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significant and positive, it has a positive relation with the trade, and vice versa.

Empirical Results

Now we will focus on the empirical analysis. This paper selects 16 countries and area which have relative big trade volume with China mainland than the else countries to establish a panel data. They are Australia, Brazil, Canada, France, German, China Hongkong, Indonesia, India, Japan, South Korea, Malaysia, Russia, Singapore, Thailand, Taiwan, Britain and America. This is ranked according to China’s Statistic Yearbook in 2011, which these countries above have accounted for more than 75% of the total trade of China. Since all of the 16 countries except Russia are members of the WTO/GATT, thus I can make a comparison about the trade change before and after accession into this organization for ten years. Of course, though the WTO was established in 1995, its predecessor GATT has already existed since in 1947. So I regard the membership of the WTO and the GATT as the same, which for the period from 1990 to 1995, the dummy of the WTO is one if a country is a member of the GATT. Additionally, Indonesia, Malaysia, Singapore and Thailand and China join the China-ASEAN Free Trade Area (CAFTA), which the free trade agreement was signed in 2003, therefore I add a dummy variable RTA in the gravity equation to estimate whether it would affect the bilateral trade among CAFTA. All data in this paper is picked up from 1990 to 2011, quarterly. The equation will be regressed with a pooled Ordinary Least Squares (OLS). Then table 1 below would contain the core results used to explain whether each variable in my model has a significant relation with China’s bilateral trade or not. If the coefficient of a variable is positive, it is positively related with the trade.

R2= 0.852530 F-statistics= 653.7035

Table 1 Pooled OLS Analysis

Table 1 show that the model has a well goodness of fit with the sample data since R square is high. We know that the F-statistic is to estimate the significance of all variables to the dependent variable, thus from the result in table 1, it could be expected that all variables in this model has a significant effect to China’s external trade with other countries. However, F-statistic cannot show the exactly significance of each variable to the whole model, thus we need to analyze the t-values of variables one by one to see whether all variables in the model is significant or not.

In general, GSP is given by those developed countries to developing countries via providing preferential duty-free entry for most of trade products when import from their partners. Seen from the table above, we can identify that GSP is a significant variable which means it encourages bilateral trade between China and its partners. In fact, as the GSP is generally given by developed countries to developing countries, and the most important article in GSP is the tariff reduction, thus it is evident that enjoying GSP from those advanced countries do benefit China’s trade. Although the developing countries seem to benefit more from the GSP, those countries providing it also are reciprocated. Taking the USA as an example, ===owing to the country carrying out of the GSP program, not only multinational trade is increased by 18.5 billion dollars in 2011, but also more than 80,000 occupations which are relevant with the GSP policy were created since 2005. China began to enjoy th