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SCHOOL FUNDING AND MANAGEMENT IN SOUTH AFRICA

Findings from the school survey

Department of Education

3 December 2009

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The collection of the data used for this report was funded by USAID, and the production of

this report was funded by UNICEF.

The overall study of which this report forms a part was managed by S-cubed Consulting. The

data used for the report were collected by Khulisa Management Services, which was sub-

contracted by RTI International.

The author of this report is Martin Gustafsson, of the Policy Research Group at the

Department of Economics at Stellenbosch University. Comments and guidance from a

number of people are gratefully acknowledged, including (but not limited to) Firoz Patel,

Paddy Padayachee, Dorothy Masipa, Bobby Soobrayan, and Luis Crouch. The author can be

contacted at mgustafsson@sun.ac.za. Views expressed in the report are those of the author,

and not necessarily those of the organisations involved in the broader study or referred to

here.

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Table of contents

EXECUTIVE SUMMARY ..................................................................................................... 7

Acronyms used ....................................................................................................................... 16

1 Introduction ..................................................................................................................... 17

2 Details on the dataset ...................................................................................................... 18

2.1 Sampling parameters ...................................................................................................18

2.2 Questionnaires and fieldworker interviews .................................................................19

3 Overall approach of the analysis ................................................................................... 21

4 Categorising schools by level of learner performance ................................................. 23

5 Optimal levels of school funding .................................................................................... 28

5.1 The funding levels in poor schools ..............................................................................28

5.1.1 Compliance with the allocation targets ................................................................. 30

5.1.2 Adequacy in terms of expenditure patterns ........................................................... 42

5.1.3 The backlogs problem ........................................................................................... 50

5.1.4 School opinions on funding adequacy .................................................................. 52

5.2 Public funding levels and no fee schooling .................................................................55

5.3 The funding levels in schools with fees .......................................................................62

5.4 The funding levels for Grade R ...................................................................................66

5.5 The funding of public schools and educational quality ...............................................78

6 Appropriate controls over school funding and related matters .................................. 84

6.1 Sharing of vital financial information with schools ....................................................84

6.2 Transfer of funds and resources to schools..................................................................87

6.3 Appropriate quintile, no fee and section 21 classifications .........................................91

6.4 Financial management and spending decisions in schools ........................................100

6.5 Fee-setting practices ..................................................................................................104

6.6 Schools-based democratic governance ......................................................................109

6.7 Grade R funding and resourcing modalities ..............................................................111

6.8 The merits of key proposals to reshape the funding system ......................................113

6.9 The funding of inclusive education in ordinary schools ............................................117

7 Protecting the poor through fee exemptions ............................................................... 120

7.1 Parent awareness of the exemptions system ..............................................................120

7.2 Compliance with the exemptions rules and their workability ...................................121

8 Conclusion and recommendations for future research ............................................. 131

References ............................................................................................................................ 132

9 Appendix: The question-by-question results .............................................................. 133

9.1 How to interpret the tables with interval data ............................................................133

9.2 How to interpret the tables with nominal data ...........................................................134

9.3 How confidence intervals were calculated ................................................................135

9.4 The calculation of weights .........................................................................................136

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List of tables and figures

Table 1: Sampled schools by province and district ..................................................................19

Figure 1: Distribution of school-level average scores ..............................................................24

Table 2: Scores and provinces ..................................................................................................24

Table 3: Scores and ex-department ..........................................................................................25

Table 4: Scores and type of area ...............................................................................................25

Table 5: Scores and quintiles ....................................................................................................26

Table 6: Scores and fees ...........................................................................................................26

Table 7: Scores and race of learners .........................................................................................26

Table 8: Scores and race of educators ......................................................................................27

Table 9: Scores and provinces with all schools included .........................................................27

Table 10: Indicators of the reliability of the financial data ......................................................30

Figure 2: Distribution of 2008 per learner transfer ...................................................................31

Figure 3: Distribution of 2008 per learner transfer by province ...............................................32

Table 11: Financial transfer details by province ......................................................................32

Table 12: Transfer figures from survey and budget statements ...............................................33

Table 13: Surveyed schools by province and quintile ..............................................................33

Table 14: Financial transfer details by province and quintile...................................................34

Table 15: Information available on Departmental purchases ...................................................35

Figure 4: Imputed Departmental purchases ..............................................................................36

Table 16: Department purchase figures from survey and budget statements ...........................37

Table 17: Departmental purchase details by province ..............................................................38

Table 18: Departmental purchase details by province and quintile ..........................................38

Table 19: Total Department spending details by province (I) ..................................................39

Table 20: Total Department spending details by province and quintile (I) ..............................39

Table 21: Percentage of learners attaining the allocation target (I) ..........................................40

Table 22: Percentage of learners attaining the no fee threshold (I) ..........................................40

Table 23: Percentage of allocation coming as a transfer ..........................................................40

Table 24: Total Department spending details by province (II) .................................................41

Table 25: Total Department spending details by province and quintile (II) .............................41

Table 26: Percentage of learners attaining the allocation target (II).........................................42

Table 27: Percentage of learners attaining the no fee threshold (II) .........................................42

Table 28: Extent of non-arrival of funds and goods .................................................................42

Table 29: Information available on school expenditure ...........................................................43

Table 30: Percentage of schools with expenditure data............................................................44

Table 31: School expenditure by item ......................................................................................45

Table 32: Departmental purchases by item ..............................................................................46

Table 33: School expenditure plus Departmental purchases by item .......................................47

Table 34: Relative spending in schools with lower scores .......................................................48

Table 35: Total norms-related spending by province ...............................................................49

Table 36: Total norms-related spending by province and quintile ...........................................49

Table 37: Percentage of learners with sufficient norms-related spending ................................49

Figure 5: Possible crowding out effects ...................................................................................50

Table 38: Backlogs situation ....................................................................................................51

Table 39: Adjusted percentage of learners attaining the allocation target ................................51

Table 40: Adjusted percentage of learners attaining the no fee threshold ................................52

Figure 6: Principals saying there is more funding now ............................................................52

Table 41: Uncompensated out-of-pocket spending by school personnel .................................53

Figure 7: Opinions on the adequacy of the school allocation ...................................................54

Figure 8: Opinions on the adequacy of the transfer ..................................................................54

Figure 9: Principal’s preference for additional spending .........................................................55

Table 42: Percentage of learners in no fee schools ..................................................................56

Table 43: Opinions from no fee schools ...................................................................................58

Table 44: Principal’s satisfaction with no fee schooling ..........................................................58

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Figure 10: Private revenue patterns ..........................................................................................59

Figure 11: Non-fee contributions .............................................................................................60

Table 45: No fee schools and private revenue ..........................................................................61

Table 46: Patterns of household spending on education inputs ................................................61

Table 47: Opinions on the sufficiency of public funding by quintile .......................................62

Figure 12: Profile of total monetary revenue ............................................................................63

Figure 13: Profile of total monetary plus in kind revenue ........................................................64

Figure 14: A counterfactual funding scenario ..........................................................................65

Table 48: Possible quintile misclassifications ..........................................................................66

Table 49: Percentage of Grade 1 with Grade R ........................................................................68

Figure 15: Public funding of Grade R ......................................................................................69

Table 50: Percentage of Grade R with a public transfer ...........................................................70

Table 51: Per learner value of the Grade R transfer .................................................................70

Table 52: Surveyed schools with Grade R transfer amount .....................................................70

Table 53: Percentage of Grade R learners in schools with posts ..............................................71

Table 54: Average number of Grade R posts per school ..........................................................71

Table 55: Per learner value of Grade R posts ...........................................................................72

Table 56: Percentage of Grade R learners receiving LSMs .....................................................72

Table 57: Total Grade R funding per learner ...........................................................................73

Table 58: Total Grade R spending by province in 2008 ...........................................................73

Table 59: Total Grade R spending by province in 2008/09 ......................................................74

Table 60: Approaches to Grade R staffing ...............................................................................74

Figure 16: Grade R class sizes ..................................................................................................75

Table 61: Annual pay of SGB-employed Grade R educators...................................................76

Table 62: Cohort coverage of Grade R .....................................................................................76

Table 63: Percentage of Grade R learners charged fees ...........................................................77

Table 64: Grade R fee charged .................................................................................................77

Table 65: Percentage of Grade R funding that is public ...........................................................77

Table 66: Challenges in the way of higher Grade R enrolment ...............................................78

Table 67: Views on what makes a quality school ....................................................................79

Table 68: Effect of teacher response ........................................................................................80

Figure 17: The principal’s education priorities ........................................................................81

Figure 18: The SGB parent’s education priorities ....................................................................82

Figure 19: The teacher’s education priorities ...........................................................................83

Table 69: Views on performance rewards for schools .............................................................83

Figure 20: Dissatisfaction with Departmental support .............................................................85

Table 70: General access to policy information .......................................................................85

Table 71: Knowledge of targets amongst principals ................................................................86

Table 72: Knowledge of targets amongst SGB parents ............................................................86

Table 73: Receipt of school-specific financial information .....................................................87

Table 74: Year in which transfers to schools began .................................................................88

Table 75: How financial transfers are implemented .................................................................88

Table 76: How Departmental purchases are implemented .......................................................89

Table 77: Ringfencing of the school allocation ........................................................................89

Table 78: Configuration of funding for school feeding ............................................................90

Table 79: Control over school feeding funds ...........................................................................90

Table 80: The poverty table ......................................................................................................93

Table 81: Distribution of ex-department across quintiles.........................................................94

Table 82: Distribution of type of area across quintiles .............................................................94

Table 83: Distribution of race across quintiles .........................................................................94

Figure 21: Quintiles and parent years of education ..................................................................95

Table 84: Quintile adjustment patterns .....................................................................................95

Table 85: Between-school enrolment dynamics .......................................................................96

Table 86: Non-SGB parents not liking no fee schooling ..........................................................96

Table 87: Percentage of principals wanting no fee status.........................................................97

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Table 88: Percentage of SGB parents wanting no fee status ....................................................97

Table 89: Percentage of non-SGB parents wanting no fee status .............................................97

Table 90: Percentage of schools with section 21 status ............................................................98

Table 91: Percentage of schools not assessed ..........................................................................98

Figure 22: Section 21 status and the use of a transfer ..............................................................99

Table 92: Opinions relating to financial controls .....................................................................99

Table 93: Background statistics on the principal ....................................................................101

Table 94: Details on budgets and financial statements ...........................................................101

Table 95: Details on financial decision-making .....................................................................103

Table 96: Percentage of schools where parents decide on the budget ....................................103

Table 97: What influences poor financial management .........................................................104

Table 98: Opinions on the fee-setting process ........................................................................106

Figure 23: Relationship between fees and voter turnout ........................................................107

Table 99: Distribution of fees charged in 2009 ......................................................................108

Table 100: Opinions on a maximum fee ................................................................................108

Figure 24: A reasonable maximum school fee .......................................................................109

Table 101: Opinions on the SGB ............................................................................................110

Table 102: Training of SGB members ...................................................................................110

Table 103: Education level of SGB parents ...........................................................................110

Table 104: Difference between SGB parent and other parent education ...............................111

Table 105: Year in which public funding of Grade R began ..................................................112

Table 106: Access to Grade R policy information .................................................................112

Table 107: Grade R roll-out situation .....................................................................................113

Table 108: Grade R ringfencing issues...................................................................................113

Table 109: Efficiency of the Department’s salary management ............................................114

Table 110: Opinions on the devolution of personnel powers .................................................115

Table 111: Percentage of principals supporting school employment of staff .........................115

Table 112: Percentage of teachers supporting school employment of staff ...........................115

Table 113: Percentage of SGB parents supporting compensations ........................................116

Table 114: Stakeholder opinions on school fees at the primary level ....................................116

Table 115: Stakeholder opinions on school fees at the secondary level .................................117

Table 116: Percentage of schools with Departmental assessment processes .........................118

Table 117: Percentage of schools with Departmental special needs support .........................118

Table 118: School opinions and actions on special needs ......................................................119

Table 119: Parent awareness of the exemptions policy ..........................................................121

Table 120: Opinions on the fairness of fee exemptions .........................................................121

Figure 25: Non-payment of fees .............................................................................................124

Table 121: Aggregate private charges and deductions ...........................................................126

Table 122: Learners with deductions by ex-department.........................................................127

Table 123: Learners with deductions by quintile ...................................................................127

Figure 26: Value of total deductions ......................................................................................128

Table 124: Exemptions applications ......................................................................................129

Table 125: Opinions on the exemptions formula ...................................................................130

Table 126: Responses to non-payment of fees .......................................................................130

Table 127: Example of statistics on an interval variable ........................................................134

Table 128: Example of statistics on an nominal variable .......................................................135

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EXECUTIVE SUMMARY

The main aim of this report is to provide a comprehensive policy-focussed analysis of a

dataset of 525 schools in order to inform a wider study dealing with the financing and

management of schools in South Africa. The data were collected in March 2009, and the

process involved the completion of five questionnaires by fieldworkers through various

interviews all conducted within one day at each school. The questionnaires were a financial

data questionnaire, a principal questionnaire, an SGB parent questionnaire, a teacher

questionnaire (administered to two teachers in the school) and a non-SGB parent

questionnaire (administered telephonically to two randomly selected parents who were not on

the School Governing Body (SGB)). 331 of the 525 schools were from the 2004 run of the

Systemic Evaluation (itself a data collection based on a random sample), allowing for

relationships between average learner performance (in 2004) and other school characteristics

to be investigated.

The rest of this executive summary focuses on the findings of the report. Starting from section

5.1 of the report, each second-level heading (5.1, 5.2, 5.3 and so on) begins with a boxed

summary. Instead of simply reproducing those boxed summaries here, a more synoptic

overview is provided here, arranged around three over-arching policy design questions used in

the overall study. The reader wanting more detail, without all the detailed tables and graphs,

should refer to the boxed summaries in the remainder of the report. Moreover, this executive

summary sums up key province-specific findings within a provincial ‘scorecard’.

But at the very highest level, how could one summarise the findings of the report in a

nutshell? An attempt is provided in the next paragraph.

The survey finds that around half of schools are funded at a level below that specified in the

policy, and that these schools tend to be poorer schools. Importantly, in seven of nine

provinces this is not because the budget is not large enough, but because provinces distribute

funds in a way that deviates from the policy. Whether this deviation is a problem is debatable.

It could be that provinces are responding to real needs, and that the distribution required by

the policy needs to be revised. Responses from schools suggest that what is a greater problem

than the amount of funding received, is the fact that schools do not have sufficient controls

over existing funds. Specifically, the Department tells schools what to spend transferred funds

on when this may not be ideal (and runs contrary to the policy), and where the Department

spends funds on behalf of the school these schools experience numerous problems such as

goods arriving late, or the goods not being the right ones. Turning to the overall architecture

of the funding system, on the whole this seems to serve its intended purpose and schools tend

to be satisfied with it. There is one exception, however. The classification of schools into

socio-economic quintiles, whilst overall in line with socio-economic realities on the ground,

seems to contain too many errors to work well in its current form. Responses from schools

suggest that they are not satisfied with this element of the architecture. The survey itself did

not take this matter further and investigate what the responses of schools may be to alternative

classification systems.

The first of the three key policy design questions runs as follows: What should the level of

non-personnel funding of different schools be?

Attainment of the existing funding norms targets. The answer in the existing funding

norms to the question of how much should be spent with respect to non-personnel in schools

is rather straightforward. Per learner spending amounts are specified for each of the poverty

quintiles into which schools have been divided. As examples, for quintile 1, the poorest

quintile, this amount is R775 (in 2008), in quintile 3 it is R581 and in quintile 5 it is R129.

The quintile 3 amount is moreover considered a ‘no fee threshold’, or an absolute minimum

amount of non-personnel funding required by a school that has no private revenue. Analysis

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of the survey data suggest that in seven of nine provinces (all except for North West and

Mpumalanga) overall spending on the non-personnel ‘school allocation’ is sufficient for the

targets to be met. The fact that none of the provinces do fully meet the targets is a result of a

flattening of the distribution across quintiles by provinces. Put simply, they give more than

what is implied by the targets to the rich (specifically quintile 5) and less than what the targets

specify to the very poorest quintiles (in particular quintile 1). The result is still a pro-poor

distribution of funding, but one that is not as pro-poor as what the policy requires.

Specifically, whilst provinces fund quintile 1 three times as much as quintile 5, the policy

requires a ratio of six to one. This deviation from the policy is so widespread that there must

be some systematic explanation. Either the relatively rich are successfully lobbying for

funding above the target level, or provinces see no need to fund quintiles 1 and 2 above the no

fee threshold level. What would support the latter explanation is that the policy does not

clearly state what additional items quintiles 1 and 2 should purchase with the additional funds

to deal with their socio-economic disadvantage. Of course both explanations could be true.

Overall, around 62% of learners seem to attain their national target levels. In quintile 1, 65%

of learners do. However, it is important to bear in mind that the survey did not involve a fully-

fledged financial audit, and there are a number of additional reasons why the financial values

may not reflect precisely the reality on the ground (in particular, they are more likely to be

under-estimates than over-estimates). If one artificially adjusts the school financial values up

by a wide 10% margin, then the overall percentage of learners attaining their national target

levels rises from 62% to 75%. Put differently, one can be highly certain that not all learners

are funded at the right level, at it is very unlikely that more than 75% of learners are. If one

uses the alternative benchmark of the no fee threshold, one finds that around 85% of learners

in quintile 1 do reach this level (section 5.1).

Adequacy in terms of physical inputs. Though the survey was not designed to provide a

detailed audit of what schools need and what they have, if one adds up what the Department

delivers to the school in the form of goods and services, and what the school spends its money

on (whether this money is from a public or private source), it is possible to gain an idea of

where the resourcing inadequacies may lie. In particular, it is useful to compare the value of

inputs in quintile 5 schools (which are often considered to be adequately resourced) to those

in quintile 1 to 4 schools. Few differences seem great enough to be critical. One difference

that does stand out, however, is that poorer schools spend about one-tenth of what quintile 5

schools spend on document reproduction. This is likely to impact negatively on the ability of

poorer schools to deliver the curriculum (for instance through the reproduction of worksheets

and tests) and implement effective governance (for instance by sending circulars to parents).

With respect to certain items, poorer schools appear better funded than rich schools. More is

spent on classroom furniture, which is probably a reflection of the infrastructure backlogs

experienced in poorer schools. They also spend more on textbooks. This is largely a reflection

of the fact that in richer schools textbook purchases feature to a smaller degree in the accounts

of schools as many schools expect parents to purchase textbooks themselves (section 5.1).

The effect of personnel spending pressures. Spending on non-personnel items in poorer

schools would have been better if schools had not diverted public funds intended for non-

personnel items towards the hiring of personnel, in particular non-educator support staff. This

is in clear contravention of the funding policy. Such diversion of funds does not happen in

richer schools as their private revenue is sufficient to deal with their additional personnel

needs. If one subtracts the diverted funding from the original non-personnel amount in poorer

schools, then the percentage of learners attaining the national targets drops substantially, for

instance from 50% to 30% for the poorest learners (section 5.1).

Key funding thresholds. The relationship between public funding levels and the level of

satisfaction amongst principals and SGB parents with this funding were analysed to see

whether there were critical funding thresholds. The analysis was limited to no fee schools and

schools charging R100 or less in fees. Rather clear patterns emerged. If the per learner school

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allocation amount went below R500, then this was associated with substantially greater levels

of frustration. Moreover, if the part of the school allocation coming to the school in the form

of a financial transfer went below around R400 (as it does for half of the schools analysed),

then this too was associated a sharp increase in the level of frustration. The data suggest that

at low levels of funding below these threshold levels, schools tend to focus on maintaining the

school infrastructure to the detriment of more educational inputs. In other words, there could

be a hierarchy of priorities within which infrastructure precedes educational materials, and if

schools do not receive enough funding, education suffers (section 5.1).

No fee schools. Despite the funding problems mentioned above, schools that have been

declared no fee schools are rather happy with their status. Just 19% of school principals, 8%

of SGB parents and 5% of non-SGB parents are dissatisfied about their no fee status.

Presumably some of this dissatisfaction relates to ‘teething problems’ with the new system, in

particular access to public funds. 98% of all schools do receive a financial transfer from the

Department, but as we have seen it is often frustratingly low. It is important to note that 68%

of no fee schools collect what can be called non-fee contributions, amounts that parents agree

should be contributed on a voluntary basis by all parents. This is not a problem in terms of the

policy, as long as the contributions are truly voluntary and there is no marginalisation of non-

paying parents. However, it seems as if these conditions are not always met. A quarter of

SGB parents in these schools said the contributions were compulsory, and 7% of non-SGB

parents clearly viewed them as school fees. Very importantly, there is no clear correlation

between having non-fee contributions and the level of public funding, meaning the non-fee

contributions are as likely to occur in well-funded schools as poorly funded schools. Raising

the level of public funding may therefore not affect the extent of these contributions. It seems

necessary to clarify the status of these contributions in the policy, and establish ways of

ensuring that they remain voluntary. Banning them would probably not be desirable,

particularly if they represent legitimate attempts by the community to contribute towards

special projects, as opposed to core needs. The receptiveness of fee-charging schools to

becoming a no fee school is rather high. 70%, 48% and 23% of principals in fee-charging

schools in quintiles 3, 4 and 5 are interested in becoming no fee schools. This enthusiasm is

stronger at the primary school level. The figures suggest that it is better for schools

themselves to select the no fee status, than for this to be imposed on a quintile-by-quintile

basis. For instance, it would be better for the 23% of schools in quintile 5 wanting to be no fee

schools to assume this status, than for the 30% of schools in quintile 3 not wanting this status

to be forced into it (sections 5.2, 6.3 and 6.8).

The ranking of schools by quintile. It has been said that there is a ‘middle quintiles’

problem whereby schools in quintiles 3 and 4 are under-funded because they are not poor

enough to qualify for the more generous quintiles 1 to 2 allocation amounts, yet not rich

enough to collect the fees collected in quintile 5. The data seem to confirm that to some

degree this problem exists, which could explain why provinces have been flattening the

distribution of the targets (basically they were avoiding an even more serious ‘middle

quintiles’ problem). However, the under-funded ‘middle’ is largely quintile 4 and a part of

quintile 5. Part of the problem lies with the way schools have been placed into quintiles. A

very simple analysis using fee charged as an indicator of socio-economic status suggests that

schools with 2.5 million learners should move another quintile, with the largest rearrangement

being between quintiles 3 and 4. At the same time, it should be emphasised that the existing

quintile placements do seem to correlate rather well with important factors, such as the

ruralness of the school. The survey data confirm that relatively popular myths such as that

quintile 5 schools are mainly ex-white schools are not true. In fact, the data indicate that

around half of quintile 5 schools are historically African (see Table 81). There is considerably

more dissatisfaction with the quintiles than there should be. Half of schools have complained

to the Department about their quintile placement, and a third of all schools have done so

through a formal letter. Complaints have been most common in quintile 3. 9% of all schools

have seen their quintile change as the result of a complaint. This raises the risk that

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insufficiently accurate data is being replaced by a school’s ability to complain as a criterion

for higher poverty-targeted funding, especially given that provinces have little objective data

that they can use when they respond to complaints. The figures moreover suggest that a large

number schools not qualifying for a change will feel unfairly disadvantaged. One obvious

difficulty with using data on the community around the school, as is currently the case, is the

fluidity of learner movements. Estimates by the school principal suggest that overall more

than half of the learners in the public schooling system attend a school that is not the closest

one offering their grade (sections 5.3 and 6.3).

Funding Grade R. Determining an optimal design for a national Grade R funding policy is

made difficult by the fact that currently fundamentally different funding modalities, in

particular with respect to personnel, exist and have existed for many years. 80% of schools

were already receiving public funding for Grade R by the time the 2008 Grade R funding

norms were introduced. And given that Grade R enrolments in public schools are still rising,

there is still an important trade-off between per learner funding and coverage. By 2009 around

71% of the Grade R cohort was enrolled in Grade R in public schools, and 85% of enrolled

learners were receiving public funding of some sort. Of those receiving this support, 65% are

in schools where all Grade R teachers are employees of the Department, 13% are in schools

where just SGB-employed teachers are used, and 22% have a mix of the two. SGB-employed

teachers is what Education White Paper 5 had envisaged for all schools, but 81% of principals

prefer the option of Departmentally employed teachers. This is understandable given that on

average the Department pays six times more per teacher than the SGB, even though around

half of principals indicate that the Department specifies what SGB-employed teachers should

be paid. Two critical problems, both of which relate to educational quality, can be identified.

Firstly, the extremely low pay of SGB-employed teachers raises questions around the

qualifications of these teachers and the incentives for them to do a good job. Secondly, it is

clear from the data that the more costly option of Departmentally employed teachers is

associated with larger classes. A quarter of learners under the second option are in classes

exceeding 50 learners, whilst this is rare amongst those learners following the first option. In

the end, however, the option of Departmentally employed teachers remains the more costly

one (class sizes are not six times as high within this option), raising concerns around the

equity of per learner funding. What is confusing is that the different options do not appear to

follow clear patterns with respect to provinces and quintiles, which begs the question of what

criteria are being used to determine which school gets what. The Grade R funding norms are

designed to deal with these problems, but either they are not understood, or the policy is being

superseded by other factors. It is possible that the survey data, perhaps combined with other

data such as Persal data from the Departments, can be interrogated further to explore what the

right policy response should be. As emphasised by White Paper 5, it is important that the

quality of Grade R should receive sufficient attention (sections 5.4 and 6.7).

Funding and learner performance. A rewards system whereby whole schools would be

paid a bonus for improving their results is generally regarded in a favourable light by schools.

Around 80% of parent, principals and teachers would support this. The data also suggest that

better advocacy of the right things would be a low-cost way of improving educational quality.

Interviewees were asked what makes a quality school, and were able to select good learner

performance, discipline, a physically attractive school and community participation. Around

half of teachers selected good learner performance. A regression model focussing on primary

schools revealed that that there was a statistically significant relationship between having a

teacher define quality schooling in this way, and actual learner performance reflected in the

Systemic Evaluation results. This suggests that simply explaining to schools in a more

persuasive manner what educational quality means might make a difference. The survey data

support the widely held view that there is not sufficient standardised performance data

available on schools. Specifically, the conditional correlation between parent ratings of their

school and actual learner performance reflected in the Systemic Evaluation is surprisingly

poor, indicating that in general parents are not well informed about how well their children

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learn in their schools. This reduces the chances that parents will put the right kind of pressure

on schools, and hold schools accountable for better teaching and learning (section 5.5).

Priorities for the future. Despite the fact that overall the funding targets in the policy have

not been met, the above discussion seems to point towards a situation which is more positive

than negative. In fact, the non-attainment of the funding targets appears partly to be a

compliance problem, and partly a problem with the targets themselves. The picture is

especially positive if one considers the recent trend. Close to half of principals believe that the

funding situation has improved in the last five years (see Figure 6). Yet there are a number of

complex policy problems to revolve, in particular with respect to Grade R. Two relatively

straightforward policy actions seem to stand out. Firstly, getting every school to receive at

least R400 as a financial transfer is a priority that is achievable and easy to understand, and

the data suggest that this would improve the situation substantially in affected schools.

Secondly, developing and implementing a policy that would ensure that poorer schools were

sufficiently staffed with respect to support staff would remove much of the current pressure to

spend money intended for non-personnel items on personnel.

The second over-arching policy question is the following: Which schools should manage their

own public non-personnel funds?

Currently both the approach of having the Department provide goods and services to schools

and the approach of transferring funds to schools are widely employed. Changing the current

mix of these approaches is a key matter, but so is the matter of how either approach can be

improved.

Improving the system of Departmental procurements. The Departmental purchases

approach can be improved. A third of affected principals say that goods procured in this way

arrive late at the school and a third say that they are not asked what goods they need. The

latter complaint clearly violates the funding policy. In some provinces the approach of

allowing schools to deal directly with suppliers, and having the Department pay suppliers on

instruction from the school, is employed. The data indicate that following this approach

reduces the lateness of deliveries and the problem of the wrong goods being delivered (section

6.2).

Improving spending by schools. The approach whereby schools procure goods using their

financial transfer is also subject to improvement. Despite the fact that the funding norms do

not allow Departments to ringfence parts of the financial transfer for specific items (though

the Department may and in fact should provide a recommended breakdown), a third of

schools report that ringfencing is applied. Many principals find that this hinders their work

(section 6.2). The policy requirement that schools should begin receiving a financial transfer

only after they have passed a management readiness assessment has mostly not been applied.

It seems as if section 21 functions have not formally been transferred to many schools when

this should have occurred – though 98% of schools receive a financial transfer, only 78% of

them say they have section 21 status (section 6.3). Despite these shortcomings, key financial

management processes in schools are in place. Virtually all schools compile a budget and

(with the exception of Eastern Cape) all seem to prepare annual financial statements. But a

third of principals are not aware of any standard chart of accounts for the financial statements.

Without such standards training and monitoring become difficult (section 6.4). Parent

participation in the school’s budget process, in particular in poorer schools, is not what it

should be according to the policy. In poorer schools parents influence the budget directly in

about a half of schools. In the other half, it is the school’s staff that prepare the budget on their

own. In this regard, the situation in Eastern Cape is completely unlike that of any other

province. In this province, 71% of schools report that teachers, and not the principal nor

parents, exercise most influence in preparing the budget. The influence of teacher unions in

the budget process is, however, not unusually strong in Eastern Cape. A serious problem is

12

reflected in the fact that 8% of SGB parents report that they believe financial irregularities

have occurred in their school. This statistic would perhaps be reduced if parent participation

were increased and a standard chart of accounts were utilised in all schools. In a multivariate

analysis, however, the only variable that appeared to exert a statistically significant impact on

financial mismanagement was having a female principal. Female principals are clearly

associated with better financial management.

Information on Departmental commitments to schools. The literature on school financing

emphasises the importance of making information on public funding widely available to

stakeholders. In the South African context, the greater the awareness of the national targets,

the greater will be the pressure on provinces to reach these targets and provide the funding on

time. In five provinces, the awareness amongst principals of the targets is good, but not in the

remaining four provinces (see the provincial ‘scorecard’ below). An obvious problem here is

that principals do not have easy access to the relevant policy documents. One-third of

principals indicate that this is a problem (section 6.1).

Preferences of schools with respect to funding modalities. Asked whether they would

prefer school control over all public non-personnel funds, or a Department that provided an

optimal resourcing service, 90% of principals and SGB parents said they would prefer school

control. This suggests that there are high levels of trust between these two stakeholder groups,

given that they share responsibility for the school’s finances, and that principals are willing to

take on additional work and responsibilities. Data from provincial officials may reveal

problems inherent in transferring more financial powers to schools, yet the very clear

receptiveness of schools to comprehensive financial responsibilities is an important factor that

should influence the way forward (section 6.3).

The workability of the current school governance model. There appear to be no serious

system-wide problems with the current governance model. 90% of principals believe it is a

good model, and 86% of non-SGB parents believe the principal is doing a good job.

Extensive training has occurred. 77% of SGB parents indicate that they have received training

in school governance (section 6.6).

Control over the school nutrition programme. Three-quarters of school principals whose

schools receive food through this programme believe a better service would be offered if

funds were transferred to the school and the school were allowed to manage this service

locally (section 6.2).

Inclusive education. The inclusive education policies place a strong emphasis on the agency

of the district. A district-focussed approach is advantageous insofar as it prevents a situation

where schools overstate their special needs requirements in order to attract more funds into

the school fund. The survey data show what where the Department is proactive with respect to

the implementation of inclusive education, schools are more receptive to this philosophy for

dealing with special needs learners and are more inclined to have special or remedial classes.

A key challenge is that Departmental support in the area of special needs is still biased in

favour of better off schools (section 6.9).

Control over personnel functions and funds. The radical proposal of transferring all

personnel funds to schools so that they can employ all staff does not find much support in

schools. However, in quintile 5 a sizeable minority of 36% of teachers (and a majority of 56%

of principals) support this. Even in quintiles 1 to 3 10% of teachers favour this proposal. This

is likely to be linked to frustrations around the Department’s handling of specific issues. Half

of teachers believe that the Department handles salary problems poorly. Perhaps surprisingly,

half of teachers would prefer to see disciplinary action devolved to the school level (section

6.8).

13

Regulating school fees. Linked to the matter of school powers over public funds, is the

matter of the rights of schools with respect to the collection of private revenue, in particular

school fees. The South African policy on school fees in public schools is strongly premised on

the assumption that parents will participate to a sufficient degree in the fee-setting process.

This appears to be the case. 70% of non-SGB parents reported that someone in the household

had at some point participated in the vote for a school fee. At the same time, around a quarter

of non-SGB parents describe the school fee they pay as ‘very high’ and a further 15% say it is

‘a bit high’. This seems consistent with the correct application of the fee-setting rules, which,

implicitly, are only designed to keep a majority satisfied. The data suggest that changing the

rules around the quorum required for the vote to proceed may not change the outcome of the

vote, even if it increases the involvement of parents. One should keep in mind that voting for

a school fee is rather un-dynamic, in the sense that one’s preference is largely dependent on

one’s income, which is fixed. When voters do not come to the budget meeting, it could be that

they have assessed that their presence would not make a difference to the outcome. A policy

intervention that is likely to make a difference is the imposition of a maximum permitted fee.

Perhaps surprisingly, a third of principals in fee-charging schools support this, though support

drops the higher the fee charged. Those schools that do oppose a maximum fee, however, are

likely to oppose the intervention strongly. The data show that schools with high fees, despite

already having below average learner/educator ratios, are exceptionally keen on reducing

class sizes even further. Their high fees are thus linked to an aspect of schooling they value

very highly (sections 6.5 and 5.5).

The third policy question is: What should happen to the fee exemptions system?

Attitudes towards the principle of fee exemptions. Fee exemptions as they have existed up

till now involve maintaining an implicit cross-subsidy within individual schools, whereby

better off parents subsidise worse of parents. This makes them a particularly difficult and

contentious policy area. It is important to note that overall support for the principle of this

cross-subsidisation is rather high. 80% of parents and principals support it. However, 16% of

principals categorically state that exemptions are unfair (section 7.1).

The extent of fee exemptions. Fee exemptions are fairly widespread. 71% of fee-charging

schools implement them (though this figure should be close to 100%). 11% of learners

receive exemptions. This global figure seems neither too high nor too low. Yet the situation in

individual schools differs greatly (sections 7.1 and 7.2).

Non-payment of fees in the absence of exemptions. There appear to be two major policy

challenges relating to fee exemptions. Firstly, non-payment of un-exempted fees is high. The

problem is not that the affected schools do not implement exemptions. The great majority of

them do, and many also incur large financial losses due to exemptions, apart from losses due

to non-payment. Overall the value of revenue lost due to non-payment is greater than revenue

lost due to exemptions. Non-payment of fees is likely to lead to even more serious tensions

than exemptions. There is some indication in the data that it is schools that lack the capacity

to take legal action against non-paying parents that experience the largest non-payment

problems. These problems are more prevalent in more disadvantaged schools. The data do not

allow for a satisfactory analysis of the problem, however. In particular, without parent income

data, it is not possible to assess whether the non-payment problem is mostly a matter of

parents who are able to pay taking advantage of the situation, and to what extent it is a matter

of the exemptions formula not being sufficiently generous (section 7.2).

Automatic exemptions and poor households. The second major problem is that the 2006

exemptions provision stating that recipients of social grants should enjoy automatic

exemptions is not widely known or implemented. The result is that around 1.2 million

particularly poor learners do not enjoy an exemption when they should. Clearly, the solution

14

to this problem needs to be considered together with plans to extend the system of no fee

schools.

Below, a tentative provincial ‘scorecard’ is presented. It is tentative because it does not follow

any formal scorecard model, and is mainly intended to capture key points made about

provinces in the report. Crosses indicate problems, whilst ticks indicate a good situation. The

approach was to highlight exceptional problems and exceptional achievements. This means

that not having a cross does not necessarily mean there is no problem, just as not having a tick

does not necessarily mean there has been no achievement. A blank cell simply means that the

situation in the province is not exceptional.

15

A TENTATIVE PROVINCIAL ‘SCORECARD’

EC FS GP KN LP MP NC NW WC

1 Exceptionally poor overall funding of the school

allocation. r r

2 A low per learner amount paid as a financial

transfer for the poorest schools. r

3 High levels of satisfaction amongst school

principals with the implementation of no fee

schooling.

b b

4 Low level of Grade R enrolment in schools,

accompanied by a very limited presence of financial

transfers for Grade R.

r

5 High levels of Grade R enrolment in public schools. b b b

6 Low level of knowledge amongst school principals

of the public funding that is due to them according

to the funding norms. r r r r

7 A high percentage of principals reporting that their

financial transfer is received through one, and not

several, payments, as required in the policy.

b b

8 A high percentage of principals reporting that

supplies delivered by the Department arrive late. r

9 High level of prescription with respect to how

financial transfers should be used by the school, in

contravention of the funding norms.

r

10 High levels of dissatisfaction around quintile

placements. r r

11 High percentage of schools not compiling an annual

financial statement, though they receive a financial

transfer. r

12 High percentage of annual financial statements

prepared according to a standard chart of accounts. b

13 High percentage of principals not being aware of

the existence of a standard chart of account. r

14 High apparent prevalence of financial management

problems (including financial management

irregularities) in schools.

r r

15 Low percentage of SGB members with training in

school governance. r

16 Difficulties experienced by principals in obtaining

the Grade R funding policy or information on the

roll-out of publicly funded Grade R. r r r

17 Particularly low levels of special needs assessment

and Departmental support in this area. r r

18 Low degree of implementation of exemptions

policy in fee-charging schools. r

19 High degree of implementation of exemptions

policy in fee-charging schools. b

16

Acronyms used

LSM Learning support materials

SGB School Governing Body

UNICEF United Nations Children’s Fund

USAID United States Agency for International Development

The following abbreviations for the provinces are used:

EC Eastern Cape

FS Free State

GP Gauteng

KN KwaZulu-Natal

LP Limpopo

MP Mpumalanga

NC Northern Cape

NW North West

WC Western Cape

17

1 Introduction

This report is part of a series of reports emerging from a study formally titled ‘Evaluation of

the implementation and impact of the National Norms and Standards for School Funding and

the development of a monitoring and evaluation framework and strategy’. The study is

conducted for the Department of Education and funded by UNICEF and USAID.

The current report focuses on primary findings from a survey of 525 nationally representative

schools. In order to enhance the policy focus of this report, the analysis itself is broken down

by the same headings as those appearing in the main ‘composite findings and

recommendations’ report, a report into which this report feeds. A detailed discussion of the

policies that the study is intended to inform can be found in the ‘composite findings and

recommendations’ report. That discussion is not repeated here, and it is assumed that the

reader of this report is already more or less familiar with the policies themselves.

This report contains a large amount of detailed discussion of how empirical findings are

arrived at. Within this report there are two levels of summarisation for the reader wishing to

extract the essence of this analysis. Firstly, each of the policy sections, beginning with section

5.1, contains a boxed summary. Secondly, the executive summary appearing above provides

an even briefer summary of the findings. Finally, the findings from this report are brought

together with other findings within the ‘composite findings and recommendations’ report.

It should be emphasised that the dataset used for this report is rich, and the analysis presented

here cannot exhaust all the analytical opportunities presented by the dataset. The dataset

allows for a variety of analyses falling beyond the policy scope of this study and it is hoped

that such analyses are taken forward beyond the current report.

Accompanying this report are five annexes, each of which provides question-by-question

statistics in a standard format from virtually every question in each of the five questionnaires.

Sections 9.1 and 9.2 of this report provide a guide on how to read these annexes.

18

2 Details on the dataset

Details on the dataset that are directly relevant for understanding this report are discussed

below. A separate and more detailed report on the data collection process has been compiled

by the data collection organisation Khulisa Management Services. This report, which includes

copies of the five questionnaires, is titled Finance and Management: Final survey report.

2.1 Sampling parameters

The original intention had been to collect data from around 1,000 public schools and 100

independent schools across the country. Due to budget constraints, the sample had to be

reduced to 525 public schools, and it was decided that no independent schools would be

surveyed.

The decision was taken to sample an equal number of schools per province in order to make

up the 525 schools. An equal number of schools per province would allow for similar

confidence intervals, or similar levels of statistical reliability, for each of the nine provinces.

Contrary to what is often believed, it is not the percentage of the sample that determines how

reliable the statistics are, but the raw number of sampled units (for instance schools). This is

why, for instance, international testing systems such as TIMSS require a minimum number of

schools per country, regardless of how many schools there are in the country.

It was recognised from the outset that having around 58 schools per province would render

certain provincial statistics unusable, as the confidence intervals would be too wide. However,

as certain provincial statistics would have a sufficiently narrow confidence interval to be

useful for the analysis, having an equal number of schools per province seemed optimal.

An equal number of schools per province implies that national statistics would need to weight

schools from more heavily populated provinces more, and weight schools from less populated

provinces less. Details on the derivation of weights is provided in section 9.4.

In five provinces schools were selected in just six districts in order to reduce travelling costs.

This approach, called clustering, makes it necessary to adjust confidence intervals in a way

that takes into account the clustered nature of the data. Section 9.3 provides details in this

regard.

The following table indicates how many schools were sampled per province and (where

clustering occurred) per district.

19

Table 1: Sampled schools by province and district

Eastern Cape Butterworth 10 Cofimvaba 8 Dutywa 10 Libode 11 Lusikisiki 10 Mthata 9 58

Free State No clustering 58 58

Gauteng Ekurhuleni South 10 Gauteng East 8 Johannesburg Central 12 Johannesburg East 9 Johannesburg North 9 Tshwane South 11 59

KwaZulu-Natal Empangeni 11 Ilembe 7 Obonjeni 9 Pinetown 9 Umgungundlovu 10 Vryheid 13 59

Limpopo No clustering 58 58

Mpumalanga No clustering 58 58

Northern Cape No clustering 58 58

North West Greater Taung 8 Klerksdorp 10 Lichtenburg 11 Mafikeng 12 Rustenburg 8 Zeerust 9 58

Western Cape Metropolitan Central 8 Metropolitan North 9 Metropolitan South 10 Overberg 11 Southern Cape/Karoo 10 Westcoast/Winelands 11 59

Total 525

2.2 Questionnaires and fieldworker interviews

Five questionnaires were administered in the 525 schools by fieldworkers during March 2009.

In the case of each school one fieldworker spent a day at the school. The five questionnaires

are as follows:

� Financial questionnaire and follow-up survey. A 12-page questionnaire designed to

collect financial data (but also some non-financial data) was sent to schools before the

school visit with the request that schools fill in the questionnaire beforehand. The

fieldworker came to the school with a 22-page ‘financial questionnaire and follow-up

survey’, which was the original 12-page questionnaire with some additional questions on

the data. The fieldworker was asked to re-enter the data from the 12-page questionnaire

into the corresponding tables in the 22-page questionnaire, if the school had managed to

fill in the 12-page questionnaire beforehand. If not, the fieldworker, working with the

school principal, had to fill in the entire 22-page questionnaire from scratch during the

visit. Some details relating to the process of this questionnaire are provided in Table 10

below.

� Principal questionnaire. This 25-page questionnaire was filled in by the fieldworker

during an interview with the school principal.

20

� SGB parent questionnaire. This 16 page questionnaire was filled in by the fieldworker

during a face-to-face interview with a parent member of the School Governing Body

(SGB), preferably the chairperson of the SGB. An SGB parent was requested to come to

the school on the day of the school visit. If this was not possible, the fieldworker

attempted to visit an SGB parent in his or her home or workplace to conduct the

interview. Data were collected from an SGB parent in 516 of the 525 schools.

� Teacher questionnaire. This 7-page questionnaire was meant to be administered twice in

each school through two face-to-face interviews with two randomly selected teachers.

Responses from 1,037 (out of a maximum possible of 1,050) teachers were collected.

� Other parent questionnaire. This 7-page questionnaire was also meant to be

administered twice in each school, through two telephonic interviews with parents who

were not members of the SGB. Fieldworkers were given a methodology to select parents

who had telephones randomly using the school’s list of learners. Data were collected from

1,061 parents from 525 schools (in a few schools data from more than two parents were

collected).

The response rates in the survey can be regarded as very high.

Confidentiality was an important matter in the survey given that sensitive questions were

asked, for instance a respondents opinion on the services of the Department, or the

effectiveness of the school principal. In accordance with standard confidentiality procedures,

any values which could reveal the identity of respondents or even schools have been removed

from the dataset that will become generally available for possible follow-up studies.

21

3 Overall approach of the analysis

The study required the questionnaire design and the data analysis to be very focussed on

policy issues which to some degree are specific to South Africa. Both the questionnaire

design and data analysis were thus preceded with a careful analysis of the relevant policy

documents, and discussions with various people, inside and outside the Department of

Education, who have dealt with the policy design issues in the past. The separate ‘composite

findings and recommendations’ report provides the outcome of the policy interrogation, as

well as a review of the academic literature dealing with school funding, management and

governance. The empirical findings in this report are organised according to the same series of

policy questions that are found in the composite report.

The question-by-question results in the five annexes to this report were examined to gain an

idea of how the responses in the survey could add to the existing policy discourse. This led to

the analysis that is reported below.

The analysis is largely focussed on painting a picture, in quantitative terms, of the school

funding and management situation in 2008 and 2009. The questionnaires were not designed to

provide a historical picture. To this would have entailed a far more ambitious data collection

process. The financial picture is largely provided with respect to 2008, as relatively good

figures for 2008 would have been available from schools when the interviews were

conducted, in March 2009. However, certain statistics relate to 2009 where this was practical.

For instance 2009 fees charged and 2009 enrolment patterns are reported on.

In an analysis of this nature it is crucial that the reliability of statistical findings be very

carefully assessed. No statistic provided in this report should be regarded as a perfectly

accurate reflection of the situation in schools. In this analysis, as in most policy analyses,

there are many reasons why a statistic may not be perfect. The fact that the data are from a

sample imply that the statistics are only approximations (though confidence intervals can be

statistically determined). Though the response rate in the survey was good, many of the

questions are complex (partly because the policy itself is complex) and respondents and/or

fieldworkers may not have understood the questions fully (though fieldworkers were

workshopped on the policy as part of their training). In some cases it is necessary to estimate

statistics for which the questionnaires were not specifically designed, using a combination of

responses, because these statistics are so important for the policy analysis.

The following cautionary measures can be regarded as necessary if one is to avoid misuse of

the statistics in discussing policy:

� One should keep confidence intervals in mind. It is necessary to be familiar with the kinds

of confidence intervals applicable to the statistics at the national level and at the

provincial level (what a confidence interval is and how it should be interpreted is

explained in the appendix). Confidence intervals for virtually all the questions, at the

national and provincial level, are provided in the question-by-question results in the

annexes. It would have been impractical to report confidence intervals for all the statistics

presented in this report. Occasionally they are made explicit, but mostly they are not. The

reader should bear in mind that just because confidence intervals are not made explicit

does not mean they do not exist. Importantly, where more variation in the responses can

be expected, one should expect wider confidence intervals. For instance, people’s

opinions are often associated with wide confidence intervals, because opinions are not

very predictable. On the other hand, statistics relating to something administrative, such

as the level of funding per learner within one quintile in one province, would normally

have narrower confidence intervals (unless of course, the funding is being distributed

unequally within the quintile, in contravention of the funding rules).

22

� One should keep in mind the definition of the statistic. Median values are used extensively

in the report, in particular where financial values per quintile and province are given. The

advantage with the median, or the mid-point, value is that it is not sensitive to strange

outlier values, which could simply be errors. Median values are useful indicators where

one does not expect much inequality, for instance within a provincial quintile. However,

median financial values cannot be used to calculate total funding, in the same way as

mean (or average) values can (by multiplying the mean by, say, the number of learners). It

is obviously important to keep in mind from which respondent a statistic is obtained.

Principals may be inclined to report differently to, say, teachers. Importantly, when a

statistic such as ‘80% of principals say X’ is referred to in this report, this is 80% of those

who gave some response. In other words, principals who did not provide any response are

ignored.

� One should be aware of how learner weights work. The statistics provided in this report

are mostly calculated using learner weights. This means that a statistic like ‘in 80% of the

system there is adequate funding’ means that 80% of learners are adequately funded. If all

schools were of an equal size, then 80% of schools would also be adequately funded.

However, schools are not equally sized. If small schools are more likely to be under-

funded then the percentage of schools that are adequately funded would be lower than

80%. And if small schools were more likely to be adequately funded, then the percentage

of schools that are adequately funded would be higher than 80%. The advantage with the

approach used in the report of treating learners as the main unit of analysis is that the

extent of, for instance, funding problems becomes clearer. Often in the report a phrase

such as ‘in 80% of schools’ actually means in 80% of learner-weighted schools. This

should always be clear from the context of the discussion.

� One should be especially careful with financial values. Financial values are more prone to

error than non-financial values in general. The number of learners in the school is a much

more difficult figure to get wrong than, for instance, the amount of spending on stationery

in 2008, to take an example. Just because a financial statistic in the report looks very

precise, as in say R3,294, does not mean that it is precise. The problem is exacerbated by

the fact that in the analysis there was no option but to use 2008 financial values with 2009

enrolment figures in order to calculate per learner financial values. The best financial

figures in the dataset are the 2008 ones, largely because in many schools financial

statements would have been finalised or almost finalised for that year, yet the figures

would be fresh in people’s minds. However, the best enrolment figures are those from

2009. 2008 enrolment figures were only collected for fee-charging schools, and even then

they were not grade-specific. An option would have been to link the dataset to 2008 EMIS

enrolment figures, but given the possibility that those enrolment values are inflated, it

seemed better to use enrolment figures from the following year collected by a fieldworker

(working, coincidentally, for the same firm that has conducted enrolment verification

visits to schools for a couple of years on behalf of the DoE).

23

4 Categorising schools by level of learner performance

This section explains how a link between the surveyed schools and the 2004 Systemic

Evaluation allowed statistics in this analysis to be reported according to the level of learner

performance of the school.

Given the critical importance of learner performance, a decision was made to stratify the

sample of schools according to whether a school had Grade 6 or not, and where schools did

have Grade 6 to use, as far as possible, schools that had taken part in the 2004 Systemic

Evaluation, which focussed on Grade 6. The 2004 Systemic Evaluation was based on a

random sample of around 1,000 schools drawn from a population of all public ordinary

schools offering Grade 6 with the exception of very small schools with fewer than 15 Grade 6

learners (Department of Education, 2005: 13). This would allow the analysis of funding and

management to draw some linkages with learner performance. The drawback of this approach

is that very small schools offering Grade 6 might be excluded, though small schools without

Grade 6 would not be excluded. 331 schools in the survey were 2004 Systemic Evaluation

schools. Small schools were rather well represented despite the exclusion factor related to the

Systemic Evaluation. 29 schools had fewer than 100 learners overall, and of these 17 had a

Grade 6 enrolment figure of between 1 and 14 learners. It is possible that this is the result of

changes in enrolment patterns since 2004, when the Systemic Evaluation was run.

The fact that there should be a five year gap between the 2004 Systemic Evaluation and the

2009 finance and management survey is obviously a drawback for the analysis. At the same

time, the literature indicates that learner performance trends change very slowly, suggesting

that test results from 2004 are, on average, likely to represent 2009 levels of performance

relatively well. Unfortunately it was not possible to use the 2007 Systemic Evaluation dataset

(which focuses on Grade 3 performance) as that dataset was not ready at the time.

The following graph illustrates the distribution of the school averages of the 2004 Systemic

Evaluation scores amongst the 331 schools in the 2009 survey that also participated in the

2004 wave of the Systemic Evaluation. Schools are weighted by their learners. The score for

each school is the weighted average of the school averages of the mathematics, science and

language scores. All science and mathematics scores were adjusted upwards so that the two

overall mean values for these two subjects became equal to the overall mean for language.

This way each of the three subjects carried an equal weight.

24

Figure 1: Distribution of school-level average scores

Note: In obtaining the composite scores the mathematics and science scores were weighted so that their two overall means across all schools became equal to the language mean.

The top 20 percentiles of the schooling system display an incline that is clearly different from

that in the rest of the system. It is partly this pattern that informs a breakdown used in the

analysis whereby the bottom 40 percentiles of learners (actually learner-weighted schools) are

considered a ‘lower scores’ group, the next 40 percentiles of learners are considered a ‘middle

scores’ group and the top 20 percentiles of learners are considered a ‘higher scores’ group. As

will be shown in this section, this three-way breakdown corresponds rather well to other

categorisations of schools that are sometimes made.

Table 2 provides a distribution of surveyed schools and weighed learners across the three

performance categories. Clearly, three provinces, GP, WC and NC are almost exclusively

situated in the top two categories. On the other hand, several provinces, notably LP and KN

are situated mostly in the bottom two categories. To a large extent this is due to the socio-

economic profiles of the profiles. The poorer and less educated the parents in a province are,

the worse one can expect their children to do at school, on average. Though good schooling

can weaken this pattern, it is a pattern that is found in schooling systems, to a greater or lesser

degree, everywhere.

Table 2: Scores and provinces

Lower scores Middle scores Higher scores

Schools Learners Schools Learners Schools Learners

EC 30 969,000 11 474,000 6 221,000 FS 10 103,000 10 126,000 9 65,000 GP 3 88,000 21 527,000 15 385,000 KN 23 741,000 11 612,000 3 123,000 LP 27 636,000 6 205,000 2 37,000 MP 9 150,000 18 304,000 7 103,000 NC 1 4,000 19 108,000 16 77,000 NW 15 116,000 11 169,000 7 124,000 WC 1 34,000 15 239,000 25 303,000

Total 119 2,842,000 122 2,764,000 90 1,437,000

25

The next table points to an important distinction. Historically African schools with a

homeland background (see the categories Bophuthatswana up to Venda), which are often rural

schools, do considerably worse on average than ex-DET schools, which are largely African

urban schools. Very few ex-homeland schools fall into the ‘higher scores’ category, whilst a

relatively low number of ex-DET schools fall into the ‘lower scores’ category. A substantial

number of ex-DET schools fall within the ‘higher scores’ group. Whilst historically white and

Indian schools are situated almost exclusively in the ‘higher scores’ group, historically

coloured schools are split fairly evenly across the middle and higher groups. What all this

means is that the ‘lower scores’ group is largely made up of historically African schools in ex-

homelands. This historical angle can assist in concretising the analysis and the search for

policy solutions. Part of the analytical challenge lies in identifying what defines the

exceptions, for instance the six ex-Transkei schools which fall into the ‘higher scores’ group.

Table 3: Scores and ex-department

Lower scores Middle scores Higher scores

Schools Learners Schools Learners Schools Learners

DET (African) 9 150,000 49 992,000 31 304,000 Bophuthatswana 16 127,000 8 71,000 3 25,000 Gazankulu 2 86,000 0 0 0 0 KaNgwane 4 71,000 10 172,000 1 10,000 KwaNdebele 1 21,000 0 0 0 0 KwaZulu 22 720,000 7 448,000 1 30,000 Lebowa 18 356,000 5 163,000 1 24,000 Qwa Qwa 5 35,000 0 0 1 10,000 Transkei 30 969,000 10 402,000 6 221,000 Venda 7 194,000 0 0 0 0 HOA (white) 0 0 0 0 11 105,000 HOD (Indian) 0 0 1 4,000 5 177,000 HOR (coloured) 0 0 26 294,000 18 225,000 Not known/not on list 3 78,000 2 115,000 11 302,000 Post-1994 school 2 34,000 4 103,000 0 0

Total 119 2,842,000 122 2,764,000 89 1,434,000

Table 4 largely confirms the points made about the previous table. The fieldworker was asked

to categorise the type of area the school is in. The great majority of schools in the ‘lower

scores’ group are rural village schools. Yet a third of the rural village schools are not ‘lower

scores’ schools.

Table 4: Scores and type of area

Lower scores Middle scores Higher scores

Schools Learners Schools Learners Schools Learners

Rural village 101 2,481,000 45 1,258,000 14 290,000 Farm 6 46,000 8 95,000 2 7,000 Town 1 10,000 10 106,000 33 352,000 Inner city 0 0 0 0 4 87,000 Suburb in a city or town 0 0 10 141,000 28 541,000 Township 9 180,000 46 1,036,000 8 156,000 Informal settlement 1 49,000 3 128,000 1 4,000

Total 118 2,766,000 122 2,764,000 90 1,437,000

The correspondence between the quintiles determined by the funding policy and learner

performance is fairly high. Most quintiles 1 and 2 schools are within the ‘lower scores’ group.

Most quintile 3 and 4 schools have ‘middle scores’. And most of quintile 5 is within the

‘higher scores’ group. There are, however, many exceptions.

26

Table 5: Scores and quintiles

Lower scores Middle scores Higher scores

Schools Learners Schools Learners Schools Learners

Q1 59 1,398,000 19 375,000 7 80,000 Q2 34 763,000 25 430,000 6 147,000 Q3 21 535,000 44 964,000 16 207,000 Q4 4 106,000 24 800,000 19 306,000 Q5 0 0 3 77,000 39 650,000

Total 118 2,802,000 115 2,645,000 87 1,390,000

Fees, a strong indicator of socio-economic status, correlate with learner performance, as seen

in Table 6. As pointed out in many empirical studies, this is not mainly because fees buy

better results, but because higher fees are an indication of a higher socio-economic status and

hence home background advantages for the learner. Again exceptions, such as schools with no

fees falling within the ‘higher scores’ group, are noteworthy.

Table 6: Scores and fees

Lower scores Middle scores Higher scores

Schools Learners Schools Learners Schools Learners

R0 102 2,367,000 70 1,296,000 24 369,000 R1-R100 12 405,000 34 872,000 1 10,000 R101-R500 3 52,000 17 580,000 18 182,000 R501-R3,000 0 0 0 0 16 300,000 >R3,001 1 15,000 0 0 31 575,000

Total 118 2,838,000 121 2,747,000 90 1,437,000

Table 7 classifies schools according to the largest race category amongst learners in the

school. Though the great majority of predominantly African schools are not in the ‘higher

scores’ group, the number of learners in predominantly African schools within the ‘higher

scores’ group is large enough to comprise around half of this group.

Table 7: Scores and race of learners

Lower scores Middle scores Higher scores

Schools Learners Schools Learners Schools Learners

Majority African 116 2,792,000 92 2,361,000 34 724,000 Majority coloured 0 0 26 247,000 28 261,000 Majority Indian 0 0 1 8,000 2 92,000 Majority white 0 0 0 0 19 241,000 Majority other 3 50,000 3 148,000 2 47,000 No majority 0 0 0 0 5 71,000

Total 119 2,842,000 122 2,764,000 90 1,437,000

Table 8 uses the predominant race of the teachers to categorise schools. The key difference

between this table and the previous one relates to the reduction in the ‘majority African’ and

‘majority white’ values within the ‘higher scores’ group. This is indicative of the fact that

many better performing schools where learners are predominantly African, have a teaching

staff which is predominantly white. This, in turn, is indicative of the apartheid legacy in

teacher training but also the difficulty in recent years in recruiting a sufficient number of

African youths into the post-apartheid teaching training system.

27

Table 8: Scores and race of educators

Lower scores Middle scores Higher scores

Schools Learners Schools Learners Schools Learners

Majority African 117 2,828,000 90 2,317,000 17 360,000 Majority coloured 0 0 29 289,000 25 263,000 Majority Indian 0 0 0 0 2 92,000 Majority white 0 0 0 0 41 606,000 Majority other 2 14,000 3 158,000 2 31,000 No majority 0 0 0 0 3 84,000

Total 119 2,842,000 122 2,764,000 90 1,437,000

The preceding tables deal only with the 331 schools for which a link to the Systemic

Evaluation 2004 results was possible. A regression model (with a relatively high explanatory

power reflected in an R2 value of 0.78) was used to impute likely Systemic Evaluation-like

scores for all the 525 schools of the survey. Table 9 provides the breakdown of the 525

schools. The patterns seen in the preceding tables would persist if all 525 schools were

included, largely due to the fact that variables such as quintile, learner race, and type of area

were used in the imputation process.

Table 9: Scores and provinces with all schools included

Lower scores Middle scores Higher scores

Schools Learners Schools Learners Schools Learners

EC 34 1,098,000 18 719,000 6 221,000 FS 21 222,000 24 310,000 13 124,000 GP 3 88,000 33 947,000 23 680,000 KN 34 1,208,000 19 1,118,000 6 400,000 LP 38 1,016,000 16 609,000 4 111,000 MP 19 332,000 31 571,000 8 131,000 NC 1 4,000 30 139,000 27 120,000 NW 22 162,000 27 441,000 9 163,000 WC 1 34,000 20 350,000 38 554,000

Total 173 4,164,000 218 5,205,000 134 2,504,000

28

5 Optimal levels of school funding

5.1 The funding levels in poor schools

Are poor schools being funded adequately?

The financial values in the survey dataset are not the outcome of a fully-fledged financial

audit, but rather a process within which respondents were allowed to provide rough estimates

where the timeframes (or availability of the relevant source documents) did not allow for

precise figures. Despite these limitations, drawing broad conclusions about the adequacy of

school funding, using data from schools themselves (as opposed to official provincial data,

which can present different problems), seems permissible.

It is not easy to determine what percentage of learners receive a school allocation that is at

least as high as their target. Estimates based on what schools receive as a transfer and as

Departmental purchases suggest that only 60% of learners are funded at the target level (Table

21). On the other hand, estimates based on what the Department promised to pay give a

higher level of 62% (Table 26). A large part of the problem is that provinces are not able to

fund the poorest quintile 1 and 2 learners at their higher target levels. Only between 45% and

65% of quintile 1 learners attain their targets (depending on which estimate one uses). If one

uses the quintile 3 target, the so-called no fee threshold, as one’s target, then the situation

improves – the funding of quintile 1 learners lies more or less between 65% and 85%.

However, only around 50% of quintile 3 levels attain this level. More is spent on learners in

the poor quintiles, but the pro-poor slope is not as steeply pro-poor as what the norms specify.

For instance, whilst the norms specify that quintile 1 should get six times as much as quintile

5, the ratio in reality is around 3.1 (see Table 20), and even the commitments reflect a ratio

that falls short of the policy, of 4.8. The proportion of the funding that comes in the form of a

transfer, as opposed to Departmental purchases on behalf of the school, is around two-thirds,

and this proportion is more or less consistent across the five quintiles (see Table 23).

The adequacy of funding can also be viewed from the perspective of resources consumed by

the school (as opposed to public revenues of schools). Overall, quintile 5 learners are three

times as well resourced as quintiles 1 to 4 learners with respect to the Rand value of resources

consumed (see Table 33 – this comparison essentially includes all public and private

resources except for Departmentally employed staff). The differences between the four

poorest quintiles are relatively small. The data suggest that pro-poor public funding is the

explanation behind above-average per learner spending on textbooks and classroom furniture

amongst poorer schools, suggesting that the public funding is being utilised for the right

things (relatively low school spending on textbooks in richer schools is probably the result of

the fact that parents themselves are expected to buy this item). One recurrent spending gap

that stands out is that poorer schools spend only one-tenth of what better off schools do on the

reproduction of documents. This is likely to have an impact on how the curriculum is

delivered.

The detrimental effect of the ‘backlogs problem’ on the implementation of the school funding

norms is examined. It is often said that physical infrastructure backlogs in poorer schools

force these schools to spend money intended for non-personnel recurrent items on improving

their physical infrastructure. The data suggest that this is indeed the case, but that a larger

problem is the fact that poorer schools feel compelled to spend money intended on non-

personnel recurrent items on personnel items (though this is not allowed according to the

funding norms). Non-personnel recurrent items are thus crowded out in poorer schools. In

poorer schools, the personnel spending is directed mainly towards non-educator pay, but there

is even some private employment of educators in these schools. The magnitude of this effect

is reflected in the percentage of learners in quintiles 1 and 2 attaining the allocation targets

after one has subtracted the cost of non-intended spending items. Whilst around 50% of

29

quintiles 1 and 2 learners reach the allocation targets if one looks at just revenue, the

percentage drops to around 30% if one takes into consideration what schools spend their

money on (Table 39). Similarly, the percentage of poor learners reaching the no fee threshold

drops from over 60% to around 50% (Table 40).

An analysis of the opinions of school principals and SGB parents regarding the adequacy of

funding, and actual funds received by each school, suggests there are critical thresholds that

need to be taken seriously. Specifically, a school allocation below R500 (in 2008) in schools

with little or no fee income is associated with a sense of exceptional frustration over the level

of funding, yet around one-third of no fee or low fee schools find themselves in this situation

(see Figure 7). Similarly, if the transfer does not reach R400 per learner there is a sense of

exceptional frustration (yet around half of the schools in question receive less than this

amount). An analysis of what principals say they would like to spend any additional funds on

confirms the importance of having a school allocation of at least R500. Schools below this

level are inclined to prioritise building maintenance above educational inputs, perhaps

because their existing allocation does not allow them to keep up with the maintenance of their

physical infrastructure.

Overall, what stands out is that between 30% and 40% of learners in quintiles 1 to 3 appear to

have been funded below the no fee threshold amount of R581 in 2008. This problem is

brought about by an uneven level of funding across provinces. Funding below around R500 is

associated with markedly higher levels of frustration amongst principals in poor schools,

meaning the no fee threshold seems a rather useful and safe barometer of a basic level of

adequacy. Getting all poor learners up to this level should be a priority. Part of the solution

seems to lie in ensuring that sufficient funds (perhaps at least R400) flow directly to schools

in the form of a transfer.

It is important to note that the recent school funding trend has on the whole been a good one.

Almost half of school principals say that there is more funding available now than five years

ago (Figure 6). Thus whilst there are problems, a sense that things are getting better is

widespread.

Some provincial details. The public funding situation seems particularly poor in NW, where

only 18% of learners attain their funding norms targets, and 21% attain the no fee threshold

(even committed funding by NW implies that only 41% of learners would attain their targets).

MP, whilst having healthier funding figures than NW, distributes an exceptionally low

proportion of its funding as a transfer, which could result in a situation where schools have

insufficient control over their resources (see Table 14). MP’s overall level of funding is

moreover low, whether one considers committed or actual funding. EC transfers an

exceptionally low amount (around R100) to its poorest schools (see Table 11 and Figure 30).

The fact that overall spending by seven of the nine provinces is equal to or exceeds slightly

what is required to fulfil the pro-poor targets suggests that attaining the spending targets for

the poor (at least in these seven provinces) is more a matter of redistributing existing funds

than increasing the overall budget. The provinces which seem to be clearly under-funded at

the aggregate level are MP and NW (Table 20).

Section 5.1 is large, and is thus divided into four sub-sections. It is large because it introduces

a large range of variables from the survey, many of which are re-examined in subsequent

sections. Moreover, though the policy focus of this section is on poor schools, data for all

schools are considered, partly because poverty and need must be viewed in the larger context.

The first sub-section deals with compliance with the allocation targets stipulated in the policy,

the second sub-section examines the adequacy of school funding from the perspective of

detailed expenditure data, the third sub-section examines the so-called backlogs problem and

30

the fourth sub-section looks at data on what school stakeholders think of the adequacy of

public funding.

5.1.1 Compliance with the allocation targets

How reliable are the financial data from the survey? This is a key question given that the

survey did not involve a rigorous audit with an independent examination of source

documents. School principals were encouraged to fill in the ten financial tables of the survey

beforehand using source documents but, as the following table indicates, only 30% of school

principals did this. Moreover, not all these school principals would have filled in every table.

In the case of 70% of schools, the financial tables would have been filled in when the

fieldworker visited the school, and if source documents were not readily available, the

principal (or someone else from the school) was encouraged to provide a rough estimate.

Table 10 indicates that mostly it was the principal who took responsibility for filling in the

financial data, that mostly the school respondent understood the financial tables with minimal

assistance from the fieldworker, and that mostly the principal was present during the

finalisation of the financial tables. All schools filled in at least some financial data.

Importantly, the Table 10 indicators do not vary greatly by category of school, suggesting that

the reliability of the data would not change greatly by type of school. All in all, the financial

statistics presented in this report need to be interpreted carefully, with a realisation that they

are not always precise. Having said that, the financial data do seem good enough to shed new

light on the funding patterns of schools. At a few critical points, financial aggregates from the

survey data are compared to provincial budget figures, and the correspondence seems

relatively good.

Table 10: Indicators of the reliability of the financial data

Lower scores

Middle scores

Higher scores Overall

Completed beforehand 28 27 38 30 Mostly principal completed 72 66 59 66 Little assistance from fieldworker 62 53 72 61 Principal present throughout 77 72 66 72

Note: Figures represent percentages of the 525 surveyed schools.

The financial survey form aimed to collect data for the years 2007, 2008 and 2009. Here it is

mainly the 2008 data that are analysed. Fieldworkers were asked to pay particular attention to

getting the 2008 figures right, even if that meant not collecting figures for the other two years.

This seemed logical given that many 2009 figures would not be available, and 2007 figures

might not be fresh in the memory of the respondents. To some extent 2007 values were used

to impute 2008 values, where 2008 values were missing. Where this happened, inflationary

increments were used.

In this sub-section, two resource streams from the Department to the school that are governed

by the funding norms are examined, namely the financial transfer and purchases of goods and

services by the Department on behalf of the school. The value of these streams is compared to

the targets in the policy. Moreover, the commitments formally communicated by provinces to

schools are compared to the target figures.

Of the 525 surveyed schools, the financial transfer situation could be determined for 505

schools after fairly minimal cleaning. Of these, 12 schools received no financial transfer in

2008. The 20 schools for which the transfer could not be determined were fairly randomly

spread across the provinces. The difference between the curves ‘Original’ and ‘Adjusted’ in

Figure 2 indicate that the effect of the cleaning was minimal. One problem in dealing with the

survey data was that certain figures could include Grade R whilst others might not. According

to the policy, schools are supposed to differentiate between Grade R finances and Grades 1 to

31

12 finances, but clearly many schools do not appear to do this. There were questions in the

financial survey asking whether the tables not dealing with Grade R (only one of the ten

tables specifically dealt with Grade R) did in fact include Grade R values. So it is possible to

gain some idea of the mixing, even though it is often not possible to know exactly which

items have a Grade R element. The curve ‘Adjusted*’ in Figure 2 indicates what the

distribution of financial transfer values looks like if one excludes the 149 schools which said

that their transfer value included funding for Grade R. The difference between ‘Adjusted’ and

‘Adjusted*’ is so small that it seemed safe to ignore the Grade R mixing problem and to

consider the values indicated by ‘Adjusted’ to represent just Grades 1 to 12 (the funding of

Grade R is dealt with separately in section 5.4 below).

Figure 2: Distribution of 2008 per learner transfer

Note: Learner weights used.

The above graph also includes the funding levels specified by the funding norms. That there

should be a gap between the transfer and the policy-stipulated levels should not surprise as

given the official school allocation may include transfer plus purchases by the Department.

One consideration was whether hostel expenditure had ‘contaminated’ the financial data.

Schools were asked not to include hostel information in the survey. (The sample frame was

not suited for examining hostel issues.) The 35 schools which said they had hostels did not

have exceptionally high financial values, so it was assumed that there was no serious problem

in this regard.

The next graph provides ‘Adjusted’ from the previous graph by province. The graph is useful

for identifying provinces that display unusual patterns. MP has a distribution that is clearly

much lower than that of the other provinces (though at the left-hand side of the graph it is

similar to EC). LP and NW tend not to pay more than around R500 per learner at the high

end. EC has as interesting profile. Whilst it does appear to provide generous transfers at the

high end, almost half of the system in EC is receiving very low transfers of R100 per learner

and less.

32

Figure 3: Distribution of 2008 per learner transfer by province

Note: Learner weights used.

The next table provides figures for the three performance categories. Though nationally the

pattern is clearly pro-poor and pro those schools with the lower scores, a clear departure from

this pattern is seen in EC where it is the poorest who receive least in the form of a financial

transfer. EC is also the province with the highest percentage of learners receiving no transfer

at all – 9% against a national average of 2%. It is important to bear in mind that the majority

of learners in EC fall within the ‘lower scores’ category – see Table 9.

Table 11: Financial transfer details by province

Median per learner transfer (2008) % with zero

% <R50 per

learner

Estimate of total

cost (Rm) Lower scores

Middle scores

Higher scores

EC 114 560 522 9 12 703 FS 729 487 318 2 3 289 GP 495 558 168 2 2 738 KN 630 183 398 0 3 1,045 LP 384 319 290 2 2 601 MP 255 98 41 0 17 173 NC 586 576 493 6 6 115 NW 314 293 279 0 5 244 WC 627 365 387 0 9 374

SA 388 352 290 2 6 4,282 Note: Learner weights used. The calculation of median values includes schools with a zero transfer.

The last column of Table 11 weights the total funds transferred by the learner weight and

arrives at a national total of about R4.3bn in 2008. This is based on the 95% of learner-

weighted schools for which reliable transfer data were available. The next table indicates that

the R4.3bn figure is lower than the official expenditure figure by 12%. Around 5% would be

accounted for by the survey schools excluded from the estimate, leaving a variance of around

33

7% attributable to sample effects, data reliability and possibly alignment problems between

the provincial financial year (April to March) and the school’s financial year (January to

December). Moreover, 15 schools which had a per learner transfer of over R1,000 were

truncated to the R1,000 level as levels higher than R1,000 seem unrealistically high. The

variance can be considered remarkably low. Yet Table 12 underlines the need to interpret the

survey figures with caution. This is especially so in the case of NC, where the survey level is

40% below the official level.

Table 12: Transfer figures from survey and budget statements

Estimate from survey

(Rm)

Actual 2008/09

values (Rm) Survey /

Actual

EC 703 652 1.08 FS 289 366 0.79 GP 738 901 0.82 KN 1,045 1,017 1.03 LP 601 764 0.79 MP 173 182 0.95 NC 115 191 0.60 NW 244 307 0.79 WC 374 471 0.79

SA 4,282 4,852 0.88

Source: Provincial budget statements published in 2009 used for middle column.

Before transfer values by official school quintile are examined, it is necessary to assess how

the surveyed schools are spread across province and quintile in order to determine where we

can expect reliability problems due to an insufficient number of schools. Table 13 indicates

that there are a number of province-quintile cells where there are zero schools, or very few

schools. Insofar as we can expect provinces to fund schools within the same quintile similarly,

it is not necessary to have very large numbers of schools. But clearly, if there are only, say,

two schools the risk is high that one will obtain a figure that erroneous.

Table 13: Surveyed schools by province and quintile

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Total

EC 26 18 13 1 0 58 FS 20 9 16 6 6 57 GP 1 4 20 21 12 58 KN 19 9 12 9 6 55 LP 19 22 12 2 1 56 MP 10 16 8 9 9 52 NC 2 15 16 13 11 57 NW 20 9 21 2 4 56 WC 10 4 11 16 15 56

SA 127 106 129 79 64 505

The above table begs the question as to why there are not an equal number of schools per

quintile. There are a number of reasons why one would not expect this. Firstly, the national

quintiles are weighted differently within each province according to the school funding

norms. Secondly, though quintiles are weighted equally at the national level in the norms, the

way provinces actually implement school quintiles, and discrepancies between the historical

statistics that inform the ‘poverty table’ of the norms and actual enrolment numbers have been

shown to result in unequally sized quintiles in terms of learners even at the national level.

Lastly, school sizes are not equal across quintiles.

34

Table 14: Financial transfer details by province and quintile

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall mean

Target

EC 463 477 9 392 604 FS 763 165 350 318 247 455 554 GP 495 741 581 463 158 457 458 KN 704 663 388 346 161 402 555 LP 385 384 271 99 80 346 621 MP 274 255 100 72 37 169 541 NC 576 650 608 505 410 492 538 NW 439 293 252 78 264 318 555 WC 766 627 480 392 204 422 394

SA 416 421 316 370 158 379

Target 775 711 581 388 129 517

Note: Quintile values are median values, with learner weights used. Median values include schools with zero. Values in bold equal or exceed the target values. The target values are from Government Notice 869 of 2006.

The above median per learner transfer values come from the 486 schools for which there was

both a transfer value and a reliable quintile value. Perhaps the most striking thing that

emerges from this table is that the less poor the quintile, the more likely it is for the transfer

figure on its own (without Departmental purchases) to exceed the target values in the norms.

The pro-poorness of the distribution at the national level is very clear between quintile 5 and

the other four quintiles, but barely noticeable within the group of quintiles 1 to 4 schools.

We now turn to the second component of the school allocation, namely Departmental

purchases on behalf of the school. The value of these purchases is far more difficult to assess

than that of the transfer. Though the Department is supposed to indicate to schools the value

of the purchases it has made, this does not always occur, and even where it does the figures

might not be as directly accessible as figures relating to the school’s own income and

expenditure, which the school must by law maintain.

A critical table in the financial survey form is one where schools indicate, firstly, whether

they received one of 24 items from the Department in the period 2007-2008, secondly,

whether the Department ever told them the value of these goods and services and, thirdly, the

estimated or actual value of the goods and services received during 2008. (The reason why the

first question covers both 2007 and 2008 is that certain items might not be delivered every

year and important resourcing patterns could be missed if only one year was covered.) Table

15 describes what information was available. The ‘Schools with delivery’ column is based on

responses from 356 schools which indicated that at least one item was delivered. The average

number of items with a ‘Yes’ per school amongst the 356 schools was 5.2 items. Clearly the

most common items in order of school count are (a) textbooks, (g) stationery for learners, (w)

school feeding, (b) other learning support materials and (h) building maintenance and repairs.

The resource categories used in the survey questionnaire are from the school funding norms.

A rather large number of schools were told the monetary value of purchases by the

Department. In 43% of instances where there was a ‘Yes’ in the first column, there was also a

‘Yes’ in the second column. The top per learner prices, starting from the top, are associated

with (k) refurbishment of buildings and new buildings, (w) school feeding, (a) textbooks, (g)

stationery for learners and (p) rates and taxes. About 38% of ‘Yes’ responses in the first

column are accompanied by a price in third column. Overall, the response rate seemed good

enough to allow for an imputation of missing values.

35

Table 15: Information available on Departmental purchases

Schools

with delivery

Schools told the

price

Median per

learner price

a. Textbooks 259 122 169

b. Other Learning Support Materials (e.g. wallcharts, science laboratory materials)

133 56 53

c. All reproduction costs, including photocopier contracts, photocopier paper, toner, etc.

76 42 38

d. Office consumables (other than reproduction costs) 72 n/a 18

e. Computers and related items 91 28 16

f. Sports 39 14 12

g. Stationery for learners 202 82 81

h. Building maintenance and repairs 118 74 56

i. Cleaning materials 80 40 18

j. Classroom furniture 95 31 47

k. Refurbishment of buildings and new buildings 56 28 231

l. Telephone 55 40 12

m. Internet connection 44 22 5

n. Electricity 84 46 46

o. Water 58 26 17

p. Rates and taxes 31 6 62

q. Security services 31 17 9

r. Catering 31 17 4

s. Professional services (e.g. auditors, bookkeepers) 32 23 5

t. Scholar transport 33 7 32

u. School trips 15 7 3

v. Transport for workshops and meetings 39 9 6

w. School feeding 135 48 199

x. Any other expenditure 19 16 74

68 schools indicated that they mixed Grade R purchases with the Grades 1 to 12 purchases. It

was impossible to clean this aspect of the data, but it was hoped that the Grade R factor had a

similarly small impact to the one noted earlier with respect to the financial transfers.

An analysis of five different responses within the survey (apart from the Table 15 responses)

indicated that 392 schools actually received Departmental purchases in 2008 (even if not all

of them provided the Table 15 statistics), that 70 schools were told by the Department they

would receive goods and services but did not provide any indication that they received

anything (this could of course simply be a reporting omission), and that 63 schools did not

receive any goods and services, nor did the Department say they would. This breakdown

formed the basis for the calculation of four different estimates of the value of Departmental

purchases in 2008. The estimates are as follows:

� Estimate A: This is Departmental purchases where schools provided an exact or estimated

price for these purchases. There were 218 schools which did this.

� Estimate B: This is estimate A plus imputed price values for schools which said they

received Departmentally purchased goods and services but did not price them. The

imputation would be based on the median estimate A price values (in per learner terms)

by province and quintile. Province and quintile would be the obvious factors by which

departmental purchases would differ. Where prices by province and quintile were not

available a conservative approach that erred on under-estimating expenditure was used.

Specifically, if a price was not available for the quintile and province in question, a price

from the closest higher quintile was used. Given that higher (i.e. less poor) quintiles tend

to receive fewer resources this would tend to result in an under-estimate. At no point were

figures for one province imputed from those of another province. This was in recognition

36

of the fact that provincial practices could differ substantially. Estimate B would thus add

prices to individual items amongst the 218 schools where prices were missing for these

items, as well as to all items amongst the 138 schools (356 minus 218) which said they

received goods and services but indicated no prices at all.

� Estimate C: This is estimate B plus imputed price values for those schools which did not

indicate receipt of specific goods and services from the Department in the Table 15

format, but which judging from responses to other questions did receive this (for instance

they said goods were delivered on time). There were 36 schools that fell into this

category. The approach was to use the trimmed mean spending levels per Table 15 item,

province and quintile, amongst the 356 estimate B schools, to impute the value of goods

received for the 36 schools.

� Estimate D: This is estimate C plus imputed prices values for those schools, 70 of them,

which indicated in the financial questionnaire that the Department committed to

delivering goods and services to the school, but which did not indicate in the

questionnaire that anything was delivered to the school. One of two problems could be

occurring. Either the school did not fill in the relevant questions relating to goods

received, or the school did not receive anything (despite the fact that the Department

committed itself to this).

One assumes that the truth would lie somewhere between estimate C and estimate D. At the

same time, it is useful to keep an eye on estimates A and B in order to see how much

imputation occurred. The following graph illustrates the distribution of per learner amounts

for the 510 schools where it seemed one could be sufficiently sure that one had a fairly

reliable overall value for Departmental purchases. As one should expect, judging from the

number of schools, the largest difference is that between estimates A and B. For the purposes

of the graph any overall value for a school that exceeded R1,000 was truncated at R1,000

given that in the original group of schools virtually no school exceeded this level overall.

Figure 4: Imputed Departmental purchases

Note: Learner weights used.

37

What is the correspondence between the values obtained for Departmental purchases and

provincial expenditure statements? Table 16 provides a few comparisons against the official

budget programme 2 expenditure figures. A comparison of all non-personnel spending

excluding transfers from programme 2 of all the provinces yields a considerably higher figure

than the corresponding weighted values from the survey, even if one does not truncate to

R1,000 per learner as is done in Figure 4. The gap is reduced if one excludes capital spending

(in the survey this would be the item ‘Refurbishment of buildings and new buildings’). And if

one excludes the nutrition programme from the programme 2 figure and excludes ‘school

feeding’ from the survey figures, one gets an even closer correspondence (if one does not

truncate the survey values). One might expect both capital spending by the Department and

school nutrition delivered by the Department to be under-estimated in the survey as the

Department is not under an obligation to report these spending figures to schools (though the

funding norms allow the use of the school allocation for ‘food’, school nutrition proper falls

under a completely separate programme). Does the third line of Table 16 suggest that the

survey data can be relied on? It is very difficult to tell. Despite the closeness of the statistics to

the R6.0bn, this could be a coincidence, especially given all the risks associated with the

values from the survey. In particular, it is suspicious that one should arrive at ‘good’ figures

from the survey by not truncating to R1,000 per learner when the schools that did fill in the

required information virtually never exceeded this level.

Table 16: Department purchase figures from survey and budget statements

Programme

2 figure

Estimate C without truncation

Estimate C with

truncation

Estimate D without truncation

Estimate D with

truncation

All non-personnel without transfers 11.5 8.1 4.1 8.7 4.7 Above minus capital 7.9 6.3 3.8 6.8 4.3 Above minus nutrition programme 5.9 5.6 3.4 6.1 3.9 Only school allocation items ? 3.9 2.1 4.3 2.5 Note: All figures are billions of Rand. Provincial budget statements published in 2009 were used.

The fourth line subtracts two more items from the survey totals: new textbooks associated

with the introduction of the new curriculum and scholar transport. It is possible to estimate

what part of the item ‘textbooks’ in Table 15 corresponds to new curriculum textbooks and

what part corresponds to ‘replacement textbooks’, or textbooks that are supplied to replace

existing ones (these replacement textbooks may be counted within the school allocation). A

separate table in the survey requests information in this regard. Overall, 21% of textbook

purchases by the Department seem to be purchases of replacement textbooks, the rest are

associated with the introduction of the new curriculum. The fourth line then represents the

value of purchases that may be counted within the school allocation. This value is not

published in the Provincial Budget Statements. In the fourth line there is also an enormous

difference between the values with and without truncation to R1,000 per learner. It seems

very unlikely that a large amount of money would be going towards purchasing more than

R1,000 worth of goods and services per learner in some schools when this would be well

above the highest school allocation target. In the end it was decided to use estimate D with

truncation in the tables that follow, even if the total value of goods and services would only

reach R2.5bn. Later in this section when the survey data relating to Departmental

commitments are analysed it will appear that this choice yields figures that are probably the

closest to the truth.

The next table provides a provincial breakdown of the R2.5bn. At the national level there

seems to be a rather clear bias in favour the poor. The interpretation of the provincial figures

should be guided by how many schools are covered in each cell – see Table 9 above. In

particular, the zero values can be ignored.

38

Table 17: Departmental purchase details by province

Median per learner purchases (2008) % with zero

% <R50 per

learner

Estimate of total

cost (Rm) Lower scores

Middle scores

Higher scores

EC 213 316 309 25 25 467 FS 190 371 13 17 20 165 GP 347 187 52 28 29 264 KN 17 153 0 43 43 378 LP 255 28 206 5 7 475 MP 216 205 120 3 4 310 NC 0 201 285 25 26 65 NW 225 132 59 15 15 88 WC 2 225 171 20 21 222

SA 216 187 79 23 24 2,434

Note: Learner weights used. The calculation of median values excludes schools with zero purchases by the Department. Note that the non-funding norms items school feeding, scholar transport, new textbooks and refurbished or new buildings are not included in the above figures.

Table 18, which must also be read with much caution, seems to suggest that as with the

transfer, quintile 5 receives considerably less, but that the other four quintiles, whilst they are

funded at a higher level, do not display a strong pro-poor distribution.

Table 18: Departmental purchase details by province and quintile

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall mean Target

EC 213 388 316 0 258 604 FS 124 310 371 276 0 282 554 GP 347 409 187 147 52 169 458 KN 297 0 153 338 45 148 555 LP 204 255 19 22 364 288 621 MP 216 677 118 195 317 328 541 NC 49 199 471 201 62 279 538 NW 116 154 132 51 51 135 555 WC 62 494 235 211 171 256 394

SA 213 310 184 148 52 224

Target 775 711 581 388 129 517 Note: Quintile values are median values with schools with zero purchases included, with learner weights used. Note that the non-funding norms items school feeding, scholar transport, new textbooks and refurbished or new buildings are not included in the above figures.

The next table provides the median values for the sum of the transfer and the Departmental

purchases. It also indicates that virtually no learners are not funded through at least one of the

two streams.

39

Table 19: Total Department spending details by province (I)

Median per learner purchases (2008) % with zero

% <R50 per

learner

Estimate of total

cost (Rm) Lower scores

Middle scores

Higher scores

EC 545 711 801 0 2 1,157 FS 727 887 344 0 1 436 GP 842 696 290 0 0 914 KN 722 399 386 0 0 1,418 LP 679 368 447 0 0 1,084 MP 491 359 216 0 0 475 NC 586 664 510 0 0 161 NW 449 390 352 0 0 332 WC 629 623 588 0 2 554

SA 683 535 394 0 0 6,530

Note: Learner weights used. Note that the non-funding norms items school feeding, scholar transport, new textbooks and refurbished or new buildings are not included in the above figures.

Table 20 breaks the above figures down by quintile. It seems encouraging that six of the nine

provinces attain an overall mean that exceeds the provincial target. This should make it

possible to attain the targets in each of the five quintiles on condition that funds are correctly

distributed across the quintiles. It is noteworthy that when the transfer and Departmental

purchase values are brought together, a far more pro-poor national distribution of funding

becomes evident than if each of the two are dealt with separately. In fact, three distinct

funding levels appear: one for quintiles 1 and 2 of around R700 (almost the target), another

for quintiles 3 and 4 of around R500, and a third one of around R200 in quintile 5.

Table 20: Total Department spending details by province and quintile (I)

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall mean Target

EC 692 779 364 645 604 FS 803 676 700 787 241 707 554 GP 842 1,104 768 529 175 578 458 KN 868 683 499 414 206 554 555 LP 655 678 319 124 445 638 621 MP 491 818 229 270 359 491 541 NC 625 716 1,004 641 423 701 538 NW 544 373 404 129 302 440 555 WC 825 629 738 626 299 653 394

SA 711 711 481 474 228 591

Target 775 711 581 388 129 517

Note: Learner weights used. Quintile values are median values. Note that the non-funding norms items school feeding, scholar transport, new textbooks and refurbished or new buildings are not included in the above figures.

Whilst the national distribution in Table 20 seems clearly pro-poor, it is not as pro-poor as

what the funding norms require. The targets imply a ratio of 6.1 between quintile 5 and

quintile 1, the actual ratio is 3.1, mainly because quintile 5 appears well funded relative to its

target.

If the figures in the previous two tables were right, 59% of learners would be funded at or

above their target amount, as indicated in the next table. The situation is NW seems

exceptionally bad. The situation in EC, LP and MP also seems problematic. The correlation

between the last column of Table 21 and the provincial targets is negative, meaning that

provinces with lower targets find it easier to attain their targets. This is to be expected.

40

Table 21: Percentage of learners attaining the allocation target (I)

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 42 65 34 100 50 FS 59 46 70 69 91 64 GP 100 100 85 87 100 91 KN 58 43 45 90 92 64 LP 38 38 39 0 100 38 MP 36 63 0 23 98 50 NC 0 58 86 78 86 75 NW 7 5 8 30 90 18 WC 81 47 90 75 92 81

SA 45 51 48 75 95 59

Note: Learner weights used.

Table 22 indicates what percentage of learners attain the no fee threshold level of R581.

Table 22: Percentage of learners attaining the no fee threshold (I)

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 55 76 34 100 59 FS 72 56 70 69 0 62 GP 100 100 85 40 9 52 KN 84 75 45 22 0 49 LP 57 73 39 0 0 54 MP 36 63 0 0 0 27 NC 95 93 86 68 13 69 NW 41 20 8 30 30 21 WC 100 100 90 57 23 63

SA 63 72 48 36 9 50

Note: Learner weights used.

Finally, Table 23 indicates what percentage of the school allocation comes to the school in the

form of a financial transfer. The situation seems rather consistent across provinces and across

quintiles. Across the system it appears as if a fairly constant percentage of the school

allocation is transferred to schools. The striking exception is MP, where, as has been noted

above, the transfer is exceptionally low.

Table 23: Percentage of allocation coming as a transfer

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 62 49 41 0 54 FS 78 50 51 40 100 62 GP 59 67 78 76 85 77 KN 66 97 72 55 82 72 LP 68 66 66 84 18 67 MP 49 41 53 37 19 39 NC 93 81 50 79 84 73 NW 78 62 69 68 76 70 WC 87 60 68 60 73 67

SA 66 63 64 62 70 65

Note: Learner weights used. Statistics are the medians of the school-specific percentages.

So far, data on what schools actually receive has been examined. The survey data also allow

one to examine what schools have been promised. As will be seen, these data are largely in

line with the data on what is actually received. 387 schools have the required data on what the

Department promised to spend, either in the form of a transfer or through Departmental

purchases, in 2008. Table 24 provides a reworked version of Table 19, using the data on

funding promised. What is striking is that at the national level the commitments favour the

41

schools with the higher scores as opposed to those with the lower scores (this is unlike the

pattern seen in Table 19).

Table 24: Total Department spending details by province (II)

Median per learner purchases (2008) % with zero

% <R50 per

learner

Estimate of total

cost (Rm) Lower scores

Middle scores

Higher scores

EC 606 1,674 811 0 0 991 FS 681 354 685 2 2 312 GP 772 161 490 5 5 695 KN 472 392 690 0 0 1,369 LP 592 401 747 2 2 1,071 MP 294 172 304 2 2 331 NC 947 638 812 3 3 114 NW 600 498 591 0 0 401 WC 417 378 391 9 12 265

SA 580 391 644 2 3 5,549

If the total cost estimate in the above table is inflated to cover the entire survey, the resultant

figure is R7.3bn, which is not that different from the R6.5bn seen in Table 19. This seems to

support the methodology used earlier for obtaining the value of actual Departmental

purchases.

Table 25 suggests that at least as far as the commitments were concerned, there were in fact

five distinct levels of funding across the five quintiles, and not three as suggested by Table 20.

It moreover indicates that as far as commitments were concerned, there was a rather strong

adherence to the targets in the policy, even though the ratio of quintile 1 to quintile 5 funding

still falls short of the policy – it is 4.8 instead of the stipulated 6.1.

Table 25: Total Department spending details by province and quintile (II)

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall mean Target

EC 825 826 482 787 604 FS 789 734 547 347 245 654 554 GP 902 574 894 479 158 577 458 KN 870 704 791 418 294 719 555 LP 807 775 572 487 132 717 621 MP 633 660 291 195 130 392 541 NC 929 1,108 812 394 563 820 538 NW 633 692 488 1,244 476 647 555 WC 1,381 138 516 415 177 507 394

SA 807 754 580 409 170 639

Target 775 711 581 388 129 517

If we compare Table 26 to Table 21, whilst the overall percentage of learners attaining the

target remains at around 60%, Table 26 indicates a somewhat greater commitment to the

poorest quintiles. For instance, here 65% and not 45% of quintile 1 learners attain the target.

Given the various reasons why the data may not be a precise reflection of reality, it is

important to consider how the Table 26 statistics would change if one adjusted the funding

values for all schools slightly. If one artificially made the funding levels 5% higher than they

really are, then 68% of learners (instead of 62%) would be funded at or above their target

amount. And if one made the funding levels 10% higher, then the figure of adequately funded

learners would be 75%. It seems reasonable to conclude that the Table 26 values may be

under-estimates, but almost certainly not by more than about 13 percentage points. Of course,

the Table 26 values could also be over-estimates. If one drops the funding levels by 10% only

41% of learners are adequately funded. However, given how questionnaires were structured

42

and the funding system works, it is more likely that funding values will be under-estimates

than over-estimates.

Table 26: Percentage of learners attaining the allocation target (II)

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 80 77 27 0 0 65 FS 45 69 51 29 100 54 GP 100 48 100 82 100 90 KN 87 45 85 84 100 81 LP 60 66 19 11 100 49 MP 19 28 0 0 60 23 NC 100 79 80 56 100 80 NW 22 23 36 100 100 41 WC 33 0 41 71 60 55

SA 65 56 49 67 87 62

Note: Learner weights used.

If one uses the no fee threshold of R581 as one’s target, then the overall quintile 1 and quintile

2 values in the above table become 87% and 81% as seen in the following table. However, if

one counts quintiles 1 to 3 jointly, the percentage is just 70%. A somewhat lower target of

R500 would yield attainment percentages for these two quintiles of 91% and 84%.

Table 27: Percentage of learners attaining the no fee threshold (II)

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 90 93 27 0 0 74 FS 79 100 51 0 0 59 GP 100 48 100 21 0 42 KN 96 88 85 6 0 52 LP 95 92 19 11 0 65 MP 57 49 0 0 0 21 NC 100 90 80 39 43 70 NW 68 65 36 30 33 47 WC 33 0 41 11 0 18

SA 87 81 49 12 4 51

Note: Learner weights used.

Are the discrepancies between the committed funding and actual funding a function of the

data issues (both the methodology for the actual Departmental purchases and having only 387

schools with commitments data), or is it perhaps true that commitments exceed actual

spending in certain schools, in particular quintiles 1 and 2 schools? Statistics from the next

table suggest that the latter could be true. Around 20% of historically disadvantaged schools

complain that either goods arrive late or not at all. On the other hand, problems with the

transfer are more serious for more advantaged schools.

Table 28: Extent of non-arrival of funds and goods

Lower scores

Middle scores

Higher scores Overall

% of schools indicating that not all of the 2008 transfer arrived

0 3 4 2

% of schools reporting serious delivery problems 21 20 13 18 Note: Learner weights used.

5.1.2 Adequacy in terms of expenditure patterns

School expenditure data in the survey allow one to examine what specific items schools are

spending their money on. This, in combination with the details on Departmental purchases

43

mentioned earlier, permits an overall picture of what resources schools have to deliver

education. Knowing what resources are bought for the available funding can assist in the

assessment of the adequacy of public funding for the poor.

402 schools had detailed data on 2008 expenditure, whilst for a further 85 schools it was

possible to impute 2008 figures using available 2007 figures (an inflationary increment was

applied). This meant that expenditure data were available for 487 schools. Table 29 indicates

the spread of information across expenditure items.

Table 29: Information available on school expenditure

Number of schools with

non-zero figure

Median per learner

expenditure

a. Textbooks 240 128

b. Other Learning Support Materials (e.g. wallcharts, science laboratory materials)

204 44

c. All reproduction costs, including photocopier contracts, photocopier paper, toner, etc.

297 31

d. Office consumables (other than reproduction costs) 220 40

e. Computers and related items 209 17

f. Sports 330 20

g. Stationery for learners 272 16

h. Building maintenance and repairs 397 61

i. Cleaning materials 364 49

j. Classroom furniture 99 14

k. Refurbishment of buildings and new buildings 99 25

l. Telephone 351 54

m. Internet connection 80 23

n. Electricity 327 6

o. Water 125 26

p. Rates and taxes 77 23

q. Security services 273 11

r. Catering 324 13

s. Professional services (e.g. auditors, bookkeepers) 371 11

t. Scholar transport 97 5

u. School trips 226 20

v. Transport for workshops and meetings 291 25

w. School feeding 121 18

x. Examination fees 14 35

y. Educator salaries 197 10

z. Non-educator salaries 197 133

aa. Any other expenditure 249 43

141 schools indicated that they had mixed Grade R and Grades 1 to 12 expenditure in the

same figures (this is of course quite understandable). It was impossible to make an adjustment

to deal with this. The median values actually rose when schools which said they included

spending on Grade R were removed, though this should not surprise one as this could be the

result of higher school fee revenue in secondary schools.

One can assume that virtually all schools spend money. 523 schools in the survey said they

had bank accounts, which suggests that the 38 schools which did not provide expenditure data

simply omitted to report on this. This was confirmed by an examination of a few other

variables. As the next table indicates, the spread of missing expenditure figures is rather even

across the system.

44

Table 30: Percentage of schools with expenditure data

Lower scores

Middle scores

Higher scores

Overall

EC 90 75 83 85 FS 90 99 96 95 GP 100 90 90 90 KN 81 71 100 80 LP 100 93 75 96 MP 96 96 100 96 NC 100 98 93 96 NW 100 90 100 95 WC 100 100 100 100

SA 93 91 95 93

Two key policy questions with respect to the expenditure data stand out. (1) Are there any

obvious gaps in the expenditure profiles of (in particular poor) schools? (2) Do all schools,

including those that are assumed to be capable of supplementing low public subsidies with fee

income, reach a reasonable level of overall per learner spending?

Firstly, obvious gaps in the expenditure patterns are investigated. Table 31 provides

expenditure by the school itself, based on the data of the 487 schools which filled in the

relevant table. There are a few noteworthy gaps, like the fact that only two-thirds of quintile 5

schools reported spending money on office consumables. One would have expected 100%

here. This could be the result of an omission, or the school may have included the item within

another item, such as ‘reproduction costs’ due to the structure of its financial statements.

Despite these types of problems, the Table 31 non-zero median statistics seem to provide a

useful basis for assessing what schools spend their money on. Quintiles 1 to 3 appear to be

rather homogenous in terms of their spending patterns, with one notable exception. Quintile 1

schools appear to spend an exceptionally large amount on textbooks. However, an

examination of the confidence intervals suggests that it is not possible to conclude with great

certainty that this difference is real. There is a similarly low certainty around whether quintile

5 schools in fact spend less on textbooks than the other quintiles. Statistics indicating that

quintile 5 parents spend considerably more on textbooks (see section 5.2) could suggest that

household spending displaces school expenditure with respect to textbooks, thus supporting

the possibility that school expenditure on textbooks would be exceptionally low in quintile 5.

It is noteworthy that personnel spending by the school is rather high in poorer schools. The

school funding norms are based on the assumption that poorer schools do not need to spend

money on personnel. In fact, unless this is done using fee revenue, it is prohibited. Yet in

quintiles 1 to 3 spending on personnel comes to around one-tenth of what it is in the ‘fee-rich’

quintile 5 schools. The crowding out of non-personnel spending by personnel spending in

poorer schools receives attention in section 5.1.3.

Table 32 includes just Departmental purchases. The reason why the totals are higher here than

in Table 18 is, firstly, that items not associated with the school allocation are included and,

secondly, that non-zero observations are excluded from the calculation of the median. Again it

seems as if spending on textbooks may be higher in quintile 1. Spending on classroom

furniture for quintiles 1 and 2 is considerably higher than in the other quintiles. Scholar

transport and school feeding is higher in the poorest three quintiles.

Finally Table 33 provides the combination of expenditure by the school and purchases by the

Department. All textbooks bought by the school were considered to be replacement textbooks,

and not textbooks needed for the implementation of the new curriculum – there was no way of

testing the validity of this assumption. Overall spending on each quintile 5 learner is around

three times what it is in the other quintiles, and the differences between quintiles 1 to 4 seem

insignificant. .

45

Table 31: School expenditure by item

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Mean % R % R % R % R % R

a. Textbooks 43 209 32 119 39 121 54 118 79 93 79

b. Other Learning Support Materials (e.g. wallcharts, science laboratory materials) 37 14 41 19 45 25 57 40 60 32 30

c. All reproduction costs, including photocopier contracts, photocopier paper, toner, etc. 55 27 49 15 67 21 69 46 87 108 41

d. Office consumables (other than reproduction costs) 49 12 42 21 44 11 53 6 67 27 18

e. Computers and related items 36 20 43 14 44 9 59 13 66 66 21

f. Sports 64 13 66 15 71 11 80 12 86 49 27

g. Stationery for learners 51 67 59 72 55 54 67 63 65 73 82

h. Building maintenance and repairs 77 41 82 39 86 25 77 33 89 144 67

i. Cleaning materials 75 9 69 10 77 8 81 22 81 30 17

j. Classroom furniture 18 57 25 20 27 78 26 8 26 31 11

k. Refurbishment of buildings and new buildings 28 22 24 91 29 61 29 51 31 56 31

l. Telephone 58 4 51 8 75 12 94 15 91 58 18

m. Internet connection 6 2 5 4 14 3 19 3 35 8 1

n. Electricity 68 7 63 6 71 8 70 30 77 100 35

o. Water 16 11 18 10 31 6 31 36 33 104 10

p. Rates and taxes 16 9 8 3 15 7 20 5 17 20 5

q. Security services 58 15 53 15 60 17 75 14 80 56 21

r. Catering 76 10 60 8 75 7 72 9 72 18 12

s. Professional services (e.g. auditors, bookkeepers) 72 4 62 5 78 4 89 3 87 6 5

t. Scholar transport 11 12 17 17 27 10 14 9 34 68 16

u. School trips 44 19 53 19 59 20 40 16 65 25 18

v. Transport for workshops and meetings 59 24 58 18 63 12 67 11 64 15 14

w. School feeding 10 39 19 23 19 17 23 3 14 6 11

x. Examination fees 4 36 3 3 4 1 7 1 1 63 1

y. Educator salaries 19 23 29 20 28 40 68 71 73 1,163 188

z. Non-educator salaries 28 38 31 25 42 19 48 26 75 281 55

aa. Any other expenditure 58 24 45 28 58 27 60 46 60 236 80

TOTAL 100 383 100 390 100 399 100 530 100 2,275 914 Note: For each quintile “%” refers to the percentage of learner-weighted schools that reported having expenditure for this item. “R” refers to the median Rand per learner spent with schools with zero for the item being left out of the calculation.

46

Table 32: Departmental purchases by item

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Mean % R % R % R % R % R

a. Textbooks – for replacement 38 17 40 43 44 21 39 105 19 15 36

a. Textbooks – new 69 194 70 125 73 132 59 96 60 96 137

b. Other Learning Support Materials (e.g. wallcharts, science laboratory materials) 46 31 50 205 36 41 53 21 18 12 39

c. All reproduction costs, including photocopier contracts, photocopier paper, toner, etc. 28 13 32 22 24 15 39 30 4 9 20

d. Office consumables (other than reproduction costs) 29 25 33 25 33 3 11 11 6 5 6

e. Computers and related items 35 8 34 15 29 8 38 7 23 19 9

f. Sports 19 21 15 4 14 1 16 2 4 8 2

g. Stationery for learners 62 82 56 34 64 37 61 68 40 51 101

h. Building maintenance and repairs 32 47 35 32 42 64 33 17 26 5 70

i. Cleaning materials 28 9 27 14 25 10 42 12 5 12 6

j. Classroom furniture 35 86 31 118 21 24 28 21 12 24 27

k. Refurbishment of buildings and new buildings 26 81 15 80 26 134 17 4 11 77 165

l. Telephone 15 6 15 6 17 11 43 7 18 12 2

m. Internet connection 1 16 4 2 4 4 7 1 13 3 0

n. Electricity 20 5 33 14 36 50 28 38 11 16 25

o. Water 17 3 14 46 17 21 23 1 9 9 18

p. Rates and taxes 2 62 8 1 1 62 19 0 7 61 2

q. Security services 15 44 1 0 25 2 14 1 2 3 3

r. Catering 4 0 12 0 12 1 16 1 1 16 0

s. Professional services (e.g. auditors, bookkeepers) 14 1 6 5 12 1 28 1 2 5 0

t. Scholar transport 9 510 25 62 16 10 15 0 2 62 22

u. School trips 7 0 16 0 4 8 14 0 17 51 2

v. Transport for workshops and meetings 12 1 10 0 14 2 16 3 3 0 0

w. School feeding 43 124 39 97 47 54 44 10 20 43 65

x. Any other expenditure 12 16 7 4 12 48 19 91 1 48 8

TOTAL 91 472 89 504 96 356 77 453 77 212 768 Note: For each quintile “%” refers to the percentage of learner-weighted schools that reported having expenditure for this item. “R” refers to the median Rand per learner spent with schools with zero for the item being left out of the calculation.

47

Table 33: School expenditure plus Departmental purchases by item

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Mean % R % R % R % R % R

a. Textbooks – for replacement 65 91 63 129 61 99 77 118 84 93 115

a. Textbooks – new 69 194 69 125 74 132 55 119 60 96 140

b. Other Learning Support Materials (e.g. wallcharts, science laboratory materials)

66 31 71 75 61 55 81 45 65 42 68

c. All reproduction costs, including photocopier contracts, photocopier paper, toner, etc.

71 18 68 22 74 29 77 57 88 113 63

d. Office consumables (other than reproduction costs) 65 19 63 25 61 11 60 10 71 25 23

e. Computers and related items 50 33 64 16 60 16 75 10 75 64 31

f. Sports 77 17 76 15 72 11 82 14 87 50 30

g. Stationery for learners 85 96 88 63 84 54 87 83 80 92 151

h. Building maintenance and repairs 79 49 94 51 94 28 83 32 95 147 141

i. Cleaning materials 87 9 82 17 82 12 85 27 81 30 24

j. Classroom furniture 42 100 47 41 39 80 42 21 36 31 38

k. Refurbishment of buildings and new buildings 50 71 38 85 44 101 42 54 40 66 206

l. Telephone 63 5 56 8 77 14 97 19 92 58 20

m. Internet connection 7 2 6 3 16 3 22 3 44 7 1

n. Electricity 73 9 80 9 82 13 80 56 81 100 61

o. Water 31 11 29 24 41 9 40 36 39 81 30

p. Rates and taxes 19 9 16 1 16 7 26 9 24 33 7

q. Security services 63 15 53 15 66 17 81 13 80 56 24

r. Catering 77 10 69 7 77 7 76 10 72 18 12

s. Professional services (e.g. auditors, bookkeepers) 74 4 64 5 79 4 92 3 88 7 6

t. Scholar transport 18 19 37 24 38 10 28 1 34 78 36

u. School trips 48 19 59 22 62 20 43 12 72 28 21

v. Transport for workshops and meetings 63 20 65 18 63 12 72 11 66 15 14

w. School feeding 46 124 43 97 53 51 49 6 28 12 75

x. Examination fees 14 46 11 4 17 26 11 134 2 48 7

y. Educator salaries 19 23 29 20 28 40 68 71 73 1,163 191

z. Non-educator salaries 28 38 31 25 42 19 48 26 75 281 56

aa. Any other expenditure 58 24 45 28 58 27 60 46 60 236 81

TOTAL 100 981 100 944 100 1,062 100 1,105 100 2,829 1,673 Note: For each quintile “%” refers to the percentage of learner-weighted schools that reported having expenditure for this item. “R” refers to the median Rand per learner spent with schools with zero for the item being left out of the calculation.

48

To assist an item-specific comparison, Table 34 provides the ratio of mean per learner

spending in the schools with the lower scores relative to schools with the higher scores. The

calculation of the mean includes observations with zero. Perhaps the most worrying difference

from an educational perspective is the large difference with respect to spending on

reproduction. The mean total values are R19 in schools with lower scores and R199 in schools

with higher scores. This suggests that there is much less use of texts other than textbooks in

poorer schools, which is likely to narrow the scope of the education offered in these schools.

The fact that overall spending on scholar transport is considerably higher in better off schools

would partly be linked to the greater cost effectiveness of scholar transport in urban areas.

Table 34: Relative spending in schools with lower scores

School expenditure

Total amount

a. Textbooks – for replacement 1.43 0.96

a. Textbooks – new 1.62

b. Other Learning Support Materials (e.g. wallcharts, science laboratory materials)

0.50 1.04

c. All reproduction costs, including photocopier contracts, photocopier paper, toner, etc.

0.16 0.10

d. Office consumables (other than reproduction costs) 0.39 0.47

e. Computers and related items 0.24 0.23

f. Sports 0.23 0.23

g. Stationery for learners 0.86 0.25

h. Building maintenance and repairs 0.40 0.17

i. Cleaning materials 0.59 0.60

j. Classroom furniture 0.85 2.05

k. Refurbishment of buildings and new buildings 1.13 5.51

l. Telephone 0.16 0.12

m. Internet connection 0.53 0.13

n. Electricity 0.10 0.07

o. Water 0.22 0.05

p. Rates and taxes 1.26 0.48

q. Security services 0.42 0.42

r. Catering 0.51 0.65

s. Professional services (e.g. auditors, bookkeepers) 0.59 0.54

t. Scholar transport 0.21 0.49

u. School trips 0.60 0.51

v. Transport for workshops and meetings 0.94 0.89

w. School feeding 1.87 1.08

x. Examination fees 0.29 3.43

y. Educator salaries 0.04 0.01

z. Non-educator salaries 0.10 0.06

aa. Any other expenditure 0.11 0.11 Note: The first data column is the ratio of the mean total per learner expenditure (whether by the Department or the school) in schools with the lower scores relative to the figure for schools with the higher scores. The second data column is calculated in the same way but considers only expenditure by the school itself.

An attempt was made to regress the various spending values on average learner performance.

Just schools with lower and middle scores were considered in order to investigate what inputs

may a considerable difference amongst poorer schools. The regression results (not presented

here) seemed difficult to interpret. The most striking result was the prominence of spending

on the item ‘telephone’ as a factor associated with better scores. It could be that this item was

masking the effects of being isolated and rural, effects which include serious difficulties

relating to the recruitment of good teachers. In short, the analysis did not seem to yield any

new information that could inform policy or practices with respect to the optimal mix of

inputs.

49

The following two tables present total spending levels, including Departmental purchases and

school spending of funds from all private and public sources, but only in relation to items

assumed to be part of the school allocation. The median values and the overall mean all

exceed the no fee threshold of R581. However, overall spending in quintiles 1 and 2 do not

reach the target levels of R775 and R711.

Table 35: Total norms-related spending by province

Median per learner purchases (2008) Estimate of total

cost (Rm) Lower scores

Middle scores

Higher scores

EC 596 708 422 1,507 FS 698 585 1,802 613 GP 588 820 1,878 2,343 KN 1,000 520 1,731 2,798 LP 549 363 1,533 1,063 MP 493 356 1,777 681 NC 3,669 914 1,111 343 NW 483 472 1,825 707 WC 425 1,056 1,363 1,140

SA 656 622 1,533 11,194

Note: Learner weights used.

Table 36: Total norms-related spending by province and quintile

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall mean Target

EC 596 708 377 1,000 739 604 FS 698 488 667 692 2,767 960 554 GP 588 1,134 869 820 1,983 1,392 458 KN 1,000 1,460 637 520 1,633 1,042 555 LP 543 450 270 363 1,533 625 621 MP 469 742 327 493 629 699 541 NC 841 1,054 914 993 2,226 1,323 538 NW 483 529 456 1,825 1,688 937 555 WC 1,500 589 883 1,001 1,515 1,266 394

SA 647 708 604 794 1,633 962

Target 775 711 581 388 129 517 Note: Learner weights used. Quintile values are median values.

Finally, Table 37 indicates what percentage of students attain their school funding norms

targets if one examines total spending by the school and Department. The statistics are very

similar to those of Table 21. This points to a strong correspondence between the school

expenditure data and the public revenue data in the survey dataset.

Table 37: Percentage of learners with sufficient norms-related spending

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 42 44 26 100 42 FS 40 35 63 54 100 53 GP 0 81 74 85 100 83 KN 62 90 53 67 92 62 LP 36 16 48 11 100 33 MP 36 70 24 70 98 63 NC 100 90 89 96 100 94 NW 24 31 31 100 100 41 WC 70 47 83 97 100 87

SA 44 49 51 77 98 58

Note: Learner weights used.

50

5.1.3 The backlogs problem

A problem that is often referred to in the policy discourse is that poorer schools, because they

experience great infrastructure backlogs, end up spending public funds intended for non-

personnel recurrent items (including, of course, LSMs) on improving the infrastructure, or

simply not letting it deteriorate further. The following graph facilitates an examination of this

‘crowding out’ effect. The horizontal axis refers to the total value of public resourcing per

learner (transfer plus Departmental purchases) whilst the vertical axis refers to the breakdown

of overall expenditure (which could be higher than public resourcing due to private revenue).

‘Norms-related spending’ is spending (by the Department or the school) on items considered

as falling within the ambit of the school allocation. The fact that on the right-hand end of the

graph (where poorer schools would be situated) norms-related spending should be more or

less R1,000 on average suggests that the crowding out effect is not large. However, when

other expenditure items are added, what is interesting is that it is not so much spending on

capital items (‘refurbishment of buildings and new buildings’) that may crowd out norms-

related spending, but spending on personnel. Unfortunately the survey data do not allow an

in-depth analysis of the personnel pressures experienced in poorer schools, but it appears from

Table 31 that in quintiles 1 and 2 most of pressure relates to the need to hire non-educator

staff privately.

Figure 5: Possible crowding out effects

Note: Lowess smoothing was used to derive the curves.

The survey dataset does allow for some probing into the physical infrastructure backlogs

situation. The principal was asked some questions with respect to three infrastructure items,

toilets, doors and learner desks, in order to obtain an idea not only of the extent of the

backlogs situation, but of responses to the situation. The next table suggests that poorer

schools have a greater backlogs problem, but also that the problem is prominent across most

of the system. The Department appears to be focussing on the poorest schools, where the

greatest problem lies, but what is interesting is that it is common for the Department to

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indicate to schools that the school allocation should be used to deal with infrastructure

backlogs. This is not in line with the provisions of the policy, which state clearly that the

Department should deal separately with backlogs.

Table 38: Backlogs situation

Lower scores

Middle scores

Higher scores Overall

New toilets % of schools in need 83 75 55 73 Average need per 500 learner school 15 18 11 15 New doors % of schools in need 73 64 52 65 Average need per 500 learner school 10 9 8 9 New learner desks/tables % of schools in need 73 73 62 71 Average need per 500 learner school 207 161 117 170

Department has indicated... it will deal directly with the problem 38 27 15 28 school should use school allocation money 44 37 28 38 School has... budgeted for the problem 49 48 54 49 a plan to deal with problem 56 57 60 57

Note: Values such as the number of toilets needed per school were adjusted upwards or downwards to make each school seem like it had 500 learners.

In Table 21 above it was assumed that the school was spending all of its transfer on items

within the ambit of the school allocation. Clearly, however, this is not always the case. It is

possible to make an adjustment to take this into account. One simply takes the minimum of

two values per school: the transfer and the expenditure by the schools on norms-related items.

The result of the adjustment is given in Table 39. The overall percentage of learners attaining

their targets drops slightly, from the 59% of Table 21 to 47%. However, the drop for just

quintiles 1 and 2 learners is larger.

Table 39: Adjusted percentage of learners attaining the allocation target

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 15 20 27 100 0 24 FS 30 25 57 69 91 47 GP 0 81 63 77 100 76 KN 58 26 45 72 92 59 LP 27 12 39 0 100 26 MP 36 63 0 23 98 47 NC 0 35 82 78 86 68 NW 3 3 0 30 90 12 WC 51 47 78 75 92 76

SA 29 28 40 67 95 47

Note: Learner weights used.

Finally, the next table indicates what happens to the percentage of learners attaining the no fee

threshold when the adjustment is applied.

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Table 40: Adjusted percentage of learners attaining the no fee threshold

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 31 66 27 100 0 44 FS 63 25 57 69 0 49 GP 100 81 63 38 9 43 KN 84 57 45 21 0 44 LP 40 46 39 0 0 38 MP 36 63 0 0 0 22 NC 95 90 82 59 13 65 NW 10 10 0 0 30 7 WC 100 64 78 53 23 55

SA 49 55 40 34 9 40

Note: Learner weights used.

5.1.4 School opinions on funding adequacy

Principals were asked whether they thought the non-personnel recurrent funding situation had

improved in the last five or so years. Importantly, close to half of principals nationally said it

had. A further 30% said there had not been any significant change, and about 20% said the

level of funding had got worse. Figure 6 provides the provincial details, with confidence

intervals. The provinces EC and GP appear to lie clearly at the top of the range, whilst MP

and FS appear to lie clearly at the bottom. Of course an improvement does not necessarily

indicate that funding is adequate. And the converse could also hold true. No improvement

does not necessarily mean the funding is inadequate. Of particular importance would be a

situation of exceptionally low funding combined with no medium term improvement. MP

seems to experience such a situation.

Figure 6: Principals saying there is more funding now

Note: Points in the graph indicate mean values in the sample, whilst vertical lines indicate the range between the lower and upper bounds of the 95% confidence interval.

Before opinions on the adequacy of funding in 2008 are examined, information on personal

spending by school staff on work-related items is viewed as this could provide an indication

of funding adequacy. Table 41 reveals that ‘out-of-pocket’ spending is common, but common

across all the three performance categories. However, whilst amounts of, for instance, R3,000

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are large for individual employees, they are small relative to the expenditure of most schools.

In a 500 learner school R3,000 would work out to R6 per learner in a year. It seems unlikely

that the overall level of funding is the main cause for ‘out-of-pocket’ spending. It would be

part of the cause, for instance the level of public funding may explain why principals in

poorer schools pay somewhat more than principals in less poor schools (R3,000 versus

R2,000). But other causes such as difficulties around the rules around compensation for ‘out-

of-pocket’ spending and perhaps cultural issues (a principal who claims for everything may be

seen as non-committed) are likely to be important too.

Table 41: Uncompensated out-of-pocket spending by school personnel

Lower scores

Middle scores

Higher scores Overall

% of schools where personnel are not compensated for out-of-pocket spending Principal response 65 67 61 65 Teacher response 60 45 58 54

Median non-zero annual value of this out-of-pocket spending Principal response 3,000 2,400 2,000 3,000 Teacher response 600 500 900 600

% spending money on petrol and transport Principal response 64 64 60 63 Teacher response 56 39 46 48

% spending money on photocopies Principal response 25 15 7 18 Teacher response 24 17 33 23

% spending money on materials used in the classroom Teacher response 15 11 6 12

% spending money on other items Principal response 33 24 23 28 Teacher response 11 11 15 12

Principals and SGB parents were asked to assess the adequacy of non-personnel recurrent

spending by the Department along a scale where lack of money ‘is not a serious problem’, ‘is

a bit of a problem’, or ‘is a serious problem’. Figure 7 illustrates the relationship between the

total school allocation actually received (based on the financial transfer and Departmental

purchases data) and the proportion of respondents saying inadequate funding was not a

serious problem and was a serious problem. Only schools charging R100 or less were

included in the analysis as the aim was to examine opinions amongst historically

disadvantaged schools. This meant the analysis covered 358 schools in the survey.

Interestingly, there is a clear pattern whereby funding below a level of around R500 clearly

made opinions far less favourable. Above this level, opinions did not change greatly. This

suggests there is an important threshold at around R500. 37% of learners in schools charging

R100 or less in fees find themselves below the R500 level. A similar analysis focussing just

on the transfer is provided in Figure 8. Here it appears that dissatisfaction begins to mount

when the transfer is below around R400 per learner. 49% of the analysed learners are below

this level. None of this means that funding above these levels is not inadequate, yet it is

important to acknowledge these key thresholds in the planning process somehow.

54

Figure 7: Opinions on the adequacy of the school allocation

Note: Lowess smoothing was used to obtain the curves in this and the next graph. The respondents considered here are both the school principal and the SGB parent (the average of the two per school was found, meaning the value could be 0.5).

Figure 8: Opinions on the adequacy of the transfer

Figure 9 illustrates what principals at different levels of the school allocation would like to

spend an additional R200 per learner, if this was made available to the school. Principals

could each choose just one of ten items. Again, only schools charging R100 or less in school

fees were analysed. What is striking is how strongly schools with a low allocation prioritise

building maintenance. This could be the ‘backlogs problem’ having an effect, but it could also

55

be that schools below a certain level of funding are not able to keep up with the maintenance

of their physical infrastructure and therefore prioritise this item above more educational items.

Again, the threshold for not worrying too much about infrastructure seems to lie at around

R500.

The fact that other LSMs appear to precede computers in the hierarchy of needs suggests that

an overly zealous policy focus on computers could be problematic. The demand for

computers is high, but mostly after the demand for other LSMs (the questionnaire specifically

mentioned wallcharts and science laboratory equipment) has been satisfied.

Figure 9: Principal’s preference for additional spending

Note: Lowess smoothing was used to obtain the curves.

The patterns emerging from the responses of the SGB parent are similar to the patterns seen in

Figure 9 except for the fact that the ‘Other LSMs’ bulge is almost absent amongst the parents.

5.2 Public funding levels and no fee schooling

Does the funding of no fee schools make no fee schooling possible?

By 2009, 53% of learners were in no fee schools (see Table 42). These schools, which may

not charge fees, are supposed to receive public funding to at least the no fee threshold level. In

fact, only between 65% to 84% of the learners concerned receive sufficient funding – the

level depends on which estimate one uses. It seems clear that a sizeable minority of no fee

learners are not funded at the level they should be.

For no fee schooling to work, principals and parents must understand what the policy says.

Levels of understanding of the policy by principals and SGB parents seem relatively high.

Almost 100% of non-SGB parents seem aware that their school is a no fee school when it is.

56

Moreover, satisfaction levels with the no fee schooling policy are relatively high amongst

both principals and non-SGB parents. Despite the public funding problems, only 19% of

principals and 8% of SGB parents are not satisfied (Table 43 and Table 44). The intended

impact of no fee schooling, for instance better enrolment patterns amongst poor learners,

seems to have been felt within the schools concerned.

If the assumption is that the no fee schooling status should completely eliminate the need for

funding from private sources, then it is clear that the bulk of no fee schools are not adequately

funded. The data indicate that though the charging of fees is virtually non-existent amongst no

fee schools, 68% of schools do collect non-fee contributions from parents (Table 45). Though

the contributions may not be compulsory in a formal sense, around a quarter of non-SGB

parents concerned regard them as compulsory, and in fact 7% of parents say they are charged

a school fee, meaning they think of the non-fee contribution as no different from a school fee.

Importantly, the correlation between attaining the no fee threshold with respect to public

funding and having non-fee contributions is either non-existent or slightly positive (meaning

better funded no fee schools are more likely to collect non-fee contributions). This suggests

that there are important factors other than the level of public funding that influence the level

of private revenue and that improving public funding for no fee schools, whilst important, is

not likely to reduce the collection of non-fee contributions.

Insofar as the ultimate intention with having no fee schools was to improve the enrolment and

learning situation for poor learners, and not necessarily eliminate all private contributions, the

no fee schooling policy introduced in 2006 appears to have been a success.

Some provincial details. Two provinces, LP and WC, stand out as having principals that are

exceptionally satisfied with the no fee schools policy, suggesting the policy fulfils a real need

and that the Department has been successful in implementing the policy.

The variable in the survey dataset used to determine whether a school had been declared a no

fee school was year in which the school had officially been declared a no fee school. This

year should have been in the range of 2006 to 2009. However, 5% of schools with a value

indicated a year prior to 2006, when no fee schools as an official category did not exist. The

approach was to regard even these 5% of schools as official no fee schools.

In 2009 53% of learners were in no fee schools. The breakdown by province and quintile is

given in Table 42. Nationally, virtually all of quintiles 1 and 2 learners are in no fee schools,

and about half of quintile 3 learners.

Table 42: Percentage of learners in no fee schools

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Total

EC 97 100 90 0 91 FS 100 100 100 0 0 79 GP 100 100 80 0 0 32 KN 93 91 11 0 0 37 LP 100 100 10 0 0 66 MP 100 89 10 3 0 41 NC 100 99 5 15 3 32 NW 94 63 37 0 0 47 WC 100 100 68 0 0 33

SA 97 95 46 1 0 53

No fee schools should all, according to the policy, be receiving at least the no fee threshold

level of funding (R581 in 2008). The extent to which this target is reached is largely dealt

with in Table 22 and Table 27 in the preceding section. A count of just no fee schools if 2008

(when 44% of learners were in no fee schools) reveals a target attainment figure of 67% using

57

the funds and resources actually received (as in Table 22), 83% using funds committed by the

Department (as in Table 27), and 87% using a lower threshold of R500. The justification for

using a lower threshold is that the policy requires the Department to use the most recent

enrolment statistics to fund the school, but it is assumed that these statistics will always be at

least a year old, implying that schools with enrolment increases would not be funded precisely

at the correct level. The statistics suggest that whilst the Departments are close to funding no

fee schools at the level they should, they have not reached 100% yet.

How popular is no fee schooling? This question is obviously important for understanding how

optimal the public funding of no fee schools is. Table 43 breaks statistics on responses within

no fee schools down by two learner performance categories (the small number of no fee

schools with ‘higher scores’ were include in the ‘middle scores’ group for the purposes of the

tables in this section). A large majority of principals and SGB parents are satisfied with the

system. The intended impact of no fee schooling, including greater enrolment amongst the

poor, less learner absenteeism and less marginalisation of learners, seems to have been clearly

felt. A relatively small percentage of respondents, of 10%, believe that the risk of an exodus

of learners from no fee schools (possibly caused by perceptions that no fee schools offer a

lower quality of schooling) has been realised. At the same time, there are clearly problems.

Half of principals indicated that no fee schooling was causing cashflow problems. Whilst all

no fee schools in the survey receive a transfer, this is insufficient in many cases and it is

possible that there is a problem with the timing of transfer. (There were a few no fee schools

that reported no transfer had been received for 2009 yet by March, but as all of these schools

had received a transfer in 2008 it was assumed that the transfer had not arrived yet.)

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Table 43: Opinions from no fee schools

Lower scores

Middle scores

Overall

% understanding no fee schooling policy Principal 76 80 77 SGB parent 92 96 94

% of non-SGB parents knowing that school is a no fee school 96 95 95 Response rate for above 99 98 98

% of non-SGB parents approving of the no fee status 96 87 92 Response rate for above 92 94 93

% believing the following to have occurred due to no fee schooling More enrolment from poorer households Principal 88 77 83 SGB parent 92 83 88 Less learner absenteeism Principal 61 35 50 SGB parent 61 37 51 Less marginalisation of learners Principal 35 31 34 SGB parent 40 31 36 Exodus of learners Principal 11 7 10 SGB parent 11 9 10

% of principals saying no fee schooling has caused cashflow problems 57 46 52

% of principals feeling as follows about the no fee status Very satisfied 50 33 42 Satisfied 31 39 34 A bit unsatisfied 16 16 16 Very unsatisfied 1 6 3 Indifferent 2 6 4

% of SGB parents feeling as follows about the no fee status Very satisfied 79 73 76 Satisfied 13 19 16 A bit unsatisfied 7 4 6 Very unsatisfied 0 3 2 Indifferent 0 0 0

The following table breaks the level of satisfaction of the principal up by province and

quintile. WC and LP stand out as provinces with particularly a particularly low percentage of

principals not being satisfied (less than 10% in both cases).

Table 44: Principal’s satisfaction with no fee schooling

Very satisfied Satisfied

A bit unsatisfied

Very unsatisfied Indifferent

EC 31 49 15 1 2 FS 47 22 22 0 8 GP 1 61 7 8 17 KN 58 11 26 1 0 LP 46 40 5 4 2 MP 49 22 26 2 0 NC 53 19 16 3 8 NW 34 39 18 8 0 WC 46 29 0 4 20

SA 42 34 16 3 4

An important question is whether no fee schools are in fact charging no fees. Before this

question is answered, some examination of the private revenue patterns in all schools (not just

no fee schools) is in order. Figure 10 illustrates the distribution of private revenue streams for

the 506 schools where sufficiently reliable data were available. Just under 40% of learners

were in schools with no private revenue according to the revenue table in the survey. This

59

corresponds rather well to the 44% of learners being in no fee schools in 2008. 53% of

learners were in schools declaring no school fee revenue. Clearly, most private revenue comes

in the form of school fee revenue, with fundraising revenue being a small but relatively

important alternative, and private donations and non-fee contributions being relatively low

(the meaning of this latter term is discussed below). Of course these are the aggregate trends.

It is possible for there to be individual schools where fee revenue is smaller than the sum of

other revenue streams.

Figure 10: Private revenue patterns

Note: Learner weights used. Note that the curves are calculated separately so, for instance, schools at the 50th percentile for ‘Total’ are not necessarily the same schools as those at the 50th percentile for ‘School fees’.

The survey includes relatively unstructured (in the sense of being non-tabular) questions on

non-fee contributions in 2009, directed at the principal and parents, apart from the more

structured revenue table. This was deliberate as the intention was to probe the extent of

relatively informal contributions that may not be included in the financial statements of the

school. Because the questions included an indication of the maximum and minimum paid per

learner, and the percentage of learners contributing, it was possible to roughly estimate the

average per learner value per school. The results of the non-tabular questions from 491

schools with sufficiently reliable data are illustrated in Figure 11. Responses from the

principal and SGB parents yield very similar curves, but a slightly higher curve results if one

takes the highest of the two (there would be schools where the principal’s response is lower

than that of the SGB parent, and vice versa). 25% of schools did not have non-fee

contributions. However, in the 75% of schools that did, a comparison with the tabular

60

responses suggests that around 90% of the revenue is not included in the formal financial

accounting of the school. The non-fee contributions are in general not high (less than R100

per year in most schools), but to a large degree they are not formally accounted for.

Figure 11: Non-fee contributions

Note: Learner weights used.

The following table indicates that largely informal non-fee contributions exist in around 68%

of no fee schools, but that its median or typical level is around R55 per learner (in 2009).

About a quarter of non-SGB parents think of the contribution as fee-like in the sense that it is

compulsory, and 7% think of it as being no different from a school fee.

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Table 45: No fee schools and private revenue

Lower scores

Middle scores Overall

% of no fee learners in 2008 in schools with School fee revenue 5 17 10 Any private revenue 35 59 45

% of no fee learners in 2009 in schools with Fees charged 0 3 1 Non-fee contributions 67 70 68

Median value of non-fee contribution where it exists 55 55 55

% of no fee principals encouraging private contributions 58 70 63

% of non-SGB parents in no fee schools making financial contributions

63 62 62

Response rate for above 99 97 98

% of non-SGB parents in no fee schools viewing private contributions as compulsory

20 27 23

Response rate for above 63 63 63

% of non-SGB parents in no fee schools saying there is a fee 7 8 7 Response rate for above 99 96 98

An analysis of the correlations between the public funding variables and private revenue or

informal non-fee contributions in no fee schools does not point to a clear negative correlation,

meaning more public funding being associated with lower private contributions. This is

important, as it suggests that it is not primarily the inadequacy of public funding that is

causing the private contributions. There are other factors at play. For instance, the school

community may think of the school as a community organisation that is able to realise

educational, sporting and cultural goals to which parents are willing to contribute. In fact, the

strongest correlation was that between having non-fee contributions in 2009 and having

attained a level of public funding of R581 (according to the Departmental commitment). This

correlation, of 0.15, was not high and was positive.

Poor households contribute not only money to the school, but also spend money on a number

of items such as scholar transport. If the ultimate aim of no fee schooling is to remove

monetary barriers from the schooling of the poor, then the full range of household costs need

to be considered. The survey does not cover these costs in much depth, but the following

statistics could be obtained. They suggest that pressures on poorer households, whilst they

exist, are not very high. For instance, only 3% of most disadvantaged households spend

money on textbooks whilst the figure for stationery (a much cheaper item in general) is only

14%.

Table 46: Patterns of household spending on education inputs

Lower scores

Middle scores

Higher scores Overall

% of non-SGB parents paying for transport 6 15 44 18 Response rate for above 98 93 98 96

% of non-SGB parents paying for stationery 14 31 59 31 Response rate for above 98 93 98 96

% of non-SGB parents paying for textbooks 3 5 29 10 Response rate for above 99 92 99 96

% of SGB parents believing it is very true that The cost of transport is a barrier to attendance 17 16 21 17 The cost of uniforms is a barrier to attendance 24 16 6 17

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5.3 The funding levels in schools with fees

Are schools with fees adequately funded?

Schools which are considered well-off according to their quintile classification are paid

substantially less by the Department, the assumption being that well-off schools can

compensate for this through private fee income. If the quintile classification is inaccurate,

then there is a risk that schools considered well-off will in fact not be able to raise fees as

expected, and will suffer an overall revenue shortfall. The extent to which this happens has

been the subject of much debate.

The survey data indicate that public plus private under-funding is most common in quintiles 3

and 4 where around half of learners are under-funded in the sense that funding falls below the

no fee threshold level. The situation is slightly better in quintiles 1 and 2. This lends support

to the hypothesis that it is there is a ‘middle quintiles’ public funding problem. Some quintile

5 schools also seem under-funded, but here the proportion of under-funded schools, at around

15%, is much lower.

The causes for the under-funding of schools with fees are complex. Non-attainment of the

official funding targets, having targets which are too low, and quintile misclassifications all

seem to play a role. An analysis of what the targets should perhaps be reveals that the quintile

4 target should be around R150 higher, and that part of quintile 5 (perhaps a quarter) should

have a target that is R300 higher than the current target of R129 (see Figure 14). An analysis

of what the quintile classifications should be suggests that as many as 2.5 million learners

could be in the wrong quintile, with the largest misalignment being greatest between quintile

3 and quintile 4 (though there are other substantial misalignments too) – see Table 48.

The question of whether schools that charge fees receive adequate public funding is partly

answered by section 5.1 above, which looks at the attainment of official funding targets, and

by section 7 below, which examines the pressures caused by the inability of poorer parents in

fee-charging schools to pay the fee. In this section the focus is largely on the overall public

plus private revenue situation in fee-charging schools, and on likely public funding levels that

would ensure a basic minimum overall level of funding in these schools.

The next table does not provide clear support for the hypothesis that many quintiles 3 to 5

schools, or schools that depend to a greater or lesser degree on fee revenue, are under-funded

by the state. Principal responses in fact suggest less serious funding problems in quintiles 4

and 5 schools than in other schools. If the hypothesis had been right, one may have expected a

clear picture of exceptionally high dissatisfaction amongst quintiles 3 to 5 schools. The

hypothesis may be true, but these statistics do not support it. (The principal opinion data

analysed here are the same as the data that were used for Figure 7 and Figure 8.)

Table 47: Opinions on the sufficiency of public funding by quintile

Quintile

1 Quintile

2 Quintile

3 Quintile

4 Quintile

5

% of principals believing insufficient public funding is A serious problem 23 25 28 0 0 A bit of a problem 25 34 24 21 61 Not a problem 52 40 49 79 39 Not sure 0 0 0 0 0

% of SGB parents believing insufficient public funding is A serious problem 16 34 28 35 0 A bit of a problem 35 18 32 31 42 Not a problem 48 45 37 34 58 Not sure 1 3 3 0 0

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The next two graphs illustrate the overall revenue situation of schools, counting all public and

private revenue, except for the partially informal contributions discussed in section 5.2 above.

These contributions were excluded as their exact nature is not clear (they could fund relatively

non-educational activities) and because it seemed better for this analysis to under-estimate

rather than over-estimate private income. Figure 12 excludes in-kind revenue from

Departmental purchases, whilst Figure 13 includes this. The first graph indicates that the

quintiles 1 to 4 revenue situation is rather uniform, whilst the quintile 5 situation is

considerably more favourable. The second graph, however, suggests that quintiles 1 to 4 are

different, with quintiles 3 and 4 being disadvantaged in the sense that fewer learners attain the

no fee threshold level of funding of R581 (in 2008). Specifically, 50% of quintile 4 learners

do not reach the no fee threshold, whilst 43% of quintile 4 learners do not. This is not

supposed to happen, according to the policy. The data give credence to the idea, commonly

expressed, that the ‘middle quintiles’ experience exceptional funding pressures. They are not

poor enough to receive preferential public funding, and yet not rich enough to charge fees that

can fully compensate for the shortfall on the public side.

Figure 12: Profile of total monetary revenue

Note: Learner weights used. The horizontal line indicates the no fee threshold of R581.

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Figure 13: Profile of total monetary plus in kind revenue

Note: Learner weights used.

The problem is a complex one. There are at least three key factors at play. Firstly, public

funding targets in quintiles 3 to 5 could have implicitly over-estimated the ability of schools

to collect fee income. Secondly, quintile classifications could be wrong, resulting in a

situation where some schools are considered better off than they really are (the corollary

being that other schools are considered worse off than they really are). Thirdly, the factor of

Departments not reaching targets (discussed in section 5.1) plays a role. A rough

quantification of the first two factors was attempted by creating a counterfactual public

funding scenario in which the only public funding rule was that public funding had to close

the gap (of any) between the fee revenue level and the no fee threshold. Moreover, fee

revenue per learner was considered to be an accurate measure of the socio-economic status of

the school, and hence its placement within one of the five quintiles. The latter assumption is

of course risky, partly because fee revenue is to some extent influenced by the level of public

funding (which may be inappropriate and not correlated to socio-economic status). Figure 14

illustrates the outcome of the analysis. Clearly, fee revenue increases steeply between more or

less the 85th and 90th percentiles, in other words points that should lie within quintile 5. The

required (and counterfactual) public revenue, which is simply the inverse of private revenue,

drops sharply between the 85th and 90th percentiles. A negative gap between the

counterfactual public revenue curve and the steps-like actual public revenue curve is most

marked in quintile 4 and in a part of quintile 5, more or less from the 60th percentile to the 85th

percentile. Specifically, all quintile 4 schools seem to be under-funded by between around

R150 on average, and a portion of quintile 5 schools seem to be under-funded by around

R300.

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Figure 14: A counterfactual funding scenario

Note: Learner weights used.

An added complexity is that in reality each quintile is not 20% of all learners. In particular,

the survey data indicate that quintile 5 is much smaller than what it should be. It includes only

around 12% of learners. It seems as if this is not just a question of the survey being

unrepresentative of the quintiles. Separate analysis of EMIS data point to the same

conclusion. This anomaly has in fact positive implications for the funding situation, because it

implies that the quintile 5 under-funding situation would be smaller than what is implied by

the previous graph (see the right-hand arrow). However, the problem in quintile 4 would

largely remain.

An assessment was made of how many learners might be in the wrong quintile, by comparing

actual quintile to the quintile implied by the per learner fee revenue level. In the analysis, it

was assumed that each quintile would retain its current size, so for instance quintile 5 would

only contain 12% of learners. However, because most quintiles 1 to 3 schools do not charge

fees, schools in these schools were lumped together and called ‘quintile 3’. This should not be

a concern as the main purpose of the analysis is to examine dynamics between quintiles 3, 4

and 5. Table 48, which provides the result of the assessment, indicates that 8% of all learners

(around 900,000) should switch from quintile 4 to quintile 3, 3% (around 350,000) should

move from quintile 5 to 4, 1% (about 100,000) from quintile 5 to quintile 3, 2% (around

200,000) from quintile 4 to quintile 5 and 1% (100,000) from quintile 3 to quintile 5.

Altogether 23% of all learners, or 2.5 million learners, should move if a more accurate

poverty classification were to be realised. For many reasons, these are rough estimates. The

2.5 million learners needing to move could easily be 2 or 3 million learners, though it is

unlikely to be as low as 1 million learners or as high as 4 million learners.

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Table 48: Possible quintile misclassifications

New quintile 1-3

New quintile 4

New quintile 5 Total

Old quintile 1-3 61 8 1 69 Old quintile 4 8 8 2 19 Old quintile 5 1 3 8 12

Total 70 19 11 100

Section 6.3 examines further problems and possible solutions relating to the quintile

classification approach.

5.4 The funding levels for Grade R

Is there sufficient spending on Grade R in public schools?

The percentage of Grade 1 learners in the public system in schools which also had Grade R

offered in the same school stood at 74% in 2009 (see Table 49). The percentage of the Grade

R population cohort enrolled in public school Grade R was at least 71% (Table 62). The

intention of Education White Paper 5 was for these indicators to reach 100% and 90%

respectively by 2010. The coverage situation seems to suggest that there has not been

sufficient Grade R investment by the state.

Amongst the 71% or so of the cohort that is enrolled in public school Grade R, the extent of

public funding is rather high. The percentage of Grade R learners receiving some public

funding, either in the form of publicly funded teaching posts or a transfer, is in the range of

81% to 89%. (In the case of some schools it is difficult to tell from the survey data whether

the general transfer to the school is intended for Grade R learners as well, or only Grades 1 to

12, hence a precise estimate could not be given.) Moreover, in total Rand terms around 79%

of overall Grade R funding in public schools is estimated to be public, as opposed to private

(Table 65). The White Paper envisages that 70% of overall funding, regardless of school type,

should be public.

The White Paper and the Grade R funding norms have very specific recommendations

relating to per learner spending. It is said that this should be 70% of the Grade 1 cost in

quintile 3 (though 50% is permitted during an interim period), the rationale for a lower level

of funding than in Grade 1 being that employment by the school, and not the Department, of

Grade R educators will reduce costs. The other quintiles have recommended funding levels

based on a pro-poor gradient. There were two key difficulties encountered in the estimation of

Grade R per learner spending. The difficulty relating to the financial transfer has already been

mentioned. With regard to Departmentally employed educators (despite the White Paper 5

intentions, such educators are common in Grade R) it was assumed that on average the

educator was placed at the lowest salary notch possible for an educator with four years of

professional training. This seemed like a reasonable assumption. However, it is possible that

this assumed cost could be around 15% to high or 7% too low. The cost estimates for Grade R

need to be viewed in this light.

At a national level, public spending on each Grade R learner (counting only those learners

receiving some public spending) was found to be around 55% of the level of spending on each

Grade 1 learner (Table 58). Relative to the 70% target of the Grade R funding norms, the

actual level of funding seems low. Yet, curiously, the approach considered the more costly

one, where Departmental posts as opposed to SGB-employed educators are utilised, is

widespread. Specifically, 65% of funded learners are in schools where only Departmentally

employed educators teach Grade R, 13% are in schools where only SGB-employed educators

teach Grade R, and the remaining 22% are in schools where both approaches are followed

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simultaneously. The result of this situation seems to be that class sizes are higher than they

would otherwise have been. Amongst funded Grade R learners in quintiles 1 to 4, 24% are in

classes exceeding 50 learners, and 38% in classes exceeding 40 learners. A comparison of

class sizes in schools with just public posts to class sizes in schools with just SGB-employed

educators indicates that the problem with excessive class sizes is considerably more common

amongst the first group than amongst the second group (in this analysis quintile 5 schools

were excluded to factor out exceptionally well endowed schools). This suggests that in

moving forward, the Departments must either secure more funding so that a sufficient number

of posts are created in schools, or, if additional funding cannot be secured, make greater use

of the option of transferring funds to schools so that schools employ Grade R educators

themselves. However, there are further complexities. SGB-employed educators are paid

salaries that are extremely low relative to the pay of Departmentally employed educators. This

is true even in the one-third of schools employing their own educators where the Department

prescribes what educators should be paid. On average, the cost of each Departmentally funded

post is six times the cost of an SGB-employed educator. This suggests that SGB-employed

educators are paid well below what was assumed in the White Paper, and could explain why

the approach of using Departmentally funded posts is so popular (at least as far as parents and

unions are concerned). The low pay of SGB-employed educators as well as the widespread

existence of excessive class sizes (a phenomenon which is more common when the more

costly option of posts is used) both raise serious questions about the quality of Grade R being

offered. Moreover, the fact that resourcing approaches should not appear to correlate with

specific provinces and quintiles raises the question of what criteria are being used to

differentiate between schools, especially when differences could result in different levels of

quality in the education service.

The no fee schooling policy does not explicitly cover Grade R learners, but to a large extent

the situation in Grade R follows the situation in the other grades. This can perhaps be taken as

a sign that the public funding of Grade R, in particular the non-personnel funding, is adequate

(or at least note more inadequate than in the other grades). In quintiles 1 and 2 only around

5% of Grade R learners are charged fees (Table 63). Where fees are charged for the poor, they

tend to be low, around R60 in quintiles 1 and 2 (Table 64).

Some provincial details. NW appears to experience a particularly unfavourable situation with

respect to Grade R. A smaller proportion of schools seem to offer Grade R, which could be

related to an exceptionally low percentage of schools receiving a financial transfer from the

Department for Grade R and a high percentage of overall revenue for Grade R being private.

It appears as if the three large provinces with particularly extensive problems of poverty,

namely EC, KN and LP, experience notably higher levels of Grade R enrolment in public

schools. This is likely to be linked to reduced opportunities with respect to private Grade R

services in these provinces. On the one hand, this could point towards a factor that needs to be

taken into account in the national division of revenue process, whilst on the other it points

towards the success of targeting public Grade R to those areas where it is most needed.

Table 49 is based on a simple comparison of Grade 1 and Grade R enrolments. Overall, 74%

of Grade 1 learners are in schools which also offer Grade R. One should bear in mind that

only 357 of the 525 surveyed schools have Grade 1, so confidence intervals would be even

wider than they would be for the entire sample. Differences between provinces and quintiles

should thus be viewed with much caution. Arguably, the only thing one can conclude with

respect to these differences is that there seems to be no strong evidence of major deviations

from the national average by province or quintile (major deviations may in fact exist, but the

data do not permit us to identify them). Even the exceptionally low overall level for NW may

mean nothing given how this is largely based on what is happening in quintile 3 in this

province.

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Table 49: Percentage of Grade 1 with Grade R

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 100 100 97 0 94 FS 98 65 100 36 20 68 GP 0 67 100 67 64 70 KN 100 100 100 92 100 99 LP 95 100 45 100 81 MP 57 96 100 100 69 78 NC 96 99 60 87 88 80 NW 80 95 37 87 54 WC 25 100 73 97 68 74

SA 65 82 75 79 70 74

Target 100 100 100 100 100 100 Note: Learner weights used.

Importantly, the above table does not tell us that enrolment in Grade R is 74% of what it is in

Grade 1. In fact, overall Grade R enrolment is 47% of what it is in Grade 1. The reason for

this is twofold. There is much higher grade repetition in Grade 1 than in Grade R, and many

schools do not aim to enrol all of the local Grade R cohort in the school’s Grade R (the

Department may place limitations based on budget availability, there may not be enough

physical space in the school, and demand for Grade R may be dampened by strong private

provisioning in the area and by the fact that Grade R is not yet compulsory).

With regard to the extent of public funding of Grade R, a problem in analysing the data is that

the questionnaire design did not sufficiently take into account the fact that the Department

might not make a clear separation between transferred funds intended for Grade R, and those

intended for other grades. This separation is required in the policy, but is clearly not always

practiced. However, it is possible to estimate the situation in schools without a specific Grade

R financial transfer amount by examining responses to various questions dealing with the

matter. The percentage of Grade R learners in schools which receive a transfer for this grade

lay between 40% and 72% in 2008, depending on how interprets the data. The correct

percentage is much more likely to be around 72% than 40%. The 72% level assumes that if a

school principal implicitly indicated that there was a Grade R transfer in responding to any

relevant question, then the school did in fact receive a Grade R transfer (even if the amount

could not be ascertained). It is unlikely that a principal would imply there was a public

transfer intended for Grade R when one did not exist.

The situation with respect to Departmentally employed Grade R educators in 2009 is very

clear, on the other hand. 72% of Grade R learners are in schools with such educators. The

percentage of Grade R learners receiving either a transfer or Departmentally employed

educators, or both, lies between 81% and 89%.

The following graph illustrates the spread of per learner spending, without imputing any

transfer values. In other words, if no value was given, it was assumed that the school received

no transfer for Grade R. This would obviously result in an under-estimate of the transfer

amount. All learners receiving some known level of public funding (either educator posts or a

transfer) are covered along the horizontal axis. These learners are from 225 surveyed schools.

Despite appearances, 36% of Grade R learners receiving something receive a known transfer.

However, the figure is so low in many schools that it is not distinguishable from zero in the

graph. (The reason why the 36% is not at least the 40% referred to earlier is that in a few

schools the amount is not given, even though it is absolutely clear that the school does receive

a Grade R transfer.) To arrive at the value of Departmentally funded educators, the number of

such educators was multiplied by R153,722, the total cost to the employer of a person at the

lowest salary notch applicable to teachers with a four year qualification. The actual value

could be lower than this if the teacher only has three years of training. The actual value could

also be higher than R153,722, depending on years of experience (even a teacher with a three

69

year qualification and enough years of experience would cost more than R153,722). The

likely range of salaries in the official scales suggest that the R153,722 assumption could be

around 15% too high or around 7% too low. This would also be the margin of error for the per

learner cost of Departmentally paid educators, and more or less the margin of error for total

public cost given how small the transfer is in most cases.

Figure 15: Public funding of Grade R

Note: Learner weights used.

It was mentioned above that it can be estimated that up to 72% of Grade R learners receive a

transfer (even if the amount is not known). Table 50 breaks down the 72% by province and

quintile. Again, the fact that confidence intervals would be wide should caution against

reading too much into the differences seen in the table. However, the exceptionally low level

in NW is spread across all four quintiles for which data are available, suggesting that this

province may indeed be exceptionally under-funded with respect to the Grade R transfer.

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Table 50: Percentage of Grade R with a public transfer

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 77 85 81 80 FS 95 100 77 0 100 82 GP 100 4 41 0 22 KN 95 90 94 100 64 93 LP 57 88 100 100 78 MP 100 58 56 100 52 74 NC 100 94 48 89 80 77 NW 24 38 42 44 39 WC 100 55 94 90 100 90

SA 77 82 63 76 66 72

Target 100 100 100 100 100 100 Note: Learner weights used.

The following table provides the median per learner amount of the Grade R transfer in 2008,

but these figures are based only 85 surveyed schools (Table 52 provides the breakdown of the

school count). In compiling Table 51, schools which reported the same total amount of the

transfer for Grade R as for Grades 1 to 12 were excluded. The figures are rather telling. Even

if very few schools are considered, one would expect schools within the same quintile and

province which receive a Grade R transfer to receive an amount which was the same. Four

provinces for which we have figures, FS, MP, NC and WC, display rather high per learner

transfer values, in some cases exceeding R1,000 per learner in the poorest quintiles. KN, on

the other hand, clearly follows a different approach with per learner amounts not exceeding

R50. Generally, adherence to a pro-poor distribution appears to be the norm.

Table 51: Per learner value of the Grade R transfer

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 167 167 FS 689 1,059 1,506 1,200 GP KN 43 31 22 28 17 32 LP MP 1,014 1,171 283 511 545 643 NC 265 1,469 1,043 730 555 723 NW WC 1,571 1,483 1,269 1,201 433 1,112

SA 43 43 283 547 410 58 Note: Learner weights used. All statistics are the median of non-zero values.

Table 52: Surveyed schools with Grade R transfer amount

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 0 1 0 0 0 1 FS 7 3 8 0 0 18 GP 0 0 0 0 0 0 KN 10 6 5 1 1 23 LP 0 0 0 0 0 0 MP 3 3 2 1 1 10 NC 1 4 1 4 6 16 NW 0 0 0 0 0 0 WC 2 1 2 6 6 17

SA 23 18 18 12 14 85 Note: Learner weights used.

72% of Grade R learners are in schools which have Departmentally employed educators

working in Grade R. The next table provides the breakdown for this 72%. The high values

across provinces and quintiles (seldom less than 50%) suggest that there has been a

71

substantial deviation away from the position of White Paper 5, which envisaged having only

SGB-employed Grade R educators in public schools, using funds transferred to the school by

the Department. Only 9% to 16% of Grade R learners receive a transfer but no public posts.

Had the White Paper approach been followed, these figures would have been much higher.

Table 53: Percentage of Grade R learners in schools with posts

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 79 76 86 80 FS 64 72 85 100 0 77 GP 100 78 11 0 44 KN 100 90 100 100 65 95 LP 87 87 81 0 83 MP 90 86 92 35 93 79 NC 100 85 93 71 70 80 NW 44 77 30 44 42 WC 35 55 58 72 41 58

SA 76 82 81 53 57 72

Note: Learner weights used.

The number of Grade R posts per school, where these exist, is not high, as the next table

indicates. Given the inherent simplicity of these figures, and the fact that one would expect

more or less uniform treatment of schools within groups defined by province and quintile, the

differences seem meaningful. Again, the pattern of exceptionally low provisioning of public

Grade R resources in NW is evident.

Table 54: Average number of Grade R posts per school

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 1.1 1.1 1.0 1.1 FS 1.6 2.0 2.0 1.0 1.8 GP 1.5 1.4 1.0 1.3 KN 1.1 1.3 1.5 1.7 2.0 1.3 LP 1.2 1.1 1.5 1.2 MP 1.8 2.3 3.0 1.3 2.0 2.1 NC 1.0 1.9 2.0 2.2 2.0 2.0 NW 1.0 1.0 1.0 1.0 1.0 WC 1.0 2.0 1.3 1.3 1.5 1.4

SA 1.1 1.3 1.4 1.5 1.8 1.3

Note: School weights used.

Though Table 54 suggests there could be an anti-poor bias in the distribution of

Departmentally employed educators, the next table, which focuses on the per learner value of

these educators (using the cost assumptions mentioned earlier) suggests that that there is not a

clear pattern across the quintiles, at least at the national level. The patterns seen in the

previous table would largely be the result of the fact poorer schools tend to be more rural and

hence smaller.

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Table 55: Per learner value of Grade R posts

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 3,617 4,658 3,749 3,749 FS 4,521 4,330 3,843 2,196 4,330 GP 3,494 3,575 4,804 3,575 KN 4,392 4,521 3,366 6,684 3,454 4,392 LP 4,212 4,521 3,704 4,212 MP 6,495 5,590 5,490 4,045 3,379 5,590 NC 2,261 5,490 5,124 4,155 4,804 4,804 NW 3,014 4,270 3,208 3,271 3,271 WC 6,987 4,270 3,843 5,124 5,490 4,392

SA 4,270 4,521 3,843 5,124 3,454 4,270 Note: Learner weights used. All statistics are the median of non-zero values.

There is the possibility that schools receive physical resources other than educators from the

Department. This is permitted in the Grade R funding norms. The survey asks whether

delivery of learning support materials occurs, but not their monetary value. Table 56 indicates

that such deliveries are widespread across quintiles 1 to 4. Of course it is possible that

deliveries concentrate on materials needed to introduce the new curriculum, and should

therefore not be counted within the calculation of the value of the school allocation. There is

no way of telling from the data to what extent this is the case.

Table 56: Percentage of Grade R learners receiving LSMs

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 72 47 68 0 0 64 FS 24 28 19 0 0 21 GP 0 100 75 37 0 51 KN 21 22 75 94 0 41 LP 64 18 19 0 0 35 MP 100 54 49 79 43 68 NC 100 30 20 10 21 24 NW 68 60 77 0 0 63 WC 65 55 0 49 8 30

SA 58 36 59 54 8 49

Note: Learner weights used.

The next table provides estimates of the overall level of public funding per learner, based on

figures discussed above, and some imputation. The imputation involved using province,

quintile and whether the school had Departmentally employed educators to find out what

missing transfer values were. This was possible for the six provinces with transfer values in

Table 51. For the remaining three provinces (GP, LP and NW) no transfer values were used in

calculating the overall level of public funding. The existence of Departmentally employed

educators was used as an imputing factor given that schools with such educators tended to

receive a lower transfer, something one would expect. The imputation raised the overall

national total in the next table by just 2%, partly because the transfers account for a small

portion of overall spending, but also because the data were not rich enough to allow for the

level of imputation one would have wanted.

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Table 57: Total Grade R funding per learner

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall median

Overall mean

EC 3,784 4,658 3,749 3,880 4,057 FS 4,000 3,850 5,249 2,196 4,857 4,456 GP 3,494 3,575 4,804 3,575 4,092 KN 4,435 4,565 3,388 6,700 3,471 4,435 4,569 LP 4,212 4,521 3,704 4,212 4,381 MP 7,509 5,873 5,490 545 3,379 4,804 4,638 NC 2,525 6,534 5,854 4,884 5,285 5,318 4,917 NW 3,014 4,270 3,208 3,271 3,271 3,460 WC 8,471 5,753 5,112 4,206 1,112 4,008 3,969

SA 4,212 4,658 4,155 4,657 3,379 4,212 4,318 Note: Learner weights used. All quintile statistics are the median of non-zero values. The overall mean considers only non-zero values.

How far are the above figures from the per learner spending targets in the policy? The

progressivity of the distribution is supposed to be 120:110:100:80:20 according to the Grade

R funding norms. The national values in Table 57 indicate that the slope, with quintile 3 set to

100, is 101:112:100:112:81. Essentially, quintile 5 is receiving more than it should, and the

distribution in the quintiles 1 to 4 range is possibly flat, and not slightly sloped in favour of

the poorer quintiles. The pattern should be understood in the context of widespread use of

public posts for Grade R. It is inherently more difficult to adhere to per learner spending

targets in Grade R if one has public posts, which by their nature are indivisible.

The policy also states that the level of public Grade R per learner spending in quintile 3

should be about 70% of the level of the figure in Grade 1. However, the level could be as low

as 50% during an interim period whilst Grade R is being rolled out, in order to allow for a

faster roll-out process. The following table suggests that overall the spending level of 55% of

the Grade 1 cost is low, but within the permitted range. Yet the figures beg a question. How

can spending per learner be on the low side when the more costly approach of using

Departmentally employed educators is the most commonly used approach? An obvious

explanation would be that classes are larger than originally anticipated.

Table 58: Total Grade R spending by province in 2008

A: Quintile 3 per learner total

B: Programme 2 per learner spending 2008 A / B

EC 3,749 7,595 0.49 FS 5,249 8,180 0.64 GP 3,575 7,649 0.47 KN 3,388 7,091 0.48 LP 3,704 7,522 0.49 MP 5,490 7,571 0.73 NC 5,854 8,698 0.67 NW 3,208 7,714 0.42 WC 5,112 7,843 0.65

SA 4,155 7,559 0.55 Note: The figures in column B are based on the total 2008/09 programme 2 spending levels reported in the 2009 Provincial Budget Statements, enrolment figures in the DoE’s 2008 Education School Realities publication plus the assumption that the ratio of spending on each Grades 8 to 12 learner to spending on each Grades 1 to 7 learner is 1.12. This last ratio is based on the author’s analysis of education expenditure data in a previous project.

Before class sizes are analysed, a comparison is made between the overall public spending on

Grade R implied by the survey data, and the official expenditure figures. Table 59 suggests

that the correspondence is not good at all, and much lower than what was found for Grades 1

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to 12 above. In the case of Grade R, however, this poor correspondence should not surprise

us. Reportedly, there are problems with the official expenditure figures. It is widely believed

that there are budget classification problems with respect to budget programme 7, in particular

as far as the accounting of publicly funded educator posts in public ordinary schools are

concerned. Specifically, it appears if a part of this spending is incorrectly accounted for under

programme 2, which should deal just with Grades 1 to 12. It is possible that this largely

explains why the official figures reflected in Table 59 are so much lower on average than the

estimates derived from the survey. Moreover, the preceding discussion of the cost estimates

derived from the survey make it clear that these figures could be incorrect by perhaps 15%.

Table 59: Total Grade R spending by province in 2008/09

A: Estimate based on survey data (Rm)

B: Programme 7 spending on Grade R (Rm) A / B

EC 566 239 2.4 FS 121 70 1.7 GP 88 192 0.5 KN 675 205 3.3 LP 298 59 5.0 MP 226 72 3.1 NC 84 39 2.2 NW 40 157 0.3 WC 111 168 0.7

SA 2,210 1,202 1.8 Note: The figures in column B are the 2008/09 sum of sub-programmes 7.1 and 7.2, the two sub-programmes focussing on Grade R. Provincial Budget Statements published in 2009 were used.

With respect to publicly funded staffing of Grade R, one could say there are three approaches.

In some schools only publicly funded posts are used. In other schools only SGB-employed

educators are used. Thirdly, both types of educators may co-exist in the same school. The

following table indicates the extent of the three approaches, considering just those schools

which receive either a transfer or publicly funded posts (or both). Each value represents the

number of surveyed schools. At the national level, if number of schools is translated to

weighted Grade R learners, then 13% of learners have just SGB-employed teachers, 22% have

both types of teachers, and 65% have just Departmentally employed educators (here 100%

means all Grade R learners receiving some public funding).

Table 60: Approaches to Grade R staffing

Just SGB-employed

Both SGB-employed and Dept.

posts Just Dept.

posts Overall

EC 2 2 35 39 FS 7 3 17 27 GP 2 6 6 14 KN 4 28 32 LP 1 2 23 26 MP 7 6 16 29 NC 4 6 22 32 NW 3 6 9 WC 9 7 7 23

SA 32 39 160 231

Clearly, both types of teachers exist in each of the nine provinces. A breakdown of the above

figures by quintile reveal almost no pattern, though there is a slight tendency for

Departmentally employed educators to be used more extensively in the poorer quintiles.

75

Turning to the matter of class sizes raised earlier, Figure 16 provides the spread of class sizes,

with a breakdown according to the staffing approach pursued. The graph covers the situation

in 231 schools. Quintile 5 schools, which experience a rather different public funding

situation (and more private funding) are excluded as the aim was to focus on relatively non-

rich schools. Though the learner/educator ratio and class size are clearly different statistics,

for the purposes of this analysis they can be regarded as synonymous as the L/E ratio as

calculated here clearly excludes any managers (such as the principal) and as the Grade R

school day is relatively short and one educator would tend to teach the class all the time.

Figure 16: Grade R class sizes

Note: Learner weights used.

What is clear is that many Grade R learners experience rather large classes. The problem is

accentuated in schools that have Departmentally employed educators. In such schools 24% of

learners experience a class size exceeding 50 and 38% experience a class size exceeding 40.

In schools with just SGB-employed educators, on the other hand, only 6% of learners

experience a class size exceeding 50.

How much are SGB-employed educators paid? The next table indicates that in general their

total cost is well below the R153,722 assumed to be the annual cost of a Departmentally

employed educator in Grade R. Even if the R153,722 level is as much as 15% too high (a

possibility referred to above), the difference in the cost of the two types of educators is very

large. The gap is so large that some time was spent assessing whether the figures for the SGB-

employed educators could be incorrect, or could perhaps be referring to the monthly and not

annual cost. In the end this did not seem like a plausible explanation for the low values in

Table 61. If one excludes schools which have both Departmentally employed and SGB-

employed educators, the median overall pay level in Table 61 goes up from R24,000 to

R36,000, which could be an indication that where both types of educators exist, SGB-paid

educators tend to work as teaching assistants with a lower than average pay. However, even a

total annual cost of R36,000 is well below the R153,722 cost of a public post. Principals are

76

asked whether the Department prescribes what the pay of SGB employed Grade R educators

should be. In 35% of schools employing Grade R educators the response was that this is the

case. However, this prescription does not result in a markedly higher level of pay.

Specifically, the overall median of R24,000 seen in the next table remains unchanged when

only schools where a prescription is reported are considered.

Table 61: Annual pay of SGB-employed Grade R educators

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall median

EC 3,500 900 500 3,500 FS 120,000 60,000 120,000 48,084 169,734 120,000 GP 24,000 12,500 36,000 111,250 24,000 KN 3,800 13,000 13,000 1,750 13,000 LP 4,800 4,800 MP 2,000 12,000 2,000 25,800 24,000 24,000 NC 49,200 3,000 51,600 3,000 60,500 49,200 NW 18,000 15,600 15,600 91,440 91,440 WC 36,000 42,600 42,000 24,500 66,300 42,600

SA 18,000 15,600 13,000 25,800 66,300 24,000 Note: Learner weights used. Statistics represent the median of non-zero values.

The next table provides an attempt to gauge the proportion of the Grade R population cohort

that is enrolled in public schools. This is done by comparing enrolment in Grade R to

enrolment in Grade 2. A comparison with Grade 1 is avoided given the widely known and

exceptionally high levels of grade repetition in Grade 1. The comparison assumes, firstly, that

the population cohorts for Grades R and 2 are similar in size and, secondly, that grade

repetition in both grades is negligible, or at least equal. The estimate arrived at the national

level is 0.71, meaning around 71% of the Grade R cohort is enrolled in public ordinary

schools. If grade repetition in Grade 2 is slightly higher than in Grade R (this is likely) then

the 0.71 level represents a slight under-estimate. The breakdown by province suggests that it

is the poorer provinces, in particular EC, KN and LP, that make greater use of public school

Grade R, whilst richer provinces, in particular GP and WC, have lower enrolment levels in

public schools due to greater uptake in private institutions. It is perhaps best not to make too

much of the apparently low level of enrolment in quintile 4 (at the national level), especially

as a similar pattern was not seen in Table 49 above.

Table 62: Cohort coverage of Grade R

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 0.96 0.97 0.94 0.96 FS 0.54 0.72 0.64 0.90 0.50 0.65 GP 0.50 0.79 0.37 0.66 0.55 KN 0.90 0.76 0.93 0.46 0.90 0.78 LP 0.74 0.84 0.86 1.37 0.80 MP 1.08 0.57 0.92 0.40 0.53 0.58 NC 0.40 0.80 0.60 0.69 0.87 0.70 NW 0.79 0.72 0.57 0.44 0.57 WC 0.26 0.38 0.49 0.61 0.72 0.55

SA 0.85 0.73 0.79 0.46 0.68 0.71 Note: Learner weights used.

Table 63 indicates the percentage of Grade R learners charged fees, whilst Table 64 indicates

what the fee levels are. Both tables refer to the situation in 2009. Few learners in the poorest

two quintiles are charged fees, suggesting that the no fee policy is being applied to a large

extent in Grade R, though the policy is arguably unclear on whether it should. Much of the fee

charging of poorer learners is concentrated in GP and WC. The fee levels are generally low,

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and lower the poorer the quintile is. (The R1,200 value in WC in quintile 1 is based on the

situation in just one school.)

Table 63: Percentage of Grade R learners charged fees

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 0 0 7 0 0 2 FS 24 36 10 100 100 25 GP 0 77 95 100 100 96 KN 6 0 71 94 100 39 LP 0 0 100 100 0 15 MP 21 13 100 93 100 56 NC 0 13 70 66 100 58 NW 16 23 49 0 100 40 WC 65 45 90 100 100 91

SA 5 6 56 96 100 34 Note: Learner weights used.

Table 64: Grade R fee charged

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 10 10 FS 50 20 300 1,200 5,610 300 GP 70 700 1,980 6,000 740 KN 50 55 50 800 55 LP 80 100 100 MP 25 150 360 250 330 330 NC 20 240 480 1,200 480 NW 30 30 300 4,400 300 WC 1,200 20 300 1,080 1,000 660

SA 50 70 100 480 3,120 330

Note: Learner weights used. Statistics are all median values.

If fee income and public funding are compared, it becomes clear that most funding of Grade

R, specifically 79%, is public. As one would expect, this percentage is lower in the less poor

quintiles. Overall, the 79% level exceeds the policy target of 70%. The notable provincial

exceptions in Table 65 are GP and NW, both of which display private funding levels that are

in excess of public ones.

Table 65: Percentage of Grade R funding that is public

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 100 100 100 100 FS 98 99 99 64 0 96 GP 98 15 0 0 13 KN 100 100 98 99 68 96 LP 100 100 0 0 97 MP 100 99 93 51 61 90 NC 100 100 94 67 64 75 NW 99 0 0 0 15 WC 64 100 91 78 61 78

SA 100 100 91 69 34 79

Note: Learner weights used.

The last table provides statistics based on the school principal’s opinions regarding the

challenge of increasing Grade R enrolments. According to the principal, lacking demand

amongst parents is an important constraining factor, suggesting strongly that a supply-

oriented approach of just increasing recurrent spending will not be sufficient. Advocacy and

eventually legal action against parents who do not enrol their children in Grade R is important

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too. Moreover, insufficient physical space in the case of the minority of schools not yet

offering Grade R is the largest single constraining factor in these schools.

Table 66: Challenges in the way of higher Grade R enrolment

Percentage of Grade 1 learners who have been through Gr R (principal's estimate) 84 67 80 77

Percentage of principals seeing the main factor behind the above problem as being the following Parents do not understand the importance of Grade R. 58 36 50 47 Parents could not afford to send their children to Grade R. 20 36 33 30 No Grade R being offered close enough to the home. 26 30 36 31

Percentage of principals not offering Grade R selecting the following as the main reason Insufficient classrooms 28 48 39 40 Insufficient funding 19 20 20 20 Grade R is offered in other places near the school 37 18 27 25 People are not interested in Grade R in this school 0 5 0 2 The Department has not approved Grade R for this school 16 10 14 13

5.5 The funding of public schools and educational quality

Are the funding norms sufficiently geared towards improving educational quality in public

schools?

Parent views on how good their school is do not appear to correlate with the actual

performance of learners in a regression model, suggesting that without clearer learner

information flowing to parents, parent involvement in school governance and financing may

not focus optimally on the key matter of learner performance. What is positively correlated

with actual learner performance is the teacher’s view on what makes a quality school.

Teachers believing that learner performance defines a quality school, as opposed to

community involvement or discipline, are associated with better performing schools (within

groups defined by province and quintile). Only half of teachers hold this belief. This could

point to the need for greater clarity and advocacy on the part of the Department around what

really defines quality schooling.

Responses on what principals, SGB parents and teachers view as the key priorities in

improving education reveal patterns that are important for policymakers. Perhaps surprisingly,

few of the respondents, even amongst the principals, select increasing the powers of principals

as a number one or even a number two priority. Better performing schools, though they

already have relatively lower learner/educator ratios, place an exceptionally large emphasis on

reducing class sizes even further. This has important implications for the regulation of school

fees. High fees are largely associated with the employment of additional teachers. If fees are

capped or reduced through a policy intervention many schools which value strongly the

reduction of class sizes, would see their class sizes increase. This is likely to cause tensions

between the Department and schools.

Support for a system that would reward schools in monetary terms for improving their learner

performance is widely supported by principals, SGB parents and teachers. Around 80% of

each of the three respondents support this, with the support being slightly stronger amongst

historically disadvantaged schools.

The non-SGB parent is asked what he or she thinks of the teachers at the school – 93% said

they were either ‘very good’ or ‘good’. The non-SGB parent is also asked what he or she

thinks of the principal (95% said ‘very good’ or ‘good’), and whether he or she believes the

school performs better or worse or the same as other schools in the area. One might expect a

correlation between the responses to these questions and the average Systemic Evaluation

score of the school, if one controls for a few key variables, in particular province and quintile.

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Various permutations of the responses were entered in a regression model where the school

SE score was the dependent variable. Only the original scores were used, meaning only

schools with Grade 6 were included in the model. Other independent variables were dummies

for province and quintile. The model was run with and without the higher score schools to

cater for the possibility of different dynamics in the less successful four-fifths of the system.

Whilst the provincial and quintile variables explain as much as 59% of the variation in the

scores, the non-SGB parent responses were not statistically significant in any of several

permutations. This suggests something important, namely that in general parents do not know

how well their children learn at school. A question in the survey asks the non-SGB parent

whether he or she receives enough information from the school regarding the learner’s

performance. Only 4% said no, suggesting that parents do receive reports from school, even if

those reports may not be very good indicators of performance. Given the general lack of

standardised and externally marked or moderated assessment in primary schools, and the

absence of standardised examinations, the fact that parent responses should not be good

predictors of learner performance should not come as a surprise. The implications of this for

the funding system and school governance in general is that parent participation in the school

may not put the right kind of emphasis on the right things, at least not from the perspective of

improving learner performance. This could change if better performance accountability

systems existed, for instance in the form of reports to parents based on more standardised

assessments than is currently the case.

A question in the survey asks principals, SGB parents and teachers to select one of four

definitions of a quality school. The details appear in the next table. Whilst the most common

selection is the third one, namely that learner performance defines quality schooling, over half

of respondents selected other options. The similarity of the aggregate statistics for the three

respondents suggests that respondents in one school may tend to provide the same responses,

reflecting the culture of the school. In fact, the correlation between the three respondents is

rather low (between 0.10 and 0.20), indicating that there is a large degree of variation of

views within schools. What is interesting is that better performing schools are less inclined to

say that learner performance defines quality schooling. It could be that these schools believe

they have reached their full potential in terms of learner performance and hence focus on

other things, in particular discipline.

Table 67: Views on what makes a quality school

A school where the

community is involved

A school with an attractive

physical environment

A school that achieves good

learner results in examinations and

assessments

A school with disciplined

staffmembers and learners Total

Principal Lower scores 32 1 48 19 100 Middle scores 25 1 45 29 100 Higher scores 14 0 29 58 100 Overall 25 1 43 31 100

SGB parent Lower scores 35 0 51 15 100 Middle scores 29 2 42 27 100 Higher scores 20 1 39 39 100 Overall 29 1 45 25 100

Teacher Lower scores 26 4 46 25 100 Middle scores 26 2 49 24 100 Higher scores 17 0 33 51 100 Overall 24 2 45 30 100

Correlations between the responses to the Table 67 question and actual performance in

schools with Grade 6 were tested and one important regression model yields a statistically

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significant positive correlation. Teachers who selected the third option are associated with

schools that perform better when schools with higher scores are excluded. The results in Table

68 indicate that a teacher believing learner performance defines quality schooling is

associated with an average score that is 2.2 higher than if the teacher selecting one of the

other three options. To provide some context, the average score for the ‘lower scores’ group

of schools is 18, that for the ‘middle scores’ group 28 and for the ‘higher scores’ group 49. A

difference of 2.2 associated with just one variable describing what is, in a sense, the teacher’s

educational ideology, is considerable.

Table 68: Effect of teacher response

Coeff. t-stat Level of

significance

Is in FS 5.1 2.84 *** Is in GP 6.0 4.66 *** Is in KN 0.3 0.35 Is in LP 1.3 1.16 Is in MP 3.7 2.65 *** Is in NC 9.3 3.84 *** Is in NW 4.0 2.46 ** Is in WC 8.4 5.02 *** Is in quintile 2 0.9 1.01 Is in quintile 3 3.4 3.88 *** Is in quintile 4 6.5 6.16 *** Is in quintile 5 5.1 1.75 * Teacher selected (c) 2.2 2.37 **

Intercept 17.3 17.29 ***

R2 0.39 n 241 Dependent variable is the average of the normalised mathematics, science and language results in the 2004 Systemic Evaluation as explained in section 4. *** means significant at the 1% level (i.e. highly significant), ** at the 5% level and *** at the 10% level.

The following three graphs are based on a question which asks respondents to select five

education priorities, and number them from 1 to 5, from most to least important. Only

priorities one and two were used. The first priority was given a weight of 2, and the second

priority a weight of 1. The spread of responses is illustrated according to the average learner

performance of the school (see the horizontal axis). Virtually all surveyed schools are

included in the analysis as the response rate to the question was high. The fact that all of the

ten priorities should feature fairly prominently in each of the three graphs underscores the

notion that the problems of the schooling system require a mix of interventions. There is no

single ‘magic bullet’. At the same time, the narrowness of some of the wedges is notable. The

more powers to principals priority is not particularly popular amongst any of the respondents,

not even the principal. This points to the need for caution if there is further devolution of

powers to schools. Specifically, the feasibility of transferring specific functions to schools

needs to be interrogated (the survey question deals with the matter in a very general manner),

and it needs to be made clear to principals why powers are devolved. The fact that better

learner assessment is not highly prioritised suggests that expansion in this area must be

carefully justified to schools. What is particularly prominent is the high priority assigned to

smaller classes amongst better performing schools. These schools already have

learner/educator ratios that are below average – schools with higher scores have a median L/E

ratio of 27, against 30 and 31 in the ‘lower scores’ and ‘middle scores’ groups. This is due to

the private employment of educators.

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Figure 17: The principal’s education priorities

Note: Learner weights used.

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Figure 18: The SGB parent’s education priorities

Note: Learner weights used.

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Figure 19: The teacher’s education priorities

Note: Learner weights used.

Versions of the above graphs for just primary or just secondary schools did not display

markedly different patterns.

Respondents were asked the following question: ‘What do you think of the idea of providing

more money (rewards) for schools that show big improvements in standardised assessments?’

The results appear in Table 69. Clearly, support for such an intervention is high, even

amongst teachers, and is in fact slightly higher in the schools with the greatest learner

performance challenges.

Table 69: Views on performance rewards for schools

Lower scores

Middle scores

Higher scores Overall

Principal This is a good idea 81 82 72 79 This is not a good idea 19 18 28 21

SGB parent This is a good idea 82 85 77 82 This is not a good idea 18 15 23 18

Teacher This is a good idea 83 83 71 80 This is not a good idea 17 17 29 20

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6 Appropriate controls over school funding and related matters

6.1 Sharing of vital financial information with schools

Is the essential financial information being shared?

School principals must obviously understand the school funding policies. Generally, they do

not have serious difficulties understanding the policy once they have access to the

information. However, a notable problem is that a third of principals complain that they are

not able to obtain the necessary policy information.

It is particularly important that principals, and arguably parents, should know what the school

allocation targets are, as without this knowledge they are not able to demand from the

Department the funding that is legally due to them. In several provinces, knowledge amongst

principals is good, but parents are far less likely to know.

When it comes to the funding information on specific schools, the Departments are relatively

good at providing information on what funding is actually committed to schools for the

coming year. 95% of schools seem to have received this information. However, few schools

know what funding will be provided beyond the next year, implying that the required MTEF

framework is not being applied to schools, and that medium-term planning in schools would

be difficult.

Some provincial details. Principals in EC and KN seem to experience exceptionally serious

problems in obtaining policy information and understanding it. Around half of the principals

in these provinces are affected. Principals in EC and KN, but also LP and MP, are clearly not

as knowledgeable about the school allocation targets as they should be, suggesting there are

considerable information sharing problems in all these four provinces (see Table 71). In these

four provinces, school-specific funding information is generally not provided as it should be,

in other words as a per learner amount, multiplied by an assumed number of learners,

resulting in a total amount. In MP, the ability of the Department to communicate the monetary

values of goods and services bought for schools to these schools seems particularly poor

(Table 73). WC is the only province where schools tend to know what the level of actual

funding will be more than one year into the future.

The following graph illustrates the percentage of principals describing the support they

receive from the Department with respect to school funding as poor or non-existent. In most

provinces the problem is fairly limited to around 10% to 15% of principals, but the situation

in KN is notably worse, with close to 30% of principals not being satisfied.

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Figure 20: Dissatisfaction with Departmental support

Note: Learner weights used. Points in the graph indicate mean values in the sample, whilst vertical lines indicate the range between the lower and upper bounds of the 95% confidence interval.

The next table suggest that principals in KN and EC have particular difficulties in obtaining

policy information with respect to school funding, and understanding it. It is worth bearing in

mind that where policy information is obtainable, the majority of principals make use of

secondary materials such as manuals and brochures. NC is exceptional here, however, with

most principals making use of the actual policy text as their main source of information. The

second set of responses in Table 70 (obtained from a different survey question to those in

Figure 20) confirm that KN experiences problems with respect to Departmental support. The

high value for GP in this line may not mean much, especially given that principals in GP

appear to have the least difficulties in understanding the policy.

Table 70: General access to policy information

EC FS GP KN LP MP NC NW WC SA

% of principals who have difficulty obtaining school funding policy information 53 23 16 44 33 30 20 29 27 35

% of principals who have not received useful support from the Department in the last two years 3 8 24 30 11 16 23 8 7 15

% of principals saying the funding policy is difficult to understand 49 29 19 40 24 30 20 31 36 33

Note: Learner weights used.

The last row of Table 71 indicates what percentage of principals responded to the question of

what the national school allocation target was for the current year (2009) and the quintile of

the school. Clearly the response rate varied greatly, from 10% in MP to 95% in FS. The table

also indicates the median values from the principal responses. Those that agree with the actual

target values (published in Government Notice 1089 of 2008) appear in bold. There are two

distinct reasons why responses might deviate from the national target values. Principals may

be poorly informed about what they should receive. On the other hand, provinces may

publicise their own targets, which may differ slightly from the national ones, and principals

may have referred to these (for understandable reasons) and not the national targets (the

question did clearly refer to national targets, however).

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Table 71: Knowledge of targets amongst principals

Quintile Correct EC FS GP KN LP MP NC NW WC

1 807 540 804 795 807 400 97 775 804 807 2 740 550 711 775 740 364 85 740 740 711 3 605 32 605 757 510 320 240 605 706 581 4 404 404 466 369 105 581 404 430 5 134 240 155 160 82 527 134 206

Response rate 45 92 90 32 22 10 70 97 73

Awareness of the national targets seems relatively good in five provinces. However,

principals in EC, KN, LP and MP seem poorly informed in this area (in KN responses

provided are not far off the mark, but the response rate is low). Notably, NC seems to be

communicating a target for quintile 5 to schools which is far above what the policy stipulates.

The following table suggests that awareness of the national targets amongst SGB parents is

much lower than that for principals, though parents in FS fare rather well.

Table 72: Knowledge of targets amongst SGB parents

Quintile Correct EC FS GP KN LP MP NC NW WC

1 807 540 100 600 250 45 700 807 2 740 540 711 450 500 250 740 580 711 3 605 60 340 750 550 320 20 605 650 404 4 404 404 466 200 581 400 350 5 134 240 150 404 100 555 400 127

Response rate 28 70 43 5 8 6 19 46 24

95% of schools were able to provide at least one or a few figures relating to the funding

commitments of the Department (not counting whether this commitment occurred with

respect to a financial transfer or Departmental purchases). Communication of school-specific

commitments to individual schools thus seems good. In four provinces, EC, KN, LP, MP, the

Department does not communicate this information in the form of the three basic components,

namely per learner funding, enrolment figure used, and the total to be spent on the school (see

Table 73 below). One presumes that in these provinces the tendency is for the Department to

simply communicate what the total figure is. This makes it more difficult for the school to

query errors. The error could relate to the per learner amount or the enrolment figure. By the

time the survey was run, half of schools had been informed of what their final school

allocation for 2008 was – the survey was run in March, the same month in which budgets for

the next April to March financial year of the Department would be finalised. However, only in

WC were a substantial number of principals aware of what the allocation would be in 2010.

Only around one-third of those schools that do receive Departmentally purchased goods and

services reported being told the monetary value of everything the Department bought, as

required in the policy. In the case of the remaining two-thirds, it would be difficult for the

school to hold the Department fully accountable to its funding commitments. MP seems to be

in a notably worse position in this regard, with only 7% of principals reporting that they

received all the necessary financial information.

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Table 73: Receipt of school-specific financial information

EC FS GP KN LP MP NC NW WC SA

% of schools that were able to provide a Rand amount of funding committed by the Department 88 100 94 96 98 93 95 100 98 95

% of schools that get told both the per learning funding and the total funding 13 98 79 20 33 7 81 98 86 44

% of schools that had been informed of the final 2009 amount 24 94 60 75 0 72 26 55 75 52

% of schools that had been informed of the 2010 amount 11 1 8 15 0 0 0 10 64 12

% of schools told what the value is of at least some Departmentally purchased goods and services (where this is received) 80 35 55 71 50 26 47 45 83 56

% of schools told what the value is of all Departmentally purchased goods and services (where this is received) 45 35 29 37 33 7 19 31 65 33

Note: Learner weights used.

6.2 Transfer of funds and resources to schools

Are funds and resources transferred to schools as they should?

Though close to 100% of schools seemed to be receiving a financial transfer in 2008, this is a

rather recent phenomenon in many schools. For 25% of schools 2008 was only the second

year in which they received a transfer. Implementation problems need to be seen in the light

of the relative newness of the system for many schools.

43% of schools received their financial transfer in the form of one payment. A further 43%

received two payments. The policy requires the Department to channel the transfer through

just one payment. Around two-third of principals complain that the funds arrive too late. This

seems to be more of a systems problem than an implementation problem. The Department

appears to stick to its commitments in general, but these commitments seem inadequate.

In the around 80% of schools that receive Departmentally purchased goods and services the

most common practice is for the Department to manage the whole procurement process.

However, around 20% of principals report being able to place orders directly with suppliers

and to instruct the Department to pay the supplier from their account (see Table 76). Around a

third of schools complain that deliveries are late and a third indicate they are not asked what

goods and services they need. However, these problems are substantially reduced when the

approach of a direct link between the supplier and the school is employed.

Though a breakdown by the Department of the school allocation funds by item is permitted to

guide schools, a third of schools report that these breakdowns are not merely guidelines, but

mandatory. This is not in line with the policy. Half of school principals find the ringfencing of

the school allocation around specific items a hindrance.

Around three-quarters of those principals receiving food from outside for the feeding

programme believe that the programme would be improved if funds were transferred to the

school so that the school itself could manage the programme.

Some provincial details. It is only in GP and LP that the bulk (90%) of schools receive the

financial transfer through just one payment in the year, as required in the policy. Principals in

NC are most inclined to complain that Departmentally procured goods and services arrive

late. WC is most inclined to allow schools to deal directly with suppliers, and to instruct the

Department to pay the supplier. The Department in NC appears to be particularly prescriptive

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when it comes to how the school allocation should be spent, though it is in GP that principals

seem most opposed to these prescriptions.

Table 11 in a previous section indicated that virtually all schools receive a financial transfer.

Only 2% do not, and these schools are found mainly in EC and NC. The next table provides

the median values for the principal response indicating when financial transfers to the school

began. GP clearly has more experience with these transfers than the other provinces. For 25%

of schools nationally, 2008 was only the second year in which financial transfers occurred,

and for 4% it was the first year. These changes are undoubtedly linked to the introduction of

the no fee policy (which created a strong need for having at least some public funding flowing

to the school fund), but even schools that are not no fee schools appear to have seen an

increase in the proportion of schools receiving money from the Department.

Table 74: Year in which transfers to schools began

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 2007 2007 2007 2007 FS 2004 2007 2007 2007 2007 2006 GP 2006 2003 2002 2000 2000 2001 KN 2006 2005 2005 2006 2005 2006 LP 2006 2007 2007 2006 2005 2006 MP 2006 2005 2006 2004 2004 2005 NC 2001 2007 2006 2007 2008 2007 NW 2006 2007 2006 2004 2004 2006 WC 2004 2004 2006 2000 2006 2005

SA 2006 2007 2006 2004 2005 2006

Table 75 indicates the extent to which provinces effect the financial transfer through one

payment, as required in the policy, or through more than one payment. Just under half of

schools receive one payment in the year, with most of the remainder receiving two payments.

GP and LP are best at complying with the policy in this regard. Two-thirds of schools

complain that funds arrive too late, and the figures below suggest that it is the schedule of

payments itself which is at fault (as opposed to non-compliance with the schedule).

Table 75: How financial transfers are implemented

EC FS GP KN LP MP NC NW WC SA

% of schools with different numbers of financial transfers in 2008 1 38 20 90 12 92 46 30 5 0 43 2 40 8 10 79 5 44 34 91 70 43

>2 22 72 0 9 3 9 35 3 30 14

% of principals saying the transfer arrived too late 89 38 59 42 88 79 72 83 48 66

% of principals saying the Department did not stick to its schedule of payments 33 6 14 1 12 0 5 12 10 12

% of principals saying the Department did not indicate when the funds would arrive 15 5 21 5 22 45 16 4 9 16 Note: Learner weights used.

Section 5.1.1 above referred to the fact that between 392 and 462 schools receive

Departmentally purchased goods and services (the difference of 70 schools comprises schools

which were clearly supposed to receive something, but did not explicitly report receiving

anything in the survey questionnaires either because they in fact did not receive anything, or

because they failed to indicate what they received). Put differently, between 70% and 84% of

learners are in schools that received goods and services from the Department in 2008. The

Department’s control is intended to reduce fraud and mismanagement, but runs the risk of

over-bureaucratising the resourcing of schools. Maintaining just the right level of

Departmental control is clearly a critical matter. The next table indicates that in the case of at

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least 68% of schools the Department manages the procurement of the goods and services

directly. However, in the case of over 19% of schools the school order the goods directly from

the supplier and instructs the Department to pay the supplier from the account of the school.

WC adopts the latter the approach to a particularly large degree. A sizeable minority of

schools complain that goods are delivered late, though the figure exceeds 50% in the case of

NC. FS principals are most inclined to question the appropriateness of the goods and services

delivered. Tellingly, schools where only the Department deals with the supplier are 3.1 times

as likely to complain about the lateness of deliveries compared to schools which themselves

deal with the supplier (though the Department pays the supplier). Similarly, the schools in the

former group are 2.4 times as likely to complain about the appropriateness of the deliveries as

those in the latter group.

Table 76: How Departmental purchases are implemented

EC FS GP KN LP MP NC NW WC SA

% of schools that receive goods experiencing particular Departmental purchase modalities Department 71 74 76 90 57 72 42 88 14 68 Via supplier 13 18 12 6 33 14 20 10 54 19 Mixed 13 9 0 0 7 14 13 2 24 9 Other 3 0 12 4 3 0 25 0 8 4

% of principals who receive goods complaining about severe lateness of deliveries 46 43 24 36 16 19 54 14 5 27

% of schools that receive goods but are not asked what goods they need 27 14 51 28 24 25 61 23 63 31

% of principals who receive goods complaining about the appropriateness of the delivered goods 9 26 9 14 9 18 17 10 0 12 Note: Learner weights used. ‘Mixed’ means both of the preceding modalities are employed simultaneously.

Principals in GP and WC appear to be least consulted when it comes to what the Department

procures for them. This might be an indication that principals in these two largely

metropolitan provinces are more selective when it comes to what is delivered. For instance,

they may not be happy with a simple question of how many mathematics textbooks they

should receive. They may also wish to select the author.

Ringfencing the school allocation amount according to key items is clearly common practice

in all provinces according to Table 77. A third of principals nationally (but 72% in NC) say

the ringfencing is compulsory and not just a guideline (as the policy says it should be).

Principals in GP and MP are most likely to regard this ringfencing as a hindrance. This could

be a reflection of the prescriptiveness of the Department, but it could also be a reflection of

how keen school principals are to take management decisions in an independent manner,

without restrictions imposed by the Department.

Table 77: Ringfencing of the school allocation

EC FS GP KN LP MP NC NW WC SA

% of schools with specific items ringfenced LSMs 95 98 95 88 63 88 87 62 81 85 Stationery 85 37 30 39 33 80 87 48 64 52 Equipment 20 26 27 75 33 46 4 25 29 39 Services 79 95 89 83 36 89 84 33 79 74 Maintenance 83 96 88 76 35 77 82 34 77 72

% of principals saying that compulsory ringfencing is applied by the Department 5 48 52 44 25 20 72 38 35 34

% of principals who find the ringfencing or recommended breakdown a hindrance 56 37 72 42 38 63 47 45 51 52 Note: Learner weights used.

90

Only a few questions in the survey focus on the funding of school feeding, partly because

school feeding falls under a separate programme, and not under the school funding norms,

and partly because there have been official and in-depth analyses of school feeding in schools

in previous years. The next table illustrates the mix of funding modalities for school feeding

according to the survey data amongst the 274 schools which reported having a school feeding

scheme. 73 schools received a transfer from the Department to offer school feeding, whilst a

further 68 schools received food from a Department-paid supplier. 47 schools received both

food funded by the Department and funding from the Department. This is perhaps explained

by the fact that schools consider their school funding norms transfer as being, at least in part,

a top-up for the school’s school feeding scheme even though this is not official (the 47

schools either listed school feeding revenue from the Department as a separate revenue stream

or indicated that the Department required them to ringfence a part of the school allocation for

school feeding). Altogether 48 schools indicated as a separate revenue stream money for

school feeding from a government body other than the Department of Education. Here it is

possible that schools are not aware that the school nutrition programme was transferred from

health to education some years back. (The structure of the questionnaires suggests that if a

school was receiving food from what it thought was a non-education public body, it would be

classified in the first Table 78 row, under ‘Department spends for the school’.) 38 schools

received no public resourcing at all linked in any way to school feeding, but spent money

from the school fund on this activity anyway. Importantly, amongst the 135 schools receiving

deliveries of food, 52 schools also spent money from the school fund on school feeding.

Table 78: Configuration of funding for school feeding

Department spends for the school 1 1 1 1

Department transfers funds 1 1 1 1

Other public organ transfers funds 1 1 1 1

No public funding but school funds own scheme 1

Surveyed schools 73 68 47 38 20 11 9 8

The next table provides a profile by province. One thing that is striking is that a particularly

high percentage of schools in two poor provinces, KN and NW, report having no school

feeding (the percentages are 73% and 78%). EC seems to favour providing food and not funds

to schools, whilst FS, NC, NW and WC clearly favour providing funds and not food. The

other provinces present a relatively even spread across the two approaches. At the national

level, about half of those schools which do have publicly funded school feeding receive funds

and the other half receive food. Private spending on school feeding in the absence of public

funding is most widespread in GP. 72% of the principals in schools receiving food from

outside believe that the programme would be better run if all the funds were transferred to the

school. This belief is particularly strong in EC, FS and NC, suggesting that there could be

problems with the design of the official programme, especially in these provinces.

Table 79: Control over school feeding funds

EC FS GP KN LP MP NC NW WC SA

School feeding situation Department spends 42 25 8 15 33 34 38 4 14 23 Public transfer 10 53 6 20 29 26 75 18 37 22 Both the above 5 21 2 9 12 18 34 3 12 10 Private school feeding only 4 7 32 1 0 5 3 2 11 7 No school feeding 49 37 57 73 50 53 17 78 50 57

% of principals with an outside supplier saying school feeding funds should be transferred to the school

93 95 61 59 73 74 89 56 55 72 Note: Learner weights used.

91

6.3 Appropriate quintile, no fee and section 21 classifications

Are schools appropriately classified with respect to quintiles, no fee schooling and section 21

functions?

The survey data confirm that the school quintiles are not equally sized in terms of numbers of

learners. This is not necessarily a serious problem. What is more important is that the

quintiles should provide an accurate ranking of socio-economic status. Some analysis of how

ex-departments, type of area, race and the educational level of parents correlate with quintiles

suggests that the quintiles do broadly provide an appropriate socio-economic breakdown of

the schooling system. However, such an analysis cannot show us to what extent a minority of

schools may be misclassified within the system.

Over one-third schools have complained formally, through a letter, to the Department of their

quintile placement. Half of schools have complained, if one also counts verbal complaints to

the Department. Complaints are most common amongst schools originally placed in quintile

3, which contradicts the notion that it is mainly quintile 5 schools, with their exceptionally

low official school allocations, which complain most (see Table 84). 9% of schools have seen

their quintile classification change as a result of a complaint. What these figures suggest is

that the quintiles could be causing a sense of unfairness, but also ‘gaming’ of the system

whereby schools try to compile a sufficiently strong case of greater poverty in order to secure

more funding. The survey data confirm the commonly made argument that data on areas

surrounding schools as a basis for socio-economic status are problematic, because there is so

much fluidity between school catchment areas when it comes to enrolment. Responses from

school principals suggest that over half of the learners enrolled in schools are not attending

the school that is closest to them (and offers their grade). This phenomenon is common across

all the quintiles, and quintile 5 is not exceptional in this regard. However, quintile 5 principals

are slightly more inclined to say that the learners coming from other catchment areas are from

communities that are poorer than the community surrounding the school. Specifically, 52% of

quintile 5 principals say this, against 40% nationally. These findings need to be seen in the

context of the analysis made in section 5.3 where fees charged were used as a basis for a more

enrolment-oriented (as opposed to geographically-oriented) measurement of socio-economic

status. In that analysis, it was estimated that schools accounting for 23% of total enrolment

could be in the wrong quintile. Of course the quintile classification system should be assessed

in the light of what the alternatives are.

In general, people in no fee schools are highly satisfied with this setup. Table 44 above

indicated that only 3% of principals are ‘very dissatisfied’ whilst a further 16% are ‘a bit

dissatisfied’. One can expect satisfaction levels to improve once teething problems, in

particular blatantly insufficient public funding (as discussed in section 5.1.1), are resolved.

Table 86 in this section indicates that only 5% of parents in no fee schools are dissatisfied

with the new setup. If one focuses on the opinions of people not in no fee schools (they would

largely be from quintiles 3 to 5), the receptiveness towards no fee schooling in future is

relatively good. The percentage of principals wanting no fee status is 70%, 48% and 23% in

quintiles 3, 4 and 5 respectively (Table 87). SGB parents are slightly more keen than school

principals, and non-SGB parents are even keener.

Though 98% of schools seem to receive a financial transfer from the Department (see Table

11 from a previous section) only 78% of school principals report enjoying section 21 status,

the status one would normally associate with the receipt of a financial transfer (Table 91). The

phenomenon of receiving a transfer despite not having section 21 status is fairly evenly spread

across quintiles, suggesting that the explanation is not to be found in the introduction of no

fee schooling in quintiles 1 and 2. An analysis of the percentage of the school allocation

provided as a financial transfer indicates that section 21 status is associated with a

substantially larger percentage of funding taking the form of a financial transfer (Figure 22).

92

This does suggest that section 21 status is not meaningless, and that with this status comes

increased rights to a financial transfer. 33% of principals report that there was no management

readiness assessment of the school before the Department began transferring funds into the

school’s bank account (Table 91). Such an assessment is required by policy. Around half of

principals say it has become easier to access funds in the last five years, whilst 10% say it has

become more difficult (Table 92). Moving forward, it seems significant that given the choice

between a Department that managed the school’s funds and resourcing efficiently and

transparently, and a scenario where the Department transfer all non-personnel funds to the

school, the great majority of principals and SGB parents (around 90% in both cases) prefer it

if the school receives the funds. Given that principals come from an educator background, and

might be more oriented towards managing pedagogy than managing funds and procurements,

it is perhaps surprising that so many principals would prefer to exercise financial management

responsibilities themselves, even in the case of an ideally efficient Department. Transferring

further financial responsibilities to principals than is currently the case might not be

welcomed by some principals (10% perhaps) but the great majority would apparently

welcome this.

Some provincial details. Many of the inter-provincial differences, for instance with respect to

the percentage of principals that have complained about their quintile placement, seem too

small to warrant comment. However, it does appear noteworthy that NC and NW display

rather exceptional figures across several tables. In these two provinces parents tend to be more

dissatisfied with no fee schooling (Table 86), schools are more inclined to complain formally

about their quintile placement (Table 84), and there have been more quintile adjustments

following complaints by schools. All this seems to suggest that there could be relatively

serious problems with the way the quintiles were originally set up in these two provinces. KN

stands out as a province where an exceptionally low percentage, 62%, of schools having

section 21 status. WC has a particularly low percentage of principals saying that a

management readiness assessment was run before financial transfers began. In MP the median

percentage of the school allocation coming to the school as a financial transfer is particularly

low, at 39%, and principals in this province are least inclined to say that access to public

funds has improved over the last five years.

In section 5.1.1 above the fact that the five quintiles are not of an equal size at the national

level, despite this being an intention of the policy, was discussed. Table 13 showed this with

respect to the number of surveyed schools. Table 80 illustrates the same point with reference

to the number of weighted learners. The table also provides the ‘poverty table’, or breakdown

of the national quintiles within each province, as published in 2006 and again in 2008.

Currently, the 2006 breakdown still applies, though it is envisaged that the 2008 breakdown

would be implemented in future years. The greatest disparities between the breakdown

amongst the surveyed schools and the 2006 official breakdown are clearly those for quintile 5

of EC and quintile 1 of NC.

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Table 80: The poverty table

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

Government Notice 869 of 2006

EC 34.8 21.6 21.0 11.6 10.9 100.0 FS 30.8 14.9 20.1 18.8 15.4 100.0 GP 10.5 11.4 27.4 27.2 23.6 100.0 KN 24.2 18.8 25.6 17.3 14.1 100.0 LP 34.0 22.3 24.9 11.6 7.2 100.0 MP 16.7 20.2 29.8 19.9 13.5 100.0 NC 26.3 17.7 21.6 14.8 19.6 100.0 NW 22.7 15.2 30.5 20.5 11.0 100.0 WC 6.5 8.0 23.1 27.7 34.6 100.0

SA 23.7 17.5 25.1 18.2 15.5 100.0

Government Notice 1089 of 2008

EC 28.2 21.7 19.7 17.4 13.1 100.0 FS 19.7 22.0 18.9 21.8 17.6 100.0 GP 12.7 15.4 19.3 23.0 29.6 100.0 KN 20.9 22.2 21.1 20.2 15.4 100.0 LP 28.1 24.7 23.9 15.6 7.6 100.0 MP 25.3 22.4 21.0 18.7 12.7 100.0 NC 22.3 22.6 21.6 20.6 12.9 100.0 NW 23.5 23.4 18.7 17.0 17.3 100.0 WC 9.5 13.6 16.9 22.1 37.9 100.0

SA 21.6 20.9 20.4 19.4 17.7 100.0

Surveyed schools

EC 45.9 27.6 20.6 5.9 0.0 100.0 FS 29.5 20.9 28.2 12.5 8.9 100.0 GP 2.9 6.6 28.6 39.0 22.9 100.0 KN 23.6 13.7 23.0 26.7 13.1 100.0 LP 31.4 32.3 28.0 5.5 2.8 100.0 MP 15.2 26.9 16.5 21.2 20.2 100.0 NC 4.1 22.2 29.6 23.6 20.5 100.0 NW 20.8 15.3 48.0 5.4 10.5 100.0 WC 7.2 10.5 21.8 31.3 29.1 100.0

SA 23.4 19.5 25.6 19.3 12.3 100.0 Note: Learner weights used for surveyed school statistics.

Of the 525 surveyed schools, 17 did not provide quintile values. A further three schools

provided invalid quintile values (quintile 6 or 7). Of the 20 schools for which no valid

quintiles were available, six were in MP. The remainder were distributed rather evenly across

the remaining eight provinces. Only four schools explicitly stated, in response to one

question, that they did not know what their quintile was – two of these schools were in MP. It

is possible that there are special quintile placement problems experienced in MP.

The following three tables indicate patterns in the relationship between the quintiles, on the

one hand, and ex-departments, types of areas and race on the other. The patterns support the

notion that the quintiles do correlate with real historical and socio-economic patterns rather

well. For instance, ex-homeland and rural schools have a very strong presence in quintiles 1

and 2, whilst quintiles 4 and 5 are mainly urban, with quintile 5 being mainly suburban (or

historically white). However, the correlatyions should not be exaggerated. White learners are

a minority in quintile 5, though white teachers are a majority. There is a sizeable minority of

suburban schools in quintile 4 and of ex-homeland schools in quintiles 4 and 5.

94

Table 81: Distribution of ex-department across quintiles

Quintile 1

Quintile 2

Quintile 3

Quintile 4

Quintile 5

Department of Education and Training (African, largely urban)

9 18 32 37 40

Homeland department (African, largely rural) 85 74 59 28 9 House of Representatives (Coloured) 2 4 5 14 10 House of Delegates (Indian) 0 0 0 7 10 House of Assembly (White) 0 0 1 1 8 Other 5 3 3 14 23

Total 100 100 100 100 100 Note: Learner weights used.

Table 82: Distribution of type of area across quintiles

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Rural village 85 78 54 18 1 Farm 5 3 2 0 0 Town 2 1 5 13 22 Inner city 0 0 0 1 7 Suburb in a city or town 1 1 2 23 54 Township 5 16 33 45 16 Informal settlement 2 1 3 0 0

Total 100 100 100 100 100

Note: Learner weights used.

Table 83: Distribution of race across quintiles

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Learners African 97 91 91 75 48 Coloured 3 9 9 19 14 Indian 0 1 0 5 5 White 0 0 0 1 31 Other 0 0 0 0 2

Total 100 100 100 100 100

Teachers African 96 89 89 55 27 Coloured 3 9 8 27 10 Indian 0 1 0 7 6 White 0 1 4 11 56 Other 0 0 0 0 1

Total 100 100 100 100 100 Note: Learner weights used.

The level of education of parents, seen in the following graph, follows the quintiles relatively

well, though there is obviously much overlapping. For instance, around half of the quintile 5

parents are less educated than the top 20% of quintile 4.

95

Figure 21: Quintiles and parent years of education

Note: Learner weights used.

In the following table, the quintile classifications are those that would have applied after any

quintile adjustments, which were applied to 9% of schools. However, if in the first panel of

the table one excludes schools that experienced an adjustment, the pattern would not change

greatly. Specifically, 60%, 45% and 30% of schools in quintiles 3, 4 and 5 respectively would

have made formal complaints about their quintile status.

Table 84: Quintile adjustment patterns

EC FS GP KN LP MP NC NW WC SA

% of schools that have made formal and written complaints to the Department about their quintile placement Quintile 1 4 1 10 18 24 9 6 Quintile 2 17 55 9 41 32 11 29 51 27 Quintile 3 73 58 39 62 94 57 50 75 32 64 Quintile 4 100 58 27 62 27 65 100 53 47 Quintile 5 15 42 54 75 12 44 33 All 27 35 25 44 36 32 53 56 37 36

% of schools that have made written or verbal complaints to the Department about their quintile placement 34 49 46 51 47 51 60 67 66 49

% of schools that have seen their quintile placement change as a result of a complaint Quintile 1 4 10 26 36 9 7 Quintile 2 4 6 32 30 32 17 15 Quintile 3 3 26 14 23 2 24 11 Quintile 4 27 4 8 70 4 Quintile 5 25 12 10 4 All 3 11 5 3 17 12 15 27 3 9

Note: Learner weights used.

96

Principals were asked to estimate what percentage of learners in the school were from other

catchment areas, meaning they lived closer to other public schools that offered their current

grade (this was explained clearly within the questionnaire). More or less the median value

reported by principals was 33%, and hence this was used as a cut-off in the first panel of

Table 85. Principals were also asked if in general the learners from the other areas were

poorer, less poor or more or less socio-economically equal to the learners from the direct

vicinity of the school.

Table 85: Between-school enrolment dynamics

EC FS GP KN LP MP NC NW WC SA

% of schools where the principal says over 33% of learners are from the catchment areas of other schools Quintile 1 68 57 40 64 92 95 15 58 Quintile 2 20 65 100 76 42 47 67 36 45 Quintile 3 31 72 34 92 39 73 72 45 24 51 Quintile 4 100 82 76 49 100 89 44 100 65 70 Quintile 5 55 48 100 100 64 52 30 36 59 All 47 66 59 66 54 71 61 40 37 57

% of schools where the principal says the learners from other areas come from poorer (less wealthy) households Quintile 1 34 8 100 61 2 32 13 42 31 Quintile 2 41 39 72 76 12 9 80 100 39 Quintile 3 76 27 30 72 33 75 19 22 41 Quintile 4 34 23 57 100 31 41 70 31 39 Quintile 5 23 49 68 58 29 60 51 52 All 44 25 37 65 10 30 36 38 38 40 Note: Learner weights used.

Table 42 above indicated that almost all of quintiles 1 and 2 learners and about half of quintile

3 learners were in no fee schools in 2009. Non-SGB parents were asked how satisfied they

were with the newly acquired no fee status in their school. As indicated below, very few

parents felt dissatisfied.

Table 86: Non-SGB parents not liking no fee schooling

% of parents in no fee schools not happy with the

no fee status

EC 2 FS 9 GP 13 KN 6 LP 2 MP 0 NC 19 NW 12 WC 8

SA 5

The next three tables are based on the responses from schools which were not already no fee

schools in 2009.

97

Table 87: Percentage of principals wanting no fee status

Quintile 3 Quintile 4 Quintile 5 Overall

EC 100 0 22 FS 40 23 33 GP 93 45 0 34 KN 81 76 38 69 LP 66 0 0 50 MP 75 28 63 55 NC 47 52 8 37 NW 48 30 12 34 WC 55 47 20 36

SA 70 48 23 48 Note: Learner weights used.

Table 88: Percentage of SGB parents wanting no fee status

Quintile 3 Quintile 4 Quintile 5 Overall

EC 100 0 22 FS 29 23 27 GP 93 45 0 34 KN 100 82 68 84 LP 53 11 0 41 MP 80 47 65 59 NC 37 33 16 30 NW 65 0 12 50 WC 55 55 14 37

SA 75 52 29 53 Note: Learner weights used.

Table 89: Percentage of non-SGB parents wanting no fee status

Quintile 3 Quintile 4 Quintile 5 Overall

EC 100 100 FS 6 14 10 GP 68 48 6 36 KN 100 72 76 82 LP 86 100 50 85 MP 91 53 49 61 NC 47 11 19 33 NW 74 65 17 66 WC 50 23 11 19

SA 85 52 30 56

Note: Learner weights used.

78% of the 525 surveyed schools (using learner weights) indicated that they had section 21

status in terms of the South African Schools Act (SASA). Principals were also asked whether

an assessment of the school’s financial management capacity had been conducted before

financial transfers to the school began. As indicated in Table 91, 33% of principals stated that

no assessment took place. Here all 98% of schools receiving a transfer are counted. If one

only considers those schools with section 21 status, the percentage drops to 19%.

98

Table 90: Percentage of schools with section 21 status

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 69 82 69 100 75 FS 80 90 69 54 100 77 GP 100 100 100 85 100 94 KN 61 84 58 57 62 62 LP 100 90 61 100 100 86 MP 89 72 59 76 85 76 NC 100 92 87 99 89 92 NW 91 93 79 30 100 83 WC 96 68 67 70 83 75

SA 78 85 72 74 86 78

Table 91: Percentage of schools not assessed

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 34 18 50 33 FS 3 0 15 101 33 21 GP 0 35 38 75 17 48 KN 5 0 0 95 120 42 LP 36 24 31 100 0 33 MP 63 25 61 8 15 31 NC 0 23 11 0 13 11 NW 21 8 17 0 58 20 WC 1 0 14 13 0 7

SA 25 16 26 65 41 33

Figure 22 illustrates the distribution of the percentage of the total school allocation taking the

form of a financial transfer. This graph thus refers to the same statistics as were referred to in

Table 23 above. It is clear that section 21 schools tend to receive a larger portion of their

school allocation as a financial transfer.

99

Figure 22: Section 21 status and the use of a transfer

Note: Learner weights used.

The first row of Table 92 repeats figures provided in Table 23 as background information.

Apart from being asked whether access to the school’s non-personnel funds had become

better, worse, or remained the same, the principal was asked which of the following two

options was preferable: (1) Having an effective Department manage most of the school's

public non-personnel funds, and delivering the required goods to the school on time. (2)

Transferring all or most of the public non-personnel funds into the bank accounts of schools

and letting schools make all purchases.

Table 92: Opinions relating to financial controls

EC FS GP KN LP MP NC NW WC SA

Median % of the school allocation coming as a financial transfer 54 62 77 72 67 39 73 70 67 65

% of principals saying that it has become easier in the last five years to access the school's non-personnel funds 58 44 47 54 65 32 49 63 45 53

% of principals saying that it has become more difficult in the last five years to access the school's non-personnel funds 9 12 10 13 2 17 12 10 14 10

% of principals choosing as the ideal locus of financial control Department 17 11 7 3 15 13 7 4 4 9 School 83 89 93 97 85 87 93 96 96 91

% of SGB parents choosing as the ideal locus of financial control Department 10 16 9 6 7 26 21 20 12 11 School 90 84 91 94 93 74 79 80 88 89

100

6.4 Financial management and spending decisions in schools

Are schools managing their public funds as they should?

Certain elements of the financial management systems required by the policy are extensively

implemented. Virtually all schools compile a budget, have a bank account, are aware that

annual financial statements must be compiled and (in eight of the nine provinces) virtually all

schools do in fact compile a financial statement. The finalisation of financial statements

appears not to be unduly delayed – by March 2009 virtually all schools had completed their

2007 statements, and about half had completed the 2008 statement. There is a problem with

respect to the implementation of a standard chart of accounts within each province, however.

A third of school principals believe there is no standard chart of accounts, whilst the data from

the schooling system as a whole suggest there is (see Table 94). Departments seem not to be

advocating the use of accounting standards, and taking action when this does not occur. The

current situation suggests that Departments do not analyse financial statements submitted by

schools.

The policy intention that parents should have the final say in deciding what the school spends

its money on is to a fairly large extent a reality. However, parents are more likely to dominate

the budget process is historically more advantaged schools. Specifically, whilst parents seem

to play a decisive role in 82% of quintile 5 schools, this figure drops progressively to 46% in

quintile 1 schools (Table 96). No fee schooling could make it more difficult to increase parent

involvement in the poorer quintiles. As a minimum, Departments should aim to ensure that

involvement does not drop below the current levels in no fee schools. On the whole,

principals do not appear to be opposed to the existing power of parents with respect to the

budget. Most principals are happy that the current situation continue, and substantially more

principals say they would like to see the power of parents increase as opposed to decrease

(Table 95). 71% of SGB parents would welcome more parent influence in the school’s

spending decisions. At the same time, 94% of parents expressed satisfaction with the

principal’s financial management capacity (and this figure is similar across the levels of

historical disadvantage), suggesting that parents’ reasons for wanting more financial powers

has to do with wanting to improve on a situation that already works, not step in to fix a

situation they regard as dysfunctional.

The minority of schools that do experience financial management problems may experience

rather serious ones. 8% of SGB parents believe there have been financial irregularities in their

school during the last two years. A multivariate regression model indicated that though the

principal’s age, the principal’s years of education and the level of the school (primary or

secondary) were not significantly associated with an index of financial management

problems, the gender of the principal was. Women principals are significantly less associated

with these problems than their male counterparts.

Some provincial details. In EC, a quarter of schools do not put together an annual financial

statement, though they are aware that this is a requirement. The fact that a notably low

proportion of EC SGB parents believe there have been financial irregularities in their schools

could simply be a reflection of how little they know about the finances of their schools. EC is

also unusual with respect to how budgets are compiled. In 71% of EC schools teachers as a

whole play a decisive role in determining the budget, whilst teachers are rather un-influential

in this regard in other provinces. At the same time, EC principals do not report above average

involvement by teacher unions in setting the budget, indicating that it is teachers within

schools taking decisions fairly independently of their unions. Whist the EC situation clearly

deviates from the policy intention of giving parents most budgeting powers, it is difficult to

tell from the data how exactly teacher influence affects the efficiency of financial

management in schools. In WC almost all principals report using a standard chart of accounts,

whilst GP displays the highest percentage of principals not being aware of such a standard.

101

The multivariate model indicated that NC and NW were more likely to experience financial

management problems in schools, and that this finding was statistically significant.

Table 29 provides some background statistics. It is noteworthy that LP, NC and WC appear to

have a rather low percentage of schools with woman principals.

Table 93: Background statistics on the principal

EC FS GP KN LP MP NC NW WC SA

% of schools with a principal's office 62 95 99 96 56 86 100 95 100 84

% of schools where principal is a woman 41 22 34 34 17 20 15 34 16 29

Average age of principal 51 49 53 50 51 50 52 49 51 51 Note: Learner weights used.

What is not shown in the next table is that 99% of schools have a bank account. The approval

of expenditure procedures in schools seem somewhat cumbersome insofar as they mostly

involve more than one person. However, this may be necessary to maintain the required

degree of trust and control. Where principals indicated that the authorisation occurred through

some ‘other’ means, an examination of comments in the completed questionnaires reveals that

this largely refers to the use of a finance committee, presumably a committee consisting of at

least one parent and one school member of staff. SGB parent responses to the question of how

spending is approved were very similar to those of the principal. Virtually all principals

indicated the Department requires the school to compile an annual financial statement, yet this

is not implemented by a sizeable minority (24%) of principals in EC. This problem obviously

raises questions about the quality of the EC financial data analysed in previous sections of this

report. If schools do not compile financial statement, do they really know, for instance, what

the annual revenue and expenditure of the school is? The fact that at least 40% of principals in

each province said there was a standard chart of accounts suggests that this exists in every

province. However, a sizeable minority (or 57% in the case of GP) of principals are not aware

of a standard chart of accounts.

Table 94: Details on budgets and financial statements

EC FS GP KN LP MP NC NW WC SA

% of schools without a budget 0 0 0 3 0 2 0 0 0 1

% of schools where ... authorises spending from the school fund Principal 15 5 3 5 6 0 2 4 8 6 Parent 5 9 12 8 14 9 16 30 2 10 Both the above 74 76 80 83 66 53 75 64 81 74 Other 6 10 6 5 14 38 7 2 9 10

% of schools without financial statements 24 4 0 2 0 3 0 4 0 5

% of schools with the last finalised financial statements covering 2008 49 29 41 55 37 52 26 37 28 43 2007 49 71 56 42 63 46 66 63 72 55 <2007 2 0 3 3 0 2 8 0 0 2

% of principals saying there is not a standard chart of accounts 10 36 57 33 31 22 16 45 5 30

% of principals with specific problems relating to the financial reporting requirements Lack of capacity 20 11 25 5 12 27 26 9 6 15 Unreasonable demands 0 3 2 0 3 0 3 0 2 1 Note: Learner weights used.

According to the following table, in between 60% and 75% of schools parents have the

greatest say when it comes to the determination of the budget (the figure depends on whose

102

response one uses). That in many schools the principal should have the greatest say is not

necessarily a contravention of the policy, which says parents should vote on the budget. It is

possible that the principal does most of the technical work, and that parents tend to trust the

work of the principal and approve of this through a vote. There is no separate question in the

survey on whether parents approved the budget through a vote, but since 96% of schools have

a vote on what the school fee should be (see section 6.5 below), and as the school fee and

school budget are meant to be determined at the same meeting, one can assume that that the

existence of parent vote for the budget would be widespread. This is as far as fee-charging

schools is concerned. Table 96 indicates that the poorer a school is, the less likely it is that

parents will play a decisive role in determining the budget. The constant gradient in the Table

96 statistics by quintile seem more consistent with the explanation that in poorer schools

principals are more active in guiding parents on what to vote for, than the explanation that a

vote for the budget simply does not take place in no fee schools. One should remember that

no fee schools did all charge fees until a couple of years ago. However, one cannot be certain

from the data in the survey that there is no decline in the tendency to vote for the budget in no

fee schools.

The very different budget decision-making pattern in EC, where teachers (but not so much the

teacher union) play a decisive role in the majority of schools, is very noteworthy.

An index of possible financial management problems was constructed. A value was assigned

to each school. Each non-SGB parent saying there was insufficient financial information

available counted for 0.5. Each SGB parent saying the principal withheld information counted

for 1.0. And where an SGB parent thought there had been recent financial irregularities in the

school a value of 3.0 was added. The resultant index, which had a mean of 0.54 and a

standard deviation of 1.06, was used in a regression model that is explained below.

Few principals would want to reduce the decision-making powers of parents in schools, in

fact many (30%) would like to see this increase. 71% of sgb parents would be happy to take

on more financial responsibilities. However, 21% of them said they would not like to take on

additional responsibilities, and amongst the higher score schools half of parents took this

position.

103

Table 95: Details on financial decision-making

EC FS GP KN LP MP NC NW WC SA

% of principals saying that ... have the greatest say when it comes to determining the budget Parents 43 87 72 96 75 71 88 73 71 74 Principal 6 3 20 3 6 15 6 20 8 9 Other staffmember 0 0 2 0 8 8 0 4 1 3 Teachers in general 51 10 6 0 11 6 6 3 20 14

% of SGB parents saying that ... have the greatest say when it comes to determining the budget Parents 18 79 80 75 67 55 75 65 57 61 Principal 8 9 16 20 17 34 12 25 30 18 Other staffmember 3 1 0 4 5 11 13 5 7 4 Teachers in general 71 11 3 1 10 0 0 6 7 16

% of SGB parents believing the principal withholds important financial information 13 9 5 24 6 17 7 26 5 14

% of non-SGB parents believing they do not receive enough information on the school's finances 8 8 19 6 8 15 24 13 12 11

% of SGB parents believing there have been financial irregularities in the last two years in relation to the school fund 1 17 8 9 10 10 16 15 5 8

% of principals who say the influence of teacher unions in the management of the school is strong 0 8 1 4 8 14 6 2 3 4 noticeable 10 11 32 18 25 38 24 6 4 19 negligible 90 80 68 78 67 48 70 92 93 76

% of principals who believe they should have more influence over how the money in the school fund is spent 92 76 64 66 73 74 60 62 51 71

% of principals believing parents should have ... influence over how money in the school fund is spent unchanged 56 53 76 55 47 72 78 55 78 61 more 39 35 8 35 40 29 20 35 9 30 less 3 9 12 3 15 2 3 8 14 8

% of SGB parents believing they should have more influence over how the money in the school fund is spent 72 83 53 78 86 87 67 64 31 71 Note: Learner weights used.

The next table is based on the SGB parents’ responses to the question on who has the greatest

say in the determination of the school budget.

Table 96: Percentage of schools where parents decide on the budget

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 20 21 12 0 0 18 FS 78 84 62 94 100 79 GP 0 81 65 86 100 80 KN 59 82 85 77 89 77 LP 64 71 73 89 0 68 MP 51 53 51 36 83 55 NC 95 78 80 62 73 74 NW 63 74 66 70 52 66 WC 82 83 52 34 68 57

SA 46 59 61 71 82 61 Note: Learner weights used.

To the above statistics should be added the fact that 94% of SGB parents found the financial

management skills of the principal adequate, whilst 88% found their own skills in this area

adequate. These statistics are similar across the socio-economic levels.

In the multivariate model described in Table 97, the dependent variable is the index of

financial management problems described above. The reference province is EC, a province

which does not acquire a significant coefficient when any other province is made the

reference province. The fact that having a woman principal should be negatively and

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significantly associated with financial management problems is made more relevant by the

fact that the model controls for the grade level of the school. It is possible that secondary

schools are inherently more complex than primary schools, and hence more prone to financial

mismanagement. Primary schools are twice as likely to have a woman principal than a

secondary school.

Table 97: What influences poor financial management

Coefficient (effect on the index) t-stat

Level of significance

Has Gr 10 0.19 1.44 Has office 0.11 0.80 Principal is woman -0.25 -2.32 ** Principal's age 0.00 -0.16 Principal has child in other school -0.14 -1.00 Principal's years of education -0.03 -0.71 SGB parent's years of education -0.03 -0.84 Non-SGB parent's years of education 0.02 0.79 Is in FS 0.41 1.69 * Is in GP 0.27 1.01 Is in KN 0.36 1.83 * Is in LP 0.13 0.85 Is in MP 0.33 1.60 Is in NC 0.51 2.04 ** Is in NW 0.55 2.09 ** Is in WC 0.01 0.07 Is in quintile 2 0.12 0.77 Is in quintile 3 -0.01 -0.09 Is in quintile 4 -0.09 -0.41 Is in quintile 5 -0.38 -1.77 *

Intercept 0.92

R2 0.07 n 502 Dependent variable is an index of financial management problems compiled from responses in the SGB parent and non-SGB parent questionnaires. *** means significant at the 5% level (i.e. most significant), and * at the 10% level.

The above figures imply that if all principals were women the index of financial management

would be 0.25 lower than if all principals were men (currently the average value for the index

is 0.54). The magnitude of the gender effect implied by the model is thus substantial.

6.5 Fee-setting practices

Are school fees being determined transparently and responsibly?

In around 70% of households someone has taken part in the vote for the school fee at some

point in the past. This suggests a rather high level of participation. If one only considers the

last vote, a voter turnout of just over 50% is seen in lower and middle scores schools, though

in schools with higher scores (which tend to charge higher fees) voter turnout is a bit lower, at

between 15% and 30%. In perhaps as many as 44% of (learner-weighted) quintile 5 schools,

moreover, voter turnout is less than 10%. Of course low voter turnout does not necessarily

mean that non-voters are not satisfied with the process or the outcome of the vote. Around

80% of non-SGB parents believe the vote is a fair one, even in schools with higher fees. A

quarter of non-SGB parents believe the fee is ‘very high’ and a further 15% believe the fee is

‘a bit high’. All in all, the figures suggest that a minority of parents would genuinely like a

lower fee, but are forced to accept the current fee because they are outvoted (or know they

would be outvoted if they attended the fee-setting meeting). In many ways this is a logical

outcome of the combination of the fee-setting policies and income inequality amongst parents

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in schools. The figures do not suggest that introducing a quorum (for instance one that says

that 60% of parents must be present at the fee-setting meeting for the vote to proceed) will

make a substantial difference to the fee levels charged, or the level of satisfaction of parents.

Around 32% of school principals in schools which charge fees would be in favour of a

maximum limit for the school fee. The corresponding figure for SGB parents, at 40%, is

somewhat higher. In quintile 5 schools, around 80% of respondents did not support the idea of

a maximum fee. Where respondents agreed to suggest a specific level for the maximum fee

that would be reasonable, figures varied from R200 in quintile 3, to R400 in quintile 4, to

R850 in quintile 5. These were the median responses of the principal. SGB parents provided

figures that were about half those of the principal. A substantial percentage of respondents

suggested rather high maximum fee levels. For instance 10% of principals who provided a

response said the maximum fee should not be less than R3,000. As an indication of impact, a

maximum fee of R800 would have affected just over 10% of learners in 2009 (these are the

learners in schools charging more than R800), but around 70% of learners in quintile 5.

In section 5.2 the widespread existence of non-fee contributions was discussed. 23% of

learners make non-fee contributions – see Figure 10 above. This could be indicative of a fee-

setting problem in schools. The policy states that all charges should be included within the

school’s official school fee, in order to avoid hidden costs that can cause unforeseen tensions

and hardships for certain households. Insofar as non-fee contributions are considered

compulsory, they should be incorporated into the school fee.

The focus in this section is mainly on two key policy issues. Firstly, the issue of the voting

process whereby school fees are determined is examined. Secondly, the possibility of having a

maximum allowed school fee is explored.

Table 98 indicates opinions towards the fee-setting process. Just over 60% of school

principals and teachers believe that school fees are a good thing. The figure for SGB parents

is somewhat lower, at 51%. And as one might expect, the figures for all respondents are

higher the less disadvantaged the school. Amongst no fee schools, 51% of principals and 34%

of SGB parents believe fees can be a good thing. In 96% of schools charging a school fee,

there was a parent vote to determine what this school fee should be. The percentage of parents

attending the fee-setting vote is relatively high in lower and middle scores schools, at between

50% and 60%, but lower in higher scores schools, at between 15% and 30%. Higher scores

schools are also more inclined to have fewer than 10% of parents attending the vote. This

seems problematic. Yet few principals and SGB parents seem to believe that a higher voter

turnout would change the fee substantially. Of course this could be wishful thinking on the

part of these respondents, who might have an interest in keeping the fee at a particular level.

On the other hand, the high percentage of non-SGB parents who have at some point voted for

the fee suggests that parents tend to stay away from the vote not because they are unable to

attend, but because they know what the voting patterns are and are either happy with them, or

feel that they are in a minority and would not be able to change them. It is important to bear in

mind that one’s fee preference is to a large degree a function of one’s income. This results in a

rather un-dynamic voting pattern as incomes are rather static. Voting for a fee is undoubtedly

less dynamic than voting for a leader, where voters would be far more susceptible to

persuasion.

One very clear policy compliance problem emerging from Table 98 is that a third of parents

say the main reason for not attending the fee-setting meeting is that they are not informed of

the meeting. However, this needs to be seen in the light of the finding that a high percentage

of parents find the vote fair. As a matter of principle, parents should be informed of when the

fee-setting meeting occurs. However, the figures in Table 98 do not suggest that non-

attendance by parents is keeping fees at different levels to what they would otherwise be. The

fact that a clear majority of parents describe their school fee as ‘fine’ seems to confirm this.

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Table 98: Opinions on the fee-setting process

Lower scores

Middle scores

Higher scores Overall

Median fee charged (2009) 55 150 2,750 230 % of schools charging fee (2009) 16 55 83 47

% believing fees can be a good thing Principal 45 63 84 61 SGB parent 30 55 76 51 Teacher 49 66 80 63

Median % of parents attending last fee vote according to Principal 55 50 15 50 SGB parent 60 60 30 50

% of schools with less than 10% for previous question according to Principal 4 5 44 18 SGB parent 2 3 25 11

% saying the following would have happened if all parents had voted Principal The fee would have been much higher 0 10 6 7 The fee would have been much lower 0 10 4 7 SGB parent The fee would have been much higher 1 16 4 9 The fee would have been much lower 0 3 1 2

% of non-SGB households who have ever voted for fees 77 67 68 70 Response rate for above 94 89 98 93

% of non-SGB parents claiming main reason for not voting was We are not told about meetings 3 39 35 32 Meetings are at bad times 66 48 63 55 Meetings take place far away 9 2 0 3 We are not interested in voting 17 10 1 9 There are tensions in the school between parents 5 1 0 1 There are tensions in the school 0 0 0 0 Response rate for above 4 11 11 8

% of non-SGB parents believing fee vote is fair 86 80 80 82 Response rate for above 83 73 76 77

% of non-SGB parents believing the fee charged is Low 7 5 5 6 Fine 52 59 72 63 A bit high 15 16 18 16 Very high 26 19 5 15 Not sure 0 1 0 1 Response rate for above 19 51 80 46 Note: Learner weights used.

The next graph reveals an interesting pattern. Generally, the higher the fee the higher the

higher the voter turnout. However, an exception to this pattern seems to be the fee range

R2,421 to R4,840, which is associated with a rather low voter turnout. (The fee ranges are

defined as multiplies of the no fee threshold figure in 2009 of R605.) It is understandable that

in schools where fees are likely to be higher, more parents would want to attend the fee-

setting meeting in order to influence the vote, or at least see what the school intends to do

with the money. However, it is possible that the range R2,421 to R4,840 represents a segment

of the system where the fee is high enough for a large proportion of parents to be working

(and hence experience difficulties in attending meetings) and yet not high enough to motivate

parents to monitor the situation by attending the meeting. Figure 23 is useful insofar as it

allows one to gauge the impact of a minimum quorum imposed by the policy. For instance, if

a minimum quorum of 20% were imposed, then this would force around 20% of (learner-

weighted) schools to change their fee-setting practices, though over half of schools charging

up to once the no fee threshold, or R1 to R605. On the other hand, if a quorum of 50% were

imposed, this would imply a change in approach across half of the system, but over 90% of

those schools charging the lowest fees.

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Figure 23: Relationship between fees and voter turnout

Note: Learner weights used. Fee values are from 2009.

The question of imposing a maximum fee that schools may charge must obviously be

informed by the current distribution of fees charged. Table 99 provides details in this regard.

This table tells us, for instance, that around 25% of learners pay a fee of over R200 in the

system as a whole, whilst in quintile 4 just over 55% of learners pay a fee exceeding R200.

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Table 99: Distribution of fees charged in 2009

Percentile All Quintiles 1 and 2 Quintile 3 Quintile 4 Quintile 5

1 0 0 0 0 47 5 0 0 0 62 108

10 0 0 0 76 180 15 0 0 0 100 205 20 0 0 0 100 300 25 0 0 0 148 700 30 0 0 0 148 800 35 0 0 0 148 800 40 0 0 0 160 1,300 45 0 0 0 220 3,000 50 0 0 8 296 4,190 55 46 0 42 296 4,446 60 80 0 55 296 4,620 65 99 0 80 400 4,900 70 147 0 80 400 5,170 75 200 0 98 435 5,373 80 295 0 120 550 6,187 85 470 0 150 675 8,500 90 1,283 0 170 2,750 9,700 95 4,724 0 352 3,970 11,853

100 14,000 160 4,850 15,200 14,000

Note: Learner weights used. The fee charged values presented here and elsewhere in the report were obtained by first finding the average fee per school, using grade-specific enrolment as a weight where schools charged different fees for different grades.

The survey included questions on what the principal and SGB parent thought about a

maximum fee stipulated in the funding policy. The next table indicates that around a third of

principals who responded to the question approved of the idea (mostly those principals in fee-

charging fees did respond to the question). The figure is 40% for SGB parents. Support for the

idea was lowest amongst the least poor schools. For instance, one could expect around 80% of

quintile 5 principals and 75% of quintile 5 non-SGB parents to disapprove of the idea.

Table 100: Opinions on a maximum fee

Quintiles 1 and 2 Quintile 3 Quintile 4 Quintile 5 All

% who could support a cap on school fees Principal 0 40 37 18 32 Response rate for above 4 54 99 100 47 SGB parent 63 50 41 25 40 Response rate for above 5 50 94 98 45

Reasonable maximum school fee (median) according to Principal 36 200 400 850 300 Response rate for above 0 29 47 28 20 SGB parent 120 100 200 400 200 Response rate for above 2 31 48 32 22

Note: Learner weights used.

Table 100 suggests that SGB parents preferred maximum fee values that were half of the

values proposed by principals. One should note that only around 20% of respondents did

propose a specific maximum fee level. Figure 24 illustrates the whole range of proposals

provided. Clearly, a sizeable proportion of respondents proposed rather high maximum values

exceeding R1,500.

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Figure 24: A reasonable maximum school fee

Note: Learner weights used.

6.6 Schools-based democratic governance

Is schools-based democracy working as intended?

To a large degree what was said about parent participation in fee-setting in the previous

section applies to parent participation in school governance in general (at least as far as

schools with fees are concerned).

Faith in the system of schools-based democratic governance through SGBs seems fairly high.

For instance, 90% of principals believe it is a good system, though support is slightly higher

amongst historically disadvantaged schools (see Table 101). 86% of non-SGB parents believe

the SGB is doing a good job.

77% of SGB parents interviewed (these tended to be the SGB chair or a very active parent

member) had been through some SGB training, and of these around 84% reported that there

was training in school finances and that this training was good.

As one might expect, SGB parents tend to be more educated in more socio-economically

advantaged schools. In quintiles 1 and 2 the median years of education of these parents (at

least the ones interviewed) is 10 years, in quintiles 3 and 4 it is 12 years and in quintile 5 it is

14 years. SGB parents tend to have around two years more of education than parents in

general, suggesting that communities understand the importance of having qualified parents

on the SGB.

Some provincial details. KN seems to be the province with the weakest SGB training

programme. This province has the lowest proportion of SGB parents with training, and of

those trained, the lowest proportion with some training in school financing (Table 102).

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In Table 101 it is understandable that the teacher responses should indicate the lowest level of

support for the representativity of SGB parents and for the SGB system generally, given how

teachers, relative to the principal and parents, play a lesser role in the SGB. The fact that even

teachers should display rather high levels of optimism towards the SGB is thus particularly

indicative of the fact that, on the whole, the system works.

Table 101: Opinions on the SGB

Lower Middle Higher Overall

% of respondents who believe that the SGB parents represent the general parent body well Principal 91 85 90 88 Teacher 88 78 80 82

% of respondents who believe having an SGB results in a better run school Principal 95 90 85 90 SGB parent 97 90 96 93 Teacher 88 80 77 82 Note: Learner weights used.

The following table is based on responses from the SGB parent.

Table 102: Training of SGB members

EC FS GP KN LP MP NC NW WC SA

% of SGB parents who have received school governance training 72 81 93 62 76 96 83 69 81 77

% of training recipients saying the training dealt with school funding matters well 88 90 80 84 74 88 88 92 85 84 poorly 12 7 11 1 17 4 7 1 3 8 not at all 0 3 9 14 9 7 5 7 11 8 Note: Learner weights used.

The next table is based on the number of years of education of SGB parents, which in turn is

obtained from a question asking respondents about their education qualifications. What is

rather surprising are the low values in the case of WC, and the high values in the case of LP

are noteworthy.

Table 103: Education level of SGB parents

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 10 10 12 10 FS 10 12 12 12 14 12 GP 10 12 10 14 15 14 KN 10 10 12 12 10 10 LP 12 12 12 14 15 12 MP 10 10 14 12 12 12 NC 12 10 12 12 14 12 NW 10 10 12 14 12 12 WC 10 12 10 10 12 10

SA 10 10 12 12 14 12

Note: Learner weights used. Statistics are median values.

The high values for quintile 4 in the next table are perhaps suggestive of high levels of

inequality in this quintile with respect to parent education, resulting in large differences

between SGB parents and non-SGB parents. Put crudely, in quintile 5 the great majority of

parents are well educated, whilst in quintiles 1 to 3, the great majority of parents have

relatively low years of education.

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Table 104: Difference between SGB parent and other parent education

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 3.0 1.5 1.5 2.5 FS 2.0 2.0 2.5 5.0 1.0 2.0 GP 3.0 3.0 0.0 3.0 0.5 1.5 KN 1.0 2.5 1.5 3.0 -1.0 1.5 LP 4.0 3.0 4.0 4.0 0.0 3.5 MP -0.5 1.0 4.0 5.0 2.0 2.0 NC 0.0 0.0 1.5 3.0 0.0 0.0 NW 2.0 1.5 1.0 0.0 5.0 1.0 WC -1.0 3.0 0.0 1.0 0.0 1.0

SA 1.5 2.0 1.5 3.0 0.5 2.0 Note: Learner weights used. The average years of education per school of the non-SGB parent respondents was found and this was subtracted from the years of education of the SGB parent respondent. The medians of the values obtained are reported above.

6.7 Grade R funding and resourcing modalities

Are funds and resources for Grade R transferred to public schools as they should?

Whilst section 5.4 focussed largely on the amounts being by the Department for Grade R, this

section focuses largely on the conditions attached to this funding.

In 79% of schools receiving some form of public funding for Grade R in 2009, this began

before the introduction of the national Grade R funding policy in 2008. This must surely

explain why there is so much divergence between the 2008 policy and the situation within

schools. 54% of principals in schools that do or could offer Grade R say it is difficult to

access the policy information (see Table 106). This is worse than the 34% level seen for the

Grades 1 to 12 policy. This is probably understandable given the newness of the 2008 Grade

R policy. Despite a relatively low awareness of the details of the policy, 95% of principals are

aware of the policy imperative that all public schools with Grade 1 should eventually offer

publicly funded Grade R. Yet 80% of principals who do not enjoy publicly funded Grade R

yet do not know in which year they are scheduled to begin receiving public funding, though

the Department is meant to have informed all schools of this according to the policy.

Though only 14% of schools receiving a Grade R transfer from the Department indicate that

they are forced to comply with Departmentally specified ringfencing (one presumes this is in

relation to non-personnel items), only a quarter of schools indicate that they may use their

transfer to pay Grade R educator salaries, and half of the principals saying this also indicate

that the Department specifies what the pay of SGB employed educators should be (Table

108).

It is clear that the option of SGB employed Grade R educators is not popular. 81% of

principals prefer educators to be employed by the Department. This is understandable given

how much more public funding goes towards each Departmentally employed educator (see

section 5.4).

In 21% of schools with publicly funded Grade R, the Department limits the number of

learners or classes in the school. This is permitted, according to the Grade R funding norms,

but only during an interim period before the Grade R roll-out process in public schools is

complete.

Some provincial details. In EC and LP a particularly high percentage of principals reported

having difficulties finding out what the Grade R funding policy was. In EC, FS and LP no

principals without publicly funded Grade R yet reported knowing when this funding would be

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introduced in the future. This points to inadequate planning of the roll-out of public Grade R

in these provinces. The provinces FS, MP, NC and WC are clearly more willing to allow

schools to use their financial transfer to employ Grade R staff (or may be more explicit and

clear in communicating this to school principals). In all these four provinces at least half of

schools are told what the pay of SGB employed educators should be. WC is clearly most

inclined to limit enrolments or number of classes in schools receiving public funding for

Grade R, whilst EC is least inclined to do this.

The breakdown of when public resourcing of Grade R began, shown in the following table, is

similar to that for Grades 1 to 12 seen in Table 74 above. Each value in the table is the median

year reported. The question asked when any form of public funding of Grade R began,

whether this was in the form of a transfer or funded educator posts or transferred physical

resources. The greatest difference between the two tables is perhaps that in GP there is not a

particularly long history of public funding for Grade R (though there is for Grades 1 to 12).

79% of schools with publicly funded Grade R in 2009 began receiving this before 2008, when

the national Grade R funding norms were promulgated.

Table 105: Year in which public funding of Grade R began

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 2007 2007 2006 2007 FS 2007 2007 2006 2009 2007 GP 2006 2006 2008 2007 KN 2006 2007 2004 2009 2003 2006 LP 2006 2007 2007 2006 2007 MP 2006 2007 2006 2009 2007 2006 NC 2002 2008 2007 2006 2002 2006 NW 2007 2008 2006 2006 WC 2007 2003 2008 2002 2002 2003

SA 2007 2007 2006 2007 2003 2006

Note: Learner weights used.

Table 106 indicates that 54% of principals offering Grade 1 (in other words principals that

should all eventually be offering Grade R) had difficulty obtaining information on the Grade

R funding policy. But only 5% did not know that eventually all schools should, according to

the policy, offer publicly funded Grade R.

Table 106: Access to Grade R policy information

EC FS GP KN LP MP NC NW WC SA

% of principals offering Grade 1 who have difficulty obtaining Grade R funding policy information 86 31 34 49 66 47 34 36 24 54

% of principals offering Grade 1 without public funding of Grade R who did not know that this was coming 8 0 2 0 28 0 9 0 0 5 Note: Learner weights used.

The following table indicates that in 64% of schools offering Grade 1 (using the weighted

sum of Grade 1 enrolments for the calculation) there was already public resourcing of some

kind for Grade R by 2009. A further 7% had been told that this would begin in 2010. 28% of

schools did not know when public funding of Grade R would begin. This percentage was

higher in quintile 5, which is less of a priority than the other quintiles according to the policy,

but even in quintile 1 the percentage was 28%.

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Table 107: Grade R roll-out situation

EC FS GP KN LP MP NC NW WC SA

Already receives 84 66 35 96 73 80 77 32 72 64 2010 0 0 19 2 0 6 5 12 3 7 Year unknown 16 33 46 2 27 15 18 56 25 28 Note: Learner weights used.

The 14% of principals reporting that ringfencing occurs in the financial transfer for Grade R

(see the next table) is lower than the 34% seen for Grades 1 to 12 funding in Table 77 above.

Yet in only 27% of schools receiving a transfer was the principal permitted to use the funding

for Grade R personnel, and of this 27%, 53% were told what each educator should be paid.

Clearly a large majority of principals prefer Departmentally employed educators to SGB

employed educators. This statistic is also high, at 75%, if one counts only those schools that

do not have Departmentally employed educators.

Table 108: Grade R ringfencing issues

EC FS GP KN LP MP NC NW WC SA

% of schools receiving a Grade R transfer where specific items are ringfenced and the ringfencing is mandatory 4 33 35 0 8 65 26 0 29 14

% of principals receiving a Grade R transfer who say that funds may be used to pay Grade R staff 0 91 12 20 8 91 77 0 60 27

% of principals responding yes to the previous question who say that the Department prescribes what the pay of SGB-employed Grade R educators should be 0 61 0 50 0 53 50 0 64 53

% of principals preferring Departmentally employed educators to SGB employed educators in Grade R 81 86 72 82 85 78 56 89 93 81

% of principals with publicly funded Grade R saying the Department limits the Grade R enrolment numbers 2 44 34 7 10 24 39 8 66 17

% of principals with publicly funded Grade R saying the Department limits the number of Grade R classes 2 22 25 17 10 5 15 8 34 13

% of principals with publicly funded Grade R saying the Department limits either enrolment or number of classes 2 44 38 21 10 24 46 8 69 21 Note: Learner weights used.

6.8 The merits of key proposals to reshape the funding system

What are the merits of key proposals that have been made in the public debates on

fundamentally redesigning the funding system?

The survey probed how respondents felt about problems and solutions relating to key policy

dilemmas. With regard to powers over educator employment, a substantial percentage of

principals (40%) and teachers (49%) felt that the Department handled salary problems

inefficiently (see Table 109). The situation is similar across the quintiles. A high percentage

of principals are interested in obtaining more powers with respect to the strategic functions of

the hiring of educators (74%), disciplinary action against educators (69%) and the in-service

training of educators (66%) (Table 110). Teachers are also rather interested in the devolution

of educator employment powers to schools, though not as interested as school principals.

Notably, the function teachers would most like to see devolved is disciplinary action (47%

support this) suggesting perhaps that the Department’s handling of this is either inefficient or

too heavy-handed in the view of teachers. Of course this could also reflect a feeling amongst

teachers that it would be easier for them to manipulate, say, the school principal than the

Department in any disciplinary proceeding. Overall, schools seem rather receptive to the

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transfer of more employment powers from the Department to the school. However, support

for handing all these powers over to schools and making the school the employer, using

public funds, is not widespread, though it exists. 10% of teachers in quintiles 1 to 3 would

support this, and this percentage rises to 26% in quintile 4 and 36% in quintile 5 (Table 112).

Principals are more keen on this kind of devolution than teachers – for instance 53% of

principals in quintile 5 are in favour (Table 111).

The proposal to compensate schools for revenue lost due to fee exemptions is almost

unanimously supported by principals. This is to be expected, given how attractive this

proposal is from the school’s perspective. In fact, it is strange that only 88% of SGB parents

support the proposal (Table 113).

The proposal to make all public schools in South Africa no fee schools is likely to meet

considerable resistance in both quintiles 4 and 5, where principals, teachers and SGB parents

are around half as inclined as their colleagues in quintile 3 to say no to school fees as a matter

of principle. For instance, only around 20% to 30% of SGB parents in quintiles 4 and 5 are

opposed to school fees, against almost 60% in quintile 3 (this is at the primary level – see

Table 114). Stakeholders are much more likely to see school fees as a good thing in schools at

the secondary level, as opposed to schools at the primary level. It is noteworthy that only 3%

of principals in quintile 5 at the secondary level believe that fees are a bad thing.

Some provincial details. The problem of inefficient Departmental handling of salary problems

is most pronounced in EC, KN and LP. These three provinces are also slightly less interested

than others in the devolution of more educator employment powers to schools (Table 110).

The profile of opinions presented in the next table do not differ greatly by quintile, though

there could be a very slight trend whereby the poorer the quintile, the less likely it is to

complain about the Department’s handling of salary problems. Handling of salaries was the

only employment-related matter where the current performance of the Department was probed

by the survey.

Table 109: Efficiency of the Department’s salary management

EC FS GP KN LP MP NC NW WC SA

% of principals finding the Department's handling of salary problems inefficient 53 24 21 47 47 37 26 35 32 40

% of teachers finding the Department's handling of salary problems inefficient 63 39 28 64 52 40 34 41 38 49

Note: Learner weights used.

In Table 110 ‘Count’ refers to the average number of complaints made per respondent, where

the maximum number was five, corresponding to the five educator employment areas

specified in the table. ‘Leave management’ in the table refers only to the approval of when a

teacher takes leave, not to the determination of the number of leave days a teacher is entitled

to.

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Table 110: Opinions on the devolution of personnel powers

EC FS GP KN LP MP NC NW WC SA

Principals believing it would be good to devolve more powers in the following areas to the level of the school Hiring 77 85 86 50 71 83 85 88 89 74 Performance incentives 49 57 67 59 55 47 43 58 64 56 Disciplinary action 55 79 72 60 71 83 80 79 80 69 Leave management 40 43 67 39 35 58 71 59 63 48 In-service training 58 64 76 56 61 80 86 66 79 66 Count 2.8 3.3 3.7 2.6 2.9 3.5 3.7 3.5 3.7 3.1

Teachers believing it would be good to devolve more powers in the following areas to the level of the school Hiring 44 63 48 26 27 65 64 59 46 42 Performance incentives 25 39 50 40 19 30 25 36 32 34 Disciplinary action 32 59 58 45 35 51 64 59 58 47 Leave management 14 22 32 26 15 31 25 34 20 23 In-service training 33 51 47 42 35 52 50 49 48 43 Count 1.5 2.3 2.3 1.8 1.3 2.3 2.3 2.4 2.0 1.9

Note: Learner weights used.

What is perhaps surprising about the following table is that an exceptionally high oveall

percentage of principals in NC and WC are in favour of a situation where personnel funds are

transferred to the school so that the school itself can employ educators. The question referred

explicitly to educators only, and not support staff. There was no equivalent question on suppot

staff.

Table 111: Percentage of principals supporting school employment of staff

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 23 21 16 FS 1 22 40 67 18 GP 10 29 91 35 KN 26 48 39 22 23 31 LP 53 11 22 11 27 MP 22 15 27 11 26 20 NC 39 22 46 86 44 NW 20 12 30 13 WC 10 85 32 40 61 47

SA 27 22 20 25 53 27 Note: Learner weights used.

Teachers are substantially less inclined to support employment of educators by the school.

Table 112: Percentage of teachers supporting school employment of staff

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Overall

EC 7 6 5 FS 11 5 23 38 61 21 GP 50 36 11 43 59 37 KN 12 8 7 21 38 16 LP 16 6 6 50 50 13 MP 2 14 27 18 12 NC 47 27 23 31 32 29 NW 3 2 50 4 WC 2 25 7 10 17 13

SA 10 10 9 26 36 16

Note: Learner weights used.

The questionnaire asked respondents whether the Department should compensate schools for

the income they lose as a result of exemptions. School principals who responded to the

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question (only those in fee-charging schools were asked to respond) were almost all in favour

of the idea. SGB parents were somewhat more sceptical about the proposal, and hence only

statistics on them are presented. The particularly low level of support from FS SGB parents is

notable and difficult to explain. Yet even in that province the great majority support the idea.

Respondents were moreover asked if a compensatory amount of R700 per learner

(presumably in 2009) would be reasonable. Over 90% of respondents indicated that it would

be.

Table 113: Percentage of SGB parents supporting compensations

Quintile 3 Quintile 4 Quintile 5 Overall

EC 100 100 FS 66 82 73 GP 100 74 100 82 KN 81 100 77 89 LP 98 100 82 MP 85 100 100 96 NC 100 81 67 83 NW 88 100 100 95 WC 100 94 91 93

SA 90 85 91 88 Note: Learner weights used.

Table 98 has already reported on the general support amongst stakeholders for school fees.

The next two tables provide a further disaggregation of these important variables. All

principals and SGB parents were asked the question, regardless of whether they were in a no

fee school or not. In the tables that follow, quintiles 1 and 2 statistics are not provided as the

focus here is on the receptiveness of schools which still charge fees to a complete elimination

of school fees (the statistics for quintiles 1 and 2 are more or less reflected by the ‘lower

scores’ statistics in Table 98). If one focuses on the ‘not a good thing’ statistics, the greater

differences are those between quintile 3 and quintile 4, as opposed to quintile 4 and quintile 5.

This is breaking down what was already in Table 98 – note all q3 responded even..... As one

might expect, not a good thing clearly declines with q tho some ambiguity in q4 and q5. And

school staff less inclined to reject than parents. The overall percentage of SGB parents

rejecting school fees – 56% for the primary level and 37% for the secondary level – can be

compared to the 56% of non-SGB parents wanting their school to become a no fee school

according to Table 89 (though that statistic is derived only from schools which do charge

fees). For the purposes of the tables below, any school offering Grade 10 was considered to be

a secondary school, and if Grade 10 was not offered the school was considered a primary

school.

Table 114: Stakeholder opinions on school fees at the primary level

Quintile

3 Quintile

4 Quintile

5 Overall

Principal's position on school fees can be a good thing 16 47 57 21 can be a good thing in certain circumstances 34 35 21 35 not a good thing 50 18 22 44

SGB parent's position of school fees can be a good thing 20 39 51 21 can be a good thing in certain circumstances 23 30 27 22 not a good thing 57 30 21 56

Teacher's position on school fees can be a good thing 26 41 56 25 can be a good thing in certain circumstances 35 42 21 33 not a good thing 39 17 23 41

Note: Learner weights used.

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Table 115: Stakeholder opinions on school fees at the secondary level

Quintile

3 Quintile

4 Quintile

5 Overall

Principal's position on school fees can be a good thing 28 47 83 39 can be a good thing in certain circumstances 41 42 14 36 not a good thing 31 11 3 25

SGB parent's position of school fees can be a good thing 26 48 77 32 can be a good thing in certain circumstances 39 35 7 31 not a good thing 35 17 16 37

Teacher's position on school fees can be a good thing 26 40 54 30 can be a good thing in certain circumstances 47 39 31 39 not a good thing 27 21 15 30 Note: Learner weights used.

6.9 The funding of inclusive education in ordinary schools

How should inclusive education be funded in ordinary schools?

The survey data indicate that 38% of schools have had to apply special needs identification

techniques specified by the Department. The statistic is not notably different across levels of

socio-economic disadvantage (see Table 116). In 34% of schools specialists have worked

directly with teachers or learners in the school to support special needs education. This

statistic does appear to be regressively distributed – only 23% of the most disadvantaged

schools have received this support, whilst the figure is 48% for the least disadvantaged (Table

117). It is possible that this pattern relates to geographical challenges. In rural areas it is more

difficult for support staff to reach schools.

Awareness or support of the national special needs policies is not widespread amongst

principals. Only 50% of principals believe they have special needs learners in their school

(Table 118). Given the definitions in the policies, one would expect a response close to 100%.

Perhaps more seriously, 49% of principals believe that all special needs learners should be

educated in special schools, and not ordinary schools. Clearly these principals do not

understand or support the inclusive education policy position. These worrying statistics are

similar across different socio-economic strata.

The presence of special or remedial classes for special needs learners is greater in more

advantaged schools – 9% of schools have this facility in the least advantaged schools, against

32% in the most advantaged schools.

Where the Department takes action, either by insisting on special needs assessments or by

providing direct support, this is associated with better statistics in the school. Specifically, if

the school reported that the Department took either of these two actions, it was twice as likely

to have a principal that admitted there were special needs learners in the school, and it was

seven times as likely to offer remedial classes.

The analysis provided here supports the policy position that support programmes from outside

the school make a difference, and that good special needs assessment is important. The

analysis also underlines the need for more policy advocacy in the area of inclusive education.

Some provincial details. KN and LP display particularly low levels of action when it comes to

the implementation of inclusive education in schools (Table 116 and Table 117).

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The questions relating to inclusive education and special needs education are all asked in the

principal questionnaire. The statistics in the next table are based on a question asking whether

the Department has ever required the school to assess the special needs of learners, and to

classify learners according to these needs. Whilst the figure is higher for more advantaged

‘higher scores’ schools, the overall pattern is not sufficiently clear to draw hard conclusions in

this regard.

Table 116: Percentage of schools with Departmental assessment processes

Lower scores

Middle scores

Higher scores

Overall

EC 63 48 85 60 FS 60 44 87 58 GP 21 33 41 36 KN 20 22 18 21 LP 15 22 37 19 MP 54 45 48 48 NC 0 44 42 42 NW 50 39 37 41 WC 100 46 49 50

SA 37 35 45 38

Note: Learner weights used.

For the following table, the principal was asked whether special needs specialists from the

Department or the district office have ever worked with learners or teachers from the school.

The pattern of higher values in more advantaged schools seems clearer in this table than the

previous one. Both tables suggest that Departmental intervention in KN and LP is particularly

low. This pattern is also observed when one considers whether either of the two interventions

analysed in the two tables have been implemented.

Table 117: Percentage of schools with Departmental special needs support

Lower scores

Middle scores

Higher scores

Overall

EC 32 26 28 30 FS 71 60 69 66 GP 45 46 46 46 KN 10 19 36 18 LP 8 9 25 10 MP 29 45 36 38 NC 0 72 49 60 NW 44 40 60 45 WC 100 70 65 68

SA 23 36 48 34 Note: Learner weights used.

The first two rows in the next table gauge the opinion and understanding of the principal with

respect to special needs, whilst the third row gauges the extent of activity in the school.

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Table 118: School opinions and actions on special needs

Lower scores

Middle scores

Higher scores

Overall

% of principals believing they have special needs learners in the school 53 42 60 50

% of principals believing that all special needs learners should be educated in special schools 50 44 55 49

% of principals saying that there are special or remedial classes for special needs learners 9 15 32 17

120

7 Protecting the poor through fee exemptions

7.1 Parent awareness of the exemptions system

Are parents aware of their right to fee exemptions?

Understandably, in provinces where fee-charging schools are less inclined to have formal fee

exemptions, parent knowledge of the exemptions policy tends to be lower. Surprisingly, the

profile of parent knowledge in this area hardly changes if one considers only parents from

schools that implement the exemptions policy. So whilst provinces clearly differ from each

other in their level of implementation, it is possible that within each province it is relatively

unsystematic factors such as the preferences of individual principals and district officials that

determine which schools offer exemptions.

One would expect the implementation of fee exemptions to be almost universal in fee-

charging schools given the inequalities of South African society and the high incidence of

poverty. Even in schools with low fees of, say, R150, one would expect at least some parents

to be eligible for exemptions. A household with an annual income below R15,000 and with

one learner would be eligible for a full exemption in a school with a R150 fee. Around 30% of

South African households earned below this level in 2005 (Statistics South Africa, 2008: 31).

The fact that 29% of schools with fees should not be implementing the exemptions is clearly a

problem. This statistic is 43%, 20% and 9% in quintiles 3, 4 and 5 respectively, so clearly

there is a more serious problem is less rich schools.

People’s awareness of their rights and their willingness to act on this awareness is influenced

by the general respect in society for these rights. In general, the fee exemptions system is

regarded as fair and justifiable. Almost 80% of parents and principals hold this view (Table

120). However, 16% of principals categorically state that the exemptions system is unfair.

Amongst parents, support for exemptions is highest in richer schools, whilst amongst

principals it is the other way round. Discrimination against non-paying learners appears to be

more widespread in quintile 3 schools than in quintiles 4 and 5 schools.

Some provincial details. Parents in LP and NW seem particularly unaware of their fee

exemptions rights, and in NW the percentage of fee-charging schools implementing

exemptions is rather low. In WC, on the other hand, parent awareness and the roll-out of the

exemptions policy is particularly high.

The statistics presented in this section and the next one all refer only to schools that charge

fees (this is around half of learner-weighted schools as seen in Table 99). Exemptions

statistics all relate to 2008. In the next table the EC statistics can essentially be ignored as they

are based on, at most, two schools, which is the number of fee-charging schools in EC within

the survey (the bias towards poorer schools in the EC sample has been discussed above). The

correlation between the provincial statistics excluding EC in the first two rows of Table 119 is

high, at minus 0.88. Parent awareness of the exemptions system is thus highest in those

provinces where fee-charging schools implement exemptions most.

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Table 119: Parent awareness of the exemptions policy

EC FS GP KN LP MP NC NW WC SA

% of schools charging fees and implementing fee exemptions 37 90 81 64 69 78 75 61 87 71

% of non-SGB parents in fee-charging schools saying they do not know they have right to apply for exemptions 100 21 24 36 50 31 30 49 13 32

% of non-SGB parents in fee-charging schools which implement exemptions saying they do not know they have right to apply for exemptions 0 10 27 29 50 35 24 46 14 31

% of SGB parents who know about the 2006 revisions to the exemptions policy 0 49 70 62 39 28 46 29 58 52 Note: Learner weights used.

The problem of the non-implementation of fee exemptions is more serious the less rich the

quintile. The percentage of fee-charging schools implementing the exemptions is 57%, 80%

and 91% in quintiles 3, 4 and 5.

As seen in the next table, 12% of SGB parents believe that learners unable to pay the school

fee are discriminated against. Teachers, on the other hand, almost all responded that there was

no discrimination against non-paying learners in schools.

Table 120: Opinions on the fairness of fee exemptions

Quintile 3 Quintile 4 Quintile 5 Overall

% of non-SGB parents believing it is fair for poorer parents to pay less in school fees 69 79 84 78

% of non-SGB parents who believe learners who are unable to pay the fee are discriminated against 18 9 10 12

% of principals who believe that exemptions are fair 88 73 70 77 unfair 10 20 19 16

% of SGB parents who believe that exemptions are fair 84 91 83 87 unfair 10 9 11 10

Note: Learner weights used.

7.2 Compliance with the exemptions rules and their workability

Are the exemptions rules followed, and are they workable?

An account of the overall private charges and the deductions from these charges in schools

reveals certain patterns that one would not expect if all policies were ideally implemented.

Non-fee contributions by parents should not exist, but their magnitude is limited. They

account for around 4% of total private revenue (see Table 121).

More seriously, it was envisaged that with a fee exemptions system, there would not be a

major problem with parents not paying what they legally owe the school. Yet this is clearly a

problem. Non-payment of fees legally owed (after exemptions) translates into an overall loss

in revenue of R958m for the schooling system, compared to the financial loss of R803m

associated with exemptions. Non-payment of fees is slightly more common when fees are

higher, but this link is not very strong. The percentage of school revenue lost through non-

payment is twice as high in historically black schools as in historically white schools, though

even in historically white schools the number of learners not paying is almost as great as the

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number of exempted learners (Table 122). Schools that are ‘generous’ in the sense that

exemptions approved as a ratio of exemptions applications are high and in the sense that they

are not strongly inclined towards taking legal action against parents, are more likely to

experience non-payment problems (Table 126). Importantly, high levels of non-payment of

fees is not associated with non-implementation of the exemptions system. The great majority

of schools with large non-payment problems also implement exemptions, and around half of

them also experience high revenue losses from the exemptions, apart from the problem of

non-payment. Because the survey data do not include information on the incomes of parents,

it is impossible to say whether the root problem is that schools are not implementing the

policies as one might expect (for instance by making use of the right to take legal action

against non-paying parents) or whether the problem lies within the policies (in particular the

assumed link between ability to pay and household income within the exemptions formula).

However, the fact that the number of applications for exemptions should be lower than

expected suggests there is a problem in implementing the exemptions policy. Specifically,

whilst 50% of learners do not pay the full fee amount (for whatever reason), only 13% apply

for exemptions within the most problematic schools.

Turning to fee exemptions themselves, both the statistics from the school principal and the

parent responses indicate that, paradoxically, more learners received exemptions than applied

for exemptions. Specifically, around 8% applied for exemptions, and 11% received

exemptions in fee-charging schools. Yet not everyone who applies for an exemption gets one.

In fact, only 72% of applicants are successful (Table 124). This implies that around half of

exemptions granted are not granted in response to a formal application. This is much more

pronounced in quintile 3 and quintile 4 schools than in quintile 5 schools, suggesting a less

legalistic and perhaps more community-sensitive approach in these schools. This is not

necessarily a bad thing, especially if one considers that the net result is a higher percentage of

formally exempted learners, in particular in quintile 3 (‘formal’ here is in the sense of the

principal regarding this as an exemption, and not simply as non-payment).

Undoubtedly the greatest problem with respect to exemptions is that the provision that all

learners who are recipients of social grants should be automatically exempted, is not being

followed through. The provision was introduced with the 2006 revisions to the exemptions

rules. Only half of SGB parents know about the 2006 revisions (Table 119 above). In primary

schools, where most child support grant recipients would be enrolled, 40% of parents report

receiving a child grant, yet only 16% of these parents enjoyed a fee exemption (the figure

should be 100%). In the minority of cases where grant recipients apply for an exemption,

100% of applications are successful, suggesting that the problem is largely one of awareness

amongst parents. Roughly, around 1.2 million learners who should be receiving exemptions,

because they receive a social grant, do not. This has important implications for government’s

overall poverty alleviation strategies. If the policy were to be correctly implemented in this

regard, the financial losses for schools would moreover be considerable and the need for

public funding for schools would increase.

The limited awareness of the 2006 revisions could explain why many respondents favour

making the exemptions policy more generous (basically they do not realise how generous they

already are). In quintiles 3 and 4 a large percentage of principals and SGB parents believe it

should become easier, and not more difficult, for a parent to obtain an exemption. The attitude

in quintile 5 is very different, however. Here a clear majority favour making the policy less

generous.

The following graph illustrates distributions of key variables from 226 schools that seemed to

provide sufficiently reliable data in the relevant table. A maximum of 274 schools was

possible, this being the number of schools charging fees in 2008. Some imputation of values

occurred, but this did not alter the basic patterns seen in Figure 25. The most frequent

imputation was completing the number of learners who paid the full school fee amount.

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Perhaps understandably, given that principals might focus more on the number of learners not

paying as opposed to the number of learners paying, this variable was in several cases omitted

but could easily be imputed using the available data. The graph illustrates that amongst the

226 (learner-weighted) schools the median percentage of learners paying the full school fee

was around 70%. Within the questionnaire and this analysis, automatic exemptions, generally

granted to learners receiving child support grants, were considered a sub-category of ‘fully

exempt’. The sum of the ‘fully exempt’ and ‘partially exempt’ curves would thus represent

overall exemptions. Importantly, this sum clearly exceeds the percentage of learners applying

for an exemption. This paradox is discussed below. The percentage of learners who didn’t

pay, either wholly or in part, whilst not having an exemption, is clearly high in many schools.

It is likely that some fees not paid in one year would be paid by owing parents in the

following year, so the ‘didn’t pay’ curve is likely to exaggerate the problem somewhat. The

survey data do not allow us to tell to what degree. However, even if some learners would pay

late, the fact that a large number of learners are not paying during the year in which the fee is

charged, despite not having an exemption, obviously has serious implications for the cashflow

situation in the school.

The ‘not paying other’ curve reflects an estimate of the percentage of learners not paying what

was described in section 5.2 as ‘no-fee contributions’. It was assumed that the average no-fee

contribution collected from parents for sundry activities such as school trips was compulsory

for all learners, and that where a percentage lower than 100% of learners paid the

contribution, non-paying learners were considered to be ‘exempt’ in a sense. It is unlikely that

things work quite like this in reality. In reality, there are probably learners who are not

expected to pay contributions in a particular year, perhaps because their class is not going on a

school trip. Yet, as seen in Figure 25, in about 25% of schools charging fees all learners pay

these non-fee contributions. The ‘exemptions’ applicable in the other 75% of schools are

included below in order to offer a more holistic picture.

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Figure 25: Non-payment of fees

Note: Learner weights used.

Table 121 provides key aggregates with respect to any private charges made in schools, and

deductions from these charges, in 2008, using the survey data. Values were incremented to

reflect what the likely values in the schooling system are for schools charging fees. Around

6.8 million learners, or 57% of all learners, were charged fees. These learners came from 45%

of schools, reflecting the fact that fee-charging schools tend to be larger than no fee schools.

The total value of fees charged was around R9.0bn (or almost double the amount of public

spending going towards public schools and reflected in Table 12), which gives an average per

learner of R1,327. High inequalities with respect to fee charged means the median fee value is

only R150 (see Table 99 for the full distribution, but note that that table refers to 2009). Of

the R9.0bn charged in fees, one should deduct R803m for exemptions granted and a further

R958m for non-payment where there was no exemption. This results in a total fee revenue

figure of R7.3bn. The gap in terms of numbers of learners is even larger – 739,000 exempted

against 1.3 million not paying – given that the per learner exemption Rand amount is

relatively high, at R1,087 on average (though the median is much lower). There are slightly

more schools experiencing non-payment of fees as opposed to exemptions – 7,681 against

7,036. Of those learners exempted from fees, around two-third receive a full exemption (this

includes some automatic exemptions) whilst partial exemptions account for one-third of

exempted learners. Automatic exemptions come to around 20% of all exemptions. 6% of all

learners, but 11% of learners in fee-charging schools (739,000 over 6.8 million) are exempted.

The last line of the table indicates that 40% of all learners (or 70% of learners charged fees)

pay the full fee amount.

The most important non-fee contributions value in the table is perhaps the R285m actual

revenue figure. We can be relatively sure that this is more or less the value of this revenue

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stream. What is less certain is whether R826m is charged, and the remainder is deducted (as

was explained above). Revenue from non-fee contributions thus comes to around 4% of total

private revenue in schools, counting fee-charging schools only (as discussed earlier, there are

also non-fee contributions that exist in no fee schools).

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Table 121: Aggregate private charges and deductions

Learners

(thousand) % of learners Schools % of schools Total value (Rm)

Average Rand per non-zero

learner Median Rand per non-zero learner

Charges 6,796 57 11,120 45 9,847 1,449 Fees 6,796 57 11,120 45 9,021 1,327 150 Non-fee contributions 4,774 40 7,708 31 826 173 100

Deductions 2,430 20 10,074 41 2,303 948 Fee exemptions 739 6 7,036 28 803 1,087 Partial 230 2 3,901 16 230 1,000 135 Full 509 4 6,917 28 573 1,127 150 Automatic 157 1 3,816 15 147 932 150 Fee non-payment 1,271 11 7,681 31 958 754 148 Non-fee exemptions 2,430 20 7,905 32 541 223 15

Charges minus all deductions 7,545 Fees 7,260 Non-fee contributions 285

Full payment of fees 4,786 40 10,960 44 6,900 1,442 200

Note: Learner weights used. The indentation of the row headings indicates what is a sub-category of what.

127

The next two tables are partly aimed at determining which schools experience high levels of

non-payment of fees, a phenomenon that is not anticipated by the national policies regarding

fees and exemptions. In Table 122 and Table 123 100% means all learners in fee-charging

schools (100% meant all public school learners in the previous table). The first graph suggests

that historically white schools experience a situation that is somewhat more like what the

policy envisaged. Fewer learners are charged non-fee contributions (yet half are). Moreover,

the percentage of learners simply not paying is lower. The second table indicates that it is in

quintile 3 that the greatest proportion of learners are exempted, and that there is more non-

payment of fees in quintiles 4 and 5. What is not reflected in the table is that the proportion of

fee-charging schools implementing fee exemptions in quintile 3 is exceptionally low, at 57%,

and that the percentage of learners in quintile 3 schools which apply exemptions is as high as

25% on average.

Table 122: Learners with deductions by ex-department

African Coloured Indian White

Learners charged 100 100 100 100 Fees 100 100 100 100 Non-fee contributions 69 81 100 49

Learners with deductions 40 43 29 23 Fee exemptions 10 8 11 12 Partial 2 2 5 6 Full 8 6 7 6 Automatic 2 3 1 2 Fee non-payment 20 30 18 10 Non-fee exemptions 40 43 13 21

Full payment of fees 70 63 71 77

Total learners (thousand) 4,666 583 287 801

Note: Learner weights used.

Table 123: Learners with deductions by quintile

Quintile 3 Quintile 4 Quintile 5

Learners charged 100 100 100 Fees 100 100 100 Non-fee contributions 69 77 72

Learners with deductions 44 41 31 Fee exemptions 14 11 10 Partial 3 5 5 Full 12 6 6 Automatic 3 2 1 Fee non-payment 14 25 20 Non-fee exemptions 44 41 24

Full payment of fees 72 64 69

Total learners (thousand) 2,257 2,176 1,394

Note: Learner weights used.

The following graph illustrates the distribution of charged fees lost through just exemptions,

and through exemptions plus non-payment. Both are given in Rand terms (see the left-hand

vertical axis) and in percentage terms (see the right-hand vertical axis). The graph, and

perhaps an intuitive sense of what is possible, suggests that one can regard schools with a

non-payment rate of over 20%, or an exemptions rate of over 15%, as ‘extreme’. This

category ‘extreme’ is used in the tables that follow in an attempt to identify what may

distinguish these schools from other school. There is a low correlation between the exemption

rate illustrated in Figure 26 and the level of the fee charged, which is perhaps contrary to what

one may expect. High fees do not go together with higher exemptions rates. However, there is

a bit of a positive correlation (of 0.22) between the overall rate of revenue loss (from

exemptions plus non-payment) and the level of the fee charged. However, the median fee for

‘non-extreme’ schools is R133 against R185 for ‘extreme’ schools, so the difference is not

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great. One may ask whether high levels of non-payment go together with the non-

implementation of the exemptions policy. This would make intuitive sense. However, this is

not the case. Of the 86 surveyed schools which experience a non-payment rate in excess of

20%, only 10 do not have exemptions. And of the remaining 76 schools, half also have an

exemptions rate in excess of 15%. It is thus common for the two problems to co-exist in the

same school.

Figure 26: Value of total deductions

Note: Learner weights used.

The principal and non-SGB parent responses tally rather well when it comes to applications

for exemptions, and applications granted. As seen in the next table, 9% of learners (in fee-

charging schools) applied for exemptions according to parents, whilst the principal’s data

yields a figure of 8%. Moreover, the 13% of learners actually exempted agrees rather well

with the 11% figure referred to earlier. However, the parent responses indicate that only 72%

of those who apply obtain an exemption. This implies that 5% of learners in fee-charging

schools obtain an exemption without an application ((11% - 8%) + (100% - 72%) × 8%),

whilst 6% obtain the exemption after an application process (using the principal’s 11% value

for exemptions). In other words, only around half of exemptions are the result of an

application. It is possible that applications for automatic exemptions are not considered proper

applications, because they do not involve a means test conducted by the school. But there

could also be other reasons, such as principals excluding applications occurring after a cut-off

date in the survey responses. Applications for exemptions in ‘extreme’ schools is high, at

13%, yet this is well below the average percentage of learners not paying the full fee amount

– that figure stands at 50%. On the whole, parents find that schools assist in the exemptions

applications process, though it is notable that in quintile 5, 7% of parents describe the school

as ‘not helpful at all’. The rate at which applications become approved exemptions is

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exceptionally high in ‘extreme’ schools, at 95%, suggesting that these schools are perhaps

victims of their own ‘generosity’ (though this ‘generosity’ may in fact be a correct

implementation of the exemptions policy). Overall, in 48% of schools with exemptions

parents are not involved in the decision-making process around the granting of exemptions, as

required in the policy.

Table 124: Exemptions applications

Quintile 3 Quintile 4 Quintile 5 Extreme Overall

% of non-SGB parents in fee-charging schools saying they applied for exemptions in 2008 or 2009 6 8 13 13 9

% of learners applying for exemptions in 2008 according to principal 7 7 13 12 8

% of non-SGB parents who applied for an exemption and found the school helpful in the matter 72 89 62 78 70

% of non-SGB parents who applied for an exemption and found the school unhelpful in the matter 0 0 7 0 5

% of non-SGB parents who applied for an exemption and obtained one 72 69 80 95 72

% of all non-SGB parents who obtained an exemption (regardless of whether they applied for one) 11 10 19 22 13

Exemptions granted as a % of exemptions applied for according to the principal 96 94 90 98 92

% of non-SGB parents with only primary level learners who say someone in the household receives a child support grant 63 41 12 44 40

% of the above non-SGB parents saying they applied for a fee exemption 8 2 54 16 9

% of the above non-SGB parents who obtained a fee exemption 100 100 100 100 100

% of all non-SGB parents with only primary level learners and with a social grant who obtained a fee exemption (regardless of whether they applied for one) 15 8 81 26 16

% of principals saying ... exercise the most influence in assessing a parent’s eligibility for an exemption parents 26 19 31 29 24 principal or school staff 36 26 25 32 28 a mix of the above 38 55 44 39 48 Note: Learner weights used.

With respect to possible improvements to the exemptions policy, the next table indicates that

a large majority of principals find the household income data submitted as part of the

exemptions applications process unreliable (SGB parents express a very similar view). This

suggests that a better methodology, or more training in the existing mean test methodology,

may be required. 70% of principals believe the current formula is too complex, again

implying the need for either a policy revision or better packaging and communication of the

existing policy. For instance, a web-based exemptions calculator maintained by the DoE may

assist.

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Table 125: Opinions on the exemptions formula

Quintile 3 Quintile 4 Quintile 5 Extreme Overall

% of principals who find the following information untrustworthy Household income 67 74 67 67 70 Fees paid in other schools 48 44 52 51 47

% of principals who believe the 2006 revisions to the exemptions policy did the following improved things 41 18 34 22 29 made things worse 12 20 35 26 23 made little difference 49 37 32 33 38

% of principals believing that the exemptions formula should be simplified 70 81 53 71 69

% of principals believing it should be ... for parents to obtain an exemption Easier 79 64 28 66 56 more difficult 6 16 40 21 21

% of SGB parents believing it should be ... for parents to obtain an exemption Easier 77 68 26 56 58 more difficult 9 22 44 26 25

Note: Learner weights used.

The last table indicates that whilst quintile 3 and 4 schools tend to use persuasion to get non-

paying parents to pay their fees, quintile 5 schools tend to employ legal means. A problem on

the margin is that there are still a few schools (2% of them) which withhold report cards from

parents who do not pay their fees.

Table 126: Responses to non-payment of fees

Quintile 3 Quintile 4 Quintile 5 Extreme Overall

% of principals saying the following happens when parents are unable to pay fees

School tries to persuade parents to pay but without taking legal action

61 59 31 54 51

School holds back the learner's report card

3 4 0 3 2

School takes legal action against the parents or uses debt collectors

1 26 63 26 30

School asks for in-kind assistance. 18 11 3 9 10

School does nothing. 16 0 3 9 7

Note: Learner weights used.

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8 Conclusion and recommendations for future research

This section concludes the report with suggestions for future work. For a summary of the

report and key policy conclusions the reader is referred to the executive summary of the

report.

This report has attempted to shed light on the complex policy area of the funding of schools.

The analysis is greatly assisted by the existence of a new dataset on schools, collected

specifically for the study at hand, but hopefully also a useful input for future work. Proposals

for such future work are the following:

� The analysis undertaken for this report could be deepened. For instance, the multivariate

modelling appearing above is very basic, and could be elaborated on with a view to

understanding better what results in a well managed school and what kind of management

and resourcing is associated with superior learner performance, when a range of

contextual factors are controlled for. The costing done for the report rests on a number of

assumptions about how the institutions concerned work, and what the data are saying.

These assumptions were carefully thought through, but they are not infallible, and it is

possible that different and equally valid assumptions could lead to different findings.

� The dataset used for the study is perhaps the best there is for those looking for a baseline

dataset on which to build scenarios of alternative funding policies. The financial and

enrolment data would be useful in this regard, but so would the opinion responses, which

allow for a better understanding of behavioural responses to specific policy changes.

� The dataset lends itself to analysis beyond the area of school management and finance.

For instance, data on the personal characteristics of respondents (age, gender, race and

years of education) permit an analysis of a more sociological nature.

With a view to facilitating future work, a short technical report on features of the data has

been compiled. Moreover, the analysis steps followed for the production of this report are

documented in the form of two Stata .do files.

132

References

Blalock, H.M. (1979). Social statistics. Singapore: McGraw-Hill.

Department of Education (2005). Grade 6 Systemic Evaluation: National. Pretoria. Available

from:

<http://www.hsrc.ac.za/research/output/outputDocuments/3580_Grade6National.pdf>

[Accessed February 2006].

Statistics South Africa (2008). Income and expenditure of households 2005/2006: Analysis of

results. Pretoria. Available from: <http://www.statssa.gov.za> [Accessed March 2008].

133

9 Appendix: The question-by-question results

This appendix provides background information on the calculation of confidence intervals and

weights, and explains how the five annexes containing the question-by-question statistics

from the survey should be read.

9.1 How to interpret the tables with interval data

In the annexes interval and nominal variables are reported on differently. Interval variables,

often referred to as continuous variables, contain meaningful numeric values, such as the

school fee charged at the school. Nominal variables contain code values corresponding to

different responses in a multiple choice question.

Table 127 is a copy of the results reported in the school principal annex dealing with the

question on the school fee charged in 2007. It illustrates how interval data are dealt with. The

following should be noted about Table 127:

� 17% of unweighed schools did not respond to this question.

� 27% of unweighted schools gave a response of zero, meaning no school fee was charged.

� The distribution of values is indicated by the minimum, the maximum, and the percentile

values inbetween. As an example, the 10th percentile value is the value ten one-

hundredths (or one-tenth) from the bottom of the distribution, in other words close to the

minimum. The 50th percentile is what is often referred to as the median. It is the value in

the middle of the distribution. The distribution seen in Table 127 indicates that at least

75% of schools had a fee value of R150 or less. The percentile values are calculated using

the school weight (see section 9.4 for details on the weights).

� The weighted mean (or average) for South Africa was R611. In calculating this mean,

schools with a zero response are included. The school weight, and not the learner weight

was used to calculate this mean.

� The confidence interval for the mean for South Africa was R187 to R1,035. This means

we can be 95% certain that the mean (or average) lies in the range of R187 to R1,035.

This is a very wide confidence interval and in fact the survey is not very useful at reliably

telling us what the average actually is. The survey was too small, and the variance of the

school fee variable examined here was too high (there was too large, or unequal, a range

of fee values). However, the results for all variables appearing below indicate that for

many variables the confidence interval was much better (narrower) than the one for the

2007 school fees. Generally, where there is less inequality across schools, the confidence

intervals will be narrower. Whilst it may seem disheartening to discover that certain

statistics are as unreliable as the average fee of R611 reported on in Table 127, it is vital

that this kind of unreliability be considered in the policy discussions to avoid a situation

where conclusions are drawn on statistics that are too unreliable. What constitutes ‘too

unreliable’? This must be determined through professional judgment, depending on the

nature of the policy question and the general picture provided by the survey as a whole.

The statistical software cannot make this judgement.

� The trimmed mean is the weighted mean (using the school weight) with the bottom 2%

and the top 2% of values excluded. This implies that around 20 schools are excluded. The

trimmed mean of R625 may be a more accurate mean than the untrimmed mean of R611

if we consider that very high and very low values could be errors.

134

� The provincial statistics work like the national statistics. The confidence interval for EC

(R7 to R33) is narrower than even the national confidence interval because there is less

inequality amongst the sampled EC schools. The confidence intervals are particularly

useful in determining which provinces have higher values than others. For instance, we

can be rather confident that the fee in EC was lower than the fee in all the other provinces

except FS and NW (the upper bound in EC is below the lower bound of these other six

provinces). Similarly, we can say that the fee in NC was higher than the fee in EC and LP.

� Arguably, in Table 127 the distribution statistics are more useful than the mean statistics.

Specifically, one can be relatively certain that nationally around 75% of schools were

charging R150 or less in fees. In fact, the confidence interval for this percentage is 71% to

85%, meaning we can be 95% certain that between 71% and 85% of schools which did

respond to the question were charging R150 or less in fees. This provides a relatively

good idea of the pattern of fee-charging in schools across the country. (Confidence

intervals relating to the distribution of values are not calculated in the tables that follow,

but in general those confidence intervals would be around the size discussed here.)

Table 127: Example of statistics on an interval variable

T3(a)/2007 Average fee charged per learner per year (before any exemptions) (Rand only)

Null Zero Min p10 p25 p50 p75 p90 Max

0.17 0.22 0 0 0 50 150 860 94720

SA EC FS GP KN LP MP NC NW WC

Mean 624 20 3193 2165 290 140 353 655 726 1010

Upper bound 1054 33 7537 3859 532 230 621 974 1463 1780

Lower bound 194 7 -1150 472 48 49 84 336 -11 240

Trimmed mean 644 20 3193 1972 290 140 353 655 726 806

9.2 How to interpret the tables with nominal data

The next table provides an example of statistics relating to a nominal (or categorical) variable,

in this case the school principal’s assessment of the correctness of the Department’s payment

of transfers to the school. The following should be noted:

� The possible responses are indicated with a letter, in this case (a) to (g). Moreover, the

possibility of a blank (or null) response is considered. In the country as a whole, 27% of

schools agreed most with statement (a), 40% agreed most with statement (b), and so on.

We call (b) the mode because it is the response that attracted most respondents. For this

reason the 40% value is bolded. The fractions in the table are calculated using the school

weight.

� The provincial fractions work in the same way as the national ones.

� The two last lines of the table give the confidence interval for the mode. Thus the

confidence interval for the percentage of nationally schools choosing option (b) is 31% to

48%. Note that in the case of FS the confidence interval of 42% to 68% refers to the

percentage of schools choosing option (a), as this and not option (b) is the mode in the

case of FS.

� How should one interpret the statistics, given the confidence intervals? Obviously there

are different interpretations but, for instance, it seems permissible to say that ‘around

40%’ of schools nationally selected option (b). It is certainly permissible to say that

clearly over half of schools in NW chose option (b).

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Table 128: Example of statistics on an nominal variable

2.9 Which statement best describes the timing of the transfers by the Department in 2008?

a. The funds arrived when we needed them.

b. The funds arrived too late, but the Department stuck to its schedule of payments.

c. The funds arrived too late, and the Department did not stick to its schedule of payments.

d. The funds arrived too late, and the Department does not tell us when the funds will arrive.

e. The funding the school was supposed to receive did not all arrive.

f. Note sure.

g. Not applicable.

SA EC FS GP KN LP MP NC NW WC

a .27 .10 .55 .36 .46 .10 .17 .17 .12 .53 b .40 .38 .26 .24 .32 .59 .40 .31 .72 .27

c .11 .24 .10 .17 .02 .12 .00 .07 .12 .07

d .12 .12 .03 .17 .07 .16 .40 .12 .02 .07

e .01 .00 .02 .00 .00 .00 .00 .19 .00 .00

f .02 .02 .02 .00 .03 .00 .00 .09 .02 .02

g .06 .12 .02 .05 .07 .03 .03 .05 .00 .05

blank .01 .02 .00 .02 .03 .00 .00 .00 .00 .00

Mode UB .48 .50 .68 .56 .74 .72 .53 .43 .94 .72

Mode LB .31 .26 .42 .15 .17 .46 .27 .19 .50 .33

9.3 How confidence intervals were calculated

A challenge in the analysis of the Financial and Management Survey was to obtain confidence

intervals that would take into account the non-randomness of the selection of schools in the

five provinces where clustering on the basis of districts was employed. The approach

followed was that described by Blalock (1979: 569) whereby confidence intervals obtained

assuming no clustering, in other words assuming a completely random selection, are adjusted

upwards to take into account the effect of the clustering. This is done by increasing the

variance of the values in a variable by a ratio, sometimes called the design effect, which

indicates the degree to which the clustering affected the design of the sample. The design

effect ratio is always greater than 1, and is calculated as follows:

( )11var

var−+= N

R

C ρ

varC is the variance one would expect when clustering is employed, varR is the variance one

would expect in a completely random sample without clustering, ρ is the intra-class

correlation co-efficient (the degree to which variance occurs between clusters as opposed to

within clusters), and N overscore is the average number of units (in our case schools) sampled

per cluster. This was the approach taken to obtain provincial statistics in the five provinces

where clustering was used. For the other four provinces, confidence intervals were calculated

using the regular approach applicable to random samples.

For the country-level statistics the four provinces without clustering (NC, FS, LP and MP)

and the 30 clusters in the remaining five provinces were each treated as a cluster, making 34

‘clusters’ in total, and confidence intervals were calculated using the design effect approach.

By how much does the use of design effect widen the confidence intervals? To illustrate, if

the confidence intervals in Table 127 had been calculated without the design effect one would

have obtained the following:

� The confidence interval for the national mean of R611 in school fees becomes R254 to

R928, instead of the R187 to R1,035 seen in Table 127.

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� The confidence interval for the EC mean of R20 in school fees becomes R11 to R29,

instead of the R7 to R33 seen in Table 127.

The confidence interval is thus slightly wider as a result of the design effect.

It is important to note that the design effect has no influence on the mean statistics. It is just

the confidence intervals for these mean values that are affected.

9.4 The calculation of weights

Two weights were created in the dataset. A school weight for each school was calculated as

follows:

n

NWs =

N is the actual number of schools in the province, and n is the number of schools sampled in

the province as part of the survey. Using the school weight ensures that provinces with more

schools, such as KN, are weighted more in the calculation of statistics than provinces with

fewer schools, such as NC. To illustrate, the mean school fee of R611 at the national level

reported in Table 127 becomes R918 if the school weight is not used.

A learner weight was also calculated for each school as follows:

EN

nWp ×=

Here n is the number of Grades 1 to 12 learners in the school, N is all the Grades 1 to 12

learners in the sampled schools and E is the actual enrolment in public ordinary schools in the

province according to the DoE’s 2008 Statistics at a Glance (official 2009 enrolment figures

were not available yet, but they would have made hardly any difference to the weighting). The

learner weight is not used for the calculation of any of the question-by-question statistics

shown below, but it is used for some of statistics reported in the main body of the report

above, where necessary. For instance, to obtain the percentage of learners paying above a

particular school fee, the learner weight and not the school weight is required. To illustrate, if

the learner weight is applied, the national mean for the school fee becomes R420 instead of

R611. What this implies is that schools with more learners tend to charge less in school fees.