and 1980—when the government made a major effort at building primary schools. The program built about one new school for every 500 children of primary school age. The analysis exploits the fact that the program represented a significant change in policy and therefore older cohorts were not exposed to it, and that it was implemented at different rates in different regions, in order to identify the causal impact. Based on the results presented it is not possible to assess what the results of a cross-sectional analysis would have been. Duflo (2001) estimates that the program resulted in 0.27 additional years of schooling for each child (from a starting point of an average of almost 7.98 years—or an increase of 3.4 percent in years of schooling) with a larger effect in poorer areas.

Jalan and Glinskaya (2003) use difference-in-difference propensity score matching to compare school enrollment in districts of India that received a large-scale education program (that included a large school construction component) with those that did not receive the program. Their data from 1993/1994 and 1999/2000 bracket the intervention which was launched in 1994 and which targeted districts with high female illiteracy. They find that simple estimators that compare enrollment before and after the program _overstate_ the impact of the program on enrollment. For example, enrollment of 6 to 10 year olds in program districts increased by 5.4 percentage points, but this was only 2.0 percentage points above the increase in non-program districts. Moreover, it was only 1.3 percentage points higher than the increase in comparable non-program districts (as selected by their propensity score matching methodology). In addition, the enrollment of 11 to 13 year olds in program districts increased 3.3 percentage points, but this was actually 2.6 percentage points less than those in comparable non-program districts. Their main finding is the overall limited impact on enrollment of what was a large and expensive intervention.

The cross-sectional nature of DHS data (and limited complementary information such as the timing of school construction) makes it difficult to systematically deal with the potential endogeneity of schools. In three of the countries analyzed here there have been multiple surveys that can be matched, to some degree or another, across time according to geographic location.<sup>5</sup> A fixed effects approach can therefore be used in these cases. Interpreting the results as the causal impact of the availability of schools on enrollment rests on the assumption that any cause of the selective placement of schools is geographically fixed over time. As such, this approach is most similar to Pitt, Rosenzweig and Gibbons (1993).

Table 2 reports selected results from models that estimate the association between distance to schools and enrollment:

- in each of the years for which data are available;

- for a sample that pools data across the two years;

- and for the pooled sample using models that include fixed effects for geographic location.

> 5 Many countries have multiple DHS surveys but do not have the requisite cluster level variables on the availability of schools and other services.

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