For Each Child a Teacher: Effective Reassignment of Surplus Teachers Through the Use of Geospatial and Administrative Data

• In Event: Data, Technology, and Talent in Education Systems: Policy Pathways to Address Global Learning

Proposal

Motivation
Teachers are not equitably distributed across the world, and as a result, many children are still without access to the education they seek. Pupil-teacher ratios (PTRs) are generally much higher in LMIC than in high-income countries (HIC) (Walter, 2018). Furthermore, within countries, cross-school variation of PTR is greater in LMIC–indicating that teacher capacity is distributed unevenly, misaligned with enrolment needs (Walter, 2018). Indeed, misallocation of teacher capacity, rather than an overall teacher shortage, seems to be the real problem, with sizable teacher surpluses in some schools and vacancies in others (Datta and Kingdon, 2021).
That said, the solution cannot be a simple redistribution of teachers without regard to contextual factors. Previous studies have highlighted the difficulty of incentivising teacher transfers to areas perceived as less desirable (Evans and Acosta, 2021; Crawfurd and Pugatch, 2020; Ramachandran et al., 2017). However, these studies reveal preferences on a more general level, which tend to avoid remote rural areas or other geographical pockets of disadvantage (Bird, 2019; Fagernäs and Pelkonen, 2012). While broader efforts must be made to eliminate such pockets, one first step toward increasing equitable access to education is through a finer-grained approach that allocates teacher capacity more effectively within current constraints.
We provide evidence that leveraging GIS (“Geographic Information System”) data can identify possible teacher transfers within regions, i.e., between schools located in close proximity, and therefore of similar desirability. These fiscally-neutral transfers, together with thoughtful management and monitoring, could enable much more effective allocation of human capital across public education systems.

Research Questions
How unevenly is teacher capacity currently allocated in Lagos and Jigwara, Nigeria?
How would using a tool that leverages GIS data help education systems allocate teacher capacity more effectively?

Methods and Data
We have developed a tool that identifies potential teacher reallocations, providing recommendations that either prioritise political feasibility by minimising the distance between transfers or that maximise equity by prioritising the greatest need. We first analyse the initial distribution of teachers and enrolled pupils across public Primary schools in Lagos, Nigeria, using location and staffing data from the EKOEXCEL programme. We then extend the analysis to Jigawa State, where school locations are more rural and dispersed. Unlike in Lagos, where our analysis is simulated, the Jigawa case reflects actual implementation: the government applied our tool in practice to guide real teacher transfers, allowing us to evaluate its feasibility in a live policy setting. For both states, we use the tool to obtain a simulated picture of the teacher distribution after reallocation if recommended transfers were to be made. We weigh the results of the reallocation by analysing changes in the proportion of classroom vacancies filled, estimated number of children reached, and potential reductions in wasted teacher salary. The tool was developed using Stata, and will be publicly available as an open-source package for other practitioners.
Preliminary Findings
Lagos
Using data from the EKOEXCEL program in Lagos, Nigeria, we find that an estimated 41,000 children (about 10% of the school-aged population) are enrolled in primary school but do not have a teacher, while 689 teachers (8% of the teaching corps) remain unassigned after all classrooms are staffed. Within the same state, there exist both schools with a teacher surplus – where there are teachers not assigned to a class– and schools with a teacher shortage, where there are fewer teachers than classes.
Teacher vacancies are not concentrated in a few remote LGAs, nor do teacher surpluses follow any pattern suggesting preferences for certain locations. In fact, throughout the state, there appear to be schools with teacher surpluses and schools with teacher shortages located in close proximity to each other. These patterns suggest considerable potential for teacher reassignments that do not require prohibitive increases in travel distances for teachers but nevertheless fill classroom vacancies.
Reallocating teachers as recommended by the tool, with the maximum distance between sending and receiving schools set at 3km, would fill 149 teacher vacancies and reach an estimated 3,600 children. As a result of the transfers, 54 schools would go from having shortages to being fully staffed, and many other schools would go from being severely understaffed to only being moderately understaffed. The resulting reduction in the number of surplus teachers would save Lagos state an estimated $241,000 in wasted teacher salary, based on an estimate of 1617.41 USD per teacher.

Jigawa
Results are similar in Jigawa State, a more rural and dispersed setting. Among 679 primary schools in the state, 479 do not have a teacher for every class, and within this group, 223 schools do not even have one teacher for every two classes. At the same time, 766 teachers are recorded as not being assigned to a class. Using our reallocation tool in close collaboration with the state government, and setting a maximum transfer distance of 12km to account for Jigawa’s wider school dispersal, the government approved and supported the transfer of 332 teachers. As a result of these transfers, 200 schools received a teacher, 69 of which went from being short-staffed to fully staffed. Many other schools improved substantially, moving from severely short-staffed to only moderately understaffed. These results highlight the potential of the tool to produce meaningful improvements even in geographically challenging settings.

Contribution
We offer an algorithm that can help education systems effectively reallocate teacher capacity by leveraging fine-grained geospatial data. In Lagos State, Nigeria, we demonstrate—through a simulated exercise—how the tool can identify potential transfers under different policy priorities, whether minimising the burden on teachers or prioritising schools with the greatest need. Additionally, in Jigawa State, we demonstrate a practical application; with collaboration from the government, the tool directly informed the approval and implementation of over 300 teacher transfers, resulting in hundreds of schools receiving additional teachers, and dozens moving from severely short-staffed to being fully staffed. This tool will not fully solve teacher shortages, but it offers actionable recommendations, identifying politically feasible transfers that would be fiscally neutral and enable thousands more pupils to have access to a teacher.

Authors

• Keuna Cho, NewGlobe Education

• Priscilla Lu, NewGlobe Education

• Pranav Bhargava, NewGlobe Education

• Timothy Sullivan

• Demi Chen

• Olusanya Omojola, NewGlobe Education