effects for the same teacher over time, this result is arguably causal. Consistent with recent work by Bold et al. (2017) in Africa, we also find that higher content knowledge is associated with significantly higher TVA, a correlation that emerges clearly once we account for measurement error using multiple test scores from the same teacher.

Second, there is no link between TVA and wages in the public sector.<sup>2</sup> Public sector wages reward seniority and education – both of which have small associations with TVA – but not TVA directly. Although this “zero-gradient” result is widely believed to be true, to our knowledge this is the first direct empirical test in a low income country. Using similar data from _private schools_ , we compare the gradient in the “market” with that in the public sector. Rewards to seniority are one-fifth as high in the private sector and, strikingly, a 1sd increase in TVA increases wages by 11%. Even in the absence of a formal testing regime, TVA is somewhat observable and can be rewarded, but the public sector does not have a mechanism to do so.

Third, lowering the wages of public sector teachers has no effect on the TVA of new entrants.<sup>3</sup> We compare the TVA of teachers hired just before and after a change in the hiring regime that moved all new hires to temporary contracts with significantly lower wages. In our data, 93% of new hires in 1997 were permanent teachers and by 2002, 89% were contract teachers, who received salaries that were 35% _lower_ , not counting further cost savings from benefits such as pensions.<sup>4</sup> Instead of a decline, we typically find a _positive_ , though not consistently significant, impact of contract status on TVA. When we compare contract and permanent teachers with more similar levels of experience, we find larger and more significant positive effects. It is possible that a worsening macroeconomic outlook following 1998 increased the attractiveness of teaching relative to other professions, compensating for the contractual change (for example, see Nagler et al. (2015)). However, we also do not find evidence that the pool of new teachers worsened over time (as the economy improved) with similar TVA estimates for later and earlier hires.<sup>5</sup>

> 2Here, we assume that TVA is a useful measure of productivity for teachers. We recognize that teachers have other functions that we do not capture, and how these are rewarded is an important agenda in its own right. Nevertheless, children’s test scores remain a key component of educational performance in any system, especially since they are a strong predictor of adult outcomes (for example, see Chetty et al. (2014b)).

> 3Determining whether wages are “too high” is typically difficult since researchers must determine how teachers are compensated relative to their outside options. To do this, they must adjust for different schedules (summer vacation), education levels, cognitive ability and the type of teacher, and different adjustments lead to different conclusions about the relative size of teacher compensation. Weissman (2011) is a non-technical summary of the studies and these issues.

4We arrive at 35% by regressing log teacher salaries on teacher characteristics, including seniority, as well as an indicator variable for contract status. Thus, we report the difference between contract teacher and non-contract teacher wages after accounting for any differences in observable teacher characteristics.

> 5The main threat to identification is the systematic allocation of contract teachers to students who were likely to learn more. We test for such systematic sorting and find no evidence that contract teachers were systematically allocated to better performing schools or students. Furthermore, there is no correlation between students’ test score trends and future assignment to a contract teacher, either at the school or at the student level. We further account for unobserved selection of students or teachers to schools by comparing contract and permanent teachers _within_ the same school.

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