Egypt. A parallel study in Kenya found that school environment factors (in particular the differential treatment of girls relative to boys) contributed to differential dropout among girls and boys (Lloyd and others 2000).

Since the DHS data do not have any information on the quality of the nearest school, they cannot be used to for this type of analysis. The associations and simulations described in this paper can therefore only be interpreted conditional on the average quality actually observed in the data. For example, to anticipate the results for some countries: if an increase school availability can only be expected to raise enrollments by a small amount this should be interpreted as the impact of an increase in the availability of schools with the same average quality as existing schools.

# **4. An empirical model assessing the relationship between enrollment and availability**

The methodological approach used here is to estimate a multivariate model relating school enrollment to child, household, and community variables—including distance to the nearest schools. For the purposes of the analysis, community is defined as the sampling “cluster” used in the survey. A cluster is the primary sampling unit in the DHS surveys. There are typically between 20 and 30 households interviewed per cluster. Community questionnaires were administered at the level of these clusters. Since these questionnaires were limited to rural areas in many countries, and since distance to schools is likely to be more of a salient issue in rural areas, the analysis is limited to households living in rural areas.

The specific variables included in the model are:

- _Child level_ : gender (dummy for male), age, age squared

- _Household level_ : household wealth (dummy variable for being in the top 50 percent of an index derived from household asset ownership and housing characteristics), the highest years of schooling of adult males in the household, the highest years of schooling of adult females in the household, the gender of the head of the household (dummy for male), the years of schooling of the head of the household

- _Cluster level_ : distance to nearest primary school, distance to nearest secondary school, distance or availability of various cluster level facilities.

The cluster level variables include more than just the distance to a primary school, the school level that 6 to 14 year old children are likely to be attending. Although these children are not likely to be attending secondary school, the access to secondary places may have an impact on primary schooling and the dummy variables for secondary school are therefore included in the multivariate analysis (consistent with Lavy 1996).

The equation includes a set of community facility and infrastructure variables in order to ensure that a relationship with school presence is not simply reflecting the fact that communities with more facilities and infrastructure in general, including schools, may tend to have higher enrollment (consistent with Pitt, Rosenzweig and Gibbons 1993). While the exact list of variables varies from survey to survey, the typical list includes: the distance to the nearest urban 7