37 On the other hand, the main natural rubber export destination countries of
Indonesia are the US and Canada. The value of Indonesian natural rubber exports to the US in 2011, for example, is the highest value of Indonesian natural rubber
exports during the period from 2003 to 2013, amounting to 2.7 billion US. This figure is directly proportional to the average RCA value of Indonesian natural
rubber in the US, which reached 85.60 during the period between 2003 and 2013. It is noteworthy that the average RCA value for Indonesian natural rubber in
Canada, as shown in Figure 14, is very high at 274.72. This is due to Indonesia being the main natural rubber exporter for Canada, which had an average export
value of 0.4 billion dollars from 2003 to 2013, far exceeding the value of exports from other exporter countries. The proportion of the value of Indonesian natural
rubber exports to the total value of natural rubber exports to Canada in 2013 was more than 50 percent. Additionally, the average proportion of worlds natural
rubber exports to Canada out of the total world exports of all commodities to Canada accounted for only 0.20 percent during the period from 2003 to 2013.
Finally, Malaysia is the EUs second largest trading partner with the ASEAN countries, with bilateral trade in goods reaching 31.9 billion Euros in
2010 and the EUs 22
nd
largest trading partner overall MGCC, 2012. Germany has intensive trade relations with Malaysia and is one of the main foreign
investors into the Malaysian economy. Moreover, among members of the European Union, Germany is Malaysia’s leading trading partner, especially in the
natural rubber sector. In 2011, the value of Malaysian natural rubber exports to Germany amounted to 0.5 billion US, which is the highest natural rubber export
value to the Germany when compared with the export value from other exporting countries. This is in line with a very high RCA value for Malaysian natural rubber
in Germany, which reached 76.72 during the period from 2003 to 2013.
6.2 Factors Affecting Natural Rubber Trade in International Markets
The natural rubber trade to the top ten export destinations is influenced by many factors. These factors can be derived from either the exporting the importing
countries that will affect the natural rubber export value that is traded. These factors need to be identified and analyzed to determine whether they have
significant positive or negative effects on the value of exports. In this section, the results of the estimation of the factors that affect the value of exports of natural
rubber in the international market will be presented.
6.2.1 Estimation of Natural Rubber Export Model in International Markets
This research is completed with the use of panel data, which is a composite of the time series and cross-sectional data. There are two common approaches
applied to panel data, namely Fixed Effect Model FEM or Random Effects Model REM. Both are distinguished based on the assumption of either the
presence or absence of correlation between the errors with independent variables. The estimation approach used for this study is the FEM. The FEM is useful when
the individual effects and the explanatory variables are correlated with Xit or have patterns that are not random. This assumption allows for the error term of
individual and time effects to be part of the intercept. There are several techniques
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
