IAFOR Journal of Education: Inclusive Education

Volume 9 – Issue 1 – 2021

Understanding Cross-National Differences in Inclusive Education Coverage:
An Empirical Analysis
Steve R. Entrich
University of Potsdam
Germany

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IAFOR Journal of Education: Inclusive Education

Volume 9 – Issue 1 – 2021

Abstract
With the adoption and ratification of the United Nations’ Convention on the Rights of Persons
with Disabilities in 2006, inclusive education became an international human right and a global
norm. But, “Education for All” remains a worldwide challenge. It appears that some countries
achieved higher inclusive education rates than others. Why this is the case has barely been
empirically investigated. To address this gap, this study analyzes cross-national differences ininclusive education coverage in over 50 societies. First, the data gap is addressed by providing
an overview of inclusive education rates in 52 societies. In the theoretical part of the paper,
hypothetical causes for the cross-national differences are discussed from a new institutionalist
perspective, before concrete, testable hypotheses are derived. Third, a secondary meta-analysis
based on self-assembled data from national and international sources is conducted predicting
the cross-national differences by means of ordinary least squares regressions. Findings showthat national income or educational expenses have no impact on the level of institutionalizationof inclusive education in a society; the cross-national differences in school inclusion are mainly
due to the structural conditions of the school system and its own institutional logic (especiallythe degree of institutional differentiation); and the definition of what is recognized as special
educational needs and promoted in a national education system largely affects the extent of
inclusive education coverage. The findings of this analysis prove to be a good start for futureendeavors in macro-sociological and educational analyses of international inclusive educationand have major policy implications.
Keywords: cross-national comparison, country-level analysis, inclusive education, newinstitutionalism, secondary meta-analysis, special education

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IAFOR Journal of Education: Inclusive Education

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With the adoption and subsequent ratification of the United Nations’ Convention on the Rights
of Persons with Disabilities (UN-CRPD) in 2006 (United Nations, 2006), inclusive education
became an international human right and a global norm. Most of the 182 countries that signed
the petition also ratified it soon after, thus expressing their commitment to reducing theexclusion of pupils with special educational needs (SEN) (United Nations Office of Legal
Affairs [UN-OLA], 2020). Nevertheless, “Education for All” remains a worldwide challenge
and is seen as a process and goal rather than an achieved status (Powell, 2018; United Nations
Educational, Scientific and Cultural Organization [UNESCO], 2015). Whereas some societies
with comparatively advanced inclusive education systems (e.g., the Nordic countries ofEurope) were praised for their efforts to provide education for all, in countries with highly
stratified education systems, such as Germany and Switzerland, students with special
educational needs remain predominantly or exclusively schooled in special schools (Biermann& Powell, 2014; Powell, 2016, 2018; Werning, 2014).
The objective of this study is to find explanations for the cross-national difference in the levelof institutionalization of inclusive education. Therefore, this study for the first time analyzes
cross-national differences in Inclusive Education Coverage (IEC) in over 50 societies. The
article is structured as follows. First, the still existing gap in valid and reliable data on inclusive
education provision across the world is addressed by providing self-assembled inclusive
education rates for 52 societies. Second, hypothetical causes for the cross-national differencesin IEC are discussed from a neo-institutionalist perspective, before concrete, testable
hypotheses are derived. Third, the cross-national differences in IEC are empirically analyzed
by means of ordinary least squares (OLS) regressions using self-assembled data from national
and international sources. Finally, findings are discussed for their meaning and significance forthe further development of inclusive education, before future directions and policy implications
are discussed.
Literature and Data Review
Mapping Inclusive Education Coverage
The need for reliable data on inclusive education to enable evidence-based policy-making forlong-term development of inclusive education systems is well recognized (Ramberg &Watkins, 2020). However, although more attention is devoted to collecting data on crosscountry special and inclusive education coverage (European Agency for Special Needs andInclusive Education [EASIE], 2020; Organisation for Economic Co-operation andDevelopment [OECD], 2005; UNESCO, 2015), serious gaps in data and research remain.
First of all, the data collection related to inclusive education is still quite problematic (Watkins
et al., 2014). Apart from the European Agency Statistics on Inclusive Education, whichcollected longitudinal, comparative national data of 31 European societies, data collected byother international organizations (e.g., OECD, UNESCO, World Health Organization [WHO],
United Nations International Children’s Emergency Fund [UNICEF]) are rarely harmonizedor suited to fill the existing data gap for countries outside Europe (Ramberg & Watkins, 2020).
Second, there is still a lack of systematic and sophisticated empirical studies examining thedifferences regarding inclusive education provision. Comprehensive cross-national analyses ofthe determinants and effects of Special Education Coverage (SEC) significantly contributed to
our knowledge about the influence of economic and educational factors on the level of special
education provision in a country (Anastasiou & Keller, 2014; Anastasiou et al., 2018).
According to this research, whether more or less special education is generally provided in a

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IAFOR Journal of Education: Inclusive Education

Volume 9 – Issue 1 – 2021

country depends on economic resources (i.e. gross national income per capita) and educational
context (e.g., adult literacy rate, net enrollment in primary education, pupil teacher ratio, andschool life expectancy). However, this kind of study provides few answers to the question why
more pupils are schooled in inclusive settings (in the sense of the UN-CRPD) in some countries
than in others. To answer this question, valid and reliable cross-national data on inclusive
education coverage are necessary to enable evidence-based education policy-making.
Based on the data at hand, in most countries there has been a noticeable improvement in theprovision of special needs education (EASIE, 2020; UNESCO, 2015). However, more than adecade after the adoption of the UN-CRPD, the worldwide transition from an exclusive to an
inclusive organization of special education must be described as rather slow. A large proportionof SEN students is still either schooled in special schools (segregation: students are enrolled infully separate special schools) or special classes (separation: students are enrolled in specialclasses in mainstream schools for more than 20 % of their time). Sometimes this kind of
instruction within the regular schooling system allows for shared lessons (integration), but only
if SEN students receive 80% or more of their instruction in regular classes among students
without SEN can we truly speak of inclusive education according to international standards(EASIE, 2020; Ramberg & Watkins, 2020). Often, what is officially recognized as inclusive
schooling does not necessarily comply with this definition (D’Alessio & Watkins, 2009;
Ramberg & Watkins, 2020). In societies where the more exclusive forms of special education
are socially deeply rooted and have been institutionalized over many years, it is likely that they
prevent the further expansion of inclusive education (Powell, 2018). Although inclusive
educational structures have been developed in all regions, there are currently very few
educational systems around the world in which all pupils learn together in inclusive settings
within the mainstream schooling system (Ramberg & Watkins, 2020).
Following the definition of EASIE (2020), Figure 1 reports inclusive education participation
rates of SEN students based on self-assembled data from 52 societies (details on data collectionin the methodology section). The data show that the proportion of SEN students in inclusive
settings has considerably increased between 2008 and 2018 in many societies. In Australia,
Czech Republic, Germany, Hungary, France, and Turkey, for example, this proportion
significantly increased. In other societies, not much seems to have happened since 2008. In
many societies with traditionally high inclusive education rates (over 90%), such as Italy,
Norway, Portugal or the United States, continuation is observed. In other societies withtraditionally low inclusive schooling, such as Japan or, especially, Switzerland, only marginal
or no improvement is visible. Surprisingly, in Finland, Estonia, and, especially, Sweden,
inclusive schooling rates considerably decreased even.

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IAFOR Journal of Education: Inclusive Education

Volume 9 – Issue 1 – 2021

XKX
CHE
HKG (CHN)
DNK
SWE
BEL
JOR
JPN
RUS
NLD
EST
LVA
FIN
WAL (GBR)
DEU
LUX
ENG (GBR)
CHN
KOR
ISR
MAC (CHN)POLFRANIR (GBR)
SVK
HUN
CZE
AUT
AUS
MYS
GBR
GRC
QAT
SVN
VNM
IRL
ESP
BRA
BGR
CYP
HRV
TUR
CAN
PRT
ALB
TAP (CHN)
LTU
ISL
NOR
THA
SCO (GBR)
USA
MLT
ITA
TUN
RWA

0%

2018
2008

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Figure 1: Percentage of students with official SEN-status attending inclusive classes inregular schools as a proportion of all students with SEN status in 52 societies, 2008 and 2018
Note: Data for 2008 for Albania from 2012; for Brazil from 2009; for China from 2007; Data for 2018 for Korea,
Russian Federation, and Vietnam from 2017; for China, Canada (Ontario), Croatia, Cyprus, Denmark,
Hungary, Macao and Thailand from 2016; for Brazil from 2015. Data for Rwanda and Tunisia are based
the actually schooled number of students with SEN status; it is unclear whether all SEN students areenrolled in education.

The data presented imply a higher level of commitment to the provision of inclusive education
opportunities in some countries compared to others. Why this is the case and these massive
cross-national differences occur is all but clear. To address this issue, I apply neoinstitutionalism in the following section and derive testable hypotheses.

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IAFOR Journal of Education: Inclusive Education

Volume 9 – Issue 1 – 2021

Theoretical Approach: New Institutionalism
In order to clarify under which national conditions inclusion is implemented or institutionalized
more comprehensively, the present work draws on theoretical assumptions of the new
institutionalism. Although this theoretical perspective has so far found little application in thediscussion on inclusive education, it still offers important insights for the introduction andinstitutionalization of inclusive school systems (Nohl, 2018). According to Berger andLuckmann (2003), institutionalization takes place as soon as habitualized actions arereciprocally typified by types of agents. Every typification that is carried out in this way is an
institution (p. 58). Hence, instead of simply accepting the existence of institutions, the new
institutionalism points to the importance of institutions as connecting structures between
society and the individual actor. Only then, the formation, nature and change of institutions canbe understood. The institutional development and establishment of inclusive education
therefore depends on the overall societal recognition of instruction for all students as a common
good. In most societies, however, the learning environment appropriate to the needs of students
with SEN was – if at all – for the longest time located in exclusive settings such as special
schools rather than in regular schools.
The institutionalization of special education support systems took place in three phases.
Around 1900, guiding and structural principles of the general school system for theestablishment of “institutions” for the schooling of children with disabilities were adopted (e.g.,
auxiliary schools in Germany; special classes in the United States). Between 1900 and 1970,
schools for children with disabilities were expanded and institutionalized, with a following
differentiation in the course of educational expansion. It was not until 1970 that options forcommon lessons for all children were pursued in different school experiments and models.
However, these new concepts often found it difficult to assert themselves. Instead, theinstitution “special school” was accepted as necessary or even inevitable in many countries(Powell, 2009, p. 216). In these societies, the institutionalization of the special school system
was extremely effective, because schools orient themselves in their organizational form on theinstitutionalized expectations of their environment (Meyer & Rowan, 2009): that is, in the
segregating or separating schooling of children classified as possessing disabilities or SEN.
With the inclusion of SEN students, schools often fear to risk their own legitimacy and thus
their continued existence, regardless of whether school inclusion is more effective or not (Nohl,2018).
Without a doubt, the resolutions passed by the UN-CRPD became a powerful instrument of
global educational governance, which made inclusive education a new element of the “world
culture” of education (Meyer et al., 1992) and thus part of the globally institutionalized
expectation structure for education (Biermann & Powell, 2014). The effective implementationof inclusive education ultimately depends on forms of isomorphism; that is, processes ofaligning school organizations to this globally institutionalized expectation structure. Threedifferent types of isomorphism can be distinguished (Powell, 2009): the imitation of highly
successful inclusive schools (mimetic isomorphism); the change of standards, classifications
and expectations within special education (normative isomorphism), which led to a global
diffusion of school inclusion (Biermann & Powell, 2014); and the pressure on states and theireducation systems to introduce inclusive education (forced isomorphism). The latter case isquite problematic, as it deviates from Berger and Luckmann’s concept of institutions. Here thecreation of institutions underlies no longer reciprocally typified habitualized actions, but rather
explicit rules that have been internationally and/or nationally codified (Nohl, 2018).

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The UN-CRPD is a perfect example of forced isomorphism, because all countries that ratified
the UN-CRPD are obliged to guarantee inclusion in schools. However, school organizations
are still confronted with the respective historically grown expectations and previously
institutionalized support systems, which can conflict with the resolutions of the UN-CRPD.
There are therefore different forms of coupling: close coupling where the school’s educational
practices correspond to institutionalized environmental (and policy) expectations; loose
coupling wherein expectations in educational practice are reinterpreted by the schools; anddecoupling which is when educational practices are completely detached from the externallycommunicated image of the schools and the postulated policy goals (Nohl, 2018).
The replacement of the special school system as an institution by the inclusive school istherefore a lengthy process and strongly dependent on the willingness to consistently
implement inclusion. The different ways in which organizations deal with inclusion, which is
influenced by internal logics and practices and carries the risk of lip service, is particularly
problematic. To reduce the risk of decoupling of schools from the official goal of providing
education for all and thus explain cross-national differences in IEC, four national factors are
deemed especially important: economic resources, education system, political conviction, andclassification of SEN in a country. The hypothetical impact of these factors on IEC is briefly
discussed and the derived hypotheses to be tested in the analysis section.
Hypotheses
Economic resources. The implementation of inclusive instruction in mainstream schools is
believed to be quite a costly endeavor. Whether more or fewer children receive any form ofspecial education was found to be positively related to the economic resources of a country, inother words, to the gross national income (GNI) per capita. With higher mean income, theproportion of SEN students who receive education increases (Anastasiou & Keller, 2014;
Anastasiou et al., 2018). However, these arguments fall short in countries in which an extensive
special education system is already in place. In fact, cost arguments speak against themaintenance of special needs schools and in favor of inclusion. Not only is the average cost oflessons per pupil in mainstream schools lower than in special schools; human resources can be
distributed differently and transport, possible accommodation and operating costs can be savedby closing special schools. Overall, it can be seen that rising costs for teachers and othereducational staff are foreseeable, but falling investment and operating costs for special schools
and student transport costs should more than compensate for this. In addition, demographic
change is making special schools less profitable, as falling numbers of pupils per school
increase operating costs, while space is freed up in mainstream schools (Sibanda, 2018). Hence,in contrast to SEC, no significant effects of national education expenditure or the mean income
per capita of a country on the level of inclusive schooling are to be expected. This leads to thefirst hypothesis:
Hypothesis 1. Higher mean income and educational expenditure in a country does not
significantly enhance IEC.
Education system. The implementation of inclusive schooling is ultimately a question of theinstitutional logic with regard to selection criteria of the established school system. Educational
institutional differentiation in the form of tracking and standardization arguably influences theextent to which the principle of “Education for All” is supported or prevented by theinstitutional logic of the education system. Tracking describes the level of stratification of an
education system (Bol et al., 2014). External tracking refers to the formal differentiation ofschools by tracks (school types), school maintainer (public versus private) or specializations,27

IAFOR Journal of Education: Inclusive Education

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or the informal differentiation according to reputation (ranking), resources, or student
composition. Internal tracking (within schools) often refers to formal specializations and ability
grouping, among others (Blossfeld et al., 2016). Based on in-depth evidence covering the fullrange of educational differentiation and institutional arrangements across 17 countries,
Blossfeld et al. (2016) developed a framework to categorize four dominant models of
secondary schooling with different levels of institutional differentiation, including tracking andage of first selection (from least selective to most selective): the Nordic Inclusive Model, theIndividual Choice Model, the Mixed Tracking Model, and the Early Tracking Model. Entrich(2021) extended the number of societies covered by this classification to over 60. In countries
with lower levels of differentiation, inclusive education should be more readily accepted andeasier to integrate and implement than in highly stratified education systems. In combination
with higher levels of differentiation, centrally administered high-stakes exit or entrance
examinations as a form of rigid standardization of learning contents should foster higher
competition for placement in elite educational tracks or the most prestigious institutions (Bolet al., 2014). Based on these institutional differences and underlying institutional logics inrelation to selection criteria, varying organizational forms of learning support are to be
expected (Powell, 2018). Countries with higher levels of differentiation and standardization
are thus expected to have experienced more difficulties in the implementation of inclusive
education.
Hypothesis 2. In societies with higher levels of differentiation and standardization of
education, IEC will be significantly lower.
Political conviction for inclusion. Whether inclusion is actively pursued and how many
resources are made available to achieve the internationally set goal of education for all is ofcourse largely related to when and to what extent the representatives of a country decided to
introduce inclusion. Countries in which the resolutions of the UN-CRPD were signed andratified immediately after they were presented in 2007 and in which the additional protocol
was also ratified show a heightened interest in meeting the international expectations of aschool for all. Although other countries have also ratified these resolutions, this may have been
more a result of international pressure than based on the conviction that inclusion is absolutely
necessary and should be implemented as soon as possible. There are still examples where the
UN-CRPD has been ratified, but exclusive forms of special education continue to dominate
and were even expanded in recent years (e.g., Switzerland). In many societies, the rhetoric is
more ambitious than school realities (Powell, 2018).
Hypothesis 3. In societies where the UN-CRPD was ratified earlier, the conviction to
implement inclusive education is higher and thus IEC will be significantly higher as
well.
Classification of SEN. In order to enjoy inclusive school education, pupils with disabilities
must be officially classified as SEN students. According to estimates by the World HealthOrganization, around 15.3% of people across all age groups are counted in the category“moderate and severe disabilities” (WHO, 2011, p. 30), which refer to severity classes III andabove. Of the 0–14-year-old population about 5.2% have SEN of this severity. However, there
are very large differences in the proportion of students with SEN among the consideredsocieties. Who ultimately receives a SEN status and who does not depends largely on what is
officially recognized as SEN (D’Alessio & Watkins, 2009; Kim et al., 2019; Mithout, 2016;
Ramberg & Watkins, 2020). Hence, it is not surprising that the proportion of students with an
official SEN-status can vary greatly from country to country (see Figure 2).

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Figure 2: Special Education Coverage (SEC) and Inclusive Education Coverage (IEC) in
52 societies, 2018
Note: Data for 2018 for Korea, Russian Federation, and Vietnam from 2017; for China, Canada (Ontario),
Croatia, Cyprus, Denmark, Hungary, and Macao from 2016; for Brazil from 2015; for Albania from 2012.

While in Sweden and Korea, for example, only about 1% of all students possess an attested
SEN status, about 14% of US students are recognized as SEN students. Contrasting to countries
like Korea or China, for instance, in the US almost 40% of all SEN students are recognized ashaving specific learning disabilities – a category that is marginal in Korea and completely
missing in China (Kim et al., 2019). These significant differences in SEC between countries

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refer in particular to the internal logic according to which a SEN status is assigned at all. How
many students in a country receive inclusive schooling basically depends on the intrinsic logicof the system, that is, on the conditions under which someone is certified with the special SEN
status and can accordingly qualify for special educational support. Perhaps unsurprisingly, theoverall IEC rates shown in Figure 2 imply a clear relationship between SEC and IEC. In
countries with larger proportions of students with an official certification of SEN, the potential
proportion of inclusively schooled students is larger as well. Of course, the difference between
SEC and IEC can be quite enormous across countries. Still, there seems to exist a generaltendency. In sum, higher IEC is expected in countries where the SEN classification covers alarger range of disabilities and learning disadvantages and thus more students receive the SENstatus.
Hypothesis 4. In societies with generally higher special education coverage (SEC), IEC
will also be significantly higher.
Methodology
Data
The data used in this analysis cover 52 societies and come from different international (World
Bank, UNESCO, United Nations, and EASIE) and national sources (ministries of education as
well as national statistics bureaus). Data were targeted for the year 2018 or the closest available
to that year. Table 1 provides a detailed description of all variables.
Measures
Outcome Variable: Inclusive education coverage (IEC). This measure reflects the overall
percentage of students who are schooled in inclusive settings as a percentage of the overall
student population in a country. Data was obtained from the European Agency Statistics on
Inclusive Education (EASIE) for the 31 European countries in the sample and supplemented
by data from national ministries of education and their statistics bureaus for 21 societies forother regions of the world, including the Chinese special administrative regions of Hong Kongand Macao. To achieve comparable measures of IEC, instead of the postulated inclusive
schooling rates in the considered societies, which do not necessarily match (D’Alessio &Watkins, 2009; Ramberg & Watkins, 2020), the operational definition of an inclusive settingfrom the UNESCO and the EASIE was used. Accordingly, the IEC rate for all consideredsocieties “refers to education where the child/learner with SEN follows education inmainstream classes alongside their mainstream peers for the largest part – 80% or more – ofthe school week” (EASIE, 2020, p. 11). Furthermore, the IEC rates used are restricted tocompulsory schooling, that is, to primary and lower secondary education in most societies.
According to these data, the proportion of students schooled in inclusive settings within themainstream schooling system varies between 0 % (Hong Kong/Switzerland) and 14.53 %(Canada) with a mean of 3.75 %.
Economic resources. To test the economic argument, 2018 data on Gross Domestic Product[GDP] per capita as direct measurement of the level of national economic development wereused. Data was obtained from the World Bank (https://data.worldbank.org) and national
statistics. According to these data, the GPD per capita is lowest in Rwanda (782.62 US Dollar)
and highest in Luxembourg (116654.26 US Dollar), with a mean of 29,702.1 US Dollar. The
data on GDP were transformed into a natural logarithm (logged GDP) to resemble normal
distribution. To also control for governmental spending on education as a proxy for national
value of education, national educational expenditure on education as a percentage of GDP was30

IAFOR Journal of Education: Inclusive Education

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used. Data were obtained from the UNESCO Institute for Statistics (UIS:
http://data.uis.unesco.org) showing that governmental spending on education among theconsidered societies is lowest in China (1.89 % of GDP) and highest in Norway (7.91 % ofGDP), with a mean of 4.72 % of GDP.
Educational differentiation and standardization. To account for educational institutional
differentiation across societies, following the framework put forth by Blossfeld et al. (2016)
and the work of Entrich (2021), a series of dummy variables was created for the different
secondary schooling models. Based on in-depth evaluation of data on educational
differentiation, the most selective secondary schooling model (Early Tracking) was assigned
to those 9 societies where the first major selection occurs very early (age 10 or 11) and whereformal external differentiation into different school types is found. The second most selectivemodel (Mixed Tracking) was assigned to those 32 societies where selection typically occurs at
age 14 or 15 and is followed by strong formal and/or informal external differentiation. For
another 5 societies, the second least selective model (Individual Choice) was classified. Hereformal internal differentiation is the norm. Finally, the least selective model (Nordic Inclusive)
was assigned to those 6 societies with comprehensive systems focusing on informal internal
differentiation.
To control for educational standardization, the existence of centrally administered high-stakes
exit or entrance examinations is included drawing on the framework and data of Bol et al.
(2014) and Entrich (2021). Additional data was obtained from national reports of UNESCO(http://www.ibe.unesco.org/en/resources/world-data-education) and national ministries of
education. In total, 36 societies possessed central exams (1), 12 did not (0), whereas in 4
countries these exams were present only in subnational regions and are represented byrespective proportions (i.e., Australia: 0.81, Canada: 0.51, Germany: 0.44, and the UnitedStates: 0.09).
Political conviction for inclusion. To measure when and to what extent the representatives ofa country decided to introduce inclusion, 2 variables were introduced based on data from theUnited Nations (https://indicators.ohchr.org): years since the ratification of the UN-CRPD: thatis, the number of years which have passed since the resolutions of the UN-CRPD were ratified
referencing the year 2007, where the first ratifications are documented (0 to 11 years); andratification of the optional protocol (1 = yes; 0 = no).
Classification of SEN. The proportion of students counted as SEN students reflects the special
education coverage (SEC) of a country and is thus measured as the percentage of students
schooled in any special education context: in special schools, special classes, andintegrated/inclusive settings. In most of the considered societies, all students with SEN areschooled in one way or the other. In some, however, there may be students with SEN not
counted in these statistics, because they are not schooled in state recognized institutions but at
home or elsewhere. Nevertheless, the author is confident that the data used cover the actual
situation of special education in the considered countries quite well.

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Table 1: Descriptive statistics
Country
code

Country/
Society

ALB
AUS
AUT
BEL
BGR
BRA
CAN
CHE
CHN
CYP
CZE
DEU
DNK
ESP
EST
FIN
FRA
GBR
GRC
HKG
HRV
HUN
IRL
ISL
ISR
ITA
JOR
JPN
KOR
LTU
LUX
LTA
MAC
MLT
MYS
NDL
NOR
POL
PRT
QAT
RUS
RWA
SVK
SVN
SWE
TAP
THA
TUN
TUR
USA
VNM
XKX
Mean

Albania
Australia
Austria
Belgium
Bulgaria
BrazilCanadaSwitzerland
China
CyprusCzech Rep.
Germany
Denmark
Spain
Estonia
FinlandFranceUnited Kingdom
GreeceHong Kong
Croatia
Hungary
Ireland
IcelandIsraelItaly
JordanJapan
Korea, Rep.
Lithuania
Luxembourg
LatviaMacao
Malta
Malaysia
Netherlands
NorwayPolandPortugal
Qatar
Russian Fed.
RwandaSlovak Rep.
Slovenia
SwedenTaiwanThailandTunisia
TurkeyUnited States
VietnamKosovo, Rep.

IEC
(totalinclusive
students)
0.55
5.30
2.29
1.13
2.41
1.73
14.53
0.00
0.95
6.68
6.95
2.37
0.26
2.97
2.75
2.91
2.00
3.28
4.49
0.00
5.09
4.99
5.51
14.08
6.16
3.52
1.98
0.82
0.41
12.05
0.75
2.62
1.19
9.47
0.93
1.13
7.20
2.15
6.09
3.35
0.65
0.68
9.45
5.72
0.64
4.89
1.83
0.10
2.26
13.41
2.16
0.00
3.75

SEC
(total
SEN
students)
0.64
7.70
3.34
8.25
2.96
2.14
17.30
3.86
1.80
8.10
10.19
5.45
5.13
3.69
7.38
7.45
3.39
4.59
6.25
1.16
6.13
7.47
6.93
15.34
11.00
3.55
10.00
3.50
0.77
13.48
1.50
6.71
2.10
9.90
1.32
3.20
7.84
3.77
7.21
4.60
2.40
0.68
15.07
7.75
1.02
5.62
1.97
0.10
2.72
14.10
2.81
0.08
5.60

GDP
per capita(in US$)

Educational
expenditure(% of GDP)

Model ofsecondaryschooling

Centralexams

5284.38
57395.92
51525.05
47491.32
9423.56
9001.2346313.17
82818.11
9976.6828689.71
23046.95
47639.00
61390,69
30337,68
23258,47
50021,29
41631,0943043,2320324,3148542,89
14920,19
16150,77
78621,23
72968,70
41719,7334520,094241,7939159,4233340,27
19080,62116654,26
17805,28
87208,5430133,47
11373,2353048,10
81734,47
15460,64
23461,57
68793,78
11370,81
782,62
19428,1226054,5454589,06
22294,00
7295,48
3438,79
9370,1862840,02
2566,60
4419,91
35038.48

3.61
5.12
5.36
6.41
4.09
6.32
5.27
5.13
1.89
5.78
3.85
4.91
7.82
4.21
4.97
6.38
5.45
5.44
3.96
3.33
3.92
4.67
3.51
7.66
6.09
4.04
3.03
3.18
4.33
3.81
3.57
4.40
2.74
4.82
4.48
5.18
7.91
4.56
5.02
2.86
4.69
3.07
3.94
4.78
7.57
5.05
4.12
6.60
4.37
4.99
4.17
3.30
4.73

Mixed Tracking
Indiv. Choice
Early Tracking
Early Tracking
Mixed Tracking
Mixed Tracking
Nordic Inclusive
Early Tracking
Mixed Tracking
Mixed Tracking
Early Tracking
Early Tracking
Nordic Inclusive
Mixed Tracking
Mixed Tracking
Nordic Inclusive
Mixed Tracking
Indiv. Choice
Mixed Tracking
Mixed Tracking
Mixed Tracking
Early Tracking
Indiv. Choice
Nordic Inclusive
Mixed Tracking
Mixed Tracking
Mixed Tracking
Mixed Tracking
Mixed Tracking
Indiv. Choice
Early Tracking
Mixed Tracking
Mixed Tracking
Mixed Tracking
Mixed Tracking
Early Tracking
Nordic Inclusive
Mixed Tracking
Mixed Tracking
Mixed Tracking
Mixed Tracking
Mixed Tracking
Mixed Tracking
Mixed Tracking
Nordic Inclusive
Mixed Tracking
Mixed Tracking
Mixed Tracking
Mixed Tracking
Indiv. Choice
Early Tracking
Mixed Tracking
—

1
.81
0
0
1
0
.51
0
1
0
1
.44
1
0
1
1
1
1
0
1
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
1
1
1
1
0
1
1
1
1
.09
1
0
.73

UN-CRPD
Years
Rat.
of opt.
since
prot.
rat.
5
No
10
Yes
10
Yes
9
Yes
6
No
10
Yes
8
No
4
No
10
No
7
Yes
9
No
9
Yes
9
Yes
11
Yes
7
Yes
2
Yes
8
Yes
9
Yes
6
Yes
10
No
11
Yes
11
Yes
0
No
2
No
6
No
9
Yes
10
No
4
No
10
No
8
Yes
7
Yes
8
Yes
10
No
6
No
8
No
2
No
5
No
6
No
9
Yes
10
No
6
No
0
Yes
8
Yes
10
Yes
10
Yes
10
No
10
Yes
10
Yes
9
Yes
0
No
3
No
0
No
7.25
—

Analysis Strategy
In this secondary meta-analysis, first, correlation statistics for the country-level predictors arepresented. These correlations allow statements about fundamental relationships between
variables and are important for the subsequent identification of possible mediation effects.
Second, using the percentage of IEC per country as a dependent variable, hypotheses are tested
using the most popular quantitative approach to analyze country effects: OLS regressions.

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IAFOR Journal of Education: Inclusive Education

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Although many multi-country data sets contain thousands of individuals, most include rarely
more than 30 countries. With more than 50 cases (societies) the data used here offer acomparatively high number of cases and greater reliability than most other studies (see Bryan& Jenkins, 2016).
In Table 3, the different predictor groups are independently tested before they are combined in
one overall model. This way, the hypotheses are tested individually before checking which
factors best describe the differences in IEC across countries.
Finally, to ensure the reliability of results, robustness and multicollinearity checks are briefly
discussed.
Findings
Bivariate Correlation Statistics
Correlation statistics between all country-level variables are reported in Table 2. Cross-national
differences in IEC are positively correlated with Nordic (r = .279) and Individual Choice
schooling models (r = .367), and, above all, SEC in a country (r = .900), but negatively
correlated with the Mixed Tracking schooling model (r = -.278). These bivariate statistics
provide first support for hypotheses 1, 2, and 4.
Table 2: Bivariate correlation statistics(1) DV: Inclusive Education Coverage (IEC)

(1)
1.00

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9) (10)(2) Mean income (logged GDP per capita, in US$) .265 1.00
(3) National educational expenses (as % of GDP) .194 .327 1.00
(4) Nordic Inclusive Model (vs other).279 .325 .649 1.00

(5) Individual Choice Model (vs other).367 .217 -.037 -.118 1.00

(6) Mixed Tracking Model (vs other)-.278 -.466 -.425 -.457 -.413 1.00

(7) Early Tracking Model (vs other)-.164 .156 .027 -.165 -.149 -.579 1.00

(8) Standardized exit/entrance exams (vs no)-.147 -.222 -.256 -.120 .040 .157 -.132 1.00

(9) UN-CRDP: Years since ratification-.150 .074 -.013 -.142 -.189 .223 -.020 .052 1.00

(10) UN-CRDP: with optional protocol (vs no)-.075 -.038 .245 -.014 .053 -.049 .033 -.126 .378 1.00

(11) Special Education Coverage (SEC).900 .311 .231 .293 .292 -.328 -.053 -.111 -.102 -.028

Note: Significant correlations (p < .05) are printed in bold and highly significant correlations (p < .01) in bolditalics.

Correlations between the predictor variables are mostly weak to moderate. Notable positive
correlations exist between national mean income and educational expenditure; Nordic
inclusive model and mean income and educational expenditure; both UN-CRPD variables,(time since the resolutions of the convention were ratified in a country) and whether the
optional protocol was ratified at all correlate with each other; and SEC and mean income,
Nordic Inclusive and Individual Choice Models. These correlations indicate that while SEC isclearly related to the economic resources of a country, thus confirming past findings(Anastasiou & Keller, 2014; Anastasiou et al., 2018), IEC is not. Furthermore, in societies withmore egalitarian schooling systems, SEC and IEC are generally more advanced.

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IAFOR Journal of Education: Inclusive Education

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Multivariate Regressions
To explain the cross-national differences in IEC, OLS regressions test the impact of national
characteristics (Table 3). In model 1, the possible impact of economic resources on IEC istested. Results show that neither with higher mean income (logged GDP per capita) nor withlarger proportions of educational expenditure in a society the proportion of students receiving
education in inclusive settings increases, thus confirming hypothesis 1.
Table 3: OLS Regression predicting cross-national differences in inclusive education
coverage (IEC) (showing standardized beta-coefficients, N=52)
M1
Predictor groups:

M2

Economic Education
Resources System
B
p
B
p

M3

M4

M5

Political
Conviction
BpClassificationBpAll
predictors
Bp

Economic resources
Mean income(logged GDP per capita, in US$)National educational expenses(as % of GDP)
Educational differentiation andstandardization
Model of secondary education(Early Tracking Model omitted)
Nordic Inclusive Model
Individual Choice Model
Mixed Tracking Model
Standardized exit/entrance exams
yes (vs no)
Political conviction for inclusion
UN-CRDP: Years since ratification
UN-CRDP: with optional protocol
yes (vs no)
SEN classification
Special Education Coverage (SEC)

.226

-.039

.120

.015

Adjusted R²

.046

.360*
.455**
.095
-.136

.121
.199*
.126
-.085

-.142
-.022

.198

-.017

-.007
-.060

.900***.851***

.805

.809

Note: ***P<0.001; **P<0.01; *P<0.05

Model 2 then tests the influence of educational differentiation and standardization on IEC,
showing that in societies with lower educational institutional differentiation (Nordic Inclusive
or Individual Choice Models) the likelihood that students will be schooled in inclusive settings
is significantly higher than in societies with highly differentiated systems (Early Tracking
Model). Evidence for the suspected relationship between standardized examinations and IEC
is not found.
As already reported in the correlation statistics, a clear impact of the official political conviction
for inclusion as expressed in the speed of ratification of the UN-CRPD and its optional protocolcannot be confirmed (model 3).
Model 4 tests the assumptions related to the classification of SEN in a society. Results show
similar effects to those reported in the bivariate correlations, thus further supporting hypothesis 4.

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IAFOR Journal of Education: Inclusive Education

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Model 5 brings together all predictors, testing for dominant effects. Results show robust effects
for Individual Choice Model and SEC. It appears that SEC largely mediates other effects in themodel, such as those previously found for societies with the Nordic Inclusive model of
education. This model provides clear support for hypotheses 1, 2, and 4.
Robustness and Multicollinearity Checks
Given the limited number of observations, the difference in measurement of the variables fromdiverse data sources (possibility of noisy data), and the considerable correlations between
several predictor variables, robustness and multicollinearity checks are in order. First, to checkfor country outliers and influential cases which could considerably affect the results due to the
low number of cases (N = 52), robust regressions were carried out using the STATA routine
for robust regressions (rreg). Results show no indication of biased results through the selectionof countries.
Second, to make sure the estimation of coefficients is not biased due to strong correlations
between predictor variables (multicollinearity), collinearity statistics were carried out and show
a variance inflation factor (VIF) between 1.166 (standardized tests) and 2.240 (Nordic
Inclusive model). Hence, the tolerance for all measures was well beyond the critical 0.1 level(.446 to .858), wherefore multicollinearity can be ruled out for this analysis.
Discussion
The results of the OLS regressions confirmed that there are generally no higher rates ofinclusion in high-income countries or those with higher educational expenditure (hypothesis1). It is true that there is a connection between national income and the provision of special
education (Anastasiou & Keller, 2014; Anastasiou et al., 2018). However, this connection
cannot be extended to inclusive education. In fact, resources that have already been provided
and are thus available for special education can be redeployed for effective schooling ininclusive settings. This way, exclusive forms of special education, such as special schools andclasses, could be shut down, while at the same time enhancing inclusive education provision.
Ultimately, this requires conviction and the will to effectively implement and expand inclusive
education in a country.
Analyses also showed that less differentiated education systems generally have higher rates ofinclusion (Hypothesis 2). Institutional differences and underlying institutional logics in relation
to selection criteria clearly affect the degree to which education for all is implemented. Even
though no evidence was found that standardization in the form of central exams results in lessinclusive schooling, the basic principles of inclusion seem to gain more recognition in countries
with less stratified education systems than in those with more stratified systems.
In contrast, neither the earlier ratification of the UN-CRPD nor the additional ratification of
the optional protocol, contrary to the assumptions of Hypothesis 3, lead to a noticeable increase
in IEC. This implies the possibility of decoupling educational practices from fixed politicalgoals. The effectiveness of already established special education support systems may actually
prevent or at least slow down the implementation of inclusive education. Whether the politicalconviction regarding the absolute necessity of inclusion in school is sufficiently well coveredby the two indicators chosen remains questionable though. In future work, more reliableindicators need to be used, such as national directives and reform plans with concretely
formulated milestones.

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The general SEC of a country is the most powerful influencing factor. The extraordinarilystrong effect of the coefficient as well as the high R2, which alone explains 80% of the variance,are proof of the decisive connection between the inclusion rate and the general belief in theimportance of special needs education in general. In countries with a higher proportion ofschoolchildren with certified SEN, after the adoption of the UN-CRPD, a higher proportion ofinclusively schooled schoolchildren can be found on average.
Taking into account the results of the bivariate correlations, an important mediation effect canbe isolated: SEC mediates significantly the effects of the secondary schooling model. Thissuggests that the cross-national variation in IEC is not only highly linked to SEC in a country,
but that higher SEC and IEC are generally more common in societies with lower educational
institutional differentiation (e.g., societies employing the Nordic Inclusive or Individual Choice
Models).
For future research, it will become increasingly important to clarify whether increasing
inclusive education rates are actually due to improved conditions and organizational change or
due to the softening of the SEN classification.
Conclusion
The findings of this work lead to the following conclusions. Firstly, the theoretical approach
employed in this analysis, the new institutionalism, proved beneficial for the understanding of
the institutional development and establishment of inclusive education based on the societal
recognition of “Education for All” as a common good. The further development of education
systems towards this goal largely depends on determined educational policies, which can only
have success if politics and public commonly value and push inclusive education. Since the
UN-CRPD and other similar agreements are examples of forced isomorphism, the danger of
decoupling the set goal of education for all from educational practices in school is likely. So
long as school systems are characterized by high educational institutional differentiation andits immanent institutional logic, a fully inclusive education system will not be achieved. Hence,
whether a shift towards full inclusion becomes reality largely depends on how willing policymakers and societies at large are to change their education systems, focusing stronger on
equality instead of competition, stratification and excellence. Comprehensive education
reforms targeting the whole education system are necessary to enable fully inclusive education.
Second, and connected to this point, the cross-national differences in inclusive education are
not least due to what is officially recognized as SEN and promoted in an education system. The
national differences in the classification or attribution of what is considered SEN are highly
problematic and often serve to overlook students with actual need for inclusive schooling. The
results of this work thus call for an international measure of SEN and corresponding
international standards and subsequent education reforms targeting the general education
system.
Third, this analysis also shows how different factors generally assumed to be influential (e.g.,
economic resources, political conviction) were found to exert no effect on the provision ofinclusive education. Additional financial expenses due to the change from an exclusive to an
inclusive school system are not necessarily required. Instead, policy makers need to create legal
and administrative options for a dovetailing and reallocation of existing resources. Politicalpostulates must ultimately be followed by action in order to fully establish inclusive education.

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In sum, the findings of this analysis are a good start for future endeavors in macro-sociologicaland educational analyses of international inclusive education. The quite challenging collectionof data to meet the international standard definition of inclusive education was and remains a
prerequisite for this kind of comparative analysis. Future studies should extend the range ofcountries, consider different and other country-level measures as well as multi-level modelingto connect macro- with micro-level data, and stronger concentrate on the collection andanalysis of trend-data.

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Corresponding author: Steve Entrich
Contact email: entrich@uni-potsdam.de40