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How many students need to be reached and accounted for in an education system? This is a fundamental question for planners. However, the answer is far from simple. This is partly because precise school-age population estimates are hard to come by. When they are available, the data are normally in rigid five-year age groups that do not always match actual educational levels. However, a step-by-step methodology is now available to help educational planners with determining school-age populations for all school levels – in any territory worldwide. IIEP’s Amélie A. Gagnon breaks it down.
There is a lot of population data available, but what is the challenge with school-age data?
It’s true that national population censuses provide governments with key information to help deliver social services, such as education. However, depending on the size of the geographic census units, estimates can be quite rough, and not necessarily made available by single years of age. Instead, they may be by 5-year age groups. This is important because only a handful education programmes worldwide are mapped on these typical 5-year age groups. Education systems cater to various age groups, so we can think of 3 to 5 years old for pre-primary, 6 to 11 for primary, 12 to 15 for lower secondary, and so on. Strategic indicators, such as gross intake ratios, are also calculated for a single year of age.
Such precise information may be available at the national level, but actual programming and implementation often takes place at the extremely local level.
A planner needs to know how many primary school-age children there are in a neighbourhood, to anticipate the need for teachers, textbooks, or school meals, for example.
Therefore, we are proposing a solution for the contexts school-age populations by single years of age are not available for small geographical areas.
Can you tell us more about the methodology?
The methodology uses gridded population estimates prepared by WorldPop, which is based at the University of Southampton in the UK. By using national census data and surveys, alongside aerial photography and satellite imagery, the researchers developed a method to estimate the density of populations within a territory for virtually every country in the world. As this data is available in 5-year age groups, it takes us to the second step of the approach where we apply Sprague multipliers to rasterized data to estimate population by single years of age. These estimates are often produced collaboratively with national statistical offices, as was the case in the Zambia.
Sprague was a demographer who fixed an interpolation method in the 1880s. His algorithm allows us to break these 5-year age groups for any population into single years of age, and then reconstruct school-age populations that exactly match any educational programme.
From here, we move to the third and final part of the methodology where we reconstruct the school- age population. Because we broke down population estimates by single years of age, we can now recompose any combination we want. This code –and QGIS plugin – allows planners to build all the school age groups they need, for any area. The school-age population are available at the hyper local level, which is about 100 meters by 100 meters at the equator. These estimates can now be aggregated for any other area, which may be needed by the planner.
The methodology can be applied to any population estimates available in 5-year age groups. For example, a planner having access to 5-year age groups for their district population could use the methodology through this simple Excel workbook made available by IIEP’s Development team. For planners working on multiple customized areas (e.g. the irregular catchment areas around schools), a more automatized process might be efficient, and the code could therefore be relevant to use.
This is a game changer for planning and implementing programmes. As planners build or strengthen the collaboration between the ministry of education and the national statistical office, school-age population estimates could be produced and adjusted to national population estimates and shared through official channels.
What impact can the Sprague multipliers model have on educational equity and quality?
Quality learning opportunities for all hinges on the provision of age-appropriate opportunities that meet the educational expectations of communities. The model presented here allows planners and managers to really embrace a micro-level approach to planning and ensure that all communities can benefit equitably from educational services. The model can support ministries of education, development partners, and humanitarian actors in allocating scarce resources, especially when there is no time for leading a full population census or collecting survey-based data. Based on population estimates from WorldPop or the most recent national census, the model can also be used for other purposes, such as estimating school-age populations exposed to a natural hazard (IDMC background paper, 2022).
If you and your team are interested in learning more, check out the Technical Note with explanations on this methodology with examples. You can freely use the code that is available on GitHub and follow the set of instructions in the Technical Note to make your own estimations (you can even share the results with your National Statistical Office). Please contact our team with any questions at development@iiep.unesco.org.
