Conducting the Survey

When conducting the survey at Mangwele, there were numerous houses where no persons were present, which made it problematic to obtain enough samples (only 33 households were surveyed). This problem, however, did not occur at Sane.

The initial intention was to have a randomly picked sample from each village. The households were numbered on a map and a random sample was generated using Excel. This proved to be impractical on site as one would have had to visit a household much further away from the previous one and then have to return somewhere close to the initial one. The problem of having no one present in some houses also enlarged the problem of using a random sample. The survey was conducted by going from door to door per street.

3.4.2.1 Variables for data collection

Below is a list of the variables collected at each household. • Household size (HHS)

• Grade of each student

• Mode of transport (j)for each student (walk or public transport) with cost for public transport (PTCST) and travel time for walking (WT)

• Gender of each student. This, together with the school grade information gives the following variables:

o Number of secondary females (SEC FEM) o Number of primary females (PRI FEM) o Number of secondary males (SEC MAL) o Number of primary males (PRI MAL)

• Household income per month (INC GRP). Income group obtained from using Table 3.14 on page 69

• Clinic which each household visits (k)

• Mode of transport to clinic (walk or public transport) (j) • Travel cost for public transport to clinic (PTCST)

• Travel time for walking to clinic (WT)

• Waiting time at clinic before attendance (WAITt)

• Frequency of doctor visitation (DOC). This was not included in the survey paper but was asked on numerous occasions

Below is a list of the variables collected at schools • Grades offered

• Number of students

• Number of classes in each grade or overall (CLASS)

• An estimate of the number of textbooks made available to students (e.g. 90% of all students have the required textbooks) (TXTBKS)

The survey design is shown in Appendix B.

Some households did not declare their monthly income; this was than estimated using the average income of Sane available at the Stats SA website. Mangwele could not be found in the Stats SA website and it was decided to use the same average monthly income as listed for Sane. This was done because the villages are and very similar. The average was found to be in the same income category as that of Khakhu.

3.4.3 Analysis

The analysis of the data involved substituting the variables in the SP models with the variables obtained from the data collection survey. This was done as discussed in Chapter 3.3.1. using Equation 3.3 to determine the probability of an individual household using a particular facility or travel mode. Ideally, the probability of using a facility or travel mode should decrease as the conditions become unattractive (expensive, far to walk, long waiting times, etc.) and increase when they become attractive.

3.4.3.1 Education facilities analysis

The probability of a household making use of a particular school and transport mode to reach it was estimated for each school. This probability depends on the significant variables identified in the logit regression, as shown in Equations 3.5 and 3.6.

-(|)_},~ = '

'/01(3453676538785...53979) (Equation 3.5)

-(f)_} = '

'/01(3453676538785...53979) (Equation 3.6)

where

P(j) = Probability of household h using transport mode j to go to school l P(l) = Probability of household h using school l

These probabilities were then aggregated according to the schools, using the number of households making use of the school as shown in Equation 3.7.

-(|),~ = D}Ä% -(|)~,,} ÅÅ(|)~, (Equation 3.7)

where

P(j)v,l = Probability of average household from village v using transport j to travel to

school l

HH(j)l,v = Number of households from village v using transport mode j to travel to

school l

This probability was also aggregated according to the number households using that school as shown in the Equation 3.8.

-(f)_ = D -(f)_,}

}Ä% ÅÅ(f) (Equation 3.8)

where

P(l)v = The probability of an average household in village v using school l

P(l)v,h = The probability of household h from village v using school l

HH(l)v = The number of households in village v using school l

3.4.3.2 Health care facility analysis

For health care, the two villages were identified as using the same clinic facility. The probability of using the facility was estimated just as it was for educational facilities using Equations 3.9 and 3.10.

-(|)_},Ç = '

'/01(3453676538785...53979) (Equation 3.9)

-(p)} = _{'/01(3453676538785...53979)}' (Equation 3.10)

Where

P(j)h,k = Probability of household h using transport mode j to go to clinic c

P(c)h = Probability of household h using clinic c.

The probability of an average household using the facility and/or transport was calculated similarly to that of the educational facilities as shown in Equations 3.11 and 3.12.

Where

P(j)v,c = Probability of average household from village v using transport j to travel to

clinic c

P(j)v,c,h = Probability of household h from village v using transport mode j to travel to

clinic c

HH(j)c,v = Number of households from village v using transport mode j to travel to

clinic c

This probability was also aggregated according to the number of households using that particular school as shown in the Equation 3.12.

-(p)_ = D -(p)_,}

}Ä% ÅÅ(p) (Equation 3.12)

Where

P(c)v = The probability of an average household in village v using clinic c

P(c)v,h = The probability of household h from village v using clinic c