Research on the Relationship between Foreign Trade and the GDP Growth of Southwest Minority Region of China

--Empirical Analysis Based on the Panel Causality

Yuhong Li1，2, Xiaoyin Wang (Corresponding author) 3

1College of Economics and Management, Agricultural University, Wuhan，Hubei，430070，P.R.China 2 Business School, Jinggangshan University, Ji’an, Jiangxi, 343009, P.R.China

3 College of Science, Huazhong Agricultural University, Wuhan，Hubei，430070，P.R.China wxqwxy@126.com

Abstract：In open economy, development of foreign trade greatly impacts GDP growth. Adapting modern metering methods like roots of unity, panel co-integration analysis and error correction model for researching the causalities between foreign trade including total foreign trade, total export and total import as well as GDP growth of southwest minority of China. The result suggests that there exist long time or short time causality between GDP and total foreign trade as well as between GDP and export, foreign trade is the long time and short time reason of GDP growth, but no evident can prove that there exists long time stable causality between foreign trade and GDP. This paper ends up with recommendations to develop the economy of southwest minority region of China.

Keywords: southwest minority region, GDP growth, foreign trade, panel causality analysis

I. INTRODUCTION Since the reform and opening up, China's foreign trade, which is playing a significant role in the world, becomes more and more important. But the proportion of China's total import volume in the GDP cannot match the average level of the developed countries. Obviously, the foreign trade is closely related to economic growth in China. The importance of foreign trade for a country is increasingly prominent, though there are many researches like Xu Qifa and Jiang Cuixia (2002) related to contribution that foreign trade to GDP growth, researchers particularly focus on one region or region is not common. Since the reform and opening up, foreign trade in Southwest minority region has experienced rapid development. From 1978 to 2008, exports and imports in Southwest minority region increased from 0.407 billion dollars to 27.015 billion dollars. The increase of foreign trade is faster than the increase of GDP, and the proportion of foreign trade in GDP is increasing too. However, is there serious internal logical causality between GDP and foreign trade? That is to say, is there long time or short time causality between them? Thus, this paper will try to research and discover it. Chinese southwest minority region mainly consists of Sichuan province, Chongqing city, Yunnan province, Guizhou province, Guangxi autonomous region of zhuang people and Xizang autonomous region. Though

each of the regions consists of variety of nationalities, Yunnan, Guangxi, and Xizang all consist of almost minorities, thus, the data from these regions would be considered as the data of minority regions. Chongqing although consists of more than 50 nationalities including Tu people, Miao people and so on, the proportion of the minorities there is just 6.37%. Therefore, the foreign trade in the minority region has little impact on Chongqing’s GDP growth. Then data of the minority region in Chongqing will not be dealt with as data of southwest minority region. Even there must be difference with truth, but it won’t change the characteristics of this research much. In this case, the region mentioned in this paper is just the region including Yunnan province, Guizhou province, Guangxi province and Tibet. According to the provincial China yearbooks (1978-2008) , we collected and sorted out the relative data of southwest minority region of China, including (1978-2008) total foreign trade volume( IE), the total export volume(EXP) and the total import volume (IMP) as well as GDP. From the result, it is could be seen that the mentioned indexes present a trend to increase on the whole. In order to further grope for the relationship between GDP and certain index, the concept of foreign trade dependency is cited to describe it. It is just the ratio of certain index of foreign trade and GDP, which can reflect the relationship of dependency between GDP and certain index. It can be written in a formula as,

Ratio of dependency on foreign trade

= certain index of foreign trade / GDP (1)

With the result calculated by the above formula, the dependency of each index of foreign trade presents a trend to increase on the whole, while in some years, they occasionally present fluctuations. With the result, we could see a trend of dependency. The ratio of dependence on foreign trade in Southwest minority region increased from 1978 to 2008, from 3.08 % in 1978 to11.4 % in 2008. Especially, in 1994, it reached the top (15.45%); the ratio of dependence on export increased from 2.44% in 1978 to 6.16% in 2008, it reached the top (9.96%) in 1995; the ratio of dependence on import increased from 0.64% in 1978 to 5.17% in 2008, it reached the top (5.77%) in 1994. These figures intuitively indicate that each index of foreign trade of southwest region of China contributes to GDP growth at different levels. But,

978-1-4244-4870-8/09/$26.00 ©2009 IEEE 798

whether there exists internal logical causality should be further tested. Therefore, the panel data and latest more stable analysis methods are adapted for testing the relationships of the indexes of foreign trade and GDP of southwest minority region of China. We will use roots of unity, panel co-integration long time causality and short time causality analyses to expect more stable results.

Ⅱ. LITERATURE REVIEW

Adam Smith, British classical economist, put forward surplus product export model, and he pointed out that foreign trade is a significant symbol of expanding market, so the expansion of foreign naturally leads to GDP growth. His statement includes the primary thought of international trade can pull GDP growth. The comparative cost theory built by David Ricardo suggests that importing cheap primary products can guarantee capital accumulation and GDP growth. J．S．Mill earliest pointed out that international trade has indirect effect, and his follower Rhode Meyer generalized his statement in three: first, expanding market can improve productivity; second, increasing savings and capital accumulation; third, cultivating new demands and hobbies, transferring technologies and arts and the educational effect brought about by entrepreneurships. Since middle of 1980s, American economists including Paul, Lucas, and British economist Scott and so on broke through the transitional growth theories. Paul thought that new growth theory regards knowledge as the source of growth. Scott’s theory suggests that developing countries can absorb advanced technologies, human resources from foreign countries through international trade to reduce fouls and form a special overtaking to develop national economy rapidly, which emphasizes the position of international trade in GDP growth. In modern stage, relevant theories of foreign trade and GDP growth generally suggest the following three views. One is that foreign trade is the motor of GDP growth, and international trade cannot only bring direct dynamic interests, but also bring indirect dynamic profits. Another is that Core and Periphery Theory suggesting developed countries and developing countries separately lie in the core and the periphery, and they have equal economy positions. The third is that trade is a maid rather than an engineer of growth. What’s more, it’s unnecessarily good for economy. Jeffery and Andrew (1999), professors of Harvard University, found after research that the GDP growth reached 4.5% in 1970s-1980s in the developing countries with open economy every year, while the growth rate just reached only 0.7% in those developing countries with close economy. At the same time, research indicated that the average growth rate of developed countries with open economy reached 2.3%, and average growth rate was only 0.7% in developed countries with close economy. In China, a number of scholars have done much

empirical studies on the relationship between Chinese foreign trade and GDP growth, as the methods and the data are different; the results were not the same. But the methods roughly include the following: 1) Correlation analysis. This analysis mainly adapts correlation coefficients for reflecting the dependency relationship between GDP growth and export and import, and the measurement reflects the ratio of dependence on each other. An empirical study done by Dong Bigang (2000) indicated that, in 1978-1998, the correlation of China’s foreign trade and GDP growth was significant, the coefficient was r>94%.Guo Xin (2004) drew the conclusion by recursion model that there exists significant positive correlation between foreign trade and GDP growth, that is to say, the contribution of foreign trade to GDP growth is considerable.

2) Regression analysis. This analysis assumes that the impact of other factors on GDP growth is constant or stable, then to build simple linear regression model. Yi Xiangshuo (1997) found by research that the pull effect of export on non-export sectors or the whole GDP growth was not strong at all, never perform a role of engineer on GDP growth. Yang Quanfa (1998) took a regression analysis with the data (1978-1995) of relevant indexes through the model built by Balasa and Vedur, and he drew a conclusion that growth of export didn’t meet the expectation of promoting the GDP growth. Sun Lin, Wang Qifan (2003) researched, by the improved Vedur Model, and pointed out that the mechanism and approaches of the effect of China’s foreign trade on GDP growth has strong time limitation. 3) Co-integration analysis. This analysis adapts causality test for analyzing whether there exists long time stable co-integration between each variable and the concrete form accordingly. The empirical analysis done by Shi Chuangyu (2003) suggested that growth of export could greatly promote GDP growth in short time, while that of import didn’t impact GDP significantly. Liu Xiaopeng (2001) did a co-integration analysis with the data of import and export and GDP, and he suggested that growth of import greatly promotes GDP growth of China, while that of export performs an opposite one. 4) Measurement of contribution ratio. Traditional method usually direct comment the effect of foreign on GDP growth through export and import based on the equation of national income. Lots of scholars revised or improved it, Wei Weixian (1999) drew the conclusion through co-integration analysis and variance analysis that 31% of GDP growth of China ascribes to the export -oriented strategy, while the contribution of GDP growth to that of export is less than 10%, his accounts for that the fast growth of China’s GDP growth didn’t realize the scale effect of the growth of export. Lin Yifu and Li Zhengjun improved the traditional single equation model and built combined equations to calculate the contribution of foreign trade to GDP growth. The result suggests that 10% of the export growth can lead to 1% of GDP growth The above methods all have separate advantages and drawbacks, they commonly adapt single cross-set (time

Proceedings of the 2009 IEEE IEEM

799

series) analyses for test, these methods all have the problem of potential on the low side, and they may lead to errors of calculating long time causalities and to the

instabilities. In addition, simply dealing the data equals to considering them coessential, ignoring their heterogeneity.

Ⅲ. MODELS AND METHODS

This paper will adapt panel co-integration analysis for

testing whether there exists long time or short time stable causality between foreign trade and GDP growth, testing the roots of unity of each variable to confirm their stabilities. The following process presented by the model,

yit =ρi yi,,t - 1 + Xitδi +εit (2)

where i = 1 , …, N present the number of panel data; t=1, … ,T presents time span; xit are the exogenous variables in the model including fixed effect or time trend of each panel unit; ρi is autoregressive coefficient, suppose that disturbance terms εit are mutual independence. If | ρi | < 1, yit presents the stable process; if | ρi | = 1, yit presents the process of roots of unity. Take the logarithms of the gross domestic products (GDP), total import and export volume (IE), total export volume (EXP) and total import volume (IMP) separately, they are LnGDP、LnIE、LnIMP、LnEXP. Then test their logarithm values and first order difference values through panel roots of unity. Logarithm is cited here is for convenience to get stability more easily, and helpful to eliminate the heteroscedasticity of time series and the characteristics of time series and relationships would not be changed. This paper will test the relationships between relevant indexes by three steps. First of all, test through roots of unity using panel data; then, use two-step method pointed by Engle and Granger (1987) to test the mutual long time causalities of relevant indexes; if the long time causality exists, then further test their short time causalities. A. Test of Panel Data by Roots of Unity

In order to overcome the deviation brought out by one method, LLC test, B test and IPS test are all used in this paper to test the relationships between GDP and relevant indexes (IE, EXP and IMP) of foreign trade of southwest minority region of China by roots of unity.

B. Co-integration Analysis of Panel Data and Long Time Causality Test In order to test the long time cassations between variables, two-step test method pointed by Engle and Granger (1987) is used. When measuring the long time causalities between GDP and relevant indexes of foreign trade, the measured variables are mutual simple integration, and then take the regression through the following panel equation (3), we get residual errors Eit

and test it through roots of unity to determine their stabilities. If Eit is stable, the mutual long time causalities are proved to exist.

Ln(*)= α+βLn(**)+εit (3)

Where (*) and (**) separately present GDP, IE, EXP and IMP. C Panel Data Error Correcting Model and Short Time Causality Test

Co- integration relationships just reflect the long time

balanced relations between relevant variables. In order to cover the shortage, correcting mechanism of short time deviation from long time balance could be cited. At the same time, as the limited number of years, the above test result keeps doubtful (Christpoulos and Tsionas, 2004). Therefore, under the circumstance of long time causalities, short time causalities should be further tested as well. The error correcting models could be built as,

dLnGDPit=ηi+Σα1dLnGDPi,t-1+Σβ1dLnIEi,t-1 +λECMit+εit (4)

dLnGDPit=ηi+Σα1dLnGDPit-1+Σβ1dLnEXPit-1

+λECMit+εit (5) dLnGDPit=ηi+Σα1dLnGDPit-1+Σβ1dLnIMPit-1

+λECMit+εit (6)

dLnIEit =ηi+ Σα1dLnIEi，t-1 +Σβ1dLnGDPi,t-1 +λECMit+εit (7)

dLnEXPit=ηi+Σα1dLnEXPit-1+Σβ1dLnGDPi,t-1

+λECMit+εit (8)

dLnIMPit=ηi+Σα1dLnIMPi,t-1+Σβ1dLnGDPi,t-1 +λECMit+εit (9)

Where t presents year, d presents one order deviation calculation, ECMit present the errors of long time balance. If λ = 0 is rejected, error correcting mechanism happens, and the tested long time causality is reliable, otherwise, it could be unreliable. Ifβ1=0 is rejected, then the short time causality is proved, otherwise the short time causality doesn’t exist.

Ⅳ. RESULT AND ANALYSIS A. Test of Panel Data by Roots of Unity

Through calculating by software Eview 5.0 and test the four variables LnGDP, LnIE, LnEXP and LnIMP by LLC test, B test, IPS test separately(see table 1). The result indicates that LnGDP, LnIE, LnEXP and LnIMP all

Proceedings of the 2009 IEEE IEEM

800

perform non-steady through the tests by the mentioned method. However, after one order deviation, through the same methods, it’s found that all of them passed the

significant test by 1%. So we can say GDP growth, total foreign trade volume, total export volume and total import volume are all one order integration.

Table 1 Result of Panel Data Test by Roots of Unity

Cross-set:4 Time span:31

LnGDP LnIE LnIMP LnEXP

Level test One order deviation Level test One order

deviation Level test One order deviation Level test One order

deviation

LLC value 0.81645 (0.7929)

-3.67434** (0.0001)

0.26527 (0.6046)

-7.24624** (0.0000)

-0.08885 (0.4646)

-9.61254** (0.0000)

-1.26904 (0.1022)

-5.39061** (0.0000)

B value -1.04845 (0.1472)

-4.48781** (0.0000)

-2.1479* (0.0159)

-7.06638** (0.0000)

-1.39733 (0.0812)

-8.53662** (0.0000)

-1.5727* (0.0472)

-2.42717** (0.0076)

IPS value 3.79089 (0.9999)

-5.10384** (0.0000)

2.98218 (0.9986)

-7.96837** (0.00000

1.93436 (0.9735)

-9.98096** (0.0000)

1.31968 (0.9065)

-7.14549** (0.0000)

Notation: 1.the figures in the brackets are p values; 2. * presents pass the test by 95% significance level, ** presents pass the test by 99% significance level; 3.testing form is only intercept, lagging exponent number is chosen as Schwarz rules. B. Co-integration Analysis of Panel Data and Long Time Causality Test

Through the test by roots of unity, GDP, IE, EXP and

IMP all perform one- order simple- integration I (1), so it’s possible that there exist mutual co-integration between relevant variables. The results of their long time causalities and corresponding residual errors Eit can be seen as table 2.

Table 2 Result of Panel Co-integration Test (Test of Residual Errors by Roots of Unity)

Variables LLCvalue Bvalue IPSvalue LnGDP is the induced variable of LnIE LnIE is the induced variable of LnGDP

-1.68807*(0.0457) -1.65855*(0.0486)

0.11622(0.6463) -0.58299(0.2600)

-1.93487*(0.0255) -2.36116**(0.0094)

LnGDP is the induced variable of LnIMP LnIMP is the induced variable of LnGDP

0.02895(0.5115) 0.33248(0.6302)

-0.98286(0.1628) -1.87357*(0.0305)

-2.24397*(0.0124) -1.56894(0.0583)

LnGDP is the induced variable of LnEXP LnEXPis the induced variable of LnGDP

-1.67812*(0.0472) -2.84338**(0.0022)

0.84866(0.8020) 0.46127(0.6777)

-2.06297*(0.0200) -2.78182**(0.0027)

Notation: * presents pass the test by 95% significance level, ** presents pass the test by 99% significance level.

From table 3, it’s found that there exist mutual long time causality between LnGDP and LnIE, LnGDP and LnEXP, two of the three tests (LLC test, B test and IPS test) passed by 95% significant level or 99% significance level. For at least two of the tests didn’t pass 95% significance level, nothing can prove there exist long time mutual causality between GDP and LnIMP.

Corresponding co-integration equations are the following,

Notation: Figures in ( ) and [ ] are separately t-test values and p values of t-test.

The above equations all passed t-test and F-test by 95% level, from them, in long time, LnGDP and Ln IE are positive. And the elasticity between them is 0.946573; this means one unit of LnIE increment will lead to 0.946573 units of LnGDP increment. Similarly, one unit of LnGDP increment will lead to 0.965796 units of LnIE increment; the elasticity between LnGDP and LnEXP is 0.836868, one unit of LnEXP increment will lead to 0.836868 units of LnGDP increment, at the same time, one unit of LnGDP increment will lead to 1.072914 units of LnEXP increment.

C. Panel Data Error Correcting Model and Short Time Causality Test

It’s found, according to the co-integration test of panel data, that there exist mutual long time causality between LnGDP and LnIE, LnGDP and LnEXP. For the limited number of years, short causality between them should be tested to test their stability (see table 3).

Table 3 Short Time Causality Test of Panel Data

Variables LnGDP 与 LnIE

Variables LnGDP 与 LnEXP

Model1 Model 2 Model 3 Model 4 D(LnGDP) D(LnIE) D(LnGDP) D(LnEXP)

C 1.52341** 4.32145** C 1.46261** 2.32546** D(LnGDP(-1)) 0.15348** 3.45687** D(LnGDP(-1)) 0.156874 0.04578*

LnGDP= -4.294046 + 0.946573lnIE (-15.26985) (36.05346) [0.0000] [0.0000] F=1299.852, R2=0.913493

LnIE= 5.0539154 + 0.965796LnGDP (31.62920) (36.05346) [0.0000] [0.0000] F=1299.852, R2=0.913493

LnGDP= -2.691144 + 0.836868LnEXP (-10.30035) (32.75690) [0.0000] [0.0000] F=1073.015, R2=0.797909

LnEXP= 3.912185 + 1.072941LnGDP (20.02974) (32.75690) [0.0000] [0.0000] F=1073.015 R2=0.797909

Proceedings of the 2009 IEEE IEEM

801

D(LnGDP(-2)) -0.45214 -0.4678 D(LnGDP(-2)) 0.1547** 0.7813*- D(LnIE(-1)) 0.007541 0.06781 D(LnEXP(-1)) 0.05789** 0.14577** D(LnIE(-2)) 0.00257* 0.38461* D(LnEXP(-2)) 0.48717 1.24575

ECM 0.65566** 0.824457** ECM 0.918445** 0.845778** R2 0.864541 0.91254 R2 0.894025 0.920315 F 1160.321 2032.182 F 1987.35 2241.32

D-W 1.9632 2.124 D-W 1.978 2.0453 Notation: * presents pass the test by 95% significance level, ** presents pass the test by 99% significance level.

The result in table 4 makes the following clear: ECM in model 1, of which test equation is equation (4), is positive and passes the test by 0.01 level, indicating correcting error mechanism happens, and long time pull effect of foreign trade on GDP has been proved. ECM in model 2, of which test equation is equation (7), is also positive and passes the test; further indicating that foreign trade and GDP are mutually causal. From the co-integration equations, we can see they are positive, and GDP growth will lead to the growth of foreign trade, in reverse, export and import have active impacts on GDP growth. ECMs in model 3 and model 4, of which test equations are separately equations (5) and (8), are all positive and pass the tests, indicating the causality between EXP and GDP, and they both have mutual promoting effects. From the significance of other parameters in models 1-4, the short time causalities between foreign trade and GDP are testified, indicating foreign trade is the short reason of GDP growth.

Ⅴ. CONCLUSION AND RECOMMENDATIONS The result of test indicates foreign trade is the long

time and short time source of GDP growth of southwest minority region of China. Total foreign trade , total export and total import all have positive relationship with GDP there, and they are mutual causal. It has proved the intuitional dependence relationship mentioned at part 2- issue and specification. Developing foreign trade is good for promoting GDP growth of southwest minority region, and GDP growth, in reverse, is also good for promoting the development of open economy.

The result suggests that there exists long time stable causality between export and GDP; export can contribute to GDP growth for long. Although export of southwest minority region of China cannot provide GDP growth with a power which would be less than the others provided by other regions like east region because of the lack of advantages of location, as long as every kind of resources can be made full of to develop foreign trade, the open economy of southwest region must be considerably good.

No evident can prove that there exists long time stable causality between import and GDP growth; it’s possible that import cannot directly contribute to GDP growth. As the crowding-out effect of imported products from foreign countries and the indirect promoting effect couldn’t be measured, it cannot be proved whether there exists long time causality between import and GDP,

whether there exists long time stable internal logical causality could not be proved yet.

Keynes ever said that export is for better future import. That is to say, export is to bring about good condition to import what we don’t have or to import more effective products and rare resources. Therefore, in order to promote more and more effective GDP growth, foreign trade should be strongly recommended to develop to develop open economy as final destination, and the export and import could be balanced relatively to the most extent, protectionism is bad for southwest minority region as well as the world to utilize the resources effectively. Although strengthening export can improve the resources utilization and improve employment status to promote GDP growth, import should not be restricted while expanding export, for import can help to obtain advanced technologies and rare resources we don’t have.

ACKNOWLEDGMENT

This paper is assisted by the project A Study on the Development of Service Industry and Service Trade in Jiangxi Province. Project ID: 09YJ249 REFERENCES: [1] Brainard , S. Lael, 1993 , A Simple Theory of Mul-

tinational Corporations and Trade with a Trade-Of f Between Proximity and Concentration [C] , NBER Working Papers 4269.

[2] Brainard , S. Lael , 1997 , An Empirical Assessment of the Proximity Concentration Trade-of f between Mul- tinational Sales and Trade , [J ] , American Economic Review , 87 : 520～544.

[3] Helpman , Elhanan , 1984 , A Simple Theory of International Trade wi th Multinational Corporations [J ] , Journal of Political Economy , 92 : 451～471.

[4] Helpman , Elhanan and Paul R. Krugman , 1985 , Market Structure and Foreign Trade [M] , MITpress.

[5] Horstman , I. J. and J. R. Markusen , 1992 , Endogenous Market Structures in International Trade [J ] , Journal of International Economics , 32 : 109～129.

[6] Im , K. S , H. Pesaran and Y. Shin , 2003 , Testing f or Unit Roots in Heterogeneous Panels [J ] , Journal of Econometrics , 115 : 53～74.

[7] Kao, C, 1999, Spurious Regression and ResiduaL2based Tests f or Co-integration in Panel Data [J], Journal of Econometrics , 90 : 1～44.

Proceedings of the 2009 IEEE IEEM

802