension”, Personnel Psychology, Vol. 49, pp. 429- 452. Cohen, J. (1988) Statistical power and analysis for the behavioral sciences (2nd ed.), N. J., Law- rence Erlbaum Associates, Hillsdale. Creasy, T., Carnes, A. (2017), “The effects of workplace bullying on team learning, innovation and project success as mediated through virtual and traditional team dynamics”, International Journal of Project Management, Vol. 35, Issue 6, pp. 964-977. Davison, B. (2003), “Management span of control: how wide is too wide?”, Journal of Business Strategy, Vol. 24, Issue 4, pp. 22-29. Delbecq, A. L. (1968), “The world within the “span of control”, Business Horizons, Vol. 11, Issue 4, pp. 47-56. Eubanks, D. L., Palanski, M., Olabisi, J., Joinson, A., Dove, J. (2016), “Euba”, Computers in Human Behavior, Vol. 61, pp. 556-568. Gelbarda, R., Carmeli, A. (2009), “The interactive effect of team dynamics and organizational support on ICT project success”, International Journal of Project Management, Vol. 27, Issue 5, pp. 464-470. Ghiselli, E. E., Johnson, D. A. (1970), “Need satisfaction, managerial success, and organizational structure”, Personnel psychology. Vol. 23, Issue 4, pp. 569-576. Haleblian, J., Finkelstein, S. (1993), “TOP management team size, CEO dominance, and firm performance: the moderating roles of environmental turbulence and discretion”, Academy of Management Journal. Vol. 36, Issue 4, pp. 844-863. Hanak, R. (2016), Data analysis for social sciences, Econom, Bratislava (in Slovakian). Holm-Petersen, CH., Østergaard, S., Andersen, P. B. N. (2017), “Size does matter – span of control in hospitals”, Journal of Health, Organisation and Management, Vol. 31, Issue 2, pp. 192-206. Hopej, M., Martan, J. (2006), “The determination of span of control”, Operations Research & Deci- sions, Issue 2, pp. 55-62. House, R. J., Miner, J. B.(1969), “Merging Management and Behavioral Theory: The Interaction Between Span of Control and Group Size”, Administrative Science Quarterly. Vol. 14, Issue 3, pp. 451- 464. Klug, M., Bagrow, J. P. (2016), “Understanding the group dynamics and success of teams”, Royal Society Open Science. Vol. 3, Issue 4. McMullen, T., Nethersell, G. (2009), “Do you have a plan for spans? New research on executive span of control”, Journal of Compensation and Benefits, Vol. 25 Issue 1, available at: http://eds.a.ebscohost.com/eds/detail/detail?vid=3&sid=f68c937b-6a5c-42c4-8513-b953ec 164

Katarina Remenova, Zuzana Skorkova and Nadezda Jankelova / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 155-165

55402a%40sessionmgr4008&bdata=JkF1dGhUeXBlPWNvb2tpZSxpcCx1cmwsY3BpZCZjdXN0aWQ9c2tsa WIzJmxhbmc9c2smc 2l0ZT1lZHMtbGl2ZQ%3d%3d#AN=edsbig.A193809086&db=edsbig (accessed on 27 January 2017) Nasrallah, W. F., Ouba, C. J., Yassine, A. A., Srour, I. M. (2015), “Modeling the span of control of leaders with different skill sets”, Computational & Mathematical Organization Theory, Vol. 21, Issue 3, pp. 296-317. Neilson, G. L., Wulf, J.(2012), “How Many Direct Reports?”, Harvard Business Review, Vol. 90, No. 4, pp. 112-119. Nikolowa, R. (2015), “Career dynamics and span of control”, Economics Letters, Vol. 128, Issue C, pp. 6-8. Ouchi, W. G., Dowling, J. B. (1974), “Defining the span of control”, Administrative Science Quarter- ly, Vol. 19, Issue 3, pp. 357-365. Peltokorpi, V., Merv, H. (2014), “How Participative Safety Matters More in Team Innovation as Team Size Increases”, Journal of Business & Psychology. Vol. 29, Issue 1, pp. 37-45. Pendharkar, P. C., Rodger, J. A. (2009), “The Relationship between Software Development Team Size and Software Development Cost”, Communications of the ACM, Vol. 52, Issue 1, pp. 141- 144. Schyns, B., Maslyn, J. M, Weibler, J. (2010), “Understanding the relationship between span of control and subordinate consensus in leader-member exchange”, European journal of work and organizational psychology. Vol. 19, Issue 3, pp. 388-406. Staats, B. R., Milkman, K. L., Fox, C. R. (2012), “The team scaling fallacy: Underestimating the de- clining efficiency of larger teams”, Organizational Behavior and Human Decision Processes, Vol. 118, Issue 2, pp. 132-142. Udell, J. G. (1967), “An Empirical Test of Hypotheses Relating to Span of Control”, Administrative Science Quarterly. Vol. 12, Issue 3, pp. 420-439. Urwick, L. F. (1974), “Graicunas and the span of control”, Academy of Management Journal, Vol. 17, Issue 2, pp. 349-354. Wallin, L., Pousette, A., Dellve, L. (2014), “Span of control and the significance for public sector managers' job demands: A multilevel study”, Economic and Industrial Democracy, Vol. 35, Is- sue 3, pp. 455-481. Walter, M., Zimmermann, J. (2016), “Minimizing average project team size given multi-skilled workers with heterogeneous skill levels”, Computers and Operations Research, Vol. 70, pp. 163-179. Zagorsek, B. (2014), “Working Hours and Its Impact on Quality of Sleep and Motivation” Societas et iurisprudentia, Vol. 2, Issue 4, pp. 98-110.

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Montenegrin Journal of Economics

Vol. 14, No. 2 (2018), 167-174 ‘

Network Topology of Renewable Energy Sector in Stock Exchange

MANSOOREH KAZEMILARI1, ALI MOHAMADI1, ABBAS MARDANI2, and DALIA STREIMIKIENE3

1 Department of management, School of Economics, Management & Social Sciences, Shiraz University, Shiraz, Iran e-mail: [email protected] 2 Faculty of Management, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia, e-mail: [email protected] 3 Lithuanian Institute of Agricultural Economics, V. Kudirkos g. 18, Vilnius, Email: [email protected]

ARTICLE INFO ABSTRACT Received April 18, 2018 In today's global economy, the most prominent position clean energy Revised from May 11, 2018 is basically viewed as the highest-speed growing branch. Sustaina- Accepted May 25, 2018 ble energy, perpetual climate change, and technological advance- Available online June 15, 2018 ments are the reasons from which this foreground position results from. Regarding the debate of effects of pollution and the importance of the alternative fuels, the more awareness people im- JEL classification: prove, the more interested they are to invest in clean energy. This M12, M54. paper brings to a focus the inspection of clean energy and the way any market would analysis the influential stocks which have an DOI: 10.14254/1800-5845/2018.14-2.11 effect on the other. In this regard, correlation network approach has extensively applied to explore the ﬁnancial markets properties. In Keywords: econophysics, technical topology network is defined for analyzing the interaction between stocks to find signiﬁcant implications to Topological network analysis, optimize the portfolio. Network topology shows the physical layout of Stock market, a network. It refers to the way in which per stock is located and Renewable energy sector interconnected to other stocks. This study analyse the topological . properties of network on a set of 62 stocks in renewable energy companies from 30th February 2015 to 3th March 2016 to aid to the interpretation of relationships in the network structure and find influencing stocks.

INTRODUCTION A group of alternative markets, settled in varies industries like renewable energy, constitute the global financial system through which wide-ranging financial products are being traded. These markets are various and multiple, though; index movements are primarily toward the same economic report that reveals the corresponding, correlated characteristics (Andersen, et al., 2007; Balduzzi et a., 2001). This fact shows there are identical characteristics among financial time

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Mansooreh Kazemilari, Ali Mohamadi, Abbas Mardani, and Dalia Streimikiene / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 167-174 series which may be correlated as well. Renewable energies play substantially a crucial in order to reinforce the Regional development policy. On the basis of the renewal penetration, the principal obstacle is mainly high-up front costs and related inadequate cost effectiveness. Subsequently, this fact necessitates the introduction of financial support mechanism alongside with approving promotion scheme, to be specific the one that evokes private finance for energy sector and reduces the financial burden. Markets all around the world are indeed various members of a unit multiplex system (Bukarica and Vrhovcak, 2006; Nalan, Murat and Nuri, 2009). Consequently there is a need to highlight the analysis of structural interactions on a global based, indeed. This unit complex system is defined as a network. With regard to the investment theory and risk management, correlation among stock markets is the predominant factor which plays a paramount role with respect to the optimization problem in the Markowitz Portfolio Theory (Markowitz, 1952). With reference to the paramount role of Network Analysis which provokes the representation of financial markets, the network topology essentially improves the understanding of structural. Stock markets have performed Innumerable analysis of stock markets (Mantegna, 1999; Bonanno et al., 2004; Coelho et al., 2007). Moreover, in accordance with the growth of renewable energy, multiple companies involved in these sectors have emerged. Their presence on stock exchanges worldwide has fueled the creation of indices that aims to track the performance of renewable, allowing us to analyze how this sector performs compared to other assets. However, the branch of the renewable energy has not been highlighted yet. Therefore, the present research considers the 62 renewable energy stocks from 30th February 2015 to 3th March 2016 to the analysis of the renewable energy exchange market by using network theory. This approach helps us to analyze the interaction between stocks and the performances of them that determine the level of importance to find significant implications in stock market structure.

1. RENEWABLE ENERGY SECTOR IN STOCK MARKET Policy measures and financial supports extensively support the renewable energies in order to lessen the given expenses. On the basis of technological and infrastructural renovations, the cost of the reliable and abundant renewable energy, such as solar, wind, geothermal, hydropower, tidal energies, and biofuels, is basically going to be diminished. Unsustainable energy (coal and petroleum), demanding a greater and further effort of exploration, will be substantially costly and seriously hazardous regarding mining and drilling. Whereas, it only takes a minute that renewable energy leads to Carbon dioxide emissions. Climate changes, which actually caused by fossil fuel usage, have been essentially fortified in support of sustainable energy (Yanine et al., 2014). Over the past decades, overall growth in the global economy has been expanded based on the sustainable energies (Katsaprakakis and Christakis, 2016; Lu et al., 2016; Rafindadi and Ozturk, 2016). As table (Bürer and Wüstenhagen, 2009) suggests, competent merchandiser and investors tend to view policy environments as the leading factor based on which technologies of clean energy may be supported (Boyer and Filion, 2007). Previous studies have illustrated the effects of energy and prices of stock markets, though the researchers do not identify any kind of substantial relationship between price and stock markets.

2. CORRELATION NETWORK CONSTRUCTION The existence of the relationships among stocks as a complex structure is a known fact. Since the behavior of each stock in stock market are inﬂuenced by the others, the relationships among all stocks seems to be complicated system. The interrelationship or, equivalently, similarity among stocks is customarily measured by using Pearson correlation coefficient (PCC) among the logarithm of returns in which stocks are characterized by univariate time series of its price. Logarithmic vola- 168

Mansooreh Kazemilari, Ali Mohamadi, Abbas Mardani, and Dalia Streimikiene / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 167-174 tility refers to the changes of the logarithm of closing price t time , which is defined by, , for (number of stocks). Therefore, The correlation coefficient between the -th log price of stock ( and -th log price of stock ( is for all where the is called covariance of stocks and . The level of linear association between stocks and , is quantified by the coefficient . Correlation coefficient for all stocks form a matrix C in which C is a 62-by-62 symmetric matrix. This correlation matrix plays a significant role since it shows the degree of interrelationship as the main source of economic information and it provides an overall conception of system’s behavior. However, it cannot tell us anything which we would not in principle obtained readily from the large matrix C itself. Therefore, we initial need to transform the similarity into dissimilarity among all pairs of stocks by using the nonlinear mapping . The correlation structure by transforming the to appropriately defined distances form a distance matrix D or, equivalently, a weighted complete graph. The graph is considered as a network of 62 stocks. However, by concerning with all connections in the network, it would not be interpretable even for small networks. In this regard, a more applicable approach is Minimum Spanning Tree (MST) related to its aptitude in providing the meaningful information from network. MST is used to reduce and simplify this network from connections of complete graph to only 61 connections. The MST constructs the network topology of 62 stocks which is determined by Kruskal’s al- gorithm. The information contained in MST is summarized by using centrality measures (Borgatti, 2005). From network analysis view point, the importance of per stock can be determined in terms of a measures of centrality to provide the result of role played for each specific stock. Centrality measure is known as a fundamental concept in the network analysis and help us to find the influential stocks in the network (Espino and Hoyos, 2010) as recommended by Borgatti (2005; Siec- zka and Hołyst, 2009). In the area of centrality measure, many interesting research issues are investigated. Borgatti et al. (2006) explored robustness of betweenness centrality measure in random graphs. To the aid of interpretation of information contained in network, we conduct analysis based on betweenness centrality measure formulated in (Ibid.) which is defined based on shortest paths. This measure is useful to know the importance and influence of particular stock relevant to the other stocks (Ibid.).

3. DATA COLLECTION As a case study of renewable energy sector, 62 popular companies in the stock markets are chosen in terms of some information provided in websites (http://www.renewable-energy- industry.com/stocks and http://www.investorideas.com/Companies/RenewableEnergy). The historical data collection of the stock price are taken from http://finance.yahoo.com based on the daily close price of stocks for the period of 30th February 2015 to 3th March 2016. Table 1 shows a set of 62 stocks, corresponding sectors and regions.

Table 1. A set of 62 stocks in renewable energy and corresponding stock symbols, sectors and regions

# Company and Symbol Sector Region 1 Acciona, S.A. (ACXIF) General Americas 2 ALEO SOLAR( AEORF) Solar Americas 3 Alterra Power Corp. (MGMXF) General Europe 169

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4 Ameresco, Inc. (AMRC) General Americas 5 Americas Wind Energy Corporation (AWNE) Wind Asia 6 Amyris, Inc. (AMRS) Fuel cell Americas 7 APPLIED SOLAR, INC. (OEGY) Solar Europe 8 AREVA SA (ARVCF) Nuclear & Renewable energy Americas 9 Ascent Solar Technologies, Inc. (ASTI) Solar Australia 10 Ballard Power Systems Inc. (BLDP) Fuel Cell Australia 11 Brookfield Renewable Partners L.P. (BEP) Hydroelectric, Solar& Wind Asia 12 Canadian Solar Inc. (CSIQ) Solar Americas 13 Capstone Infrastructure Corp (CSE) General Asia 14 CAPSTONE INFRASTRUCTURE CORP PR (CSE-PA.TO) Wave Asia 15 Ceramic Fuel Cells Limited (CEFLF) Fuel Cell Asia 16 China Longyuan Power Group Corporation Limited (CLPXF) Wind Australia 17 China Sunergy Co., Ltd. (CSUNY) Solar Europe 18 Crosswind Renewable Energy Corp. (CWNR) General Americas 19 DAYSTAR TECHNOLOGIES INC (DSTI) Solar Americas 20 EDP RENOVAVEIS ADR (EDRVY) Wind Asia 21 Enel Américas S.A. (ENIA) Electric power Americas 22 Enlight Renewable Energy Ltd (ENLT) Solar and Wind Americas 23 ENVIROMISSION LTD SP (EVOMY) Solar Asia 24 First Solar, Inc. (FSLR) Solar Asia 25 Gamesa Corporacion Tecnologica (GCTAY) Wind Asia 26 General Cable Corporation (BGC) General Australia 27 Gintech Energy Corporation (3514.TW) Solar Europe 28 Green Plains Inc. (GPRE) Fuel Cell Europe 29 Iberdrola, S.A. (IBDRY) Wind Americas 30 Innergex Renewable Energy Inc. (INGXF) General Americas 31 JA Solar Holdings Co., Ltd. (JASO) Solar Europe 32 LDK Solar Co., Ltd. (LDKYQ) Solar Europe 33 Mass Megawatts Wind Power Inc. (MMMW) Wind Americas 34 Meyer Burger Technology AG (MYBUF) Solar Europe 35 Motech Industries, Inc. (6244.TWO) Solar Europe 36 Muenchmeyer Petersen Capital AG (MPC) General Europe 37 Neo Solar Power Corp (3576.TW) Solar Europe 38 NextEra Energy, Inc. (NEE) Solar Australia 39 Norex Exploration Services Inc. (NRX.TO) Wind Americas 40 Ocean Power Technologies, Inc. (OPTT) Wave Asia 41 Ormat Technologies, Inc. (ORA) Geothermal Asia 42 Phoenix Solar AG (PS4) Solar Europe 43 Plug Power Inc. (PLUG) hydrogen and fuel cell Asia 44 Power REIT (PW) General Americas 45 Premier Power Renewable Energy, Inc. (PPRW) Solar Europe 46 Real Goods Solar, Inc. (RGSE) Solar Americas 47 REC Silicon ASA (RNWEF) Solar & Electronic Americas 48 ReneSola Ltd (SOL) Solar Europe 49 Renewable Energy Group, Inc. (REGI) General Americas 50 Renewable Energy Holdings Corp. (PREHC) General Americas 51 SAG Solarstrom AG (SAG) Solar Americas 52 SMA Solar Technology AG (SMTGF) Solar Americas 53 Solar-Fabrik AG (SLFBF) Solar Americas 54 Solartech Energy Corp (3561.TW) Solar Americas 55 SolarWorld Aktiengesellschaft (SRWRY) Solar Americas 56 Solco Limited (ASX) Solar Europe 57 SunEdison, Inc. (SUNEQ) Solar Europe 58 SunPower Corporation (SPWR) Solar Europe 59 Suntech Power Holdings Co Ltd (STPFQ) Solar Americas 60 Suzlon Energy Limited (SUZLON.NS) Wind Americas 61 Trina Solar Ltd (TSL) Solar Europe 62 Vestas Wind Systems ADR (VWDRY) Wind Asia

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4. TOPOLOGICAL ANALYSIS To elaborate the findings more clearly, based on the MST, we present its network topology in order to analyze the topological properties with respect to degree of connections. The degree of connection for node refers to the adjacency matrix. The adjacency matrix of network (C) is a symmetric matrix consists of 62×62 elements of 0 and 1. indicates the existence of relationship between a pair of and stocks in MST. We use Pajek software to visualize the topological network in the form of graphical representation (De Nooy et al., 2011; Batagelj and Mrvar, 2004). It helps us to understand the complex network in a simple structure. Figure 1 shows the correlation–based MST for a set of 62 renewable energy stocks in which size of nodes is based on degree of connections. Each stock is labeled by its symbol and colored by its corresponding sector include solar (yellow), general (orange), wind (red), wave (blue) electric power which is the main source of the other stocks (green).

Figure 1. The topological network of 62 renewable energy stocks

This section discusses the results of topological network analysis obtained by using centrality measures. Betweenness stock-level centrality measure capture the interaction between the stocks of the network. In this network, General Cable Corporation (BGC) with highest score of betweenness centrality measure (0.662) and 8 links is located in central position. BGC has directly relationship and influence to First Solar, Inc. (FSLR), Ameresco, Inc. (AMRC), Brookfield Renewable Partners L.P. (BEP), Innergex Renewable Energy Inc. (INGXF), Muenchmeyer Petersen Capital AG (MPC), Enel Américas S.A. (ENIA), Ormat Technologies, Inc. (ORA) and Acciona, S.A. (ACXIF). After BGC in general sector, FSLR and Trina Solar Ltd (TSL) in solar sector play important roles in network. This MST shows the situation where the FSLR and TSL are dominated by BGC. In terms of centrality also Acciona, S.A. (ACXIF) and EDP RENOVAVEIS ADR (EDRVY) have the highest scores, respectively. Therefore, the most influential and dominant stock in renewable energy is General Cable Corporation. The top five stocks based on betweennees centrality measure score are classified in Table 2. 171

Mansooreh Kazemilari, Ali Mohamadi, Abbas Mardani, and Dalia Streimikiene / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 167-174 Table 2. most important stocks and centrality measures

Symbol Stock Degree of connections Betweenness General Cable Corporation BGC 8 0.662 FSLR First Solar, Inc. 3 0.577 TSL Trina Solar Ltd 6 0.532 ACXIF Acciona, S.A. 3 0.446 EDRVY EDP RENOVAVEIS ADR 3 0.445

Figure 2 shows the number of stocks in each sector. From 62 companies of renewable energy, solar with 28, general with 11 and wave with 9 companies have the highest rank among other sectors, respectively. In Figure 3, the number of regional stocks are classified. As can be seen, Ameri- ca with 28 and Europe with 21 have the most companies of renewable energy sector in the world.

Figure 2. Companies' distribution for each sector region Figure 3. Companies' distribution for each

5. CONCLUDING REMARKS However, in todays' world the growth of global economic has peaked while environmental pollution and the crisis of energy deficiency will surely broaden the burgeoning problem. In case the dispute has not been managed efficiently, it would influence the human beings' lives in such a way that any human community will not support the sustainable developmental issue which have an effect on their living quality, moreover. Thereupon, taking serious issues of energy and environment into account and accelerating the renewable energy sources may be considered quite com- pelling. The stock market considered as an extremely complex network of the relationships between time series of stock price therefore, they are particularly worth of analysis. The purpose of this re- 172

Mansooreh Kazemilari, Ali Mohamadi, Abbas Mardani, and Dalia Streimikiene / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 167-174 search is to examine the 62 major stocks behaviour in stock market and find affecting stocks associated with renewable energy sector. In order to determine the most influential stocks, this research considered the similarity measure among stocks by using Pearson correlation coefficient. After that, the network visually constructs the interaction among stocks, which is extracted a topological influence map for stocks by the MST. We discussed the results obtained from topological network analysis by using centrality measures. These measures are useful to know the importance and inﬂuence of particular stock relevant to the other stocks. Based on those findings, study can conclude that the most influential stocks are General Cable Corporation (BGC) from general sector, First Solar. Inc and Trina Solar from solar sector in renewable energy stock market.

ACKNOWLEDGMENT The first author acknowledge financial support from the National Elites Foundation, Iran. The authors also would like to thank the Shiraz University for the facilities and providing us with a pleasant working environment.

REFERENCES Andersen, T.G, Bollerslev, T, Diebold, F.X., Vega, C. (2007), “Real-time price discovery in global stock, bond and foreign exchange markets”, Journal of International Economics, Vol. 73, pp. 251-277. Balduzzi, P, Elton, E.J, Green, T.C. (2001), “Economic news and bond prices: Evidence from the US Treasury market”, Journal of financial and Quantitative analysis, Vol. 36, pp. 523-543. Batagelj, V., Mrvar, A. (2004), Pajekanalysis and visualization of large networks, Springer. Bonanno, G., Caldarelli, G., Lillo, F., Miccichè, S., Vandewall,e N., Mantegna, R.N. (2004), “Net- works of equities in financial markets”, The European Physical Journal B-Condensed Matter and Complex Systems. Vol. 38, pp. 363-371. Borgatti, S. P. (2005), “Centrality and network flow”, Social networks, Vol. 27, No. 1, pp. 55-71. Borgatti, S. P., Carley, K. M., Krackhardt, D. (2006), “On the robustness of centrality measures under conditions of imperfect data”, Social networks, Vol. 28, No. 2, pp. 124–136. Boyer, M.M., Filion, D. (2007), “Common and fundamental factors in stock returns of Canadian oil and gas companies”, Energy Economics, Vol. 29, pp. 428-453. Bukarica, V., Vrhovcak, M.B. (2006), Renewable energy policy in Croatia: current status and future activities, World Renewable Energy Congress, Hrvatska znanstvena bibliografija i MZOS- Svibor. Bürer, M.J., Wüstenhagen, R. (2009), Which renewable energy policy is a venture capitalist's best friend? Empirical evidence from a survey of international cleantech investors. Energy Policy.; 37:4997-5006. De Nooy, W., Mrvar, A., Batagelj, V. (2011), Exploratory social network analysis with Pajek. vol. 27. Cambridge University Press. Espino, J. M. Hoyos, J. R. C. (2010), “Stability of centrality measures in social network analyses to identify long-lasting leaders from an indigenous boarding school of northern Mexico” Estudios sobre las culturas contempor´aneas, Vol. 32, pp. 155–171. Markowitz, H. (1952), “Portfolio selection”, The journal of finance, No. 7, pp. 77-91. Mantegna, R.N. (1999), Stanley HE. Introduction to econophysics: correlations and complexity in finance, Cambridge university press, Cambridge. Nalan, Ç.B, Murat, Ö, Nuri Ö. (2009), “Renewable energy market conditions and barriers in Turkey”, Renewable and Sustainable Energy Reviews, Vol. 13, pp. 1428-1436.

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Mansooreh Kazemilari, Ali Mohamadi, Abbas Mardani, and Dalia Streimikiene / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 167-174 Coelho, R., Gilmore, C.G., Lucey, B., Richmond, P., Hutzler S. (2007), “The evolution of interdependence in world equity markets — Evidence from minimum spanning trees”, Physica A: Sta- tistical Mechanics and its Applications, Vol. 376, pp. 455-466. Katsaprakakis, D.A., Christakis, D.G. (2016), “The exploitation of electricity production projects from Renewable Energy Sources for the social and economic development of remote communities. The case of Greece: An example to avoid”, Renewable and Sustainable Energy Reviews, Vol. 54, pp. 341-349. Lu, H-f, Lin, B-l, Campbell, DE, Sagisaka, M, Ren, H. (2016), “Interactions among energy consumption, economic development and greenhouse gas emissions in Japan after World War II”, Re- newable and Sustainable Energy Reviews. Vol. 54, pp. 1060-1072. Rafindadi, A.A., Ozturk, I. (2016), Effects of financial development, economic growth and trade on electricity consumption: Evidence from post-Fukushima Japan, Renewable and Sustainable Energy Reviews, Vol. 54, pp. 1073-1084. Sieczka, P., Hołyst, J. A. (2009), Correlations in commodity markets. Physica A: Statistical Mechanics and its Applications,; 388(8): 1621–1630. Yanine, F.F., Caballero, F.I., Sauma, E.E., Córdova, F.M. (2014), “Homeostatic control, smart meter- ing and efficient energy supply and consumption criteria: A means to building more sustainable hybrid micro-generation systems”, Renewable and Sustainable Energy Reviews, Vol. 38, pp. 235-258.

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Montenegrin Journal of Economics

Vol. 14, No. 2 (2018), 175-182 ‘

The Effect of Word of Mouth Marketıng on the Purchase Behavıor Vıa Brand Image and Perceıved Qualıty

ZÜHREM YAMAN1,

1 Assistant Professor, Selcuk University, Faculty of Health Sciences Healthcare Administration, Konya, Turkey, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received April 21, 2018 In terms of businesses in today's world, the most important way of Revised from May 12, 2018 sustainability is through a good sense of quality and a brand image. Accepted May 24, 2018 Especially when the uncertainties about the service to be offered in Available online June 15, 2018 the service sector are excessive, people can postpone the decision stage as long as they are not confident. For a service that they do not already have experience, the customers choose the way to JEL classification: prefer perceived quality perception and businesses with high brand M31 image with the help of word of mouth marketing. In this study, it is aimed to determine whether word of mouth marketing has effect on DOI: 10.14254/1800-5845/2018.14-2.12 the purchasing behavior via brand image and perceived quality. In order to collect the data in the study, scales which consists of 23 Keywords: items, 89 for Cronbach Alpha internal consistency coefficient word of mouth marketing, 80 for brand image, 92 for perceived quality, word of mouth marketing, developed by Goyette vd (2010), Ural and Perk (2012) and Pappu, purchase behavior, Quester ve Cooksey (2006) was used. As a result of the analyzes brand image, made, it has been found out that word of mouth marketing activities perceived quality. develop faster on the institutions which had high percieved quality and brand image before, and on the purchasing behaviors of the individuals.

INTRODUCTİON Today, with technological developments, individuals are exposed to a lot of information or messages every day, and it is difficult to distinguish between institutions that produce similar products or services. Particularly when it comes to service, the individual is having difficulty in the stage of deciding. The perceived quality and brand image created by those who previously bought the product or service constitutes the most important factors affecting the purchasing preferences of potential customers. The recent competitive success is achieved by being closer to the customer and establishing a healthy communication with them. Customers prefer the brand image-intensive businesses that can best meet their needs and wants, understand the questions and produce early solutions.

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Zührem Yaman / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 175-182 Businesses that are aware of this, have an individual interaction with the customers and take a hint about what to do with them. In an ever-increasing competitive environment, organizations will be able to communicate more closely with customers in order to be able to sustain their assets and to do so with the right marketing communications. People who demand service through word- of-mouth marketing realize purchasing by obtaining the most important information they trust, which reduces their ambiguity.

1. CONCEPT OF WORD OF MOUTH MARKETİNG In today's increasingly competitive world, it has become possible to reach every individual through various electronic mass media, but the importance of communication with word of mouth marketing has never changed and even this traditional communication type has become even more important. Rather than researching or experimenting with the product for which they do not have information about the consumer, they may be able to access information that they can use for their work through daily word of mouth communication (Ellison and Fudenberg, 1995, p. 114). Word of mouth marketing is the most customer-focused form of communication in all forms of communication. The consumer decides who to talk to and what to ask (Silverman, 2007, p. 47). Word of mouth marketing is expressed in the form of verbal communication between two or more consumers who are not engaged in a commercial, brand, product or service (Woodside and Delozier, 1976, p. 13). Richins describes word of mouth marketing as an exchange of voluntary information between consumers about an organization, product, or consumer’s experience of this product. This voluntary information exchange is a powerful source of information that organizations can use for their own benefits (Richins, 198, p. 69). In the context of marketing;it is seen as transmitting products or services, activities of profit-oriented or non-profitable organization to any other person from any other person than the sales personnel (Gulmez, 2008, p. 318). Communica- tion is also a science of art, which provides mutual benefits between the customer - the customer and the customer - the marketer. (Sernovitz, 2007, p. 2). Word of mouth marketing is a form of personal communication between the buyer and the sender and can be seen as a personal influence process that can change the behavior or attitudes of the buyer (Sweeney et al., 2008, pp. 344-345).

2. BRAND IMAGE AND PERCEİVED QUALİTY A brand can be defined as a combination of name, term, sign, symbol, design, shape, or all of them to describe a product or distinguish a product from rivals (Odabası and Oyman, 2007, p. 360). While the brand (Ozdemir, 2008, p. 114), can also be thought of as the sum of the perceptions and feelings of consumers related to a product, on the other side while consumers buy a product carries the aim of "differentiation" (Ar, 2007, p. 5). The image is defined as the impressions that emerge in the buyer's memory in the form of emotions or thoughts revolving around the brand, and these impressions are expressed by the knowledge gained from various sources, by the experiences of other people, or by the person's own experience (Yukselen and Guler, 2009, p. 22). The brand image concept can be evaluated from different angles. It is interested in evaluating the perception of a brand in the mind of the consumer when buying behavior is examined. While the consumer is satisfied by establishing an emotional connection with the brand, this level of satisfaction improves the brand's differentiation. The brand image is the sum of the emotional and aesthetic positive or negative impression that the product creates in the target market (Ker, 1998, p. 25). For the formation of the brand image, it is absolutely necessary for the consumer to have the experience of purchasing or using the product or service belonging to a mark (Hung, 2005, p. 239). 176

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The brand is a promise of mutual trust and a quality guarantee. The brand establishes relationships between the firm and the firm's customers. A strong brand,  Can be distinguished  Creates a profile  Stands as an element of preference (Perry and Wisnow, 2003, p. 12).

Brand image can vary depending on the difference between expected and perceived quality. The customer satisfaction is achieved when the quality which the customers perceive is higher than expected quality, whereas it shows that a business does not provide satisfaction when the expected quality is higher than percieved quality. Businesses can contribute positively to the brand image with the importance they give to perceived quality and customer satisfaction and also at the same time they can contribute to their the customer portfolio via past experience of the satisfied customers and word of mouth marketing. Through word of mouth marketing, a positive brand image effect can be created in the minds of consumers. For this reason, it is possible with high perceived quality for the brand image to come out to daylight with full potential. Brand image is formed in the minds of consumers in the direction of positive or negative perceptionsThrough word of mouth marketing, both conditions are reflected in the buying behaviors of the customers as positive or negative perceived quality by the customers who have experienced the product or service before. When the dimensions of service quality developed by Parasuraman are examined, the perceived quality, in other words the expected quality, includes the perception of what and how consumers are offered services according to their needs, the past experiences of customers and expectations of them from current service process. (Buttle, 1996, p. 27; Acuner, 2001, p. 2). Perceived quality is evaluated as an input of the customer satisfaction process. The quality of the product or service takes place before the customer's perception and satisfaction process, and the corporations are expected to produce the product or service at a certain quality. The customer buys the product or service. Highly perceived value and satisfactory products or services continue to positively impact buy-in behaviour through word-of-mouth marketing after people become con- scious of the brand image of the corporations. According to Hung (2005), consumers have an image of that brand with the help of associations in their minds about that brand (Ozdemir, 2005). Most of the connotations related to the brand convey the quality perception that some consumers have perceived previously about the product or service to others through word of mouth marketing. In addition, recent searches reveal that service quality perceptions are related to purchase intention. (Zeithaml et al., 1996; Boulding et al., 1993). While brand image, one of the most important elements of an effective marketing, can process positive brand perception and brand awareness with the perceived service quality as a whole, this positive situation will be more influential on the consumer through oral marketing. The elements that make up a brand value are brand name awareness, brand loyalty, perceived quality and brand associations. (Aaker, 2009, p. 21). It has been determined that products or services with high brand image are evaluated as more qualified,safe and more preferable by consumers. Businesses' desire to make a difference among other products in order to survive in market conditions where businesses are dominated by intense competition, reveals the concept of brand- ing. It has been found that there is a positive relationship between the various characteristics of the brand (image of the brand, brand love, brand satisfaction, brand reputation, attitude and behavioral commitment) and trust in the brand (Cabuk ve Orel, 2008, p. 103) , and it has also seen that this stuation affects the purchasing decisions of the consumers (Cabuk and Orel, 2008, p. 115). 177

Zührem Yaman / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 175-182 3. BRAND IMAGE AND THE EFFECT OF WORD OF MOUTH MARKETING ON PURCHASING THROUGH PERCEIVED QUALITY The purpose of this study is to investigate the effect of word of mouth marketing on consumers' purchasing behaviours through brand image and perceived quality in health institutions operating in the service sector and to try to emphasize brand image and perceived quality effect in oral mind marketing activities of enterprises in the direction of research findings. In order to collect data in the research, the oral communication marketing scale developed by Goyett et al. (2010), the brand image scale developed by Ural and Perk (2012) and the scales developed by Pappu, Quester and Cooksey (2005) were used. People who participated in the research have got service from the same hospital in Konya- Meram district at least once; opinions of 256 patients were collected using an individual questionnaire and data analysis was conducted.

3.1 Demographic Findings towards the Research

Table 1. Demographic Characteristics of People Involved in Research

Gender Number Percentage Age Number Percentage Male 139 54,3 20 yr. & under 27 10,5 Female 117 45,7 21-30 yr. 54 21,1 Total 256 100 31-40 yr. 72 28,1 Education Number Percentage 41-50 yr. 68 26,6 Primary Sc. 45 17,6 51 and over 35 13,7 High School 109 42,6 Total 256 100 Undergraduate 70 27,3 Income Number Percentage 1000 TL and Postgraduate 32 12,5 23 9 under Total 256 100 1001-2000 TL 121 47,3 Occupation Number Percentage 2001-3000 TL 59 23,1 Student 61 23,8 3001-4000 TL 15 5,8 Officer 59 23,1 4001-5000 TL 20 7,8 5000 TL and Self-employed 73 28,5 18 7 over Unemployed 63 24,6 Total 256 100 Total 256 100 Marital Status Number Percentage Married Single Total 256 100

When the demographic characteristics of the participants were examined, 54.3% of the respondents were women, 56.6% of them were married, and 28.1% of them were between 31-40 years of age. While 28.5% of the participants are freelancers, 23% are officers. It has been stated that the participants with a maximum of 47.3% of the income level are between 1001 and 2000 TL, while at least 7% are between 4001-5000TL. Regarding the educational status, it is seen that 42.6% of the participants have high school education, 27.3% have university education, 17.6% have primary education and 12.5% have graduate education.

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3.2 Survey Findings Related to Variables of Word of Mouth Marketing, Per- ceived Quality, and the Brand Image of the Participants of the Research

Table 2. Adaptive Value of Scale

X2 df X2/df GFI CFI RMSEA AAP Scale 3,672 1 3,672 1 1 0,073 Brand Image Scale 0,876 2 0,438 1 1 0,000 Perceived Quality Scale 2,784 5 1,392 1 1 0,000 Good Adaptive Value ≤3 ≥0,91 ≥0,96 ≤0,05 Acceptable Adaptive ≤4-5 0,90-0,86 ≥0,93 0,06-0,08 Value

p>.05, X2 =Chi-Square; df= Degree of Freedom; GFI=Goodness Of Fit; CFI=Comparative Fit Index; RMSEA=Root Mean Square Error of Approximation. In order to test the validity of the scales used in Table 1, one-factor constructions of all variables were verified by confirming factor analysis for all variables by Amos program.

Figure 1: The Mediating Role of Brand Image on Word-of-Mouth Marketing

In Figure 1, the mediating role of brand image on word-of-mouth marketing is evaluated and it is observed that the model's adaptive values are within acceptable limits. In the structural equation model, the independent variable has a meaningful effect on the mediator variable (p <0.05), so it can be said that the brand image is influenced by word-of-mouth marketing. In Figure 2, the mediating role of perceived quality on word-of-mouth marketing is evaluated and it is found that the model's values are within acceptable limits. In the structural equation model, because the independent variable has a significant effect on the mediator variable (p <0.05), it can be said that perceived quality has a partial effect on word-of-mouth marketing. Positive word of mouth communication is related to the customer's perception of products and services as good and valuable. In other words, if the service or product performance and the perceived support after the service are satisfied, it is inevitable that positive word of mouth communication is realized. (Derbaix and Vanhammn, 2003, pp. 7-8).

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Zührem Yaman / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 175-182 Figure 2 The Mediating Role of Perceived Quality on Word-of-Mouth Marketing

Figure 3. The Mediating Role of Brand Image and Perceived Quality on Word-of-Mouth Marketing

In Figure 3, the mediating role of brand image and perceived quality on word-of-mouth marketing is evaluated and it is found that the model's adaptive values are within acceptable limits. In the structural equation model, brand image and perceived quality have a significant effect on word-of- mouth marketing due to the fact that the independent variable has significant effect on the mediator variables (p <0,05). The vast majority of customers tell their dissatisfaction to the people around them. If dissatisfaction arises as a result of the service, the customer tells this dissatisfaction to 10 to 20 people. (Kitapcı, 2008, p. 118). Especially in small cities, this can have a negative impact on business operation and the business image may suffer. This happens often because positive experiences are within expectations and are soon forgotten. Unresolved negativities, however, make people frustrated, disappointed,

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Zührem Yaman / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 175-182 and negatively affect word of mouth communication. (Silverman, 2007, p. 44). Especially in the last decade, dissatisfied consumers now share their complaints in complaint web sites that allow them to spread all over the World. These sites are available to other dissatisfied consumers, and they can all unite to create a negative power union for the purchase of the company or product (Tuk, 2008, pp. 16-17).

CONCLUSİON The brand symbolizes the nature of customer perceptions of businesses in any sector (Kim and Kim, 2005). The brand image in health institutions contributes to the establishment of strong and long-term relationships with patients. In hospitals, which is a service sector as it is in all sectors, achieving competitive superiority is now one of the most important goals. It can be considered as an effective factor in increasing the number of patients, demanding no cost such as word-of- mouth marketing, within the scope of large-cost promotional activities to take a step forward for customers. Since the brand image is also related to the perceived quality perceptions of the people, careful attention should be paid to the ambiance elements of the hospital, functionality and sensitivity to customer complaints and taking care of all kinds of behavior that may affect perceived quality. As a result of these findings, an assessment can be made that patients who are satisfied with the institution and who are affected by the brand image of the institution and the quality of service will loyally mention the brand image and hospital as the best health institution where the best doc- tors work. Also, patients will encourage other people to go to the same institution when needed. It can be said that patients can communicate easily with other people whom they are in communication. Making the health institution a brand, the company can have a vision of reaching more patients and requesting them when they need, thanks to advertising through word-of-mouth marketing without any expense. According to the results of the research, it is concluded that word-of- mouth marketing affects brand image with 57% alone, 71% with brand image and perceived quality. Given that intense competition is important for businesses in all sectors, companies need to pay more attention to word-of-mouth marketing. The increase in the quality perception will positively contribute to the brand image, while it will create an opportunity to attract more customers to companies with less cost through word-of-mouth marketing at the same time.

REFERENCES Aaker, D. A. (2009), Creating Strong Markets, Mediacat Publishinh, İstanbul (in Turkish). Acuner, T., Acuner, S. A. (2001), "It's Role in Total Quality of Service Management and Customer Satisfaction ", Marketing World Magazine, No:4, July-August (in Turkish). Ar, A. A. (2007), Brand and Brand Strategies, 2. Printing, Nobel Publishing Distrubition, Ankara (in Turkish). Boulding, W., A. Karla, R. Staelin, Zeithaml, V. A. (1993), “A Dynamic Process Model of Service Quality: from Expectations to Behavioral Intentions”, Journal of Marketing Research, Vol. 30, pp. 7–27. Buttle, F. (1996), “Servqual: Review, Critique, Research Agenda”, European Journal of Marketing, Vol. 30, No. 1, , pp. 8-32. Cabuk, S., Orel, F. D. (2008), “Determination of Relations Between Brand Characteristics and Con- fidence in Brand and Manufacturer: A Research in Cukurova University Sca-le”, Cukurova Uni- versity Social Sciences Institute Magazine, Vol. 17, No. 1, pp. 103-116 (in Turkish). Derbaix, C., Vanhamme, J. (2003), “InducingWord-Of-MouthByElicitingSurprise – A Pilot İnvestiga- tion”, Journal of Economic Psychology”, Vol. 24, No. 1, pp. 99-116.

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Zührem Yaman / Montenegrin Journal of Economics, Vol. 14, No. 2 (2018), 175-182 Ellison, G., Fudenberg, D., (1995), “Word-of-mouth Communication and Social Learning”, The Quar- terly Journal of Economics, Vol. 110, No. 1, pp. 93–125. Goyett, I., Ricard, L., Bergeron, J., Marticotte, F. (2010), “e-WOM Scale: Word-of-Mouth Measure- ment Scale fore-Services Context”, Canadian Journal of Administrative Sciences, Vol. 27, pp. 5–23. Gulmez, M. (2008), Oral Communication and Marketing, Selections from Current Marketing Ap- proaches, Editors: Varinli, İ., Çatı, K., Detay Publishing, Ankara, pp. 315- 334 (in Turkish). Hung, C. (2005), “The Effect of Brand Image on Public Relations Perceptions and Customer Loyal- ty”, International Journal of Management, Vol. 25, No. 2, pp.237-246. Ker, M. (1998), ), “Place and importance of professional image in the scope of image management”, Marketing World, Vol. 71, No. 12, pp. 25-28 (in Turkish). Kim, H. B., Kim, W. G. (2005), “The Relationship Between Brand Equity and Firms’ Performance in Luxury Hotels and Chain Restaurant”, Tourism Management, 26, 549–560. Kitapci, O. (2008), RestCustomer Complaints Behavior in Restaurant Services: An Application in Sivas”, Erciyes University Faculty of Economics and Administrative Sciences Magazine, Vol. 31, pp. 111-120 (in Turkish). Sernovitz, A. (2007), “Word of Mouth Marketing: How Smart CompaniesGet People Talking”, Sum- maries.Com. (İnternet delivered Business Book Summaries). Odabaşı, Y., Oyman, M. (2007), Marketing Communication Management, Mediacat Publishing, Printing, İstanbul (in Turkish). Ozdemir, G. (2008), Destination Marketing, Detay Publishing, Ankara (in Turkish). Ozdemir, H. (2009) “The Impact of Corporate Social Responsibility on Brand Image”, Istanbul Commerce University Social Sciences Magazine, Vol. 15, No. 8, pp. 57-72 (in Turkish). Pappu, R., Quester, P. (2006), “A Consumer-Based Method for Retailer Equity Measurement: Re- sults of an Empirical Study”, Journal of Retailing and Consumer Services, Vol. 13, No. 5, pp. 317–329. Perry, A., Wisnom, D. (2004), DNA of Brands, Rules of Creating Unique and Durable Markets, trans, MediaCat Books, Zeynep Yılmaz, İstanbul (in Turkish). Richins, M. L. (1983), “Negative word-of-mouth by dissatisfied consumers: a pilot study”, The Jour- nal of Marketing, Vol. 47, No. 1, pp. 68-78. Silverman, G. ,(2007), Word to Mouth Marketing – World's Most Powerful Marketing Method, WOMM's Secret, Mediacat, İstanbul (in Turkish). Sweeney, J. C., Soutar G. N., Mazzarol, T. (2008), “Factors Influencing Word of Mouth Effective- ness: Receiver Perspectives”, European Journal of Marketing”, Vol. 42, No. 3/4, pp. 44-364. Tuk, M. A. (2008), “s Friendship Silent When Money Talks?: How People Respond to Word-of- Mouth Marketing, Erasmus Research Institute of Management (ERIM), Erasmus University Rot- terdam, ERIM Ph.D., Series Researchin Management 130. Ural, T., Perk, G., (2010), “The Impact of Consumer Based Brand Value on Personal Computer Pur- chasing Intent A Study in Antakya”, Anadolu University Social Sciences Magazine, pp. 11-25 (in Turkish). Woodside, A. G.ve Delozıer, W., (1976), “Effects of Word of Mouth Advertising on Consumer Risk Taking”, Journal of Advertising, Vol. 5, No. 4, pp. 12- 19. Yukselen, C. Ve Guler, G. E.(2009), Antakya Brand City Opinions and Suggestions, Detay Publish- ing, Ankara (in Turkish). Zeithaml, V., Berry, L., Parasuraman, A.,(1996), "The Behavioural Consequences of Service Quali- ty", Journal of Marketing, Vol. 60, pp. 31-46.

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Montenegrin Journal of Economics

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