out of 725 teachers were absent at least once during the last month. Two-thirds of the students live with both parents (7 percent of the children had lost both parents, and another 14 percent had lost one parent), and parental education is relatively high–a majority of the mothers reported studying to levels “more than primary schooling” and among fathers, this proportion increases to 72 percent. Despite the high levels of parental and teacher education, learning gains over the academic year were low. On average, children answered only 3.2 questions more in Mathematics from a starting point of 17.2 correct answers (from 45 questions) and 2.4 more in English starting from 11.1 correct answers (from 33 questions). In terms of the standardized score, children gained 0.42 standard deviations in Mathematics and 0.40 in English.<sup>14</sup> Table 2 summarizes our data on head-teacher reports of absenteeism. Teacher illness accounted for 35 percent of all absence episodes, and illnesses in the family and funerals for another 27 percent, suggesting that health-related issues are a major source of shocks to teacher inputs. The head-teacher reported a median absence duration of two days for teacher and family illness and three days for funerals.

Table 3 disaggregates the teacher and student characteristics by “more” and “less” absent teachers, restricting attention to the sample of non-movers (table 1 in appendix 3 provides a similar table for the full sample). While there are some differences in teacher characteristics, these differences are not signi fi cant at the usual con fi dence levels. In terms of student characteristics, there are no differences between the students with less and more absent teachers (an exception is the proportion of students that live within 15 minutes of their schools). Note in particular, that there is no statistically signi fi cant difference in the number of days the student was absent (1.42 versus 1.47) across less and more absent teachers. Finally, the characteristics of the sample change somewhat as we progressively exclude those teachers and students on whom we have no information–those excluded tend on average to be male teachers and teachers in rural areas.

Table 4 shows differences between non-movers and movers: Schools with non-movers are larger and are more urban. These differences are signi fi cant at the usual levels. Teachers who teach nonmovers are more urban (signi fi cant), female (signi fi cant), more experienced (signi fi cant) and have more training (signi fi cant). Similarly, there a signi fi cant differences in the student characteristics.

> 14 We used item response theory methods to arrive at a scaled score for every student; essentially the method constructs optimal weights for every question and estimates a latent variable, interpreted as the ”knowledge” of the child, using a maximum likelihood estimation procedure. Differencing across the base and fi nal year, the estimated change in ”knowledge” is used as the dependent variable in our regressions (see Das and others 2004a for details). The distribution of knowledge in the base year is standardized to have mean zero and variance 1, so that the coefficients of the regression can be interpreted as the impact of the independent variable on standard deviations of estimated (change in) knowledge.

14