20 CHAPTER 2


(Education Management Information Systems [EMIS] codes) after 1996, pre- venting an accurate merge of the 1994 and 2000 datasets.4 In the section on empirical findings we see, first, that the LER distribu- tion for formerly African schools differs from that of formerly white, colored, Indian/Asian, and new schools in both 1996 and 2000. In particular, the dif- ference between formerly African schools and white or Indian/Asian schools was found to be statistically significant. A large number of formerly African schools exhibit LERs above the targets set by the government: 40:1 and 35:1 for primary and secondary schools, respectively.


To identify how the number of educators was adjusted in response to changes in the number of learners, the estimation strategy takes into account community-school-level unobserved fixed components, using specifications drawn directly from the model in the appendix. First, the adjustments of edu- cators in responding to changes in the number of learners (with budget con- straints) differ statistically across racial groups in primary schools, especially the adjustments of subsidized educators. Formerly African schools are more budget (liquidity) constrained than non-African (white, colored, and Indian/ Asian) schools when they employ educators. Second, among secondary schools, the gaps are smaller than those found in primary schools. Interestingly, for- merly white secondary schools do not show any significant adjustments to changes in the number of learners during this period, probably because their condition was already optimal. Third, in combined schools (both primary and secondary levels), the gaps between formerly African and Indian/Asian or new schools are significant. This observation reflects the fact that combined schools are regionally concentrated in certain districts and that there are few formerly white schools of this type. Fourth, in the analysis restricted to nonsubsidized (privately employed) educators, the number of educators does not significantly respond to changes in the number of learners. In this sense, the liquidity constraint is more binding at the school level than at the government level.


Framework Setting


I factor out possible reasons for changes in the number of learners. First, natural population growth contributes to cohort size, and therefore the number of school-age children in a community. Second, after the abolition of apartheid, households could freely migrate from formerly African areas to


4 Since its population size is large, exclusion of Gauteng may potentially affect our results.


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