Having developed an accurate database of teachers, we then combined this with school-level enrollment data, derived from EMIS, to develop an up-to-date database of school-level PTRs. This enabled the first systematic analysis of PTR variation using reliable and complete data. The findings are presented in Section 4.

We then turned to the potential determinants of school-level PTR variation. Focus group discussion with key stakeholders, including teachers and headteachers, suggested a wide range of potential variables which affect the willingness of teachers to work at a school. The most common factors identified by discussants were availability of electricity at the school; accessibility of school by road during rainy seasons; availability of housing; the distance from the school to the nearest trading center; and the availability of amenities – medical facilities, banking facilities, and water/electricity – at the nearest trading center.

Information on these variables was collected from administrative databases. First, information on school-level characteristics – availability of electricity, housing, and year-round road access – was obtained from EMIS and matched to the staffing database using a unique identifier.

MoEST maintains a database of the geospatial coordinates of all public schools in Malawi, while the National Statistical Office (NSO) maintains a database of larger villages and towns, commonly known as trading centers, which includes geospatial coordinates. We obtained these data and validated them through comparison with Malawi Spatial Data Platform (MASDAP), a public platform for geospatial data, by confirming the government coordinates for each school coincided with the district boundaries drawn from MASDAP. DEMs’ offices were also given the opporutnity to propose additional trading centers; the final database included 503 centers across Malawi.

Combining these records, we were able to identify the closest trading centers to each school and calculate the Euclidean distance to the center from the school.

Responding to feedback from qualitative discussions, we added an additional element of data in the form of the level of amenities available at trading centers. With the help of DEMs, the authors gathered data on the amenities available near trading centers. DEMs’ offices were asked to assign each trading centers a score from 0 to 3 based on how many major types of amenity – banking, medical facilities, and piped water or electricity – were available within 3 km of the center.<sup>8</sup> Combining these data sources, it was possible to construct a full picture of the remoteness of Malawi’s schools, in terms of both geographical distance and levels of facilities; the variation in PTRs between Malawi’s schools; and the relationship between the two. Descriptive and analytical findings are presented in the following section.

> 8 DEMs classified trading centers according to the following formula:

> 0 = None of the following available: bank, hospital, or piped water/electricity;

> 1 = One of the following available: bank, hospital, or piped water/electricity;

> 2 = Two of the following available: bank, hospital, or piped water/electricity;

> 3 = All of the following available: bank, hospital, and piped water/electricity

> 68 out of the 507 trading centers (13 percent) were identified as in category 0, with no amenities. 184 (36 percent) were classed as category 1, 190 (37 percent) as category 2, and 61 (12 percent) as category 3, including all district capitals or government centers. On average, each district had three centers in category 0, seven in category 1, seven in category 2, and two in category 3.

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