Our analysis is based on two years of extensive work in liaison with the Ministry of Education, Science and Technology (MoEST) of Malawi, as well as other official sources, to rationalize and analyze administrative data on teacher management. First, we developed the first full and accurate database of the actual whereabouts of all Malawi’s primary school teachers. The exercise revealed the level of inconsistency in related data sets from different government sources, with large proportions of teachers recorded at different schools, or even districts, between databases; and the extent of inequities in teacher distribution, with PTRs<sup>2</sup> within even a single district ranging from a very low 9 students to one teacher to the extremity of 1,417 students to one teacher.

In order to test our first proposition – that the persistence of PTR disparities reflects political capture of the system by teachers’ interests – we first conducted focus groups and qualitative discussions to identify the key variables which inform teachers’ preferred school choice in Malawi. Using multiple regression analysis, we then demonstrate that these preferences – concerning the level of facilities available at a school, the distance of a school from its nearest town center, and the level of amenities available at the center – are closely correlated with PTR variation, demonstrating the key role teacher interests play in driving their allocation to schools.

Through a network mapping exercise, we developed a model of the local-level political economy of teacher allocations. This illustrated the level of pressure district-level public officials face over teacher allocations; the central role played by politicians and other public officials, who support teachers in applying pressure; and significant asymmetries of power, with communities exercising a much weaker voice than teachers.

In order to explore our second proposition – that improvements to administrative data systems can strengthen systems to resist political capture – we develop and simulate the impacts of new datadriven reforms to policies around teacher distribution. First, employing the key variables of teacher preference, we develop a data-driven classification model of school remoteness. Our system classifies schools into three categories of remoteness, employing information on the location of schools and trading centers and the levels of amenities available. Descriptive statistics demonstrate the effectiveness of the proposed categorization in predicting PTR variation.

Using statistical simulation, we then project the potential impact on PTR disparities of two planned policy applications of this categorization – targeting of newly qualified teachers to the most remote schools, and more targeted financial incentives for teachers working in the most remote schools. We find that, if fully implemented, these policies could achieve rapid reductions in PTR disparities within a short period.

Although the potential policy applications of our work are in the early stage of implementation,<sup>3</sup> analysis of the 2016 deployment of new teachers suggests that the improvements in data and information sharing engendered by the process have already led to improvements in the allocation of new teachers.

> 2 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.

> 3 The Government of Malawi has agreed to implement these policy applications for the results-based component of Malawi Education Sector Improvement Project (MESIP), funded by the Global Partnership on Education.

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