The second law states that there is a strong positive correlation between growth in manufacturing output and growth in productivity in the manufacturing sector. The third law states that there is a strong positive correlation between growth in manufacturing output and productivity growth outside the manufacturing sector or in the non-manufacturing sector.
Research Problem
Therefore, the situation in South Africa did not differ from the Greek experience during a roughly similar period. Another interesting finding for South Africa, supported by the findings of Orakopoulos and Theodossiou (1991), is the declining contribution of the primary sector as a whole and agriculture in particular.
Objective of the Study
Procedure and Data
Employment in the primary sector, for the purposes of this study, consists only of agriculture, mining and quarrying. The exclusion of employment statistics for the forestry and fisheries sub-sectors from the overall primary sector should not prejudice the study in question.
Thesis Organisation
It should also be noted that accurate employment figures for the forestry subsector are difficult to obtain as this figure excludes independent contractors. However, because this study measures productivity growth, we do not expect a significant impact on the estimated results by excluding employment in the forestry and fisheries subsectors.
Introduction
The Structural Approach to Growth
Pioneering growth studies tended to follow the neoclassical approach, but the introduction of the structural approach (1950s to present) ushered in a new era. The Harrod-Domar approach explains economic growth in terms of the interaction between the savings rate, the labor force growth rate, and the capital-output ratio.
The KaIdor Growth Model
Introduction
The thesis evaluates the statistical relationships related to growth, which are derived from the predictions of the Kaldor growth model. The sections then elaborate on Kaldor's three laws of growth, which will be empirically analyzed in this thesis.
Basic Model Propositions
Because of increasing returns in manufacturing, and induced productivity growth in non-manufacturing, we expect that the faster the growth rate of manufacturing output, the faster the growth rate of productivity in the overall economy. The first law postulates that there is a positive relationship between the growth of GDP (gGDP) and the growth of manufacturing output (gm) (Drakopoulos and Theodossiou, 1991). This implies that the greater the growth rate of manufacturing output (gm) above the growth rate of the overall economy (gGDP), the faster the overall growth rate.
This means that a relationship should be expected between industrial production growth and industrial labor productivity growth. Faster growth in manufacturing output generates faster productivity growth in the manufacturing sector, due to static and dynamic returns. For the manufacturing sector, this relationship shows that there is a positive relationship between the growth rate of labor productivity in the manufacturing sector, and the growth rate of manufacturing output (Kaldor Thirlwall Drakopoulos and Theodossiou Delivani.
Therefore, Kaldor's second law assumes that there is a positive relationship between the growth rate of labor productivity in the manufacturing sector (Pm) and the growth rate of manufacturing output (gm) (Drakopoulos and Theodossiou. Equation (2.2) assumes that there is a positive relationship between the growth rate of employment in the manufacturing sector sector (em) and the growth rate of manufacturing output (gm).
Kaldor's Third Law
Much of this process has to do with the fact that productivity growth has accelerated as a result of transfers at both the winning and losing ends. From equation (3.1), we can say that the faster the increase in production output, the faster the rate of labor transfer. Long-term output growth, in the neoclassical sense, cannot be seen as exogenously determined by the long-term rate of growth of the total labor force and technical progress.
Finally, according to Kaldor, the remarkable correlation between the growth of GDP and the growth of manufacturing output asserts that labor absorbed in manufacturing does not reduce output in the rest of the economy. This means that growth in manufacturing output has a more pervasive effect in that it leads to productivity growth in the other non-manufacturing sectors of the economy, thus driving overall economic growth. When a study of this nature is undertaken, controversy may arise as to the precise meaning of the industrial sector of an economy.
Kennedy stated that Kaldor (1966) argued that the relationship between output and productivity growth is usually associated with the secondary sector (industrial production, including utilities, construction and manufacturing) rather than the primary (agriculture and mining) or tertiary sectors economy. . In this thesis, production is used in the narrowest sense of the word and adheres to the SIC definition of production.
Conclusion
Remedial Measures: When the Structure of Autocorrelation Is Known
When reporting econometric results, the presence of serial correlation (autocorrelation) in the disturbances (Ut) has a direct impact on the reliability of the estimated regression coefficients, as the OLS estimators are inefficient. Thus, under an OLS estimate that ignores autocorrelation, the usual t and F significance tests are no longer valid, and may therefore yield erroneous conclusions regarding the statistical significance of the estimated regression coefficients (Gujarati. Because, for our purposes, the structure of autocorrelation (p ) is known (since SHAZAM uses the Cochrane-Orcutt iterative procedure to estimate, (Cochrane and Orcutt, 1949)), we can apply the GLS method to transform all observations.
Note that, other procedures for estimating p give "quite similar" results (Gujarati, so using the procedure ( undertaken by SHAZAM is more than adequate for our purposes. Since the mathematical derivation of the GLS procedure is beyond the scope of this thesis, the reader may refer to Gujarati for further details.The procedure is given below and will be applied where the regression results in level form exhibit serial correlation.
Note that when observing both the OLS (planar variables) and GLS (transformed variables) regression results reported, there is generally no significant difference in the predictive power (R2) of the models, coefficients, t and F-tests , but rather for which the problem of serial correlation is now corrected.
Summary
Analysis of Empirical Results
- Introduction
- Kaldor's First Law
- Kaldor's Second Law
- Kaldor's Third Law
- Summary
The regression results reported for equation (1.4) support the hypothesis that there is a correlation between GDP growth and service sector growth (tertiary sector of the economy). It is clear from the regression results reported for equation (1.6) that there is not a strong relationship between primary sector output growth (explanatory variable) and overall GDP growth (dependent variable). The regression results reported for equation (1.9) show that the overall predictive power and coefficient for g·Secondary are quite similar to those reported in equation (1.1) and help to substantiate the previous claim that production is a good proxy for the total industrial sector in South Africa. Africa.
Note, however, that the RESET (2) test implies that the model is not correctly specified, but the RESET (3) and (4) tests imply that the model is correctly specified at the 1 percent level. The results obtained support the view that there is a reasonably strong relationship between the rate of growth in manufacturing productivity and the growth in manufacturing output for the South African economy. An increase of 1 percent in the growth of manufacturing production thus leads to an increase of approx. 0.54 percent in manufacturing productivity growth, ceteris paribus.
Thus, a 1 percent increase in industrial output growth leads to a roughly 0.46 percent increase in industrial employment growth, ceteris paribus. Overall, industrial production growth explains about 50 percent of industrial employment growth.
Summary and Conclus ions
- Kaldor's First Law
- Kaldor's Second Law
- Kaldor's Third Law
- Conclusion
The objective regarding 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. The a priori prediction is that the regression coefficient for equation (1.1) is positive since the growth of manufacturing output, as the independent variable, is positively related to the growth of overall GDP. Equation (1.5) indicates that the growth of manufacturing output does determine the growth of services to a large extent.
The conclusion is that there is a relatively strong positive correlation between growth in manufacturing output and productivity growth in the manufacturing sector for the South African economy. The regression coefficient for equation (3.1) is assumed to be positive since manufacturing output growth and productivity growth in the non-manufacturing sector are positively related. The regression coefficients for manufacturing output growth in both equations (3.1) and (3.2) are assumed to be positive.
This finding is also supported by the positive regression coefficient estimated in equation (3.2) for industrial production growth. The conclusion is that for the South African economy there is a strong positive correlation between industrial production growth and productivity growth in the non-manufacturing sector.
Recommendations
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1949) Fattori che Regolano 10 Sviluppo della Produtivita del Lavoro, L'Industria, Vol. 1991) 'The Kaldorian Approach to Greek Economic Growth', Applied Economics, Vol. 1980) 'Verdoorn's Law in Retrospect: A Comment', Economic Journal, Vol. REQUIRED RED MEMORY IS PAR= 6 CURRENT PAR= 500 DEPENDENT VARIABLE = G 52 OBSERVATIONS REGRESSION COEFFICIENTS. MEMORY REQUIRED IS PAR= 6 CURRENT PAR= 500 DEPENDENT VARIABLE = N 52 OBSERVATIONS REGRE SSION COEFFI CI ENTER.
MEMORIAE RUFUS EXPECTATUS EST PAR=4 PARS=50 0 PARS VARIALIS = E 26 I REGRESSI DE COEFFICIENTIBUS SERVANDIS.
