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M & D FORUM Empirical Analysis of the Impact of China's Foreign Trade on Economic Growth in China WANG Lijuan, XU Ye Shijiazhuang University of Economics, Shijiazhuang, China, 050031 [email protected] Abstract: Econometric method is applied in the ADF testing on the stability of relevant economic variables time series data, the cointegration analysis on the stable variables after adjustment, and the research on whether equilibrium relation exists in the long run. Analysis is conducted on the cause-and-effect impact of various economic variables in long run equilibrium and short run fluctuation, via cointegration equation, error correction model and Granger Causality Test. The result of the research shows that in the short run, economic growth is inclined to be restrained by needs, while in the long run, it is determined by the improvement of production effectiveness. Therefore, China should continue to promote exporting trade to facilitate economic growth, and attach importance to the impact of importing on economic growth, to polish up the economic effect of importing. Keywords: Foreign Trade, Economic Growth, Cointegration Analysis, Granger Causality Test 1 Introduction On the relationship between foreign trade and economic growth, researchers domestic and abroad have conducted huge amount of studies. Foreign researchers study the relationship between trade and economic growth from the perspective of developed countries. Their applications require re-examination on China, a major developing country, as China’s dependence on trade is continuously increasing. Domestic researchers take few considerations on the stability of economic data when conducting studies, which impacts on the accuracy of model estimation. Therefore, this paper applies cointegration theory and Granger Causality Test method, takes into consideration the impact of variables’ short run fluctuation on variables long run equilibrium, and analyzes the relationship between China’s foreign trade and economic growth, to further grasp the essence of the economic development mode of China, and on a certain degree, provide references when making decision on China’s development on foreign trade. 2 Positive Analysis 2.1 Data Selection and Processing Instructions This paper conducts analysis on China foreign trade and economic growth with data of 1978-2008. The data originates from the database of China Bureau of Statistics and China Ministry of Commerce. The model applies major analytical variables with GDP, EX, IM and TEI. To eliminate the heteroscedasticity existed in time series, various variables are switched with logarithm. The switched variables are LNGDP, LNEX, LNIM and LNTEI respectively. 2.2 Positive Analysis 2.2.1 Correlation Analysis To test the relationship between the variables on a preliminary level, Eviews5.0 is applied in the calculation of correlation coefficient. The correlation coefficients of the various variables are thus generated. as shown in table 1. 193 M & D FORUM Table 1 Correlation Coefficients between LNGDP and LNEX, LNIM, LNTEI LNTEI LNGDP LNEX LNIM LNGDP 1.000000 0.995856 0.993824 0.995434 LNEX 0.995856 1.000000 0.997685 0.999269 LNIM 0.993824 0.997685 1.000000 0.999447 LNTEI 0.995434 0.999269 0.999447 1.00000 We can tell from table 1 that the correlation coefficients are extremely high (close to 1). These imply that evident correlations exist between China GDP and EX, IM and TEI; and evident correlation exists between IM and EX. 2.2.2 Stationary Analysis Most of the economic data are non-stable variables, as they have strong change tendency with the change of time. If regression equation is established on these data, the issue of “Fake Regression” will emerge. Therefore, this paper applies unit root test to determine whether the variable is stable before establishing proper regression equation. Table 2 ADF Unit Root Test Result Variable LNGDP LNGDP LNEX LNEX LNIM LNIM LNTEI LNTEI △ △ △ △ Test form ADF value (C,N,4) (C,N,3) (C,N,O) (C,N,O) (N,T,1) (N,N,2) (C,T,1) (C,T,1) -1.33.69 -3.56852 -1.56125 -5.04243 -1.61537 -3.57801 -1.90607 -3.65890 5%TLV -2.97626 -2.97626 -2.96041 -2.96347 -2.96041 -2.96397 -2.96041 -2.96397 p.s. (C, T, K) represent the constant, time trend and lagging rank figure in the model. N does not include C and T. refers to rank 1 difference. △ We can tell from table 2 that although LNGDP, LNEX, LNIM and LNTEI take on non-stable feature, their rank 1 difference is steady. This implies that they all belong to rank 1 single full series, namely I (1). Hence, cointegration analysis can be conducted on the difference of the original data. 2.2.3 Cointegration Analysis To make regression meaning for non-stable series, horizontal series difference can be applies to make them steady. And then difference series can be used for regression. However, if this method is applied, the information contained in the original horizontal series will be neglected. And the information is of crucial importance to the analysis. This paper adopts Johansen’s maximum likelihood estimation method, to test the cointegraion of various variables. Optimized lagging figure is determined as 2 based on AIC information optimization standards. Please refer to table 3 for the test result. Table 3 Johansen Cointegration Analysis table Cointegration equation N=0* ≦ N≦2 N≦3 N 1 eigenvalue Trace statistic 1%TLV Prob. 54.68150 35.45817 19.93711 6.634897 0 0.0157 0.1493 0.0975 0.868377 94.7881 0.516328 33.95376 0.269406 12.16330 0.087481 2.746377 p.s. * indicates that the null hypothesis is rejected on 1% evidence level. 194 M & D FORUM According to the test result, on 1% evidence level, the test result rejects the null hypothesis that the cointegration equation number N=0. This implies that cointegration exists between the variables. At least, one cointegration vector exists. That is to say, long run equilibrium cointegration exists in the system consisted by the variables. Based on the above cointegration test result, cointegration equation exists in between the various variables. This suggests that long run stable equilibrium exists among GDP, EX, IM and TEI. Although the development routes of the various variables turn on relatively big fluctuation, and the routes divert from equilibrium scale, these diversion are haphazard and temporary. In the long run, the equilibrium relation is stable. Giving that TEI is composed of IM and EX, and linearity may engender in the estimation of cointegration, cointegration regression only takes into account the relations between GDP and IM, EX. LNGDP = 4.86287 + 0.6705 LNEX + 0.0369 LNIM ( )( )(0.213985) t= 33.76083 4.118307 R =0.992697 DW=0.683621 2 We can tell from the coefficient denotation of various variables that EX and IM are all positive. And this indicates that the development of EX and IM all have positive impact on GDP in the long run, and the output elasticity of EX is bigger that IM. 2.2.4 Error Correction Model The above analysis indicates that long run equilibrium relation exists between China’s economic growth and the various variables of foreign trade. The time series of these variables present unstable features, and relatively big fluctuation are engendered in the development process, which may impact on the equilibrium relation between economic growth and the various variables. In the short run, the equilibrium relation may lose balance. Error correction model can be applied to analyze the amendment degree of the short run fluctuation and long run equilibrium diversion of various variables. ∆LNGDP = 0.0932 + 0.1059∆LNEX + 0.0437 ∆LNIM + 0.03317∆LNTEI − 0.2335 Et −1 ( t= 5.0416 R2=0.6532 ) (0.6495) DW=0.7813 (0.1826) (0.8441) (-3.8815) The results show that the short run fluctuation of the various variables has positive impact on economic growth. As Et-1 coefficient is statistically evident, 23% (0.233576) of the diversion between annual actual economic growth figure and long run equilibrium is amended. The above analyzes the correlation between foreign trade and economic growth variables from the perspective of regression. Nevertheless, the correlation is not necessarily cause-and-effect. To thoroughly study the relation between import, export trade and economic growth, statistical causality can be analyzed between the variables. 2.2.5 Granger Causality Test Cointegration exists between foreign trade and economic growth. Below Granger Causality Test is applied, to determine whether the expansion of trade brings in economic growth, or economic growth brings in the expansion of trade. Before applying the Granger Causality Test, the lagging rank figure p of the series of LNEX, LNIM and LNTEI and LNGDP needs to be set. As the standards stipulate that the statistical quantity AIC or SC should reach minimum integer, p=5. Please refer to Figure 4 for the test results. 195 M & D FORUM Table 4 Granger Causality Test table F-Statistical Prob. Null Hypothesis Quantity The reason why LNEX is not LNGDP 3.56486 0.02529 The reason why LNGDP is not LNEX 4.56242 0.01296 The reason why LNIM is not LNGDP 1.60854 0.21786 The reason why LNGDP is not LNIM 2.56240 0.07225 The reason why LNTEI is not LNGDP 2.36252 0.09034 The reason why LNGDP is not LNTEI 3.49278 0.02716 The reason why LNIM is not LNEX 2.62972 0.06708 The reason why LNEX is not LNIM 0.45643 0.80233 p.s. Evident level is 5%. * indicates close to the threshold. Conclusion Reject hypothesis Reject hypothesis Do not reject hypothesis Reject hypothesis Do not reject hypothesis Reject hypothesis Reject hypothesis * Do not reject hypothesis The results show that, in the long run, export trade is closely related to economic growth. Causality lies between the two. Meanwhile, import trade’s impact on economic growth is not evident. But the impact of importing is significant on exporting. In the short run, TEI increase leads to economic growth; in the long run, economic growth leads to TEI increase. 3 Conclusion In conclusion, exporting in foreign trade contributes a huge part in China’s economic growth, while importing has negative impact on the increase of GDP. Although the results of metric testing imply that importing does not directly have propelling impact on the increase of GDP, importing in fact has un-neglectable indirect propelling impact on the increase of GDP. Hence, in the future development, China should not only continue to utilize exporting’s pulling effect on economic growth, but also bring into play the introduction of advanced production requisites and propelling competition effect by importing trade. The allocation of production requisites should be optimized, to facilitate the improvement in production effectiveness. On the aspect of importing trade policy, quality foreign investment should be brought in, to improve domestic production technology and effectiveness, and the upgrade of industry structure. In this way, the domestic economy can sustain growth in the long run, on a fundamental level; and economic growth and foreign trade can have positive inter-impact. However, on the matter of on what level can economic growth reaches equilibrium, under what circumstances it diverts, and how to amend it, still require further research. References [1]. Keller W. Trade and the transmission of rhe technology. NBER Working Papers, 1997(6):113~120 [2]. Mcnab R M, Moore R E. Trade policy, export expansion, human capital and growth. Journal of International Trade and Economic Development, 1998(7):237~256. [3]. Lin Qingquan. Domino Offect Analysis on the J Curve of China Importing and Exporting Trade,The Journal of Quantitative & Technical Economic. Beijing: Chinese Academy of Social Sciences, 2007(11):19~25(in Chinese) 196