Research into the Long-Run Relationship between Logistics Development and Economic Growth in Turkey
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1 Journal of Logistics Manageent 204, 3(): -6 DOI: /j.logistics Research into the Long-Run Relationship between Logistics Developent and Econoic Growth in Turkey Sultan Kuzu, Erah Önder * School of Business, Departent of Quantitative Methods, Istanbul University, Istanbul, 34322, Turkey Abstract In this study, the long ter relationship between econoic growth and developents in the logistics sector are investigated. The basis of an econoetric odel, the relationship between logistics developent and econoic growth are established. The findings derived fro analysis are annotated. Following ethods are used in the analysis: Unit Root Tests, Engle-Granger Cointegration test and Granger Causality test. Two variables including GDP (represent to econoic growth) and Turnover Index of Transportation and Storage (represent to logistic developent) are used in this study. Through the analysis findings show that two variables are cointegrated. And in long run a Granger causality relation fro econoic growth to logistic developent found. So it can be said that econoic growth plays active role on the logistics developent. Consequently, a Granger causality relation fro logistics sector and econoic growth is found in long run. Keywords Cointegration Analysis, Econoic Growth, Granger Causality Test, Logistics Developent. Introduction Logistics deals with the planning and control of aterial flows and related inforation aong and between suppliers to consuers. Iproveents in transportation and logistics ake valuable contribution to production and consuption activities. Logistics has becoe a leading industry, playing an iportant role in social and econoic developent. Industrial developent, on the other hand, has a great effect for iproved logistics sector[]. With econoic globalization and the deepening of social division labor, logistics as a sophisticated organization and anageent technology, which showed ore and ore iportant strategic position in the econoic developent, and gradually cause for concern. Thus the interaction between logistics and econoic growth has also thus becoe a research spot[2]. In the world, the huge contribute of the logistics to econoic developent was already approved by the practice in any countries. Especially recently, the syste and intensives of logistics also exhibits the iportant value of reducing environent pollution and accelerating the sustainable developent. So the logistics are attracted broadly the attention by the people[3]... Overview of the Sector / Global Sector Logistics industry is of great iportance for the * Corresponding author: eronder@gail.co (Erah Önder) Published online at Copyright 204 Scientific & Acadeic Publishing. All Rights Reserved copetitiveness of a country's foreign trade. As a result of the growth of the econoies of the countries, the logistics sector has grown in iportance. In addition, the location of a country is of great iportance in ters of the logistics industry. According to World Trade Organization (WTO), Transportation services in the value of the total services was around 28% in the last ten years and in 200, the rate was over 30%. When other service-ites are exained, Transportation and Logistics industry sector appears to have an iportant place. In 200, Transport has been the fastest growing coponent of exports of coercial services to increase of 4% and $ billion to reach..2. Logistics Industry in Turkey Turkey, in ters of its strategic location, it holds a very iportant place in the World. Geography of Turkey, there are iportant advantages fro the perspective of logistics. Our country's geo-strategic point of view is the bridge between Asia and Europe and the Mediterranean and the Black Sea and at the intersection of three continents[4]. Logistics copanies in Turkey are serving ostly sectors, which are concentrated foreign trade. Textiles and apparel, autootive, consuer products, retail and food, petrocheicals, achinery anufacturing and construction sectors are aong the sectors. Published by the World Bank Logistics Perforance Index (LPI), Turkey is iproving its logistics perforance. LPI, which operates worldwide logistics operators in their respective countries on the ease of logistics based on feedback.
2 2 Sultan Kuzu et al.: Research into the Long-Run Relationship between Logistics Developent and Econoic Growth in Turkey 2. Literature Review Held et al., (2000) accentuate that the regional logistics ust be oriented on the developent of regional econoy through the developent of logistic infrastructure and services.[5] Wen-Jie Zhang (2002) exercises the econoics and the trade theory to analyze the relationship between econoy and logistics, finally derives that the relationship of econoy and logistics represent the econoic globalization, the regional econoic integration, the pursuit of profit and core copetence in the regional enterprise, and the status quo of Chinese econoic developent prooting the developent of Chinese odern logistics[6]. Gong Zhong (2003) defined the exact eanings of regional logistics and regional econoic growth. He selected three variables (freight, freight turnover, logistics network) as indicators to describe logistics fro different aspects and also took every region s GDP as its description indicators of econoic growth. He had established two single-equation regression odels about the effects of regional logistics ipacted on the regional econoic growth[7]. Li and Zhang (2007) puts forward the evaluation syste of regional logistics adaptability fro the point of view the atching between the regional logistics and the regional econoy, arks out or iproves the regional logistics syste to fit the regional logistics structure, the service level, the legal policy, the infrastructure, the inforation network, the personnel quality[8]. Ates and Isik (200) are investigated in their study, The effects of developent in logistics services on export led growth in Turkey. In conclusion, no relation can be found between logistics sector and export in short run but, in long run a Granger causality relation fro logistics sector to export is found. In addition, a duplex causality relation fro logistics sector revenue to industries production index. Consequently, a Granger causality relation fro logistics sector and export is found in long run[9]. Navickas et all (20), In global econoy the activity of logistics systes, its infrastructure usage for the purpose to achieve the growth of econoy becoe a necessity, whereas the scope and ais of logistics systes and its infrastructure use becoe specific in a reason of the ipact of country s econoic policy, focused on long-ter copetitiveness. Main factors of logistics systes ipact on country s econoic growth could be ebodied through developent levels of logistics systes and its infrastructure, the ipact of business environent, the obility and effective use of resources, logistic flexibility and receptivity to innovations[0]. 3. Data and Methodology To predict future events and conditions, and any other areas of the econoy is of great iportance. To better predictions about the future, in ters of decision-aking and policy-aking, is required. The tie series prediction odel using a variable current and previous values are obtained. The concept of cointegration, in 980, entered into the analysis of tie series. Cointegration analysis is a ethod of attepting to deterine whether a long-ter relationship of two or ore non-stationary tie series. To be exained closely associated with econoic variables in the long-run equilibriu. The presence of co-integration relationship between the variables indicates that these variables ove together in the long ter. Also if there is cointegration relationship between the variables investigated short-and long-ter causal relationship. In this study, the long-ter acro-econoic relationship between the two variables exained. The data of the variables, which is used in this study, taken fro the Central Bank of the Republic of Turkey s National Databases and the official web page of Turkey Statistical Institute. The variables used in seasonal periods were obtained during the periods of []. Eviews 7 package progra was used for the econoetric analysis. The ai of this study is to be established with an econoetric odel of the logistics developent and econoic growth will be investigated whether long-ter relationship. The absence of nuerical healthy data and details of the logistics sector cause to the use of data fro the transportation and storage data. Nuerical reflections of all the developents in the logistics industry also directly reflected in the transport and storage sub-sector is considered. First, unit root tests for this purpose whether the variables are deterined degree of stability. To conduct cointegration analysis of the variables are the sae degree. And then to apply Granger causality test of the variables are cointegrated. 4. Epirical Analyses To conduct the Engle-Granger Cointegration test two variables are used in this study. They are Turkey s GDP and turnover index of Transportation and Storage. Firstly investigate the stationary all of the variables with unit root test. 4.. Unit Root Test Analysis of the tie series data by the ethod of traditional regression analysis are iplicitly assued the tie series is stationary. The ost coonly used techniques are Dickey-Fuller, Augented Dickey Fuller and Phillips-Perron tests. To give a brief inforation about the Dickey-Fuller test; Consider the autoregressive odel, Yt = ρyt + et ()
3 Journal of Logistics Manageent 204, 3(): -6 3 Where Y 0 = 0, ρ is a real nuber, and {e t } is a sequence of independent noral rando variables with ean zero and variance σ 2. The tie series Y t converges (as t ) to a stationary tie series if ρ <. If ρ =, the tie series is not stationary and the variance of Y t is t σ 2. The tie series with ρ= is soeties called rando walk. If ρ >, the tie series is not stationary and the variance of the tie series grows exponentially as t increases[2]. Given n observations Y, Y 2, Y n, the axiu likelihood estiator of ρ is the least squares estiator, n 2 n p= Yt YY t t (2) t= t= graphs and Figure 2 shows Logarithic and Seasonal Adjustent Gross Doestic Product s tie series graphs. Figure. Gross Doestic Product s tie series graphs p is a consistent estiator for all values of ρ[3]. Augented Dickey-Fuller; DF test are confronted with the proble of autocorrelation. To eliinate this proble, DF equation enough to eliinate autocorrelation lagged value of the dependent variable is added to the right side of the equation. After the addition DF regression equation becoes Augented Dickey-Fuller equation. The following odel is used for the ADF test; k yt= δyt + αi yt i+ εt (3) i= Hypotheses are the sae DF tests. Phillips-Perron test; This test proposes new tests for detecting the presence of a unit root in quite general tie series odels. Their approach is nonparaetric with respect to nuisance paraeters and thereby allows for a very wide class of weakly dependent and possibly heterogeneously distributed data. The tests accoodate odels with a fitted drift and tie trend so that they ay be used to discriinate between unit root non-stationary about a deterinistic trend. The liiting distributions of the statistics are obtained under both the unit root null and a sequence of local alternatives. The latter non-central distribution theory yields local asyptotic power functions for the test and facilities coparisons with alternative procedures due to Dickey&Fuller[4]. The following odel is used for the PP test; yt = α0 + αyt + εt T yt = α0 + αyt + α2 t + εt (4) 2 This paper used the ethod of ADF (Augented Dickey-Fuller Test) and PP (Phillips Perron test) GDP Series Figure shows Gross Doestic Product s tie series Figure 2. Logarithic and Seasonal Adjustent Gross Doestic Product s tie series graphs Unit Root Test Result of GDP The conclusion of the unit root test is shown below in table. Table. GDP s stationary tests Null Hypothesis: Ln_gdp_sa has a unit root ADF PP Test % Critical %5 Values % ,Trend ADF PP Test % Critical %5 Values % ADF PP Critical % Null Hypothesis is not rejected. GDP series is not stationary. So first differences of this series is shown table 2 ;
4 4 Sultan Kuzu et al.: Research into the Long-Run Relationship between Logistics Developent and Econoic Growth in Turkey Table 2. The first differences of GDP s stationary tests Null Hypothesis: D(Ln_gdp_sa has a unit root) ADF PP Test % Critical %5 Values % ,Trend ADF PP Test % Critical % Values % ADF PP Critical % Null Hypothesis is rejected so, first differences of this series are stationary Turnover Index of Transportation and Storage (TITS) Unit Root Test Result of TITS The conclusion of the unit root test is shown below in table 3. Table 3. TITS s stationary tests Null Hypothesis: Ln_turnoner _sa has a unit root ADF PP Test % Critical % Values %0-2.69, Trend ADF PP Test % Critical % Values % ADF PP Critical % Null Hypothesis is not rejected. GDP series is not stationary. So first differences of this series is shown table 4; Table 4. The first differences of TITS s stationary tests Figure 3. TITS s tie series graphs Figure 4. Logarithic and Seasonal Adjustent TITS s tie series graphs Figure 3 shows Turnover Index of Transportation and Storage s tie series graphs and Figure 4 shows Logarithic and Seasonal Adjustent Turnover Index of Transportation and Storage s tie series graphs. Null Hypothesis: D(Ln_turnoner _sa has a unit root) ADF PP Test % Critical % Values % , Trend ADF PP Test % Critical % Values % ADF PP Critical % Null Hypothesis is rejected so, first differences of this series is stationary. These series are stationary at the sae degree. We can apply co-integration test between this variables Engle-Granger Cointegration Test Cointegration is used to describe the long-ter stable relationship of the level value of soe econoic variables. To conduct cointegration, all variables ust be stationary
5 Journal of Logistics Manageent 204, 3(): -6 5 with the sae degree[5]. This paper selected ethod of Engle-Granger cointegration test because of the nuber of variables. Cointegration analysis is copleted in two steps. Firstly, regression odel is established. Then, stability of the error ters are exained. If it is deterined that the error ter is stationary, we can say two variables are cointegrated. Step: Regression Model Dependent Variable: Ln_GDP_SA Method: Least Squares Date: 08/04/3 Tie: 2:5 Saple: 2005Q 202Q4 Included observations: 32 Figure 5. Correlogra Q statistics Variable Coefficient Std. Error t-stat.. Ln_Turunover_SA C R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Su squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat (F-statistic) The econoetric odel: GDP= TURNOVER e t 2. Step: The Stationary of Error Ter Table 5. Residuals stationary test Null Hypothesis: Ln_turnoner _sa has a unit root ADF Test % Critical % Values % ,Trend ADF Test % Critical % Values % ADF Critical % Table 5 shows that the stationary of the error ter. And Figure 5 shows that correlogra Q statistics. Figure 6 shows residuals graph. Error ter of the odel is stationary so GDP and Turnover index are cointegrated. Then we choose appropriate lag length to use odel criteria for applied the causality test. Table 6 shows that appropriate lag length. Figure 6. Residuals Graph Table 6. Appropriate lag length = Lag LogL LR FPE NA 7.90e * 3.58e-06* e e e-06 Lag AIC SC HQ * * * Appropriate lag length = 4.3. Granger Causality Test Granger Causality Test: Two variables y and x, Granger Causality test requires the following regression is estiated[6]: yt= α0 + α0xt i+ β0yt i+ ut (5) i= i= xt= β0 + λiyt i+ δixt i+ u2t i= i= (6)
6 6 Sultan Kuzu et al.: Research into the Long-Run Relationship between Logistics Developent and Econoic Growth in Turkey y represent Gross Doestic Product; x represent turnover index of Transportation and Storage. In general, if the x ipacted y, that x is a Granger cause of y, and the changes of x ust before the changes of y. Therefore, when do the regression analysis of y ipacted on the on the other variables, if the past or lagged values of x were encopassed can significantly enhance the explanatory power of regression, which can be considered x is the Granger reason of y. It also set up in turn. The conclusion of the Causality test indicated in table 7. Pairwise Granger Causality Tests Lags: Table 7. Causality Tests Null Hypothesis: Obs F-Statistic. Ln_TURNOVER_SA does not Granger Cause Ln_GDP_SA Ln_GDP_SA does not Granger Cause Ln_TURNOVER_SA Thus, there is one-sided causal relationship fro GDP to index of Transportation and Storage. 5. Conclusions The world, especially in the last years, has changed rapidly. Main reason of these changes is the phenoenon of globalization. Logistics industry in our country in recent years is showing developents as a young industry. The result of the econoetric analysis shows a long-ter relationship between the GDP and turnover index of Transportation and Storage in Turkey. Interpreted the results of the econoetric odel is established, explanation of GDP, eaningless out turnover index of Transportation and Storage whereas turnover index of Transportation and Storage explaining GDP was effective. So a one unit change in the turnover index of transport and storage of the resulting changes of 30% of GDP and the relationship between the is directly proportional. According to the results of the Granger causality test for odel, there is one-sided causal relationship fro GDP to index of Transportation and Storage. Increasing econoic growth has been achieved in the developent of logistics. The results of this study for Turkey are supporting the results of the previous siilar studies for other countries. Generally, the econoic developent of the country has ade progress in logistics. Our recoendation, if possible, is to find and exaine the long-ter data for the sae analysis. With using long-ter data it can be analyzed in structural breaks. Thus, coparing results with and without considering structural breaks will be possible. REFERENCES [] Bayraktutan, Y. &Tüğlüoğlu, Ş. & Özbilgin M. 202 Lojistik Sektöründe Yoğunlaşa Analizi ve Lojistik Gelişişlik Endeksi: Kocaeli Örneği, Uluslararası Alanya İşlete Fakültesi Dergisi, C:4, S:3, s. 6-7 [2] Wang, A. 200, Research of Logistics and Regional Econoic Growth, Scientific Research, ibusiness, pp [3] Yang, S. & Jiangou, Z. 20, The Panel Co-integration Analysis between the Logistics Industry and Econoics Growth in China, International Business and Manageent, Vol. 2. No. 2. Pp [4] Babacan, M. 2005, Lojistik Sektörünün Ülkeizdeki Gelişii ve Rekabet Vizyonu, İMPY Prograı. [5] Held, D., Mcgrew, A. Goldblatt, D., Perraton, J. (2000). Global transforation: Politics, Econoics and Culture. New York: Polity Press&Blackwell Publishers. [6] Zhang, W. 2002, The Relationship between the Regional Econoic Growth and Logistics, Logistics Technology, pp.5-7 [7] Zhong, G. 2003, Regional Logistics Role in Regional Econoic Growth Beijing University of Technology, Beijing. [8] Li, Z., Zhang, Y. 2007, The Study on the Suitability Evaluation of Syste Regional Logistics. Logistics Technology, pp [9] Ateş, İ., Işık, E. 200, Türkiye de Lojistik Hizetlerinin Gelişiinin İhracattaki Büyüeye Etkileri, Ekonoi Bilileri Dergisi, Cilt:2. Sayı:, s [0] Navickas, V. Sujeta L., Vojtovich S. 20, Logistics Systes As A Factor Of Country s Copetitiveness, Econoics And Manageent, pp [] [2] Dickey, D.A & Fuller, W.A. 979, Distribution of the Estiator for Autoregressive Tie Series with a Unit Root Test, Journal of the Aerican Statistical Association, Volue: 74, Issue: 366, pp [3] Rubin, H. 950, Consistency of Maxiu-Likelihood Estiates in the Explosive Case in Statistical Inference in Dynaic Econoic Model, ed. T.C. Koopans, New York, John-Wiley&Sons [4] Phillips, P.C.B. & Perron, P. 988, Testing for a Unit Root in Tie Series Regression, Bioetrica,75, 2, pp [5] Engle, R.F. & Granger, C.W.J, 987, Co-Integration and Error Correction: Representation, Estiation, and Testing, Econoetrica, pp [6] Granger, C.W.J. 969, Investigating Causal Relations by Econoetric Models and Cross-spectral Methods, Econoetrica, Vol. 37, No. 3 pp
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