What you need to know:
- At 74 percent, Uganda is one of the four countries with the largest out of school rates of upper secondary school age. Neighbouring Tanzania (87 percent) leads, with Niger (85 percent) and the Central African Republic (73 percent) joining Uganda to complete the top four.
Decades after Uganda adopted universal education programmes, the country ranks top among those with the largest out-of-school rates of upper secondary school age.
At 74 percent, Uganda is one of the four countries with the largest out of school rates of upper secondary school age. Neighbouring Tanzania (87 percent) leads, with Niger (85 percent) and the Central African Republic (73 percent) joining Uganda to complete the top four.
The out-of-school rate, which is a Sustainable Development Goals (SDGs) thematic indicator, is defined as the “proportion of children and young people in the official age range for the given level of education who are not enrolled in pre-primary, primary, secondary or higher levels of education.”
The figures are derived from new estimates from the United Nations Educational, Scientific and Cultural Organisation (Unesco) based on a novel estimation method, which combines administrative and survey data.
The result is a damning indictment given that Uganda has had Universal Primary Education (UPE) for more than two decades and Universal Secondary Education for more than 15 years.
This newspaper’s own analysis based on Uganda National Examinations Board (Uneb) data has consistently shown that less than half of the students who enrol for Primary One in any given academic cohort complete the seven years. Of those who finish, more drop out after Primary Seven. The number of those whose transition is impacted by various reasons and keeps growing as a particular cohort is tracked at both O and A-Levels.
In 2011, for instance, 1,839,714 pupils enrolled in Primary One. Of these, 923,089 were males and 916,625 females. Seven years later, only 646,080 pupils wrote their Primary Leaving Examinations. Of these, 312,585 were male and 333,495 female.
This means 610,504 boys and 583,130 girls either dropped out or did not progress in the system normally. Perhaps some died along the way. The other reason could be down to “ghost” pupils recorded in the system.
Globally, inequalities in access to education are keeping some 244 million children out of the classroom, with sub-Saharan Africa accounting for an unparalleled 98 million. It is also the only region where this number is increasing.
“It is estimated that the out-of-school population stood at 244 million in 2021, including 67 million children of primary school age (about six to 11 years), 57 million adolescents of lower secondary school age (about 12 to 14 years) and 121 million youth of upper secondary school age (about 15 to 17 years),” the report notes.
The Unesco report concludes that while low-income countries—like Uganda—have continued bringing down the out-of-school rate among children of primary school age, they struggled in the 2010s to reduce the out-of-school rates among those of secondary school age. The implication is that one in three adolescents and more than one in two youth remain out of school. Many of those who are in school, in fact, still attend primary school.
Overall, nine percent of primary school age children, 14 percent of lower secondary school age adolescents and 30 percent of upper secondary school age youth remain out of school.
Although the model incorporates administrative data from the 2021 school year, the report authors conclude that there is not yet enough evidence to capture the impact of Covid-19, which disrupted not only school attendance but also education management information systems all over the world.
Uganda is the only country on the African continent where schools remained fully closed for close to two years despite registering low Covid-19 infections and deaths, according to a United Nations Children’s Fund (Unicef) 2021 report that tracks the educational impact of Covid-19 globally.
Preliminary evidence, according to the Unesco report, suggests that while primary and lower secondary education enrolment might not have been affected, there might be some impact on upper secondary enrolment.
In 2007, the government introduced the Universal Secondary Education (USE) policy in order to increase access to quality secondary education for economically vulnerable families. It was a follow up to the Universal Primary Education (UPE) introduced in 1997. Studies have shown that the intention of increasing access to quality education through USE has not been achieved to date. The staggering out-of-school rates of upper secondary school age dim the success of the programme further.
Estimates of out-of-school rates were produced within a Bayesian hierarchical framework, which allows information sharing between countries, years and types of data to improve estimates in the context of low data availability. A choice was made to model out-of-school rates by individual age rather than by education level since children enter and exit the school system at every age, resulting in substantial within-level variability. In making this decision, the model is, therefore, designed to capture age-to-age patterns experienced by student cohorts as they progress through their school cycles.
The modelling process is divided into three main steps:
Baseline out-of-school rates: The starting point to estimate out-of-school rates for each country-year-age is the entry-age out of school rate. This is the percentage of students not in education at the official entry age dictated by national policy.
Cohort progression: Building on the baseline out-of-school rates, the out-of-school rates at each subsequent age are modelled using a cohort process. Net changes in out-of-school rates from grade-to-grade are estimated using vector-valued splines. Each vector corresponds to a cohort out-of-school rate first difference curve, adjusted to ensure the resulting cohort trajectory forms a smooth curve. Further, the vectors themselves are adjusted over time so that transitions between patterns are smooth in time as well. The out-of-school rates are then recovered from the changes in out-of-school rates by starting with the baseline out-of-school rate for a given cohort and accumulating all of the changes until the appropriate age is reached.
In the final step, observed data interact with the underlying out of-school rates generated in the previous two steps, depending on the data source. Administrative data are assumed to be unbiased but subject to potentially large errors. These errors are shared regionally to capture similarities in data infrastructure and globally to reflect the minimum plausible error on administrative data resulting from the two-source mismatch challenge. The error structure is designed such that negative observations can be accepted into the model, but since the underlying out-of-school rates are constrained to the [0, 1] interval, they trigger expansion of the regional and global variances. This variance expansion propagates to the rest of the data, thus reflecting the true level of uncertainty in the positive observations.
After executing the models and extracting estimated age-specific out-of-school rates, post-processing steps are also taken. First, age-specific out-of-school rates are aggregated by the corresponding school ages to produce out-of-school rates by education level. Then, the level-specific out-of-school rates are aggregated into regional, income level and other groups using population-weighted averages. Finally, each out-of-school rate is converted into an excluded children and youth population by multiplying by the appropriate population figures.