Demographics - Pre-entry correlates of HE access and success

3.5 Pre-entry correlates of HE access and success

3.5.1 Demographics

3.5.1.1 Age

Themajority of first-time enteringundergraduate studentsin South Africa areindividualswho transition

fromsecondaryschoolintotheHEsystemwithlittleornodelay.However,asalreadynotedinSection3.2.1,

HEparticipationamongolderindividualsisnotuncommon.Between2000and2013,roughly22%ofallfirst-

timeentering undergraduatestudents in SouthAfrican publicHEIs were25yearsof age orolder (HEDA,

2015).Thatsaid,SouthAfrica’sHEparticipationratesdecreaserapidlyovertheageprofile(SeefigureE.1in

Appendix E).

TheinternationalevidenceontheassociationbetweenageofentryintoHEandthelikelihoodofHEsuccess

ismixed,butmoststudiesfindthatyoungerstudentswhoenterHEshortlyafterfinishingsecondaryschool

aremorelikelytoachievesuccessthanolderstudents.Thisispartlybecauseyoungerstudentsareoftenmore

accustomedtodealingwiththeacademicdemandsofformaleducation,butalsobecauseolderstudentstend

3.5. PRE-ENTRY CORRELATES OF HE ACCESS AND SUCCESS 66

One of the problems with analysing the association between age and HE outcomes is that individuals who

enter HE at older ages also tend to be those who postponed HE entry. Given the discussion of the association

between delayed HE entry and HE throughput in Section 3.4.2, it is to be expected that there would be a neg-

ative association between age and success in undergraduate studies. Rarely is any distinction made between

senior first-time entering undergraduate students who are older because they postponed HE enrolment and

those who are older purely because they were already older by the time that they completed secondary school.

This means that it is difficult to disentangle the parts of the association between age and HE success that are

respectively due to delayed entry and due to differences in age.

The WCED matric data contains information both on when learners were born and when they wrote the

SCE. It is therefore possible to account for differences in age at HE entry that are due to delayed enrolment

and differences due to other factors. Specifically, since most children from the 1987 birth cohort should have

entered Grade 1 in 1994 and, conditional on not dropping out or repeating any grades, progressed to Grade

12 by 2005, the date of birth information in the data can be used to determine if the learners who wrote the

2005 SCE were under-aged, appropriately-aged, or over-aged for Grade 12 (Burgeret al., 2012:6 - 7).

Critically, whether or not learners are of the appropriate age in Grade 12 conveys important underlying

information about their entry into and pathways through the primary and secondary schooling system. If

Grade 12 learners are under-aged, for example, one can conclude with reasonable confidence that it is because

they entered the formal schooling system at a younger age than the rest of their peer group. By contrast, over-

aged Grade 12 learners may be over-aged because they entered the schooling system later than their peers,

because they repeated one or more grades during school, or because of some combination of these factors.

This implies that any unconditional association between HE outcomes and age (as it is measured here) that is

identified in the data will not only reflect any pure age effects, but also any underlying school entry and school

progression effects associated with reaching Grade 12 at a given age. Nevertheless, the primary focus at this

stage is not on the causal mechanism underlying the association between age and HE access and success, but

rather on establishing whether such an association exists in the first place.

Table 3.12: Matric pass type and HE access, completion, and dropout rates for the 2005 WCED matric cohort by age group

Appropriate age Under-aged Over-aged

Share of matric cohort 50.7 13.2 36.1

Passed with endorsement 46.1 40.1 13.0

Passed without endorsement 44.8 50.9 49.3

4-year access rate 35.9 33.8 13.2

1-year access rate 25.7 23.7 7.6

- Bachelor’sa 75.8 64.6 56.3

4-year completion rate 52.7 47.3 33.4

3-year dropout rate 17.6 23.2 30.8

NOTES: Estimates are weighted and are calculated only for the sample of learners from the 2005 WCED matric cohort. Completion and dropout rates are estimated only for those learners who were part of the WCED 2006 first-time entering undergraduate cohort.[a]Figures reflect the estimated percentage of the WCED 2006 first-time entering undergraduate cohort who were enrolled in 3- or 4-year undergraduate Bachelor’s degree programmes in 2006. For the 2005 matric cohort, learners born in 1987 are categorised as being appropriately-aged whereas learners born before or after 1987 are respectively categorised as over-aged and under-aged.

3.5. PRE-ENTRY CORRELATES OF HE ACCESS AND SUCCESS 67

A substantial proportion (36.1%) were over-aged and a smaller, yet non-negligible, number of learners were

under-aged (13.2%). It is evident that appropriate-age learners performed better, on average, in secondary

school than over-aged learners in terms of the likelihood of passing matric and the type of pass obtained.45 Appropriately aged learners were also not just more likely to access HE between 2006 and 2009 than over-

aged learners, but far more likely to do so immediately after 2005. In fact, the proportion of appropriately-

aged learners who proceeded to HE in 2006 was more than three times as high as the proportion of over-aged

learners who did so. Furthermore, for the learners who did enter HE in 2006, those in the appropriate age group

were far more likely to have enrolled in three or four-year undergraduate Bachelor’s degree programmes than

those in the over-aged group and, to a lesser extent, those in the under-aged group.

Since admission to HEIs explicitly takes matric academic performance into account, one would expect the

extent of the association between age at HE entry and HE success to be mitigated to a degree by the nature

of selection into HE. It is therefore somewhat surprising that large throughput differentials are found to exist

between the different age groups from the 2005 WCED matric cohort. The 4-year completion rates for over-

aged students (33.4%) from the cohort are far lower than those in the appropriate age group (52.7%). Similarly,

those over-aged matric learners who entered HE in 2006 were far more likely to drop out within three years

than the HE entrants from the appropriate age group. In slight contrast to the findings for matric performance

and HE access, the under-aged cohort of students is also found to have performed worse than the appropriate-

age group in terms of HE completion and dropout, albeit to a far lesser extent than the over-aged group.

3.5.1.2 Gender

In South Africa, females account for a larger share of HE enrolments than males and this share appears to be

rising steadily over time. HESA (2012), for example, shows that the female share of headcount enrolments in

public HEIs rose from 55% to 58% between 2004 and 2011.46 In line with most of the international literature, Bhoratet al. (2010:103) also find that females generally perform better than males in terms of HE throughput and retention.47This finding is also supported by CHE (2014b:11), which shows that the course success rates for female students between 2007 and 2012 were consistently between 4 and 5 percentage points higher than

they were for males.

Table 3.13 provides a summary of matric performance, HE access, and HE throughput by gender for the 2005

WCED matric cohort. The estimates show that there were no major differences between males and females

in terms of 4-year or 1-year HE access rates or in terms of the types of qualifications for which students

enrolled.48 Given the similar levels of matric performance (in terms of the types of passes achieved), the fact that females accounted for a larger share of the WCED 2006 first-time entering undergraduate cohort (56.7%)

than males (43.3%) is thus a direct reflection of the gender composition of the 2005 WCED matric cohort

and not because female matriculants had a higher propensity to access HE than males. In fact, the gender

composition of the 2005 WCED matric cohort and the WCED 2006 first-time entering undergraduate cohort

was exactly the same because the 1-year participation rates for both genders were exactly the same.

The difference in pass rates and pass types between appropriate-age and under-aged learners in the cohort is less pronounced. 46

The HEMIS data indicates that the female share of first-time entering undergraduates also rose from 53.4% in 2000 to over 57% in 2013 (HEDA, 2015).

See Van Zyl (2010:58) for a summary of some of the international literature. 48

Nonetheless, the 4-year HE access rate for females from the cohort was statistically significantly lower than it was for males at the 5% level of significance.

3.5. PRE-ENTRY CORRELATES OF HE ACCESS AND SUCCESS 68

Table 3.13: Matric pass type and HE access, completion, and dropout rates for the 2005 WCED matric cohort by gender

Male Female

Share of matric cohort 43.3 56.7

Passed with endorsement 34.7 32.3

Passed without endorsement 47.4 47.1

4-year access rate 28.2 26.9

1-year access rate 18.9 18.9

- Bachelor’sa 69.9 72.1

4-year completion rate 44.1 52.8

3-year dropout rate 22.1 19.2

The most notable difference between genders in the WCED 2006 first-time entering undergraduate cohort was

in terms of 4-year completion rates, which were significantly higher for females than they were for males.

More than half (52.8%) of the females in the cohort successfully completed an undergraduate qualification by

the end of 2009. Similarly, the extent of dropout within the first three years of study was marginally lower for

females than for males. These findings are consistent with the HE throughput literature in South Africa which

finds that females generally tend to outperform males in terms of course success, retention, and programme

completion rates (Soudien, 2010:15).

3.5.1.3 Race

As discussed in Section 3.2, race remains perhaps the single most prominent demographic correlate of HE

access and success in South Africa. To investigate the association between race and HE outcomes in the

Western Cape, Table 3.14 disaggregates the matric pass type and HE access, completion, and dropout rates

among learners from the 2005 WCED matric cohort by race.

The racial composition of learners in WCED schools differs substantially from that of the rest of South Africa.

The estimates in Table 3.14 show that 28.5% of the learners from the 2005 WCED matric cohort were Black,

47.1% were Coloured, only 1.3% were Asian, and 22.2% were White.49 By contrast, of all the learners who sat the 2005 SCE nationally, 80.6% were Black, 6.2% were Coloured, 2.8% were Asian, and 8.6% were White

(Table E.3). On the basis that race is strongly correlated with observed secondary schooling and HE outcomes,

these compositional differences provide yet another reason why the extent of HE access and success among

learners in the Western Cape is unlikely to be representative of the country as a whole.

In addition to compositional differences, learners from the 2005 WCED and national matric cohorts also per-

formed differently in terms of the extent and types of matriculation passes achieved in the 2005 SC. It is

Due to the small number of Asian learners (524) in the 2005 WCED matric cohort, the confidence intervals surrounding the HE access, completion, and dropout rate point estimates for this group are quite large. For example, the width of the respective 95% confidence intervals around the estimated 1-year access, 4-year access, and 4-year completion rates for Asian learners from the cohort are all in excess of 12 percentage points. This should be taken into consideration when interpreting the various estimates for Asian learners/students presented below as well as when the results for Indians are compared with those for other race groups.

3.5. PRE-ENTRY CORRELATES OF HE ACCESS AND SUCCESS 69

Table 3.14: Matric pass type and HE access, completion, and dropout rates (%) for the 2005 WCED matric cohort, by race

Black Coloured Asian White

Share of matric cohort 28.5 47.1 1.3 22.2

Passed with endorsement 15.0 26.3 73.3 67.9

Passed without endorsement 44.2 58.8 20.4 29.2

4-year access rate 21.5 20.0 63.0 47.8

1-year access rate 11.7 14.6 52.1 34.4

- Bachelor’sa 48.3 66.7 86.9 83.3

4-year completion rate 31.9 43.0 46.9 62.1

3-year dropout rate 30.1 26.1 9.6 12.2

evident that far greater proportions of learners from WCED schools passed with endorsement than was the

case nationally. Moreover, this hold true for all race groups. Yet, while learners from different race groups

in the 2005 WCED matric cohort may have performed well relative to the national average, there remain

substantial racial differentials in the extent and types of passes achieved within the cohort.

Only 15% of Black and 26.3% of Coloured learners from the cohort passed the 2005 SC with matriculation

endorsement. By contrast 73.3% of Asian and 67.9% of White learners in the cohort followed suit. Given the

importance of matriculation endorsement for HE admissions, as discussed in Section 3.5.2 below, it is therefore

to be expected that far fewer Black and Coloured learners would have accessed HE between 2006 and 2009

than White or Asian learners. The data confirms that this was indeed the case. The estimated 4-year HE

access rates for Black (21.5%) and Coloured (20.0%) matrics were less than half of what they were for White

learners (47.8%).

Closer inspection of the various estimated access rates in Table 3.14 reveals two further interesting findings.

First, in contrast to the case for the other race groups, a greater percentage of Black learners from the cohort

enrolled in HE at some stage between 2006 and 2009 (21.5%) than passed with endorsement in 2005 (15.0%).

In fact, only 48% of the Black learners who accessed HE within four years of writing the SCE passed the 2005

SC with endorsement. Second, the differences in the 1-year and 4-year access rates show that the prevalence

of delayed HE entry also differs significantly between race groups. Only just over half of the Black learners

from the cohort who enrolled in HE between 2006 and 2009 did so in 2006. The proportion of HE participants

from the cohort who entered HE immediately after matric was much higher for Coloured (73%), Asian (83%),

and White learners (72%).50

In addition to the significant HE access differentials, there are clear differences in terms of the types of quali-

fications for which students from different race groups in the WCED 2006 first-time entering undergraduate

cohort enrolled. Where more than 80% of White and Asian students enrolled in 3- or 4-year undergraduate

Bachelor’s degree programmes in 2006, only 66.7% of Coloured and 48.3% of Black students did the same.51

These figures express the estimated 1-year access rates for the respective race groups in Table 3.14 as a percentage of their 4-year access rates.

3.5. PRE-ENTRY CORRELATES OF HE ACCESS AND SUCCESS 70

Table 3.15 shows that further differences are also apparent in terms of the HEIs where learners from differ-

ent race groups tended to enrol. While 73.6% of Black and 62.5% of Coloured students from the WCED 2006

first-time entering undergraduate cohort enrolled at either CP UT or UWC, only 37.4% of Indian and 18.9% of

White students in the cohort enrolled at these institutions.

Table 3.15: Enrolments for different race groups in the WCED 2006 first-time entering undergraduate cohort, by HEI attended in 2006

% of group enrolled in at ...

Black Coloured Asian White All

CP UT 48.8 32.8 10.8 17.4 28.4 US 2.8 14.0 9.2 47.3 25.2 UWC 24.8 29.7 26.6 1.5 17.1 UCT 10.0 12.2 40.8 15.7 14.7 UNISA 6.3 8.2 7.3 10.2 8.6 Other HEIs 7.3 3.1 5.3 8.0 5.9

NOTES: Estimates are weighted and are calculated only for the students in the WCED 2006 first-time entering undergraduate cohort. Figures represent the percentage of students within each race group who were enrolled at specific HEIs in 2006.

Given the racial differentials in matric endorsement rates and in the underlying institutional and qualification-

specific composition of enrolments among the WCED 2006 first-time entering undergraduate cohort, it is

perhaps not surprising that Table 3.14 reports significant differences in the extent of throughput and dropout

between the different race groups. 62.1% of White students and 46.9% of Asian students successfully com-

pleted their undergraduate studies within four years. Yet, only 43.0% of Coloured and 31.9% of Black students

respectively completed HE qualifications over the same period. Dropout was also far more prevalent among

historically disadvantaged groups with nearly three times as many Black students and more than twice as

many Coloured students dropping out of HE than Asian or White students before 2009.

The ratios between the estimated 4-year completion rates and 3-year dropout rates for the respective race

groups in Table 3.14 are also telling. More than five times as many White students completed their under-

graduate programmes within 4 years than dropped out of HE within the first three years of study with the

ratio for Asian students being nearly as high. Although the ratio was considerably less favourable for Col-

oured students (1.6:1), the situation was by far the worst among Black students where the proportion who

dropped out of HE within three years was very nearly as high as the proportion who completed their studies

within four years.

The implications of the racial differentials in HE access, throughput, and retention among the 2005 WCED

matric cohort for HE graduation outputs is simple. While 24.3% of White learners and 28.8% of Asian learners

from the cohort had completed at least one HE qualification by the end of 2009, only 6.9% of Coloured and

4.8% of Black learners managed to do the same. This is particularly worrying in light of the fact that Black

and Coloured students were far less likely to have enrolled for Bachelor’s degree programmes than White or

Asian students.

It is clear that the HE outcomes for learners from the 2005 WCED matric cohort are highly inequitable. How-

ever, in order to address these inequalities, it is necessary to understand why they obtain. Perhaps the most

3.5. PRE-ENTRY CORRELATES OF HE ACCESS AND SUCCESS 71

and retention might be explained by underlying differences in secondary school performance between race

groups. Clearly, the degree to which this is the case firstly depends on the extent to which secondary school

performance influences HE access and success. This is the primary focus of the analysis in the next section.

In document Graduate unemployment, higher education access and success, and teacher production in South Africa (Page 82-88)