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Relationship BETWEEN FDI AND EXPORTS

1. Mean Differences Using the Independent sample T-test
 Mean H Mean L Mean difference T ratio Sig. 1990 FDI per capita 51090.40759 2622.03559 48468.37200 2.460 0.000 Export per capita 59056.64042 4997.21796 54056.64042 2.960 0.000 GDP per capita 43264.0650 333389.84420 399252.2208 2.221 0.001 2007 FDI per capita 333296.0003 49712.56448 305105.5897 2.799 0.000 Export per capita 205232.8941 28190.41055 155520.3296 2.519 0.004 GDP per capita 1067668.551 166582.3215 901086.2295 2.044 0.005 2014 FDI per capita 389665.3619 66275.77171 389665.3619 2.357 0.01 Export per capita 261173.7262 90509.4320 170664.2942 1.835 0.07 GDP per capita 1242393.99 385942.9448 856451.0452 1.455 0.07

The independent t-test shows the mean difference between the groups L and H, the T ratio the mean and the level of significance is used determine when to reject a null hypothesis while testing the significant difference.

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H0: There is no significant difference in means H and L

H1: There is a significant difference in means H and L(Libguides.library.kent.edu, 2017).

In all the cases there is a difference in the means of H and L, but is the difference statistically significant?

In 1990 and 2007, the sig value is less than 0.05. Therefore, we reject H0 and conclude that there is the significant difference in the means H and L of the categories FDI, GDP and Exports per capita.

In 2014, Export per capita and GDP per capita had no significant difference since 0.07>0.05, we, therefore, do not reject the null hypothesis while the FDI per capita has a significant difference between the means since 0.01<0.05(Hinton, 2004).

1. Correlation between the FDI, GDP, and Exports per capita Through the Years
 FDI per capita Exports per capita GDP per capita 1990 FDI per capita 1 0.884 0.928 Exports per capita 0.884 1 0.842 GDP per capita 0.928 0.842 1 2007 FDI per capita 1 0.697 0.938 Exports per capita 0.697 1 0.766 GDP per capita 0.938 0.766 1 2014 FDI per capita 1 0.681 0.287 Exports per capita 0.681 1 0.851 GDP per capita 0.287 0.851 1

Conclusion

The stated correlation coefficients indicate the change in variable 1 as a result of a unit change in variable 2 (Anon, 2017).

In 1990 and 2007, all the variables have a strong positive correlation with each other. This is because the correlation coefficient is greater than 0.05. FDI per capita has a strong positive correlation of 0.884 with the Exports per capita(Hinton, 2004).

In 2014: FDI per capita has a strong positive correlation with the Exports per capita.

FDI per capita has a weak positive correlation of 0.287 with the GDP per capita.

GDP per capita has a strong positive correlation of 0.851 with the GDP per capita(SPSS 15.0 brief guide, 2006).

1. Regression on the ln of the variables
2. Regression on FDI only
 Coefficient values Significance of coefficients R2 B0 B1 1990 lnFDI per capita 3.618 0.628 0.000 0.577 2007 lnFDI per capita 0.197 0.945 0.000 0.691 2014 lnFDI per capita -1.332 1.064 0.000 0.707

H0: The coefficient is not significant in predicting the Exports per capita

H1: The coefficient is significant in predicting the Exports per capita

Using the linear regression model, I was able to check the significance of coefficients in predicting the Exports per capita variable and the same time finds the coefficients of the various variables.

The sig value of the years 1990, 2007 and 2014, 0.000<0.05, we thereby reject the null hypothesis and state that all the coefficients are significant in predicting Exports per capita.

TheB0 indicates the values the change in Export per capita whenever the FDI per capita is zero. B1 shows the change in Exports per capita for every unit increase in the FDI per capita.

R2 depicts the percentage of the model that predicts the Y variable. In 1990, R2=57.7% indicating that the model explains 57.7% of the Exports per capita. In 2007, R2=69.1% indicating that the model explains 69.1% of the Exports per capita. In 2014, R2=70.7% indicating that the model explains 70.7% of the Exports per capita.

1. Regression on FDI and GDP
 Coefficient value Significance of coefficient R2 1990 Constant -1.725 0.000 0.875 lnFDI per capita 0.103 0.000 0.875 lnGDP per capita 0.915 0.000 0.875 2007 Constant -2.614 0.000 0.897 lnFDI per capita 0.016 0.000 0.897 lnGDP per capita 1.091 0.000 0.897 2014 Constant -2.537 0.000 0.821 lnFDI per capita 0.737 0.000 0.821 lnGDP per capita 0.520 0.000 0.821

H0: The coefficient is not significant in predicting the Exports per capita

H1: The coefficient is significant in predicting the Exports per capita

The sig value of the years 1990, 2007 and 2014, 0.000<0.05, we thereby reject the null hypothesis and draw the conclusion that all the coefficients are significant in predicting Exports per capita.

The Constant indicates the values the change in Export per capita whenever the FDI per capita is zero. lnFDI per capita indicates the change in Exports per capita for every unit increase in the FDI per capita all factors held constant. lnGDP per capita indicates the change in Exports per capita for every unit increase in the FDI per capita all factors held constant(Huizingh, 2007).

In 1990, R2=87.5% indicating that the model explains 87.5% of the Exports per capita. In 2007, R2=89.5% indicating that the model explains 89.5% of the Exports per capita. In 2014, R2=82.1% indicating that the model explains 82.1% of the Exports per capita.

1. Regression per Sample

L countries

 Coefficient value Significance of coefficient R2 1990 Constant -1.371 0.000 0.859 lnFDI per capita 0.154 0.000 0.859 lnGDP per capita 0.844 0.000 0.859 2007 Constant -2.925 0.000 0.903 lnFDI per capita 0.210 0.000 0.903 lnGDP per capita 0.958 0.000 0.903 2014 Constant -2.734 0.000 0.877 lnFDI per capita 0.848 0.000 0.877 lnGDP per capita 0.393 0.000 0.877

H countries

 Coefficient value Significance of coefficient R2 1990 Constant -2.717 0.000 0.858 lnFDI per capita 0.030 0.000 0.858 lnGDP per capita 1.098 0.000 0.858 2007 Constant -2.537 0.000 0.859 lnFDI per capita -0.111 0.000 0.859 lnGDP per capita 1.197 0.000 0.859 2014 Constant 1.010 0.000 0.855 lnFDI per capita 0.083 0.000 0.855 lnGDP per capita 1.025 0.000 0.855

From the tables above, it is clear that the L models predict the Exports per capita better than the H values. This is due to the increasing R2 values from L to H. The coefficients remain significant in predicting the Y variable.

E. Regression as per the Dummy Variables.

H0: The coefficient is not significant in predicting the Exports per capita

H1: The coefficient is significant in predicting the Exports per capita(Anon, 2017)

The sig value of the years 1990, 2007 and 2014, 0.000<0.05, we thereby reject the null hypothesis and conclude that all the coefficients are significant in the prediction of the variable Exports per capita.

The Constant indicates the values the change in Export per capita whenever the FDI and GDP per capita are zero. lnFDI per capita indicates the change in Exports per capita for every unit increase in the FDI per capita all factors held constant. lnGDP per capita indicates the change in Exports per capita for every unit increase in the FDI per capita all factors held constant(Anon, 2017).

In 1990, R2=85.9% indicating that the model explains 85.9% of the Exports per capita. In 2007, R2=90.3% indicating that the model explains 90.3% of the Exports per capita. In 2014, R2=87.7% indicating that the model explains 87.7% of the Exports per capita(Liu et al., 2003).

H0: The coefficient is not significant in predicting the Exports per capita

H1: The coefficient is significant in predicting the Exports per capita

The sig value of the years 1990, 2007 and 2014, 0.000<0.05, we thereby reject the null hypothesis and conclude that all the coefficients have significance in the prediction of Exports per capita.

The Constant indicates the values the change in Export per capita whenever the FDI per capita is zero. lnFDI per capita indicates the change in Exports per capita for every unit increase in the FDI per capita all factors held constant. lnGDP per capita indicates the change in Exports per capita for every unit increase in the FDI per capita all factors held constant.

In 1990, R2=85.8% indicating that the model explains 85.8% of the Exports per capita. In 2007, R2=85.9% indicating that the model explains 85.9% of the Exports per capita. In 2014, R2=85.5% indicating that the model explains 85.5% of the Exports per capita.

References

Anon, (2017). [online] Available at: https://statistics.laerd.com/spss_tutorials/linear-regression-using-spss-statistics.php [Accessed 4 Apr. 2017].

Hinton, P. (2004). SPSS explained. 1st ed. London: Routledge.

Huizingh, E. (2007). Applied statistics with SPSS. 1st ed. London: SAGE.

Libguides.library.kent.edu. (2017). LibGuides: SPSS Tutorials: Independent Samples t Test. [online] Available at: http://libguides.library.kent.edu/SPSS/IndependentTTest [Accessed 4 Apr. 2017].

Liu, R., Kuang, J., Gong, Q. and Hou, X. (2003). Principal component regression analysis with spss. Computer Methods and Programs in Biomedicine, 71(2), pp.141-147.

SPSS 15.0 brief guide. (2006). 1st ed. Chicago, Ill.: SPSS Inc.

Anon, (2017). Using SPSS for Correlation. [online] Available at: http://http://academic.udayton.edu/GregElvers/psy216/spss/cor.htm [Accessed 4 Apr. 2017].