from MWK10,000 (US$14) to MWK25,000 ($US35), equivalent to about 35 percent of the average teacher’s salary in Malawi **<u>[Table 8]</u>** .<sup>24</sup> Furthermore, because the categorization is based on an objective formula for remoteness, employing data on school and trading center facilities from established sources, it is expected that the revised allowance can be appropriately targeted without the large incidence of dispute observed under the original scheme, where DEMs exercised considerable discretion in the identification of eligible schools.

**<u>Table 9</u>** <u>shows a simulation of the impact of the revised rural allowance scheme on PTR inequities.</u> The simulation employs an assumption that the scheme would incentivize eight percent of teachers from Category C schools, net of ‘leakage’, to move to Category A and B schools, in equal proportion. This is a conservative estimate intended to reflect the impact of the allowance in preventing ‘leakage’ from remote schools as well as encouraging movement to remote schools. Applying the impact of the allowance following the influx of 5,000 new teachers, but distributing these teachers across categories according to the current distribution and allowing for attrition (as in **<u>Table 6</u>** <u>), this movement of existing teachers further reduces the PTR in Category A from 86.3</u> to 78.2, and the difference in category-level PTR between Category A and Category C from 22 pupils-per-teacher to just seven.

**<u>Table 10</u>** presents simulations of the impact of the allowance with a smaller proportion of teachers responding to the allowance by moving to more remote schools. With only four percent of teachers moving to more remote schools, the category-level PTR in Category A is reduced from 86.3 to 82.0.

In practice, the MoEST expects to implement both policies – with the 2017 targeting of new teachers prioritizing Category A and B <u>schools, and the revised allowance expected to be</u> introduced during the 2017/18 school year. **<u>Table 11</u>** shows the combined impact of both policies – allocating 5,000 new teachers with a view to equalizing PTR, coupled with a three percent reallocation of Category C teachers to Category A and B schools from the revised allowances. The results are notable – according to this simulation, it is possible that per-category PTRs could be substantially equalized within one year, with Categories A-C having an overall PTR of between 72.3 and 73.4.

# **5.4. Impact of improved data on 2016 allocation of IPTE graduates**

The process of consultation, collection of data, validation and reconciliation of inconsistencies through which the data underpinning this analysis was prepared began in 2015 and involved all Malawi’s DEMs as well as MoEST, DHRMD, and non-education officials such as District Commissioners. The process required discrepancies between data sets, and within data sets between district records, to be identified and addressed for the first time. Therefore, if our proposition is true that improvements to the quality and sharing of data can enable improvements in teacher allocation decisions, we would expect to see such impact as a result of the process of engagement as early as 2016.

> 24 Under this framework (“Option 3”), only teachers in Category A and B schools would receive allowance. Other options include some allowance for teachers at Category C schools in addition.

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