Case study education rwanda laterite
Case study

Evidence to inform educational policy

What causes dropout and repetition in Rwandan basic education?


Rwanda has made impressive strides in access to schooling, ensuring nearly universal education enrollment. The next major challenge is to increase rates of promotion through the system and completion of basic education by reducing dropout and repetition.

Currently Rwanda’s education system is skewed heavily towards the early years, with over 50% of all students in school in primary 1 to primary 3. To put it another way, in 2016, there were close to 600,000 children enrolled in primary 1, compared to less than 100,000 in primary 6.

This is because children enter a cycle of frequent class repetition starting in primary 1 that leads to high over-aging. Once children are over-age, they are much more likely to drop out of school.

To address this issue, policy-makers need to understand the causes repetition and dropout in Rwandan basic education.


Laterite created Rwanda’s first nationally representative study on dropout and repetition, linking child, parent, community and school data for the first time.

In this major survey, Laterite interviewed more than 8,000 children aged 6 to 18, 3,000 parents, 500 communities and about 200 schools.

To do this, Laterite worked with children and parents to recreate a child’s entire schooling trajectory. For the first time, this work provided an overview of how children in Rwanda progress through their education. It shows how what happens at one point in a child’s education affects their future trajectory.


This work generated concrete policy ideas on how to reduce repetition and dropout and rates and many new insights on dropout and repetition patterns. It also informs the design of interventions intended to reduce dropout and repetition rates in basic education in Rwanda.

In addition, Laterite:

  • developed a system dynamics model for the Ministry of Education, to help predict future grade-based enrollment levels and resource requirements under different scenarios;
  • developed algorithms to help the Ministry of Education transfer its school level data from thousands of excel sheets into one comprehensive time-series dataset; and
  • worked with students from Harvard University to create a scorecard based on machine learning algorithms. Teachers and schools can use  this scorecard to detect which children are the most at risk of repetition early-on.

Read more about the findings and recommendations in our policy brief.