teaching the student or having taught the student in the previous year. We collected information on the current teacher for 85 percent of the students and on the past teacher for 62 percent of these students since some teachers had left the school.<sup>10</sup> Consequently our sample drops as we include present and past teacher controls. Moreover, this change is probably not random–particularly in the case of the present teacher, it is very likely that we lose information on those who are prone to high absenteeism.

To assess learning achievement, a maximum of 20 students in grade 5 were randomly chosen from every school in 2001 and an achievement test was administered in Mathematics and English.<sup>11</sup> The same tests were administered in 2002 to the same students leading to the construction of a two-year panel of test scores. Sampled children were also asked to complete a student questionnaire in every year with information on basic assets and demographic information for the household.

Our source of variation for shocks to teacher inputs is variation in teacher absenteeism, where absence is de fi ned as a teacher being away from school during regular school hours.<sup>12</sup> Unfortunately, schools in Zambia (and in most other low-income countries) do not maintain records of teacher’s time away from school. To the extent that such records are available, they tend to under-estimate absence by 5-10 percent (Chaudhury and others 2004). Our information on absences is instead based on three different measures that we collected as part of the school survey; spot absence, selfreported absence during the last 30 days and the head-teacher’s report of teacher absence during the last 30 days. The most satisfactory measure is the head teacher’s report, whereby head teachers provided independent reports of teacher absence over the last 30 days for the entire matched teacher sample. As an indicator for teacher shocks, this is a noisy measure and as usual, measurement error implies that our estimates are likely to be biased towards zero.<sup>13</sup> Table 1 summarizes the school, teacher and student samples. The schools are evenly divided among rural and urban locations, with an average of 23 teachers teaching 912 pupils in every school. There are more female teachers, the majority have teaching certi fi cates and about half have been teaching for fi ve years or more. Absenteeism is a big problem. Head-teachers reported that 304

> 10 As in a number of other countries in the region, student teachers are typically used to teach a class for a year before returning to teacher training college to complete their training.

> 11 In schools with less than 20 students in grade 5, the entire grade was sampled.

> 12 Ideally we would like to measure the time that teachers spend away from the classroom when they should be teaching. This would include absence episodes while teachers are in school. Glewwe, Kremer and Moulin.(2001) fi nd that teachers are in school but absent from class 12 percent of the time. We focus only on time away from school.

> 13 Absences and their reasons are broadly similar for different methods used to collect absenteeism data. Appendix 1 provides a discussion of these alternative measures.Despite the measurement error associated with a 30-day recall period as a measure of year-long shocks, there are established precedents in household surveys. Most household survey modules on illness, for instance, restrict themselves to recall periods of 30-days or less. Nevertheless, these measures have been extensively used and validated in the literature on health and economic outcomes.

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