Research Findings

It is very important that the respondents' background characteristics be provided before carrying on with serious analysis so as to allow the reader to identify group dynamics. These are given in Table 12 (below)

Table 12: Percent Distribution of Resp ondents' Selected Background Characteristics by Place of Residence,1998

Urban ( %) Non-urban (%)

A~grcxp'f

15-19 18 23

20-24 17 19

24-29 15 15

30-34 15 12

35-39 15 13

40-44 ¹¹ 10

45-49 9 8

Raregrcxp':"f

Black 66 91

Coloured 18 7

White 10 2

Indian 6 0.2

Maritalstatus':'-"'f

Nevermarried 50 49

Manied 34 34

Livingtogether 7 10

Widowed 2 3

Divorced 3 1

Living alone 4 3

Hilf'estmJlmtWn'f'f'f'f

None 4 11

Primary 20 34

Incomplete secondary 46 42

Higher 30 13

Enplaymm:status'f'f'f'f'f

Working 38 22

Not working 62 78

Total 100 100

':. (X2=84.408, p<O.OOl);**(X2=1086.497,p<O.OOl); ***(X2=76.279,p<O.OOl);****(X2=717.763,p<O.OOl)*****(

X2=365.307,p<O.OO1).Sinceallp-valuesare lessthan0.001 ,the observeddifferencesare statisticallysignificant.

When firstl y looking at the results of Table 12 according

to

age group, there seems to

be no major urban-rural differentials of respondents according

to

their respective age

groups (that is, almost the same amount of people belonging

to

each age group are found in either place of residence). However major differentials occur when breaking down the results according

to

racial group whereby the highest percentage of those residing in non-urban areas

(90%)

are Black compared less than

10%

of Coloureds,

7%

Whites and less than a percent of Indians. Even in urban areas, Blacks are the majority population (66%), followed by Coloureds (less than 20 %, then Whites and lastly by Indians. These results are quite expected considering the fact that almost

80%

of the survey respondents were Black as compared to

13%

Coloureds,

7%

Whites and only

3%

Indians.

As with age group, there seems

to

be no major differentials in marital status between

urban and non-urban dwellers. Half of respondents in both urban and non-urban areas

had never been married

,35%

had been married, fewer than

10%

were cohabiting and a

further

10%

or less were either divorced, living alone or separated. As

was

expected,

there is variation in the highest level of education by place of residence. The highest

majority of those with no education were concentrated in the non-urban areas

(11%)

as

compared

to

only

4%

in the urban areas. Furthermore while the percentage of those

with incomplete secondary education is almosr similar between urban and non-urban

areas, only a few

(13%)

of those residing in non-urban areas had attained a higher

education as compared

to 30%

of urban dwellers. With these observed differentials in

educational attainment between urban and non-urban dwellers, it is not surprising that

only 22% of non-urban dwellers were employed at the time of the survey as compared

to almost

40%

of urban dwellers. Having provided respondents background

characteristics, it seems logical to now provide the various economic indicators before

carrying out data analysis. These are provided in Table

13,

which is listed below.

Table13: SelectedPovertyIndicatorsbyPovertyStatus(%)

Verypoor Poor Non-poor

Saote

if

drink it:guater"

Piped 62

Notpiped 38

Type if

toiletjadity,:-,:-

Flushtoilet(own) 39 Flushtoilet(shared) 4 Haselectricity,~,~* 63

Litereu:y':-':-'~':-

Easily 74

With difficulty 14

Not atall 12

Eduouional

attainnrnt'~-'-'r':-'r

None 11

Primary 39

Incomplete secondary 37

Higher 13

Enp/oyrrentstatus':":"r ':"r'~

~or~ng 83

Not working 17

79 21

62 6 84

88 8 4

3 25 46 26

92 8

88 12

79 3 90

94 5 1

2 9 29 60

95 5

Ex^tent

if

hmsehdd

hurrg:r~'r~:-~'r~r~:-):. ':.

Often 12 6 3

Sometimes 32 26 15

Seldom 5 7 5

Never 51 61 77

*Cx2= 294.507p<O.OOl);

**

CX2=577.327p<O.OOl)***(x2=345.734p<O.OOl)****(x2= 264.329,p<O.OOl)*****

CX2= 1007.577,p<O.OOl)****** CX2=p<O.OOl)******* (x2= 269.144,p<O.OOl).Since all p-values are less than 0.001,the observeddifferencesarestatisticallysignificant.

The availability of drinking water and proper sanitation varied across economic group.

Only 62% of the very poor and 80% of the poor had access

to

piped water as compared

to

almost 90 percent (88%) of the non-poor. Availability of a flush toilet in the house was approximately 39%, 62% and 79% for the very poor, the poor and the non-poor respectively. As with access

to

piped water and a flush toilet, there were huge differentials in access

to

electricity between the different economic groups, whereby only about 63% of very poor households had electricity compared

to

more than 80%

among the poor and the non-poor. Whil e literacy levels were not too low for all

economic groups (more than 70%), educational attainment varied greatly. Less than 4%

of the poor and non-poor had no education at all as compared more than 10% of the very poor. Moreover for the majority of the very poor and the poor, the highest level of education attained is incomplete secondary whereas the majority (60%) of the non- poor had a higher education. The figures are 37% and 46% for the very poor and the poor respectively. These low literacy and education levels can be attributed to the environments within which the majority of the poor and the very poor populations reside. High levels of education are dependent on high literacy levels which are then influenced by environmental factors. With the majority of households lacking proper sanitation, it is likely that they also cannot afford the cost of education. The lack of electricity on the other hand exacerbates the problem as it probably disables manyfrom studying or simply reading. People may be busy assisting their families on farms or carrying out other household chores by day therefore they may not be able to spare time for learning. At night those without electricity might have to either study by candlelight or not study at all as the use of the candle for reading might be seen as a waste in scenarios with tight financial situations.

Surprisingly, the low levels of education among the very poor and the non-poor are not matched by high unemployment rates. Only 17% of the very poor and 8% of the poor were not working as compared to 5% of the non-poor. These low unemployment levels can be attributed to the fact that all types of employment (formal/informal and cash/kind) are included in this category. However despite these low unemployment rates, hunger was a common phenomenon for more than 40% of the very poor and 32% of the poor but appears to be infrequent among the non-poor (18%).

The Poor as Determined by Monthly Income

Having provided some of the respondents' important background statisncs and

selected poverty indicators, it is important at this point to therefore provide the final

poverty indicator before resuming analysis. In previous sections the poor were defined

as those with monthly income levels between R601 and R1000, the very poor as those

earning below R600 and the non-poor as those earning from RlOOO and above. This definition of the poor is adhered to in this section.

Black (1378)

56% 15% 30% 100%

Coloured (274)

(92)

(283) (649)

42% 14% 44% 100%

White (87) (47) (334) (468)

19% 10% 71% 100%

Indian (32) (21) (105) (158)

20% 13% 67% 100%

Total

Note:figures in parentheses refer to theactual numberofpeople ineachcategory(X2=370.234 P <0.001) thustheobserved relationships arestatisticallysignificant

Table 14 provides the distribution of poverty according to racial group. Overall there were more respondents who were very poor as opposed to the non-poor, the figures being

47%

and

39%

respectively. As expected, Blacks and Coloureds constitute the majority of the very poor (more than

40%)

as compared to approximately

20%

for Whites and Indians whereas Whites and Indians constitute the majority of the non- poor (more than 60%). Thes e figures are somewhat consistent with those mentioned earlier.

S ECTION 2: ESTABL ISH ING THE LINK

This section is divided into two parts: the first part explores the relationship between

economic status and knowledge of HIV/ AIDS . Thus this part seeks to determine

whether low economic status is associated with low knowledge of the means of

avoiding HIV/ AIDS . The second part examines the relationship between economic

status and high HIV risk related sexual behavioural practices. This question seeks to

determine if low econonuc status

^IS

indeed a driving force behind risky sexual behavioural practices.

Part I: Exploring the Influence of Economic Status on Knowledge of