greater than between rates of 30 to 35; it also may be greater between 60 and 75 than between rates of 45 and 60.

**_Why are the correlations stronger at the school level than at the individual level?_** Inter-school variation is larger in Peru (and in other countries with a high inequality) than in most developed countries. It is probably larger than intra-school variation. The broader Junin study (Banco Mundial, 2008) found that rural, multigrade schools that teach indigenous children are far behind than urban, full grade schools. There are many reasons for this: (a) rural schools have fewer resources such as appropriate text books and teaching materials; (b) teachers assigned to rural, multigrade schools are often less qualified and less experienced than those working in large, prestigious urban schools; (c) teachers in remote schools miss more hours of class than teachers in urban schools because they have to travel larger distances and no one controls their attendance; (d) teachers in multigrade classrooms must split their attention among groups of students, and students waste time waiting for the teacher to finish with another group and to come work with them; (e) children in urban schools have parents with higher level of education and more resources to spare than children in poor, rural communities; (f) in many rural communities children enter first grade without having had any exposure to written texts (no signs in the town, no books); and (g) indigenous children learn Spanish as they learn to read, and in Junin there are no teachers qualified on bilingual education.

Beyond the obvious differences between poor rural schools and urban schools, another likely source of school-to-school variation is instructional time use, which is a mediator variable that is challenging to measure, so it often escapes scrutiny. An international study found that students are often taught for only a fraction of the intended time, particularly in lower-income countries (Abadzi, 2007). In the fieldwork in Junin, classroom observations were carried on in the classes where we tested the students, estimating that in one hour of class, teachers were not engaged in teaching –were outside the room, working in their desks, talking to school personnel, or chatting with some students– an average of 10% of the time (16% in single-teacher schools). This is on top of the delays to start class, and teacher and student absenteeism (Banco Mundial, 2008). Research suggests that students must get sufficient time to process the information and practice to be able to learn the contents and develop the skills (Abadzi, 2007).

The fact that between-school variation is relatively large compared to within-school variation suggests that accountability for whole-school results, using school averages, might be both better (since accountability perhaps needs to address the worst forms of inequality first), and more reliable (since the correlations between items are higher across schools than across individuals). It is likely that whole-school accountability might have less perverse consequences than accountability over individual student results and be more effective in confronting inequality. In terms of policy implications, it is necessary to focus more on school variation than on individual variation.

**_A new framework for understanding the fluency-comprehension relation._** Analyses based on the Rasch model to combine the ECE test and dichotomic versions of oral fluency (at several cutting points), have provided a more detailed description of the relationship between the individual items in the written test and different fluency levels. Results indicate that minimal fluency (45 wpm) is a good predictor of basic, literal comprehension, and a requisite for higher levels of comprehension but not a good predictor of performance at those levels. Yet, higher levels of fluency (e.g. 75 wpm) may be good predictors of more complex types of comprehension.

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