MoEST deployed approximately 9,000 new teachers in 2016, of which 4,653 were graduates of the IPTE training scheme.<sup>25</sup> The deployment followed a two-year period without deployment of new teachers, increasing the need for the new deployment to be allocated to schools according to the greatest need. Allocation decisions were made by MoEST and DEMs between May and August 2016. Analysis of the allocation of these teachers therefore provides a snapshot of the state of decision-making around teacher allocation one year into the process of data collaboration.

The results suggest a notable improvement in decision-making in the allocation of teachers compared to the preceding situation. MoEST appears to have broadly succeeded in allocating teachers to districts in accordance with PTR disparities: 83 percent of new IPTE graduate teachers were allocated to one of the 18 districts with an average school-level PTR of 1:85 or more.<sup>26</sup> Furthermore, DEMs appear to have improved the targeting of new teachers to schools with the greatest need. Of the 4,653 IPTE graduates recruited, 55 percent were deployed to Category A and B schools with PTRs above 60, or 80 in urban areas. This represents a significant improvement over the current distribution of existing teachers, in which only 38 percent of teachers are based at Category A and B schools. In consultations, DEMs said they had already made significant changes to practices in response to the growing awareness of the extent of PTR disparities, including introducing more strict requirements for medical and marriage certificates.

However, the 2016 deployment also demonstrates the need to deepen and institutionalize improved data management, and introduce reformed data-driven policies, to further improve allocation decisions. For example, while the deployment of new teachers to Category A and B schools represents an improvement on the current distribution, a fair distribution which aimed to minimize PTR disparities would see 84 percent of teachers allocated to these schools.<sup>27</sup> Furthermore, _leakage_ to less remote schools appears to remain a problem in the absence of a reformed allowance scheme. Fourteen percent of teachers moved to a different school within the same district within the first year after deployment; of these, 69 percent moved to a school in a less remote category.

# **6. Discussion**

Our findings provide support for both our main propositions. First, low-quality administrative data leave officials with a high degree of discretion in teacher allocation, creating space for political capture of the system by teacher interests and the maintenance of PTR disparities. Second, simulations suggest that improved data can mitigate the situation by enabling the development of more specific and precise policies, empowering officials to resist pressure to bend the rules, and enabling communities to create countervailing pressure for officials to consistently enforce agreed policies.

Throughout the system of teacher management, data are fragmented, inconsistent, and low in transparency. Sources of data from different government agencies differ in the number, location, and seniority of teachers. In the absence of accurate, up-to-date, and widely accepted data, policies

> 25 The remainder were ODL graduates; given the cancellation of the ODL scheme, our analysis here focuses on IPTE graduates.

> 26 Although we employ the term PTR for simplicity, all figures presented are for PQTR – pupil-qualified teacher ratio, which excludes volunteer, trainee, and month-to-month teachers.

> 27 Table 18, in Annex 1 (available online: see page 40).

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