European Journal of Economic Studies, 2016, Vol.(18), Is. 4
444
EUROPEAN of Economic
Journal Studies
Has been issued since 2012. ISSN 2304-9669. E-ISSN 2305-6282
2016. Vol.(18). Is. 4. Issued 4 times a year Impact Factor MIAR 2016 – 5,602
EDITORIAL BOARD
Dr. Vidishcheva Evgeniya – Sochi State University, Sochi, Russian Federation (Editor-in-Chief) Dr. Simonyan Garnik – Scientific Research Centre of the Russian Academy of Sciences, Sochi,
Russian Federation Dr. Levchenko Tatyana – Sochi State University, Sochi, Russian Federation Dr. Tarakanov Vasilii – Volgograd State University, Volgograd, Russian Federation Dr. Balatsky Evgeny – Central Economics and Mathematics Institute (RAS), Moscow,
Russian Federation Dr. Dinh Tran Ngoc Huy – Banking University HCMC Viet Nam – GSIM, International
University of Japan, Japan Dr. Gerasimenko Viktor – Odessa State Economic University, Odessa, Ukraine Dr. Gvarliani Tatjana - – Sochi State University, Sochi, Russian Federation Dr. Gunare Marina – Baltic International Academy, Riga, Latvia Dr. Kryshtanovskaya Olga – Institute of Sociology of the Russian Academy of Sciences,
Moscow, Russian Federation Dr. Minakir Pavel – Economic Research Institute of the Far Eastern Branch Russian Academy
of Sciences, Khabarovsk, Russian Federation Dr. Papava Vladimir – Ivane Javakhishvili Tbilisi State University, Tbilisi, Georgia Dr. Prokopenko Olga – Sumy State University, Sumy, Ukraine Dr. Vishnevsky Valentine – Institute of Industrial Economics of the National Academy of
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EUROPEAN of Economic
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РЕДАКЦИОННЫЙ СОВЕТ
Видищева Евгения – Сочинский государственный университет, Сочи, Российская
Федерация (Гл. редактор) Левченко Татьяна – Сочинский государственный университет, Сочи, Российская
Федерация Симонян Гарник – Сочинский научно-исследовательский центр Российской академии
наук, Сочи, Российская Федерация Тараканов Василий – Волгоградский государственный университет, Волгоград,
Российская Федерация Балацкий Евгений – Центральный экономико-математический институт РАН, Москва,
Российская Федерация Вишневский Валентин – Институт экономики промышленности Национальной академии
наук Украины, Донецк, Украина Гварлиани Татьяна – Сочинский государственный университет, Сочи, Российская Федерация Герасименко Виктор – Одесский государственный экономический университет, Одесса,
Украина Гунаре Марина – Балтийская международная академия, Рига, Латвия Динь Чан Нгок Хай – Банковский университет Хошимин Вьетнам - GSIM, Международный
университет Японии, Япония Минакир Павел – Институт экономических исследований ДВО РАН, Хабаровск, Российская
Федерация Крыштановская Ольга – Институт социологии РАН, Москва, Российская Федерация Папава Владимир – Тбилисский государственный университет имени Иване
Джавахишвили, Тбилиси, Грузия Прокопенко Ольга – Сумский государственный университет, Сумы, Украина
Журнал зарегистрирован Федеральной службой по надзору в сфере массовых коммуникаций, связи и охраны культурного наследия (Российская Федерация). Свидетельство о регистрации средства массовой информации ПИ № ФС77-50465 от 4 июля 2012 г.
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Статьи, поступившие в редакцию, рецензируются. За достоверность сведений, изложенных в статьях, ответственность несут авторы публикаций.
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C O N T E N T S
Articles and Statements
Government Expenditure, Defense Expenditure and Economic Growth: a Causality Analysis for BRICS
Salman Ali Shah, Chen He, Mao Yu, Wang Xiaoqin .......................................................
447
The Impact of Energy Consumption, Trade Openness and Financial Development on Economic Growth: Empirical Evidence from Turkey (1980-2014)
Murat Cetin ......................................................................................................................
459
Fostering the Sustainable Development of the Economy of the Russian Federation via the Creation of Small Innovation Enterprises at Institutions of Higher Learning
Mikhail N. Dudin, Natalia P. Ivashchenko ......................................................................
470
Features of Touristic Territory Branding on the Example of Sochi City (Russian Federation) and Jurmala City (Latvia)
Characteristics of Basel Principles and Standards in Banking Branimir Kalaš, Nada Milenković, Jelena Andrašić, Miloš Pjanić ..................................
486
The Effect of Credit Risk Management on Banks’ Profitability in Kosovo Aliu Muhamet, Sahiti Arbana ..........................................................................................
492
Impact of OPEC Policies over the Global Economy: Case of USA Ameer Mahdi Nassrullah Mzwri, Filiz Katman ...............................................................
516
European Journal of Economic Studies, 2016, Vol.(18), Is. 4
Published in the Russian Federation European Journal of Economic Studies Has been issued since 2012. ISSN: 2304-9669 E-ISSN: 2305-6282 Vol. 18, Is. 4, pp. 447-458, 2016 DOI: 10.13187/es.2016.18.447
www.ejournal2.com
Articles and Statements
UDC 33 Government Expenditure, Defense Expenditure and Economic Growth: a Causality Analysis for BRICS Salman Ali Shah a , *, Chen He a , Mao Yu a , Wang Xiaoqin a a Huazhong University of Science and Technology, Wuhan, Hubei, P.R China
Abstract This paper empirically examines the effects of civilian and military portions of government
expenditure on economic growth of five key emerging economies Brazil, Russia, India, China and South Africa (BRICS). We ran separate Cointegration and Granger causality tests for each country using data taken from WDI and SIPRI while taking account of the limitations of time series data. We got interestingly different effects of military expenditure on economic growth across countries especially for the three nuclear powers Russia, India and China. India and Brazil showed negative, Russia and China showed positive while South Arica showed no effect on economic growth in terms of government civilian expenditure.
Keywords: government expenditure, military expenditure, economic growth, BRICS, cointegration, granger causality, unit root, emerging economies, one way causality, feedback relationship.
1. Introduction ‘‘The single and most massive obstacle to development is the worldwide expenditure
onnational defense activity.’’* The traditional gun-butter tradeoff claims that military spending is a non-productive
expenditure. The logic behind this argument is the fact that military expenditure consumes a lot of resources thus leaving little for other economic activities, for instance, investment in public infrastructure, private consumption and investment, social security programs, etc., and thus slows down economic growth (Shieh, Lai, & Chang, 2002). Moreover, substantial military imports can also cause problems in balance of payments. On the other hand, the following quotation puts questions for researchers that need empirical answers;
“There is no way of telling from economic theory whether a greater military effort will slow down or accelerate output growth.”*
* Corresponding author
E-mail addresses: [email protected] (Salman Ali Shah) * Quote from a statement issued by a United Nations Committee for Development Planning written in the 1970s and cited in (Deger & Smith, 1983)
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Nonetheless, studies like (Benoit, 1973; Benoit, 1978; Yildirim†, Sezgin, Öcal, 2005) and (Yildirim, Öcal, 2014) have proved empirically wrong the conventional belief that military expenditure negatively affects economic growth. . On the other hand, a plethora of studies do empirically support this argument (Faini, Annez, & Taylor, 1984b), (Lim, 1983b), (Abu-Bader & Abu-Qarn, 2003), (Galvin, 2003), (Klein*, 2004), and (H.-C. Chang, Huang, & Yang, 2011). Studies that found out mixed results in cross country analysis include among others, (Chowdhury, 1991), (Kusi, 1994), (Kollias, Manolas, & Paleologou, 2004), (Chang et al., 2013) and (Pan, Chang, Wolde-Rufael, 2014).
There are several channels from both supply and demand point of view that show positive effect of military expenditure on economic growth. Regarding the supply-side effect, the defense sector provides a variety of public infrastructure (e.g., dams, communication networks, roads, airports, highways, and other transportation networks), and enhances human capital through education, nutrition, medical care, and training. Moreover, military research and development experience created by arms imports positively affects private production. From the demand-side point of view, defense spending reduces unemployment and increases aggregate demand, thus promoting economic growth. Furthermore, defense spending may favor economic growth since it provides both internal and external security, and therefore enhances private investment and attracts foreign investment. This form is known as military spending growth hypothesis. Growth hypothesis is a one-way Granger causality running from military spending to economic growth. The second form is that military spending is detrimental to economic growth (‘guns or butter’). This hypothesis is built upon the belief that if taxes or borrowings are used to finance military expenditure, it will crowd-out private investment. Otherwise, it takes the resources away from more productive government expenditures, for instance education and health services (Deger & Smith, 1983); (Lim, 1983a) (Dunne & Vougas, 1999). The second form is called the military spending growth detriment hypothesis. Growth detriment hypothesis is also a one-way Granger causality running from military spending to economic growth. The relationship between economic growth and military expenditure is bidirectional; that is to say, economic growth is caused by military spending and high military spendings are associated with economic growth. Furthermore, military sending is not exogenous when we consider changes in economic growth (Cappelen, Gleditsch, & Bjerkholt, 1984), (Kusi, 1994), (Kollias et al., 2004). The third form is a feedback hypothesis, which is a two-way Granger causality between military expenditure and economic growth. Finally, there is a fourth form of the relationship between military expenditure and economic growth which states that there is no relationship between military expenditure and economic growth (Biswas & Ram, 1986), (Grobar & Porter, 1989). The fourth form is called neutrality hypothesis, no causal relationship between military expenditure and economic growth. If the relationship between military spending and economic growth is either growth (detriment) hypothesis or feedback hypothesis, then reduction in (increase) military spending may lead to negative economic growth. For this reason, policy-makers need to analyze the relationship between military spending and economic growth to make an appropriate military strategy.
Military spending is qualitatively different from other government spending in many ways. Firstly, military procurements follow more strict acquisition processes and quality requirements than non-military spending (Hartley, 2004). Secondly, military spending is generally sanctioned by the government, independently from other types of spending. Thirdly, there is comparatively little flexibility in shifting military spending to other uses, unlike other spending. Fourthly, in almost every country, there is centralized allocation of military spending, while non-military spending may be allocated by central, state or local governments. While centralization might present different oversight, decentralization can involve more middlemen (Teobaldelli, 2011). Thus, it is highly likely that military and non-military spending have different effects on the economy. There has been an ongoing debate on the relationship between government spending and economic growth. The celebrated “Wagner’s law” postulates that government spending is income elastic and that the ratio of government spending to income tends to grow with economic development. Furthermore, government provides public goods and services (for non-military purposes) such as education,
* The authors point towards the study of Benoit (1973) that claims a positive effect of military expenditure on economic growth and argue that a single study has been used to build such a belief. They used cross country analysis and proved that military burden can slow down economic growth. (Faini, Annez, & Taylor, 1984a)
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infrastructure, and laws, are often considered as important variables in economic growth. The effects of economic growth on government expenditure have been examined by a plethora of empirical studies using different testing procedures and different measures of government spending (Peacock & Scott, 2000). Since the 1990s, it has become a common practice to test Wagner’s law using times-series techniques such as unit-root and co integration tests (Narayan, Nielsen, & Smyth, 2008). Using the Swedish data, (Henrekson, 1993) finds no evidence for Wagner’s law; he also finds that earlier results from time-series studies may be spurious because they did not test the stationarity properties of the data. On the other hand, (Akitoby, Clements, Gupta, & Inchauste, 2006) empirically supports Wagner’s law by using the co-integration method to a sample of 51 developing countries. Moreover, a number of studies have examined the effect of government spending on economic growth assuming that an inverted-U relationship exists between the scale of government and economic growth e.g. (Ram, 1986); (Dar & AmirKhalkhali, 2002). (Hansson & Henrekson, 1994) utilize disaggregated data and find that government transfers, consumption and total outlays have negative effects, while educational expenditure has a positive effect, and government investment has no effect on private productivity growth. In a framework of endogenous growth, (Barro, 1990) presented two kinds of predictions; unproductive government expenditure will have a negative effect on economic growth while the role of productive government expenditure on economic growth is unclear; it depends on how the government reacts and how much is the ratio of government spending to GDP. Later on, other studies also find support for negative effect of government spending on economic growth e.g. (Barro, 1991). The current body of literature generally suggests that developed countries may confirm Wagner’s law but it is less likely to find support for it in developing countries (Akitoby et al., 2006).
On the other hand, another strand of literature suggests that government spending could have a positive effect on economic growth if it involves public investment in infrastructure, but could have a negative effect if it involves only government consumption. Yet, previous studies have not reached a consensus on the relationship between government spending and economic growth, owing to their differences in the specification of econometric models, the measurement of government expenditures, and the selection of samples (e.g., (Agell, Lindh, & Ohlsson, 1997). As argued by (Abu-Bader & Abu-Qarn, 2003), typical regressions for explaining government spending or economic growth generally focus on the relationship between government spending and economic growth, rather than providing insight into the direction of causality. One popular approach to investigating the causal relationships between the two variables has been using the tests (Granger, 1969). Over the past decades many studies have applied the Granger causality tests to test the causal relationship between government spending and economic growth. (Halicioĝlu, 2003) applies the Granger causality tests to the Turkish data over 1960–2000 and finds neither co-integrated nor causal relationships between per capita GDP and government spending shares. In contrast, several studies find evidence on the Granger causality running from national income to government expenditure, and thus provide support for Wagner’s law e.g.,(Abu-Bader & Abu-Qarn, 2003). In particular, (Dritsakis, 2004) provides evidence on such a causal relationship for Greece and Turkey. By applying the unit-root, co-integration, and the Granger causality tests to panel data, (Narayan et al., 2008) find that Wagner’s law is supported by the panel of sub-national data on China’s central and western provinces, but is rejected by the full panel consisting of all Chinese provinces. Using the U.S. data since 1792, (Guerrero & Parker, 2007) find evidence supporting Wagner’s law but not supporting the hypothesis that the size of the public sector Granger causes economic growth.
A wave of literature concerning the BRIC countries has erupted since the term’s creation in 2001 by (O'neill & Goldman, 2001) e.g. (Armijo, 2007); (Cheng, Gutierrez, Mahajan, Shachmurove, & Shahrokhi, 2007); (Cooper, 2006); (Glosny, 2010); (Macfarlane, 2006). In (Wilson, Purushothaman, & Goldman, 2003) predicted that in less that forty years, or by 2050, the BRICs’ combined economies would catch up with – and could be larger than – the combined economies of the G6 (US, Japan, Germany, France, Italy and the UK). The BRICs would then become the world’s principal ‘engine of new demand growth and spending power’ (Wilson & Purushothaman, 2003). As ‘larger emerging market economies ‘Brazil, Russia, India and China where taken together as an analytical category based on their potential for domestic economic growth, underpinned by their large population size (Armijo, 2007).The BRIC category carries the promise of strong domestic
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economic growth and, more importantly, the prospect of becoming a global power. The acronym thus provoked an ever growing body of literature, many concerning the accuracy of including one BRIC or another in the group and the feasibility for a certain country to realize its ‘BRIC potential’ e.g. (Cooper, 2006); (Desai, 2007); (Macfarlane, 2006); (Sotero & Armijo, 2007). Later on in 2010 South Africa was included in the group of major national economies and thus the acronym is now known as “BRICS”.
The objective of this paper is to test the four hypotheses of government (military or non military) spending in case of five major emerging economies Brazil, Russia, India, China and South Africa. These four hypotheses are,
Growth hypothesis: a one-way Granger causality running from government (military or non military) spending to economic growth. (Positive)
Growth detriment hypothesis: also a one-way Granger causality running from government spending to economic growth. (Negative)
Feedback hypothesis: a two-way Granger causal relationship between government spending and economic growth.
Neutrality hypothesis : No causality between them We believe our findings will add up to the existing body of literature in two ways. One, our
Granger causality analysis will test the causality while our cointegration analysis will determine the direction as well as the nature of the relationship whether it’s positive or negative. Two, our findings will help the policy makers of these rapidly growing economies identify what could be slowing down their growth.
2. Data and Methodology Annual data ranging from 1988 to 2013 is used in our study for all the countries. All the
variables are measured in million dollars and are expressed in logarithms. Data for Gross domestic product and Government consumption is taken from World Development Indicator (WDI) while Military Expenditure’s data is taken from Stockholm International Peace Research Institute (SIPRI). The list and symbols of variables used in our study are as follows.
LGDP: Log of Gross domestic Product used an indicator for economic growth. LGE: Log of Government expenditure LME: Log of Military Expenditure 2.1 Econometric Methodology: Our econometric methodology consists of the following steps. 2.1.1 Augmented Dickey Fuller Test: Since our data set includes time series data, thus we have to test the properties of the time
series. In order to find out whether the data is stationary or not, we use Augmented Dickey Fuller test. This test was proposed by Dickey-Fuller (1979) and is widely used in the literature. Economic time series is typically non stationary and non stationary data can give us misleading results. Therefore, such time series should be made stationary or in other words such data should be differenced d times. The time series which is made stationary after differencing is called integrated of order d. When the test value comes out to be greater than the critical value, we interpret that the time series is stationary and vice versa.
2.1.2 Optimal Lag selection: After testing for stationarity; if the variables are integrated of the same order, the next step is
to choose optimal lag length. Different criterions have been used for lag selection in the literature but the most widely used method is to select the lag length suggested by majority of the criterion.
2.1.3 Johansen Co Integration Test: In order to find the cointegrating relationship among the variables, we use Johansen (1988)
test. Johansen’s procedure starts with VAR of order p and is given by
Where yt is an nx1 vector of variables that are integrated of order one – commonly denoted
I (1) – and εt is an nx1 vector of innovations. This VAR can be re-written as
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t
p
i
itiitt eyyy
1
1
Where
IAi
p
i
1 And
j
p
ij
i A
1
If the coefficient matrix Π has reduced rank r<n, then there exist nxr matrices α and β each with rank r such that Π = αβ′ and β′y t is stationary. Johansen proposes two different likelihood ratio tests: the trace test and maximum eigenvalue test,
2.1.4 Multivariate VECM Granger Causality Test: If cointegration exists between the variables then there is causality running between these
variables in at least one direction (Granger, 1988). In order to test the causal relationships among the variables we use Granger causality test proposed by Engle and Grnager (1987).
The null hypothesis of Granger causality can be formulated as: H0: Y does not Granger cause X As per the definition of Granger causality, Y does not cause X if,
0.......321 jit
And X does not cause Y if,
0.......321 jit
Granger causality can be interpreted as Y is Granger caused by X if current value of Y can be forecasted with the help of past values of X.
3. Empirical Results 3.1. Augmented Dickey Fuller Test: In the first step of our analysis, we run Augmented Dickey Fuller test so as to test stationary
of our variables. In order to go further with our analysis, our variables should be integrated of the same order. Thus, we present the results of unit root test in Table 1. As evident from the table, all of the variables are non-stationary at first level and are shown stationary after differencing it once. In other words, our variables become stationary at first difference, therefore, we can apply further tests in our analysis.
Table 1. Augmented Dickey Fuller Test
Country Variables Trend Intercept Lag
Length T value/critical value
Order of Integration
Brazil lgdp Yes Yes 8 -2.27 (-4.498)
Level
∆lgdp Yes Yes 1 -6.34 (-4.39)***
First difference
ge No Yes 5 -0.96 (-4.37)
Level
∆lge No Yes 5 -5.05 (-4.39)***
First difference
me Yes Yes 2 -1.60 (-3.61)
Level
∆lme Yes Yes 2 -3.16 (-2.99)**
First difference
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Russia lgdp yes yes 5 0.89 (4.37)
Level
∆lgdp yes yes 5 4.89 (4.41)***
First difference
ge Yes yes 5 0.37 (4.37)
Level
∆lge yes Yes 5 4.80 (4.39)***
First difference
me yes yes 5 2.76 (5.37)
Level
∆lme yes yes 5 5.38 (4.39)***
First difference
India lgdp Yes Yes 5 -2.26 (-4.37)
Level
∆lgdp yes Yes 5 -4.63 (-4.39)***
First difference
ge Yes Yes 2 -2.60 (-3.61)
Level
∆lge No Yes 6 -3.44 (-2.99)**
First difference
me No Yes 3 -2.33 (-3.60)
Level
∆lme No Yes 3 -3.99 (-3.61)**
First difference
China lgdp Yes Yes 5 2.64 (5.39)
Level
∆lgdp Yes Yes 5 5.35 (4.41)***
First difference
ge Yes Yes 6 3.14 (4.39)
Level
∆lge Yes Yes 6 4.64 (4.41)***
First difference
me No Yes 2 2.16 (3.73)
Level
∆lme No Yes 2 4.07 (3.75)***
First difference
S.Africa lgdp Yes Yes 5 3.00 (4.37)
Level
∆lgdp Yes Yes 5 4.84 (4.39)**
First difference
ge Yes Yes 5 1.33 (4.37)
Level
∆lge Yes Yes 5 3.41 (3.26)*
First difference
me Yes Yes 5 2.29 (4.37)
Level
∆lme Yes Yes 5 3.57 (3.24)*
First difference
3.2. Optimal Lag Selection: Since Vector Auto Regression needs to account for lag length, we run the optimal lag length
test and present the findings in Table 2. Studies using VAR in their analysis have used different lag length criterion, we however, choose the lag length suggested by majority of the criterion, i.e. lag length having most number of “*” will be considered the optimal lag length. Therefore, our optimal lag length for Brzail, India and South Africa is 1 while for China and Russia is 2.
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Table 2. Optimal Lag Selection
Country Lag LogL LR FPE AIC SC HQ Brazil 0 -340.5371 NA 5.44e+08 28.62809 28.77534 28.66716 1 -325.8713 24.44294* 3.42e+08 28.15594 28.74497* 28.31221* 2 -315.9379 14.07237 3.31e+08* 28.07815* 29.10895 28.35163 Russia Lag LogL LR FPE AIC SC HQ 0 -325.7297 NA 1.58e+08 27.39414 27.54140 27.43321 1 -271.6568 90.12148 3737226. 23.63807 24.22710 23.79434 2 -255.5505 22.81729* 2157727.* 23.04588* 24.07667* 23.31935* India Lag LogL LR FPE AIC SC HQ 0 81.23805 NA 2.96e-07 -6.519838 -6.372581 -6.480770
2 189.5540 13.30705 1.68e-10 -14.04617* -13.01537 -13.77270 China Lag LogL LR FPE AIC SC HQ 0 68.53548 NA 6.73e-07 -5.698737 -5.550630 -5.661489 1 96.50269 46.20670* 1.31e-07 -7.348060 -6.755628* -7.199065 2 108.1699 16.23268 1.09e-07* 7.579994* -6.543239 7.319253* S. Africa Lag LogL LR FPE AIC SC HQ 0 -109.0955 NA 2.288299 9.341295 9.488552 9.380363 1 -49.76022 98.89220* 0.034821* 5.146685* 5.735712* 5.302954* 2 -43.47541 8.903489 0.045577 5.372951 6.403748 5.646421
3.3. Johansen Cointegration Test: We present our findings of Trace statistics and Eigen Value statistics in Table 3. Furthermore,
cointegration equations for all the 5 countries obtained from Vector Error Correction Model are shown in the same table. Null hypotheses of “no cointegration” among the three variables (Economic growth, Government expenditure and Military expenditure) are rejected in case of our sample countries. Thus it is inferred, there is one cointegrating vector in case of each of the trivariate system of our variables.
Table 3. Johansen Cointegration Test
Country Hypothesized Trace Critical Max-Eigen Critical No. of CE(s) Statistic Value at
0.05 Statistic Value at 0.05
Brazil H0: r = 0 32.51* 29.84 23.56* 21.13
H0: r ≤ 1 8.94 15.49 8.88 14.26 H0: r ≤ 2 0.05 3.84 0.05 3.84 Cointegrating equation
Lgdp = 9.66 - 2.33 lge*** - 0.03 lme*** (1) (5.74) (-5.14) Country Hypothesized Trace Critical Max-Eigen Critical No. of CE(s) Statistic Value at
0.05 Statistic Value at 0.05
Russia H0: r = 0 46.42* 29.79 37.94* 21.13 H0: r ≤ 1 8.48 15.49 8.38 14.26 H0: r ≤ 2 0.09 3.84 0.09 3.84 Cointegrating equation
Our cointegration equation for Brazil shows statistically significant and negative relationship
of military expenditure and government expenditure with the economic growth. Further, a 0.03 percent change in military expenditure will reduce the economic growth by one percent while the same decrease in the economic growth of Brazil is caused by a 2.33 percent change in the government civilian expenditure. The equation for Russia shows a positive effect of government civilian expenditure on economic growth while the defense expenditure causes the economic growth to reduce. In quantitative terms, 1.07 percent increase in defense expenditure causes the economic growth to reduce by one percent. On the other hand, economic growth is enhanced by one percent with a 4.80 increase in the government civilian expenditure. It is evident from Table 3 that the economic growth of India reduces by one percent with the increase in government expenditure by 1.23 percent while it is increased by one percent when military expenditure is increased by 1.07 percent. The cointegration results show the relationship between economic growth and military expenditure of China is statistically insignificant while a one percent increase in economic growth is observed when government civilian expenditure is increased by 0.57 percent. Finally, our cointegration equation for South Africa shows no statistically significant relationship of government expenditure with economic growth while it shows the economic growth is reduced by one percent when the military expenditure is increased by 3.16 percent.
3.4. VECM Granger causality test: Now that cointegration has been found in the system of our variables, we apply Granger
causality test to detect the direction of causality among our variables. The Granger causality test
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helps us to determine the weak exogeneity among variables. This test suggests us the causal relationship of one variable with the other variable. The results of VECM Granger causality test are reported in Table 4. The significant chi-square statistic shows the dependent variable is Granger caused by the independent variable. Table 4 shows bidirectional causality between economic growth and government expenditure in case of Brazil. Unidirectional causality running from government expenditure to economic growth has been found in case of Russia, China and India while no statistically significant relationship can be detected for South Africa. In our trivariate analysis, we found unidirectional causality running from growth to military expenditure for Brazil, unidirectional causality running from military expenditure to growth in case of Russia and India while no relationship was found between military expenditure and growth for China. We found bidirectional causality between growth and military expenditure in case of South Africa.
Table 4. Multivariate Granger Causality Test
Country Independent Variables Brazil Independent Dependent lgdp lge lme
lgdp --- 5.22* 3.04
lge 14.80*** --- 0.79
lme 5.77* 2.02 --- Russia Independent Dependent lgdp lge lme
lgdp --- 12.55* 14.83**
lge 9.28 --- 13.47**
lme 8.79 16.29*** --- India Independent Dependent lgdp lge lme
lgdp --- 7.31** 12.02***
lge 0.14 --- 1.89
lme 1.44 11.94*** --- China Independent Dependent lgdp lge lme
lgdp --- 5.45** 0.77
lge 0.16 --- 0.38
lme 0.08 0.30 --- S. Africa Independent Dependent lgdp lge lme
lgdp --- 0.88 11.95***
lge 1.28 --- 0.026
lme 3.01* 1.92 ---
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4. Results We will sum up our findings from the statistical analysis for all the five countries in this
section. Our trivariate analysis for Brazil reveals there is a negative long run causality running from government civilian expenditure to economic growth which means our growth detriment hypothesis holds true for Brazil. Furthermore, two ways causality between government expenditure and economic growth was also found in case of Brazil, thus accepting our feedback hypothesis. To abridge our findings for Russia, one way positive causality from government expenditure to economic growth while negative causality from military expenditure to growth is detected. Therefore, growth hypothesis is accepted for government civilian expenditure and growth detrimental hypothesis is accepted for government military expenditure. Our findings for India affirm growth detrimental hypothesis for government civilian expenditure, i.e. unidirectional negative causality running from government spending to economic growth. These findings further affirm growth hypothesis for government military expenditure, i.e. unidirectional positive causality running from government spending to economic growth. We found government civilian expenditure to positively affect economic growth in case of China, thus proving growth hypothesis true. No statistically significant relationship was found between military spending and economic growth for Chinese data. Summarizing our findings for South Africa, bidirectional causality between military spending and economic growth is detected which confirms feedback hypothesis.
5. Conclusion Our aim in this study was to find out whether there is any causal relationship between
economic growth and both civilian and military portions of government expenditure in five emerging economies recently known as BRICS, i.e. Brazil, Russia, India, China and South Africa. Since it is generally believed that military expenditure can slow down economic growth, we examined the effects of military expenditure on economic growth of these five major emerging economies of the world. Our results for the 3 nuclear powers in our analysis, i.e. Russia, India and China were interestingly different from each other. Russian data showed negative effect of military spending on economic growth, Indian data showed positive effect while Chinese data suggested insignificant impact of military spending on economic growth for our sample period. The implications for these findings are straightforward; our sample period starts from 1988 and ends on 2015 which was a particularly rough period for Russia. The Afghan war and the separation of 6 central Asian states from USSR forced Russia to spend serious money on military which shook its economy. Chinese economy has been boosting for the last few decades and our findings might imply that Chinese economy is too strong for its military expenditure to affect it. The implication for positive impact of military spending on Indian economy might be the investment on public infrastructure, hospitals, education and etc. by military organizations. Our findings for Brazil and South Africa indicate that military spending slow down economic growth of both the countries.
Government civilian expenditure of India and Brazil showed negative effect on economic growth, therefore we suggest the policy makers of these countries to reduce their government spending and/or reallocate it to productive projects. In case of Brazil, shifting resources from military to civilian spending may not enhance economic growth since government civilian expenditure itself is reducing economic growth. Thus, the government should look for civilian productive activities to foster economic growth. Russian and Chinese data gave positive response to economic growth for our sample period. Therefore, we conclude that only the military portion of government spending has been a burden on Russian economy while Chinese economy was being neutral to military spending. Our analysis for South Africa suggested statistically insignificant relationship of government civilian expenditure with economic growth, hence we conclude by suggesting reduction in its military spending which is causing its economy to slow down.
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Published in the Russian Federation European Journal of Economic Studies Has been issued since 2012. ISSN: 2304-9669 E-ISSN: 2305-6282 Vol. 18, Is. 4, pp. 459-469, 2016 DOI: 10.13187/es.2016.18.459
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UDC 33 The Impact of Energy Consumption, Trade Openness and Financial Development on Economic Growth: Empirical Evidence from Turkey (1980–2014)
Murat Cetin a , *
a Faculty of Economics and Administrative Sciences, Namik Kemal University, Tekirdag, Turkey Abstract The developments in Turkish economy indicate that energy, trade openness and financial
development are critical determinants of economic growth. This study aims to investigate the impact of energy consumption, trade openness and financial development on economic growth in case of Turkey over the sample period 1980-2014. The results of unit root tests reveal that the variables are integrated at I(1). The results of ARDL bounds test and Johansen-Juselius technique reveal that there exists a long-run relationship among energy consumption per capita, trade openness, domestic credit provided by banking sector and real GDP per capita. Energy consumption and financial development have a positive impact on economic growth while there do not a statistically significant relationship between trade openness and economic growth in the long run. The VECM Granger causality results show that there exist a uni-directional causal linkage running from energy consumption, trade openness and financial development to economic growth in the long run. The empirical findings can provide several policy implications for Turkish economy over the period.
1. Introduction The determinants of economic growth has long been argued by theoretical and empirical
literature. It is well known that economic growth has been affected by energy, trade and financial development (Goldsmith, 1969; Yu, Choi, 1985; Barro, Sala-i-Martin, 1997). Energy-growth literature reveals the existence of four hypotheses on the link between energy consumption and economic growth. These theories explain the causal linkages between the variables. According to the growth hypothesis energy consumption is very important for economic growth implying that there exists a uni-directional causality running from energy consumption to economic growth (Altinay, Karagol, 2004). The conservation hypothesis suggests that there exists a uni-directional causality running from economic growth to energy consumption (Payne, 2010). The feedback hypothesis implies a bi-directional causality between energy consumption and economic growth (Soytas, Sari, 2003). The neutrality hypothesis assumes that there exist no causal linkages between energy consumption and economic growth (Zhang, Xu, 2012).
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Trade openness is an important determinant of economic growth. (Bhagwati, 1978; Romer 1986; Grossman and Helpman, 1990; Rivera-Batiz, Romer, 1991; Taylor, 1993) are the main theoretical studies investigating the link between international trade and economic growth. Generally, these literature reveal the presence of a consensus that trade openness causes economic growth. Financial development is linked with economic growth. Several theoretical studies such as (McKinnon, 1973; King, Levine, 1993; Warman, Thirlwall, 1994) discuss the link between financial development and economic growth. According to these literature, financial development will cause economic growth through productive and efficient use of financial resources.
Kraft and Kraft (1978) is the first study investigating the relationship between energy consumption and economic growth. The study reveals a uni-directional causality running from economic growth to energy consumption. Erol and Yu (1988) show that there exists a bi-directional causality between energy consumption and economic growth for Japan. The study finds a uni-directional causality running from energy consumption to economic growth for Canada. The study also finds a uni-directional causality running from economic growth to energy consumption for Germany. Masih and Masih (1996) find a uni-directional causality running from energy consumption to economic growth for India and Indonesia. In the study, a uni-directional causal linkage running from economic growth to energy consumption is found. In addition, there exists a bi-directional causality between the variables in Pakistan. Asafu-Adjaye (2000) finds no causality for Indonesia and India. Soytas and Sari (2003) indicate that there exists a uni-directional causality running from economic growth to energy consumption in Italy and Korea. The study aslo indicates that there exists a uni-directional causality running from energy consumption to economic growth in France, Germany, Japan and Turkey. Farhani and Rejeb (2012) reveal that there exists a uni-directional causality running from economic growth to energy consumption in low and high income countries. This study also reveals that there exists a bi-directional causality between the variables in upper-middle income countries.
Barro (1991), Edwards (1998) and Frankel and Romer (1999) examine the link between trade openness and economic growth through the cross-country regression analysis. Empirical results show that trade openness is positively correlated with economic growth. Musila and Yiheyis (2015) investigate the effect of trade openness on economic growth in case of Kenya. Regression analysis reveals that trade openness is positively linked with economic growth. But, the impact is found to be statistically insignificant.
Applying the Johansen-Juselius cointegration method and Granger causality test, Jenkins and Katircioglu (2010) explore the long run relationship among international trade, financial development and economic growth for Cyprus. Empirical results imply that there exists a long run relationship between international trade, financial development and economic growth. Empirical results also imply that there exists a uni-directional causality running from economic growth to financial development and international trade. Gokmenoglu et al. (2015) deal with the links between international trade, financial development and economic growth in Pakistan. The Granger causality analysis indicates that there exists a uni-directional causality running from financial development to economic growth.
In recent years, several studies such as Shahbaz et al. (2013), Muhammad et al. (2015) and Kumar et al. (2015) examine the relationship between energy consumption, trade, financial development and economic growth. However, these studies provide inconclusive findings and do not investigate Turkish economy. For example, Shahbaz et al. (2013) investigate the relationship between energy consumption, trade, financial development and economic growth in China by using the ARDL bounds testing approach to cointegration and VECM Granger causality method. The empirical results show that energy consumption, trade openness and financial development positively affect economic growth. The Granger causality analysis indicates that a uni-directional causality running from energy consumption to economic growth exists. The Granger causality analysis also indicates that there exists a bi-directional causality between international trade and economic growth and, financial development and economic growth.
Applying panel cointegration and PMG estimation methods, Muhammad et al. (2015) aim at exploring the impact of energy consumption, trade openness and financial development on growth in five South Asian countries. Empirical findings show that there exists a long run relationship between energy, trade, financial development and economic growth. Empirical findings also show that financial development, energy and trade are positively linked with economic growth.
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In addition, a bi-directional causality between energy consumption and economic growth, and a uni-directional causality running from trade and financial development to economic growth are found in the long run.
Kumar et al. (2015) examine the effect of energy consumption, trade openness and financial development on economic growth in case of South Africa. Using the ARDL bounds and the Bayer and Hanck cointegration tests, the study shows that trade openness and energy consumption positively affect economic growth in the long run. The study also shows that financial development negatively affects economic growth in the long run. The Toda-Yamamoto causality analysis indicates that there exists a bi-directional causality between trade openness and economic growth. The Toda-Yamamoto causality analysis also indicates that there exists no causal linkage between energy consumption and economic growth. In addition, financial development does not cause economic growth.
Energy consumption, financial development and trade openess are crucial factors for economic growth of Turkish economy. Therefore, the objective of present study is to explain the impact of energy consumption, financial development and trade openess on economic growth in case of Turkey over the period of 1980-2014. The stationarity properties of the variables are investigated through different unit root tests. The study implements the ARDL bounds testing approach to cointegration to examine the long run relationship among the variables. In addition, the study applies the VECM Granger causality approach to explore the causal linkages between the variables. The findings are expected to present several implications for energy, financial and trade policies to sustain economic growth in Turkey.
The rest of the study is organized as follows. Section 2 deals with econometric specification and data description. Section 3 describes the methodology used in the study. Secton 4 reports the empirical findings. Conclusion and policy implications are offered in Section 5.
2. Econometric Specification and Data Description In this study, the standard log-linear model is used to investigate the impact of energy
consumption, trade openness and financial development on economic growth as it can present more efficient results. Following Shahbaz et al. (2013) and Kyophilavong et al. (2015) the long run relationship between the variables is specified as follows:
where, gdpt is per capita real GDP (constant 2010 US$), energy is per capita energy
consumption (kg of oil equivalent), finance is financial development (domestic credit to private sector, % of GDP) and trade is the openness ratio (foreign trade, % of GDP). µt is the regression error term. The annual data covers the sample period 1980-2014. The Turkish economy has witnessed many radical changes and structural reforms since the 1980s (Terterov and Rosenblatt, 2006). Therefore, this sample period is selected to analyze the links among the variables. The data is obtained from the World Development Indicators (WDI) online database. All the series are converted to their logarithmic form.
The parameters, βi, i=1, 2, 3, indicate the long-run elasticities of per capita real GDP with respect to per capita energy use, domestic credit to private sector (% of GDP) and trade openness, respectively. Under the energy, finance and trade-led eonomic growth hypotheses, the signs of β1, β2 and β3 are expected to be positive (Shaw, 1973; Levine, 1997; Payne, 2010; Yenokyan et al., 2014).
The descriptive statistics and correlation matrix of the series are presented in Table 1. It shows that there exists a positive and significant relation between energy consumption and economic growth. Financial development is positively correlated with economic growth. In addition, it is found that there exists a positive correlation between trade openness and economic growth. Figure 1 shows the plots of the variables employed in the study.
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Table 1. Descriptive Statistics and Correlation Matrix (Time Series Data: 1980-2014, Observations=35) Statistics/Variables lgdp lenergy lfinance ltrade
Mean 8.909 6.984 3.119 3.692 Median 8.910 7.026 2.897 3.765 Std. dev. 0.257 0.239 0.468 0.295 Min. 8.473 6.559 2.609 2.838
Max. 9.327 7.353 4.312 4.094
Skewness 0.008 -0.145 1.324 -0.874
Kurtosis 1.931 1.994 3.548 3.446
Observations 35 35 35 35
lgdp 1.000
lenergy 0.994 1.000
lfinance 0.753 0.730 1.000
ltrade 0.879 0.889 0.605 1.000
8.4
8.6
8.8
9.0
9.2
9.4
1980 1985 1990 1995 2000 2005 2010
lgdp
6.4
6.6
6.8
7.0
7.2
7.4
1980 1985 1990 1995 2000 2005 2010
lenergy
2.4
2.8
3.2
3.6
4.0
4.4
1980 1985 1990 1995 2000 2005 2010
lfinance
2.8
3.2
3.6
4.0
4.4
1980 1985 1990 1995 2000 2005 2010
ltrade
Fig. 1. Trends of the Series in Turkey
3. Methodology The present study aims at examining the relationship between energy consumption, financial
development, trade openness and economic growth over the period 1980-2014. The unit root properties of the variables are determined by different unit root tests. The ARDL bounds testing approach to cointegration is applied to investigate the presence of long run relationship among the variables. In addition, the VECM Granger causality framework is applied to determine the causal links between the variables.
3.1 Cointegration Analysis This study applies the ARDL bounds testing approach to cointegration in order to test the
long run relationship between the variables. This approach has a flexible procedure. In this procedure, the variables can be integrated at I(0) or I(1). This procedure presents consistent results
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for small sample. In addition, a dynamic unrestricted error correction model (UECM) includes the short run and the long run dynamics (Pesaran and Shin, 1999; Pesaran et al., 2001). The equation of UECM model is expressed as follows:
where, α0, Δ and εt are the constant, the first difference operator and the random error term,
respectively. The appropriate lag order is selected by the Akaike Information Criterion (AIC). The ARDL bounds test uses F-statistic to determine the existence of cointegration between the variables. This test compares the computed F-statistic with the upper critical bound (UCB) and lower critical bound (LCB). These critical bounds are presented by Pesaran et al. (2001) and
Narayan (2005). Here, the null and the alternative hypotheses are and
, respectively. There exists a cointegration between the variables when the computed F-statistic exceeds the UCB. There exists no cointegration between the variables when the computed F-statistic below the LCB. The finding is uncertain when the computed F-statistic falls between the UCB and LCB.
Several diagnostic tests can be used to examine the robustness of the ARDL model. These are serial correlation, functional form, normality of error term and heteroskedasticity tests. Additionally, the cumulative sum of recursive residuals (CUSUM) and the cumulative sum of squares of recursive residuals (CUSUMsq) tests developed by Brown et al. (1975) can be applied to investigate the stability of the ARDL parameters.
3.2 Granger Causality Analysis The cointegration methods do not provide any information about the direction of causality.
This study uses the VECM Granger causality test as this method examines the long run and the short run causality between the variables. The VECM specification is expressed as follows:
)3(
lg
)1(
lg
)1(
4
3
2
1
1
1
1
1
1
44434241
34333231
24232221
14131211
1
4
3
2
1
t
t
t
t
t
t
t
t
t
iiii
iiii
iiii
iiii
p
i
t
t
t
t
ECT
ltrade
lfinance
lenergy
dp
x
bbbb
bbbb
bbbb
bbbb
L
a
a
a
a
ltrade
lfinance
lenergy
dp
L
where (1- L) is the lag operator and ECTt−1 is the lagged error correction term. This term is
obtained from the long run specification. ε1t, ε2t, ε3t and ε4t are error terms assumed to be N (0,σ). A significant F-statistic on the first differences of the variables implies the presence of a short run causality between the variables. In addition, a significant t-statistic on the coefficient of ECTt-1
implies the existence of a long run cusality between the variables. 4. Empirical Findings The study applies several unit root tests such as DF-GLS, PP and Ng-Perron methods to
explore the unit root properties of the series. Ng-Perron tests provide more reliable results compared to classical unit root tests. Additionally, it can be more suitable for small sample size (Alimi, 2014).
The results of DF-GLS, PP and Ng-Perron tests are reported in Table 2. The results indicate that the variables are not stationary at a level. However, after taking the first difference of the variables, the series are found to be stationary. The results imply that all the variables are
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integrated at I(1). The results also imply that the ARDL bounds testing approach to cointegration can be applied to test the presence of cointegration between the variables.
ltrade -0.720 -2.925 -0.074 -0.041 0.560 21.852 Δlgdp -6.572*** -7.857*** -16.128*** -2.836*** 0.175** 1.530*** Δlenergy -5.700*** -6.628*** -16.336*** -2.857*** 0.174** 1.499*** Δlfinance -3.925*** -4.395*** -14.176*** -2.659*** 0.187** 1.740*** Δltrade -3.983*** -5.801*** -14.089*** -2.644*** 0.187** 1.775*** Notes: The model with constant and trend is used for unit root analysis. The optimal lag length is selected automatically using SBC for ADF test and the bandwidth is selected using the Newey-West method for PP test. *** and ** denote the significant at 1 % and 5 % level of significance, respectively.
Table 3 reports the results of bounds F-test for cointegration. As noted in Table 4, we use
critical bounds obtained by Pesaran et al. (2001) and Narayan (2005). According to Pesaran et al. (2001) critical values, the results show that calculated F-statistic is greater than UCB at 1 per cent. According to Narayan (2005) critical values, the results show that calculated F-statistic is greater than UCB at 5 per cent. All the findings indicate that the series are cointegrated implying that there exists a long run relationship between per capita energy consumption, domestic credit to private sector, trade openness and per capita real GDP for Turkish economy over the period of 1980–2014. The results for diagnostic tests of ARDL model are also reported in the lower part of Table 3. The findings show that the ARDL model passes all the tests successfully.
Table 3. Cointegration Test Results Bounds testing approach to cointegration
Model ARDL lag order Calculated F-statistics
F(lgdp/lenergy, lfinance, ltrade)
[2,1,0,0] 7.411
Peseran et al. (2001) critical value bounds of the F-statistic: unrestricted intercept and unrestricted trend Significance level Lower bounds, I(0) Upper bounds, I(1)
1% 5.17 6.36 5% 4.01 5.07 10% 3.47 4.45
Narayan (2005) critical value bounds of the F-statistic: unrestricted intercept and unrestricted trend (T = 35) Significance level Lower bounds, I(0) Upper bounds, I(1)
1% 6.38 7.73 5% 4.56 5.79 10% 3.80 4.88
Diagnostic tests
R2 0.988 Adjusted-R2 0.930 F-statistic 17.259*** Breusch-Godfrey LM test 3.320(0.142) ARCH LM test 0.162 (0.689)
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J-B normality test 1.428 (0.489) Ramsey RESET test 0.636 (0.469)
Notes: The model with constant and trend is used for cointegration analysis. Optimal lag order is selected based on SBC. The values in parentheses indicate the probabilities. *** denotes the significant at 1 % level of significance.
Table 4 reports long run results. The effect of energy consumption on economic growth is
positive and statistically significant at 1 % level. A 1 % growth in energy consumption is expected to increase economic growth by 0.630 %, indicating that energy consumption plays dominant role to stimulate economic growth in Turkey. Financial development has a positive impact on economic growth. It is statistically significant at 10 % level. A 1 % increase in financial development raises economic growth by 0.023 %. In addition, trade openness has a negative effect on economic growth. But, it is statistically insignificant. These findings indicate the dependence of Turkish economic growth on energy consumption and financial development.
Table 4 also reports short run results. The impact of energy consumption is positive and statistically significant at 1 % level. A 1 % increase in energy consumption raises economic growth by 0.844 %. In addition, the impact of financial development and trade openness on economic growth is statistically insignificant. The negative and statistically significant estimate for ECMt-1
confirms the presence of long run relationship among the series in case of Turkey. The coefficient is statistically significant at 1 % level. The short run deviations from the long run equilibrium are corrected by 77.0 % towards long run equilibrium path each year.
The diagnostic tests for the long run model are presented in the lower part of Table 4. The diagnostic tests reveal that error terms are normally distributed. The diagnostic tests also reveal free of serial correlation, heteroskedasticity, and ARCH problems in the model. In addition, functional form for the long run model is well specified. The cumulative sum (CUSUM) and the cumulative sum of squares (CUSUMsq) tests inform us about the stability of long run parameters (Fig. 2). The graphs of CUSUM and CUSUMsq tests lie within the 5 % critical bounds. The results reveal that the ARDL estimates are reliable and stable. Therefore, the results will be used to provide policy implications.
Table 4. Estimated Coefficients from ARDL Model Panel A: Long-run results
R2 0.994 Adjusted-R2 0.993 F-statistic 674.409*** Breusch-Godfrey LM test 1.852 (0.186) ARCH LM test 0.211 (0.649) J-B normality test 1.015 (0.601) Ramsey RESET test 0.770 (0.388)
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Notes: The long-run and short-run coefficients are obtained on the basis of ARDL (2,1,0,0) model, decided by the SBC. The values in parentheses indicate the probabilities. *** and * denote the significant at 1 % and 10 % level of significance, respectively.
-15
-10
-5
0
5
10
15
90 92 94 96 98 00 02 04 06 08 10 12 14
CUSUM 5% Significance
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
90 92 94 96 98 00 02 04 06 08 10 12 14
CUSUM of Squares 5% Significance
Fig. 2. Plots of CUSUM and CUSUMsq Tests for the Parameter Stability The Granger causality results are presented in Table 5. The results reveal that there exists a
uni-directional causal linkage running from energy consumption, financial development and trade openness to economic growth implying that energy consumption, financial development and trade openness Granger cause economic growth in the long run. The results confirm that the energy-led growth, the finance-led growth and trade-led growth hypotheses are valid for Turkish economy in the long run.
Table 5. VECM Granger Causality Test Results
Short-run (F-statistic)
Long-run (t-statistic)
Dependent variable
Δlgdp Δlenergy Δlfinance Δltrade
Δlgdp - 0.806(0.459) 2.691 (0.090)
0.323 (0.727)
-1.747 (0.094)
Δlenergy 1.719 (0.202)
- 2.003 (0.158)
0.338 (0.716)
0.341 (0.736)
Δlfinance 1.104 (0.349)
0.429 (0.656)
- 0.412 (0.666)
-0.259 (0.797)
Δltrade 0.149 (0.862)
0.459 (0.637)
0.185 (0.832)
- -0.475 (0.638)
Notes: The values in parentheses indicate the probabilities. 5. Conclusion and Policy Recommendations This study investigates the effect of energy consumption, financial development and trade
openness on economic growth in Turkey for the period of 1980-2014. It applies DF-GLS, PP and Ng-Perron unit root tests to examine the stationarity properties of the variables. The presence of cointegration among the variables is analyzed by using the ARDL bounds test. In addition, the Granger causality within VECM is applied to test the direction of causality between the variables.
The results indicate that the series are integrated at I(1). The results also indicate that there exists cointegration among the variables. A negative and statistically significant estimate for ECMt-1
provides an evidence for cointegration between the variables. This implies that there exists a long run relationship between energy consumption, financial development, trade openness and economic growth. Energy consumption and financial development are positively linked with economic growth implying that energy consumption and financial development increases economic
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growth in the long run. A significant relationship between trade openness and economic growth is not found in the long run. In the short run, there exists a positive link between energy consumption and economic growth. Energy consumption, financial development and trade openness Granger cause economic growth in the long run. So, the energy-led growth, the finance-led growth and trade-led growth hypotheses are valid for Turkey over the period.
This study openness up new implications for policy makers in Turkish economy. Empirical findings indicate that energy consumption is the vital factor for economic growth. This implies that a reduction in energy supply will slow down economic growth. Turkish government should diversify the energy sources and improve the energy efficiency. In addition, policy makers should stimulate domestic investors to apply new technologies. Empirical findings also indicate that financial development is a significant factor for Turkey. Turkish government should mobilize financial resources to most productive investments and support the financial development to have stable economic growth. Therefore, the innovative use of technology is implemented in most of the financial services. In this study, it is found that trade openness Granger causes economic growth. Therefore, Turkish government should improve trade activities and strengthen international economic relations. Policymakers should make more effective export promotions to the small and medium sized enterprieses to improve export performance.
A comparative empirical analysis can be applied for future research on the relationship between energy consumption, financial development, trade openness and economic growth in some developing countries. The unit root and cointegration tests with single or two unknown structural breaks stemming in the series can also be applied to investigate the unit root and cointegration properties of the variables.
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Published in the Russian Federation European Journal of Economic Studies Has been issued since 2012. ISSN: 2304-9669 E-ISSN: 2305-6282 Vol. 18, Is. 4, pp. 470-475, 2016 DOI: 10.13187/es.2016.18.470
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UDC 33
Fostering the Sustainable Development of the Economy of the Russian Federation via the Creation of Small Innovation Enterprises at Institutions of Higher Learning Mikhail N. Dudin a , *, Natalia P. Ivashchenko b
a Russian Presidential Academy of National Economics and Public Administration, Russian Federation b Lomonosov Moscow State University (Department of Economics), Russian Federation
Abstract This paper’s relevance resides in the fact that, under present-day conditions, when
the national economy is going through a stage of turbulence, there is a need to seek out new solutions that could help ensure the sustainability of national social-economic development in keeping with global trends. The paper explores the issue of ensuring Russia’s sustainable economic development via boosts in the innovation activity of entrepreneurship, which it is sought to achieve via a number of ways, including the creation of small enterprises at institutions of higher learning. The authors examine the experience of Russian and foreign educational institutions as to the creation of small innovation enterprises, look into the major forms of partnership between business and college, and describe a possible mechanism for implementing innovation projects at institutions of higher learning.
Keywords: small innovation enterprise, interaction, college, institution of higher learning, support for small innovation enterprises.
1. Introduction There are several potential solutions that may help ensure the sustainable and innovation-
oriented development of the national economy. These, specifically, include: 1. The government’s furthering the development of the nation’s high-tech industrial and
service sectors; 2. Creating an optimal institutional environment for activating science-driven production
and service activity; 3. Stimulating the innovation activity of small entrepreneurship. The last aspect (i.e., stimulating innovation activity amongst small enterprises) is one of the
key conditions for ensuring well-balanced economic growth, as well as facilitating the technology-oriented development of the national economy. Issues related to creating the right incentives and conditions to boost innovation activity within the small entrepreneurship segment are currently of particular relevance to the Russian Federation, whose economy has lately been characterized by transformative changes and a shift from the industrial way of life to neo-industrialization.
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2. Materials and methods This paper utilizes as its primary sources of information some data from a number of scholarly
publications by Russian and foreign researchers and draws upon a body of legal information on the activity of small innovation enterprises set up at Russian institutions of higher learning. In addition, to better get across the paper’s main points, the authors make use of information from the websites of some of Russia’s top colleges (e.g., Lomonosov Moscow State University, Moscow Engineering Physics Institute, Bauman Moscow State Technical University, etc.).
3. Discussion It is obvious that an essential condition for the nation to achieve sound and well-balanced
economic growth is robust innovation activity within the small entrepreneurship segment. It, however, will be hardly possible to achieve tangible boosts in innovation activity without the proper interaction of the entrepreneurial segment itself with the research-and-education sector (specifically, institutions of higher learning), which is what provides a rationale for the need to explore theoretical and methodological approaches to boosting the efficiency of institutions of higher learning in this respect.
Small innovation enterprises set up at colleges serve as a sort of connecting link between science and the real sector of the economy, and there are data indicating that nearly one in two state-run colleges in Russia is engaged in innovation activity. This substantiates the significance of issues related to enhancing and boosting the efficiency of innovation activity and commercializing scientific-technical solutions developed by Russian institutions of higher learning, as well as developing relevant criteria and indicators for evaluating the operating efficiency of small innovation enterprises at these colleges.
Russia has yet to fully develop its system of government support for the production and implementation of innovations. Yet, at the same time, it is worth noting that in 2009 the government passed a special legislative act that established relevant regulations and priorities for the operation of small innovation enterprises set up at institutions of higher learning*. Subsequently, via the passage of the Federal Law ‘On Education in the Russian Federation’, the government did some fine-tuning as to objectives in the activity of small innovation enterprises set up at colleges†. Pursuant to Article 103 of the above law, small innovation enterprises are expected to not only create innovation-technological solutions but also commercialize them, as well as provide jobs to students graduating from the colleges (Loginova, Lizina, 2014).
The small innovation enterprise segment has been highly popular with the nation’s national research universities and has played a significant role in the country’s development, which may explain the fact that the last few years have seen increases in funding for research and development at these institutions. Among the major Russian institutions of higher learning involved in the process are Lomonosov Moscow State University, Moscow Engineering Physics Institute, Bauman Moscow State Technical University, Moscow Institute of Physics and Technology, and some others.
However, the situation with fostering small innovation entrepreneurship and scientific-research activity in the overwhelming majority of Russia’s colleges can hardly be called OK and, actually, leaves a lot to be desired, as in many of the nation’s colleges innovation enterprises have been created just on paper (Mashegov, Lebedev, 2016).
At present, the nation’s colleges are facing the urgent task of implementing innovations under the conditions of a knowledge economy, which is expected to facilitate boosts in the competitiveness of those institutions of learning (Dadayan, Storozheva, Shlyagina, 2015).
Government participation plays a significant role in providing funding to help small enterprises within the education system to master innovations, which signals the need to enhance the process further.
* Federal Law No. 217-FZ on Amendments to Certain Legislative Acts of the Russian Federation on Issues Related to the Creation by Budgetary Scientific and Educational Institutions of Economic Societies with a View to the Practical Application (Implementation) of the Results of Intellectual Activity of August 2, 2009 (as amended by Federal Law No. 273-FZ of December 29, 2012) † Federal Law No. 273-FZ on Education in the Russian Federation of December 29, 2012 (as amended by Federal Law No. 359-FZ on July 3, 2016)
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An optimal variant of the mechanism for government regulation and support of small innovation enterprises within the national economy is illustrated in Fig. 1.
Fig. 1. Mechanism for government regulation and support of small innovation enterprises within the national economy (Levchenko, 2015)
The organizational-economic approach to creating and managing small innovation
enterprises at institutions of higher learning is virtually unified. More specifically, the executive staff of an educational institution, in keeping with the specialization of and regional resource support potential for the college, determines the terms for investing in, and specific types of, innovation entrepreneurial activity that the state-run college is expected in engage in (Zhevora, 2015). The most sought-after area, which normally attracts the more significant amounts of state funding, is the one associated with creating and developing innovation infrastructure – above all, engineering centers.
It is worth noting that clusters, which have enjoyed support from the government, have demonstrated outperforming growth rates in the way of creating highly productive jobs, boosting investment activity, and conducting research and development activities. Thus, for instance, the use of the potential of cluster interaction between the system of secondary vocational education of the city of Moscow, employer organizations, and adjacent and supporting organizations for the purpose of facilitating the innovation development of Moscow’s economy has been determined as the primary area of focus in the Concept of the Long-Term Social-Economic Development of the Russian Federation through to 2020*.
Russia’s current state policy is clearly indicative of an orientation toward augmenting the scientific component in organizations of higher learning and shifting the focus of scientific activity from core scientific-research institutes to large colleges (Obukhova, 2015).
* Decree of the Government of the Russian Federation No. 1662-r on the Concept of the Long-Term Social-Economic Development of the Russian Federation through to 2020 of November 17, 2008 (as amended on August 8, 2009)
Government regulation and support of small
innovation enterprises
Regulatory support
System of bodies of
executive authority
Infrastructure of support
Measures and methods for
government support:
• financial support;
• information support;
• property support;
• support in the area of
innovations and industrial
production;
• research-and-education and
staffing support
Small innovation enterprises within the national
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The trend toward increases in the number of small innovation enterprises set up at institutions of higher learning is characteristic of many developed countries. In European countries, small college-based innovation enterprises have been around since the mid-20th century. Among the countries where this process has been most successful are Germany, Sweden, and the US (Kuznetsova, Ioda, 2015). This fact reflects the significance and role of universities occupying a central position in the mechanism of interaction between science, education, and innovation business and acting as a connecting link in it. In European countries, by definition, a university is a scientific-technological center operating in a standalone fashion, both in terms of budget and its own development (Khalova, Aleksandrova, 2013).
The Massachusetts Institute of Technology and its activity, which has seen a great amount of technological research, could set a good example for Russian colleges seeking to develop innovation business that will be based at them. It is worth noting that among the chief stimulators of the innovation activity of entrepreneurs at the above college are freedom of research; local venture capital; large funding for projects and scientific research, as well as a state-of-the-art business center (Popova, 2013).
Sweden utilizes a special model for the innovation activity of educational institutions that combines the independence of institutions of higher learning in terms of commercializing the results of innovation activity produced by them with instruments directed at deriving a payoff from specific solutions that have been created.
The major forms of partnership between business and college in Sweden are:
setting up special units in colleges that handle the commercialization of scientific results;
creating special consulting organizations and forums on partnership with external market participants;
creating holding companies that own and manage the company’s shares and commercially distributing the results of scientific activity conducted at the university.
Wide use in Sweden has been made of evaluation centers, which serve as a connecting link between the university’s research group and entrepreneurs from the industrial sphere. These centers specialize in conducting problem-oriented interdisciplinary research and transforming new knowledge into new products, processes, and services. A good example for Russian colleges is set by Halmstad University with its IE programme. The programme aims to ensure that students apply their knowledge as part of a real project in order to develop a new product, create a production prototype, and get their work registered.
Having gained an insight into the activity of small innovation enterprises at colleges in foreign countries, one can then consider all the pros and cons before implementing this experience in Russian colleges.
A significant impetus to the development of innovation activity at state-run institutions of higher learning in the Russian Federation has been the implementation of innovation programmes as part of a high-priority national project known as ‘Education’*.
Notwithstanding that the project’s major objective is boosting the quality of education, many Russian institutions of higher learning have managed, thanks to state funding, to augment the innovation component dealing with the development and commercialization of scientific-technical novelties (Ioda, Kuznetsova, 2015). There are at least two organizations taking part in the innovation project – a college (represented by its representative – a small enterprise) and an external partner. All the work within the college is divided into two stages:
1) scientific-research work of an applied nature; 2) developing the technology of product output (Kukota, 2016). The major sources of funding for the entrepreneurial activity of small innovation enterprises
at colleges are the educational institution’s own funds, funds obtained via state contracts and grants, as well as funds from the commercial activity of small innovation enterprises and educational institutions themselves.
Thus, the optimal formation and efficient operation of small innovation enterprises at Russian colleges provides the founder with such benefits as additional extra-budgetary funding for
* Resolution of the Government of the Russian Federation No. 715 on Amendments to the Federal Target Programme for the Development of Education for the Period 2011–2015 of July 16, 2015
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the educational institution, the possibility of getting college innovations commercialized, students gaining hands-on knowledge, boosts in the competitiveness of future specialists in the labor market, and, on the whole, higher ratings for the innovation university.
Considering that at the macro-level the innovation system incorporates legislation, education, science, science-driven production, and the market, it can be stated that institutions of higher learning are an indispensable part of the structure of Russia’s national and regional innovation system (Ruban, 2015).
The creation of a small innovation enterprise at a college facilitates the development of both the college itself and the entire system of higher vocational learning. This, in turn, creates impetuses for the national economy’s fastest transition possible to an innovation-driven development model, benefiting the whole of society.
4. Conclusion The data and conclusions derived in this work help establish that creating small innovation
enterprises at colleges helps, first of all, to reduce the length of processes related to creating, testing, and implementing science-driven solutions. Second of all, the participation of Russian colleges in setting up small enterprises provides access to new sources of funding that can be used to stimulate research-and-development activity. Third of all, the practice of creating small innovation enterprises creates a positive synergetic effect from the interaction between science and the real sector of the economy, which perfectly agrees with the scientific paradigm describing the specificity of macroeconomic sustainability under present-day conditions.
Applying these inferences in practice should help provide new impetuses for Russian colleges to boost their efficiency and research-and-development activity, which will definitely facilitate smooth and sustainable national social-economic development.
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Kukota, 2016 – Kukota S.I. (2016). Upravlencheskie riski v innovatsionnom menedzhmente vzaimodeistviya malykh predpriyatii i vuzov [Managerial risks in innovation management of interaction between small enterprises and institutions of higher learning]. Uspekhi Sovremennoi Nauki, 1, 19. (in Russian).
Kuznetsova, Ioda, 2015 – Kuznetsova E.Yu., Ioda Yu.V. (2015). Malye innovatsionnye predpriyatiya pri vuzakh: zarubezhnyi opyt i praktika funktsionirovaniya [Small innovation enterprises at institutions of higher learning: International experience and practices]. Sotsial'no-Ekonomicheskie Yavleniya i Protsessy, 10(12), 35. (in Russian).
Levchenko, 2015 – Levchenko O.V. (2015). Osobennosti mekhanizma gosudarstvennogo regulirovaniya deyatel'nosti malykh innovatsionnykh predpriyatii v Rossiiskoi Federatsii [Characteristics of the mechanism for the state regulation of the activity of small innovation enterprises within the Russian Federation]. Peterburgskii Ekonomicheskii Zhurnal, 1, 47. (in Russian).
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Loginova, Lizina, 2014 – Loginova E.A., Lizina O.M. (2014). Malyi innovatsionnyi biznes kak faktor obespecheniya konkurentosposobnosti regional'noi ekonomiki [Small innovation business as a factor in ensuring a competitive regional economy]. Kontentus, 5, 40–45. (in Russian).
Mashegov, Lebedev, 2016 – Mashegov P.N., Lebedev M.A. (2016). Formirovanie instituta “opornykh universitetov” kak instrumenta razvitiya territorial'nykh innovatsionnykh sistem [Putting together the institution of “support universities” as an instrument for the development of territorial innovation systems]. Yamal'skii Vestnik, 1, 12. (in Russian).
Obukhova, 2015 – Obukhova E.A. (2015). Osobennosti sozdaniya malykh innovatsionnykh predpriyatii pri vuzakh v sovremennykh rossiiskikh usloviyakh [Characteristics of the creation of small innovation enterprises at institutions of higher learning under present-day Russian conditions]. Sibirskaya Finansovaya Shkola, 3, 31 (in Russian).
Popova, 2013 – Popova I.I. (2013). Malye innovatsionnye predpriyatiya i ikh vzaimodeistvie s sub"ektami innovatsionnogo protsessa [Small innovation enterprises and their interaction with subjects of the innovation process]. Ekonomika i Menedzhment Innovatsionnykh Tekhnologii, 9, 8. (in Russian).
Ruban, 2015 – Ruban D.A. (2015). Dualizm ustroistva regional'nykh innovatsionnykh sistem (Dualism in the makeup of regional innovation systems). Vestnik Taganrogskogo Instituta Upravleniya i Ekonomiki, 2, 4. (in Russian).
Zhevora, 2015 – Zhevora, Yu.I. (2015). Effektivnoe funktsionirovanie malykh innovatsionnykh predpriyatii pri vuzakh agrarnogo profilya [The efficient operation of small innovation enterprises at institutions of higher learning specializing in agrarian science]. Vestnik APK Stavropol'ya, 4, 311. (in Russian).
European Journal of Economic Studies, 2016, Vol.(18), Is. 4
Published in the Russian Federation European Journal of Economic Studies Has been issued since 2012. ISSN: 2304-9669 E-ISSN: 2305-6282 Vol. 18, Is. 4, pp. 476-485, 2016 DOI: 10.13187/es.2016.18.476
www.ejournal2.com
UDC 33 Features of Touristic Territory Branding on the Example of Sochi City (Russian Federation) and Jurmala City (Latvia)
Marina Gunare a , Evgeniya V. Vidishcheva b , *
a Baltic International Academy, Riga, Latvia b Sochi State University, Sochi, Russian Federation
Abstract The brand is a harmonic combination of social and cultural life, modern infrastructure,
authorities and business activities, attractive investing climate and historical heritage. In a modern world territory brand formation and its successful integration is a key aspect in ensuring of well-being and development of society. Geographic mobility is increasing and it influences welfare of territories. Complex of branding activities provides a sustainable place in a market for territory. Russian experience in tourism development demonstrates that each ruble spent on tourism promotion returns to growth of tourist flow, tax revenues and local producers’ incomes. So territory brand promotion expenditures can give a heavy impact to socio-economic development of the region. The brand of territory is directed to diverse target audiences, the most important of whom are investors, tourists, consumers of goods and services in the external market, residents.
The article considers the touristic territory branding experience of Russia and Latvia. Research aims to analyze how the implementation of branding activities contributed to achieving a new level in tourism and investment attractiveness of cities, and if it allowed creating an image of unique place for visiting.
Keywords: touristic territory, brand, promotion, strategy, branding activities. 1. Introduction In globalization time and intense competition for investment brand reputation
is undoubtedly important. Brand can be both challenging and constraining factor in the territory development. Brand-building of resort towns consists of several stages, including the development of strategy, analysis of key market segments, brand positioning and so on.
There are different approaches for branding tourist destinations, depending on the potential, infrastructure development and funding opportunities. This article considers various approaches to the formation of territories brand, and compare brand formation processes of resort towns – Sochi and Jurmala.
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2. Relevance Territory branding relevance caused by the fact that the competition for tourist flows between
cities and countries is increasing every year. Working out of territory developing strategy and improving its image – is a necessary element of efficient development policy of the regions and the whole country. This process includes identifying of the hidden capacity and the unique characteristics of the region, which together will provide the region long-term competitiveness, sustainable development, and capital inflows. Thus, the branding of territories is a key element in national strategies for sustainable socio-economic development.
3. Materials and methods The methodological bases of the study are the works of Russian and foreign scientists in the
field of territory branding, formation and successful implementation of branding strategies in Russia and Latvia. The study was based on the works of (Anholt, 2005; Kotler, Gertner, 2002). The paper used methods of system, factor and comparative analysis, as well as the method of statistical processing of empirical data.
4. Discussion Branding territory study has not a long-term history. The concept of "place branding»
wasfirstly used in 2002. Simon Anholt – the author of term. However, the lack of terminology does not imply the absence of the process. For the first time to promote the territory brand (namely the country brand) suggested David Ogilvy in the 1950s. Later, in 1980, T. Levitt raised the issue of creating a unique image (brand) to achieve a positive effect in his book "Marketing success through differentiation."Scientists who studied the essence of branding processes, issues to increase their tourist attractiveness are Ward, S. V. (1998), A.P. Pankrukhin (2002), S. Devis (2005), F. Sharkov (2006), Danny K. (2013) and others.
In the scientific literature there are a lot of different definitions of the brand and brand essence. In the most complete meaning brand can be described as a set of ideas about product, service or territory, which forms an integral image, determines their differences from competitors in the perception of potential consumers. The brand creates the unique properties of the goods in the minds of consumers by symbols and through communication channels. From an economic point of view,the brand of territory is a demonstration and guarantee of competitive advantage (Brand and image...).
Brands can be classified according to certain criteria. Firstly, brand is closely connected with its potential market. Depending on the spread in the world market brands are divided into global and local. If we compare the ability of different brands influence the minds of consumers and shape their preferences, we will reveal the concept of brand strength. On this basis, there are strong and weak brands. The most famous classification of brands compiled by L. Apshou (Upshaw, 1995), it is based on the brands division according to branding object: trade, service, personality and so on (Moilanen, Rainisto, 2009). There are also a number of concepts that are worth paying attention to. In the hierarchical classification there is isolated umbrella brand, which is a union of a few products or services under a single brand, which may be completely unrelated to each other. The basic classifications of the brand are presented in Table 1.
Table 1. Brand Classifications according to different features
According to…
Market Object
(L.Apshou) Recognition
Degree Hierarchy
Promotion way
1. Global 2. Local
1. Goods 2. Services 3. Persons
4. Organizations 5. Events
6. Territory
1. Strong 2. Developing
3. Weak
1. Corporative 2. Umbrella
Brand 3. Sub-brand 4. Individual
1. Consumer 2. Hi-tech
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Territory branding becomes popular nowadays. The trait of post-industrial time is the development of services sector and tourism as one of the types of service, accordingly. Today, when it is possible to get to any place in the world for a few hours, the question of raising recognition and attractiveness of the tourist areas is most relevant. Branding of the territory is a strategy to improve the competitiveness of certain territories, aimed at different target groups of customers: tourists, investors, highly qualified migrant workers. The main objective of the strategy − the promotion of tangible and intangible resources of the territory as a result of the distribution of information about its uniqueness among a wide audience of potential customers. Territory brand is a tool for implementing the regional development strategy.
Territory branding attracts attention of many scientists, so in the scientific literature there are so many different definitions of the term. The most interesting and precise concept of the territory brand shown in Figure 1.
Fig. 1. Some definitions of territory brand
Analysis of presented definitions helps to conclude a complete definition of the brand of
territory. The brand of territory is a positive image in consumers’ notion, distinguishes area among identical territories, as well as the sum of all the unique characteristics of the area, defining its competitive identity.
It’s impossible to find one universal script of effective branding; there are only general methods for brand development. As branding process of any product, working-out of territory branding should be based on the principles of marketing planning, including 4 stages:
1. Marketing research of the situation, emphasizing of a unique image of the territory; 2. Creation of the brand development strategy; 3. Positioning of the brand, i.e. the definition of the brand niche in the market; 4. Promotion of the brand, namely the implementation of the development and introduction
brand strategy. As mentioned above, the territory is a special commodity, and its consumer value lies in
discovering and using its potential. Prospective basis (potential) include such characteristics of the region, which distinguish it from the neighboring areas. Besides geographical position and climatic conditions the uniqueness of the territory may be in its resource base, historical and cultural heritage, infrastructure development, and other features. In addition, high-quality functioning of the territory depends on the condition and operation of housing and roads.
Today territories have to compete on the foreign and domestic market. Cities and countries are involved in a highly competitive fight for investment, professionals, tourists, and therefore an important task of areas is to provide "something outstanding or unique" in the market (Kotler, Asplund, 2005). Something "unique" should help to ensure a unique position in the market and positive image among other areas. Therefore, branding of territory, according to Kotler and
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Asplund, consists of four components: the development of an attractive image, creating incentives for potential customers of goods and services, delivery of goods and services in an effective form, information about distinctive advantages.
Formation of touristic territory brand, as a strategic development tool aimed at attraction of tourists by displaying their exclusivity by means of various communication technologies, affords many advantages for resorts. Thus, a strong, well-built and attractive territorial brand will allow the tourist area to increase:
• Awareness of potential tourists; • Attractiveness of the territory in the minds of potential tourists; • The number of tourists due to the formation of tourist loyalty; • The flow of public and private investment in the development of the territory; • Attractiveness of the territory for the population with certain qualifications and skills
demanding in the tourism sector. Thus, territory branding as a managerial process aimed both to the creation and development
of the brand, and to its promotion and increase of tourist demand, it is one of the most valuable intangible assets of a tourist area.
Well-developed territory brands are the source and mover for successful development of tourism, and not only today but also in the future as strong brand can bring considerable profit in the long term.
For a more detailed study of territory branding process, we consider the features of branding such touristic cities as Sochi (Russia) and Jurmala (Latvia).
Jurmala – the biggest and the only official resort in Latvia, for many years it is a favorite destination of tourists from all over the world. Every season, the resort hosts big cultural events: art exhibitions, local and international festivals and concerts. The Government of Latvia pays serious attention to the building of a national brand. In 2007, in the development of a national branding strategy was engaged one of the most famous experts in the branding of areas –Saimon Anholt. The results of his study were not as optimistic as expected, and he suggested basing the branding strategy not on the country's positioning as a whole, but to attract attention to the capital – Riga. Brand of Latvia is formed by a number of internet resources, including official sites and accounts in social networks, designed to attract the world's attention to the country and its history. In recent years, insufficient attention is paid to the largest seaside resort of the Baltic States – Jurmala. For a long time, the city was a center for various festivals and competitions ("New Wave", KVN, "Anshlag», Comedy club etc.). Since 2015 holding the majority of Russian-speaking events in Jurmala was suspended due to political confrontations. International music festival "New Wave" is now held in Sochi, attracts many tourists, creates new jobs and increases the brand awareness of the city. Governor of Krasnodar Region on this occasion said: "We have won the right to hold the"New Wave" (Tourism in Jurmala).
According to official statistic data about 180 thousand people were on vacation in Jurmala in 2014, in comparison with 2013 year increase of visitors is 21 %. Despite the loss of Russian consumers and visitors of "New Wave" festival, positive dynamics remains. Increased attendance and, consequently, the popularity of the city can be achieved by moderate price policy.
In the modern economic situation, when the economic crisis has yet affected many countries, more and more consumers prefer to rest for a modest pay. Hotel rooms in Jurmala became cheaper by 15-20 % in 2015 compared to 2014.The dynamic of tourists’ number annually visiting Jurmala is presented in Figure 2.
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Fig. 2. Number of foreign visitors in millions people, 2010–2015 (Tourism Developing Strategy...)
In 2016 Jurmala plans to attract 5 % more tourists than in 2015 due to marketing activities.
The main strategic markets for Jurmala are the Baltic States, Finland, Belarus and Scandinavian countries. The priorities of resort are health and business tourism.
The strategic target of Tourism Development Strategy for Jurmala 2007–2018 is the increase of number of tourists served in Jurmala lodgings from 101 447 in 2005 up to 140 000 in 2020. Equal increase in the amount of tourists during off-season is another target: annual 8 % increase out of season and 20 % increase during the season (Tourism Developing Strategy...). As we can see foreseen number of tourists has been achieved in 2013. It proves the efficiency of marketing activities.
The most popular time for Latvian brand was in the Soviet Union period. To have a rest in Jurmala was considered as prestigious. At that time, Jurmala took 2nd place by the number of holidaymakers after Sochi and Yalta. However, after the collapse of the USSR Jurmala reached a turning point. The country faced wide range of competitors; it led to a decrease of popularity of resorts of Latvia. This point is clearly visible in the chart of tourism's contribution to GDP (See Figure 3)
Fig. 3. Tourism total contribution to GDP of Latvia, % share (Advertising Expenditure...)
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In 2015 total contribution of tourism reached 9 % of GDP. Graph shows a sustainable growth of tourism since 2003.
Advertising expenditure in Latvia demonstrate moderate growth since post crisis period. The dynamic is shown on the Figure 4.
Fig. 4.Advertising Expenditure in Latvia, 2007–2015, million U.S. dollars (Official page of National Competitiveness Council)
Despite the promotion activities by tourism professionals, Latvia's recognizability remains
rather low which is one of the problems in the tourism industry, representatives of other industries should also make effort to increase Latvia's recognizability so that people in other countries would know where and what Latvia was and what it could offer. Moreover, Latvia had to compete not only with neighboring countries that had similar offers for tourists but also, for example, with the Mediterranean countries that had plenty of sun and warm sea. For the Latvian tourism industry to be competitive, it had to find its niche. (Latvia’s recognizability...).
The history of brand’ formation of Sochi city can be divided into several stages: Old Sochi, Olympic Sochi and post-Olympic Sochi. Until 2007, the resort's fame was limited by the territory of the former Soviet Union, during that time Sochi was one of three most visited resorts in the Soviet Union with Yalta and Jurmala. In 2007 Sochi was recognized throughout the world as the future Olympic capital. Since that moment an active policy of promoting the "Sochi 2014" brand had started. To turn subtropical resort to the capital of the Winter Olympics country had to fully rebrand the territory.
The first step of the re-branding of the city was the creation of the brand in the perception of the world’s community. In the second part of 2013 the amount of advertising with the brand "Sochi 2014" was more than any other brand. In total, sponsors have spent more than $ 1 billion for branding promotion of the Olympic Sochi. Olympic rebranding affected the appearance of the city as a whole. Soviet-style resort was destroyed and instead was erected a new Sochi – a world-class resort. Before the Olympic Games Sochi was considered as a middle class resort, now it is a "luxury" resort, provides with a variety of world-class chain hotels. Sochi became a venue for high-level meetings: meetings of President of the Russian Federation Vladimir Putin with heads of foreign countries are often held in Sochi.
Sochi is not the only resort of the Krasnodar Region, but it stands out among the neighboring territories, demonstrating its competitive identity. This is direct evidence of the city promoted brand. The dynamics of the tourist flow in the Krasnodar region in the period from 2004 to 2015 is shown in Figure 5.
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Fig. 5. Tourist flow in Krasnodar region, million people (Municipal Program…)
The diagram shows that the holding of the Olympic Games was the most effective branding
event for the resorts of Krasnodar region. Despite the huge brand promotion of the Olympic capital, since 2007, real tourist’s interest to the region emerged only after 2014. To trace how branding activities contributed to touristic traffic’ increase, consider the dynamic of the budget of tourist potential promotion of the Krasnodar region, see Figure 6 (Municipal Program…)
0
20
40
60
80
100
120
140
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Fig. 6. Dynamic of marketing funding in Krasnodar region, million rub
As we see funding of marketing activities and information promotion of resorts rose more
than 2 times from 2007 to 2009, and the flow of tourists grew by only 3 %. Latersince 2009 to 2013 growth of investments fluctuated and eventually reached 4.8 %, while the number of tourists during this period decreased by 3.4 %. In 2014-2015 financing has been cut by more than 70 %, but there still was a growth in the number of tourists by 6 % and 20 % in 2014 and 2015 accordingly.
According to the results of the analysis, we can conclude that the branding activities can vary depends on strength, have both positive and negative effects and, most importantly, they are cumulative.
In the Olympic capital the number of tourists is increasing every year, a significant growth is seen in the post-Olympic period. (See Figure 7)
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2,8
2,9
3,8
4,7
5
6,7
0 1 2 3 4 5 6 7 8
2011
2012
2013
2014
2015
2016
Fig. 7. Number of tourists in Sochi, million people
There is only preliminary data about 2016, but it is already clear that the post-Olympic Sochi
attracts an increasing number of tourists. Popularity growth was also affected by external factors, such as sanctions, restricting entry of Russian citizens on the territory of the European resort; temporary difficulties in the relations with Turkey, as well as the unstable economic situation which affected the exchange rate of the ruble.
Analysis of brands formation processes in tourist areas on the example of two cities-resorts allows comparing the methods of territory branding. Both resorts had been quite popular in the Soviet Union times, and with it the collapse had to resist foreign competitors. Today Jurmala and Sochi are expanding their travel services markets and claim to get the title of world resorts. Sochi attracts the attention by the unique post-Olympic legacy, by organizing major world-class events (Olympic Games, Hockey Championship, New Wave, etc.). A significant part of tourist visited Jurmala consisted of guests of such large events as the “New Wave”, “KVN” etc., but introduction of sanctions for many Russian artists causedloss of this segment of the market for Jurmala. Latvian resort implements promotion activities throughinternet channels, participates in international tourism fairs and cooperates with many European and Scandinavian countries. A brief comparative description of the resorts is shown in Table 2.
If we evaluate the effectiveness of branding activities in the number of tourists, we can conclude that the results of Jurmala policy are intangible. However, this is not true, Jurmala actively implement Tourism Developing Strategy and targets of 2018 have almost been achieved in 2015. There is a reason for the huge difference in the number of tourists in these cities (in Jurmala the number of tourists are in the hundreds of thousands, in Sochi –in millions). Latvian resort strongly depends on the season and attracts guests only during the summer, while Sochi has been positioned as a year-round resort for several years.
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Table 2. Comparison of Tourist territories branding activities
Sochi Jurmala
Priority branding methods
Promotion as Olympic city; Act as World Events area; Distribution of Symbolics;
Mass media
Internet promotion; International cooperation; Participation in Tourism
Exhibitions
Costs
Government spent about 550 million rub;
Sponsors invested more than 1 billion U.S. $ since 2010
Since 2010 Latvia spent 808 millions U.S. $ for marketing
Results
Since 2011 the number of tourists increased by 3.7
million people in whole region, and by 3.9 million people in
Sochi city
Increase of tourist flow from 104 000 people till 179 000
since 2011
5. Conclusion Research of branding policy of resorts proved that in a variety of modern methods of brand
promotion there is not universal safe version, each territory requires a special approach. Sochi implements more aggressive tactics of brand promotion, and becomes a world-class resort in a short term. Such a fast-moving tactic requires significant financial investments, the support of the authorities and the use of bright events (event marketing) to attract global attention. Olympic Games played an important role in city marketing; after Games Sochi "wakes up" famous in a moment. However, to hold large event is not simple way to form a successful brand of the city. There are many examples of unsustainable management of post-Olympic cities, which leads to non-use of the Olympic legacy and the fall of the city's popularity. Sochi continues to support a brand even after its successful formation and so stimulates the growth of tourist flows after the Olympic Games.
The tactic of Jurmala is rather quiet. Latvia annually participates in the international tourism fairs, and in this way demonstrates potential of territory, attracts tourists and investors, and also in marketing policy of Latvia promotion through the Internet channels has a significant impact. During the brand formation of Jurmala event marketing also took place, for many years the resort hosted the famous festival "New Wave", and other events attracted tourists.
Brand development of both cities took great efforts; authorities invested large sums and developed strategies, and definitely made some progress. But the formation of a sustainable brand of territory is affected by many external factors which may constrain the growth of tourism. The overall effect of long-term marketing activities results in the growth of tourist flows during 5 years at 79 % and 69 % in Sochi and Jurmala accordingly.
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Draft Budgetary Plan... – Draft Budgetary Plan of The Republic of Latvia 2016 [Electronic resource].URL: ec.europa.eu (Accessed: 07.12.16).
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Kotler, Gertner, 2002 – Kotler P., Gertner, D. (2002). Country as brand, product and beyond: a place marketing and brand management perspective. Journal Brand Management, Vol. 9, Is. 4-5, April, pp. 249-261.
Kukina, 2011 – Kukina E.N. (2011). Branding territories: essence and design principles. VestnikVolgograd State Technical University, Vol. 11, Is. 4.
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Morgan et al., 2002 – Morgan, N. Pritchard, A. Pride, R. (EDS) (2002). Destination Branding, Creating the Unique Destination Proposition, Butterworth-Heinemann, Oxford.
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Published in the Russian Federation European Journal of Economic Studies Has been issued since 2012. ISSN: 2304-9669 E-ISSN: 2305-6282 Vol. 18, Is. 4, pp. 486-491, 2016 DOI: 10.13187/es.2016.18.486
www.ejournal2.com
UDC 33 Characteristics of Basel Principles and Standards in Banking Branimir Kalaš a , *, Nada Milenković a, Jelena Andrašić a, Miloš Pjanić a
а University of Novi Sad, Serbia
Abstract International banking devotes considerable attention to the minimum supervisory standards,
which banks have meet in order to get and therefore retains the license to do business. Today, banking organizations have to ensure three fundamental principles like security, stability and profit. Security and stability in banks is relevant, because only in this way they can gain the customer trust and confidence. The subject of this paper is importance and implementation of Basel standards in banking. First, it's presented history of Basel Committee on Banking Supervision and basic principles for effective banking. Second, authors reflect three main Basel standards like Basel I, Basel II and Basel III and determine difference between them by using equations and tables.
Keywords: banks, Basel, principles, standards. 1. Introduction Banks are faced with problem of high level of instability and concentration of different risk
types which is basic characteristic of current state of the world financial system and banking markets. Acceptance of exposure to high risk caused the emergence of crisis and inability to collect overdue receivables, which implied destruction of the large number of banks in the world. Inadequate collateral and highlighted volatility of fundamental economic parameters as price, interest rates and exchange rates requires exceptional expertise and professionalism in risk managing, as well as precisely determined mechanisms and measures of supervision by regulatory financial institutions. With development of modern banking and industry, exposure to different types of risk is growing in business. Timely identification and quantification of all kinds of risk as well as adequate preventive measures of protection has become extremely relevant factors of business success in an increasingly complex economic conditions. It's illusory to not notice that there isn't sincere encouragement towards improving of banking regulation at the right time and thereby support the Basel Principles. Namely, all previous principles were adopted only after escalation of the crisis period and they were corrective characters, rather than preventive and protective function of the entire banking system. Their relevance is manifested through the regulatory framework raise the level which is necessary for successfully overcoming all obstacles in the banking field. It's important to give greater support to these standards as they are a prerequisite for successful business in the banking sphere. With the aim of stability in the
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development of their operations and equal participation in the robust market competition, banks have to include them in their strategic objectives and strategies of banking risk management. Application management and risk management in the banking sector promote a necessary process of expansion of a stable and sound banking system. Avoiding insolvency of banks and maximizing the rate of return on risk adjusted capital are the two main objectives of the risk management policy in the banking business. Without the presence of relevant rules in the banking sphere it's unlikely that the banking system can operate adequately. Reinhart and Rogoff (2009) researched the historical experience that regulation, surveillance and macroeconomic policy aren't enough to prevent crisis. Many authors highlight importance of financial regulatory framework and reform (Goodhart, 2009; Dewatripont et al., 2010; Kotlikoff, 2010; Duffie, 2011; Admati et al., 2013; Myerson, 2014). Dewatripont et al. (2010) defined banking as one of the handful industries subject to prudential regulation on top of consumer protection. They notice that banks are faced with accelerating financial innovations which incurred as a result of customers' desire and pure regulatory arbitrage. Some financial institutions used weakness of regulatory framework which was inadequate designed to achieve abnormal high profits wherein they didn't respect basic principles of ethics and endangering social interests. Myerson (2014) determined that banks are vital financial institutions which intermediate between surplus and deficit units. In particular, banks get substantial funds from deposits and therefore security has to be one of the top principle in banking operations.
2. Basel principles and characteristics Basel Committtee on Banking supervision was established under the auspices of BIS in
Switzerland by the central bank governors from G10 countries (Belgium, Canada, France, Germany, Italy, Japan, Netherlands, Sweden, UK and USA) in cooperation with the monetary authorities of Luxembourg and Switzerland (Jablecki, 2009). The Basel Committee on Banking Supervision was founded in 1974 as one of the committee at the BIS (Bank for International Settlements) with aim of improving the banking supervision at the global level. This is a forum for discussion between national supervisory authority for mutual exchange of information, including the exchange of experiences on implementation of performance techniques and methods of surveillance activities of internationally active banks. The Basel Committee on Banking Supervision provides a platform for regular cooperation between countries – members on issues related to the control of banking organizations. This important aspect of international coordination was caused by major turmoil in the global economy emerged from the beginning of 1970. The firs oil shock and bankruptcy of the German bank Bankhaus Herstatt. It's also the debt crisis had an impact on development of Basel principles with the task of equalizing conditions for banks which operate on the global market (Milenkovic, 2011). The main objective of BCBS is to improve the understanding of key supervisory challenges and increase the quality of banking supervision worldwide (BIS, 2006). The Basel Committee on Banking Supervision defined the core principles for effective banking supervision which promote safety and soundness of banks and the banking system.
An effective system of banking supervision needs to be able to effectively develop, implement and monitor supervisory policies under normal and stressed economic conditions. There are a number of preconditions for effective banking supervision (BIS, 2012): a) sound and sustainable macroeconomic policies; b) a well established framework for financial stability policy formulation; c) a well developed public infrastructure; d) a clear framework for crisis management, recovery and resolution; e) an appropriate level of systemic protection and f) effective market discipline.
3. Tendency from Basel I to Basel III Global standards for capital are a relatively recent innovation. Basel I came into force in
1988, related only to credit risk and before then, there were no standardized rules on capital adequacy for banking organizations. In 1996, market risk rules were added and in 1998, the BCBS recognized Basel I revision by using more sophisticated internal models to measure risk. After that, Basel II was born as new regulatory framework which included calculation of three method like SA (Standardized Approach), FIRB (Foundation Internal Ratings Based approach) and AIRB (Advanced Internal Ratings Based approach) that was the most popular method at large international banks (Barfield, 2012).
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Table 1. Core principles in banking
Supervisory powers, responsibilities and functions
Prudential regulations and requirements
Responsibilities, objectives and powers Independence, accountability, resourcing and legal protection for supervisors Cooperation and collaboration Permissible activities Licensing criteria Transfer of significant ownership Major acquisition Supervisory approach Supervisory techniques and tools Supervisory reporting Corrective and sanctioning powers of supervisors Consolidated supervision Home-host relationships
Corporate governance Risk management process Capital adequacy Credit risk Problem assets, provisions and reserves Concentration risk and large exposure limits Transactions with related parties Country and transfer risks Market risks Interest rate risk in the banking book Liquidity risk Operational risk Internal control and audit Financial reporting and external audit Disclosure and transparency Abuse of financial services
Source: BIS (2012)
Table 2. Key aspects and differences among Basel I, II and III
Basel I Basel II Basel III Released rule July 1988 Released rule December 2007 Released rule July 2013 with
phased in implementation by 2019
Providing a paradigm to address risk management from a bank capital adequacy perspective
Somewhat forward looking risk sensitive approach to capital calculation
Emphasis on reducing systemic risk by minimizing procyclicality and promoting countercyclicality via capital conservation and countercyclical buffers
Not as risk sensitive as Basel II and Basel III
Provided smaller banks the option of adopting the more risk sensitive advanced approaches or a less sophisticated standardized approach which was created after Basel I
Forward looking, addresses risks relevant to bank specific portfolio and the macroeconomic environment
Focused on existing assets rather than the future composition of a bank portfolio
Introduced a three pillar approach to risk management
Mandates requirements for higher minimum capital and higher quality capital
Credit risk only – no other risk types
Pillar I – established minimum regulatory capital requirements for credit, market and operational risks
Introduces leverage ratios with the intent of improving financial system resilience Introduces liquidity risk: thirty day liquidity ratio (LCR), one year net stable funding ration (NSFR) and liquidity monitoring
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tools Fixed, predetermined risk weights for different asset classes
Pillar II – established principals for a banks Internal Capital Adequacy Assessment Process (ICAAP) which is intended to identify additional risks that are material, but not easily recognized
Mandates: enhanced disclosure requirements, interaction between LCR and the provision of central bank facilities
Differentiated assets between banking and trading books
Pillar III – established enhanced reporting requirements for market disclosure, such as credit risk exposure in different rating banks and credit quality of securitization holdings
Revises Basel II methodology for securitizations, enhances risk coverage by quantifying counterparty risk, credit value adjustments and wrong way risk
No advanced measurement of risk, based upon bank specific portfolio
Improved oversight by increasing the bar on supervisory responsibilities and expectations to normalize the way banks reported risk identification, measurement and management
More conservative market risk requirements
Simple tier calculations - tier 1 capital ratio of 4 % and total capital ratio (tiers 1 and 2) of 8 %
Increases the standardized approach risk sensitivity for residential mortgages, certain commercial credit facilities, exposures to foreign banks, public sector entitles and sovereigns Stricter data governance and data requirements
Source: Agarwai and Ravitz (2014) The basic requirement is that all financial institutions hold the capital at least 8 % of risk
weighted assets. The first part is called Tier 1 capital and it must represent the half of total capital or 100 % of Tier 2 capital (Hasan, 2002).
According to Basel I, banks assign different types of risk weights to their assets:
if assets having 0 % risk weight then banks required no capital for this type of assets;
if assets having 20 % risk weight then banks must require capital 1.6 % of the value of assets;
if assets having 50 % risk weight then banks must require capital 4 % of the value of assets;
if assets having 100 % risk weight then banks must require capital 8 % of the value of assets. Basel I measures risk by next formula (Hussain et al., 2012): Risk Based Capital Ratio = Capital/ Risk Adjusted Assets (1) Mohanty (2008) determined that after ten years of Basel I implementation, many changes of
technology, finance and other things are showed a lot of weakness of this standards. BCBS decided that they have change the existing standard into more risk sensitive Basel and introduced Basel II (Akhtar, 2006). This standard measures risk by the next formula:
Risk Based Capital = Capital/Credit Risk + Market Risk + Operational Risk (2)
Ahmad (2008) emphasized fact that this regulatory framework include credit risk, market
risk and especially operational risk which is main difference between previous regulatory standard Basel I. Also, Basel I only covered minimum capital requirements and Basel II adds two other pillars which is manifested through supervisory review process and market discipline (Dierick et
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al., 2005). Blundell-Wignall et al. (2014) Basel II proposed changes to the capital requirement rules which allowed large banks to run their own internal models to calculate the riskiness of the assets.
The new regulatory framework Basel III improves the capital and liquidity requirement whereby common equity increase from 2 % to 4.5 % and Tier 1 capital reserve rise from 4 % to 6 %. Also, there is additional reserve namely Capital Conservation Buffers of 2.5 % which can be use in stress situations. Further, Countercyclical Buffer is included and it vary from 0 %-2.5 % and Liquidity Coverage Ratio with basic purpose that bank must have high quality assets which can easily transformed in cash and this ratio must not less than 100 % (Mehta, 2012).
Table 3. Individual bank minimum capital conservation standards
Common Equity Tier 1 Ratio Minimum Capital Conservation Ratios (expressed as a percentage of earnings)
4,5-5,12 % 100% > 5,12 - 5,75 80%%
> 5,75 – 6,375 60% > 6,375 – 7,0 40%
> 7,0 0% Source: BIS (2012)
Table 3 reflects the minimum capital conservation ratios of banks in the five ranges.
For example, a bank with a CET1 capital in the range of 5.75 % to 6.375 % is required to conserve 60 % of its earnings. Also, a bank with 8 % CET1 and no additional Tier 1 or Tier 2 capital would meet all minimum requirements but would have a 0 % conservation buffer and therefore by subject to the 100 % constraint on capital distribution (BIS, 2010).
4. Conclusion The global financial crisis, spurred in part by inadequate regulatory standards, provoked the
financial regulatory reform at the national and international level. The world economy and global financial market survive one of the most difficult periods in history, accompanied with a significant slowdown, whose escalation is started on the banking market or mortgage market in US. Excessive foreign debt, budget and trade deficits, expressed volatility of currencies and prices of basic products, great unemployment and a general price increase with political instability are just some of the reasons of disturbed and disrupted economic stability of modern civilization. All of this contributes to increasing the bank's exposure to risks. In the past, not a small number of banks and financial institutions have closed their doors to the harsh impact of global economic crisis, because of inadequate preparedness to respond to unfavorable global trends. Quality risk management, bank supervision and monitoring, as well as coverage of business activities through banks' capital adequacy are just some of the measures that seek to avoid the mistakes made in the past. Maintenance and control of adequate capital ratios inspire a certain dose of security and enable a base frame for appropriate international supervision and discipline. The base frame creates conditions on the reference level where banking organizations and financial institutions can respond to economic shocks and difficulties which they are exposed. It's necessary form of implementation and unification of rules and procedures to increase the free capital flows and facilitate international banking business with the adequate control to maintain all the risk on a defined level. Banks as financial drivers of national economies present a global circulation of financial funds where the stagnation can cause serious slowdown in money circulation and economic prosperity. Banks will give contribution by investment in less risky operations, where profits will be compensated by a lower probability of loss. It remains to be seen whether the international standardization and regulation on the one hand and the entire banking industry and financial establishment on the other hand, are able to face with global challenges. Only health banking system, based on realistic grounds and respecting defined standards with adequate control, can respond to the problems and difficulties.
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References Admati, Hellwig, 2013 – Admati, A., Hellwig, M. (2013). The Banker’s New Clothes: What’s
Wrong with Banking and What to Do about It. Princeton and Oxford: Princeton University Press Agarwai, Ravitz, 2014 – Agarwai, V., Ravitz, M. (2014). The ABCs of Basel I, II, & III, Key
Aspects and Differences, Capgemini, People matter, results count. April, available at: https://www.capgemini.com/resource-file-access/resource/pdf/the_abcs_of_basel_i_ii_iii.pdf
Ahmad, 2008 – Ahmad, N. (2008). Basel II Capital Adequacy Requirements: Implementation Challenges for Islamic Banks, International Islamic Finance Conference, pp. 15-17.
Akhtar, 2006 – Akhtar, D.S. (2006). Demystifying Basel II. Global Banking: Paradigm Shift at Mumbai, India, pp. 5-8.
Barfield, 2012 – Barfield, R. A. (2012). Practitioner's Guide to Basel III and Beyond, Thomson Reuters, available at: https://www.pwc.com/gx/en/financial-services/pdf/introduction-and-chapters-final.pdf ISBN: 9780414045385
BIS, 2006 – BIS. (2006). International Convergence of Capital Measurement and Capital Standards, A Revised Framework, Comprehensive Version, Bank for International Settlements, Basel.
BIS, 2010 – BIS. (2010). Basel III: A global regulatory framework for more resilient banks and banking systems, Bank for International Settlements, December, Basel.
BIS, 2012 - BIS. (2012). Core Principles for Effective Banking Supervision, Bank for International Settlements, September, Basel.
Blundell-Wignall et al., 2014 – Blundell-Wignall, A., Atkinson, P., Roulet, C. (2014). Bank business models and the Basel system: Complexity and interconnectedness, OECD Journal: Financial Market Trends 2013, Vol. 2013/2, pp. 17 available at: https://www.oecd.org/ finance/Bank-Business-Models-Basel-2013.pdf
Dewatripont et al., 2010 – Dewatripont, M., Rochet, J.C., Tirole, J. (2010). Rebalancing the Banks: Global Lessons from the Financial Crisis. Princeton and Oxford: Princeton University Press.
Dierick et al., 2005 – Dierick, F., Pires, F., Scheicher, M., Spitzer, K. (2005). The New Basel Capital Framework and Its Implementation in the European Union, European Central Bank, Occasional Paper Series, No. 42/December.
Duffie, 2011 – Duffie, D. (2011). How Big Banks Fail and What to Do about It. Princeton and Oxford: Princeton University Press.
Goodhart, 2009 – Goodhart, C.A.E. (2009). The Regulatory Response to the Financial Crisis. Cheltenham: Elgar.
Hasan, 2002 – Hasan, M. (2002). The Significance of Basel I and Basel II for the Future of The Banking Industry with Special Emphasis on Credit Institutions. Information Alliance Regional Meeting, pp. 2-5.
Hussain et al., 2012 – Hussain, M., Ramzan, M., Ghauri, M., Akhtar, W., Naeem, W., Ahmad, K. (2012). Challenges and failure of Implementation of Basel Accord II and reasons to adopt Basel III both in Islamic and Conventional Banks. International Journal of Business and Social Research, Vol. 2, Issue 4, pp. 149-174.
Jablecki, 2009 – Jablecki, J. (2009). The impact of Basel I capital requirements on bank behavior and the efficacy of monetary policy. International Journal of Economic Social Sciences and Applied Research 2(1), pp. 16-35.
Kotlikoff, 2010 – Kotlikoff, L.J. (2010). Jimmy Stewart Is Dead: Ending the World’s Ongoing Financial Plague with Limited Purpose Banking. Hoboken, N.J.: Wiley and Sons.
Mehta, 2012 – Mehta, M. (2012). Demystifying Basel III for Indian Banks, International Conference on Technology and business Management, pp. 269-272.
Milenković, 2011 – Milenković, I. (2011). Međunarodno bankarstvo, Univerzitet Novi Sad, Ekonomski fakultet Subotica.
Mohanty, 2008 – Mohanty, S.K. (2008). Basel II: Challenges and risks. Academy of Banking Studies, pp. 2-10.
Myerson, 2014 – Myerson, R. (2014). Rethinking the Principles of Bank Regulation: A Review of Admati and Hellwig's the Bankers' New Clothes. Journal of Economic Literature, Vol. 52, No. 1, March, pp. 197-210.
Reinhart, Rogoff, 2009 – Reinhart C.M, Rogoff K.S. (2009). This Time Is Different: Eight Centuries of Financial Folly. Princeton, Princeton University Press.
Published in the Russian Federation European Journal of Economic Studies Has been issued since 2012. ISSN: 2304-9669 E-ISSN: 2305-6282 Vol. 18, Is. 4, pp. 492-515, 2016 DOI: 10.13187/es.2016.18.492
www.ejournal2.com
UDC 33 The Effect of Credit Risk Management on Banks’ Profitability in Kosovo Aliu Muhamet a , * , Sahiti Arbana a
a University of Pristina, Kosovo
Abstract The concept of the credit risk management has gained momentum in recent years with
financial institutions developing techniques aiming at minimizing credit risk and regulatory bodies coming up with policies ensuring banks adequately manage their risks.
This study was carried out to quantitatively determine how risk management affects the banks profitability. PCB, RBKO, NLB, TEB were selected as the sample banks for this study. The methodology involved extracting time series data from the annual reports of the banks to calculate the return on equity which was used as a measure of profitability and also to calculate the nonperforming loan ratio which was used as a credit risk management measure along with the risk asset ratio. Return on equity was expressed as a function of the risk asset ratio and non-performing loan ratio and substituted into a multivariate regression model. The data was run using SPSS software. To further examine the relation a simple linear regression was carried out along with a trend analysis. The output showed a substantial relation between the variables and reflected that a higher risk asset ratio would result in a marginal decline in profitability while higher nonperforming loans had a positive and more substantial effect. Further analysis showed a predominantly negative effect, highlighting the possible inadequacy of the multivariate model.
1. Introduction Commercial banks are financial institutions with the primary function of carrying out
financial intermediation – this implies that they accept deposits from customers with extra funds and loan out the money to customers with a funding gap. The cost of receiving the deposits from customers, termed the interest expense, is primarily the interest paid to the customers while the money is loaned to other customers at a higher rate. The difference between the rate at which the money is loaned out and the rate paid on interest is the spread which accrues as interest income to the bank. In addition to the spread, financial institutions also invest funds at their disposal with the ultimate aim of making a return on their investments.
The industry today is globally characterized by stiff and intense competition which threatens the very survival of the institutions themselves. As the stronger banks try to consolidate their hold on the industry the smaller players develop strategies to compete. This leads to the creation of
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different banking products, varying from different types of accounts with varying attached benefits to different offers for loans and mortgages, thus increasing the pressure on the banks to extend credit and maximize profits.
These activities however come with risks which must be considered appropriately in the credit granting and investment making process to minimize loss in the event of the risks collapsing. The different banks have varying policies which determine their risk bearing capacities greatly influencing the type of credits they give and type of assets they invest in, thus may have an effect on their level of profitability. With no absolute certainty on the potential that a risky credit or asset will collapse and that the higher the risk is the higher the expected return will be, banks that give more credits or invest in more risky assets may consistently enjoy a higher rate of return in the event of those ventures, than banks that invest in less risky assets or give less credits, which equally assume a level of risk and also have a potential to crystallize.
These facts highlight the complexities in the banking business and motivated me to explore the actual effect of risk management on profitability of banks.
The importance of banks in economic growth cannot be over-emphasized as they are the primary source of credit to individuals and organizations. While the role and performance of banks research has been ongoing for the last twenty years, it has often been limited by availability of requisite data (Haselmann, Wachtel, 2006). To ensure their concern banks have continually developed policies that guide their activities. Regulatory agencies, both local and international, also exist to create boundaries for the operation of the banks and ensure a stable and sustainable banking industry. However, experiences have shown that despite periods of robustness, the banking system remains susceptible to shocks, a major source of which is due to credit risks.
1.2. Research Objectives and Questions As the global world is becoming more competitive financial institutions have attempted to
manage the risks of their exposure by introducing robust credit policy guidelines and frameworks to minimize the risk of exposures. Risk management models have also been developed to mitigate credit risk. These activities require both financial and human resources and thus it is important to determine empirically if these resources are justified to be based on the results declared by the financial institutions. My objective therefore is to analyze and determine through empirical data if risk management has any effect on the profitability of banks. This research will be restricted to credit risk management given that credit risk is one of the most important risks that commercial banks are exposed to. In addition due to availability of data, this study will be based on Kosovo banks and the research aims to answer the questions below:
1. What is the effect of credit risk management on net interest income
2. What is the of credit risk management on overall profitability of banks
1.3. Significance of the Research A highly constricted lending policy will have a negative impact on a banks’ bottom-line as the
banks have to lend money to generate income. Furthermore, modern risk management methods come at a cost to the banks; thus a combination of reduced lending due to the applied credit risk strategies and the cost of the strategies being implemented result in reduced profits for the banks as resources have been utilized without income being generated.
The aim of this study is to justify or otherwise the resources that banks channel into the development of credit risk management initiatives, processes, models and techniques.
2. Literature review A lot of studies have been carried out on banks and risk management practices in general,
however much of the previous studies related to risk management and profitability have focused mostly on determining the extent of risk management tools usage and its effect on the overall banks’ performance. These studies include Fatemi and Faloodi (2006) who carried out a qualitative investigation of large US based financial institutions to determine the extent of banks engagement in credit risk management practices and their utilization of house generated models or vendor marketed models. They found out that only a minority utilize any of the models. Fan (2004) carried out an investigation and concluded that profit efficiency is connected with credit risk while
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Al Tamimi and Al Mazrooei (2007) in conducting a research found out that UAE banks have developed a level of expertise in managing their credit risks.
Various studies have also focused on the motive beyond risk management and its applicability. Santomero (1997) identifies some of the motives such as a managerial self interest, the cost of financial distress, a non-linearity of tax structure and capital market imperfections. Tekavcic et al (2008) emphasizes the cost of bankruptcy as a motive, stating that firms face large legal, administrative and monitoring costs which ultimately reduces the firm value, while Graham and Rogers (1999) state that management of risk reduces the volatility of the firm’s pre-tax income and it benefits economically as it reduces the expected tax liability of the institution, especially if the firm has a convex tax function, a situation where tax liabilities increase with earnings’ volatility. The managerial self interest raises a special interest which introduces a concept of agency theory. Eisenbeis and Kwan (1995) referred to Jensen who stressed that the role of managers as the custodians of the business is awash with the conflict of interest which exists between managers taking the risk of management decisions to protect their interests at the expense of maximizing shareholders wealth. In addition, Fatemi and Faloodi (2006) and Eisenbeis and Kwan (1995) assert that management of an institution are more likely to make decisions which will guarantee the security of their jobs or tend to increase their performance bonus; thus they point out that managers can either be risk averse and eliminate risks, which if taken could increase profitability or be overly pro-risk and take more risk to increase their chances of higher rewards. However, in any given situation the goal of the institution should be to add value to the shareholders thus the most important aspect of the risk management process should be maximizing the risk return trade-off. The immense impact the risks of the banks in the case that they bankrupt is a clear motivation of studies to devise means to manage them. Al-Mazrooei and Al-Tamimi (2007) clearly state that the foundation of prudential banking is risk management and it is crucial to the survival of the organization; however they attach more importance to the liquidity, the interest rate, the foreign exchange and the credit risk. Many researchers seems to be in agreement that those four are the most important risks that a bank faces (Santomero 1997, Boffey, Robson 1995), thus most studies describe how these four risks are managed. This perspective however neglects counterparty risk which is quite related to the credit risk and could pose a significant threat depending on the trading volume in the question, as its magnitude directly determines the extent of the risk. A market risk is also a very important risk but it can be argued that aspects of it are covered by elements of the four mentioned above.
The focus on risk has increased over the years with increased regulations compelling banks and other financial institutions into adopting risk based measures and practices. These have not been without their challenges in particular as risk is difficult to quantify and according to Bessis (2002) may not be visible until it begins to degenerate into a loss. However more and more banks globally are integrating risk and risk management process into their system, arguably though the extent of implementation is more based on compulsion than on the perceived need to do it.
2.1. Credit Risk Credit risk remains widely regarded as the major influence on a bank’s performance and the
major cause of bank failures, largely due to their limited capacity to absorb losses from bad loans (AlMazrooei and Al-Tamimi 2007, Boffey and Robson 1995). These losses are generally categorized into three namely.
- Expected loss (EL), which is classed as predictable and counted as part of the cost of business thus is factored in the pricing
- Unexpected Loss (UL) which are unanticipated losses above the expected and - Loss Given Default (LGD), which refers to the loss incurred by the bank with a loan default. According to Boffey and Robson (1995) a bank’s capacity to absorb bad loans comes mainly
from its profits and its capital and a single substantial bad loan can have such a significant impact on the business that it is imperative that banks manage their credit risks proactively. The statistical evidence from the research conducted by Sparaford (1988) showed that 98 % of bank failures were as a result of incidents related to poor asset quality due to factors such as poor loan policies, a non compliance with policies and guidelines and a poor supervision. Sparaford (1988) further asserts that the factors highlighted above are as a result of a poor credit culture, a position corroborated by Colquitt (2007) and Boffey and Robson (1995). Expatiating on this concept, Colquitt (2007) posits
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that a bank’s credit culture determines the attitude, style, perception and behavior that will be exhibited by the bank and is largely determined by the attitude of management towards credit risk and could actually be in conflict with the policies of the bank. In a similar vein Bessis (2002) asserts that a credit culture might be more disposed towards the relationship of the bank with its customers and may not put modeling into consideration. While this may work in the short run, the long term implication is that resources committed to developing sophisticated risk models may be unjustifiable and the danger is that the bank may more prone to more risk if adequate factors and parameters are not considered and monitored to determine the loan quality. In light of these ideas McKinley (1991) identifies four main credit cultures that define financial institutions. They are the following:
1. Value driven financial institution which has a strong credit culture and consistently strikes a balance between the quality of advanced loans and the drive to increase profitability.
2. Market share driven institution which signifies very ambitious banks that may compromise on credit quality to take a significantly higher risk to keep the market share growing.
3. Immediate performance driven institution which is depicted by banks that are consistently under pressure to increase earnings. These are perceived to be banks that are trying to catch up with their competitors.
4. Unfocused institution which are yet to find their feet. The big issue is that of all the categories listed to name which is the most profitable. While it
is obvious that the market leaders will fall between the value driven and market share driven institution, there is the insufficient data to ascertain which class is the market leader, a situation which again questions the real impact of risk management on profitability. McKinley (1991) however asserts that the major difference will reflect in the volatility of the bank’s earnings.
2.2. Measures of Bank Performance: Profitability Indicators Studies in this line that have involved measuring the performance of banks have traditionally
utilized financial ratios such as Return on Assets and Return on Equity as measures of profitability (Mathuva 2009; Wet and Toit 2006) but most of them have focused on determining the efficiency of the banks. However, the ratios have proved useful in the interpretation of company’s financial and management accounting data (Halkos and Salamouris, 2004). Breaking down the components of Return on Equity (ROE) Wet and Toit (2006) assert that it is one of the best measures of company performance as it combines the components of the profitability, efficiency and financial leverage. Further stressing the relevance of financial ratios Halkos and Salamouris (2004) stress that they are useful in making both inter and intra industry comparisons while targets can be set by benchmarking. However, they not oblivious of their shortcomings. Highlighting some of the deficiencies, Oberholzer and Westhuizen (2004) and Chen and Yeh (1997) assert that these ratios have limitations in their capacity to give a robust measurement of a bank’s performance and indeed the performance of firms in general. According to them the ratios are inadequate as measures of future performance since they are drawn from the past performance thus analysis drawn from them should be seen as the starting point for any future research. They further emphasize that the ratios are measures of short term performance and that they lump together all the aspects of the bank’s performance making it impossible to identify specific areas where actual performance has been outstanding or below expectation. In addition, Lei (2005) while emphasizing that financial ratios remain a quick, useful and reliable means of analyzing the performance of banks, acknowledges that the accuracy of financial ratios may be distorted by inflation and also the timing of the release of the financial reports. Other criticisms state that ratios ignore importance of some other parameters such as the cost of capital (Colquitt 2007) while others state that they are subject to manipulation within acceptable accounting standards (Wet and Toit 2006). Consequently several other approaches have been employed as a means of measuring the comprehensive performance of banks, one of which is the Data Envelopment Analysis which is being researched, adopted and compared to financial ratios (Ho and Zhu, 2004; Halkos and Salamouris, 2004; Oberholzer and Westhuizen, 2004; Chen and Yeh, 1997).
Data Envelopment Analysis (hereafter called DEA) is a linear programming model that considers multifactor inputs for measuring the efficiency of Decision Making Units (Ho and Zhu, 2004; Talluri, 2000). Talluri (2000) reflected on the several models developed under DEA technique while Chen and Yeh (1997) show that the concept behind the models, which is to identify
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the most efficient Decision Making Unit (DMU) and make it the standard DMU for comparison with the other DMUs, is the same across the models. Ho and Zhu (2004) however express that though a lot of research has utilized the DEA concept, most have been based on the operational efficiency, thus establishing a correlation between the financial ratios and DEA as a measure of a bank’s performance has not yielded very positive results. Oberholzer and Westhuizen (2004) conducted a study to compare results of DEA and financial ratios as measures of performance and based on the obtained results, concluded that DEA should be used as a complement to the financial ratios as there was no significantly established relationship between the outputs. Similarly, a measurement of the efficiency of Greek banks by Halkos and Salamouris (2004) incorporated financial ratios into the DEA model and sought to compare the results with that of the financial ratios resulted in a recommendation that DEA to be used as a compliment, emphasizing that both suffer a common limitation of depending on accounting data and not market figures. Previous comparison by Chen and Yeh (1997) yielded similar results. Again, Ho and Zhu (2004) in their study incorporated profitability ratios as part of their input variables for the DEA model and also highlighted the limitations of the model. A significant conclusion from these studies is the practical confirmation that DEA is not a complete substitute for the financial ratios and as confirmed by Ho and Zhu (2004) means that is better for measuring efficiency within bank units. However, in terms of measuring profitability of the banks or a firm as a whole, its applicability remains questioned.
Risk adjusted performance measures have also been introduced to factor in elements of risk embedded in the transactions into the measure of performance. Topmost of these is Risk-adjusted Return on Capital (RaRoC) which is used to determine risk based profits while a variant of it is the Return on Risk-adjusted Capital (RoRaC) (Bessis, 2002). To determine RaRoC, income is first adjusted for risk by deducting probable loss from income generated and then calculating the ratio of the outcome to allocated capital (Crouhy et.al., 2006) while RoRaC is calculated by determining the ratio of income to economic capital, which is allocated capital that has been adjusted for risk by adjusting for potential loss (Crouhy et.al., 2006). Risk adjusted measures are useful in both risk management and performance measurement as they are used in quantifying the volume of capital required for all operational activities by determining the capital requirement of all business units (www.valuebasedmanagement.net). Another type of risk adjusted performance measure is the Riskadjusted Return on Risk-adjusted Capital (RaRoRaC) which as the name implies is obtained by adjusting both income and capital for risk (www.qfinance.com). These measures however have inherent complications which require deep analysis thus making them difficult for external parties to utilize (Crouhy et.al., 2006). Additionally, Hosna et. al. (2009) and Demirguc-Kunt and Haizinga (1999) assert that aspects of the data that have been adjusted for risk are information that is internally available thus not quite accessible thus limiting the use of the terms as performance measures. These factors give justification for the continuous use of financial ratios for analysis by researchers.
3. Methodology This study will be conducted via a positivist philosophical approach with an epistemological
view. The study will employ a deductive approach as the aim is to test the validity of the proposed the using the data gathered from the four international banks based in Kosovo. A quantitative technique will be utilized via the regression analysis to test the data.
Four large Kosovo banks namely PCB, RBKO, NLB, TEB Royal will be used for the study and all data pertaining to the study will be extracted from the financial reports of the selected companies from 2006-2015. The aim is to obtain the maximum number of observations possible. Given that the aim is to determine if a relationship exists between parameters utilized in credit risk management and parameters utilized in profitability, the parameter to be used as the profitability indicators is the Return on Equity (ROE) while parameters for credit risk management are non performing loan ratio (NPLR) and risk asset ratio (RAR), thus the dependent variable will be ROE while RAR and NPLR will serve as independent variables.
The more the volume of nonperforming loans on banks’ books is the greater the amount of provision that has to be made. This will likely reduce the earning capacity of the bank and drive down profits. My proposition is that a high NPLR is an indication of inadequate risk management and should reduce profitability. I also propose that banks that hold very high capital ratio have tied
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down assets that could generate revenue and will have less return. My hypothesis thus is stated with the corresponding null hypothesis as:
1a. Hypothesis 1: An increase in NPLR will result in a decrease in ROE as the two variables will have an inverse relationship
1b. Null Hypothesis: NPLR and ROE do not have a direct relationship, thus increase in NPLR will have no effect on ROE.
In addition banks need to utilize funds at their disposal to generate income. Holding substantial capital reduces their capacity to lend and generate income. My second proposition thus becomes:
2a. Hypothesis 2: Increase in capital reserves thus RAR will reduce profitability and create a negative relationship between the two 2b. Null Hypothesis: Increase in capital reserves and RAR will not reduce profitability and no relationship exists between the two variables.
This is to answer the research questions which are stated thus: What is the effect of credit risk management on net interest income?
What is the effect of credit risk management on the overall profitability of banks?
3.2. The Regression Analysis Regression analysis is a statistical technique used to analyze the relationship between a
predictor variable(s) and predicted variable(s) with the assumption that there exists a linear relationship between the two variables; a relationship which is dependent on the certain unknown parameters which will be generated through the regression exercise from the data imputed. The most common regression analysis in use is the linear regression of which the ordinary least squares method (OLS) is the most popular. The regression technique adjusts the values of the slope and intercept to determine the line that best fits the equation or that best predicts Y from values of X. In its simplest form, the linear regression model is expressed as:
Y = α + βX + ε. Where the parameters are defined as: Y is the predicted or dependent variable X is the predictor or independent variable α is the intercept of the line β represents the slope and ε represents the inherent error in the
system. The parameters α and β are determined from the regression. Being the coefficient of X, β
determines the nature of the relationship between the two variables. To account for inexplicable variations in the patterns of the variation of the dependent function Y as the independent variable X changes, a random or stochastic error function, ε, is introduced. This is because there is the tendency that the value of Y observed in reality may not be exactly equal to the predicted value based on the model, thus the function accommodates all variations between X and Y that cannot be explained by the model and thus is known as the random component of the function. Such variations could be due to a number of reasons which may range from measurement and calculation errors to the possibility that the relationship between the variables in question may be non-linear.
For the study being conducted there are two predictor variables being RAR and NPLR and one predicted variable, ROE, thus a multivariate regression model which accommodates more than one predictor variable will be used. This is mathematically expressed as:
Y = α+β1X1+ β2X2+…+ βnXn+ ε. Where: Y remains the predicted variable X1, X2...Xn are the various predictor variables β1, β2..... βn are the coefficients of the
independent variables. 3.3. Resolving the Research Questions The model equations are the framework for determining the effect of risk management on
interest income and on the profitability of banks as a whole. Based on the multivariate regression equation, the model equations for this study become:
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ROE1 = α + β1(RAR) + β2(NPLR) + ε ROE2 = α + β1(RAR) + β2(NPLR) + ε Where ROE1 and ROE2 are measures of profitability based on net interest income and profit
attributable to shareholders RAR (risk asset ratio) is the first independent variable NPLR (non-performing loan ratio) is the second independent variable, α, β and ε remain as
previously stated. The regression will be carried out for each measure using data from the banks. This will
generate the constant term (α) and (β1, β2) the coefficients of the predictor variables or the regression coefficients, which are the parameters that will significantly define the nature of the relationship between the variables.
4. Input Data For Multivariate Linear Regression
The relevant data required for the analysis is shown in this section. All the required data have been extracted from the financial reports of the banks concerned and have been used to calculate the required predicted (ROE) and predictor (NPLR) variables. The risk asset ratio (RAR) is also extracted from the financial reports.
4.1 Input data: The input data computed is shown in the tables below:
Table 1. PCB Data Input
ROE1(NETINTINC/E QUITY)
ROE2(PAT/EQUITY)
Year NPLR RAR
2015 0.033203 10.8 0.3174 0.045471905
2014 0.026497 11.4 61555 0.4547 0.061202466
2013 0.01829 13.6 76635 0.2949 0.14928995
2012 0.015634 13.5 04806 0.3182 0.145719507
2011 0.015234 12.8 77466 0.3389 0.163157781
2010 0.018121 12 95153 0.3590 0.149128984
2009 0.027755 12 15504 0.3437 0.11781451
2008 0.029122 13.3 21886 0.2950 0.119051254
2007 0.030432 13 04389 0.3202 0.108571304
2006 0.034802 13.3 54899 0.3011 0.145446566
Source: Own research
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Table 2. RBKO Data Input
ROE1(NETINTINC/EQUITY)
ROE2(PAT/EQUITY)
Year NPLR RAR
2015 0.040624 16.6 0.208654448 0.17868574
2014 0.027602 13.6 0.277049658 0.11761565
2013 0.025605 11.8 0.32140466 0.158978307
2012 0.017813 11.7 0.359663271 0.193304748
2011 0.019376 11.3 0.463388041 0.197807873
2010 0.015631 11.5 0.392834587 0.187632773
2009 0.018075 12.8 0.40089844 0.166575609
2008 0.022036 12.8 0.408089444 0.146662282
2007 0.021126 12.5 0.420733388 0.169906259
2006 0.020525 11.00 0.390915295 0.187533177
Source: Own research
Table 3. NLB Input Data
ROE1 (NET INT INC/ ROE2 (PAT/
Year NPLR RAR EQUITY) EQUITY)
2015 0.051305 16.1 0.212308326 -0.046400638
2014 0.024007 14.1 0.317175903 -0.409942424
2013 0.012841 11.2 0.238847619 0.137693729
2012 0.013459 11.7 0.268852126 0.15736324
2011 0.014098 11.7 0.279892761 0.152165938
2010 0.020109 11.8 0.31807035 0.086366771
2009 0.021435 11.7 0.290144906 0.072859678
2008 0.023586 11.5 0.248696597 0.067632151
2007 0.022317 11.5 0.250346081 0.079901367
2006 0.021906 12.1 0.41789624 0.184673965 Source: Own research
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Table 4. TEB: Input Data
ROE1 (NET INT ROE2
Year NPLR RAR INC/EQUITY) (PAT/EQUITY)
2015 0.01853 16.5 0.278822238 0.123628383
2014 0.014811 15.6 0.333649503 0.153929539
2013 0.013616 15.2 0.300465206 0.136252458
2012 0.019474 14.2 0.310567851 0.133685397
2011 0.023651 13.6 0.299107894 0.151237165
2010 0.039301 15 0.372969769 0.17462952
2009 0.060962 14.6 0.38470512 0.131950745
2008 0.071054 14.2 0.421320495 0.116093535
Source: Own research
The data consists of a total of 10 observations each for PCB and RBKO, NLB and 10
observations for TEB. This gives a total of 38 observations for the whole analysis. 5. Data Output And Analysis
The results of the regression carried out and a detailed interpretation of it is contained in this section. The values of the alpha and beta of the model equation along with the statistical parameters that determine the strength of the relationships being tested are determined by the regression and shown in this section.
5.1. The Regression Output: The output of a regression on executed on Microsoft excel is
shown below. An explanation of the parameters generated from the regression follows.
Table 5. Sample Output
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.747115548
R Square 0.558181643
Adjusted R
Square 0.477851032
Standard Error
0.112815241
Observations 14
ANOVA
Significance
df SS MS F F
Regression 2 0.176872385 0.088436193
6.94855 0.0111903
Residual 11 0.140000066 0.012727279
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The multiple R, also known as the multiple correlation coefficient, gives an insight into the
relationship between the variables by determining the extent of linearity between them thus assessing the fitness of the data to the linear model. The correlation coefficient can vary between -1 and +1 and the closer it is to either value of 1 the stronger the linear relationship between the parameters while the closer it is to zero the weaker the linear relationship between the parameters under investigation. The difference is that a multiple R that is close to +1 indicates a positive correlation between the variables while one closer to -1 implies a negative correlation. However a correlation coefficient of zero implies there is no linear relationship between the two variables. Thus in this case the multiple R determines the fitness and extent of linearity between ROE1 expressed as a function of NPLR and RAR.
The regression also determines the square of R (or R squared, also termed the coefficient of determination).The R squared is a parameter that estimates the percentage of the variance in the predicted variable that is accounted for by the model and thus the extent to which the model can be used to predict the dependent variable. It is however noted to overestimate the extent of linearity. The adjusted R squared serves the same purpose and is deemed to be more accurate relative to R squared having taken cognisance of the number of independent variables in the model. The standard error determined from the regression defines the extent of the variance of the data points along the regression line and is computed as the standard deviation of the data points as they are spread around the regression line.
The Analysis of Variance (ANOVA) gives another reflection of how the model accounts for the predicted variable is generally used to ascertain if the relationship between the variables involved is statistically significant. The Table 5 is split into three components, the first is the part that is accounted for by the model termed, regression, and the other part is not, termed residuals while the last part is the total which is the sum of the first two. Each component has a corresponding degree of freedom (df) associated with it. The df for the regression is the number of independent variables in the model while that of the total is the total number of observations (n) minus one (i.e. n-1). The df for the residual is the difference between the total df and the residual df. The sum of squares (SS) describes the variability in the predicted variable (ROE1) in the both the regression and the residual. The variance that is not accounted for by the predictor variables is termed the error. The total sum of squares defines the total amount of variability of the predicted variable and refers to the overall variation in the data that cannot be explained by the model.
MS refers to the mean square and is determined by dividing the sum of squares (SS) of each component of ANOVA (i.e. regression, residual and total) by its corresponding degree of freedom (df).
The F in the table is the result of the F-test, a test of the null hypothesis which reflects the overall significance of the model while Significance F is the associated P-value for the F-test. These are the most important aspects of ANOVA and their values are a function of the regression analysis and the confidence level selected and are the basis on which the null hypothesis is rejected or otherwise. The F-value obtained is a function of the degrees of freedom and must be compared to a critical value of which it must be greater than for the model to be valid. However, the validity of the F-value is inherently determined by its corresponding P-value. This determines the probability that the F value obtained will be statistically relevant to reject the null hypothesis. The lower the P-value the greater the significance of the model but it is also compared to a critical significance level which the P-value must be less than. In finance, for a confidence level of 95 %, the required
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significant level is 0.05, thus a P-value that is greater than 0.05 (i.e P>0.05) suggests that the relationship between the variables is not statistically significant. Conversely a P-value less than 0.05 (i.e P<0.05) suggests that the relationship between the two is statistically significant.
The last table shows the coefficients and associated statistics. The intercept defines alpha which is an estimate of the predicted variable when the predictors or their coefficients are zero; it is however not the most relevant of the data. The coefficients of the predictor variables (betas) are also shown on the table. The size of the coefficients of NPLR and RAR determines the level of influence each variable has on ROE when the other independent variable is held constant, thus the larger the coefficient the larger the influence of NPLR and RAR on ROE. However a negative coefficient signifies that NPLR or RAR has a negative relationship with ROE. The standard errors are associated errors of the regression coefficients and are defined by the square root of the variance.
The t-stat, i.e t-statistic, is a significance test that determines the statistical significance of the independent variable in predicting the dependent variable by measuring the number of standard errors by which the coefficient is close to or away from zero. The figure is obtained by dividing the coefficient by its standard error and the greater the t-stat the more reliable the coefficient is as a function in the regression model. The associated P-values serve the same function as that of the model equation as a whole, determining also the statistical significance of the coefficients. The upper and lower confidence intervals determine the range within which the coefficients are likely to fall 95 % of the time. This implies that the confidence interval is the likely region within which the true values of the coefficients will fall. The values at the boundary of each interval are termed confidence limits. Inherent within the confidence interval is the precision of estimation and the wider the confidence interval the less the precision. The confidence level is said to be statistically significant if it does not overlap to zero. Finally the regression results also show the standardized coefficient. Whilethe unstandardised coefficients are the actual coefficients of the independent variables the standardized coefficients are the coefficients that are obtained when the variables have been standardized by deducting the mean and then dividing by the standard deviation (SD). The standardised coefficients attempt to verify which independent variable has a more significant effect on the dependent variable, given that the independent variables are measured in different units. However this may not be practical as changes in SD in the independent variables may not be equivalent, they are thus not considered as relevant parameters.
Not all these parameters are relevant in interpreting the output; the most important parameters in the output are the multiple R, adjusted R square, P-values, un-standardised coefficients, and the P-values of the coefficients and these will be the focal point of the discussion.
6. Discussion of the Results
6.1. PCB: Analysis of the Regression Relevant extracts from the regression on PCB data are shown in the Table 6. The Multiple R or correlation coefficient was determined to be approximately 0.6. Being
distant from 0 which signifies no linearity, the result implies that a partial linear relationship exists between the variables being investigated. R squared is estimated to be 0.36 while adjusted R squared is 0.28. This implies that approximately 36 % of the variance in ROE1 is accounted for by the model. This level of predictability is further reduced by the R squared which by reading 0.28 implies that 28 % of the variance in ROE1 has been accounted for by the model. This leaves a residual of 72 %, implying that 72 % of the variance in ROE1 is not accounted for by the predictor variables (RAR and NPLR).Fvalue of 4.5 and a corresponding P-value of 0.028 the resulting data demonstrates a level of statistical significance being less than 0.05. The implication of this is that the probability the model can predict ROE1 is 97.2 %.
However the result of the beta coefficients of the independent variables proves to be rather interesting. With beta a coefficient of 0.003, the implication is that RAR has a small but positive impact on ROE1. Also, a coefficient of 2.261 shows a significant positive influence of nonperforming loans on the interest income. Thus for a unit increase in NPLR, ROE increases by 2.261 when RAR is held constant. Similarly for a unit increase in RAR when NPLR is held constant, ROE1 will increase by 0.003, implying NPLR has a greater influence on ROE than RAR. However the t-value of 0.332 and the associated P-value of 0.744 signifies a level of statistical insignificance
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for RAR while with a Pvalue of 2.959 and corresponding t-stat of 0.009, NPLR is of statistical significance. This shows the level and nature of influence exerted by capital ratio and non-performing loans requires further examination as both RAR and NPLR are hypothesised to have a negative impact.
Replacing ROE1 with ROE2 results in a multiple correlation coefficient of 0.857, implying that a higher degree of linearity is exhibited. R squared and adjusted R squared estimates of 0.734 and 0.701 respectively imply that approximately 70 % of the variance in ROE2, which is based on profit after tax, is accounted for by the regression model. This however has no bearing on the statistical significance. An F-value of 22 and an extremely low P-value of almost zero highlights the significance of the model to be statistically significant. The coefficients of NPLR and RAR are 0.35 and .033 respectively, indicating that both have a positive relationship with ROE2, however a t-value of 6.5 with the P-value at almost 0.00 for RAR exhibits statistical significance while corresponding values of 0.894 and 0.385 for NPLR reflects a level of insignificance. Table 6. PCB Model and Coefficient Summary
Source:Own research
These distinction hypothesis 1 and 2 results, show an increase of NPLP and RAR that refers
us to corresponding for an increase in after-tax interest income and in other comprehensive income.
With this positive results of RAR profit impact also the author did notice those distinctions [CITATION All \l 1033 ], and higher capital ratios with a reduction in profitability [CITATION Gle15 \l 1033 ]. Also the impact of the benefit of contrast results also appear at the works of researchers as Kanas et al., (2012); Ani, Ugwunta, Ezeudu and Ugwuanyi (2012); Bolt et al., (2012). Taking into account the outcome of ROE2 into our ROE1 model, both variables have displayed a similarity with profitability relationship.
6.2. RBKO: Analysis of the Regression The result of the regression on data from RBKO parameters give a multiple R of 0.93 which
implies a good fit for the model equation. R squared is 0.869 while adjusted R squared is 0.85 implying that about 85 % of the variance in ROE1 is can be explained by NPLR and RAR.
Dependent Variable: ROE 1
Adjusted
R R
R Square Square F Sig. a
.600 0.36 0.28 4.5 .028
Un-standardised
Coefficients
RAR 0.003 0.744
NPLR 2.261 0.009
Dependent
Variable:
ROE2
Adjusted
R R
R Square Square F Sig. a
.857 0.734
0.701
22.092
.000
Un-standardised
Coefficients
RAR 0.033 0
NPLR 0.35 0.385
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Table 7. RBKO Model and Coefficient Summary
With an F-value of 53 and a P-value of approximately zero the model is statistically
significant. The coefficients of the variables show that again NPLR has a high but positive impact on ROE1 which is statistically significant based on the t-values and corresponding P-value of 0.001. RAR has a smaller but negative effect and with a t-stat of -7.5 and P-values of approximately zero for RAR, the model is statistically significant. Thus for a unit increase in NPLR, ROE increases by 2.57 when RAR is constant while for a unit increase in RAR with NPLR constant, ROE will decrease by -0.057.
Substituting interest income for profit after tax as the dependable variable gives a partial linear correlation defined by a correlation coefficient of 0.574. A correlation of coefficient of 0.329 implies that 32.9 % of the characteristic of ROE2 is influenced by NPLR and total RAR. The adjusted R squared puts that figure at 24.6 %.The result can be said to be statistically significant with an F value is 3.93 and P-value of.041. At -1.462 the coefficient of NPLR is negative and has a more bearing influence on ROE relative to the coefficient of total RAR which has a smaller and negative influence of -0.01.However the P-value of 0.066 and 0.255 shows that both NPLR and total RAR are not significantly good predictors of profitability.
6.3. NLB : Analysis of the Regression The regression output is shown in Table 3. Again there is a partial linear relationship between
profitability, non-performing loans and capital reserves as exemplified by an R of 0.512. However a maximum of 26.2 % of variance in ROE is accounted for by the model due to the R square of 0.262; the predictability of is reduced by adjusted R squared of 0.128, putting the estimate at 12.8 %. The model is also statistically insignificant with Pvalue of .188.
The betas of the independent variable again show that with a beta of 6.4, NPLR has a greater controlling effect on ROE while capital reserves has a lighter effect with a beta of -0.062. However the statistical significance is at variance with NPLR exhibiting a high degree of insignificance with Pvalue of 0.186 and RAR exhibiting the same with P-value of 0.075.
Dependent Variable: ROE 1
R Adjusted
R Square R Square F Sig. a
.932 0.869
0.853
53.049
.000
Un-standardised Coefficients
RAR -0.057 0
NPLR 2.57
0.001
Dependent Variable : ROE2
R Adjusted
R Square R Square F Sig. a
.574 0.329 0.246
3.929
.041
Un-standardised Coefficients
RAR -0.01 0.255
NPLR -1.462 0.066
Source: Own research
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Table 8. NLB; Model and Coefficient Summary
Replacing ROE1 with ROE2 results in a multiple R of 0.747, this gives a higher degree of
linearity between the variables. An R square and adjusted R square of 0.559 and 0.478 respectively also show that there is improved accuracy in the predictive capacity of the model, given that between 47.8 % and 55.9 % of the variation in profit can be attributed to NPLR and RAR.
The model also proves to be statistically significant with an F-value of 6.958 and a P-value of 0.011. NPLR again shows a greater degree of influence on profits with a positive beta of 8.8 while RAR has a milder effect with a negative beta of -0.12. However with P-values of 0.13 and 0.006 respectively, the beta of NPLR is statistically insignificant while beta of RAR reflects a level of significance.
6.4.TEB: Analysis of the Regression The model showed a very strong linear relationship between the variables with an R of 0.936.
An R squared and adjusted R squared of 0.87 and 0.84 shows that statistical variance of ROE1 is significantly explained by NPLR and RAR. With an F-value of 24.7 and a P-value of 0.001 the model is statistically significant. Furthermore the betas of the model show NPLR has a greater effect which with a P-value of approximately zero is quite significant. However RAR capital has a minimal effect which is not statistically significant given the P-value of 0.981.
Again replacing ROE1 with ROE2 reflects a partial linear relationship with a multiple coefficient of 0.535. However with adjusted R square of .083 shows that very little of the variance in income is explained by RAR and NPLR. Furthermore an F-value of 1.4 with corresponding P-value of 0.306 implies a degree of statistical insignificance. Furthermore both NPLR and RAR appear to have a negative effect on net income with coefficients of - 3.97 and -0.012 respectively. Again it shows that NPLR has a greater impact on net income relative to RAR. However based on the t-stat values of -1.192 and -1.45 and corresponding P-values of 0.272 and 0.188 respectively, the statistical status of the predictors is insignificant.
This result again supports hypothesis 2 but is inconclusive on hypothesis 1. This is because the effect on ROE2 corroborates the hypothesis it while the output on ROE1 contrasts with it.
Generally the results showed that the extent of the influence of capital reserves on profitability was small in all the banks and had a predominantly negative impact. In RBKO, NLB and TEB the beta of RAR shows a negative effect on both profits after tax and net interest income while it was all positive in PCB which appeared an exception.
Dependent Variable: ROE1
R R Square
Adjusted R
Square F
Sig.
.512a 0.262 0.128 1.952 .188
Un-standardized
Coefficients
RAR -0.062
0.075
NPLR 6.472
0.186
Dependent Vari able: ROE2
R Adjusted
R Square R Square F Sig.
.747a
0.558
0.478
6.949
.011
Un-standardized
Coefficients
RAR -0.124 0.006
NPLR 8.827 0.13
Source: Own research
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Table 9. TEB -Model and Coefficient Summary
Source: Own research
Unlike capital reserves, non-performing loans had a larger and predominantly positive effect
on profitability, with beta being negative only on TEB and RBKO when ROE2 was the dependent variable but reflecting a positive effect in all other instances.
Looking at the statistical significance, the overall model was statistically significant in PCB and RBKO with P-values less than the 0.05 benchmark but was insignificant for NLB when net-interest income was the dependent variable and insignificant in Standard Chartered when profit after tax was the dependent variable. Similarly the predictive tendency of the coefficients oscillated between statistical significance and insignificance at different times.
Relations which were mostly positive between non-performing loans and profitability were opposed period provided that the increase in bad loans the banks will deny revenue and would interfere with the reduction of potential profitability. This indicates that there is approximately a link between NLP and ROE, the result is not sensitive to the preaching of the hypothesis 1. As mentioned earlier with the work of Mario (2014) which is based on his work and in other comparative studies, he did reached the conclusion that the non-performing credits show a negative holder is the probability, similar work also appear Jamal H. Zubayr and Shazia Farooq (2014). This reflects on the further analysis.
In connection with RAR, the results largely confirm the hypothesis 2 Which emphasizes that higher capital reserves are an obstacle for benefits because of the collapse of the credit and simultaneously reduces the fall of the risk taking. Although they are not absolutely convincing because in both cases the PCB were contradictory. This has a tendency to agree with the work of Bateni, Leila; Vakilifard, Hamidreza; Asghari, Farshid. (2014) who point out that the higher capital ratios represent a reduction of banking risk and reduces the repayment, while in contrast this with findings such as authors Sonia Narula, Monika Singla (2014), conclude that with the capital increase the return of the loans will increase.
Further Analysis To further check the validity of the results due to these variances, I conduct a simple linear
regression on all the banks using excel software with NPLR and RAR used independently as independent input variables. The summary of the results, shown in Table 10, confirms that NPLR has a greater effect on ROE than RAR across all the banks as its beta coefficient was larger than the beta of RAR. Also the nature of the effect of NPLR and RAR on ROE was similar to the output from
Dependent Variable: ROE 1
R R Square
Adjusted R
Square F Sig. a
.936 0.876
0.841
24.788
.001
Un-standardized Coefficients
NPLR 2.203 0.325 0
RAR -0.004 0.008 0.981
Dependent Variab le: ROE2
R
R Square
Adjusted R
Square
F Sig.
a .535 0.287 0.083 1.406 .307
Un-standardized Coefficients
RAR -0.397 0.333 0.272
NPLR -0.012 0.008 0.189
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the multivariate model. Beta coefficient for RAR was predominantly negative replicating the multivariate model, the noted difference being a negative coefficient for PCB when ROE1 is the dependent variable. For NPLR the beta coefficients again oscillated between a positive and negative effect though the negative effect was more pronounced when PAT was the dependent variable and was more pronounced compared to the multivariate model, again bringing to question the accuracy of the multivariate model. However the F and associated P-values obtained from the models again reflected a level of statistical insignificance at certain points.
To further examine the relationship between these variables I conduct 10 year trend analysis based on real net-interest income, profit after tax, total loans, non-performing loans and non-performing loan ratio. This is reflected in the folowing figures.
A comparison of net interest income across the period shows that all the banks recorded year on year growth in net-interest income from 2006 – 2015 but all experienced a decline between 2014 and 2015, a situation that could be attributed to the economic meltdown.
Table 10. Beta Coefficients from the Simple Linear Regression
Dependent: ROE2 Dependent: ROE1
NPLR RAR NPLR RAR
RBS
-5.410 -0.045 -0.62865 -0.02257
HSBC
0.491 0.014 2.274906 -0.03548
Barclays
-1.766 -0.021 4.298716 -0.0705
SCB
-0.268 -0.005 -2.205185 -0.02257
Source: Own research
Fig. 1. Net Interest Income - A 10 Year Comparison
A similar trend is displayed for profit after tax and total loans given. But parameters
experienced a decline after 2013, PAT having a more drastic drop; prior to the recession both had been growing steadily. The drop was much more significant in NLB which experienced the largest loss, however all the banks were showing signs of recovery by 2015.
50,000
40,000
30,000
20,000
10,000
TEB
NLB
PCB
RBK
O 0
2015 2014 2013 2012 2011 2010 2009 2008 2007 2006
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Source: Own research
Fig. 2. Profit After Tax - A 10 Year Comparison
Source: Own research Fig. 3. Total Loans – A 10 year Comperation
The trend also shows non-performing loans had grown steadily from 2006 to 2012 but
contrary to other parameters which dropped, it further increased between 2013 and 2014. Again this distortion in the trend can be attributed to the recession which resulted in heavy loans default.
This shows that prior to the recession practically all the parameters showed a trend similar to each other as all experienced steady growth, giving a statistical impression that growth in non-performing loans could have a positive relationship with profitability. However a look at the trend on nonperforming loans ratio and annual percentage change in non-performing loans paints a different picture; as shown in Figures 4 and Figure 5.
1,200,000
1,000,000
800,000
600,000
400,000
200,000
0
TEB
NLB
PCB
RBK
2015 2014 2013 2012 2011 2010 2009 2008 2007 2006
20,000
TEB
0 Barclays
2009 2008 2007 2006 2005 2004 2003 2002 2001 2000
10,000
-10,000 BCP
RBK
O -20,000
-30,000
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Source: Own research Fig. 4. Total Non-performing Loans - A 10 Year Comprison
The trend also shows non-performing loans had grown steadily from 2006 to 2012 but
contrary to other parameters which dropped, it further increased between 2013 and 2014. Again this distortion in the trend can be attributed to the recession which resulted in heavy loans default.
This shows that prior to the recession practically all the parameters showed a trend similar to each other as all experienced steady growth, giving a statistical impression that growth in non-performing loans could have a positive relationship with profitability. However, a look at the trend on non- performing loans ratio and annual percentage change in non-performing loans paints a different picture; as shown in Figure 5 and Figure 6.
Source: Own research
Fig. 5. Non-performing Loan Ratio (NPLR) - A 10 Year Comparison
0.0800
0.0700
0.0600
0.0500
0.0400
0.0300
0.0200
0.0100
0.0000
TEB
NLB
PCB
RBK
O
2015 2014 2013 2012 2011 2010 2009 2008 2007 2006
40,000
35,000
30,000
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20,000
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10,000
5,000
0
TEB
NLB
PCB
RBK
2015 2014 2013 2012 2011 2010 2009 2008 2007 2006
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Source: Own research
Fig. 6. Trend Analysis – Percentage Change in Non-performing Loans
Both figures show an initial decline in the non-performing loan parameters though the
decline in figure 6 was more apparent in TEB and RBKO and less apparent in PCB and NLB, which reflected an initial stagnation or an almost constant rate of change. However for PCB while the nonperforming loan ratio followed other trends with an initial decline prior to subsequent increase, the year on year percentage change in nonperforming loans showed an initial low gradient rise before it became a steady climb, indicating a different trend from the others and shows an actual increase in the percentage change in non-performing loans throughout the period. However signs of increase in the parameters across all the banks became apparent from 2008/2009 as the volume of nonperforming loans increased in all the banks, again indicated by the curve in figure 6, showing there was an upward trend as the percentage change in nonperforming loans increased.
Conducting a similar trend analysis on profitability shows that percentage change in profit initially increased then experienced a downward trend in all the banks over a corresponding time period.
Comparing figures 5 and 6 shows that over the same time period as profitability parameters increased nonperforming loan parameters decreased and vice versa. This pattern agrees with the postulate that the relationship between profits and nonperforming loans is a negative relationship. However slightly deviating is PCB whose non-performing loan parameters indicated an initial marginal rise with profitability before the negative trend took effect. This trend which reflected an initial increase in the change in non-performing loans with profitability further explains why PCB aspects of the regression output indicate into a positive relationship between NPLR and ROE.
120
100
80
60
40
20
0
-202000 2002 2004 2006 2008 2010
TEB
RBK
O
NLB
PCB
Poly. (TEB)
Poly.
(RBKO)
Poly. (NLB)
Poly. (PCB)
-40
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Fig. 7. Trend Analysis - Percentage Change in PAT
This outcome casts an aspersion on the overall statistical accuracy of the multivariate model
due to its inability firmly establish a definite trend. However, there are limitations which could have affected the overall results of the model.
7. Conclusion This study is an investigation into the effect of credit risk management on profitability with a
focus on banks in Kosovo. Using data from PCB, RBKO, NLB and TEB.I sought to establish a link between credit risk management and profitability; profitability being measured from two perspectives which were interest income and profit after tax. The study was based on two propositions; Hypothesis 1 which states that an increase in nonperforming loans will result in profit erosion and thus a decrease in profit, implying that the two variables will have an inverse relationship; and
Hypothesis 2 which states that an increase in capital reserves will be an impediment to the income generating capacity of the banks, thus result in a decline in profitability, establishing a negative relationship between the two.
The aim was to answer the research questions:
What is the effect of credit risk management on net interest income?
What is the effect of credit risk management on the overall profitability of banks? This was conducted using return on equity (ROE) as measures of profitability while
nonperforming loan ratio (NPLR) and risk asset ratio (RAR) doubled as risk management measures. The return on equity (ROE) was determined from two measures namely interest income and profit after tax and used as a dependent variable which is postulated to be predictable by the independent variables NPLR and RAR.
The methodology involved extracting the figures for net interest income, profit after tax, total loans to customers and total nonperforming loans from the annual reports for the time period under consideration and using the figures to calculate the NPLR and ROE from the two measures.
The outcome from the multivariate regression showed across all the banks that a partial linear relationship existed between the credit risk management indicators and the profitability indicators. This was reflected by the multiple R figures which were largely between 0.5 and 0.9. NLB, TEB and RBKO had results which indicated that for both measures of ROE, an increase in RAR will result in a small decline in profitability. PCB however showed otherwise. For NPLR, PCB and RBKO reflected a positive effect on both measures of ROE while Barclays and Standard Chartered reflects a positive effect on ROE1 and a negative effect on ROE2.
200
100 TEB
RBKO
0
2000 -100
2002 2004 2006 2008 2010
-200
-300
-400
NLB
PCB
Poly. (TEB)
Poly.
(RBKO)
Poly. (NLB)
Poly. (PCB) -500
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Thus except for a few deviations from PCB, the beta coefficient of RAR generally reflected a small but predominantly negative effect on RAR, thus substantiating hypothesis 2 and demonstrating that a unit increase in RAR will result in a decrease in ROE if NPLR is kept constant. However the beta coefficient of NPLR from the multivariate model showed a larger but predominantly positive influence on ROE, implying that a unit increase in NPLR will result in an increase in ROE equivalent to the value of beta if RAR is kept constant, an outcome which was at variance with hypothesis 1.
These results were cross checked with a simple linear regression model and the result corresponded with the multivariate regression on RAR but to an extent contrasted with it on NPLR. Thus while the outcome on RAR significantly replicated that of the multivariate model, further substantiating hypothesis 2, the outcome on NPLR reflected more negative tendencies in the relationship between non-performing loans and profitability with only PCB exhibiting a positive relationship for both measures of ROE. This contrasted with the multivariate model but gave more credence to hypothesis 1 though not fully substantiating it.
Both results however are to be received with caution as both models had aspects of the overall model and individual P-values of the coefficients reflecting levels of both statistical significance and insignificance at certain points, emphasizing that the outcome is not entirely conclusive. To further check the relationship a trend analysis is conducted. The outcome of this aligned with hypothesis 1. This showed that in the long run and despite deviations from PCB, and though non-performing loans had increased across the banks over the period covered following the same pattern as profitability, net-interest income and total loans, when the loan parameters were expressed as a fraction of total loans and when the annual percentage change was determined year on year, it reflected a parameter that was on the decline while profit parameters were increasing.
From the outcome of the study it becomes imperative to draw a conclusion that for both hypothesis 1 and 2, the null hypothesis is rejected as it is clear that the beta of the coefficient variables is not zero. This conclusion is not only drawn from the multivariate model but from the combination of techniques that were applied.
Future research on this subject should strive to explore with more data so as to increase the number of observations and obtain more accurate results. A level of qualitative data which would involve interviews with staff in the selected banks to elaborate more on their credit risk management involvement and level of compliance should also form part of future studies. This would give the opportunity for interaction with risk management officers of the banks to ascertain their policies and practices as well as implementation.
Also for more accurate results the effect of non-banking subsidiaries should be controlled so that the data would reflect only the financial performance of the banking aspect of the business of the involved banks. Finally the investigation could be conducted on a non-linear basis and the results compared to the linear assumption to ascertain the true nature of the relationship between credit risk management and profitability.
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Published in the Russian Federation European Journal of Economic Studies Has been issued since 2012. ISSN: 2304-9669 E-ISSN: 2305-6282 Vol. 18, Is. 4, pp. 516-526, 2016 DOI: 10.13187/es.2016.18.516
www.ejournal2.com
UDC 33 Impact of OPEC Policies over the Global Economy: Case of USA Ameer Mahdi Nassrullah Mzwri a , * , Filiz Katman a
a Istanbul Aydin University, Istanbul, Turkey
Abstract This article is focused on how OPEC policies have historically shaped global economic
outlook; with a case example of United States of America (USA). The main aim of this study is to reflect the role of USA in strengthening of global economy using OPEC’s policies as the frontline mechanism and demonstrate an understanding of the key challenges confronted by world economies (especially emerging countries) by OPEC policies influenced by the super-power USA. This also mentions how USA has made efforts in reducing its overdependence on OPEC for oil, and how it aims to preserve and enhance national resources so as to gain oil independence. The study is directed using mixed methodologies to examine how the consequences of OPEC policies are helping shape world economies, especially of the USA since it does not have very good relations with the OPEC member states in particular.
Keywords: OPEC, oil dependence, economy, economic development, sustainability. 1. Introduction In this decade, the world has faced a new big crisis since 2014. This ends the super cycle of
commodity prices which was 105 dollars per barrel during four years of comparative stability before 2014. So, in terms of microeconomics, the factors on the oil price decline will be discussed. This decline in the oil prices is important; however, research indicates there were valid reasons behind this trend. Over the past three decades, oil prices fell in other episodes by more than 30 %. The recent oil price decline has several features that can also be compared with the situations faced during 1985-1986 followed by strong expansion of oil supply from producers not belonging to OPEC and is the result of OPEC’s decision to increase production. Other incidents included weakening global oil supply and demand that happened during the US recession of the 1990s, the recession of 2000, the Asian crisis in 1997, and the global financial crisis in 2008. All these incidents were eloquent of the fact that without a well-placed policy-relevant structure and framework, oil price declines would not be largely controllable. Apart from the production of unconventional oil, there are many reasons cited for oil price decline; one of the main reasons out of this was changes in OPEC policies which the global economies could not follow. Currency appreciation of the US dollar and weakening global oil demands were also noteworthy factors behind this situation. The world, in recent years, is witnessing a new trend that has perhaps, weakened the powers and authority of the OPEC; this translates into the development of non-
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OPEC economic institutions supplying oil which are not bothered by the OPEC policies, rather they devise their own plans and policies, and act by it.
However, the mission of OPEC takes on various little steps to ensure where it wants to go; the first measure being its commitment towards preventing decrements in the prices of oil, at a high level. OPEC’s member nations realize 40 % of world oil production with almost half their share in exportation of oil reserves across the globe. In the year 2007, around 338 billion dollars was generated from eleven member states from oil exportation which constitutes an increment of around 13 % since the year 2003. A major point in perspective here is the influence of changes in US currency (i.e. dollar) which impacts on OPEC’s policies since international trade in oil is done in this currency. As a supposition, if the dollar decreases in its value (currency depreciation), the purchasing power of OPEC states would decrease as well, due to which export ratio would be decreased by these states to be able to increase the real prices of oil. On a bi-annual basis, the OPEC member states conduct meetings within themselves to assess and study policy developments while examining economic trends that might alter the performances of international oil markets. Various decisions are taken in these meetings regarding actions to stabilize the market. Transparency and accountability in these meetings is very important as major policies with regards to changes in oil production and its world-wide distribution takes place therein.
2. Discussion Global economy How OPEC evolved into a global one, and how the different events in past affected its
influence over world economies. In times where increased oil dependence of non-oil producing countries including US and widely-condemned manipulations of the cartel organization has been matters of hot debate, the actual extent of efficiency of the organization is indeed, debatable. In the light of given research along with the learning obtained from data collected for this research, OPEC countries does not hold responsibility for world oil shortages despite their control of oil production and supply (Greene, 2010). It also confirms to the belief that the cartel is definitely not an intergovernmental organization for manipulating prices of oil commodities for its own interest; rather the main purpose of it has always been related with global economic development and sustenance. Some issues related to OPEC’s internal working environment suggested that indeed, there are fallacies with the global organization that need immediate rectification, especially those in relation with monitoring mechanisms and regulatory revisions. However, on the same hand, this research lent support to the idea that oil supplies are still better regulated and managed with the presence of this organization. It has also been discussed as the main frame of debate in this project that the US government is very concerned and is visibly aware of the economic consequences its dependence on oil, that is forwarded to it via OPEC member states (Drezner, 2014). Also, OPEC’s dominant price-setting influence can be internationally noticed where surging oil dependence on OPEC members have made consuming countries and regions realize that this dependence cannot be controlled without the support and collaboration of every nation-state in maintaining fair oil distribution and supply system in the world (Reboredo and Rivera-Castro, 2013; Baumeister Peersman, 2013).
As we know that whenever OPEC effects the US’s economy, this pressure on US directly affects the whole global economy. So that, based on the findings and discussion, it was emphasized that US should pay more attention towards R&D (Research and Development) initiatives that could be beneficial for both private and public sectors while also carrying out strategic planning for domestic oil production that might be less compelling in the shorter-run but will eventually be more fruitful in the long-term. The research suggested that the position of OPEC is unlikely to change in near future and unless substantial reforms are in place to meeting growing demands from reserves within, the situation of US getting affected by OPEC’s policies is certainly not changeable. Since oil is an essential commodity for commercial and personal use, OPEC’s significance as the world oil regulator and swing producer would not be eliminated unless every emerging and developing economy finds alternative measures to rely on (Van de Graaf, Verbruggen, 2015). Another key aspect of the reforms could mean following the same strategy as Africa and former Soviet Union; keeping production steady.
Higher fuel taxes bring with it political pressures that aggravate the issue of oil dependence for the world economies. Some attention has been paid towards decreasing the usage of crude oil
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and other crucial petroleum products in the U.S automobile industry, which also directs these organizations towards findings alternative energy resources. The government of the United States also issued some standard codes of conduct on higher fuel economy for new passenger vehicles in order to limit consumption. As a result, the governmental attention towards eliminating oil dependence is somewhat recognizable. In the coming decades, many more stringent strategies will be needed to encourage automobile and other manufacturing and service sectors to limit their usage of oil through energy-efficient product design and using different green technologies to better reduce carbon emissions while also considering the perspective of the environment (Van de Graaf, Verbruggen, 2015). Fuel prices should be under control of government for domestic use which will not only encourage and compel more people to come with vehicles on the road but is most likely to increase the rate of road accidents which are negative elements attached with the initiative. One of the obvious expectations in the light of observed practices and phenomenon is that OPEC is not going to allow oil-consuming nations not to pay higher prices for consuming increasing amounts of oil (Greene, 2010). Of course, there has to be contingencies planned for, keeping in close consideration that OPEC’s policies are at the most, only slightly alterable. Deliberate control of oil supply continues to pose the most penetrating effects on US economy; although, anti-inflationary effects, as a result of timely interventions of OPEC being a price regulator, create long-lasting advantages (Fuinhas et al., 2015).
The research provides an outlook on the ways and means in which US economy is affected by OPEC’s role as the world’s swing oil producer and oil price regulator. Despite an understanding of the primary functions of the cartel, it has been attributed to give way to more price shocks in the world than there occurred before its evolution. This research contributed to the existing research on the topic but emphasizing the need of research and development towards alternating energy resources that can help the world gain more sustainability than merely depending on oil from OPEC nation-states. The surging oil prices and oil consumption rates across the world, especially in the United States, spark a wave of having more alternative routes to valuable energy resources than through OPEC, only. However, given the possibility that if US’s thirst for oil from OPEC is unabated, there will soon be a time when it will have to extract oil from unconventional reserves (Reboredo, Rivera-Castro, 2013).
OPEC as a Cartel OPEC’s objective is to coordinate and unify of member states on oil, ensure price stability on
the world markets and prevent sudden fluctuations, and provide sustainable income for oil producing countries and effective supply for oil consumer countries (Kuchyk, 2005). Also, the primary motives and main missions, which are keeping the oil market supplied and stabilizing oil market, behind the formation have not been altered since the day it has been formed. A different number of economic, technological and political changes disrupted the economies of key OPEC members; however, its fundamentals remained as they were, before (Cairns, Calfucura, 2012). With the coordination of the member countries’ oil policies start OPEC’s objectives which also include ensuring that the prices remain stable in the world’s oil market. Furthermore, stability to the nations in terms of consistent revenues from oil-productions and efficient and reliable support to the consuming nation-states remains at the heart of OPEC’s preliminary mission. OPEC also attempts to ensure that the investors making substantial investments to the oil industry get a fair return and that their rights are protected by all means. A reaffirmation to the commitments of OPEC’s member states took place in the year 2000 where its preliminary initiatives and plans were reassessed (Rowland, Mjelde, 2016). Oil, being OPEC’S main interest has contributed significantly to the world economies over the past two centuries, and due to this significance, has gained the status of a commodity. The analysts agreed to the idea that better exploration of hydrocarbons is necessary as it is the most important energy source for the future years. The World Summit ceremony of OPEC held in Johannesburg ensured that proper energy supply is given to all countries irrespective of their economic status (poor or rice) because of its contribution to their sustainable prosperity and development (Huppmann, Holz, 2012).
An important factor to take into notice here is that OPEC’s policies do not bind according to the circumstances of its members nor are time-bound, as well. Its policies are instead of a relatively permanent nature, and bind around the roles played by the organization towards energy industry progressions with the help of petroleum (Eichengreen et. al., 1995). This way, the organization
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involves close coordination amongst member states and their cooperation towards taking its policies on the international economic stage. While different researches centre on the role of OPEC not being just a cartel, majority of the people insist on calling it by this term. A sovereign organization with permanent and realistic goals of progression with a mission to serve its member states in terms of petroleum supply and consumption is what OPEC is all about.
OPEC Policies and Economic Development OPEC’s policy frameworks have created quite a number of benefits and advantages for world
economies and their national economic subsystems. Some of these benefits include creating enormous wealth for oil producing nations and consumers, while controlling and stabilizing prices for growing number of customer countries, and ensuring oil security across the world. It also guarantees the stability of oil resources to the suppliers and plays a central role in controlling the demand and supply schedules (Sovacool, 2013; Greene, 2010). The strategic position and geopolitical stance of member countries gives OPEC an edge over non-OPEC countries in playing its part for greater economic development (Mensi et al., 2014). These member countries can allocate a portion of their revenues in helping other neighbouring countries which would be a big step towards harmonizing the region and ensuring sustained oil supply. Enhancement in regional development would ensure that the political and economic cooperation between neighbouring countries is sustained which would be a lesson to the entire world (Greene, Liu, 2015). Iran, Kuwait, Saudi Arabia and Iraq are strategically located in the region where they can be a source of regional investments to Arab states while Libya, Algeria and Nigeria can contribute towards African development. While Venezuela is located to contribute towards the development of Latin America and Brazil, Indonesia can provide monetary resources for Southeast Asia. In this manner, every member country can should perform its obligatory part to enhance the world economic progress and stability on the global scale. In general sense, OPEC countries are transforming oil revenues into sustained projects for long-term investments that guarantee more economic progress than other initiatives (Balcilar et al., 2015).
Macroeconomic impact of OPEC policies The recent eras have witnessed widespread criticism directed at the policies and frameworks
of OPEC nation-states, which has sparked huge debates around its efficacy and influence in the global oil marketplace (O'brien, Williams, 2013). There is still a need to analyse the impact of OPEC’s policies on the world (especially on the United States) while assessing if the organization has actually succeeded in maintaining its power and influenced in the international petroleum sector. Strong speculations revealed that OPEC has lost considerable power in the world economy owing to its declining influence and because of the emergence of non-OPEC countries on world oil regulatory platforms. The unearthing of oil reserves in the North sea along with those in Mexico, Alaska and the Gulf have further aggravated OPEC’s miseries, while contributing towards the market modernization (Huppmann, Holz, 2012).
Economic analysts have contested several theories to determine the actual influence of OPEC policies on the macro-economic outlook of the global area; while some theories discussed the efficacy of its independent initiatives and actions, others were more deeply interested to unveil its role as a ‘classical cartel’ (Ghassan and AlHajhoj, 2016). The inside truth however, still shows that OPEC’s member states are not responsible for world oil shortages despite their control of oil production and supply (Nystad, 1988; Brown, Huntington, 2013; Van de Graaf, Verbruggen, 2015). With respect to its macroeconomic impact, the official website of the organization could be quoted whereby the organization strongly affirms its role for the stability and harmonious supply of petroleum to both oil-consuming and producing nation-states. The member to this organization, in an attempt to fulfil these stated obligations, have been known to keep strong checks on the forecasted developments within this sector as well as the fundamental forces that shape and regulates it. Uninvited surges in oil prices could be regulated and out under reasonable control followed by OPEC’s increased oil production (Mensi et al., 2014; Greene, Liu, 2015). Producing about 42 % of global crude oil, the organization however, makes it clear through its official web presence that it does not stand as the only controller of world oil marketplace (Dike, 2013). It also maintains that its primary capacity lies in increasing or decreasing the oil production due to which global oil market would automatically be affected (Nystad, 1988; Ravenhill, 2014; Mensi et al.,
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2014; Fuinhas et al., 2015; Golub, 1983). Furthermore, OPEC’s provision of fair and transparent pricing of oil regulates oil consumption practices while keeping oil sellers under stringent monitoring through coordinated efforts. Voluntary production of less oil and other such initiatives seeks to guarantee oil price stability while also preventing uncertain and swift fluctuations. A key pointer in this regard is OPEC’s deliberation that it is not responsible for setting the prices in the global oil markets as movements in three main exchanges - the Singapore International Monetary Exchange, New York Mercantile Exchange and the International Petroleum Exchange in London are responsible to influence it (Greene, Liu, 2015; Mander, 2014). Another research by Akacem, Faulkner and Miller (2015) indicates that market scepticism about OPEC’s spare capacity and problems surrounding its influence over pricing has also led several countries including U.S towards disbelief that OPEC would hamper economic growth and stability for them. The world views OPEC as an influential price controller that can decrease its production while increasing the prices so that the member countries could enjoy inflowing revenues from residual demands that have to import oil in any case from the organization.
One of the many profound impacts of OPEC policies had been related with its effects in supporting economic recession through oil price disruptions (Ftiti et al., 2015; Sovacool, 2013). The policies of pricing implementation by OPEC as well as control over its output from the member countries had known to be one of the reasons behind economic recession and its related turmoil. Moreover, policies of resource nationalism had adversely impacted on the Western investment projections through OPEC’s output and policy pricing mechanism (Colgan, 2014). Since the productive capacity of the organization has also decreased over time, the production rates at present are still insufficient to fulfil increasing demands.
US dependence on OPEC and its economic impacts US oil dependency on OPEC countries causes various consequences for national and global
security apart from other effects. Oil dependence has long-remained a national threat to energy stabilization, economic development and stability of US. Questions about national security cause threats to economic progress while challenging the national energy policies (Bremmer, Hersh, 2013; Esfahani et al., 2014). During 1980, oil dependence of US reached its peak for almost $350 billion while in 2008 (Brown, Kennelly, 2013); it reached to about $500. While this created potential losses in the Gross Domestic Product (GDP) of the nation, it also challenged the national economic systems questioning whether this dependence would ever be reduced or controlled, at least (Greene et al., 1998; Mead, 2013). A direct answer to this miserable situation comes in the form of increasing local and regional oil production while depending lesser on foreign resources. Inflated energy prices, as argued by Brown and Kennelly (2013) are the direct cause why the United States could not, as such, develop policies for its oil independence. Unless the issue is sufficiently addressed, the nation is going to suffer with increasing costs and risks of energy security while having a threatened economic position, as well. The US realizes that in order to stabilize its markets and provide them better safeguards, it has to ensure that oil dependence on OPEC countries are remarkably reduced (Rowland, Mjelde, 2016).
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The rise of American oil dilemmas in the 1970s gave boost to OPEC’s influence in world oil
markets due to two reasons: firstly, this situation was attributed to the Arab oil embargo that was a source of energy crisis in the same decade. There were extreme shortages observed in commodity goods as US economy was left to suffer. Secondly, the initiative of Carter Administration for promoting conservation through increasing gas prices further added to the issues (Bremmer, Hersh, 2013; Eckes, 2014). Price effects resulted in shrinking supplies throughout the economy whereby oil-price controls delivered as they pleased. As domestic production continued to reduce, OPEC availed the opportunity to enter through the opened doors with its imports and flooded the oil markets. As a result of US embargoing its own oil reserves, a critical share of significance was very easily taken up by OPEC in the international oil markets (Parry, Darmstadter, 2003; Guo, Kliesen, 2005).
Purchase of oil from volatile member nations of OPEC is a matter of severe consequences for US Since the funding from oil purchase goes to these member states, America is involuntarily funding wars, and is helping these states develop nuclear weaponry. Moreover, US national security is greatly threatened by its unintentional contributions towards establishing weapons and funding terrorism. The ongoing political tensions between different OPEC countries located in the Middle East takes uncertain and incidental decisions that disrupt the entire international oil market by inciting embargoes merely due to political rivalries (Bremmer, Hersh, 2013; Guo, Kliesen, 2005). There is a need to reconsider the extent to which political problems of member countries should be allowed to affect the world oil markets; otherwise, the efficacy of OPEC and the effectiveness of the organization’s initiatives would rather appear challengeable. The following Figure 2 illustrates the use of petroleum by US and indicates huge oil dependence.
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Reduction in oil dependence is an urgent economic issue for US whose stance on these economic costs of this dependence unveils their tragic economic conditions. Two major issues underlying this dependence comes from oil price shocks of OPEC and their manipulation of pricing mechanisms as indicated by US analysts. Furthermore, another common reason behind this dependence comes from a control on around over 73 % of oil reserves by OPEC which leaves no chance for US to avoid this increased dependence. While research does not depict clear-cut solutions for reduction in oil imports, there are still chances whereby the market control of OPEC
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can be somewhat minimized so as to allow US in aiming for sufficiency or at least, in reducing the impacts of price shocks through a minimal petrol use. Researches by Ghassan and AlHajhoj (2016) and Fuinhas, Marques and Quaresma (2015) indicated that the US economic decision-makers and Bureau of Economic Analysis are still unsure whether or not new fuel economy standards would be any useful in decreasing the impacts and allowing for a control over greenhouse gas emissions. Some of the possible solutions to this dependence come in the form of increasing US real-world fuel economy to about 45 mpg which could further enhance the situation. The fuel costs should further be cut down for the customer to more than $ 1.7 trillion.
Research also indicated that US is destined to reduce its oil dependence on OPEC by 2025 to about 2 million barrels per day which would be 50 % less oil that it imports from OPEC countries at present per day. This also implies the implementation of new technological solutions for the country as it needs to upgrade its vehicle technologies for more energy-efficiency while devising new methods for minimal energy use (Bremmer, Hersh, 2013; Brown, Kennelly, 2013). Moreover, investments in cutting edge technologies are another solution to address the issue. This also manifests in developing alternative energy resources for cost-effective and clean use of petroleum while undertaking substantial research to explore new means of saving it. In this wake, there is a need to devise and execute a substantially-efficient energy policy which must focus on the following aspects of the dependency issue:
There is a need to avoid and minimize abrupt disruptions in oil supply that causes a very adverse effect on every American’s lifestyle;
Costly conflicts and sudden disruptions in oil supply can lead to various actions that consumes significant financial resources (Fuinhas et al., 2015);
Subsequent conflicts over oil dependence results in a reduction in military efficiency while compromising on other peacekeeping measures and activities;
The economic reliance on Middle Eastern and other OPEC countries should be reduced and minimized;
Oil dependence is mainly intensified due to commercial and transportation uses which should be monitored and controlled to avoid future shortages;
Conflicts with oil producing nations should be controlled as it lends support to anti-peace movements and inter-regional riots in the region;
Exploration of alternative carbon fuels is necessary for economic stability while also seeking solutions to environmental hazards (Van de Graaf, Verbruggen, 2015).
3. Conclusion US's growing dependence on foreign oil is a matter of interrelated elements that carries
substantial economic, political, and security challenges for the country, to say the least. This research was one of the works on the exploration of these challenges especially those belonging to the economic side. Based on an examination of how OPEC affects the economy of the country through its policies and procedures, it is also indicative of the fact that few nations are able to exercise their price monopoly over super powers only due to their association and affiliation with OPEC. These small nations are responsible for the supply of oil and they have driven world oil demands towards their own discretion. The article explored how different, widely-criticized elements amongst OPEC policies have aggravated the problems of surging oil prices and price shocks, and how these have been a source of significant worry to the region under study. The recent shock of higher oil prices is eloquent of the US vulnerability that it will never be able to forecast in future given its nature and unexpectedly high magnitude. In this wake, volume of oil imported and consumed should be reduced by all means; something that evolves as the only emergent solution to all US problems. Based on the research conducted in this study, it can also be stated that OPEC, by all means, has a neutral policy towards oil supply and global economic sustenance issues, and does not deliberately lend a malicious hand to the political actors for running their show. Despite being more of a political than economic nature, the relationship between US and OPEC is indeed, progressive and needs to be only better channelized. This article looked into the ways in which different practices and policy ramifications over time resulted in US’s oil dependence being a more problematic issue than ever before. Situations such as economic downturn and sluggish economies
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have been frequently-noticeable in history, providing a background to sudden price shocks as well as giving world regions a hard time coping with it.
In addition, the article discussed how member states in OPEC held their responsibility in promoting opportunities of local investment projects while also reaching out for greater economic sustenance. Sectors such as agriculture, healthcare, education, telecommunications etc. can work with OPEC within different regions and territories on topics of common interest. One of the similarities related with working in partnership or collaboration of OPEC is economic gains which is every region’s agenda. In addition to this, since OPEC countries are part to a larger ecosystem, their presence is indeed influential and they can use this influence in a rational manner to help each country pursue and accomplish its objectives rather than working on it individually. Therefore, this is one of the main aims of the organization to work for progress in the region and to engage other nations in a combined pursuit of economic development and harmony within. Sufficient oil-producing experience is another facet to OPEC’s strengths that would benefit countries lacking needed skills in oil marketing or distribution specifically owing to scanty knowledge about it. It is worth understanding that the position of OPEC has always been an international one; as discussed earlier on; it is due to this position that there lies greater responsibility on OPEC being an international cartel to engage countries together regardless of political contention, and pursue economic stability with joined hands.
This article looked into some of the allegations on OPEC as an international swing producer of oil against transfers of large income from domestic oil users by price manipulation. This had been looked into with the help of literature works to determine how communication and interpersonal relationship between the cartel, world regions and their communities could be improved. This can also be concluded with the determination that future should witness OPEC having more commitment towards furthering the interests of communities outside its immediate territories and that individual country’s interest should not be preferred over collective interests, in any case. There is a need for globally integrated Middle East that could smoothen or strengthen the relationships between different world regions, as far as their economic progress is concerned. Unfortunately, that has not been apparently achieved despite having much research available for addressing this issue. While, on the other hand, the biggest unresolved mystery for the United States over these years had been only a single question: how to reduce oil dependence? With an orientation towards steady production and need for exploration of oil reserves within its region, there is much more to do for the US than is currently being planned.
With an aim to evaluate, assess and speculate the effects of OPEC policies on world economies, taking US as the study case, it can be concluded that OPEC still has to go a long road before its relationships with the global world could be strengthened. Without an exploration of common solutions to grave political contentions between them, world’s regions cannot prosper; let alone the Middle Eastern members. This research was a contribution towards unveiling how a relationship between different countries is affected by OPEC, and how different OPEC-related accusations have had a greater global aftermath. Cutting demands through alternative measures has been found as the only intervening policy action that can facilitate US in reducing the dependence on oil-exporting OPEC. With a resolution towards cutting global demands while giving more consideration towards economic and environmental effects, this article aimed to establish that undue power and influence over oil resources would naturally be harmful for all economies, be it developing, non-developing or developed nations across the world. Emphasis on oil production at home while operating in low-tax conditions should lie at the heart of US policies, which also have to be mindful of its responsibilities in delivering trickle down effects to other nations in conditions of acute economic pressures and recession.
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