Inclusion and education

FIGURE 3.8: 
The share of youth with disabilities in the out-of-school 
population is twice as large as that in the in-school 
population
Percentage of youth with functional difficulties in the 
in-school and out-of-school populations, Georgia, 
Kyrgyzstan and Mongolia, 2018
0
10
5
15
%
Kyrgyzstan
Mongolia
Georgia
Out of school
In school
Source: 
GEM Report team analysis based on MICS data.
3
References to Kosovo shall be understood to be in the context of Security Council Resolution 1244 (1999)
.
58
GLOBAL EDUCATION MONITORING REPORT 2021

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Life at the intersections of disability with ethnicity, class, 
gender, sexual orientation and gender identity is more 
than the sum of each vulnerability (Connor, 2014).
From a statistical point of view, sample size is a challenge 
for analysis of intersecting disadvantage. Standard 
household surveys suffer from rapidly shrinking samples 
and larger estimation errors as the focus shifts to 
individuals with multiple specific characteristics. But it is 
important not to underestimate the risk that, for instance, 
poor people with disabilities may be twice excluded: from 
society generally and also within the disability movement.
Intersecting vulnerabilities may mean some go 
unaddressed. Language difficulties and behavioural, social 
and emotional difficulties often coincide (Hartas, 2011). 
Yet bilingual students with disabilities, for instance, are 
likely to be in classrooms that address their academic or 
linguistic needs but not both (Cioè-Peña, 2017).
In studies of children and adolescents with epilepsy,
one-quarter met criteria for depression (Ettinger et al., 
1998) and half for learning difficulties (Fastenau et al., 2008). 
Children identified as gifted and talented often experience 
emotional difficulties coping with their exceptionality 
and social distance from peers. Giftedness may not be 
recognized in children with autistic spectrum disorders.
Assessment criteria to identify special education 
needs can be arbitrary and contentious
Not all children with disabilities have special education 
needs, nor do all children with special education needs 
have a disability (Keil et al., 2006; Porter et al., 2011).
While a consensus approach on defining disability 
in surveys improves cross-national comparisons of 
population-level estimates, countries focus on special 
education needs for their national policy discussions:
Who has special education needs, where are they 
educated and what is the quality of that education? 
Special needs identification is distinct from disability 
measurement and entails less consensus.
The share of students identified as having special 
education needs varies widely. In Europe, it ranges from 
1% in Sweden to 21% in Scotland (United Kingdom); in 
Central and Eastern Europe, it varies from 3.3% in Poland 
to 13% in Lithuania (European Agency for Special Needs 
and Inclusive Education, 2018) (
Figure 3.10
). Such variation 
is mainly explained by differences in how countries define 
special education needs, a political decision linked to 
history. Institution, funding and training requirements 
vary, as do policy implications. The variation in approaches 
also presents measurement and data challenges. 
Comparing the prevalence of disability, difficulties and 
disadvantage across education systems and over time is 
problematic, even for clinical diagnoses. For example, in 
the case of autism spectrum disorder neither medical nor 
education considerations give unambiguous guidance on 
the point at which a behaviour becomes a disorder.
The determination partly depends on context. Whatever 
the underlying biochemistry of attention deficit 
hyperactivity disorder (ADHD), in some settings the 
boundary of orderly behaviour determines the diagnosis. 
Pre-primary and even early childhood education settings 
have become more academic. Moreover, measurement 
difficulties compromise the comparability of global data 
or limit their availability. For instance, the mean prevalence 
rate of autism spectrum disorder is 0.6% in EU countries 
among children aged 2 to 17, but among the 16 countries 
reporting data, the average treatment rate was 0.08%. 
Likewise, with an estimated ADHD prevalence rate of 
5% for children aged 6 to 17, the average treatment rate 
was 1.6% (Wittchen et al., 2011; Aleman-Diaz et al., 2018).