4. Regression Analysis
7.3 DETERMINANTS OF INCOME
7.3.1 Testing differences in income
1. Differences in average daily income based on gender
To test the difference between the average daily income of male and female, the following hypotheses was developed:
NH: On an average daily basis, there is no significant difference between the incomes of Male and Female slum operators
AH: On an average daily basis, there is a significant difference between the incomes of Male and Female slum operators
Table 7.5 presents the results of the independent sample T-test in testing the difference between the average daily income of males and females. The mean income of males and females is Gh ₵ 30.3 and Gh ₵ 28 respectively.
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Table 7.5 Independent sample T-test of income based on gender Group
Statistics
Levene’s test
t-test for Equality of Means
N Mean F Sig t df Sig (2
tailed )
Mean difference
Average daily income
Male 205 30.3 Equal variances assumed
21.
8
0.00 1.86 342 0.063 2.24 Female 139 28.0 Equal
variances not assumed
1.94 331.9 0.053 2.24
Source: Own computation, results obtained from SPSS
Firstly, the assumption of homogeneity of variance is tested with the Levene’s F Test for Equality of Variances. The test proposes a null hypotheses that there is no difference between the variance of the two groups. When the significant value of the Levene’s test is greater than our alpha of 0.05, we accept the null hypotheses and conclude that there is no significant difference in the variance of the two groups. On the other hand, if the significant value of the Levene’s test is less than our alpha value, we reject the null hypotheses. In this case, the assumption of homogeneity of variance is not met, hence the table results associated with ‘Equal variances are not assumed’, is used.
Secondly, if the T-test’s significant value is less than our alpha of 0.05, we reject the null hypothesis and accept the alternate hypothesis, that there is a significant difference between the mean income of male and female operators.
From the results in Table 7.5, one can conclude that the assumption of homogeneity of variance is violated since the significant level for the Levene’s test is 0.00. This is lower than 0.05, implying that variances of the two groups (male and female) are not the same, hence we use the results of
‘Equal variances not assumed’, i.e., second row results in Table 7.5.
As the T-test’s significant value (0.053) is greater than our alpha of 0.05, one can accept the null hypotheses. As indicated in Table 7.5, the mean daily income for male is Gh ₵ 30.3 whereas that for female is Gh ₵ 28. Thus, it can be concluded that, on an average daily basis, there is no significant difference between the incomes of males and females, at the 5% level of significance.
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However, at 10% level, there is a significant difference (t=1.94), with the male operators, on average, earning more than their female counterparts.
Studies in the informal sector (Chen 2001 and Chen et al., 2005) have shown that, women are underrepresented in high income earning activities, hence, earning less than men. Results from the current study contradicts these studies, as it was found out that both male and female operators in the AL and S&G slums of Ghana statistically earn similar average incomes.
2. Differences in average daily income based on slum location
Income of slum operators may differ based on the location in which they operate. Hence, the Independent samples T-test is used to ascertain whether slum operator’s income differ based on the slum (AL or S&G) in which they operate.
In testing the difference between the average daily income of operators in the AL and S&G slums, the following hypotheses ware developed:
NH: On an average daily basis, there is no significant difference between the incomes of operators in Akwatia Line and Sodom and Gomorrah
AH: On an average daily basis, there is a significant difference between the incomes of operators in Akwatia Line and Sodom and Gomorrah
Table 7.6 shows the results of the independent sample T-test in examining the difference between the average daily income of operators in AL and S&G slums. The mean income of operators in AL is Gh ₵ 32.5 and that of S&G is Gh ₵ 26.2.
The Levene’s Test for Equality of Variances in Table 7.6 shows a p-value of 0.00, which is less than our alpha value of 0.05. Hence, we reject the null hypotheses of homogeneity of variance. We therefore read the t-test result in the ‘equal variances not assumed’ row, i.e., the second row. As the results of the t-test shows a p-value of 0.00 which is less that the critical value of 0.05, we can reject the null hypothesis. Hence we can accept the alternate hypothesis that there is a significant difference between the average daily income of operators in AL and S&G slums.
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Table 7.6 Independent sample T-test of income based on slum Group
Statistics
Levene’s test t-test for Equality of Means
N Mean F Sig t df Sig (2
tailed)
Mean difference
Average daily income
Akwatia Line
172 32.52 Equal variances assumed
14.706 0.000 5.584 342 0.000 6.33 Sodom
and Gomorrah
172 26.20 Equal variances not assumed
5.584 325.5 0.000 6.33
Source: Own computation, results obtained from SPSS
Taking a critical look at the mean difference (Gh ₵ 6.33) between the incomes of both slums, AL’s average daily income (Gh ₵ 32.5) is significantly higher than that of S&G (Gh ₵ 26.2). Overall, there is a statistically significant difference in the operators’ mean daily income between the two slum regions.
S&G slum is located in the Greater Accra region, which is the regional capital of Ghana and its operators are expected to earn more than AL’s. However, this was not the case as AL operators earn significantly more (Gh ₵ 32.5), on an average daily basis than S&G operators (Gh ₵ 26.2).
S&G operators are involved in a wider variety of economic activities than AL operators.
Nevertheless, activities such as wood processing and sales, rice processing and kola nut sales dominate in the AL slum and these activities tend to have higher value-adding and hence generate higher average incomes in AL than in S&G.
3. Differences in average income based on locus of control
Locus of control refers to a person’s views regarding the determinants of rewards or outcomes in the individual’s life. With internal locus of control, one attributes outcomes to his/her personal actions and with external locus of control, outcomes are associated with external forces.
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In this section, the T-test is employed to find out if income of slum operators differ in terms of their locus of control. In order to test the difference between the average daily incomes of operators with internal or external locus of control, the following hypotheses ware developed:
NH: On an average daily basis, there is no significant difference between the income of operators based on internal or external locus of control.
AH: Null Hypotheses: On an average daily basis, there is a significant difference between the income of operators based on internal or external locus of control.
Table 7.7 Independent sample T-test of income based on locus of control Group
Statistics
Levene’s test t-test for Equality of Means
N Mean F Sig t df Sig (2
tailed)
Mean Difference
Average daily income
External locus of control
40 17.96 Equal variances assumed
12.470 0.000 -7.6 342 .000 -12.9 Internal locus
of control
304 30.86 Equal variances not assumed
- 9.98
62.9 .000 -12.9
Source: Own computation, results obtained from SPSS
From Table 7.7, it can be seen that the mean daily income of an operator with external locus of control is about Gh ₵ 18 and that of an operator with internal locus of control is about Gh ₵ 31.
The difference is almost Gh ₵ 13. Since the Levene’s Test for Equality of Variances shows a p- value less than the alpha value of 0.05, we reject the null hypotheses of homogeneity of variance.
Results from the ‘Equal variances not assumed’ row is therefore applicable.
The results of the t-test shows a p-value of 0.00, which is less than the critical value of 0.05, hence we reject the null hypotheses of equality of means. It can therefore be concluded that there is a significant difference in the average daily income of operators based on locus of control. Assessing the mean income differences between the two groups (internal and external locus of control) it can be observed that there is a substantial difference (of about Gh ₵ 13, as indicated above) reconfirming that, operators with internal locus of control earn significantly higher (Gh ₵ 31) than
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those with external locus of control (Gh ₵ 18). Hence, operators who base life outcomes on their personal actions (those with internal locus of control), earn more than operators who attribute life outcomes on external factors such as luck, fate and circumstances (those with external locus of control), consistent with Cobb-Clark, Kassenboehmer and Sinning’s (2013) result in Australia that, persons with internal earn more than persons with external locus of control.