How can this lack of democratic influence be explained? Respondents said that the primary reason is that communities lack awareness of the extent of PTR inequities, and subsequently are unlikely to organize or advocate forcefully for additional teachers or to resist the loss of teachers to less remote schools. TAs said they were more likely to be approached by communities seeking to expel a bad teacher than to protest the departure of a well-regarded teacher. Institutions for community influence over these actors are informal or underdeveloped.

Overall, the findings of the political economy analysis support the proposition of political capture of the teacher management system by teachers. Employing informal networks, particularly through personal relationships, teachers apply pressure to officials to obtain allocations to schools close to trading centers and amenities. DEMs, as the primary local decision-making official, bear the majority of the pressure. By contrast, community-level actors have a weak and fragmented voice in the system, meaning that there is little effective countervailing pressure on DEMs to resist teacher demands.

# **5. Findings: Data-driven categorization of schools and associated policies**

In this section, we set out to develop and simulate the impact on PTR of policies based on a more accurate picture of teacher distribution and its correlates. We present the method of classification, descriptive statistics, and simulations of the impact of various policy applications of this classification on teacher distributions; as well as analysis of the 2016 allocation of new teachers to schools, which reflects the impact of a partial improvement in the quality of administrative data.

## **5.1. Classification of schools by remoteness**

We developed a single system of classification which captured the various aspects of remoteness identified as of most relevance by the preceding analysis. The resulting classification defines school remoteness according to the types and quantity of amenities schools have access to in their nearby area. Three types of amenities are considered: (i) the availability of amenities at the school, specifically availability of electricity and accessibility by road all year round, including during rainy seasons;<sup>14</sup> (ii) Euclidean distance from a school to the nearest trading center<sup>15</sup> ; and (iii) the level of amenities available at the trading center.

In the first instance, employing a simple analytical approach, we assigned schools to one of three categories based on these factors of remoteness.<sup>16</sup> Category A schools, the most remote, are far away (more than 14km, equivalent to more than one hour’s journey by bicycle) from the nearest trading center; or are a moderate distance (7-14km)<sup>17</sup> from a trading center but lack both electricity

> 14 There is very little variation in access to drinking water supply and hence is highly collinear with other factors, like access to road. With 95% of schools having this access, and the vast majority of those lacking it also lacking yearround road access, this indicator was excluded from the categorization.

> 15 Given the significant variation in terrain and road quality in Malawi, an ideal approach might include road distance. In order to employ the most reliable geographic information available, existing geospatial data sets, it was necessary to use Euclidean distance.

> 16 **<u>Table 14</u>** <u>, in Annex 1 (available online: see page 40).</u>

> 17 PTR analysis revealed 07-km, 7-14km and above 14km to be the distance categories most connected to PTR variation [ **<u>Figure 3</u>** ].

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