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In Event: A global research on the use of open school data, for more sustainable ways to administer educational services, to manage schools and involve community stakeholders
Low-income countries have achieved rapid improvements in access to school in recent years, but many have been left struggling to cope with limited resources. Inequitable distribution of the available resources exacerbates the problem significantly, with schools in remote areas often facing greater shortages of infrastructure, materials and staff than those in and near towns and larger villages. To maximize quality of schooling within a limited resource envelope, countries must develop more sophisticated capacity to identify, and target resources to, those schools with the greatest need.
Developing countries spend the majority of finance for basic education on teacher salaries. This poses a particular challenge in resource management, as teachers themselves often exhibit a strong preference for placements near towns and villages with more access to basic amenities than in often highly impoverished remote areas. This means that efforts to equitably distribute teachers to remote areas must include systems to manage these teacher preferences. However, teachers are frequently politically connected individuals who play a role as political agents or representatives of elites, giving them some power to resist placement in remote schools.
This paper examines the case of teachers in Malawi’s primary schools. There are severe geographical disparities in pupil-teacher ratios (PTR) across Malawi, even within small areas. School-level PTRs can vary even within a single sub-district neighborhood by as much as a factor of five. The disparities are strongly associated with school remoteness, with most teachers concentrated near commercial centers; among schools further from these centers, teachers are typically clustered in those with better amenities. Data on the school placement of teachers is fragmented and inconsistent across a number of administrative databases, preventing effective implementation of policies designed to redress these disparities and achieve a more equitable distribution of teachers.
Employing administrative data from several government sources, we curate a systematic database of the school placement of all teachers in Malawi and link it with data on school facilities and geo-spatial coordinates of commercial centers to develop the first comprehensive picture of the distribution of teachers between Malawi’s schools. Regression analysis reveals that school-level factors identified by teachers as desirable – including the availability of basic facilities at schools, distance to the nearest trading center, and the level of amenities available at the center – are closely associated with PTR. This suggests that teachers’ interests play a central role in PTR variation. Political economy network mapping reveals that teachers leverage informal networks and political patronage to resist placement in remote schools. Administrative officials are unable to stand up to these formal and informal pressures, in part because of a lack of reliable data and objective criteria for the allocation of teachers. A clear and consistent categorization of schools by remoteness, with clear policies to target and maintain teachers in the most remote schools, may enable more targeted deployment and empower officials to resist pressure to bend the rules.
We then develop a consistent and objective measure of school remoteness, which can be applied to develop policies to create rules for equitable deployments and targeting of incentives. In place of a simple definition of remoteness based on distance to the nearest town or commercial center, we develop a multi-factor categorization capturing the key correlates of PTR variation, including the availability of electricity at schools, their accessibility by road year-round; the distance to the nearest trading center; and the availability at the treading center of banks, medical facilities, water and electricity. categorization proves highly predictive of variation in school-level PTR, with schools in the most remote category possessing a PTR one-fifth higher than those considered not remote.
We then develop and simulate the impacts of two policy reforms employing this categorization. First, targeted deployment of new teachers exclusively or primarily to the most remote schools; and second, fiscally-neutral reforms to teachers’ hardship allowances to provide greater allowances to those teachers in the most remote schools, incentivizing teachers to accept remote postings and remain in post for longer. Simulations suggest that in combination, these policies could effectively neutralize category-level disparities in PTR within a short period of time.
Growing awareness of disparities in PTRs among district education officials is already showing promising improvements in targeting of new teachers. The government has adopted the remoteness categorization as part of the targeting of new teachers, while the proposed reforms to hardship allowances are expected to be introduced during the 2018-19 school year. Although implementation is at an early stage, the project suggests that improvements to administrative data can support more equitable use of limited resources, with significant benefits to schooling in low-income countries.