metadata: authors: 0: author: Asim,Salman
1: author: Gera,Ravinder Madron Casley
2: author: Moreno,Juan Martin
3: author: Wong,Kerry Lai Man
count: Malawi
docna: 0: docna: Can Targeted Allocation of Teachers Improve
            Student Learning Outcomes? Evidence from Malawi
docty: Policy Research Working Paper
theme: Disaster Risk Management,Disaster Response and Recovery,Disability,Gender,Human Development and Gender,Education Facilities,Social Protection,Social Development and Protection,Access to Education,Education Financing,Urban and Rural Development,Teachers,Education Governance, School-Based Management,Education
lndinstr: 0: lndinstr: Investment Project Financing
lndinstr_key: 1353855
abstracts: cdata!: Teachers are one of the most important
            inputs for learning, but in many low-income countries they
            are poorly distributed between schools. This paper discusses
            the case of Malawi, which has introduced new evidence-based
            policies and procedures to improve the equity and efficiency
            of the allocation of teachers to schools. The analysis finds
            that adherence to these policies has been highly variable
            between the country&#x2019;s districts, with the most
            successful deploying 75 percent of teachers according to the
            rules and the least successful just 22 percent. Using
            administrative data, the paper identifies the impacts on
            student repetition rates of reductions in
            pupil&#x2013;qualified teacher ratios as a result of the
            new teachers. The findings show that schools that moved from
            having more than 90 pupils per qualified teacher to a lower
            ratio experienced reductions in lower primary school
            repetition rates of 2&#x2013;3 percentage points.
            However, similar impacts on dropout are not observed.
display_title: Can Targeted Allocation of Teachers
            Improve Student Learning Outcomes? Evidence from Malawi
listing_relative_url: /research/2024/07/34362915/can-targeted-allocation-teachers-improve-student-learning-outcomes-evidence-malawi
EnvironmentalCategory: B
topicv3: Teachers Management m1326119 1523,Primary Education m1326139 480,Quality of Education m1326344 491,Education Policy and Planning m1326781 1513,Teacher Deployment and Recruitment
            m1327510 1533
publishtoextweb_dt: 2024-07-10T00:00:00Z
dois: 10.1596/1813-9450-10844
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 30
author_keys: 0: author_key: 1013813
1: author_key: 1343690
2: author_key: 881844
3: author_key: 1891246
model_extractions: Policy Research Working Paper 10844 Can Targeted Allocation of Teachers Improve Student Learning Outcomes?

Evidence from Malawi _Salman Asim Ravinder Casley Gera Martin Moreno Kerry Wong_ Education Global Practice July 2024
Policy Research Working Paper 10844

# **Abstract**

Teachers are one of the most important inputs for learning, but in many low-income countries they are poorly distributed between schools. This paper discusses the case of Malawi, which has introduced new evidence-based policies and procedures to improve the equity and efficiency of the allocation of teachers to schools. The analysis finds that adherence to these policies has been highly variable between the country’s districts, with the most successful deploying 75 percent of teachers according to the rules and the least successful just 22 percent. Using administrative data, the paper identifies the impacts on student repetition rates of reductions in pupil–qualified teacher ratios as a result of the new teachers. The findings show that schools that moved from having more than 90 pupils per qualified teacher to a lower ratio experienced reductions in lower primary school repetition rates of 2–3 percentage points. However, similar impacts on dropout are not observed.

This paper is a product of the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sasim@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Can Targeted Allocation of Teachers Improve Student Learning Outcomes? Evidence from Malawi

Salman Asim*, Ravinder Casley Gera<sup>±</sup> , Martin Moreno<sup>∞</sup> , and Kerry Wong<sup>≠</sup> JEL classification: C1; I21; I28; D73; D78 Keywords: Econometrics; Education Policy, Education Quality, Teacher, Bureaucracy; Policy implementation

*Senior Economist, World Bank; sasim@worldbank.org

±Education Specialist, World Bank ∞Economist, World Bank ≠Assistant Professor, London School of Hygiene and Tropical Medicine
# Table of Contents

|**_1._**|**_Introduction ................................................................................................................. 3_**|
|---|---|
|**_2._**|**_Rules-based targeting of new teachers to schools.......................................................... 5_**|
||**Maximizing the efficiency of allocation rules ............................................................................... 6**|
||T1: One teacher per grade ................................................................................................................................ 6|
||T2: Allocation to schools with high PTRs .......................................................................................................... 6|
|**E**|**stimating need under the revised rules ...................................................................................... 7**|
|**_3._**|**_How well did teacher allocation match the modified rules? ......................................... 10_**|
|**T**|**otal deployment at the national level ...................................................................................... 10**|
||**Allocation of teachers to districts .............................................................................................. 10**|
||**Allocation of teachers to schools ............................................................................................... 11**|
|**S**|**hare of need met at the school level ........................................................................................ 13**|
|**_4._**|**_Impact of teacher allocation on school staffing ........................................................... 14_**|
|**T**|**eacher headcounts ................................................................................................................. 14**|
|**P**|**TR change ............................................................................................................................... 15**|
|**_5._**|**_Impact of improved staffing on student outcomes ....................................................... 16_**|
|**R**|**epetition ................................................................................................................................ 18**|
||**Dropout ................................................................................................................................... 20**|
||Robustness checks ......................................................................................................................................... 21|
|**_6._**|**_Conclusion ................................................................................................................. 21_**|
|**_7._**|**_References ................................................................................................................. 23_**|
|**_8._**|**_Tables ........................................................................................................................ 25_**|

2
# **1. Introduction**

Low-income countries, particularly in Sub-Saharan Africa, have typically struggled to deploy teachers to schools in remote areas, resulting in large variations in staffing levels between schools (Mulkeen, 2010; Majgaard and Mingat, 2012; Bashir et al., 2018). These variations stem primarily from teachers’ preferences, typically for placements near to towns and larger villages, known as _trading centers_ (Asim at al., 2019). Countries employ a range of rules and procedures to attempt to address these disparities, ranging from administrative policies to legal standards. The impact of these efforts, however, is highly variable. In India for example, maximum school PTRs are legally mandated by the Right to Education Act (2009); however, as of 2013 three provinces had an overall PTR above the legal maximum (Azim Premji, 2014). Teachers employ a range of formal and informal means to apply pressure to obtain desirable postings, and in many countries officials lack adequate incentives to enforce the rules (Cummings et al., 2016; Asim et al., 2019). In some countries, including some provinces of India, elected representatives have formal right of veto over teacher movements which can interfere with efforts to enforce PTR rules (Ramachandran et al., 2018). Additional incentives for teachers to remain in posting in remote areas, such as hardship allowances, are often required in order to achieve rationalization of teacher distributions, such as successfully tried in The Gambia (Pugatch and Schroeder, 2014).

In Malawi, one of the poorest countries in Sub-Saharan Africa, inequities in teacher distribution are particularly acute. While the national pupil-teacher ratio (PTR) is 62, 10 percent of schools have a PTR of 93 or more, while a more fortunate 10 percent have a PTR of 36 or lower. These inequities occur between districts and sub-district areas, but primarily occur within a single sub-district area ( _zone_ ): school PTRs within a single zone can vary by a factor of ten or more (Ministry of Education, 2021a).

This inequitable distribution of teachers contributes to Malawi’s poor levels of student retention and progression. More than 5 percent of students drop out in Grade 1,<sup>1</sup> with dropout rates rising to almost 7 percent in upper grades (Malawi Longitudinal School Survey, 2021). Fewer than two-thirds of students entering Grade 1 are still in school by Grade 5. Those students who remain in school frequently repeat grades:<sup>2</sup> more than one-third of Grade 1 students repeat, and repetition rates are above 18 percent in all grades (Ministry of Education, 2021b). Those students who do progress in school nevertheless achieve low learning outcomes: in Grade 2, fewer than 25 percent of students achieve minimum proficiency levels in the Early Grade Reading Assessment (EGRA) conducted by the United States Agency for International Development (USAID). At Grade 6, fewer than 25 percent of students achieve minimum proficiency levels in the Southern African Consortium for Monitoring Educational Quality (SACMEQ) assessment in Mathematics, placing Malawi near the bottom in the region.

The persistence of this poor distribution of teachers reflects weaknesses in Malawi’s systems of national- and district-level resource allocation. Until recently, the rules governing allocations were excessively broad, requiring only that teachers be sent to schools with a PTR above 60. With more than half of schools above this level, this introduced a high degree of discretion in allocations.<sup>3</sup> Teachers exploit this discretion, using formal and informal channels of influence to resist placements

> 1 Grades are known as Standards in Malawi, but will be referenced as Grades throughout this paper. Grades range from Grade 1-12. Primary Education is from Grade 1-8, and Secondary Education from Grade 9-12.

> 2 Most Malawian schools require students to repeat grades if they fail to pass an end-of-year examination.

> 3 In 2021, 61 percent of public primary schools had a PTR above 60.

3
in remote schools, resulting in continued disparities. Even where teachers are successfully placed in schools in remote areas, they may not remain for a significant period of time, exploiting these same formal and informal channels to obtain transfers to more desirable postings. A ‘rural allowance’ scheme is in place to motivate teachers to remain in remote postings, but it too is inadequately targeted with minimal incentive effect (see Asim et al., 2019, for a detailed exploration of these dynamics).

In recent years, a number of steps have been taken by the Ministry of Education (MoE) of Malawi, with support from the World Bank, to introduce more well-defined rules for allocation of teachers. Beginning in 2017, districts were advised to prioritize schools with the highest PTRs, not only those with PTR above 60. At the same time, districts were instructed to prioritize schools with fewer teachers than grades, in an attempt to address the common practice of multi-grade teaching in understaffed schools.<sup>4</sup> In this paper, we assess the level of adherence to rules-based allocation of teachers in Malawi’s schools over the period 2017-2019; and the impact of improvements in allocations on school PTRs and on outcomes. We draw data from multiple rounds of administrative data from the Government of Malawi’s Educational Management Information System (EMIS) database. EMIS data is collected via an Annual School Census (ASC) and includes a wide range of data on school size, conditions, staffing, finances, infrastructure and equipment. We employ data on grades offered, staffing and enrollment to establish the schools which meet each of the criteria established by the government. In addition, we employ administrative data on the allocation of new teachers to schools provided by district-level officials via the MoE.

Our ultimate outcomes of interest are student dropout and promotion rates. These outcomes are informed by the international evidence on the impacts of improvements in school teacher staffing and reductions in PTRs. We anticipate reductions in class sizes at the most overcrowded schools as a result of improved staffing, and our selection is also informed by the literature on the impacts of reductions in class sizes.

There is substantial evidence for a relationship between reduced PTRs and improved test scores (Angrist and Lavy, 1999; Kreuger, 1999; Muralidharan & Sundararaman, 2013), although other studies find no significant effects (Hoxby, 2000; Duflo et al., 2015; Angrist et al., 2017). Reduced PTRs can lead to improvements in learning through two channels. First, an adequate supply of teachers can lead to reduced class sizes, with benefits for learning.<sup>5</sup> Second, having an adequate level of staff can reduce schools’ reliance on multi-grade teaching where multiple classes are combined under a single teacher. Although the existing evidence-base is still limited, available research on the effects of multi-grade teaching suggests that it is harmful to student performance (Checci & De Paola 2017, Jacob et al. 2008).

Using administrative data, we are unable to present direct analysis of test scores. However, in Malawi, student promotion is closely linked to test performance, with mandatory repetition in most schools for students who do not pass a year-end assessment; we therefore measure repetition rates as a proxy for learning. Walter (2018), assessing evidence from a panel of 20 high-, middle- and low-income

> 4 Although in most schools achieving this standard require eight teachers, Malawi includes a number of ‘junior primary’ schools which only teach Grades 1-4, as well as other schools without a complete set of eight grades.

> 5 As is the case in Angrist and Lavy, 1999 and Muralidharan & Sundararaman, 2013.

4
countries, estimates the gains in student promotion from optimal allocation of existing teachers between schools at between 0.1 and 4.2 percentage points.

- In **Chapter 2** , we present a stylized algorithm, based on the MoE’s rules, to identify target schools in need of more teachers. Drawing on administrative data collected in 2017, 2018 and 2019, we apply this algorithm to identify all target schools in each year. We present estimated needs for teachers according to this algorithm, at national, district and school levels.

- In **Chapter 3** , we compare our targeting algorithm to the actual allocations of new teachers in Malawi in each of the four years, and assess the extent to which allocation met with need, both nationally and at the district level.

- In **Chapter 4** , we assess the impact of the allocation of new teachers on school PTRs.

- In **Chapter 5** , we assess the impact of reduced PTRs on student repetition rates and dropout rates.

- **Chapter 6** discusses the results.

# **2. Rules-based targeting of new teachers to schools**

The allocation of newly deployed teachers – typically 3,000 to 5,000 per year – is a multi-stage process across multiple levels of government (see Asim et al., 2019, for a detailed discussion). Teachers enter the workforce having completed pre-service training under the auspices of the Department for Teacher Education and Development (DTED) of MoE, primarily through a program known as the Initial Primary Teacher Education (IPTE). Through the Directorate of Basic Education (DBE) and the Local Government Service Commission (LASCOM), MoE allocates teachers to each of Malawi’s 34 education districts;<sup>6</sup> District Education Offices (DEOs) in each district then allocate teachers to specific schools.

Allocating new teachers equitably to schools may seem a simple matter of targeting the schools with the highest PTRs, but this has not historically been the case. Malawi has an official target for school PTRs of 60 and, prior to 2017, teacher allocations were guided only by this target. However, with more than half of schools above this ratio, this approach did not significantly guide allocations. The result was that allocations of teachers to districts and, in particular, to schools was conducted with a high degree of discretion that left district officials subject to pressure from teachers to avoid postings in remote schools (see Asim et al., 2019, for a detailed description of Malawi’s teacher allocation system and the history of attempts to rationalize deployments).

Beginning in 2017, a new set of targeting rules was introduced to support more equitable allocations of teachers to schools. In 2017, national and district officials were instructed to allocate teachers to schools with PTR below 60 if they did not have at least one teacher per grade offered. This was intended to prevent multi-grade teaching. Beginning in 2018, officials were instructed that, having fulfilled this first condition, they should target the remaining teachers to the schools with the highest PTRs. A revised Primary Teacher Management Strategy codified the new guidance (Ministry of

> 6 Malawi has 28 local government authorities, including districts and municipalities. Some of these are subdivided into two or three components for the purposes of education management, producing a total of 34 education districts. In this paper, we use ’district’ to refer to these education districts. The country’s four urban districts, Lilongwe City, Blantyre City, Mzuzu and Zomba Urban, are typically excluded from receiving newly deployed teachers owing to high existing levels of staffing.

5
Education, Science and Technology, 2018) and a spreadsheet-based tool was developed to guide districts in completing allocations according to the rules.

In this section, we employ administrative data to create lists of schools that would have been eligible to receive new teachers in each of the years 2017-2019 according to the new rules.

# **Maximizing the efficiency of allocation rules**

## **T1: One teacher per grade**

The first stipulation of the revised rules – to provide adequate teachers to schools to ensure that they have at least one teacher per grade offered – is intended to eliminate multi-grade teaching. This rule is most likely to be relevant in smaller schools, often in remote areas, which are the most likely to lack one teacher per grade owing to small enrollments.<sup>7</sup> Such schools are large in number and concentrated in particular districts. In 2017, one district, Mzimba South, required 423 teachers to meet this rule, and the top five districts combined required 1,039 new teachers – more than 40 percent of all the new teachers needed to fully attain T1 in the whole country. These districts, unless experiencing severe overall shortages of staff, are poorly allocating their existing teacher resources and may not be expected to appropriately allocate additional teachers. In addition, meeting this need entirely would be likely to severely reduce the potential for PTR reduction at more understaffed schools, and would entail allocating a significant number of teachers to schools with PTR below 60 which are otherwise adequately staffed. This may not be an effective use of limited resources, given overall constraints on teacher availability.

We therefore apply the following modifications to T1:

- Schools with a PTR of 60 or below are excluded from receiving new teachers.

- Schools whose PTR would drop to 60 or below on receipt of a new teacher, are excluded from receiving new teachers.

- Districts where more than 50 percent of schools meet T1 are excluded from receiving teachers based on T1, unless their overall district PTR is above 75:1.

## **T2: Allocation to schools with high PTRs**

The second stipulation, T2, is expected to be applied following fulfilment of the modified T1. In a context of perfect information, the preferred approach to allocating teachers in order to reduce PTRs would be the “smallest achievable maximum PTR rule” (Walter, 2018), in which teachers are allocated in order to reduce the largest school-level PTR, nationally or in a district, as much as possible. However, this approach, while the most efficient way to reduce PTRs in theory, is difficult to implement in practice as it requires a complete and accurate ranking of schools by PTR. As this may be complicated to implement, in practice, many countries adopt a simpler approach of identifying a target level of PTR above which schools are considered understaffed and providing teachers to bring

> 7 Of schools in the most remote areas, known as ‘Category A’, 38 percent have fewer than eight teachers, versus only 18 percent of the least remote ‘Category C’ schools (Ministry of Education, 2022; see Asim et al., 2019, for categorization).

6
schools below that level. For example, India establishes a target of 30 for each class (Azim Premji, 2014).

In Malawi, as described above, the qualifying PTR level has historically been 60, but with more than half of schools having PTRs above this level, this targets teachers with inadequate specificity to bring down the maximum PTR effectively. Evidence from Malawi suggests that schools with PTRs above 90 typically achieve lower learning outcomes (Asim and Casley Gera, 2024). We therefore adopt a PTR of 90 as the threshold to consider schools highly understaffed and eligible to receive teachers under T2, for evaluation of the 2017 allocation of teachers.

However, as the allocation of teachers improves, we would expect the share of schools with PTR above 90 to reduce, making adherence to this rule more difficult. Indeed, the share of schools with PTR above 90 reduced from 32 percent in 2017 to just 16 percent in 2019. Applying T2 only to these schools would not identify enough beneficiary schools to utilize the full supply of teachers. Therefore, for 2018 and 2019, as the share of needy schools declines in response to optimal teacher allocations, we lower the threshold to 80, maintaining a similar share of schools identified as needy.

Over time, with continued need-based allocations, we would expect to be able to lower the threshold further and eventually to the government’s official target of 60.

## **Box 2.1** summarizes the modified rules.

## **Box 2.1. New rules for teacher allocation**

Target 1 (T1): deploys the least number of teachers needed such that schools would have at least one teacher per grade.

- Except schools with PTR of 60 or below or whose PTR would be reduced to 60 or below with addition of new teachers

- Except districts where more than 50 percent of schools meet T1 are excluded from receiving teachers based on T1, unless their overall district PTR is above 75:1.

Target 2 (T2): deploys the least number of teachers needed so that PTR reduces to below 90 (in 2017) or 80 (in 2018 and 2019).

# **Estimating need under the revised rules**

Employing these revised rules, we then estimate the need for teachers to fulfill these targets in each year from 2017 to 2019. **Table 2.1** shows the number of teachers required in each district in each year following the application of both rules, along with the number of schools needing teachers. In each year, the data on school-level PTRs is drawn from EMIS data, reflecting the previous year’s allocations of new teachers as well as other movements, deaths and retirements of teachers in the system.

7
**Table 2.1. Summary of the number of teachers needed in 29 primarily rural districts**<sup>**+**</sup>

||**2017**|**2018**|**2019**|
|---|---|---|---|
|Total number of schools|5,368|5,345|5,527|
|Total number of students|4,567,817|4,573,924|4,775,675|
|Total number of teachers|59,233|64,865|71,471|
|National PTR|77.1|70.5|66.8|
|Total number of teachers needed in 29 primarily rural districts|5904|5676|3789|
|% schools that needed at least one new teacher|36%|36%|29%|
|% schools that needed at least one new teacher to attain T1|17%|6%|5%|
|Teachers needed among schools with need||||
|Number of teachers needed – median|2|2|2|
|Number of teachers needed – 10th percentile|1|1|1|
|Number of teachers needed – 90th percentile|6|6|5|

+ Blantyre City, Lilongwe City, Mzuzu City, Zomba Urban, and Likoma are excluded.

The total need for teachers falls gradually, partly as a result of the improvement in allocations that took place during this period. This decline occurs despite the modification of T2 to require PTR above 80 in 2018 and 2019. The proportion of schools that needed at least one new teacher also drops over time.

Despite this overall trend of reducing need, however, district-level needs vary more widely over time. This reflects the fact that the extent to which allocations to districts is aligned to their needs also varies (see next section). **Figure 2.1** shows the district-level need in 2017-19 according to the modified rules. The mean number of teachers needed across districts in 2017 was 204, dropping to 196 in 2018 and 132 in 2019 (Table 3.2). The number of teachers needed across the districts varied between 25 (Rumphi) to over 550 (Mangochi) in 2017. This range narrowed in 2018 and in 2019. In terms of overall numbers (Panel A), the majority of districts reduced their need substantially, but some districts saw increasing need over time, notably Ntcheu and Mzimba North, suggesting allocations during this period were not adequate to meet these districts’ needs. Section 3 provides further analysis of this.

8
<!-- Start of picture text --> Mangochi (528)<br>500<br>Kasungu (484)<br>Machinga (457)<br>Izimba South (433)<br>400 Kasungu (401)<br>mo]<br>2<br>3 iw 9<br>2e<br>s 300 i<br>F Chikwawa (273) x \ Dowa (290)<br>&<br>‘So Zomba Rural(260) dion Mangochi (262)<br>23 Dowa (243) eeSa MzimbaMachinga N (233) (244)<br>E AT ; SS OSS: \<br>F 200 Mulanje (200) > a = Sas nai<br>os ——_* . Seis<br>Deaza (181) FS SSS “se Ntcheu (162)<br>Salima (124) * 2 gi EG OO alain SM aga, TMS Lilongwe RW (124)<br>100 ’ on of” 7 Sh ‘© Salima (114)<br>Blantyre “A nae Balaka (105)<br> Rural (93) a, Chikwawa (93)<br>Chiradzulu (79) ChitipaBlantyre Rural (80)<br>Chitipa (70)<br>(57)<br>Rumphi (24) Mwanza (33)<br>0<br>2017 2018 2019<br><!-- End of picture text --> <!-- Start of picture text --> Kasungu (10.5)<br>10.0 Mangochi (10)<br>Kasungu (9.2)<br>Machinga (8.7)<br>Mizimba South (8.2)<br>75 Dowa (7.6)<br>a ~<br>®<br>e Pe Ss Mangochi (6.8)<br>Fa@ "fa _* Mzimba N (6.4)<br>2 ee _/* Wachinga 6.)<br>28 Zo y Fs<br>3 so cane 2) j Zomba Rural (5.1)<br>=© Zomba RuralDowa (4.9) (4.6) lliea J ; if<br>5® =arenes7.<br>a <r aties<br>Mulanje (3.8) - F :<br>Nkhotakota (3.2) : — S LongweRW (3.2)<br>25 Ntcheu (2.5) $<" fo sage ci \<br>- ee ee eg ; Nichisi (2.3)<br>Palombe (2.1) $7 a -$ Blantyre Rural (2.1;<br>Rural (1.8) eeSS chains (1.8)<br>a am — Wohing(18)<br>wradzul (1. Pee<br>Chitipa (1.1)Se Mwanza (0.9)<br>Rumphi (0.5)<br>0.0<br>2017 2018 2019<br><!-- End of picture text -->
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needed, the number allocated, and the share of district-level need met, for each rural district for each year 2017-19 and on average across the three years.

In 2017, while the average district received 70 percent of their need (in line with the national picture as seen in Table 3.1), the share of need met at district level was as low as 6 percent (in Blantyre Rural district, which received only seven teachers against a need of 126); and as high as 162 percent (in Thyolo district, which received 170 teachers against a need of 105). In total, 10 districts received fewer than half the teachers needed to meet the modified rules.

In 2018, despite the overall size of the allocation being larger, three districts still received fewer than half the teachers they needed (receiving fewer than five teachers each). A total of 14 districts received more teachers than were needed to meet the modified rules, suggesting that the rules were not being consistently followed within MoE where the allocation of teachers to districts was carried out. In 2019, as the share of total need met fell to 94 percent, the allocation to districts remained only loosely aligned with need, with the result that ten districts received fewer than half the teachers needed while three received more than double the required number.

For a given district, the share of needs met was highly variable over time, with a single district, Chikwawa, receiving between one and 175 percent of the required teachers depending on the year. In total, 13 districts received fewer teachers than required across the three years while 16 received more than required. Only one district received fewer than half the required teachers across the three years, and one received more than double the required teachers.

# **Allocation of teachers to schools**

How well did districts allocate the teachers they received? **Table 3.3** summarizes the total performance of the 29 districts in allocating teachers to needy schools according to the modified rules. Panel A shows the share of the total share of all schools in these districts which were allocated a teacher along with the average number of teachers received by these schools. Panel B shows the share of schools which did not need a teacher according to the modified rules, which nevertheless received them: between 24 and 32 percent of teachers across the three years were deployed to schools which did not qualify for one according to the modified rules. Panel C shows the share of schools which did need at least one new teacher which received teachers, rising to a high of 61 percent in 2018 before falling to 45 percent in 2019.

Strikingly, in the larger allocation of 2018, the share of both needy and non-needy schools receiving teachers increased, suggesting that an opportunity was missed to target this large one-off double allocation to achieve a large reduction in school PTRs.

11
**Table 3.3 Allocation of teachers to schools (national)**

||**2017**|**2018**|**2019**|
|---|---|---|---|
|**A. All schools**||||
|% schools allocated new teachers|38%|43%|33%|
|Number of teachers allocated – mean*|2.2|2.4|1.9|
|**B. All schools that needed 0 new teacher**||||
|% schools allocated new teachers|25%|32%|24%|
|Number of teachers allocated – mean*|1.7|1.9|1.6|
|**C. All schools that needed at least one new teacher(s)**||||
|% schools allocated new teachers|59%|61%|45%|
|Number of teachers allocated – mean*|2.6|3.0|2.2|

*Denotes number of teachers allocated to schools which were allocated a teacher.

**Table 3.4** (in Section 8, Tables) shows the performance of individual districts in allocating teachers to the correct schools, as well as the national total performance. At national level, the share of deployed teachers who were allocated correctly fell from 56 percent in 2017 to 49 percent in 2018, reflecting the fact that the extra-large allocation was not well targeted as noted above. The share correctly allocated fell further to 43 percent in 2019, showing a further deterioration in adherence to the new rules following their introduction.

This aggregate picture, however, masks a high degree of variation between districts. On average across the three years, the share of teachers received which were allocated correctly varies from as low as 22-23 percent (Rumphi and Mzimba North districts) to 75 percent (Dowa district). Strikingly, each district varies considerably year-on-year in its adherence to the guidance, with only four districts (Mwanza, Kasungu, Mangochi and Machinga) allocating more than half their teachers correctly in all three years.

To provide a deeper exploration of the dynamics of district allocations over time, we focus on 13 districts which received at least 50 teachers in all three of the years 2017-19. **Figure 3.2** shows the performance of these districts in allocating teachers to schools across the three years. In a number of cases, the quality of allocations fell in 2018 in response to the larger-than-normal allocation before improving in 2019. However, in other cases the quality of allocations increased in 2019 before falling, or declined consistently. None of the 15 districts achieved a consistent improvement in the share of teachers which were allocated according to the modified rules.

12
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Although the assignation rules used in Malawi allocate teachers both to schools without one teacher per grade (T1) and schools with PTR above 90 or 80 (T2), in this analysis we focus primarily on T2 and the reduction of overall school PTRs. This is done in response to evidence from Malawi which suggests that schools with PTRs above 90 have lower overall learning outcomes (Asim and Casley Gera, 2024). In addition, in this analysis, we exclude non-qualified and student teachers and focus on school pupil-qualified teacher ratios (PqTRs).

In recognition of the fact that the dynamics of school staffing vary significantly regardless of the allocation of new teachers (see Section 4), we focus our analysis on schools which an increase in overall teacher numbers and experienced reduction in PqTR, regardless of whether they were allocated a new teacher. In other words, a school which gained an additional teacher through transfer is treated the same as one which received a newly deployed teacher.

We define a school has having been ‘treated’ with additional teachers in a given year if it meets the following conditions:

1. The number of teachers employed in the school is higher than in the previous year;

2. The school’s overall PqTR was above 90 in the previous year and is now below 90.

We define a control group of schools which had PqTR above 90 in the previous year – meaning they needed a new teacher – but did not experience a net gain in the number of teachers.

As a result of the dynamic nature of teacher allocations, the treated and control schools therefore vary across the various year comparisons. **Table 5.1** summarizes the sample.

**Table 5.1. Impact analysis sample**

||**2017-18**|**2018-19**|**2019-20**|
|---|---|---|---|
|Treatment|902|710|468|
|Control|412|296|330|

Both control and treatment schools experienced changes in PqTR, repetition rate, and dropout rate year-on-year. To capture the differential dynamics between treated and control schools, we employ difference-in-difference (DiD) analysis.

**Table 5.2** (in Section 8, Tables) presents DiD estimates for the impact of treatment on PqTRs. As expected given the definitions of treatment and control, treated schools experienced decline in PqTR compared to control schools in each year – an substantial decline of 41 pupils per qualified teacher.

**Lagged effects for dropout and repetition.** Because dropout and repetition rates are determined by schools at the end of a school year, there is an expected ‘lag’ in impacts for an improvement in school staffing. Recall that new teachers are typically deployed to schools at the start of the new school year in September, with the EMIS data collection taking place around two months later in OctoberNovember. In order to allow time for the impacts of new teachers to be felt and measured, we report lagged effects for these indicators from the following year’s EMIS data. For example, to evaluate the impact of teachers allocated in August/September 2017, we compare:

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- EMIS 2018 (collected in October-November 2017, and reflecting the dropout and repetition status at the end of the 2016/17 school year, prior to the allocation of teachers) with

- EMIS 2019 (collected in October-November 2018, and reflecting the dropout and repetition status at the end of the 2017/18 school year, following the first full year of school with the increased level of staffing and reduced PqTR).

The COVID-19 pandemic led to the closure of all schools in Malawi for seven months during 2020 and appears to have led to significant dropout.<sup>9</sup> As the EMIS data collected in October-November 2019 is the most recent available prior to the onset of the pandemic, we restrict our analysis to the 2017 and 2018 allocations of teachers for which lagged information is available prior to the pandemic.

Tables 5.3-5.6 show DiD results. In each table:

- The first column defines treated schools as those which received at least one additional teacher in 2017 and as a result whose PqTR was brought below 90 in that year.<sup>10</sup> The regression compares the repetition or dropout rate in schools at the end of the 2016/17 year to those at the end of the 2017/18 school year, reflecting a full year of the increased staffing.

- The second column defines treated schools as those which received at least one additional teacher in 2018 and as a result whose PqTR was brought below 90 in that year.<sup>11</sup> The regression compares the repetition or dropout rate in schools at the end of the 2017/18 year to those at the end of the 2018/19 school year, reflecting a full year of the increased staffing.

# **Repetition**

Tables 5.3 and 5.4 show DiD results for repetition rates. **Table 5.3** focuses on overall repetition across grades. We find that in schools treated in 2017, repetition rates were significantly reduced by 0.28 percentage points. We do not observe significant impacts on overall repetition rates from treatment in 2018.

> 9 The total enrollment in Malawi’s primary schools fell for the first time in over a decade following the closure of schools, with 4,815,286 students enrolled in public primary schools in 2020/21 versus 5,274,819 in 2019/20.

> 10 To identify treated schools, we compare data from EMIS 2016 and EMIS 2017. As repetition and dropout rates are decided at the end of the school year and reported in the following year’s EMIS, for repetition and dropout rates, we compare data from EMIS 2017 and 2018.

> 11 To identify treated schools, we compare data from EMIS 2017 and EMIS 2018. As repetition and dropout rates are decided at the end of the school year and reported in the following year’s EMIS, for repetition and dropout rates, we compare data from EMIS 2018 and 2019.

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**Table 5.3. Impact of PqTR reduction on repetition rates**

||**2017-18**|**2018-19**|
|---|---|---|
|Control|0.266***|0.253***|
||(0.010)|(0.006)|
|Time|-0.014|-0.001|
||(0.014)|(0.009)|
|Treatment|0.007|-0.016|
||(0.011)|(0.012)|
|DiD (Treatment and Time)|-0.028*|-0.021|
||(0.016)|(0.015)|
|Control (N)|118|292|
|Treatment(N)|379|134|

_Data Source: EMIS 2016-2019 Note: Robust standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01 2017/18 compares Repetition rates at end 2016/17 and end 2017/18. Treated schools are those where PqTR brought below 90 in 2017._ _2018/19 compares Repetition rates at end 2017/18 and end 2018/19. Treated schools are those where PqTR brought below 90 in 2018._

**Table 5.4** focuses on repetition rates in lower primary (Grades 1-4). Again, we find significant impacts from the 2017 allocation of teachers, with lower primary repetition rates reduced by 3.5 percentage points, but we do not observe similar impacts from the 2018 deployment.

**Table 5.4. Impact of PqTR reduction on repetition rates in lower primary**

||**2017-18**|**2018-19**|
|---|---|---|
|Control|0.276***|0.269***|
||(0.011)|(0.007)|
|Time|-0.014|-0.005|
||(0.015)|(0.010)|
|Treatment|0.016|-0.026**|
||(0.013)|(0.013)|
|DiD (Treatment and Time)|-0.035**|-0.017|
||(0.017)|(0.016)|
|Control (N)|118|292|
|Treatment(N)|379|134|

_Data Source: EMIS 2016-2019_ _Note: Robust standard errors in parentheses. *p<0.10, ** p<0.05, *** p<0.01_ _2017/18 compares lower primary Repetition rates at end 2016/17 and end 2017/18. Treated schools are those where PqTR brought below 90 in 2017._ _2018/19 compares lower primary Repetition rates at end 2017/18 and end 2018/19. Treated schools are those where PqTR brought below 90 in 2018._ 19
# **Dropout**

Table 5.5 and 5.6 show DiD results for dropout rates. **Table 5.5** focuses on overall dropout across grades; **Table 5.6** focuses on lower primary dropout rates. In both cases, we do not observe significant impacts in schools treated in either 2017 or 2018.

**Table 5.5. Impact of PqTR reduction on dropout rates**

||**2017-18**|**2018-19**|
|---|---|---|
|Control|0.031***|0.040***|
||(0.004)|(0.003)|
|Time|0.001|-0.003|
||(0.006)|(0.004)|
|Treatment|0.020***|0.003|
||(0.006)|(0.006)|
|DiD (Treatment and Time)|-0.006|0.005|
||(0.008)|(0.008)|
|Control (N)|118|292|
|Treatment(N)|379|134|

_Data Source: EMIS 2016-2019_ _Note: Robust standard errors in parentheses. *p<0.10, ** p<0.05, *** p<0.01_ _2017/18 compares dropout rates at end 2016/17 and end 2017/18. Treated schools are those where PqTR brought below 90 in 2017._ _2018/19 compares dropout rates at end 2017/18 and end 2018/19. Treated schools are those where PqTR brought below 90 in 2018._

**Table 5.6. Impact of PqTR reduction on dropout rates in lower primary**

||**2017-18**|**2018-19**|
|---|---|---|
|Control|0.029***|0.037***|
||(0.005)|(0.004)|
|Time|-0.001|-0.003|
||(0.007)|(0.005)|
|Treatment|0.020***|0.002|
||(0.006)|(0.006)|
|DiD (Treatment and Time)|-0.004|0.009|
||(0.009)|(0.009)|
|Control (N)|118|292|
|Treatment(N)|379|134|

_Data Source: EMIS 2016-2019_ _Note: Robust standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01_ _2017/18 compares lower primary dropout rates at end 2016/17 and end 2017/18. Treated schools are those where PqTR brought below 90 in 2017._ _2018/19 compares lower primary dropout rates at end 2017/18 and end 2018/19. Treated schools are those where PqTR brought below 90 in 2018._ 20
## **Robustness checks**

In order to ensure that our particular treatment definition, of schools gaining additional teachers to bring PqTR below 90, is not creating a false appearance of systemic results with regard to repetition, we conduct additional analysis employing two alternative definitions of treated schools:

1. The number of teachers employed in the school is higher than in the previous year, and the school’s overall PqTR was above 80 in the previous year and is now below 80.

2. The number of teachers employed in the school is higher than in the previous year, and the school’s overall PqTR was above 100 in the previous year and is now below 100.

For this analysis, we focus on the 2017 allocation of teachers. Using lagged effects, we compare the repetition rates in schools at the end of the 2016/17 year to those at the end of the 2017/18 school year, reflecting a full year of the increased staffing according to the adjusted thresholds.<sup>12</sup>

**Tables 5.7 and 5.8** (in Section 8, Tables), show the findings. Adopting PqTR reduction below 80 as the threshold, we observe impacts on repetition rates in lower primary and overall of a slightly larger scope than with the original treatment definition of PqTR reduction below 90. However, adopting PqTR reduction below 100 as the threshold, although we still observe a substantial reduction in lower primary repetition rates, it does not obtain statistical significance. The findings suggest that reducing PqTRs below 90 is the ‘minimum’ treatment to achieve impacts on student learning.

# **6. Conclusion**

Our findings suggest that improvements in allocation of newly deployed teachers to schools, enabled by improved rules for allocation, can lead to improvements in student learning. Adopting repetition as a proxy for learning, we find that schools which gained additional teachers and brought PqTRs below 90 achieved significant improvements in lower primary repetition rates in comparison to schools with PqTR above 90 which did not gain additional teachers. The findings mirror others from Israel, the United States and India (Angrist and Lavy, 1999; Kreuger, 1999; Muralidharan & Sundararaman, 2013) which suggest that improvements in PTR are associated with improved learning outcomes, and demonstrate that similar dynamics persist in low-income countries. However, we do not observe impacts on dropout rates.

The descriptive analysis also reveals the extent to which rules-based approaches to teacher allocations may face challenges in implementation on the ground. Despite the clarification of allocation rules and provision of software tools to support allocations, the adherence to guidance for allocation of new teachers appears to have been weak and highly variable. At the national level, the allocation of new teachers to districts was not fully aligned with the guidance, with the share of districts’ need for new teachers met through allocations varying from a low of 32 percent to a high of 224 percent across three years. At the district level, too, the quality of allocations was highly variable, with the most successful deploying 75 percent of teachers in accordance to the rules and the least successful just 22 percent. Had the teachers deployed during this period all been allocated according to the guidance, it is likely that the number of schools achieving reduction in PqTR to below 90 would have been larger,

> 12 The comparison of staffing to identify treatment and control schools remains non-lagged, e.g. comparing EMIS data from 2016 and 2017.

21
with the result that more students would benefit from increased learning and reduced repetition rates. In addition, even where schools were correctly allocated new teachers, the effects on PTR were blunted by other movement of teachers away from these schools. A new Hardship Support Scheme, expected to be rolled out during 2024, is intended to provide additional incentives to teachers in remote schools to remain in post (see Asim et al., 2019, for background). Future research will explore the impact of this scheme on teacher behavior, school PTRs, and student outcomes.

22
# **7. References**

Angrist, Joshua D.; Lavy, Victor. 1999 “Using Maimonides’ Rule to Estimate the Effect of Class Size on Scholastic Achievement.” _The Quarterly Journal of Economics_ 114 (2), 533-575.

Angrist, Joshua D.; Lavy, Victor; Leder-Luis, Jetson; Shany, Adi. 2017. “Maimonides’ Rule Redux.” NBER Working Paper No. 23486.

Asim, S., J. Chimombo, D. Chugunov, and R. Casley Gera (2019). “Moving teachers to Malawi’s remote communities: A data-driven approach to teacher deployment.” _International Journal of Educational Development_ 65 (2019) 26–43 Asim, S. and Casley Gera, R. 2024. _What Matters for Learning in Malawi? Insights from a Longitudinal School Survey._ Washington, D.C.: World Bank Publications.

Azim Premji Foundation. 2014. “Pupil-Teacher Ratios in Schools and their Implications.” Available at: https://www.issuelab.org/resources/23973/23973.pdf [1.27.22] Bashir, S., Lockheed, M., Ninan, E., Tan, J., 2018 _. Facing Forward: Schooling for Learning in Africa._ Washington, D.C.: World Bank Publications.

Checchi, D. and De Paola, M. 2017. “The Effect of Multigrade Classes on Cognitive and NonCognitive Skills: Causal Evidence Exploiting Minimum Class Size Rules in Italy.” IZA DP No. 11211. Bonn: Institute of Labor Economics.

Chin, Aimee (2005). Can redistributing teachers across schools raise educational attainment? Evidence from Operation Blackboard in India. _Journal of Development Economics_ 78(2), 0–405.

Cummings, Clare and Ali Bako M. Tahirou with Hamissou Rhissa, Falmata Hamed, Hamadou Goumey, and Idi Mahamadou Mamane Noura. 2016. “Collective action and the deployment of teachers in Niger: A political economy analysis.” Overseas Development Institute. Available at: <u>https://cdn.odi.org/media/documents/10303.pdf [2.3.22]</u> Duflo, Esther; Dupas, Pascaline; Kremer, Michael. 2015. “School governance, teacher incentives, and pupil-teacher ratios: Experimental evidence from Kenyan primary schools.” _Journal of Public Economics_ (123), 92-110.

Hoxby, Caroline M. 2000. “The Effects of Class Size on Student Achievement: New Evidence from Population Variation.” _The Quarterly Journal of Economics_ 115 (4), 1239-1285.

Jacob, Verghese; Kochar, Anjini; Reddy, Suresh. 2008. “School Size and Schooling Inequalities.” Stanford Center on Global Poverty and Development Working Paper 354.

Krueger, Alan B. 1999. “Experimental Estimates of Education Production Functions.” _The Quarterly Journal of Economics_ 114 (2), 497-532.

Majgaard, K., and A. Mingat. 2012. Education in Sub-Saharan Africa: A Comparative Analysis. A World Bank Study. Washington, DC: World Bank.

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Malawi Longitudinal School Survey, 2021. Malawi Longitudinal School Survey Endline data. Unpublished.

Ministry of Education, Science and Technology (2018). _Primary Teacher Management Strategy._ Lilongwe: Government of Malawi.

Ministry of Education (2021a). Education Management Information System (EMIS) data, 2019/20. Unpublished.

Ministry of Education (2021b). _Malawi Education Statistics 2019/20_ . Mimeo.

Ministry of Education (2022). Education Management Information System (EMIS) data, 2020/21. Unpublished.

Malawi Longitudinal School Survey (2021). Unpublished endline data.

Mulkeen, A., 2010. Teachers in Anglophone Africa: Issues in Teacher Supply, Training, and Management. Washington, D.C.: World Bank Publications.

Muralidharan, K.; Sundararaman, V. 2013. “Contract Teachers: Experimental Evidence from India.” NBER Working Paper No. 19440.

Pugatch, T and Schroeder, E. 2014. “Incentives for teacher relocation: Evidence from the Gambian hardship allowance.” _Economics of Education Review_ 41, 120-136.

Ramachandran, Vimala, Tara Béteille, Toby Linden, Sangeeta Dey, Sangeeta Goyal, and Prerna Goel Chatterjee. 2018. _Getting the Right Teachers into the Right Schools: Managing India’s Teacher Workforce._ World Bank Studies. Washington, D.C.: World Bank Publications.

Walter, T. F. 2018. “Misallocation of State Capacity?” PhD Thesis. London School of Economics and Political Science, London. Available at: http://etheses.lse.ac.uk/3852/1/Walter <u>misallocation-ofstate-capacity.pdf [9.1.22]</u> 24
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**Table 3.4 Allocation of teachers to schools (by district)**

||**2017**||||**2018**||||**2019**||||**Average**||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Teachers**<br>**allocated**|**Correctly**<br>**allocated**|**% cor’ly**<br>**allocated**|**% need**<br>**met**|**Teachers**<br>**allocated**|**Correctly**<br>**allocated**|**% cor’ly**<br>**allocated**|**% need**<br>**met**|**Teachers**<br>**allocated**|**Correctly**<br>**allocated**|**% cor’ly**<br>**allocated**|**% need**<br>**met**|**Teachers**<br>**allocated**|**Correctly**<br>**allocated**|**% cor’ly**<br>**allocated**|**% need**<br>**met**|
|Balaka|100|68|68|35|1|0|0|0|48|21|44|20|50|30|37|18|
|Blantyre Rural|8|4|50|3|35|16|46|16|48|26|54|33|30|15|50|17|
|Chikwawa|320|186|58|59|3|0|0|0|163|53|33|57|162|80|30|39|
|Chiradzulu|20|16|80|20|104|69|66|78|7|1|14|3|44|29|54|33|
|Chitipa|84|26|31|42|53|22|42|28|215|33|15|47|117|27|29|39|
|Dedza|158|81|51|36|181|122|67|50|49|26|53|16|129|76|57|34|
|Dowa|90|48|53|15|7|7|100|2|148|114|77|46|82|56|77|21|
|Karonga|150|60|40|34|294|97|33|61|35|10|29|14|160|56|34|36|
|Kasungu|618|391|63|73|520|279|54|85|208|140|67|35|449|270|61|64|
|Lilongwe Rural<br>East|118|84|71|33|279|163|58|66|79|36|46|28|159|94|58|42|
|Lilongwe Rural<br>West|91|27|30|20|139|72|52|38|143|61|43|49|124|53|41|36|
|Machinga|384|299|78|65|533|295|55|68|162|112|69|48|360|235|67|60|
|Mangochi|552|379|69|66|599|344|57|71|163|109|67|42|438|277|64|60|
|Mchinji|142|54|38|28|283|127|45|67|15|5|33|8|147|62|39|34|
|Mulanje|200|78|39|38|318|194|61|86|142|52|37|48|220|108|46|57|
|Mwanza|25|15|60|26|31|16|52|36|13|7|54|21|23|13|55|28|
|Mzimba N|26|5|19|5|1|0|0|0|362|183|51|75|130|63|23|27|
|Mzimba S|409|235|57|48|456|128|28|56|173|52|30|34|346|138|39|46|
|Neno|39|22|56|27|1|1|100|1|45|14|31|21|28|12|63|16|
|Nkhata Bay|46|13|28|22|95|33|35|36|177|39|22|54|106|28|28|37|
|Nkhotakota|104|73|70|41|174|81|47|50|178|74|42|55|152|76|53|48|
|Nsanje|103|42|41|32|124|119|96|67|200|116|58|89|142|92|65|63|
|Ntcheu|67|40|60|24|88|34|39|29|58|30|52|19|71|35|50|24|
|Ntchisi|25|11|44|11|61|36|59|43|80|23|29|26|55|23|44|27|
|Phalombe|129|60|47|50|204|100|49|67|27|13|48|19|120|58|48|45|
|Rumphi|18|3|17|12|146|34|23|45|191|49|26|65|118|29|22|41|
|Salima|51|40|78|31|159|58|36|31|51|18|35|16|87|39|50|26|
|Thyolo|165|27|16|26|293|155|53|66|16|9|56|13|158|64|42|35|
|Zomba Rural|211|110|52|38|329|112|34|33|191|39|20|20|244|87|36|30|
|Malawi|4453|2497|56|42|5511|2714|49|46|3387|1465|43|3|1|3810|1751|30|

26
**Table 5.2. Impact of treatment on PqTRs in treated schools**

||**2016-17/ 2017-**<br>**18**|**2017-18/ 2018-**<br>**19**|**2018-19/ 2019-**<br>**20**|
|---|---|---|---|
|Control|111.067 ***|108.804 ***|106.133 ***|
||(1.066)|(1.284)|(0.860)|
|Time|5.231 ***|3.585*|1.845|
||(1.789)|(2.058)|(1.508)|
|Treatment|3.193**|1.433|2.392|
||(1.393)|(1.535)|(1.549)|
|DiD (Treatment and|-46.187 ***|-40.099 ***|-37.431 ***|
|Time)|(2.036)|(2.263)|(2.085)|
|Control (N)|415|299|470|
|Treatment(N)|912|714|330|

_Data Source: EMIS 2016-2019 Note: Robust standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01_

**Table 5.7. Robustness check: Impact of PqTR reduction on repetition rate (PqTR 80)**

||**Overall**|**Lower Primary**|
|---|---|---|
|Control in 2018|0.260***|0.272***|
||(0.007)|(0.008)|
|Time (year=2019)|-0.009|-0.010|
||(0.010)|(0.011)|
|Lagged Treatment|0.012<br>(0.009)|0.016<br>(0.010)|
|Lagged Treatment (DiD)|-0.034***<br>(0.012)|-0.040***<br>(0.014)|
|Control (N)|216|216|
|Treatment (N)|424|424|
|r2|0.03|0.03|

_Data Source: EMIS 2018 and 2019 Note: Robust standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01 Compares rates at end 2017/18 and end 2018/19. Treated schools are those where PqTR brought below 90 in 2018._ 27
**Table 5.8. Robustness check: Impact of PqTR reduction on repetition rate (PqTR 100)**

||**Overall**|**Lower Primary**|
|---|---|---|
|Control in 2018|0.268***|0.277***|
||(0.013)|(0.015)|
|Time (year=2019)|-0.017|-0.016|
||(0.018)|(0.020)|
|Lagged Treatment|-0.002|0.005|
||(0.015)|(0.017)|
|Lagged Treatment (DiD)|-0.026<br>(0.020)|-0.033<br>(0.023)|
|Control (N)|73|73|
|Treatment (N)|253|253|
|r2|0.03|0.04|

_Data Source: EMIS 2018 and 2019 Note: Robust standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01 Compares rates at end 2017/18 and end 2018/19. Treated schools are those where PqTR brought below 100 in 2018._ 28