EL DIOS DE ESTE MUNDO

The main purpose of this study is to test the effects of openness and economic freedom on economic growth in a typical SADC country. However, the individual countries‘ data are inadequate. Introducing a panel-data model allows this type of research to be conducted.

The Hausman Specification test has made it clear that the fixed-effects model is statistically accurate to use for analytical purposes. The fixed-effects model captures country-specific differences and allows the intercept term to vary over cross-section units (and over time) to measure the impact arising from the independent variables. Therefore, the fixed effects model allows for the possibility that for given values of the regressors, growth rates may differ across the countries (Moinul Islam & Sulimullah 2006: 55).

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Table 6.8 below illustrates the fixed-effects model estimation output with the following results:

Table 6.8 Fixed-Effect Model Estimated Equation Results

Dependent Variable: LNGDP

Cross-section weights (PCSE) standard errors & covariance (d.f. corrected) Variable Coefficient Std. Error t-Statistic Prob.

C 6.685196 0.387025 17.27328 0.0000

POP -0.152706 0.050140 -3.045608 0.0045

OPEN 0.005110 0.001793 2.850519 0.0075

FREE 0.145436 0.067672 2.149120 0.0391

Effects Specification Cross-section fixed (dummy variables)

R-squared 0.995777 Mean dependent var 7.582346

Adjusted R-squared 0.994753 S.D. dependent var 1.371718 S.E. of regression 0.099359 Akaike info criterion -1.592737 Sum squared resid 0.325785 Schwarz criterion -1.220380 Log likelihood 42.44749 Hannan-Quinn criter. -1.456254 F-statistic 972.6753 Durbin-Watson stat 0.573760 Prob(F-statistic) 0.000000

The prediction equation is given as:

ln(GDP) = 6.685 − 0.153(POP) + 0.005(OPEN) + 0.145(FREE) + ε (6.34)

The fit of the model is significant. The R² value of 0.9958 suggests that approximately 99.58% of the sample variation in GDP growth can be ―explained‖ by using population growth, openness and freedom. The F-statistic exceeds the 5% critical value and indicates that the model is highly adequate for predicting and estimating GDP growth, using the three proposed independent

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variables.

The apparently enlarged R² value is attributed to the error term of the group regressors being averaged. When dealing with time series (panel) data, high R² are not unusual due to common trends and consequently the measure of R² is not of much importance (Kennedy 1998: 26).

The population growth variable has the expected negative sign and is highly significant. This is in line with the previously mentioned theory that any increase in the capacity of a country to produce goods and services and increase in per-capita income is absorbed by an increase in population.

The openness variable is statistically significant, but the coefficient is very small. The magnitude of the estimate is not very large -- in the sense that the change in growth rate due to change in openness is not a sizeable amount. This is attributed to the poor proxy of the sum of imports and exports as a measure of openness. The freedom variable captures the majority of this effect in the model.

The parameter of major interest is the economic freedom variable. The estimate suggests a strong indication of a statistically significant favourable effect of economic freedom on growth. A one- unit increase in freedom raises the growth rate by 14.54%, indicating that the effects of economic freedom are much stronger than that of openness. Therefore, economic freedom plays a significant role in economic growth.

The Granger-Causality test proves the economic relationship between economic freedom and

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economic growth. It confirms the direction of causality in the SADC data. Thus, economic freedom Granger-causes economic growth.

6.6 CONCLUSION

The econometric model, a growth model based on a neoclassical production function, is adopted and manipulated allowing a log-linear model to be estimated that implies a marginal effect increase for larger values of GDP growth. The independent variables used in the model include:

population growth as a proxy for labour force growth, gross domestic investment, openness (proxied by trade in goods and services) and economic freedom (as rated by the Fraser Institute).

The single-equation econometric model is estimated, based on the data for South Africa. The model fit is high and the model is adequate for prediction and estimation purposes. However, the coefficients show ambiguous results. Gross domestic investment and openness prove to be insignificant and openness has a negative coefficient, against a priori expectations. Economic freedom is positive and highly significant.

The single-equation model is tested for multicollinearity and heteroskedasticity, problems that are common in regression analysis. The VIF test is conducted on the independent variables -- to see if any are correlated with each other. The VIFs for all independent variables are below five, indicating that multicollinearity is not present in the model. However, the variables‘ openness and freedom show slight correlation, which is expected. This correlation is not severe, and does not cause overlapping information.

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The White test is a formal test used to test for the presence of heteroskedasticity in the model. A hypothesis test is conducted with the null hypothesis, assuming that the errors have a constant variance for all levels of the independent variables. The chi-squared statistic does not exceed the 5% critical value, which leads to the null hypothesis not being rejected and proves that heteroskedasticity is not present in the model, since the variances are constant.

The panel-data model is constructed by pooling the various SADC country data together. Due to the lack of available data for the SADC countries, only six of the 15 countries have been used over a seven-year period, creating 42 observations. This allows sufficient degrees of freedom and permits more complicated behavioural tests to be conducted over a single-equation, cross-sectional or time-series model.

To make sure the model produces trustworthy results, the independent variables are tested for stationarity. At first, visual tests were used to determine whether the variables are stationary or not. First differences or changes in the variables are plotted on graphs. It appears that the series is stationary. The mean is practically zero and the variance appears to be constant, However, a formal test was then used to test for stationarity.

The Levin, Lin and Chu (LLC) test was used to test the independent variables for stationarity. The LLC test makes use of separate augmented Dickey-Fuller regressions to test the hypothesis of stationarity. Panel-unit root test results show that the variables‘ population growth, openness and freedom are stationary. However, gross domestic investment contains a unit root, and is not used further in the analysis.

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The final panel-data model contains three independent variables: population growth, openness and economic freedom, to estimate economic growth. A model needed to be selected in order to fit the data and be statistically accurate to conduct the analysis. Two models are available: the fixed-effects model and the random-effects model. Each is characterised by the different assumptions made on the parameters and on the errors.

The fixed-effects model is estimated and produces the following results: The fit of the model is extremely good; the regression explains 99.85% of variation in GDP growth. The model is highly adequate for predicting and estimating GDP growth, using the proposed independent variables.

All coefficients are statistically significant and have the correct signs.

The random-effects model is then estimated to produce the following results: The fit of the model is relatively low at a R² value of only 54.52%. The F-statistic is low, but the model is still statistically significant to use for prediction and estimation purposes. All the coefficients are significant and have the expected a priori signs.

The Hausman-Specification test allows a hypothesis test to be performed that will illustrate which model to use. The null hypothesis assumes no correlation is present between the error component and the explanatory variables of the model. Thus, the random effects model can be used.

However, the null hypothesis is rejected for any reason that makes the two sets of estimates different. The rejection of the null hypothesis is interpreted as an adoption of the fixed-effects model.

The Hausman test was conducted on the model -- to jointly test the differences between all pairs

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of coefficients, except the intercept term. The results show that the chi-squared test statistic exceeds the 5% critical value, leading to a rejection of the null hypothesis and acceptance of the fixed-effects model as the correct model to use for the analysis.

A positive relationship was established between economic freedom and economic growth.

However, a test is required in order to provide evidence on the direction of the causality in this economic relationship. The Granger-causality test was used for this purpose. The test showed that economic freedom precedes economic growth -- with causality moving in the expected direction:

economic freedom, consequently, Granger-causes economic growth.

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CHAPTER 7