5.5.1. Low income countries
For low-income SADC countries, both the Pedroni and Kao tests showed no evidence of long run relationships between FDI and GDP. Thus the panels were not co-integrated. As discussed in the methodology section, if the variables are not co-integrated Granger causality can still proceed by VAR (please see Toda and Phillips, 1993; Hurlin and Venet, 2001, 2008, and Dumitrescu and Hurlin, 2012).
This study used the VAR framework proposed by Dumitrescu and Hurlin (2012). This test requires that the variables be stationary, and it takes into account both the heterogeneity of the causal relationship and the
heterogeneity of the regression model used to test for Granger causality within a panel framework. The estimated equation is Equation 7.
Since the methodology uses stationary variables, we differenced the logged variables once. We then tested whether the log-differenced variables were stationary using a summary of the four panel unit root tests, before running the causality tests.
Just like all causality estimation, lag selection is very important as it might affect the results. Hurlin and Venet (2008:13) tested for Granger causality between financial development and growth by selecting a common lag-order and computed test statistics for one, two and three lags. Fowowe (2011) also used the same lag specifications.
Table 11 presents the Granger non-causality tests for the low-income SADC countries. For both equations and across the three lags tests, the null of Granger homogeneous non-causality could not be rejected. This implies that FDI does not Granger cause GDP and vice versa. This means that there is no causality from GDP to FDI or from FDI to GDP in all the low-income SADC countries in the short-run.
Table 11: Panel Granger causality tests of FDI and GDP in low income SADC countries
Null Hypothesis ( Lags Test Statistics P-value Conclusion
W-Stat. Zbar-Stat.
DLINFDI does not homogenously cause DLINGDP
1 0.745 -0.503 (0.615) Do not reject -
2 1.719 -0.470 (0.639) Do not reject -
3 4.288 0.795 (0.427) Do not reject -
DLINGDP does not homogenously cause DLINFDI
1 1.296 0.332 (0.740) Do not reject -
2 2.249 0.078 (0.938) Do not reject -
3 3.960 0.531 (0.595) Do not reject -
Notes: Parenthesised values are the probability of rejecting the null hypothesis of Granger non-causality.
5.5.2. Middle income countries
Since both the Pedroni and Kao tests proved that FDI and GDP for middle income countries were co- integrated, we then proceed to investigate the Granger causality using the ECM framework. Granger causality under the ECM is a two-step procedure. The first step relates to the estimation of the residual from the long-run relationship (Engle & Granger, 1987). The second step is to estimate the Granger causality model with a dynamic error correction model. This includes incorporating the residual as a right- hand side variable.
The panel Granger causality model with dynamic error correction was estimated in the form of the two co- integrated equations (Equation 8 and 9). The ECM approach helps investigate the short and long run Granger causality between FDI and GDP. In summary, the analysis and interpretation of causality results under the ECM framework is as follows:
• Long-run causality is tested based on the statistical significance of the coefficient of the error
correction terms (7 and 7") in Equation 8 and 9 respectively. Separate t-tests on the coefficients of each of the error terms are applied.
• Short-run causality is tested on the coefficients of one of the explanatory variables. A Wald (1943)
F-test statistic is applied, which is a test of the joint significance of the other variable(s) in a regression that includes lags of the dependent variable.
The estimated results of Equation 8 and 9 are presented in Table 12 below46. The results of Equation 8 are in the row where ∆is the dependant variable while Equation 9 is the next row.
Table 12: Panel Granger causality tests results for middle income countries
Dependent Variable
Source of causation (independent variable)
Short-run Long-run ∆2 ∆2#$ 89: ∆ - 3.155* (0.077) -0.060 [-3.889] *** ∆ ¡ [0.013] (0.910) - -0.00055 [-0.250]
Notes: Short Run- Figures denote F-statistic values and p-values in brackets (). For the long-run coefficient of the ECT are reported, the square brackets [] are t-statistics. . Significance at the 1% and 10% is denoted by *** and * respectively.
With respect to Equation 8 (the FDI equation), the corresponding F-statistic for the GDP variable is statistically significant at 10%. This implies that economic growth Granger causes FDI in the transitory period (the short-run). As expected, the coefficient of the error correction term (ECT) on the FDI equation is negative and statistically significant at 5% level. This implies that GDP Granger causes FDI in the long- run.
The result on the GDP equation on the other hand shows that the corresponding F-statistic for the FDI variable is statistically insignificant. This means that FDI does not Granger cause GDP in the short-run. For the long-run causality, the coefficient of ECT is negative (as expected) but insignificant, thus implying that FDI does not Granger cause GDP in both the short- and long-run. Overall, causality for the SADC middle income countries is uni-directional, running from GDP to FDI.
46
The ECMs were run with different lags from 2 to 9 to see if the results were sensitive to changes but nothing qualitative changed. Lag 5 was chosen as it had a high Adjusted R-squared and results are consistent with economic literature.
On the direction of causality, Moudatsou and Kyrkilis (2011) used a similar methodology for the EU and ASEAN economic associations and found evidence of growth causing FDI in EU and two-way causality in ASEAN. Another study by Lund (2010) separated the country groups into low, middle and high income and found that (i) in low-income countries there was no short run causal relationship, while causality was uni-directional from GDP to FDI in in the long-run; (ii) in middle-income countries, causality was uni- directional from GDP to FDI in both periods; (iii) high-income countries showed evidence of bi- directional causality in the short-run and uni-directional causality from GDP to FDI, and (iv) no evidence of bi-directional in the long-run. Thus the direction of causality for lower to middle income countries (including African countries) is mainly from growth to FDI.