Table3:Summary of Empirical Results for South Africa
IndependentVariables Eq. Oep. Intercept
gm gm-gnm gTertiary gPrimary gaff gmq gSecondary em enm R2 F OW 1.1 gGOP 1.178 0.469
(4.52) (12.02) 0.74 144.60 2.20
1.2 gnm 1.673 0.432
(4.22) (7.28) 0.51 52.93 2.58
1.3 gGOP 2.774 0.44
(8.96) (5.32) 0.36 28.32 1.86
1.4 gGOP 0.072 0.948
(0.22) (8.50) 0.59 72.18 1.91
1.5 gTertiary 1.692 0.277
(5.97) (5.45) 0.37 29.73 1.67
1.6 gGOP 1.674 0.225
(5.92) (3.87) 0.23 15.01 1.86
1.7 gGOP 1.595 0.059
(5.51) (3.06) 0.16 9.37 1.98
1.8 gGOP 1.929 0.172
(6.09) (1.98) 0.07 3.94 1.90
1.9 gGOP 1.252 0.447
(4.72) (10.28) 0.68 105.78 1.73
2.1 pm 0.044 0.538
(0.09) (5.71) 0.58 32.61 1.51
2.2 em -0.044 0.462
(-0.09) (4.90) 0.50 23.96 1.51
3.1 pnm 1.649 0.166
(4.02) (2.12) 0.16 4.50 2.34
3.2 pGOP 0.975 0.369 -0.696
(4.23) (7.54) (-5.51) 0.73 31.34 2.43
3.3 PGOP 1.458 0.379 -0.538
(4.48) (3.36) (-2.84) 0.38 6.91 1.66
The summarised empirical results reported in Table 3 above represent the output obtained from SHAZAM for the application of Kaldor's three laws of growth to the South African economy (note that t values are shown in brackets and calculated values for the t and F tests have been rounded-off to two decimal places to facilitate the construction of this table). Brief conclusions will be drawn for each law in light of the empirical results obtained, and then conclude as to the relevance of Kaldorian growth theory with respect to the South African economy.
5.1.1 Kaldor's First Law
The objective with respect to Kaldor's first law is to test whether there is a strong positive correlation between the growth of manufacturing output (gm) as the independent variable and the growth of overall GDP (gGDP) as the dependent variable. Equations (1.1) to (1.9) are the estimated results for Kaldor's first law as applied to South Africa. From the results reported, all estimated values accord with the a priori predictions, as detailed in chapter three. The a priori prediction is that the regression coefficient for equation (1.1) is positive as the growth of manufacturing output, as the independent variable, is positively related to the growth of overall GDP. A reasonably high R2 value for equation (1.1) indicates a strong correlation between the growth of manufacturing output and the growth of overall GDP, which is the crux of Kaldor's first law. The a priori prediction for equations (1.2) to (1.9) is that all regression coefficients are positive. Hence equations (1.2)and (1.5)are expected to show that the growth of manufacturing output, as the independent variable, is positively related to the growth of non-manufacturing output (gnm) and the growth of services (gTertiary) respectively.
However equations (1.3), (1.4) and (1.6) to (1.9) should indicate that the growth of overall GDP, as the dependent variable, is positively related to the difference in the growth of manufacturing and non-manufacturing output (gm-gnm), the growth of services, the growth of the overall primary sector (gPrimary) , the growth of agriculture, forestry and fishing (gaff) , the growth of mining and quarrying (gmq) and the growth of the overall secondary sector (gSecondary) respectively. The regression coefficient for equation (1.4) is expected to approximate unity in that the growth in services should almost match the growth of overall GDP. However there should be no correlation between the growth of overall GDP and the
growth of agriculture, forestry and fishing in equation (1.7) or the growth of mining and quarrying in equation (1.8), as growth in the Kaldorian model is industry-led. The results reported for equation (1.9) are expected to approximate those of equation (1.1) as,the choice of term, whether to use the growth of manufacturing sector output only or the growth of overall secondary sector output,is irrelevant to the argument at hand (Thirlwall, 1983).
All estimated regression coefficients are statistically significant except for equation (1.8), as the growth of mining and quarrying is not related to the growth of overall GDP, which supports the a priori prediction made in chapter three. The regression coefficient for equation (1.7) is significant,however the R2 is low and the coefficient, although significantly different from zero, is low at 0.059. This tends to support the a priori expectation that there should be no correlation between the growth of overall GDP and the growth of agriculture, forestry and fishing. Equation (1.5) indicates that the growth of manufacturing output does determine the growth of services to a large extent. Equation (1.4) shows that the growth of services does almost match the growth of overall GDP as the regression coefficient is close to unity.
Equations (1.3) and (1.2) support the a priori predictions made in that the regression coefficients are positive. Hence the difference in the growth of manufacturing and non- manufacturing output is positively related to the growth of overall GDP and the growth of manufacturing output is positively related to the growth of non-manufacturing output.
The crux of law one is captured in equation (1.1) and the estimated results are similar to those found by Kaldor (1966) and Drakopoulos and Theodossiou (1991) for the Greek economy. If we set the growth of overall GDP equal to the growth of manufacturing output (gGDP= gm),it is shown that annual growth rates for real GDP above 2.2 percent, over the study period 1946/47 - 1997/98, are on average representative of years where the rate of growth of manufacturing is in excess of the overall growth rate of the economy or where the share of the manufacturing sector in the overall economy is increasing. The conclusion is that there is a strong positive correlation (as indicated by a relatively high R2of 0.74)between the growth of manufacturing output and the growth of overall GDP for the South African economy.
5.1.2 Kaldor's Second Law
The objective with respect to Kaldor's second law is to test whether there is a strong positive correlation between the growth of manufacturing output (gm) as the independent variable and the growth of productivity in the manufacturing sector (Pm) as the dependent variable.
Equations (2.1) and (2.2) are the estimated results for Kaldor's second law as applied to South Africa. Once again,the estimated results reported for both specifications of law two, accord with a priori predictions for the study period 1970/71 - 1995/96. The regression coefficient for equation (2.1)should approximate a value of 0.5,which is consistent with Verdoorn's law, after PJ. Verdoorn who first found such a relationship for Eastern European countries in the 1940's. The Verdoorn law states that faster growth of manufacturing output generates faster growth of productivity in the manufacturing sector. Kaldor (1975: 893) believes that a sufficient condition for the presence of static or dynamic economies of scale, is the existence of a statistically significant relationship between the growth of employment in manufacturing (em) and the growth of manufacturing output, given in equation (2.2), where the regression coefficient is positive and significantly less than unity.
Both estimated regression coefficients are statistically significant. The regression coefficient for equation (2.1) is consistent with the Verdoorn law as it has a value of 0.538, indicating that a 1 percent growth of manufacturing output leads to a roughly 0.54 percent growth of productivity in the manufacturing sector. The regression coefficient for equation (2.2) is 0.462 and significantly less than zero, indicating that a 1 percent growth of manufacturing output leads to a roughly 0.46 percent growth of employment in manufacturing. From equation (2.1) as manufacturing output increases, productivity in the manufacturing sector increases as well,but equation (2.2) shows that manufacturing output increases at a faster rate than employment in manufacturing. Hence overall productivity in manufacturing has been increasing in South Africa. The results for both equations are consistent with earlier findings by Kaldor (1966), Kaldor (1975) and McCombie (1983). The predictive power of both models is good with values for R2 of 0.58 and 0.50 respectively. The conclusion is that there is a relatively strong positive correlation between the growth of manufacturing output and the growth of productivity in the manufacturing sector for the South African economy.
5.1.3 Kaldor's Third Law
The objective with respect to Kaldor's third law is to test whether there is a strong positive correlation between the growth of manufacturing output as the independent variable and the growth of productivity in the non-manufacturing sector (Pnm) or overall productivity growth (PGDP) as the dependent variable. Equations (3.1) to (3.3) are the estimated results for Kaldor's third law as applied to South Africa. The estimated results reported for all specifications of law three,accord with the a priori predictions for the study period 1970/71-
1995/96. The regression coefficient for equation (3.1) is assumed to be positive as the growth of manufacturing output and growth of productivity in the non-manufacturing sector are positively related. However Thirlwall (1999: 80) proposes an alternative approach to estimating law three as it maybe difficult to estimate productivity growth in activities such as services. Equations (3.2) and (3.3) therefore use overall productivity growth or growth of GOP per employed person (PGDP) as the dependent variable. The regression coefficients for the growth of manufacturing output in both equations (3.1) and (3.2) are assumed to be positive. Using the approach followed by Cripps and Tarling (1973), equation (3.2) uses the growth of employment in non-manufacturing activities (enm) as an additional independent variable, which is assumed to be negative as growth in overall GOP is negatively related to employment growth in the non-manufacturing sector. An alternative specification for law three is given by equation (3.3),which uses the growth of employment in manufacturing (em) and growth of employment in non-manufacturing, as independent variables. The regression coefficient for the growth of employment in manufacturing is assumed to be positive whereas the regression coefficient for the growth of employment in non-manufacturing is assumed to be negative.
All estimated regression coefficients are statistically significant. Despite a lower R2 for equation (3.1) of 0.16, the estimated regression coefficient for the growth of manufacturing output is statistically significant and has the correct positive sign. This finding is also supported by the positive regression coefficient estimated for the growth of manufacturing output in equation (3.2). Hence as manufacturing output increases, productivity in the non- manufacturing sector or GOP per employed person increases as well, and this result accords
with the a priori prediction. The estimated regression results for equations (3.2)and (3.3) are very encouraging. The R2for equation (3.2) is high at 0.73 and equation (3.3) also indicates sound model prediction with an R2of 0.38. Most importantly, the regression coefficients for the growth of employment in non-manufacturing (both equations) are consistent with the a priori prediction in that they have the correct negative sign. Hence the slower employment growth outside of manufacturing,the faster overall productivity grows in South Africa. The results for all three equations (equations (3.2) and (3.3) in particular) are consistent with earlier findings by Cripps and Tarling (1973), Thirlwall (1987), Drakopoulos and Theodossiou (1991) and Thirlwall (1999). The conclusion is that there is a strong positive correlation between the growth of manufacturing output and the growth of productivity in the non-manufacturing sector for the South African economy.
5.1.4 Conclusion
From the study undertaken and the results obtained, there is broad agreement for the application of Kaldor's growth laws to the South African economy. Therefore this study supports Kaldorian growth theory in the South African context. With reference to Thirlwall (1983:357) we can conclude as follows:
1. Manufacturing growth can be considered as an engine of GDP growth in South Africa; 2. The faster the rate of manufacturing growth, the faster the rate of productivity growth in
the South African manufacturing sector;
3. The faster the rate of manufacturing growth, the faster the overall rate of productivity growth in South Africa as, more labour used in the manufacturing sector, does not necessarily detract from growth elsewhere in the South African economy.