Navegación

This section provides detailed results of the socio-economic characteristics of households. It contains the analysis of household’s characteristics in terms of education, employment types, income categories, household sizes, length of stay in the study areas and migration patterns.

5.3. 1 Education levels

The distribution of highest level of formal education attained by the sampled households for both areas was 57% of respondents have primary educational qualifications, high school qualifications (grade 10 to 12) 24%, whilst 19% of the respondents never went to school (Table 5.5). This shows that a considerable proportion of the residents in the study areas have very low levels of formal education, therefore making it difficult for them to find jobs in the formal economic sectors.

They are classified as low-income earners because a good salary tends to correlate with good educational qualifications. See Table 5.8, for discussion of the relationship between educational level and monthly income of respondents. People with higher school qualifications are employed in the government while people with primary education who are mostly self-employed or (appendix 10). However, there are significant negative correlations between levels of educational attainment with age of residents in the sampled settlements. This implies that younger respondents (15-20 and 25-30) have higher education qualifications than the older residents of the study area (see Table 5.6).

Table 5.6 Correlation between age and level of education

The combined samples revealed that young residents have significantly (< p-value 0.001) better paid jobs and are earning good salaries (see Table 5.7). Similarly, the results shows that respondents with high school qualifications have high incomes (<p-value 0.001) (category 6 and 7) than those with primary education and those that never went to school (see Table 5.7 and 5.8).

Table 5.7 Correlation between age and monthly incomes

Monthly income (N$)

Table 5.8 Relationship between level of education and employment types Highest level

When the income categories are collapsed into three categories only, (< 700; 701 – 2200; over 2200) then there is a statistically significant correlation (< p-value 0.001) between the years lived in the study areas and monthly income. The analysis indicates that residents who lived in the areas for a long time have better paid jobs than the new residents. This suggests that it takes time for a resident to navigate the area and therefore to secure higher paid employment, especially in the formal sector (Table 5.9).

Table 5.9 Correlation between length of stay in study areas and monthly income Monthly income (N$)

Table 5.10 Correlation between level of education and income Types of employment Self

employed

Government Private NGO’s Unemployed Total Never went to

Table 5.10 shows that there is no significant correlation between types of employment and level of education (p-value 0.66).

5.3.2 Employment types

Forty-one percent of male respondents (compared to 32% female) are employed in the private sector, this being road construction companies, building construction companies etc. Most of the residents from both study areas do not have good education; therefore can only work in the private sector which does not really require a great deal of proper educational qualifications. The private sector is mostly construction industry, where most of the work is manual. Therefore it is mostly men that work for the private sector due to the nature of the job.

The government usually requires a minimum qualification of grade 10. The analysis of respondent’s responses shows that only 8% males and 5% females are employed in the

government. NGOs are much closer to the government; therefore their recruitment process also requires certain qualifications. Only 13% of male respondents (compared to 2% female) are working for NGOs, whilst 20% of male respondents (compared to 37% female) are self-employed and 17% of male respondents (compared to 24% female) are unemployed. Table 5.11 shows the frequencies and percentages by gender with regard to their employment types.

The majority of the women make a living in the informal sector where they sell cooked food such as cakes and kapana (cooked meat) at the bus stops and construction sites around Windhoek.

Table 5.11 Comparison of employment types and gender

Gender Types of employment

Male Female Total

22 15 37

Self-employed

20.4% 36.6% 24.8%

9 2 11

Government

8.3% 4.9% 7.4%

44 13 57

Private

40.7% 31.7% 38.3%

15 1 16

NGO's

13.9% 2.4% 10.7%

18 10 28

Unemployed

16.7% 24.4% 18.8%

108 41 149

Total %

100.0% 100.0% 100.0%

The chi-square test shows that there is no-significant difference (0.65 >p-value 0.05) in employment between female and male.

Figure 5.1 Employment types by gender

5.3.3 Monthly incomes

The income varies greatly according to the gender of respondents. Men have higher incomes than women. The results of the survey indicate that there is a significant variation in the salary levels in relation to the gender of the respondents. The sample revealed that only 9.1% of the female earns N$2700² per month in comparisons to 91% of males. Men have high levels of education attainment and this makes it easier for men to be employed in the formal sector and this have a significant impact on their salaries. The level of education impact significantly on someone’s salary; hence men have higher salaries compared to women. The majority of female respondents (55%) earn an income in the ranges of N$201-700 per month (see Table 5.12). This shows that men earn better incomes in comparisons to women.

Table 5.12 Monthly incomes by gender

The chi-square statistics (Asymp.sig 0.024 > p-value 0.05) revealed that there is no difference in income between male and female from the two study areas.

2 One Namibian dollar (N$1) is equivalent to one South African rand (R1) and this is approximately t US$7at the time of writing the thesis (September 2009)

Figure 5.2 Monthly incomes by gender

5.3.4 Household sizes

The size of the households in informal settlements varies greatly. The 1991 National Housing census revealed a mode of 3.4 household sizes; this is contrasting to the 2001 census which revealed the household mode of 4.7. The results of the census differ greatly from that of the social survey. The survey revealed a household mode of 3-4 people per household.

There is a big gap between the census average households size and that of the survey. This shows that informal settlements are growing at a fast rate. Table 5.13 shows the frequencies and percentages of various categories of household’s sizes from the two study areas.

Table 5.13 Household sizes

Household size Frequency Percent

1-2 23 15.3

3-4 62 41.3

5-6 34 22.7

7-8 15 10.0

9-10 12 8.0

11-12 2 1.3

Above 12 2 1.3

Total 150 100.0

5.3.5 Years lived in the study areas

There is a significant variation by area of sampled households. The sample revealed that 21.3%

of the household heads lived in the areas for more than 11 years, 18% has been in the areas for 10- 11 years, 15% for 8-9 years, 14% for 6-7 and 2-3 years, 16.7% for 4-5 years and the remaining 0.7% have been in the area 0-1 year. Table 5.14 shows the frequencies and percentages of household’s length of stay in the areas. The survey revealed that people who lived in the city for many years have a better understanding of problems affecting the communities and also that they have better paid jobs than new people in the city. Such people have more networks and have better access to the urban economy and can easily navigate the areas for job opportunities than people that have not lived long in the area (Newaya, 2006: 17).

Some people lived longer in Windhoek and have relocated several times in informal settlements.

Oohambo dha Nehale is the newest settlement and is only 5 years old but the survey revealed that some people have stayed in this settlement for more than eight years. This simply means that some people were staying some where else before settling in the settlements because of relocation by the municipality.

Table 5.14 Comparison of years lived in the study areas

Settlement

5.3.6 Rural-urban migration

The survey has revealed all the sampled households are migrants into the city. The sample shows that 100% of the respondents are from different parts of Namibia. Table 5.15 shows the frequencies and percentages of sampled households with regard to their places of origin. Among all the households sampled, only 5 households reported having no relatives in rural areas of Namibia. The survey revealed that 78% of the sampled households are from Owamboland in the northern part of Namibia, 21% from Kavango, 0.7% from Keetmanshoop and the remaining 4.0%

from Omaruru. Since all the sampled households are migrants to Windhoek, this confirms that migration exists and that there is a strong rural-urban linkage. More than half of the Namibian population is predominantly Owambo people. The Owambo people are the majority living in informal settlements in Windhoek; therefore, 78% of sampled households were Owambo speaking people.

Table 5.15 Places of origin of respondents

Settlement between urban and rural households. From 150 sampled households, 99.1% of the respondents indicated that they receive food from their relatives in the rural areas north of Windhoek.

The majority of the respondents are Owambo people followed by Kavango people and they are both from the northern parts of Namibia. These people depend on subsistence farming such as growing crops like mahangu (millet), maize and beans for the survival. Land is very productive in the northern parts of the country and it is where the majority of the people are found compared to

the southern parts of the country where people depend entirely on livestock farming for survival.

The southern part of the country is mountainous; therefore cultivation is not possible. Windhoek is the destination for all sectors of all Namibian people; hence people are forced to migrate there for better opportunities and to improve their living conditions.