Euro-Asian Journal of Economics and Finance ISSN: 2310-0184 (print) ISSN: 2310-4929 (online) Volume: 3, Issue: 4 (October 2015), Pages: 253-261 Academy of Business & Scientific Research

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Effects of Exports and Imports on the Economic Growth of Syria Adel Shakeeb Mohsen Economics PhD student at Universiti Sains Malaysia, Penang, Malaysia

This study attempts to investigate the effect of exports and imports on the economic growth of Syria over the period 1980-2010. The ADF unit root test, Johansen cointegration test, Granger causality test, impulse response functions (IRF), and variance decomposition (VD) analysis were used in this study. The Johansen cointegration test indicates that GDP is positively and significantly related to the exports and imports. Imports have the biggest effect on the GDP. The Granger causality test indicates bidirectional causality relationships between exports, imports and GDP in the short and long run. The study result indicates that is important to improve the quality of exports and increase its diversity, as well as simplify the import and export procedures. Keywords: Syria, GDP, development, foreign trade, VAR, cointegration test INTRODUCTION Foreign trade plays an important role in developing the economy, and it is the best way for any country to obtain goods and services that it cannot produce or the cost of production is very high. Imports of investment goods such as machinery and new technology can help in increasing country’s productivity. Besides, export is a main source of foreign exchange earnings for the state budget. In the same way, foreign trade is an essential component in the national income of Syria and it has an important role in supporting the national economy. Moreover, the strategic location of Syria on major trade routes between the east and west has given it a significant role and increased its interest in foreign trade (Hamwi, 2005). Raw materials have the highest percentage share of total Syrian exports whereby it contains about 68% of the average Syrian exports from 1980 to 2010, and the share of finished products and semifinished products is 24% and 8% respectively,

while raw materials have the lowest percentage share of total Syrian imports whereby it consists about 16% of the average Syrian imports from 1980 to 2010, and the share of finished products and semi-finished products is 44% and 40% respectively (CBS, 2010). The high concentration of raw materials exports is a source of vulnerability for the Syrian economy, because it is affected by many factors that are beyond the control of the government, such as the production of raw material, prices of raw material in the global market, and the rainy seasons that affect the production of agriculture products. On the other hand, the high percentage of finished products imports and the low share of raw materials imports reflect a weakness of domestic production and its inability to cater for the economic development needs. In order to enhance the foreign trade in Syria, the Syrian government has worked since the beginning of the 21st century to change its trade

*Corresponding author: Adel Shakeeb Mohsen Economics PhD student at Universiti Sains Malaysia, Penang, Malaysia. Email: [email protected]

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policy, liberalize foreign trade, prevent monopoly, enhance transparency of the trading system, and improve the quality of exports. The government also has implemented many economic and financial procedures such as developing the administrative and legislative environment, simplifying customs procedures for imports, declining the number of prohibited goods of trade, reducing the tariffs on agricultural and industrial raw materials to 1%, and establishment of several free trade zones with different countries (NAPC, 2010). The government also simplified the import procedures gradually, removed most tariff and non-tariff barriers, lowered high tariff rates, simplified the tariff structure, lifted quantitative restrictions on imports and reduced other import duties (NAPC, 2008). Moreover, it includes the trade policy that aims to reduce the imports of materials that have alternative products in the local market, and prioritize on importing capital goods, intermediate inputs, and equipment for medical and scientific measures that contribute in the economic and social development process (NAPC, 2006). The actions that were taken by the government was not limited only to customs tariffs, but also included nontariff barriers such as the removal of import licenses for raw materials, the requirements for the certificates of origin, and allowing private banks to finance imports (NAPC. 2007). Unfortunately, the war has started in Syria since 2011, which caused a huge damage on the Syrian economy, and created a new situation quite different than in before 2011. The infrastructure has been damaged, investment has been declined, many factories have been destroyed, foreign trade has been declined, and the deficit in the trade balance has increased (SCPR, 2014). Given this backdrop, the aim of this study is to test the effect of exports and imports on the economic growth of Syria from 1980 to 2010, which may assist Syrian policy maker, after the war, to develop an economic plan that takes into account the effect of exports and imports on the economic growth. The organization of this study is as follows. The next section is the literature review and Section 3 provides a brief discussion on the methodology. Section 4 reports the empirical results, and the

Adel Shakeeb Mohsen

conclusion and recommendations are presented in Section 5. PREVIOUS STUDIES Many studies that tested the effect of exports and imports on economic growth of different countries. Tyler (1981), Balassa (1985), Ram (1987), Krueger (1990) concluded that exports have a positive effect on economic growth. Besides, Khan and Saqib (1993) of Pakistan, Sengupta and Espana (1994) of Korea, Al-Yousif (1997) of the four Arab Gulf countries, Shirazi and AbdulManap (2004) of Pakistan, Abou-Stait (2005) of Egypt, Alhajhoj (2007) of Saudi Arabia, Hye and Bel Haj Boubaker (2011) of Tunisia, and Saad (2012) of Lebanon found that there is a positive relationship between exports and economic growth. However, other researchers including Temiz and Gokmen (2010) and Cetinkaya and Erdogan (2010) of Turkey, Al-Suwaidi and AlShamsi (1997) of Egypt, and Safdari et al (2011) of thirteen Asian developing countries found that there is a positive and a unidirectional causality relationship running from economic growth to export. Some other researchers such as Hamuda et al (2010), and Hye (2012) found that there is a bidirectional causality relationship between exports and economic growth. Husein (2009) also found that there is a bidirectional causality relationship between exports and GDP in the long run, while in the short run there is a unidirectional causality relationship running from exports to economic growth in Jordan. Besides, by using annual time series data for 16 Arab countries including Syria, El-Sakka and Al-Mutairi (2000) found that there is a bidirectional causality relationship between exports and economic growth in 6 countries, there is unidirectional causality relationship running from exports to economic growth in 4 countries, there is unidirectional causality relationship from economic growth to exports in 1 country, and there is no causality relationship between exports and economic growth in 5 countries. Moreover, this study found that there is a strong evidence that improve the unidirectional causality relationship running from exports to economic growth in the case of Syria.

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AL-Bawab (2009) found both exports and imports are bringing up economic growth in Jordan. However the relationship between exports and GDP is weak, while the relationship between imports and GDP is strong. Moreover, imports have a positive relationship with economic growth according to by Shirazi and Abdul-Manap (2004) for Pakistan, Cetintas and Barisik (2009) for 13 transition economies, Cetinkaya and Erdogan (2010) for Turkey, Hye and Bel Haj Boubaker (2011) for Tunisia, Zang and Baimbridge (2012) for Japan and South Korea, and Rahman and Shahbaz (2013) for Pakistan. Besides, Thangavelu and Rajaguru (2004) found that import led growth in India, Indonesia, Malaysia, Philippines, Singapore and Taiwan. Similar result is also found by Awokuse (2008) for some South American countries, and Cetinkaya and Erdogan (2010) for Turkey. However, Iscan and Yıldırım (2012) tested the effect of the imports of capital, intermediate and consumption goods on economic growth in 132 developing countries including Syria, during the period of 1998-2010, and found that economic growth is affected significantly and positively from imports of capital and intermediate goods, while imports of consumption goods affect negatively and significantly on economic growth. METHODOLOGY The vector autoregression (VAR) model will be used in this study. Our model consists of three variables: the gross domestic product (GDP), exports (EXP), and imports (IMP) of Syria. GDP is the dependent variable. The model is presented as follows: lnGDP = α + β1 lnEXP + β2 lnIMP + εt where α is the intercept, β1 and β2 are the slope coefficients of the model, lnGDP is the natural log of the real gross domestic product (in millions of SYP), lnEXP is the natural log of real exports (in millions of SYP), lnIMP is the natural log of real imports (in millions of SYP), and εt is the error term. The analysis must began with the unit root test to determine whether the time series data are stationary at levels or first difference. The Augmented Dickey Fuller (ADF) unit root test is

used in this study to test for the stationary of the variables. After determining the order of integration of each of the time series, and if the variables are integrated of the same order, the Johansen cointegration test will be used to determine whether there is any long-run or equilibrium relationship between the GDP and the other independent variables in the model. If we found that the variables are cointegrated, the Granger causality tests will be conducted based on the VECM to determine the causality relationships among variables. On the other hand, if there is no cointegration among the variables, the VAR model will be employed to test for short-run Granger causality between the variables. Lastly, impulse response functions (IRF) test and variance decomposition (VD) analysis are used in this study to help in determining whether the independent variables play any important role in explaining the variation of GDP at short and long forecasting horizons. This study uses annual time series data of Syria during the period from 1980 to 2010. This data collected from the World Bank. All variables in this study are in real value. Besides, all data will be expressed in the logarithmic form. EMPIRICAL RESULTS AND DISCUSSION From the results of the ADF unit root test in Table 1, we can see that all the variables are not stationary at the levels, but became stationary after first differencing at least at the 5 percent level of significance. This means that all the variables are integrated of order 1, that is, I(1). INSERT TABLE 1 HERE Johansen Cointegration Test Results After determining that all the variables are stationary in the first difference, we can use the cointegration test to determine the presence of any cointegration or long-run relationship among the variables based on the Johansen cointegration test. But before running the cointegration test, we run the VAR model first to determine the optimal lag length, based on the minimum Akaike Information Criterion (AIC). The maximum lag

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has been set to 5 in the lag length selection process. The optimal lag length selection is 5 lags based on the AIC. After we have determined the number of lags, we proceed with the cointegration test for the model. Table 2 shows that there are three cointegration equations based on the trace and maximum eigenvalue tests. In other words, the results indicate that there is a long-run relationship between lnGDP, lnEXP and lnIMP. INSERT TABLE 2 HERE After having found a cointegration relationships among the variables lnGDP, lnEXP and lnIMP, the cointegrating equation was normalized using the real GDP variable. The long-run lnGDP equation is: lnGDP = 19.62314 + 0.781134 lnEXP + 1.059798 lnIMP The cointegration equation above shows that GDP is positively and significantly related to exports and imports. The coefficient of lnEXP indicates that for every one percent increases in exports, the GDP will increase by 0.78 percent. An increase in exports leads firms to increase and improve their production. In addition, exports supply the state budget with earnings and foreign currency that can be used to finance production activities, which help in increasing and improving output growth, and encourage both local and foreign investment in the country. This finding agrees with the results obtained by Tyler (1981), Ram (1987), Khan and Saqib (1993), Al-Yousif (1997), Shirazi and AbdulManap (2004), Abou-Stait (2005), Alhajhoj (2007), and Saad (2012). The coefficient of lnIMP indicates that for every one percent increases in imports, the GDP will increase by 1.06 percent. This suggests that imports play an important role in improving the economic growth in the country through supporting the country’s needs of goods and services that it cannot produce or where the cost of production is very high. In addition, imports of investment goods such as machinery and new technology help in increasing country’s productivity and motivating producers to improve and increase their production. Our finding is in line with the results of Shirazi and Abdul-Manap (2004), AL-Bawab (2009), Cetinkaya and Erdogan (2010), Hye and Bel Haj Boubaker

Adel Shakeeb Mohsen

(2011), Zang and Baimbridge (2012), and Rahman and Shahbaz (2013). Granger Causality Tests Results Since the variables in the model are cointegrated, the Granger causality tests based on the VECM are used to determine the short and long run causal relationships among the variables in the model. The Granger causality test results based on the VECM are shown in Table 4. The significance of the coefficient of the lagged error correction term shows the long run causal effect. It is clear from Table 3 that there are bidirectional causality relationships between lnEXP, lnIMP and lnGDP in the short and long run. INSERT TABLE 3 HERE Impulse Response Functions (IRF) Test Results Impulse response functions (IRF) allow us to study the dynamic effects of a particular variable’s shock on the other variables that are included in the same model. Besides, we can examine the dynamic behavior of the times series over ten-year forecast horizon. Figure 1 below shows that when there is a shock to lnEXP, lnGDP will respond positively in the following years. However, when there is a shock to lnIMP, lnGDP will respond negatively since the third year. INSERT FIGURE 1 HERE Variance Decomposition (VD) Analysis Results The variance decomposition (VD) for 1-year to 10year forecast horizons will be applied to explain how much of the uncertainty concerning the prediction of the dependent variable can be explained by the uncertainty surrounding the other variables in the same model during the forecast horizon. The forecast error variance decompositions of the variables in our model are given in Table 4. In the first year, the error variance of GDP is exclusively generated by its own innovations and has been decreasing since then for the various forecast horizons. However, at the 10-year forecast horizon, its own shocks contribute about 47% of the forecast error

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variance. On the other hand, lnEXP and lnIMP shocks explain 28% and 25% of the forecast error variance of GDP, respectively. Furthermore, the contributions of lnEXP and lnIMP in explaining lnGDP forecast error variance have increased during the 10-year forecast period. However, the contributions of lnIMP in explaining lnGDP forecast error variance still bigger than the contributions of lnEXP in explaining lnGDP forecast error variance until the 4th year.

and use the modern technology in industry and commerce.

INSERT TABLE 4 HERE

AL-Bawab, S. A. (2009). The Effects of Exports on Economic Growth: The Case of Jorda (1978 2008). Master thesis, Ajou University, Graduate School of International Studies.

CONCLUSION This study investigated the effect of exports and imports on the economic growth of Syria using annual time series data from 1980 to 2010. The ADF unit root test, Johansen cointegration test, Granger causality tests, impulse response functions (IRF), and variance decomposition (VD) analysis were used in this study. The ADF test results indicate all variables are I(1). The Johansen cointegration test showed that exports and imports have a positive and significant effect on GDP. Imports have the biggest effect on GDP. Furthermore, from the Granger causality tests, we found that there are bidirectional causality relationships between exports, imports and GDP in the long and short run. The impulse response functions (IRFs) indicated that when there is a shock to exports, GDP will respond positively in the following years, but when there is a shock to imports, GDP will respond negatively in the following years. The variance decomposition (VD) analysis showed that at a ten-year forecasting horizon, exports and imports shocks explain 28% and 25% of the GDP forecast error variance, respectively. Based on the results of this study, when the war finish, it is vital for the Syrian government to improve the quality of exports and increase its diversity, as well as boosting the productivity and competitiveness of the Syrian products vis-à-vis foreign products in the foreign and local markets. In addition, it is important to simplify the import and export procedures, develop highly qualified export agencies, provide the exporters with the necessary information to enter the global markets,

REFERENCES Abou-Stait, F. (2005). Are Exports the Engine of Economic Growth? An Application of Cointegration and Causality Analysis for Egypt, 1977-2003. Tunis: African Development Bank.

Alhajhoj, H. (2007). Exports and Economic Growth in Saudi Arabia: A VAR Model Analysis. Journal of Applied Sciences, 7 (23), pp. 36493658. Al-Suwaidi, A., & Al-Shamsi, S. (1997). Exports and Economic Growth in Egypt: Evidence from Cointegration Analysis. Journal of King Saud University, 10 (2), pp. 99-106. Al-yousif, Y. K. (1997). Exports and Economic Growth: Some Empirical Evidence from the Arab Gulf Countries. Applied Economics, 29 (6), pp. 693- 697. Awokuse, T. O., (2008). Trade Openness and Economic Growth: Is Growth Export-Led Or Import-Led?, Applied Economics, 40 (2), pp. 161-173. Balassa, B. (1985). Exports, Policy Choices, and Economic Growth in Developing Countries After the 1973 Oil Shock. Journal of Development Economics, 18, pp. 23-35. Central Bureau of Statistics (CBS). (2003-2010), Syria, Damascus. Available at: http://www.cbssyr.org Cetinkaya, M., & Erdogan, S. (2010). VAR Analysis of the Relation between GDP, Import and Export: Turkey Case. International Research Journal of Finance and Economics (55), pp. 135-145. Cetintas, H., & Barisik, S. (2009). Export, Import and Economic Growth: The Case of Transition Economies. Transition Studies Review, 15 (4), pp. 636-649.

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El-Sakka, M. I., & Al-Mutairi, N. H. (2000). Exports and Economic Growth: The Arab Experience. The Pakistan Development Review, 39 (2), pp. 153-169. Hamuda, A. M., Elbeidi, R. M., & Gazda, V. (2010). The Relationship between Export and Economic Growth in Libya Arab Jamahiriya. Theoretical and Applied Economics, XVII (1 (542)), pp. 69-76. Hamwi, B. (2005). Agricultural Exports in Syria. Damascus: National Agricultural Policy Center (NAPC). Husein, J. (2009). Export Led Growth Hypothesis: A Multivariate Cointegration and Causality Evidence for Jordan. The Journal of Developing Areas, 42 (2), pp. 253-266. Hye, Q. M. (2012). Exports, Imports and Economic Growth in China: An ARDL Analysis. Journal of Chinese Economic and Foreign Trade Studies, 4 (1), pp. 42-55. Hye, Q. M., & Bel Haj Boubaker, H. (2011). Exports, Imports and Economic Growth: An Empirical Analysis of Tunisia. The IUP Journal of Monetary Economics, IX (1), pp. 621. Iscan, I. H., & Yıldırım, S. (2012). The Type of Imported Goods and Economic Growth: Panel Evidence. International Research Journal of Finance and Economics, 91, pp. 98-108. Khan, A., & Saqib, N. (1993). Exports and Economic Growth: The Pakistan Experience. International Economic Journal, 7 (3), pp. 53-64. Krueger, A. (1990). Asian Trade and Growth Lessons. AEA Papers and Proceedings, 80, pp. 108-112. National Agricultural Policy Center (NAPC). (2006). Syrian Agricultural Trade 2005. Damascus: National Agricultural Policy Center. National Agricultural Policy Center (NAPC). (2007). Syrian Agricultural Trade 2006. Damascus: National Agricultural Policy Center. National Agricultural Policy Center (NAPC). (2008). Syrian Agricultural Trade 2007.

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Damascus: Center.

National

Agricultural

Policy

National Agricultural Policy Center (NAPC). (2010). Syrian Agricultural Trade, 2008-2009. Damascus: National Agricultural Policy Center. Rahman, M. M., & Shahbaz, M. (2013). Do Imports and Foreign Capital Inflows Lead Economic Growth? Cointegration and Causality Analysis in Pakistan. South Asia Economic Journal, 14 (1), pp. 59-81. Ram, R. (1987). Exports and Economic Growth in Developing Countries: Evidence from TimeSeries and Cross-Section Data. Economic Development and Cultural Change, 36, pp. 5172. Saad, W. (2012). Causality between Economic Growth, Export, and External Debt Servicing: The Case of Lebanon. International Journal of Economics and Finance, 4 (11), pp. 134-143. Safdari, M., Mahmoodi, M., & Mahmoodi, E. (2011). The Causality Relationship between Export and Economic Growth in Asian Developing Countries. American Journal of Scientific Research (25), pp. 40-45. Sengupta, J., & Espana, J. (1994). Exports and Economic Growth in Asian Nics: An Econometric Analysis for Korea. Applied Economics, 26, pp. 41-51. Shirazi, N. S., & Abdul-Manap, T. A. (2004). Exports and Economic Growth Nexus: The Case of Pakistan. The Pakistan Development Review, 43 (4), pp. 563-581. Syrian Centre for Policy Research (SCPR). (2014). Socioeconomic Monitoring Report on Syria. Damascus: Syrian Centre for Policy Research (SCPR). Temiz, D., & Gokmen, A. (2010). An Analysis of the Export and Economic Growth in Turkey Over the Period of 1950-2009. International Journal of Economic and Administrative Studies, 3 (5), pp. 123-142. Thangavelu, S., & Rajaguru. G., (2004). Is there An Export Or Import-Led Productivity Growth

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in Rapidly Developing Asian Countries? A Multivariate VAR Analysis, Applied Economics, 36 (10), pp: 1083-1093. Tyler, W. (1981). Growth and Export Expansion in Developing Countries. Journal of Development Economics, 9, pp. 121-130.

Zang, W., & Baimbridge, M. (2012). Exports, Imports and Economic Growth in South Korea and Japan: A Tale of Two Economies. Applied Economics, 44 (3), pp. 361-372.

APPENDIX Table 1: ADF unit root test results Level

First difference

Intercept

Trend and intercept

None

Intercept

Trend and intercept

None

lnGDP

1.117441

-1.771122

2.094763

-3.741055 ***

-4.786693 ***

-1.980987 **

lnEXP

0.195672

-2.229596

1.815048

-4.748178 ***

-2.502529

-4.306729 ***

lnIMP

0.066548

-2.871264

1.05835

-5.541511 ***

-6.157086 ***

-5.42069 ***

ADF

Note: *** Denotes significance at the 1 per cent level, and ** at the 5 per cent level.

Table 2: Johansen cointegration test results No. of CE(s)

Trace Statistic

Probability

Max-Eigen Statistic

Probability

r=0

88.10330***

0.0000

47.66417***

0.0000

r≤1

40.43913***

0.0000

30.92982***

0.0001

r≤2

9.509305**

0.0430

9.509305**

0.0430

Note: *** Denotes significance at the 1 per cent level, and ** at the 5 per cent level

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Adel Shakeeb Mohsen

Table 3: Granger causality test results Independent variables ∑ ∆ lnGDP

∑ ∆ lnEXP

∑ ∆ lnIMP

ect(-1)

∆ lnGDP

-

3.938478(5)**

4.657295(6)**

-2.157714*

∆ lnEXP

4.600792(5)**

-

1.327643(5)

-2.063373*

∆ lnIMP

3.643871(7)**

1.214486(5)

-

-2.546415**

Notes: ect(-1) represents the error correction term lagged one period. The numbers in the brackets show the optimal lag based on the AIC. D represents the first difference. Only F-statistics for the explanatory lagged variables in first differences are reported here. For the ect(-1) the t-statistic is reported instead. ** denotes significance at the 5 per cent level and * indicates significance at the 10 per cent level.

Table 4: Variance decomposition (VD) analysis results Period

S.E.

lnGDP

lnEXP

lnIMP

1

0.059489

100.0000

0.000000

0.000000

2

0.069134

96.77927

0.485519

2.735216

3

0.072322

95.06945

0.607064

4.323491

4

0.079416

87.51228

4.265108

8.222612

5

0.082524

83.93069

8.270920

7.798393

6

0.092067

67.53037

19.85696

12.61267

7

0.099620

57.80200

23.22632

18.97168

8

0.104331

53.15268

25.01270

21.83462

9

0.109939

48.28721

27.61690

24.09589

10

0.113013

47.26407

27.81886

24.91707

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Figure 1: Impulse response functions (IRF) results

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