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ip is being increased. Thus, one reason to explain it is that the international trade is growing.

Actually, Rose (2004) considered that the Generalized System of Preferences (GSP) had a strong impact on bilateral trade, thus the GSP should have been contained in the model to estimate its influence in trade. Actually, this idea was proposed in the early 1960s by the United Nations Conference on Trade and Development. In 1971, the GATT adopted this idea and allowed this kind of preference to be given to those least developing countries. The principle for this idea is to provide a generalized and non-discriminated preference for one country’s all contracted partners. In this paper, GSP would be regarded as a dummy variable added into the model. Thus, if a country provides GSP to China, it would equal one, otherwise, zero.

In fact, the gravity model also can obtain the RTA as a variable to analyze bilateral trade. Jiang (2003) made a study about the ASEAN effect of the gravity model. From his result, when the GDP of the country, belongs to ASEAN, increased by 1%, its import would rise by 1.102%, while when the population of the country increased by 1%, its export would decreased by 0.301%. As for the distance increasing 1%, the export from a country to another country would dropped by 1.068%. This result can be applied to almost countries of the ASEAN. That is to say, GDP as well as distance are the mainly factors which have a great impact on bilateral trade. By the way, RTA is also a dummy variable in this paper.

Besides, according to the definition given by the Wikipedia, 'GDP per capita is calculated by taking a measure of all sources of income in the aggregate, such as GDP, and dividing it by the total population', thus this paper defines GDP per capita approximately as , expressed in the equation as Pgdpi. Then the model used in this paper can be rewrite as:

(3)

In equation (3), besides the original variables, this paper also adds some elements which the author considers may affect the trade. Such as Bothinijt is a dummy variable equaling to one if both of the country partners are members of the WTO in year t; otherwise, zero. Since the countries picked out to study the bilateral trade with China including Russia, who is still not a formal member of the WTO, thus this paper creates a variable Oneinijt , a dummy equals one if only one of the country partners is a member of the WTO; RTAijt, a dummy equals to one if both of the two countries belong to the same regional trade agreement; Area is the geographic area of a country, here is the area of China's trade partners; Langij means the two counties share a common language; Borderij equals one if the two countries share the same border; Religij means whether the two countries have the same religion; Pgdpi indicates the GDP per capita in country i, so does Pgdpj.

5.2 The Ordinary Least Square Regression (OLS) and Panel Data

Since the paper has to estimate the parameters from to , OLS estimation must be used. However, most of the time-series data has the problem of collinearity and heterogeneity, so it must be adjusted to make time-series stable. Combining time-series with cross-section data can reduces the collinearity and leads to a greater efficiency. Thus, a panel data will be established in this paper. Using the pooled OLS can estimate the equation. If the parameter of a variable is