The poor and access to higher education in South Africa

In South Africa attendance of higher education has been dominated by students from more affluent communities for many decades. After 1994 specific attention was given to ensure that more students from poorer communities attend higher education. However, after 1994 government appropriations per full time equivalent student decreased in real term. To balance their books higher education institutions increased tuition fees by more than the inflation rate – making the already expensive higher education even more expensive. This made access to higher education for the poor more difficult as they could not afford these high fees.

NSFAS was introduced as a tool to address this problem. Funds were made available to poor students after a means test was done. Race was used as a proxy for poverty as most of the poverty in South Africa is in the black communities. The value of NSFAS funds that individual higher education institutions received were to a large extent determined by the number of Black student attending those institutions. Poor students studying at previously ‘advantaged institutions’ with not so many ‘students of colour’ had a smaller change of getting NSFAS funds.

In this paper the financing of higher education will be looked at and how it changed over the last 20 years. One aspect that complicates comparisons is the whole higher education playing field that changed in 2004 (with all the mergers). How these institutions responded to the change in state funding will also be highlighted. The history of NSFAS will be discussed to investigate what difference it made. The throughput rates of the NSFAS students will also be compared with the rest of the students to determine whether they were more successful or not.

In order to do this project data were received from NSFAS of the 2000-2004 cohort groups as well as from Higher Education Management System (HEMIS) of all the students in higher education in South Africa from 2000-2010. Unique identifiers had to be used to merge the data from these two sources to do the analysis. By doing this the progress of students and their demographic profile could be identified. The results of this analysis will be discussed at the conference.