You are here

How many out of school children are there in Kenya?

Data Issues in Kenya

Competing definitions of primary education can lead to substantial variation in estimates of out of school children within countries, and internationally accepted definitions of primary are often shorter in duration than national definitions. A comparison of data from an administrative source and household survey data for Kenya yields unexpected results, with survey data suggesting school attendance rates in excess of enrollment rates. EPDC is working to understand the cause of these reported results.

Alternative measures of out of schoolchildren of primary school age in Kenya.

Primary school in Kenya consists of eight standards, officially corresponding to ages 6-13. The UNESCO Institute of Statistics (UIS) however, treats only the first six standards of primary school (ages 6-11) as corresponding to ISCED level 1, which UIS uses as the basis for its primary-level calculations. Although the UIS justification is that this method preserves comparability across countries, most of which have 6 grades of primary (UIS, 2008), the result for Kenya is that UIS data are not fully reflective of the primary education experience there. As shown in the table to the left, treating only standards 1-6 as primary school drives the out of school rate from 11% up to 13%, because 7th and 8th graders are not considered. It also brings down the overall number of out of school children because it excludes 12-13 year olds.

Effective use of survey data requires attention to the structure of the dataset and an understanding of the context in which the data collection took place. A closer look at the 2009 Kenya Demographic and Health Survey (DHS) illustrates how important this is - in particular, adjusting children’s ages to correct for the timing of the survey relative to the beginning of the school year is essential for obtaining accurate figures.


The extended length of time over which surveys are conducted may lead to inconsistencies in age data, which in turn affects the precision of non-attendance measures for a given age group. Therefore, it is crucial that measures of school participation be calculated using children’s ages at the beginning of the school year. This is especially true in the case of the 2009 Kenya DHS because, as a result of children’s ages having been collected 11-15 months after the beginning of the 2008 school year, it is reasonable to assume that nearly every child has had at least one birthday since that reference point, and some will have had two birthdays. As a result of this 11-15 month lag, many children who were six years old (official primary entry age) when their age was reported in the survey, were only five or four years old in January 2008.

The figures to the right illustrates the distortive effect on out of school rate of failing to adjust ages with reference to the beginning of the school year. This figure suggests that 21% of primary-aged children are out of school. It is clear that out of school children are overwhelmingly 6-7 years old, children who were most likely too young to enter school in January 2008. When an adjustment is made to reflect actual ages at the beginning of the 2008 school year, the percentage of primary aged children who are out of school declines dramatically, from 21% to 11%.

Discussion Question

  • There exists a substantive discrepancy in the national-level estimate of out of school children between household survey and administrative sources. Administrative figures from the UIS database indicate that 17% of children aged 6-11 were out of school during the 2008 school year. Data from the 2009 Kenya DHS, when adjusted to match UIS methodology as closely as possible, suggest that the out of school rate for children in this age group is 13%. While it is not unusual to have a gap of several percentage points between measures from two different types of data sources, what is unusual is that the household-based attendance figure suggests a lower proportion of out of school children than the administrative-based enrollment figure. How can researchers and policy-makers account for the discrepancy between household survey and administrative sources?




brian.dooley's picture

<p>Although sufficient data are not available to say so conclusively, it is possible that this difference is explained in part by high rates of participation in non-formal unregistered schools &ndash; a phenomenon which,&nbsp;we hypothesize, household survey instruments would be sensitive to, but school census data would not.</p>

Add new comment