Equity in education means that all students, regardless of background, should have the opportunity to fulfil their potential. This chapter discusses equity in student performance by looking at four dimensions of student background: socio-economic status, gender, geographic location and language spoken at home. Socio-economic disadvantage remains the key factor driving inequitable access to quality education in the Eastern Partnership (EaP) region, though other factors also play a significant role in some EaP economies. In addition, the chapter analyses how student background relates to their expectations and sense of belonging at school. The third and last section examines equity in access to human, material and digital resources at school, as well as to pre-primary education.
Education in the Eastern Partnership: Findings from PISA
Abstract
Equity in education is a fundamental goal of education policy. It means that all students, regardless of background, should have the opportunity to fulfil their potential.
Students have no control over circumstances such as their family socio-economic status, gender or language background. Education systems are therefore considered equitable when student outcomes (such as performance, well-being or expectations for future education and careers) are independent of or not strongly related to background circumstances. Furthermore, all students need access to quality educational resources to achieve equity in education. Equity requires that all students, especially disadvantaged students and those with special learning and socio-emotional needs, receive sufficient support so that they have a fair chance to realise their full potential.
In this chapter, the first section looks at equity in student performance. The second explores equity regarding students’ future expectations and their sense of belonging at school. The third examines equity in terms of access to human and material resources at school and learning time through pre-primary education. The full scope of the chapter is shown in Figure 3.1.
The analysis of equity focuses on mathematics performance, the main subject assessed in the OECD Programme for International Student Assessment (PISA) 2022. Several education systems participating in PISA, such as Finland and Japan, are recognised for their high equity. Both countries combine high average performance with low disparities influenced by students’ socio-economic backgrounds. In this chapter, these countries are considered aspirational benchmarks. Additionally, other benchmarks are highlighted where relevant to provide context for the data concerning Eastern Partnership (EaP) countries and economies1, such as countries that have shown the most significant improvement.
EaP countries/economies have smaller socio-economic gaps in performance than on average across OECD countries but this is partly explained by the low performance of socio-economically advantaged students. These socio-economic gaps in performance have remained relatively stable over time.
When it comes to gender gaps, girls tend to perform better in Baku (Azerbaijan) and Georgia than in Moldova and Ukrainian regions (18 of 27). In Baku and Georgia, girls perform on par with boys in mathematics and excel significantly in reading. By contrast, in Moldova and Ukrainian regions, girls underperform boys in mathematics and outperform boys in reading less than in Baku and Georgia. However, there is room to improve the extent to which EaP countries/economies take advantage of girls’ skills in adult economic life. While academic performance in school translates into higher enrolment rates in tertiary education among women, the reverse is found when it comes to women’s labour market participation.
Other relevant findings emerging from this chapter include the following:
Stark gaps in performance by geographic location in favour of students in urban (compared to rural) schools, particularly in Moldova, but also Georgia and Ukrainian regions.
Stark differences in educational aspirations by gender in favour of girls (except for Moldova) and by socio-economic background in favour of advantaged students.
More pronounced shortages in educational materials than in human resources, with variation in the specific types of shortages that are most acute in different systems; also, some improvement over time for material resource shortages in Georgia and Moldova but increased concerns in Ukrainian regions in the context of war.
Large variation in overall pre-primary attendance of 15-year-olds, with the highest in Moldova, increasing but still extremely low in Baku. Pre-primary attendance is positively associated with student performance at age 15 in EaP countries/economies, except in Ukrainian regions, but students’ socio-economic status largely explains this association. Advantaged students are more likely to have attended pre-school in all EaP countries, with particularly large differences in Georgia and Ukrainian regions.
This section considers four dimensions of student background: socio-economic status, gender, geographic location and language spoken at home. These factors are crucial because they influence the educational opportunities available to students in EaP countries and economies. However, they do not shape these opportunities equally or in the same way. Some dimensions may be more influential in creating inequities in student learning outcomes in certain countries or economies. Understanding these differences is important for policy makers who aim to prioritise student needs effectively.
Equity-related policies might want to focus on factors most strongly associated with student performance. One way to set priorities among socio-economic, gender, geographic and linguistic equity dimensions is to compare the performance gaps related to each. For instance, how does the gap in PISA test scores between socio-economically advantaged and disadvantaged students compare to the gender gap between girls and boys? Additionally, how do these gaps compare to students in urban versus rural schools or between students who speak different languages at home and at school?
As shown in Table 3.1, socio-economic disadvantage is the primary factor associated with inequitable learning outcomes in the EaP region. In each EaP country/economy, the gap in mathematics performance between advantaged and disadvantaged students is substantial (between 50 and 85 score points).2 By comparison, the relationship between other equity dimensions and student performance is weaker or inconsistent across EaP countries and economies.
The gender gap in reading performance is also notable: all EaP countries and economies have a medium (between 10 and 29 score points) or large (between 30 and 49 score points) gap in favour of girls in reading. However, the gender gap in mathematics is less of an issue: it is not significant in Georgia and Moldova and is small (lower than 10 points) in favour of girls in Baku. In contrast, boys largely overperform girls in Ukrainian regions. These variations show that addressing equity in education requires different strategies in different countries.
The performance gap favouring urban over rural schools is also large or very large in all EaP countries/economies with available data.3 Finally, the language spoken at home is significantly related to learning outcomes only in Georgia and Moldova.
The following sections will examine these dimensions in greater detail to explore how they affect learning outcomes.
Size of the performance gap by dimensions of student background
Notes: The performance gap is the score-point difference in the PISA test between two groups of students. All performance gaps in the table refer to mathematics, except for the gender gap in reading.
.. : Missing value or not available.
1. All differences in reading are in favour of girls.
Very large gap - Score difference between 50 and 85 points.
Large gap - Score difference between 30 and 49 points.
Moderate gap - Score difference between 10 and 29 points.
Small gap - Statistically significant difference of less than 10 points.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Socio-economic status and student resilience
The socio-economic gap in student performance in PISA 2022
Socio-economic disparities in student performance can be measured by comparing the average mathematics performance of students from socio-economically advantaged and disadvantaged backgrounds. This difference is called the “socio-economic gap”. A smaller gap indicates less disparity in performance between these two groups, while a larger gap indicates greater disparity.
Students from advantaged backgrounds in all EaP countries/economies are less well-off than the average student in OECD countries (see Table 1.3 in Chapter 1). Those from disadvantaged backgrounds are worse off than the average OECD student. Disadvantaged students in Moldova are worse off than the other three EaP countries/economies. In Ukrainian regions, disadvantaged students are comparatively better off. The socio-economic status of advantaged students is similar across all four EaP countries/economies.
Looking at differences in mathematics performance by socio-economic background, the socio-economic gap is at least 50 score points in mathematics in EaP countries/economies. This is smaller than average across OECD countries, where the gap is over 90 points. Indeed, the gap in EaP countries/economies is similar (in Moldova and Ukrainian regions) or smaller (in Baku and Georgia) than in highly equitable systems such as Finland and Japan (Figure 3.2).
The low performance of advantaged students in EaP countries/economies can partly explain the comparatively small socio-economic gap. Advantaged students in EaP countries/economies have a higher socio-economic status than the average OECD student.4 For this reason, one would expect advantaged students in EaP countries/economies to score higher than the OECD average. However, advantaged students in Baku, Georgia and Moldova score below this threshold. The exception is Ukrainian regions, where the performance of advantaged students is not significantly different from the OECD average.
Mean performance in mathematics in PISA 2022, by national quarter of socio-economic status
Notes: Socio-economic status is measured by the PISA index of economic, social and cultural status (ESCS).
Countries and economies are ranked in ascending order of score difference in mathematics between students in the top and bottom quarters of national socio-economic status.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Trends over time in the socio-economic gap in student performance
The socio-economic gap in student performance over time can be analysed for Baku, Georgia and Moldova. Analysis for Ukraine is not possible due to reduced participation in PISA 2022 (see Box 1.3 in Chapter 1 for details).
In Baku, Georgia and Moldova, the socio-economic gap in mathematics remained unchanged from PISA 2018 to PISA 2022, as shown in Figure 3.3. This trend is also seen in most other countries with available data, including Japan. In contrast, the gap increased on average across OECD countries and in 12 countries/economies, including Finland. It only decreased in five countries.
Socio-economic gaps can shift based on changes in the performance of both advantaged and disadvantaged students. This can lead to the gap narrowing, widening or remaining the same. For example, the gap increased on average across OECD countries and Finland because disadvantaged students’ performance declined more than advantaged students. While the reasons for this are unclear and PISA data do not allow for strong conclusions to be drawn, one reason might be that disadvantaged students found it more difficult to adapt to remote learning during the COVID-19 pandemic. Among the five countries where the performance gap narrowed, this occurred in Chile and the United Arab Emirates because the performance of advantaged students declined while the performance of disadvantaged students remained the same.
Among EaP countries/economies, the socio-economic gap did not change for various reasons. In Georgia and Moldova, the performance of both advantaged and disadvantaged students remained stable between 2018 and 2022, so the socio-economic gap did not change. In Baku, both disadvantaged and advantaged students experienced a decline of 25 score points in mathematics during this period. This is more than the equivalent of 1 year of school learning, estimated at around 20 score points. The socio-economic gap stayed the same since both groups declined by the same score points. The decline in disadvantaged students’ performance could also partly explain why a larger proportion of students in Baku failed to achieve basic proficiency levels in PISA 2022 compared to 2018 (as analysed in Chapter 2, see Figure 2.4).
Change between 2018 and 2022 in mean performance in mathematics, by national quarter of socio-economic status
Notes: Statistically significant differences are shown in a darker tone.
The difference in mathematics performance between advantaged and disadvantaged students (i.e. the socio-economic gap) changed significantly between 2018 and 2022 in Finland and the OECD average-35. The change in the socio-economic gap is not statistically significant in Baku, Georgia, Japan and Moldova.
OECD average-35 refers to the average across OECD countries, excluding Costa Rica, Luxembourg and Spain.
The PISA index of ESCS measures socio-economic status. Socio-economically advantaged students are those among the 25% of students with the highest values on the ESCS in their own country or economy. Socio-economically disadvantaged students are those among the 25% of students with the lowest values on the ESCS index in their country or economy.
Countries and economies are ranked in descending order of the mean score in mathematics of socio-economically disadvantaged students in 2022.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Resilient students
PISA categorises academically resilient students as those who achieve scores in the top quarter of their national performance metrics despite being in the bottom quarter of their country or economy’s ESCS index. This classification underscores their educational success in the face of socio-economic challenges. Resilience might be shaped by a range of factors, such as individual determination and a growth mindset, as well as supportive structures and environments (OECD, 2011[2]; 2021[3]).
As pointed out above, disadvantaged students in EaP countries/economies face greater levels of adversity (i.e. socio-economic disadvantage) than their OECD counterparts. However, a similar or higher proportion of students in EaP countries/economies demonstrate academic resilience than the OECD average (Figure 3.4). Baku and Georgia have a slightly higher percentage of disadvantaged students who are academically resilient in mathematics than Finland and Japan. However, there are still systems with an even higher share of resilient students, such as Albania and Kosovo (more than 15%). In Moldova and Ukrainian regions, the percentage of academically resilient students is the same as the OECD average.
Percentage of socio-economically disadvantaged students who scored in the top quarter of mathematics performance in their own country/economy
Notes: The PISA index of ESCS measures socio-economic status.
Countries and economies are ranked in ascending order of percentage of resilient students.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Gender
Gender gaps in education can have long-term consequences for the personal and professional lives of both girls and boys. For boys, reading difficulties might hinder their further education or employment opportunities. For girls, their under-representation among top achievers in science and mathematics partly explains their under-representation in science, technology, engineering and mathematics (STEM) careers (OECD, 2015[4]). For PISA data, the “gender gap” is analysed as the score difference between boys and girls in average performance.
In mathematics, the gender gap generally favours boys, who tend to outperform girls in most OECD and PISA-participating countries. Ukrainian regions also show this pattern, with a difference in mathematics performance in favour of boys. Boys outperform girls in Ukrainian regions by 10 points, which is similar to the gap in favour of boys observed in OECD countries and Japan (Figure 3.5).
Conversely, in Georgia and Moldova, boys and girls perform at about the same level in mathematics, on average. This is similar to 24 other countries and economies that took part in PISA 2022, including Bulgaria, Greece, Romania and Türkiye.
Baku is 1 of the 17 countries or economies that took part in PISA 2022 where girls outperformed boys in mathematics. In Baku, this marks a reversal from PISA 2018, when boys outperformed girls. Since then, although both boys and girls saw a decline in their mean scores, the drop was larger for boys (30 points) than for girls (15 points) (Figure 3.6).
Mean performance in mathematics and reading in PISA 2022, by gender
Notes: Statistically significant differences between boys and girls are shown in a darker tone. All differences in mean scores between boys and girls are statistically significant except for Georgia and Moldova in mathematics.
Countries and economies are ranked in ascending order of the score difference between boys and girls in mathematics.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Change between 2018 and 2022 in mean performance in mathematics, by gender
Notes: Statistically significant differences are shown in a darker tone.
OECD average-35 refers to the average across OECD countries, excluding Costa Rica, Luxembourg and Spain.
Countries and economies are ranked in descending order of the mean score in mathematics for girls in 2022.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Looking at Georgia and Moldova, there was no significant change in mathematics performance by gender between PISA 2018 and PISA 2022 in either country. In Georgia, both boys and girls maintained their performance levels. In Moldova, boys’ performance did not change, while girls’ performance declined. However, this decline was insufficient to cause a significant change in the gender gap in this country.
These trends in EaP countries/economies differ from those observed across OECD countries, where the gender gap in mathematics favouring boys widened slightly. This was due to a steeper decline in girls’ scores than boys’ scores between 2018 and 2022.
When it comes to reading, the pattern in all EaP countries/economies mirrors that of OECD countries: girls significantly outperform boys. This gap is especially large and wider in Baku and Georgia than in OECD countries, even if some, such as Finland, have even larger gaps (Figure 3.5).
Overall, in Baku, girls perform better than boys in mathematics and reading. Conversely, in Georgia and Moldova, girls and boys perform at similar levels in mathematics and girls outperform boys in reading. In Ukrainian regions, girls underperform boys in mathematics and their advantage in reading is less pronounced than in Baku and Georgia.
Geographic location
As highlighted in Chapter 1, a large share of EaP country/economy students go to school in rural areas, except for Baku. The share of rural students is particularly high in Georgia and Moldova. Indeed, both countries are among the ten countries participating in PISA 2022 with the highest share of rural students. Moldova has the highest share after Uzbekistan (Figure 3.7). By contrast, only five countries, including Japan, have a similar or lower share of rural students than Baku. Since the share of rural students is very low in Baku, the following analysis focuses on Georgia, Moldova and Ukrainian regions.
Percentage of students attending schools in rural areas in PISA 2022
Notes: Rural areas are places with fewer than 3 000 people.
Countries and economies are ranked in ascending order of percentage of students attending schools in rural areas.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
In most countries and economies participating in PISA, students in urban areas perform above those in rural areas. This holds true for the EaP countries/economies as well. In Moldova, which has the highest share of rural students among EaP systems, the gap in average mathematics performance between rural and urban students is particularly large (70 score points). This gap is one of the largest globally, although it is still lower than in France, where the gap is 85 points. The rural-urban performance gap in Ukrainian regions is also considerable at 49 points. In Georgia, the gap is similar to the OECD average (Figure 3.8).
Much of this performance difference by geographic location is explained by the higher socio-economic status of urban students and the more advantaged socio-economic average profile of urban schools rather than intrinsic differences in student capability or education quality. After accounting for students’ and schools’ socio-economic profiles, students in urban and rural schools perform similarly in mathematics in most countries and on average across OECD countries. This is also the case in Georgia and Ukrainian regions. This indicates that rural students perform as well as urban students on a level socio-economic playing field.5 By contrast, in Moldova (as is also the case in France), the difference in favour of urban students holds even after accounting for socio-economic factors. This suggests that other factors also explain differences in performance between urban and rural students (Figure 3.8). While the reasons for this persistent gap in Moldova are not fully clear, they seem partly driven by the small size of rural schools, as analysed in the next paragraphs.
Difference in mathematics performance between students in urban and rural schools
Note: The share of students attending school in a rural area is displayed on the x-axis. For Baku, the sample is too small for rural areas.
Statistically significant differences in mathematics performance between students in urban and rural schools are shown in a darker tone. The difference after accounting for students’ and school’s socio-economic profile is not significant in the OECD average, Georgia, and the Ukrainian regions (18 or 27).
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
School size is an important dimension to consider when analysing performance differences between rural and urban schools. While smaller rural schools may benefit from smaller class sizes, more personalised attention and closer-knit communities, they can also face challenges, such as difficulties in managing multigrade classrooms and limited opportunities for peer learning among teachers.
School sizes differ greatly across countries and a large school in one country might be considered small in another (OECD, 2018[5]). To compare school sizes across countries, schools can be divided into three groups based on national quartiles: the bottom quarter as small schools, the top quarter as large schools and the middle half as medium-sized schools. This approach reveals that small schools in EaP countries/economies are generally smaller than the average small school across OECD countries. Among EaP countries/economies, the average number of students in small schools is lowest in Georgia and Moldova (fewer than 150 students per rural school in each) and largest in the Ukrainian regions (177 students per rural school). In contrast, the OECD average for small schools is 278 students. The average school size of large schools ranges from 1 275 students in Moldova to 1 735 in Baku, compared to an OECD average of 1 396 students (calculations based on 2022 PISA database (OECD, 2022[1])).
Looking at the distribution of students across schools of different sizes by location shows that in rural secondary schools, a large majority of students attend small schools, with very few attending large schools. This is observed in all EaP countries/economies (except Baku, where data are not available) and, on average, across OECD countries (Table 3.2). Conversely, in urban areas, most students in EaP countries/economies and OECD countries attend medium-sized or large schools. For example, more than 70% of rural students in Georgia attend small schools, whereas only 4% of urban students do so.
Percentage of students in small, medium or large schools, by school geographic location
Notes: Cells coloured in grey indicate statistically significant differences between urban and rural schools. For example, the percentage of students enrolled in medium-sized schools is significantly different between urban and rural areas in France but not in Finland or EaP countries/economies.
Small schools are in the bottom quarter of the national distribution of school size, calculated as the total number of students in a school. Medium‑sized schools are in the second and third quarters of the national distribution of school size. Large schools are in the top quarter of the national distribution of school size.
The sample size of students in rural areas is too small to provide reliable estimates in Baku (Azerbaijan).
Countries and economies are shown in descending order of the percentage of rural students attending small schools.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
How, then, does student performance in mathematics differ by school size? On average across OECD countries, students in large schools outperform their peers in medium-sized and small schools in mathematics, even after accounting for students’ and school’s socio-economic profiles. This pattern is reflected in Moldova but not in Georgia and Ukrainian regions. In Moldova – a country with persistent rural-urban inequities in educational outcomes – students in small schools (more common in rural areas) perform worse in mathematics than those in medium-sized and large schools. Medium-sized schools also perform below large schools in the country. These performance differences persist after accounting for socio‑economic factors, with one exception: students in small and medium‑sized schools perform similarly when socio-economic differences are considered.
In Ukrainian regions, small schools perform similarly to medium-sized schools but both perform worse than large schools. However, after accounting for socio-economic profiles, small schools in Ukrainian regions perform significantly better than medium-sized and large schools, and the performance advantage of large schools over medium-sized schools also disappears. In Georgia, large schools perform better in mathematics than small schools but there are no significant differences in mathematics performance by school size once socio-economic factors are considered.
Difference in mathematics performance between students in large, medium-sized and small schools, before and after accounting for students’ and schools’ socio-economic profile
Notes: Significant score changes are shown in a darker tone.
Small schools are in the bottom quarter of the national distribution of school size, calculated as the total number of students in a school. Medium‑sized schools are in the second and third quarters of the national distribution of school size. Large schools are in the top quarter of the national distribution of school size.
Countries and economies are ranked in ascending order of score difference in mathematics performance between students in large and small schools before accounting for students’ and schools’ socio-economic profile.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Language spoken at home
As mentioned in Chapter 1, linguistic diversity is an important factor of diversity in the education systems of EaP countries/economies. The percentage of students speaking a different language at home compared to the one they sat their PISA assessment is slightly above 10% in Baku and Ukrainian regions, and just under 10% in Georgia and Moldova (Table 1.3 in Chapter 1). On average, across OECD countries, 11% of students speak a different language at home than at school.
In Georgia and Moldova, students who speak a different language at home than at school perform lower in mathematics than those who speak the same language at home and school. This amounts to a 41-point difference in Georgia, a gap similar to Finland and the OECD average but larger than in some high-performing countries such as Singapore. In Moldova, the difference in mathematics performance is smaller (14 points) (Figure 3.9). By contrast, there is no significant difference in mathematics performance in Baku and Ukrainian regions based on language background.
Mean performance in mathematics in PISA 2022, by language spoken at home
Note: The share of students speaking a different language at home than at school is displayed on the x-axis.
Statistically significant differences in mathematics performance between students who speak and those who do not speak at home the language of the PISA assessment are shown in a darker tone. In Baku (Azerbaijan) and Ukrainian regions (18 of 27) the difference is not significant.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
This picture changes slightly once the socio-economic profiles of students and schools are accounted for (Figure 3.10). Performance differences by language spoken at home continue to be significant in Georgia and Moldova, although they become smaller in Georgia and larger in Moldova. Students with different language backgrounds also continue to perform similarly in Ukrainian regions. By contrast, in Baku, students who speak the same language at home and school perform better once socio‑economic factors are accounted for. This is because in Baku, students who speak a different language at home have a higher average socio-economic status than students who speak the same language at home as at school.
Difference in mathematics performance between students who speak and those who do not speak at home the language of the PISA assessment
Note: Countries and economies are ranked in ascending order of score difference in mathematics performance between students who speak and those who do not speak at home the language of PISA assessment after accounting for students’ and schools’ socio-economic profiles.
Statistically significant differences in mathematics performance between students who speak and those who do not speak at home the language of the PISA assessment are shown in a darker tone.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Educational expectations
As discussed in Chapter 2, educational expectations among 15-year-old students in EaP countries/economies have risen considerably. The exception is Ukrainian regions, where the share of students expecting to complete tertiary education dropped. How do these expectations differ by students’ socio-economic background and gender?
In all EaP countries/economies, socio-economically advantaged students are more likely to expect to complete a tertiary degree than their disadvantaged peers. This is also observed on average across OECD countries. The gap in expectations is particularly large in Moldova and Ukrainian regions and smaller in Baku and Georgia compared to the OECD average. In Moldova, the gap is only slightly smaller than in Korea, which has the largest difference among OECD countries. In countries like Singapore and Türkiye, the socio-economic gap in educational expectations is narrower than in Baku (Figure 3.11).
Difference in the percentage of students who expect to complete tertiary education, by socio-economic status and gender
Notes: Student socio-economic status is measured by the PISA index of ESCS. The advantaged-disadvantaged differences are the differences between the top and the bottom quarter of the ESCS index.
Statistically significant differences in the percentage of students who expect to complete tertiary education are shown in a darker tone. All differences are significant except for those between girls and boys in Moldova.
Countries and economies are ranked in ascending order of the difference in the percentage of advantaged and disadvantaged students who expect to complete tertiary education.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Girls generally have higher educational aspirations in all EaP systems, reflecting again the picture observed in OECD countries. In Baku, the gender gap in favour of girls in terms of expectations is smaller, similar to the gap in Singapore. It matches the OECD average in Georgia and Korea, while it is somewhat larger in Ukrainian regions. Notably, in Moldova, there is no significant difference in educational expectations between girls and boys.
Contrasting these educational expectations with actual opportunities in higher education and the labour market, girls achieve their aspirations at least in tertiary education but less so when it comes to work. According to a recent report by the World Economic Forum, enrolment in tertiary education is greater among women than men in all EaP countries (WEF, 2023[6]). In Moldova, the share of women enrolled in higher education is 21 percentage points greater than the share of men; in Georgia, it is 12 percentage points greater and in Azerbaijan, only 7 percentage points greater.
However, EaP countries/economies vary regarding economic participation and opportunity. Moldova shows remarkably high levels of gender equality in labour force participation, relatively even for men and women compared to other countries. Both Georgia and Azerbaijan, by contrast, show a larger gender gap in favour of men in labour force participation rates.
Sense of belonging at school
Students’ sense of belonging at school varies widely between countries. In PISA, this is measured by students’ responses to statements about their school experience, combined into an index (also see Chapter 2). The average score on this index across OECD countries is zero, meaning a positive value indicates a stronger sense of belonging than the average OECD student.
On average, boys report a stronger sense of belonging at school than girls across OECD countries, as well as in Finland and Japan. This gender gap in favour of boys is also observed in three out of the four EaP countries/economies (Figure 3.12). In Georgia and Moldova, boys score positively on the sense of belonging index, while girls score negatively, similar to the pattern seen in Finland. In Baku, both boys and girls score negatively but boys still report a stronger sense of belonging than girls. In Japan, both genders report a stronger sense of belonging than the average OECD student, with boys feeling even more connected than girls.
PISA index of sense of belonging at school, by student gender and socio-economic status
Notes: Statistically significant differences in PISA index of sense of belonging at school are shown in a darker tone. All differences are significant except those between girls and boys in Ukrainian regions (18 of 27).
Socio-economically advantaged (disadvantaged) students are those among the 25% of students with the highest (lowest) values on the ESCS in their own country or economy.
1. The PISA economic, social and cultural index measures socio-economic status.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
However, this picture is not universal. In Ukrainian regions, there is no statistically significant difference between boys and girls in their sense of belonging: both report a weaker sense of belonging compared to the OECD average.
Socio-economic status is consistently related to students’ sense of belonging across EaP countries/economies and OECD countries. On average, students from more advantaged backgrounds feel a stronger sense of belonging at school than disadvantaged students. This gap is particularly pronounced in Ukrainian regions. In Georgia and Moldova, the gap is similar to the OECD average. In Baku, the gap is smaller: both advantaged and disadvantaged students report a relatively low sense of belonging but disadvantaged students feel even less connected.
Transforming educational funding into high-quality resources for schools is crucial for educational achievement. This includes not only hiring qualified and motivated staff and supporting them in forming strong professional communities but also providing adequate facilities and materials (OECD, 2018[5]; 2019[7]).
PISA collects data on both human, material and digital resources in schools. Human resources refer to teachers and support staff, while material resources encompass educational materials and school infrastructure. PISA 2022 asked school principals about the extent to which different resource shortages hinder instruction at their schools. The findings from this analysis are mostly indicative of the resource situation in secondary schools, as are findings from the report overall. However, depending on the context, schools may offer lower levels of education as well. In this case, reports on resource shortages might also indicate shortages in earlier levels. Throughout, the analysis compares the overall resourcing of schools as reported by principals relative to OECD countries and selected benchmarking countries, and differences between different types of schools. The section also analyses how resources are associated with mathematics performance, as measured by PISA.
In addition, this section delves into the time resources that 15-year-old students were provided with as part of their cumulative learning experience that influenced their PISA scores. To do so, the section analyses students’ attendance in pre-primary education.
Teacher availability and qualification
Teachers and other educational staff are arguably the most important resource for school systems. However, schools worldwide are grappling with teacher shortages (OECD, 2019[7]). The importance of staff resources for student learning and well-being is also borne out of analysis of PISA data. Across education systems, PISA 2022 results show that high-performing education systems are staffed with high‑quality teaching and non-teaching staff in sufficient numbers (OECD, 2023[8]).
PISA uses an index of staff shortages based on school principals’ responses to four key statements concerning the lack of staff and the qualifications of teaching and assisting personnel. An index mean of zero indicates the average level of staff shortages across OECD countries; values above zero suggest more significant shortages than average across OECD countries.
Figure 3.13 shows that school principals in Baku report the most severe shortages in educational staff among EaP countries/economies. The level of concern is higher than the OECD average, similar to those reported by principals in Italy and the Netherlands*6 and lower than in nine countries/economies, including Saudi Arabia (shown in the figure). Previous research has warned about the declining status of the teaching profession in Azerbaijan and other countries in the Caucasus regions; for example, students entering pre‑service teacher education programmes have lower results on university examinations compared to other higher education programmes, thus further undermining the prestige of the teaching profession and the quality of education (Silova, 2009[9]).
PISA index of shortage of education staff, based on principals’ reports
Notes: Higher values in the index indicate greater shortages of education staff.
Countries and economies are ranked in ascending order of the index of shortage of education staff.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
In Baku, the biggest issue appears to be shortages in the number of staff, though concerns about teacher qualifications are also high (Table 3.4). In terms of what type of staff are lacking, shortages are more pronounced for teachers than for assisting staff: 59% of students are enrolled in schools whose principal reported that the school’s capacity to provide instruction is hindered to some extent or a lot by a lack of teaching staff.
Principals in Georgia reported the least shortages of human resources among EaP countries and economies, comparable with systems like Qatar, Singapore and Switzerland. Only six systems had lower levels of concern, including Bulgaria, as measured by the index of staff shortages. In Georgia, concerns are more about the qualifications and adequacy of teaching staff rather than their numbers. This is likely related to the nature of the teacher labour market, with a large supply of teachers who continue teaching beyond retirement. In TALIS 2018, Georgia had the oldest teaching population out of any country that participated in the survey (OECD, 2019[10]). An OECD policy review of the country found that the high share of older teachers limits the availability of full-time teaching posts, especially those available for new entrants, and reduces teachers’ real salaries (Li et al., 2019[11]). Georgia, however, reports more of a shortage of assisting staff: 22% of students are enrolled in schools whose principal reported that the school’s capacity to provide instruction is hindered to some extent or a lot by the inadequate or poorly qualified assisting staff.
Moldova and Ukrainian regions both experience modest levels of staff shortages, which are less severe than the OECD average and similar to countries like Brazil, Iceland and Thailand. In both countries, the primary issue is the lack of teaching staff rather than concerns about their qualifications or the availability and quality of assistant staff: the share of students who are enrolled in schools whose principal reported that the school’s capacity to provide instruction is hindered to some extent or a lot by a lack of teaching staff is 38% in Moldova and 30% in Ukrainian regions.
Percentage of students in schools whose principal reported that the school’s capacity to provide instruction was hindered to some extent or a lot by the following
Note: Countries and economies are ranked in descending order of the lack of teaching staff.
40% or more
More than 20% and less than 40%
20% or less
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
No significant disparities in staff shortages are found between advantaged and disadvantaged schools, or between urban and rural schools in any EaP country or economy. Additionally, school principals’ perception of shortages of education staff does not correlate with student mathematics performance in EaP countries/economies. This differs from what is observed on average across OECD countries and might indicate relatively weak teaching effectiveness.
Across OECD countries, both concerns about a lack of teaching staff and inadequate or poorly qualified teaching staff worsened from PISA 2018 to PISA 2022. Concerns increased the most for the lack of teaching staff. Such trends can be analysed for Georgia and Moldova. In both these countries, no statistically significant changes can be observed.
Educational materials and digital resources
Educational materials and physical infrastructure
Providing appropriate facilities and materials is essential for teachers to fully utilise their teaching skills and create effective learning environments with their students. For example, outdated infrastructure or low‑quality textbooks can harm students’ teaching and learning (OECD, 2018[5]). In PISA, as with the approach used for evaluating human resource shortages, principals are asked how much shortages and poor quality of educational materials and physical infrastructure obstruct learning. Table 3.5 provides the detailed results for each of these aspects, which are again integrated into an index measuring overall shortages in educational materials. The index could not be calculated for Moldova because of data limitations.
The situation with educational materials and resources in EaP countries/economies stands in contrast to the availability and quality of human resources. While, except for Baku, shortages of education staff are less severe across EaP countries/economies than in OECD countries, shortages of educational materials are more acute. The most severe shortages, as perceived by school principals, are found in Ukrainian regions, followed by Baku. Georgia experiences the least severe problems among EaP countries/economies, as measured by the index (Figure 3.14). These shortages concern both a lack of material and inadequate or poor-quality educational material (Table 3.5).
Ukrainian regions rank among the top ten countries in PISA with the most significant material shortages. In only six countries/economies, including Morocco, school principals reported greater shortages of educational materials than in Ukrainian regions. The situation in Baku is comparable to that in Portugal and Thailand, while the reports of principals in Georgia are similar to those in the Slovak Republic and Uruguay.
Unlike human resources, there are also apparent equity issues related to material resources in some EaP countries/economies. Shortages are more pronounced in rural areas than in cities within Georgia and Ukrainian regions, and in disadvantaged schools compared to advantaged ones in Georgia. In Baku, analysing the rural-urban disparities is not meaningful due to the small proportion of rural students.
PISA index of shortage of material resources, based on principals’ reports
Notes: Higher values in the index indicate greater shortages of educational materials.
Countries and economies are ranked in ascending order of the index of shortage of educational material.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Looking at changes in specific dimensions of shortages in material resources, the situation has improved in some respects in Georgia and Moldova and deteriorated in Ukrainian regions. Between 2018 and 2022, the lack of physical infrastructure and the quality of educational materials deteriorated in Ukrainian regions. As shown in Figure 3.15, the decline in educational materials was particularly severe, yet the share of students in schools where principals reported that a lack of physical infrastructure hinders instruction also increased considerably (by more than 10 percentage points). This illustrates the challenges created by the ongoing war, which has strained resources, crowded schools with internally displaced students and damaged or destroyed educational facilities (Human Rights Watch, 2023[12]).
In Georgia, concerns about the lack of physical infrastructure have decreased since 2018, indicating some improvement in this area (Figure 3.15). However, 40% of students in Georgia are still enrolled in schools where the principal reports that the lack of physical infrastructure hinders instruction (Table 3.5). In Moldova, concerns about the lack of educational materials have also diminished. However, 44% of students in Moldova are still enrolled in schools where the principal reports that the lack of educational materials hinders instruction, highlighting that problems persist (Table 3.5).
Percentage of students in schools whose principal reported that the school’s capacity to provide instruction was hindered to some extent or a lot by the following
Notes: ICT: Information and communication technology.
Countries and economies are ranked in descending order of the lack of educational materials.
40% or more
More than 20% and less than 40%
20% or less
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Change between 2018 and 2022 in the percentage of students in schools whose principal reported that the school’s capacity to provide instruction is hindered to some extent or a lot by the lack of or poor-quality educational materials and physical infrastructure
Notes: Significant differences between 2018 and 2022 are shown in a darker tone.
Countries and economies are ranked in ascending order of the change in lack of physical infrastructure between 2018 and 2022.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Digital resources
Awareness of the potential benefits and risks of using digital resources in education has grown globally, especially since the COVID-19 pandemic. PISA 2022 measured both the availability and quality/adequacy of digital resources such as desktop or laptop computers, Internet access, learning management systems or school learning platforms. On average, across OECD countries, about a quarter of students were enrolled in schools whose principal reported that a lack of digital resources hinders the school’s capacity to provide instruction. A similar proportion attended schools where the inadequacy or poor quality of the digital resources hinders instruction.
As shown in Figure 3.16, availability and quality/adequacy shortages are more pronounced in socio‑economically disadvantaged schools than advantaged schools and schools located in rural areas than in urban schools, on average across OECD countries. Mexico is an OECD member country that exemplifies such a pattern. In Finland, however, the availability and quality of digital resources are not significantly different between schools of different socio-economic profiles and geographic locations.
Among EaP countries/economies with the available data, inequities in the availability and quality of digital resources are large in Georgia. In Baku and Moldova, no differences between advantaged and disadvantaged schools are observed.
Percentage of students in schools whose principal reported a lack of digital resources or inadequate or poor-quality digital resources, by school socio-economic profile and geographic location
Notes: Digital resources include desktop or laptop computers, Internet access, learning management systems and school learning platforms.
Statistically significant differences are marked in a darker tone.
For Baku, the sample size is too small for rural areas.
Countries and economies are ranked in ascending order of the percentage of students in schools whose principal reported a lack of digital resources.
1. The PISA index of ESCS measures the socio-economic profile. Socio-economically advantaged (disadvantaged) students are those among the 25% of students with the highest (lowest) values on the ESCS index in their own country or economy.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
A lack of or poor-quality digital resources is linked to lower mathematics performance in EaP countries and economies, similar to OECD countries (Figure 3.17). In Ukrainian regions, the association between shortages in digital resources and lower scores is particularly strong. In Baku and Moldova, the link is significant mainly for poor-quality digital resources and this relationship is also stronger than on average across OECD countries but less so than in Lithuania. In Georgia, the lack of digital resources primarily correlates with lower performance, again more strongly than typically seen across OECD countries.
However, after accounting for the socio-economic profile of students and schools, the association disappears in all cases in EaP countries/economies. This shows that, in EaP countries/economies, students in schools with more shortages of digital devices score lower because they come, on average, from families of lower socio-economic status. Also, disadvantaged schools have disadvantages in other aspects such as teacher shortage, materials resources, etc. As PISA 2022 results at the system level also suggest, higher-performing systems ensure that every student has access to a digital device (computer or tablet). Still, the availability of these devices does not, in itself, indicate their capacity to enhance teaching and learning (OECD, 2023[8]). The use of digital devices in schools and classrooms is further analysed in Chapter 4.
Change in mathematics performance associated with principals reporting that the school’s capacity to provide instruction is hindered to some extent or a lot by the shortage of digital resources
Note: Significant score changes are shown in a darker tone.
Countries and economies are ranked in ascending order of the change in mathematics performance associated with principals reporting that a lack of digital resources hinders the school’s capacity to provide instruction.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Pre-primary education attendance
Pre-primary education has increasingly become a standard and often mandatory phase in students’ educational paths (OECD, 2017[13]). In all EaP systems, children typically start pre-primary education at the age of three, according to system-level data from PISA, although only in Moldova are years in pre‑primary education compulsory (Figure 1.3, Chapter 1). According to students’ reports, the actual attendance in pre-primary education varies widely between EaP countries/economies (Figure 3.18).
In Moldova, 95% of 15-year-olds attended pre-primary school for at least 1 year in PISA 2022. In Moldova, pre-primary education today is compulsory for four years. This is longer than in most other systems and is typical in OECD countries, where pre-primary education usually lasts three years.
In Baku, only about 60% of today’s 15-year-old students have attended pre-primary education. This is among the lowest rates among PISA-participating countries, similar to Cambodia, Kazakhstan and North Macedonia. Students in Georgia and Ukrainian regions both report higher pre-primary education attendance rates, comparable to those from Croatia and the Philippines.
Percentage of students who had attended pre-primary education for at least a year
Notes: All differences between 2018 and 2022 in the percentage of students who reported they attended pre-primary education for at least a year are statistically significant.
Countries and economies are ranked in descending order of the percentage of students who had attended pre-primary education in 2022.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Between 2018 and 2022, attendance in pre-primary education has increased in EaP economies. Ukrainian regions experienced the largest improvement with 10 percentage points. For comparison, in Türkiye, a country with one of the largest improvements, pre-primary attendance, as reported by students, increased by 13 percentage points. In Baku, pre-primary attendance increased by 7 percentage points and, in Georgia and Moldova, by 3 percentage points each (Figure 3.18). Whereas pre-primary attendance in Georgia and Ukrainian regions lagged behind Albania in 2018, it now surpasses pre-primary attendance in that country. Albania had the largest drop in pre-primary attendance between 2018 and 2022, demonstrating that gains can also be reversed.
Disparities in access to pre-primary education by socio-economic status and geographic location
The socio-economic gap between advantaged and disadvantaged students in pre-primary attendance is wider in Georgia and Ukrainian regions than on average across OECD countries. Yet, it is narrower than in Türkiye and other countries/economies (Figure 3.19). The gap in pre-primary attendance by socio‑economic status is less pronounced in Moldova, which aligns with the OECD average.
Difference in the percentage of socio-economically advantaged and disadvantaged students1 who had attended pre‑primary education for at least a year
Notes: All differences are statistically significant.
1. The PISA index of ESCS measures the socio-economic profile. Socio-economically advantaged (disadvantaged) students are those among the 25% of students with the highest (lowest) values on the ESCS in their own country or economy.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Georgia also shows large urban-rural differences in pre-primary attendance, a gap not observed in other EaP countries/economies. As noted in Chapter 1, 26% of the PISA sample in Georgia comes from rural areas. The disparities in attendance between urban and rural areas in other countries can often be attributed to differences in demand and supply, as well as varying family models in these settings (Echazarra and Radinger, 2019[14])
Difference in the percentage of students enrolled in urban and rural schools who had attended pre-primary education for at least a year
Notes: Statistically significant differences are coloured in a darker tone. The difference is not significant in Moldova and Ukrainian regions (18 of 27).
Baku is not included in the figure as the sample size in rural areas is too small.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Access to pre-primary education and student performance at age 15
Pre-primary attendance is positively associated with student performance at age 15 in all EaP countries/economies except Ukrainian regions. This association is particularly strong in Georgia and Moldova. As highlighted above, pre-primary attendance in Ukrainian regions increased substantially between 2018 and 2022.
However, the performance advantage linked to pre-primary attendance is less pronounced in EaP countries/economies than in OECD countries. In Figure 3.21, Belgium represents the OECD country with the strongest association and Korea the OECD country with the weakest. Moreover, once the socio‑economic profiles of students and schools are accounted for, any performance differences due to pre-primary attendance are not observed in EaP countries. This shows that disadvantaged students were more likely not to attend pre-primary education and also that these students tend to perform lower. By contrast, on average, pre-primary attendance is positively associated with student performance across OECD countries even after accounting for socio-economic factors.
Change in mathematics performance when students had attended pre-primary school for at least one year, compared to having attended for less than a year
Notes: Statistically significant differences are coloured in a darker tone.
Countries and economies are ranked in descending order of the change in mathematics performance before accounting for student ESCS.
Source: OECD (2022[1]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
References
[14] Echazarra, A. and T. Radinger (2019), “Learning in rural schools: Insights from PISA, TALIS and the literature”, OECD Education Working Papers, No. 196, OECD Publishing, Paris, https://doi.org/10.1787/8b1a5cb9-en.
[12] Human Rights Watch (2023), “Tanks On the Playground”: Attacks on Schools and Military Use of Schools in Ukraine, https://www.hrw.org/sites/default/files/media_2023/11/ukraine1123web_0.pdf.
[11] Li, R. et al. (2019), OECD Reviews of Evaluation and Assessment in Education: Georgia, OECD Reviews of Evaluation and Assessment in Education, OECD Publishing, Paris, https://doi.org/10.1787/94dc370e-en.
[8] OECD (2023), PISA 2022 Results (Volume II): Learning During – and From – Disruption, PISA, OECD Publishing, Paris, https://doi.org/10.1787/a97db61c-en.
[1] OECD (2022), PISA 2022 Database, OECD, Paris, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
[3] OECD (2021), Sky’s The Limit: Growth Mindset, Students, and Schools in PISA, OECD, Paris, https://www.oecd.org/content/dam/oecd/en/about/programmes/edu/pisa/publications/national-reports/pisa-2018/brochures/Sky-s-the-limit-pisa-growth-mindset.pdf.
[10] OECD (2019), TALIS 2018 Results (Volume I): Teachers and School Leaders as Lifelong Learners, TALIS, OECD Publishing, Paris, https://doi.org/10.1787/1d0bc92a-en.
[7] OECD (2019), Working and Learning Together: Rethinking Human Resource Policies for Schools, OECD Reviews of School Resources, OECD Publishing, Paris, https://doi.org/10.1787/b7aaf050-en.
[5] OECD (2018), Responsive School Systems: Connecting Facilities, Sectors and Programmes for Student Success, OECD Reviews of School Resources, OECD Publishing, Paris, https://doi.org/10.1787/9789264306707-en.
[13] OECD (2017), Starting Strong 2017: Key OECD Indicators on Early Childhood Education and Care, Starting Strong, OECD Publishing, Paris, https://doi.org/10.1787/9789264276116-en.
[4] OECD (2015), The ABC of Gender Equality in Education: Aptitude, Behaviour, Confidence, PISA, OECD Publishing, Paris, https://doi.org/10.1787/9789264229945-en.
[2] OECD (2011), Against the Odds: Disadvantaged Students Who Succeed in School, PISA, OECD Publishing, Paris, https://doi.org/10.1787/9789264090873-en.
[9] Silova, I. (2009), “The crisis of the post-soviet teaching profession in the Caucasus and Central Asia”, Research in Comparative and International Education, Vol. 4/4, pp. 366-383, https://doi.org/10.2304/rcie.2009.4.4.366.
[6] WEF (2023), Global Gender Gap Report 2023, World Economic Forum.
← 1. The EaP countries/economies forming part of this report are Baku in Azerbaijan, Georgia, Moldova and 18 of the 27 regions in Ukraine. Any reference to EaP countries/economies, as well as the EaP average, specifically pertains to Baku, Georgia, Moldova and Ukrainian regions. Armenia is also part of the European Union EaP but has not yet participated in PISA, although participation is underway for PISA 2025.
← 2. The socio-economic gap in EaP countries/economies is not larger than in OECD countries, as mentioned later in the chapter. Nevertheless, the point here is that, within each EaP country/economy, the socio‑economic gap in student performance is larger than the gender gap, the geographic gap and the language gap.
← 3. In Baku, this analysis is not possible because sample of rural students is too small.
← 4. In EaP countries/economies, the value of the ESCS index among socio-economically advantaged students is 0.7 or higher. On average across OECD countries, the value of the ESCS index for all students is 0.0.
← 5. The regression model used in this section (see Figure 3.10) is the following: the outcome variable is student performance in mathematics and the predictor of interest is the geographic location of the students’ school (i.e. the school is in a rural area, town or urban area). To account for the potential confounding effect of socio-economic factors, two variables were included as control variables: one that measures the student socio-economic status (student value in ESCS index) and one variable for the school socio‑economic profile (the average ESCS index across all students in the student’s school). After accounting for socio-economic factors, the performance difference between students in urban and rural schools in Ukrainian regions changes in favour of rural students by 12 points but the difference is not statistically significant. This change in the sign of performance difference is due to the socio-economic advantage of urban students and urban schools relative to rural students and rural schools; when rural and urban students and schools of similar socio-economic profile are compared, the performance difference disappears. In Ukrainian regions in PISA 2022, the average socio-economic status of students enrolled in rural schools is -0.78 points in the ESCS index, whereas the average socio-economic status of students in urban schools is -0.11 points. Furthermore, rural schools’ average socio-economic profile of is ‑0.78 points in the ESCS index and urban schools’ average socio-economic profile is -0.09 points.
← 6. Some countries and economies, including the Netherlands, struggled to meet PISA technical standards for student sampling in PISA 2022. These specific countries and economies are marked with an asterisk throughout this report. See Box 1.1 in chapter 1 for details.