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sheds light on how education systems vary in terms of the distribution of basic learning
opportunities vis-a-vis various social groups in the country. Decomposition of ‘human
opportunity index’ (HoI) into its component parts reveals that household wealth, parental
education, and city size explainmost of the cross-country differences in inequality in educational
opportunities. Public spending on education also helps reduce inequality of opportunity
(Balcazar, Narayan, and Tiwari 2015). To be precise, there is a positive relationship between
public expenditure per student in primary and secondary as a % of GDP per capita and the
percentage of students at or above level 2 of proficiency. However, higher spending on tertiary
education has a negative impact. In terms of country specific results, evidence onMalaysia based
on PISA 2012 identifies parental wealth and education as the main driver of inequality of
learning opportunities (regardless of the study subject) (Balcazar, Narayan, and Tiwari 2015).
Urban residency accounts for 23.4%, 12.6% and 18.4% of the inequality in math, reading and
science respectively. Pre-school attendance is also an important source of inequality of
opportunity in Malaysia. Evidence indicates that it accounts for 10%, 7.8% and 7.7% of the
inequality in math, reading and science scores in PISA 2012 (Balcazar, Narayan, and Tiwari
2015).
The country-specific patterns based on estimates of inequality of educational opportunity
reported in Balcazar, Narayan, and Tiwari (2015) are similar to those in resilience student share
(Figure 2.27). The estimate of the inequality of opportunity index is lowest in case of Turkey
and very high in case of Qatar, Kazakhstan and Jordan. Indonesia and Malaysia also have a
relatively high level of IoE when compared to the average for the OECD. Interestingly, two
member states, Qatar and UAE, has very low share of resilient students despite very high level
of per capita income. Student achievement (in PISA 2012) in these two countries is also
characterized by a high level of IoE.
Overall, Figures 2.21- 2.25 document the low level and significant economic disparities in the
level of student achievement in OIC countries compared to the rest of the world. While family
wealth (or poverty) is strongly linked to low scores, it is not destiny. Indeed in Kazakhstan,
pupils from the poorest wealth group outperform their peers from the wealthiest group in
Jordan, Albania, United Arab Emirates and Tunisia in level-1 proficiency in PISA math (Figure
2.25). The overall poor performance of two of the wealthiest OIC member states, United Arab
Emirates and Qatar, in PISA also weakens the role of family wealth in explaining performance
difference. While children in individual OIC member countries do differ in terms of family
wealth, the education system in some countries fail children from all wealth groups while in
others, it enables children from all groups to excel.
In section 2.2.5, the levels of key correlates of learning outcomes, as identified in the conceptual
framework in section 1, are analyzed jointly in a statistical model and compared between OIC
and non-OIC countries.
2.1.5. Student Achievement in Low Income OIC Countries
The majority of the economically poorer OIC and non-OIC countries do not participate in
international assessments. Emerging global evidence on these countries confirm that schooling
is not the same as learning (UNESCO 2014, WDR 2018). This is further complicated by the fact
the millions are not in school in many OIC countries. The full set of estimates of learning profiles,
the empirical relationship between years of schooling completed and gains in learning, for OIC
countries is not available. However, evidence emerging for countries such as Pakistan,
Bangladesh and Afghanistan (Asadullah and Chaudhury 2015; Asadullah, Alim and Hossain,