CHAPTER 2 A PROFILE OF SUBCONTRACTORS IN THE CAPE
2.4 INCOME
2.4.4 The Determination of Income
The handfull of studies which have attempted to explain
variations in inter-firm income have employed regression analysis (see House, 1984; Nattrass and Glass, 1986; Chuta and Liedholm, 1985 and Adamu, 1969). The approach is followed in this paper with certain reservations, since i t is not clear that
multivariate parametric statistics represent the correct approach to the issue. First, the strong assumptions of non-col linearity and homoskadasticity are probably not met in survey data of this nature. Second, there is a potential for two-way causality. The regression results presented should, therefore, be treated as illustrative rather than conclusive.
Bearing in mind the strict requirements for regression analysis and the largely qualitative nature of the data, future research in the area should rely to a greater extent on simple tabulations and scatter plots, backed by a priori economic reasoning to investigate the imporctant relationships. In this study, scatter
44, In fact a greater proportion of operators arc worse off than if they were directly employed in 1.5Es, as the data quoted include Ii return to the operators' capital. If they were directly employed in LSEs, a capital outlay would not have been required,
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plots were-~sed to check the' results derived through regression analysis. These supporting scatter plots are presented in the original dissertation (Krafchik, 1990:242-246).
The initial regression equation iricludes ten explanatory
variables. An explanation of the economic reasoning behind these variables is presented in Appendix D. The initial results, and the model with the best explanatory power, are presented below in Tables 2.21 and 2.22 respectively.
Table 2.21
Multiple Regression Analysis of Inter-firm Income Variation Variable Coeff icient Std. Error T-Stat 2-Tail Sig.
C -134.31836 2230.9833 -0.0602059 0.952 START 0.7067695 1.1272164 0.6270042 0.533 EDUC -43.442473 45.104005 -0.9631622 0.339 APP 767.29211 247.10829 3.1050845 0.003 MOTIV -77.385420 293.53270 -0.2636347 0.793 BOOKK 255.89647 263.94565 0.9695044 0.336 AMNT 0.0076114 0.02889.62 0.2634,041 0.793 TRADE -46.671980 390.74890 -0.1194424 0.905 RACE -46.183647 237.31548 -0.1946087 0.846 EMPLOY 18.178120 15.411714 1.1795002 0.242 PTOTAL 0.0241947 0.0210377 1.1500614 0.252
R.,Squared 0.347421
Adjusted R-Squared 0.255508
S.E. of regression 970.6305
Durbin-Watson stat. 1. 923708
Log Likelihood -674.4389
Mean of dependent variable 1755.100 S.D. of dependent variable 1124.926 Sum of squared residual 66890780
F-Statistic 3.779903
(a) 82 observations, dependent variable income per annum
As can be seen from the table, the apprenticeship coefficient is positive and significant at a 3% level. Strikingly, a technical qualification (APP) is of more importance to the income of the entrepreneur than is the level of formal education (EDUC) achieved. The results support the view that beyond a level of functional literacy, working experience in the industry is of prime importance (Adamu, 1969; Chuta and Liedholm, 1985).
The initial capital coefficient (AMNT) is positive, though not very significant. Firms with access to larger amounts of initial capital are not necessarily more successful than those with access to smaller amounts of initial capital. This is a function of the generally low capital requirements in most trades,
labour-intensive technology and the absence of large economies of scale.
The coefficient for total capital employed (PTOTAL) is positive and significant at a 5% level (Table 2.23). The result is logical, since the more capital employed in a firm the greater the amount of houses that can be completed in any time period, and the larger the residual income to the entrepreneur.
The total employment coefficient is positive, although not statistically significant. This could simply reflect low labour productivity and the poor management ability of operators.
The bookkeeping coefficient is weak and not significant. An explanation may be sought in the scale and complexity of most operations under review. If the firms expand operations to produce more than one house, or work on more than one site simultaneously, bookkeeping may become a greater determinant of success.
Table 2.22
Best Linear Unbiased Estimate
Variable Coefficient STD. Error T-Stat 2-Tail Sig.
C 1018.1370 178.98497 5.6883936 0.000 APP 820.77874 225.43825 3.6408140 0.001 EMPLOY 14.994208 14.300858 1.0484831 0.298 PTOTAL 0.0285315 0.0094629 3.0150875 0.004
R-Squared 0.328163
Adjusted R-Squared 0.302323
S.E. of regression 939.6176
Durbin-Watson stat 1. 929318
Log likelihood -675.6313
Mean of dependent variable 1755.100 S.D. of dependent variable 1124.926 Sum of squared residual 68864734
F-statistic 12.69986
(a) 82 observations, dependent variable is income per annum overall, the best linear estimate (Table 2.22) is able to explain only 30.2% of the variation in the firms' income. The low
adjusted R-squared is partly due to the inclusion of a measure of unemployment in each firms' income and the exclusion of an
independent variable for unemployment. By definition the unemployment coefficient (UNEMP) is negative and highly
significant. With the inclusion of the variable, in Table 2.23, the model is able to explain 47% of the variation in income per annum.
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r
,
Table 2.23
Best Linear Unbiased Estimate including Unemployment Variable Coefficient Std. Error T-Stat. 2-Tail Sig.
C 1667.2091 198.85060 8.3842297 0.000 APP 602.68919 199.57705 3.0198322 0.004 EMPLOY 1.9136965 12.630726 0.1515112 0.880 PTOTAL 0.0277009 0.0081923 3.3813233 0.001 UNEMP -195.89369 37.623230 -5.2067218 0.000
R-Squared 0.503108
Adjusted R-Squared 0.477295
S.E. of regression 813.3029
Durbin-Watson Stat 1. 666921
Log likelihood -663.2640
Mean of dependent variable 1755.100 S.D. of dependent variable 1124.926 Sum of squared rE!sidual 50932542
F-Statistic 19.49078
(a) 82 observations, dependent variable is income per annum This result is in line with the results of other case study research employing the same technique. The best explanatory variables included by House (1984) in his step-wise regression explain 33% of the variation in income, while the results of Chuta and Liedholm (1985) explain 58% of the variation in income.
Although Nattrass and Glass (1986) report a significantly higher R-squared of 0.67, i t is not clear whether or not this is an.
adjusted R-squared.
Thus, a significant proportion of income variation remains unexplained. This could either be due to random factors or i t shows the importance of the illusive 'animal spirit' or
entrepreneurship in a firm's success. Unfortunately, economic theory does not offer a solid base for defining or measuring·the entrepreneur's role (see Walker, 1988). It is perhaps for this reason that the wide range of economic variables included are able to explain so little of the variation in income.
2.4.5 Summary
Over half of the operators in the sample earn above the minimum wage for artisans in the building industry. In fact, returns to the operator are on average greater than those reported for informal sector operations in South Africa. Nevertheless, there was significant inter-firm income variation. It was determined through regression analysis that technical training embodied in the operator and the size of the f'lrm were important variables in explaining this differentiation.· Yet, regression analysis was unable to explain the major proportion ofincciml? differentiation.
It was suggested that this residual related to unquantifiable aspects of the loosely-defined term 'entrepreneurship'.