The effect of trade creation in China-Asean free trade area based on the gravity model

1 Introduction

Since the reform and opening up, the economic cooperation between China and its neighboring countries and regions has entered a new historical period with the improvement and deepening of China’s opening-up policy. Regional trade cooperation is becoming more and more extensive, among which the China-Asean free trade area (CAFTA), as the first regional trade organization proposed and participated in by China, has made great progress in recent years [1]. By analyzing the trade effect of CAFTA, it is analyzed that whether CAFTA has a creative effect on China’s trade, which can provide a good reference for the development of CAFTA and China’s participation in the construction of other free trade zones, especially the construction of “One Belt and One Road” and “maritime silk road” [2]. Since the establishment of the free trade area, China-Asean free trade area has achieved fruitful results. While the FTA has achieved phased results, it is of great significance to analyze the creation effect of China-Asean FTA trade, to seek further development of the FTA in the new era, and to further expand the China-Asean FTA to the construction of other FTAS [3].

2 Construction of the gravity model of trade in China-Asean free trade area

Jan Tinbergen was the first to apply gravity models to international trade. Tinbergen argues that two countries in trade, whose trade flows a function of the size of their economies and the size of their geographical distances. Thus, one-way trade flows between economies are positively correlated with their overall size, and negatively correlated with their geographical distance. The indicators of economic volume mainly include population, GDP and per capita income. The geographical distance is represented by the physical distance between the two capitals. It is the gravity equation that shows a positive correlation between the trade flows of two countries and the corresponding economic aggregate. The gravity equation can also reflect the negative correlation between trade flows and the size of their capital distances. The specific equation is as follows [4]: M ij = α Y i Y j D ij(1)

In the formula (1), bilateral trade flows of the two countries i and j are expressed in M _ij . The GDP of these two countries is expressed in terms of Y _i and Y _j respectively. The distance between the economic centers of the two countries is denoted by D_ij, and the proportionality constant is α. In order to better estimate the equation, the following expression can be used to represent it: ln M ij = α + β 1 ln Y i Y j + β 2 ln D ij + μ ij(2)

In this formula, the constant term is α, the coefficient of the related variable is β₁ and β₂, and the last residual term is μ_ij.

In order to better study the above trade issues, the scholar lineman further introduced in the above formula whether these two major variables exist in population and preferential trade agreements. Through the fitting analysis, the depth of the impact of these two variables on bilateral trade is further known. The specific results are as follows. If a country has a large population and more labor force, it will be more willing to produce its own products to meet its own pdemand, which will weaken the demand for foreign trade. Generally, the trade volume of different countries is closely related to the economic size, population number, physical distance, boundary factor, per capita living standard and other relevant factors of the partner countries in which the countries cooperate, so the following gravity model equation can be obtained [5]. ln M ij = α + β + ln Y i Y j + β 2 ln Y i Y j + β 3 ln D ij + μ ij(3)

In the formula (3), the total trade of the two countries, i and j, is expressed as T_ij. The gross domestic product of these two countries is expressed in terms of Y_i and Y_j, and the corresponding per capita GDP is expressed in terms of Y_i and Y_j. The distance between the economic center of i and j is denoted by D_ij, and the political docking relationship between the two countries is denoted by C_ij. Where, the constant term is expressed by α, and the coefficient of the above variables are expressed by β₁, β₂, β₃, β₄ respectively. The residual term is denoted by μ _ij .

This paper studies whether relevant trade effects exist during the period when non-tariff trade barriers gradually disappear after the supccessful construction of China-Asean free trade area. Therefore, when building this gravity model, you can consider whether to be a member of the organization as a variable of the model and use C_ij to represent it. It can also represent the trade effect when tariffs reach zero. This corresponds to the coefficient of C_ij, and if the coefficient is positive, the effect occurs. And the larger the value of the system, the more significant the trade effect will be if the FTA is successfully constructed. Of course, if this coefficient value is small, it means the trade effect is not strong. In this paper, GDP, D distance and C, that is, whether the member countries of the organization are used as independent variables, are used to introduce them to the gravity model. Therefore, the effect of them on total trade is obtained. The detailed model equation is as follows. ln TX jt = α + β 1 ln ( Y it Y jt ) + β 2 ln D ij + β 3 C ij(4)

In the formula (4), TX_ij represents the total amount of trade between country i and country j in year t. The GDP of the two countries is expressed in Y_i and Y_j respectively. The distance between the two countries is expressed in terms of D_ij. If j is a member of China-Asean free trade area, the C_j value is 1; if not, 0. If i and j are CAFTA members, then C_ij is 1, otherwise zero. The constant term is expressed in terms of α, and the coefficient of the related variable is expressed in terms of β₁, β₂, β₃, β₄. In this study, i represents China.

When selecting the relevant sample countries, the representative and large 23 trading countries will be taken as the sample countries. Their geographical location covers North America, South America, Asia, Europe and Oceania. The total trade of these countries has accounted for more than 50% of China’s total exports, which fully shows that this data has a certain representativeness. The time span of this sample data is from 2010 to 2016. The sample data of this period was selected for two main reasons. First, the data in recent years are more representative, and can also reflect the influence of many factors on trade flow after the successful construction of CAFTA. Second, these data mainly from the general administration of customs, the international monetary fund statistics. These data are highly authoritative and reliable. In addition, in this study, the distance cost of related countries was actually calculated and analyzed as an independent variable.

4 Empirical results

According to the sample data, the total trade volume of two countries is regarded as the dependent variable of the model equation. Other related variables are taken as independent variables, and then the data source is simulated and analyzed with Eviews 5.0 software. The least square method is applied in the analysis process, and the regression is carried out by combining equation (4). The regression results are shown in Table 1. In order to solve the heteroscedasticity problem of the equation, the logarithm was introduced on both sides of the equation because the sample data source involved was different. Regression analysis showed that although the years were different, the final results were similar. Moreover, the positive and negative coefficients of the related independent variables are consistent with the expected results. It also shows that the coefficient value of the correlation variable is reliable and can be referred to. Specific regression results are given as below.

Y_i and Y_j correspond to the GDP of i and j. When the current period increases by 1.0 percent, China’s total trade will increase by the corresponding percentage, which will have a positive effect on the growth of total trade. The size of the regression coefficient of the distance is negative. When the distance between the two countries increases, the corresponding transport cost increases, so the trade profit will decrease or even become negative. Therefore, the distance variable has a negative effect on the trade effect. If the distance is longer, the corresponding trade volume will decline, that is, the distance and trade volume show an inverse relationship. The following conclusions can be drawn from that. First, the increase in the GDP of relevant trading countries will effectively promote the growth of trade flows. Secondly, the success of CAFTA will lead to a significant increase in trade volume, but the effect is not significant. Third, geographical distance has a significant restraining effect on the increase of trade flow, and the geographical location of different countries is difficult to change. However, in the above regression equation, the distance variable is mainly mapped to the transport cost. If we want to increase the volume of trade, we need to actively speed up infrastructure construction, improve transport efficiency and reduce transport costs. At present, many Asean countries have borders with China, together with the improvement of transportation conditions. It has made our trade with Asean countries more frontier, and the cost of transportation has dropped significantly.