SCHOOL MONEY Funding the flaws


The quintile system, which determines amounts of funding for individual schools, was implemented in post-apartheid South Africa as the government's commitment to redress and redistribution in the education sector. But is this an effective pro-poor mechanism? Amita Chutgar and Anil Kanjee investigated whether this system is ensuring funding allocation favours the poorest learners.

The National Norms and Standards for School Funding (NNSSF), which requires the allocation of funds to schools according to their poverty score, was a key policy change implemented in 2006 to determine the funding for individual schools. The poverty score of each school assigns it to a quintile rank (Q1 to Q5) which, based on a pre-determined formula, governs the amount of funding the school receives (see Table 1). Identifying which quintile a school falls into is a crucial step in determining school resource allocation. Thus, in 2006, the allocation per learner in Q1 schools was R703 and R117 for learners in Q5 schools.

The poverty score of a school, or quintile rank, is based on the poverty level of the community in which it is located. This score is calculated using national census data: weighted household data on income dependency ratio (or unemployment rate), and the level of education of the community (or literacy rate).

While the intention of the policy has been commended, there has been great dissatisfaction with the quintile ranking system. Specifically, critics have argued that the policy misclassifies schools giving them incorrect quintile scores, and thus similarly poor learners are found in schools with different quintiles since the poverty scores are based exclusively on the geographic area within which schools are located. This approach ignores the diverse nature of households and the composition of the school's learners.

Methods used in this study

The primary data source is the 2006 application of Progress in International Reading Literacy Study (PIRLS) 2006 database, available from the department of education in South Africa. Appropriate school weights from the PIRLS data were used to generate nationally representative estimates. The variables used for this analysis are from the school dataset. School-resource variables measure the amount of resources available in schools (which is where the current quintile system is focused comprising non-personnel, non-capital expenses), and school-composition measures focus on the composition of a school's learners. Both measures draw on principals' responses to the PIRLS questionnaire.

An extensive descriptive analysis of the data, looking at the current system of quintile ranking and whether it is correctly identifying schools and thus leading to the misallocation of resources, found that for every school background variable analysed, schools in Q5 are better off than schools in Q1. It means that schools in Q1, receiving more funding support than schools in Q5, are worse off in terms of school resources and school composition compared to schools in Q5.

However, this is not the complete story.

When we focus on Q2-Q4, we find that while the quintile system may be able to identify schools at the absolute ends of the spectrum, the schools in the middle often look similar and may appear better or worse in unexpected ways. Schools from Q1-Q4 are barely distinguishable in relation to mean proportion of disadvantaged learners in the school. With respect to average proportion of affluent learners, schools in Q1 are actually better off than schools in Q2.

In terms of overall resources and non-personnel resources, schools in Q2 and Q3, which receive less money are as well off as, or worse off, than schools in Q1. Data about school resources and school composition reveal that those in the higher quintiles Q2-Q4 may have resource needs as high as or even higher than in Q1. This suggests that the quintile ranking system is misidentifying schools currently placed in Q2-Q4.

In terms of proportions of affluent children, schools in Q1 are slightly above the national average. Schools in Q4 are no better and slightly above the national average in terms of proportion of learners from disadvantaged families, or requiring free and reduced-price lunch, although they receive much less funding than schools in Q1 where the proportion of affluent learners is slightly above the national average. Schools in Q2 which receive less financial support than those in Q1 are shown to have far fewer overall and non-personnel resources.

Not surprisingly, when a cross-tabulation is done, we find that more schools in Q1 report higher percentages of non-personnel resources compared with schools in Q2, Q3 and even Q4 (33% vs. 21-29%). The same is true in terms of overall school resources, with Q1 schools being better off than Q2 and Q3 schools.

In terms of the proportion of disadvantaged learners, we find that 81% of Q1 schools have more than 50% of such learners with 87% and 84%, respectively, compared to the less well-funded Q2 and Q3 schools.

In terms of affluent learners, approximately 12% of Q1 schools report that more than 50% of their learners are from privileged families compared with between 4% and 10% reported by Q2-Q4 schools. For the free and reduced-price lunch variable, a higher proportion of schools in Q1 and Q2 report that no learners require free and reduced-price lunch compared with schools in Q3 and Q4. Forty-seven percent of schools in Q2 have no learners requiring free and reduced-price lunch compared to 24% of Q4 schools.

Quintile ranking is ineffective

The analysis indicates that the current quintile ranking system does not work effectively. The schools that are mostly disadvantaged are those assigned to the middle quintiles. Their needs are as great as, or greater than, those in Q1 but according to the current financing formula they receive less financial support.

As we have noted, differences in terms of a school's characteristics between Q2-Q4 and often between Q1-Q4 do not appear to be very large. This indicates that schools with very similar resource deprivation may be receiving widely differing amounts of financial assistance. While the difference in resource needs of schools in both these categories is arguably rather small, under the current funding scheme, schools in Q3 receive R194 less per learner than those in Q1.

In more than one instance, we find that on average a school in Q1 is better off on some indicators than a school in Q2. Looking at the average overall school resources available to schools, we find that those in Q2 have on average 1.82 units of resources. These schools receive R64 less per learner compared to schools in Q1 although they are much worse off than schools in Q1.

In terms of population of learners served, we find that the Q2 schools, which receive less money than those in Q1, serve a greater proportion of disadvantaged learners and have fewer affluent learners than their Q1 counterparts. This points to the urgent need for the regular reclassification of schools to ensure that those in greater need are allocated into the correct quintile rank and thus qualify to receive sufficient levels of funding to meet their specific needs.

Dr Amita Chutgar is assistant professor, Department of Educational Administration, Michigan State University, USA, and Dr Anil Kanjee is the executive director of the Centre for Education Quality Improvement at the HSRC.