Estimation of Natural Rubber Export Model in International Markets

38 that can be used to calculate estimates for the FEM. In this study, the FEM estimators are calculated by the Least Square Dummy Variable LSDV approach. Furthermore, the output from the STATA software is processed to estimate the model, as can be seen in Figure 15. Figure 15 The Results of The Gravity Model Testing for the basic assumption should be done to ensure that the obtained parameters are unbiased, consistent and efficient. A test of basic assumptions includes checking for heteroskedasticity, normality and multicollinearity Appendix 1. The normality test can be done by looking at the distribution of the residual data using the kernel density plot application on STATA Appendix 1. Meanwhile, the presence or absence of multicollinearity can be checked by looking at the correlation between the independent variables in the model. As shown in Appendix 1, the correlation coefficient of each independent variable is lower than the value of the coefficient of determination R 2 0.7133, thus, it can be concluded that the model does not have multicollinearity problems. It is also reinforced by the R-squared value is high and the number of significant variables. Heteroskedasticity occurs frequently in panel data due to the fact that data on different aggregation levels are combined and different sections have different numbers of observations. The variance is also dependent on specific country characteristics. In order to formally test for heteroskedasticity, the Breusch-Pagan test was employed. The null hypothesis is constant variance, i.e., homoskedasticity. The test indicated a p-value of 0.0000, which means that we can clearly reject the H : heteroskedasticity is present. In order to solve this problem, the robust and cluster country options are used in STATA. When applying the Breusch-Pagan test for the explanatory variables, the same outcome is reached p-value 0.0000 Appendix 1. Based on the estimation model, as shown in Table 9, it can be determined that the Fstat probability value is below a 1 significance level 0.00 0.01. This _cons -81.32534 15.56643 -5.22 0.000 -111.953 -50.69764 dj_bel -.4636549 .3319673 -1.40 0.163 -1.116816 .1895066 dj_ind -4.626895 1.504841 -3.07 0.002 -7.587742 -1.666048 dj_br -2.147857 1.147711 -1.87 0.062 -4.406033 .1103185 dj_can -1.824441 .8397827 -2.17 0.031 -3.476753 -.1721285 dj_ger -2.223786 1.323001 -1.68 0.094 -4.826853 .3792812 dj_kor -1.207747 .8324896 -1.45 0.148 -2.84571 .4302162 dj_sin 0 omitted dj_chn -2.414935 1.861297 -1.30 0.195 -6.077127 1.247256 dj_jpn -2.926822 1.434761 -2.04 0.042 -5.749784 -.1038611 dj_us -3.69344 2.095365 -1.76 0.079 -7.81617 .4292897 di_tha -.3759587 .510211 -0.74 0.462 -1.379823 .6279058 di_may 0 omitted di_ina .8905043 .4334498 2.05 0.041 .0376711 1.743337 erijt -.1383721 .0201704 -6.86 0.000 -.1780583 -.0986859 rjt 1.215068 1.649992 0.74 0.462 -2.031371 4.461507 gdpjt 1.573838 .529565 2.97 0.003 .5318935 2.615782 prodit 1.813713 .4403327 4.12 0.000 .9473373 2.680088 yijt Coef. Std. Err. t P|t| [95 Conf. Interval] Robust Root MSE = .86373 R-squared = 0.7133 Prob F = 0.0000 F 15, 314 = 69.01 Linear regression Number of obs = 330 39 indicates that the model is a good fit for this data and that there is at least one significant variable in the model. The obtained R 2 value is 0.7133, which means that the model is able to explain 71.33 of the diversity of natural rubber exports, while the remaining 28.67 is explained by other factors outside of the model.

6.2.2 Interpretation of The Gravity Model of Natural Rubber Export in International Markets

Natural Rubber Production of Exporting Countries Prodit Natural rubber production of exporting countries variable Prodit has a positive and significant effect on the natural rubber export value. This is because the probability value of the prodit variable is below a 1 significance level 0.00 0.01. Based on estimates, this variable has a coefficient sign that is consistent with the hypothesis. The Prodit variable coefficient is 1.813713, which means that a 1 increase in the production of natural rubber from the three main exporters will lead to a 1.813713 increase in the value of natural rubber exports to the destination country, ceteris paribus. Figure 16 shows a positive relationship between the average export value and the average production in the period between 2003 and 2013. Figure 16 The Average of Natural Rubber Export Value and Production, 2003 – 2013 Source: Author’s elaboration with data from UN COMTRADE, 2014; FAO 2013 Figure 16 indicates that if the production of natural rubber from the three main exporting countries increases, the value of natural rubber exports from these countries to the main destination countries also increases. The average natural rubber export value was the highest in 2011, at 0.7 billion US, while he highest natural rubber production volume occurred in 2013, with approximately 2.6 million tons. Upon viewing Figure 16, it is interesting that between 2011 and 2013, the average production increased, while the average value of exports declined. This is likely due to the global economic crisis, which led to the decrease in the price of natural rubber in the international market, and further 500000000 1E+09 1.5E+09 2E+09 2.5E+09 3E+09 Year Export Value US Production KG 40 resulting in a decline in the value of exports from the three main exporting countries. Gross Domestic Product of Importing Countries GDPjt The GDP of importing countries variable GDPjt has a positive and significant effect on the export value of natural rubber. This is because the probability value of the variable GDPjt is below a 1 significance level 0.00 0.01. Based on estimates, this variable has a coefficient sign that is consistent with the hypothesis. The GDPjt variable coefficient is 1.573838, which means that with a 1 increase in GDPjt, the value of natural rubber exports to the destination countries will increase by 1.573838, ceteris paribus. This occurs because the GDP in importing countries continues to increase and grow, which will drive increased demand for natural rubber in the long run due to an increase in purchasing power. The GDP growth of each importing country is presented in Figure 17. Figure 17 GDP Growth of Importing Countries in 2003 – 2013 Source: Author’s elaboration with data from USDA, 2013 Based on Figure 17, it is known that the average percentage of GDP growth in the importing countries showed positive growth in the period from 2003 to 2012. The growth of GDP in the importing countries will increase the purchasing power and the consumption for a variety of goods and services, including natural rubber. China is the country with the highest average GDP growth between 2003 and 2012. The average value of real GDP growth in China reached 9.2 per year at its highest. The increase in China’s real GDP further led to an increase in the value of natural rubber, where the average value of natural rubber exports to China from the three main exporting countries reached 0.87 billion US in the period between 2003 and 2013. China is a country that has a strong, positive development in the automotive industry. In 2010, Chinas auto sales reached 18 million units. This figure is the world record for the highest number of car sales in one country throughout the history of the automobile. The automotive industry is the industry, which most frequently used the raw material of natural rubber. Moreover, demand is -10.0 -5.0 0.0 5.0 10.0 15.0 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 GDP gr owth Year USA Japan China Singapore Rep. Korea Germany Canada Brazil India Belgium