into an index that performs well when compared to expenditures (i.e. gives similar results) and predicts enrollments even better in some data sets (Filmer and Pritchett, 2001). Individuals are ranked by the index of the households they live in and are assigned to the “richest” and “poorest” 50 percent. The approach is the similar as in Filmer (2000) where the focus is on gender and wealth differences in enrollments in a larger number of DHS data sets.

Distance to school is typically recorded at the cluster level by a community respondent. That is, an informed villager (such as a teacher) or a local government official might be asked about the availability of services in the sample cluster. Since the data analysis is carried out at the individual level, the fact that the distance to school is a cluster aggregate introduces some degree of measurement error. That is, some respondents might be closer to the school than the reported distance, some further. Short of asking each household respondent the distance to the nearest school (which is also likely to be reported with error) or GPS location identification and traveltime mapping (which had not been generally instituted at the time of the data sets collected here) it is difficult to get around this problem. One way is to use a measure of whether there is a school in the cluster, a less precise indicator of school availability but one that is much less susceptible to measurement error. As discussed below such an approach yields qualitatively similar results.

_The role of school quality_ A key aspect of using DHS data for this analysis is that the data contain no information about the nearest schools other than their distance. In particular there is no information about the quality of the schools that children have access to. While there is a relatively substantial “production function” literature that relates school characteristics—including quality indicators—to test scores (see for example a review in Pritchett and Filmer, 1999), there has been limited analysis of the association between quality and enrollment or grade attainment (Lloyd and others, 2001). The limited results do suggest that school quality matters for school attainment:

- Lavy (1996) found that some schooling quality indicators were associated with children ever having enrolled in school, as well as the number of grades they ultimately complete in Ghana.

- Bommier and Lambert (2000) found that, in Tanzania, the perceived quality of mathematics instruction was associated with younger enrollment age, and that the perceived quality of Swahili instruction was associated with higher grade attainment.

- Younger (2000, 2003) found that indicators of the quality of the school’s physical environment, as well as of the staff, were statistically significantly associated with enrollment at the primary and secondary levels in Peru.

- Paxson and Schady (2002) found that an increase in expenditures devoted to improving the physical quality of schools in Peru was associated with increases in school attendance between 1993 and 1996.

- Lloyd and others (2003) found that school quality indicators (such as the school’s physical environment, the qualifications of teachers, and some measures of the behavior of teachers) were significantly associated with the grade attainment of adolescent girls in

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