�� = �+ �1�� + +�2�� + �3�� + �4�� + �5�� + +�6�� + �7�� + �� where _i_ indexes the school, and _Pi_ denotes pupil-teacher ratio for a school _i_ . _Ei_ is a dummy variable of availability of electricity at school _i_ , it turns 1 if a school _i_ has electricity, and 0 if not. Similarly, _Ri_ is a dummy variable for school _i_ accessibility by road during rainy seasons. _Di_ is a variable of Euclidean distance in km from school _i_ to its nearest trading center and _Fi_ denotes the number of facilities available there. _Hi_ is another dummy that turns 1 if a school _i_ has housing available for majority of its teachers, and 0 if not. _Vi_ is a dummy variable that turns 1 if DPP, the ruling party, won the most recent elections in constituency of school _i_ and _Xi includes_ all other controls such as school size, share of permanent/temporary classrooms and number of toilets available in school _i_ .

The results are presented in **<u>Table 3</u>** <u>. The first specification is a simple OLS regression, while</u> specifications 2 and 3 control for territory fixed effects, at district and zone level respectively.

Results of the regression analysis are consistent with the proposition that PTR variation reflects teacher preferences. Lower PTRs at the school level are associated with the availability of all included amenities in schools. The coefficients of the variables for availability of electricity and school accessibility by road during rainy seasons are negative and statistically significant and robust across specifications. The distance to the nearest trading center also turns out to be a strong determinant of school-level PTR: on average, a 10 kilometer increase in remoteness of school is correlated with an increase in PTR of 13 pupils per teacher.

However, availability of teacher housing, one of the variables most commonly cited by senior officials as a driving factor in teacher movement, comes out to be positively associated with higher PTRs. The results suggest that teachers prefer to stay closer to town centers or school with basic amenities, but not necessarily in remote rural schools that have staff housing.

Whether a schools’ local MP was from the ruling DPP was negatively associated with higher PTR at district level, suggesting that the political linkages of DEMs play some role in the allocation and re-allocation of teachers between districts. However, within the district and zone, the dummy is no longer significant, suggesting that MP affiliation does not drive PTR variation within a single geographic area.<sup>10</sup> Our case study of three schools in Nathenje, in Lilongwe Rural East district, further illustrates the role of these variables in PTR variation.<sup>11</sup> As noted above, the PTRs of these schools vary widely despite these schools sharing a small rural geographic area. This variation reflects the same characteristics identified by the regression analysis as key correlates of PTR variation. The school with the lowest PTR, Mwatibu, is based within Nathenje trading center and has year-round road access, electricity, water, and access to banking and medical services. Chibubu school, with a PTR of 79, is only 3.6 km from the nearest trading center; however, the center, Chadza, lacks both banking and medical facilities, while the school lacks electricity. Khuzi school, similarly, lacks electricity, and its nearest trading center lacks banking and medical facilities; however, the center, and nearest tarmac road, are about 9 km away. At 131, Khuzi’s PTR is among the highest in the district.

> 10 <u>Malawi’s districts are divided into an average 3-4 constituencies each.</u>

> 11 **<u>Table 12</u>** <u>, in Annex 1 (available online: see page 40).</u>

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