SCHOOL QUALITY, CLUSTERING, AND GOVERNMENT SUBSIDY 61
correlated with income opportunities) are more significant in the way they constrain the ability to pay for school quality and the quality of schooling investments in the next generation.
In metropolitan areas (column 6), however, the effect of the proportion of Africans in the population decreases by nearly half (from 0.412 to 0.205) and becomes insignificant, while the effect of average income increases from 0.031 to 0.213 and is thus significant. Socioeconomic factors matter more in these large cities than in the country as a whole.
School Quality, Local Resources, and Government Subsidy This section summarizes estimation results on school quality determination. School quality is measured by LER and the sensitivity of the number of educa- tors to changes in the number of learners, which I construct from SRN 1996 and 2000. An increase in LER implies a decrease in school quality.15 In the education function that I estimate, inputs are (log transformed) school fee and per-learner funding from the government. As discussed in previous sections, the school fee for 1998 is taken from the Annual School Survey for 1999. School funding information comes from the KwaZulu-Natal Department of Education.
Table 3.3 shows our empirical results. Columns 1–3 use school fees in dif- ferent years. The dependent variables are changes in LER from 1996 to 2000. Former population group, school type, and circuit indicators are controlled. Parameters of interest are school fee and per-learner funding. In those col- umns, the effects of these revenue conditions are significant and negative. Thus a better school financial situation improves school quality. In a prelimi- nary analysis, the log of the 2000 school fee was included, but its effect on dynamic change in the LER from 1996 to 2000 was insignificant. Column 3 uses per-learner total revenue (excluding government funding), which also has a significant and negative effect on LER.
In columns 4 and 5, I test how school financing can change the number of privately employed educators (nonsubsidized educators), controlling changes in the number of learners. First, an increase in the number of learners increases the number of those educators. Second, the log of the 1998 school
15 The difficulty in identifying the causality arises from potential endogeneity in the number of learners and unobserved fixed components specific to school and community, which are likely to be correlated with school inputs. For example, Lazear (2001) argues that the effect of LER on learner achievement could be empirically ambiguous because of (often unobserved) hetero- geneity in learners’ quality, that is, discipline. In his model, the optimal size of a class (that is, LER) increases if learners’ discipline improves, since the probability of disruption in a classroom decreases. To avoid such a correlation between LER and unobservables, recent studies use exog- enous variations (changes) in LER and class size to identify the effect on learner achievement.
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