Out-of-school rates at upper secondary education have fallen for most OECD countries from an average of 8% in 2013 to 7% in 2022.
For most OECD and partner countries, less than 5% of lower secondary students are more than two years older than the intended age for their grade.
There are wide differences in the share of students with minimum proficiency in mathematics according to socio-economic status and immigration background. In comparison, gender differences are small across OECD countries.
Education at a Glance 2024
OECD Indicators
Education at a Glance
In 2015, at the United Nations General Assembly, member states renewed their commitment to global development by adopting the 2030 Agenda for Sustainable Development. The Agenda is divided into 17 Sustainable Development Goals (SDGs), and constitutes a universal call for action to end poverty, protect the planet and ensure that all people enjoy peace and prosperity. The fourth Sustainable Development Goal (SDG 4) is dedicated to education and aims to “ensure inclusive and equitable quality education and promote lifelong learning opportunities” by 2030 (UNESCO, 2016[1]).
Unlike previous global targets, such as the Millennium Development Goals (United Nations, 2015[2]), SDG 4 focuses on the quality of education, with indicators related to teacher training and student outcomes, alongside more traditional measures of quantity, such as access and participation. It also emphasises the importance of learning at all stages of life, by investigating education at all levels (from early childhood education and care to tertiary education) and adult learning. This chapter builds on a selection of SDG 4 indicators to investigate gender differences in participation at school, looking at gender disparities in enrolment at different levels of education, out-of-school rates, and students who are over the intended age for their grade. It also considers equity in outcomes through differences in mathematics performance across a number of equity dimensions, and information and communication technologies (ICT) skills among young people and adults by gender and locality.
The gender gap on participation in vocational education and training varies widely among OECD and partner countries. Italy, New Zealand, Norway and Poland are among the countries where the share of 15-24 year-old men who are in vocational education surpasses the share among 15-24 year-old women by at least 8percentage points.
There are wide gender differences in ICT skills, with the share of men who reported having installed and configured software 20% higher than the share of women on average.
However, in many countries the differences in ICT skills between urban and rural locations are also wide, with inhabitants of urban regions generally reporting higher ICT skills.
Indigenous adults are less likely to have achieved at least an upper secondary education than non-Indigenous adults in all the countries analysed.
This chapter focuses partly on Target 4.5 of the SDG 4, which calls for the elimination of inequalities in education. The analysis below builds on selected SDG 4 indicators to investigate equity in participation in education and in learning outcomes. Global Indicator 4.5.1 sets the parity index as the main measure of inequity in education within the SDG 4 agenda. This indicator provides a wide scope for measuring inequity, as it is meant to be applied to all other SDG 4 indicators with available data and can be used to measure equity along several dimensions. This chapter presents a number of parity indices for a number of different indicators. Due to data availability, it only analyses four dimensions of equity: gender, immigration and socio-economic status (measured using the index of economic, social and cultural status (ESCS), and locality (rural/urban). Box 1 discusses equity in school participation for First Nations and Indigenous population among OECD countries.
Among OECD and partner countries, there are small differences between boys and girls in participation in organised education before the start of primary school (Figure 2). The widest gap is found in South Africa, where 70% of boys are enrolled in pre-primary education compared to 57% of girls. The difference is smaller but important in Brazil and Indonesia, where the proportion of boys enrolled in pre-primary is at least 4 percentage points higher than that of girls (Table 1).
There is much wider variation in the participation rates in technical or vocational education among people aged between 15 and 24 (Figure 2). On average across OECD countries, around 17% of young people in this age group take part in technical or vocational education programmes. Participation rates in Austria, Czechia, Poland and Slovenia are substantially above the OECD average, at over 25%. In contrast, 5% or less of 15-24 year-olds in Argentina, India and Saudi Arabia participate in technical or vocational education (Table 1).
There are also considerable gender differences in participation rates. On average across OECD countries, young men aged 15-24 are 4 percentage points more likely to participate in technical or vocational education than young women. This difference is particularly considerable in Italy, New Zealand, Norway and Poland where the share of men doing so is at least 8 percentage points higher than the share of women. In Iceland and India, young men are approximately twice as likely to participate in technical or vocational programmes as their female counterparts. This trend is even more pronounced in Saudi Arabia, where the participation rate in such programmes among 15-24 year-old men is more than four times higher than among women, although the rates remain small (Table 1).
As mentioned in Chapter B4, which discusses differences in access and outcomes of tertiary education, over the years there has been a gender reversal in the participation at tertiary level. In almost all OECD and partner countries the share of women enrolled in tertiary education is higher than the share of men (Figure 2). The gender gaps in gross enrolment rates in tertiary education are close to parity only in India, Japan, Türkiye and Luxembourg (Table 1).
One way to capture student’s participation of studies is by measuring the out-of-school rate, which is defined as the percentage of children in the official age range for a given level of education who are not enrolled in school (SDG Indicator 4.1.5).
In most countries, the proportion of boys of upper secondary age who are not in school is higher than the proportion of girls. In Croatia, Mexico and South Africa, this share is at least 5 percentage points higher for boys than for girls. This difference is even more important in Croatia and South Africa, where boys are at least three times more likely to drop out of upper secondary education than girls. Contrary to these countries, in Bulgaria and Indonesia, the proportion of girls out of upper secondary education is 4 percentage points higher than for boys (Table 1).
Out-of-school rates at upper secondary level have fallen for most countries from 2013 to 2022, but there are some exceptions. The out-of-school rates rose by 4 percentage points in Bulgaria over this period and by nearly 8 percentage points in Romania. The rate also increased in Germany, Hungary, Iceland, Latvia and New Zealand, by 2 percentage points or more (Figure 1). In 2022, the out-of-school rate at upper secondary is above 30% in India and Mexico despite having fallen in both countries since 2013. Considering the size of these countries’ populations, particularly in India, this corresponds to a considerable fraction of the number of students who are out of school globally (Table 1).
A few countries have seen the trend of falling out-of-school rates reverse over the period. In Australia, Mexico and New Zealand, although the out-of-school rates fell between 2013 and 2019 by more than two percentage points, between 2019 and 2022 the proportion of young people not enrolled in upper secondary education increased by 3 percentage points in Australia and New Zealand and 5 percentage points in Mexico. This might be partly due to school disruptions and uncertainty caused by the COVID-19 pandemic, which could have slowed the earlier progress made by these two countries in reducing the number of young people out of school. In Australia, the pandemic has had a negative impact on school attendance among socio-economically disadvantaged secondary students (Tomaszewski et al., 2022[3]). However, a different trend is seen in Brazil, Peru, South Africa or Switzerland, where the out-of-school rate was at least 5 percentage points higher in 2019 than in 2022 (Figure 1). Government initiatives to tackle the disruptions of the pandemic have included implementing school-based mechanisms to track vulnerable student groups not returning to school and providing financial incentives such as cash, food or transport, or waived school fees to encourage vulnerable students to return to school. For instance, this last measure was implemented in Costa Rica, Estonia, Poland, Portugal, Hungary, Spain and Türkiye (OECD, 2021[4]).
The percentage of lower secondary students who are two years older than the intended age for their grade is one of the SDG 4 indicators that helps to assess whether girls and boys are completing free, equitable and quality primary and secondary education leading to relevant and effective learning outcomes. Students might be over-age because they entered school later than their country’s theorical school starting age, or because they had to repeat grades at school (see Chapters B2 and B3).
For most OECD and partner countries, less than 5% of lower secondary male and female students are two years over-age for their grade (Figure 3). For some countries the share is close to zero, as it is the case in Iceland, Ireland, Korea, New Zealand and Sweden. Boys tend to be slightly more likely than girls to be over-age at lower secondary school, more notably in South Africa and some Latin American countries. South Africa has the highest share of over-age boys in lower secondary, with almost half of all boys at least two years older than expected for their grade. The second highest is Colombia, where the share of boys over-age for their grade is 24%, compared to 17% of girls. Boys also tend to be over-represented among those repeating grades (Chapter B2). In countries where the end of compulsory education corresponds to the end of lower secondary programmes (Chapter B2), over-age students may drop out of school before they complete their lower secondary education.
SDG Target 4.5 aims to ensure vulnerable groups have equal access to all levels of education and vocational training, including those individuals belonging to Indigenous populations or to First Nations. However, many OECD countries record disparities between Indigenous and non-Indigenous populations in the attainment of at least upper secondary education among 25-64 year-olds (OECD, 2019[5]).
The concept of Indigenous peoples is complex. The term “Indigenous” varies in meaning depending on the context, it evolves over time, and can differ across and within countries. This leads to divisions within Indigenous societies, challenges in collecting statistics, and impacting public policy effectiveness. International conventions, such as the ILO’s Indigenous and Tribal Peoples Convention 169, have been formative in developing global definitions, emphasising self-identification as a fundamental criterion. This convention identifies Indigenous groups as those distinguished by social, cultural, and economic conditions or descent from pre-conquest populations, retaining unique social, economic, cultural, and political institutions. Most OECD member and selected partner countries incorporate the ILO Convention 169 framework in their legal and statistical definitions of Indigenous peoples (OECD, 2019[5]).
Indigenous adults are less likely to have achieved at least an upper secondary education than non-Indigenous adults in all the countries analysed (OECD, 2019[5]). The United States has a highly educated Indigenous population and a small attainment gap, with 79% of the Indigenous population having upper secondary attainment, 9 percentage points lower than among the non-Indigenous population. Mexico has a low upper secondary attainment rate among the Indigenous population (23% compared to 40% for non-Indigenous population). Australia has a large attainment gap (39 percentage points) (Figure 4). Educational attainment disparities between Indigenous and non-Indigenous populations may be even greater at tertiary levels in some countries, as it is the case in the United States (National Center for Education Statistics, 2023[6]).
These outcomes represent a disadvantage for Indigenous Peoples to access “knowledge economy” jobs in the future. Indeed, fundamental skills (such as literacy and numeracy) along with high-level communication, interpersonal and problem-solving skills are valued in the labour market. Upper secondary education is therefore fundamental for Indigenous adults to acquire the skills necessary to access the labour market, health and general well-being (OECD, 2019[5]).
In Australia, some progress is seen in closing the attainment gap in upper secondary education. Upper secondary attainment rate of Indigenous Australians aged 20 to 24 increased by around 21 percentage points, from around 45% in 2008 to 66 % in 2018–19. The proportion of non-Indigenous students attaining year 12 or equivalent increased by around 5 percentage points. This has narrowed the gap by 15 percentage points (Australian Government, 2020[7]). Some of the measures implemented by the Australian government to retain Indigenous students in education comprise teacher training, the adaptation of school curricula to Indigenous histories and cultures, and school funding loadings to support Indigenous students (UNESCO, 2019[8]).
The OECD Programme for International Student Assessment (PISA) provides insights about students’ performance at the age of 15. As such, it is used to monitor SDG Indicator 4.1.1, which measures the proportion of children and young people achieving at least minimum proficiency level at the end of secondary education (i.e. Level 2 or above in the PISA context) in reading and mathematics. The release of PISA 2022 focuses mainly on mathematics and includes results from almost 90 countries (including from PISA for Development (OECD, 2023[9])). The indicator is calculated using the PISA index of economic, social and cultural status (ESCS), gender and immigration status (OECD, 2023[10]).
The proportion of students who achieve at least the minimum proficiency in mathematics is higher among those in the top quartile of the ESCS index compared to those in the bottom one. Brazil and Peru have the largest gaps, while the differences in Estonia and Japan are the smallest among OECD and partner countries (Figure 5).
There is variation among OECD countries on mathematics proficiency according to students’ immigration status, but there is no uniform pattern (Figure 5). In Indonesia and Mexico, the proportion of students with an immigrant background achieving at least PISA Level 2 is at least 80% lower than for students without an immigrant background. In contrast, in Australia, Canada, Hungary and Saudi Arabia, a greater share of students with an immigrant background achieve at least minimum proficiency than those without (Table 2).
In contrast with the other two dimensions, the gender gap for minimum proficiency in mathematics is small. Among OECD and partner countries, those from Latin America have a wide gender gap, in favour of male students. In Bulgaria, Finland and Korea, the proportion of girls with at least minimum proficiency in maths is higher than that of boys by at least 3 percentage points (Figure 5).
Target 4.4 aims to increase the number of young people and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs and entrepreneurship (UNESCO-UIS, 2024[11]). Information and communication technologies (ICT) skills have become necessary to succeed, but they are not evenly distributed across the population.
There are differences in the digital skills between men and women among OECD and partner countries. Although differences in ability to use tools to copy and paste in electronic documents tend to be small, the gaps widen in favour of men when it comes to creating electronic presentations, using formulas in spreadsheets, installing software and writing computer programmes (Figure 6For example, 41% of men in Japan have used presentation software compared to only 26% of women. Similarly, gender differences in computer programming skills are large in Austria, Luxembourg, Sweden and Switzerland (Table 3).
When it comes to the downloading, installation and configuration of software among young people and adults in OECD countries, there are clear differences between men and women and also between urban and rural populations (Figure 7.). On average among OECD and partner countries with available data, the share of men who report having applied this skill is 17% higher than the share of women. But some countries have wider gaps based on locality. On average, the share of people in rural areas who applied this skill is more than 20% lower than those from urban areas. In Colombia, only 3% of those in rural areas report having installed any software, the lowest share among OECD countries, compared to 19% of those in urban areas. Luxembourg is the only country where the locality index favours those in rural areas, as well as having the narrowest difference, with 57% of people in urban areas reporting software installation skills compared to 59% in rural areas (Table 3).
There are many initiatives to address gender digital skill gaps. In Colombia and Costa Rica, there are co-operatives and foundations working to increase the visibility of women’s experiences in the digital sector. In Mexico, the Laboratoria Coding AC provided job-oriented digital skills education to women from vulnerable backgrounds. Focused on job placement, this organisation’s bootcamp programme has reached more than 1 000 applicants and worked with the technology sector to increase diversity in its recruitment and workforce (World Wide Web Foundation, 2020[13]).
Measures to bridge the digital gap between rural and urban areas have focused on providing the infrastructure needed for the use of technology. The European Union (EU) has launched Rural Digital Futures, an initiative to provide universal and affordable access to high-speed connectivity using private-sector investments with complementary funding from national or EU funds (European Union, 2024[14]).
All indicators presented in this chapter follow the agreed SDG methodology, including for recommended data sources, and may differ in some cases from other measures presented in Education at a Glance. Please see Education at a Glance 2024 Sources, Methodologies and Technical Notes for country-specific notes (OECD, 2024[15]).
The main indicator chosen to measure equity across the SDG 4 agenda is the parity index. It is defined as the ratio between the values of a given indicator for two different groups, with the value of the group most likely to be disadvantaged in the numerator. In Figures 2, 3 and 5, to measure gender parity, the numerator is girls and the denominator is boys. To measure socio-economic background parity, the numerator is students from the lowest quartile of the PISA index of economic, social and cultural status (ESCS), and the denominator is students from the highest quartile. To measure immigration status parity, the numerator is students with an immigrant background and the denominator is non-immigrants. A parity index of between 0.97 and 1.03 indicates parity between the two considered groups. A value of less than 0.97 indicates a disparity in favour of the likely most advantaged group, and a value greater than 1.03 indicates a disparity in favour of the most disadvantaged group.
The use of a parity index provides the relative magnitude of the disparity in a simple, easy-to-communicate way. However, it also has some drawbacks, such as being sensitive to low values and not being symmetrical around 1 (perfect equality). For example, if the enrolment rate is 40% for girls and 50% for boys, the gender parity index (GPI) has a value of 0.8 (UNESCO-UIS, 2010[16]). If the female and male values are reversed, the GPI has a value of 1.25, which gives the mistaken impression of greater gender disparity because 1.25 is further from 1 than 0.8. To solve this, an adjusted parity index, which is symmetrical around 1, is used in the tables and figures of this chapter whenever values for the likely advantaged and likely disadvantaged groups are switched for an observation.
For more information on measuring inequity in education, please see the UNESCO Handbook on Measuring Equity in Education (UNESCO-UIS, 2018[17]). The handbook provides a conceptual framework for measuring equity in education and offers thorough methodological guidance on how to calculate and interpret various types of equity indicators.
References
[7] Australian Government (2020), Closing the Gap 2020, Australian Government, https://ctgreport.niaa.gov.au/content/closing-gap-2020 (accessed on 21 May 2024).
[14] European Union (2024), Actions for connected rural areas, Rural Vision website, European Union, https://rural-vision.europa.eu/action-plan/connected_en (accessed on 27 May 2024).
[12] International Telecommunication Union (2024), DataHub, https://datahub.itu.int/.
[6] National Center for Education Statistics (2023), “Digest of Education Statistics”, Rates of high school completion and bachelor’s degree attainment among persons age 25 and over, by race/ethnicity and sex: Selected years, 1910 through 2023, https://nces.ed.gov/programs/digest/d23/tables/dt23_104.10.asp?current=yes (accessed on 15 May 2024).
[15] OECD (2024), Education at a Glance 2024 Sources, Methodologies and Technical Notes, OECD Publishing, https://doi.org/10.1787/e7d20315-en.
[10] OECD (2023), PISA 2022 Results (Volume I): The State of Learning and Equity in Education, PISA, OECD Publishing, Paris, https://doi.org/10.1787/53f23881-en.
[9] OECD (2023), PISA for Development, https://www.oecd.org/pisa/pisa-for-development/# (accessed on 30 May 2024).
[4] OECD (2021), The State of School Education: One Year into the COVID Pandemic, OECD Publishing, Paris, https://doi.org/10.1787/201dde84-en.
[5] OECD (2019), Linking Indigenous Communities with Regional Development, OECD Rural Policy Reviews, OECD Publishing, Paris, https://doi.org/10.1787/3203c082-en.
[3] Tomaszewski, W. et al. (2022), “Uneven impacts of COVID‐19 on the attendance rates of secondary school students from different socioeconomic backgrounds in Australia: A quasi‐experimental analysis of administrative data”, Australian Journal of Social Issues, Vol. 58/1, pp. 111-130, https://doi.org/10.1002/ajs4.219.
[8] UNESCO (2019), Indigenous Peoples’ Right to Education: Overview of the Measures Supporting the Right to Education for Indigenous Peoples reported by Member States in the Context of the Ninth Consultation on the 1960 Convention and Recommendation against Discrimination, UNESCO, https://unesdoc.unesco.org/ark:/48223/pf0000369698.
[1] UNESCO (2016), Education for People and Planet: Creating Sustainable Futures for All, Global Education Monitoring Report, UNESCO Publishing, Paris, https://doi.org/10.54676/AXEQ8566.
[11] UNESCO-UIS (2024), Metadata and Methodological Documents, https://tcg.uis.unesco.org/methodological-toolkit/metadata/.
[17] UNESCO-UIS (2018), Handbook on Measuring Equity in Education, UNESCO Institute for Statistics, Montreal, http://uis.unesco.org/sites/default/files/documents/handbook-measuring-equity-education-2018-en.pdf.
[16] UNESCO-UIS (2010), Global Education Digest 2010: Comparing Education Statistics Across the World, UNESCO Institute for Statistics, Montreal, http://unesdoc.unesco.org/images/0018/001894/189433e.pdf.
[2] United Nations (2015), We can end poverty, Millenium Development Goals and Beyond 2015, https://www.un.org/millenniumgoals/.
[13] World Wide Web Foundation (2020), Women’s Rights Online: Closing the Digital Gender Gap for a More Equal World, World Wide Web Foundation, https://webfoundation.org/docs/2020/10/Womens-Rights-Online-Report-1.pdf.
Cut-off date for the data: 14 June 2024. Any updates on data and more breakdowns can be found on the OECD Data Explorer (http://data-explorer.oecd.org/s/4s).
Note: See under Chapter SDG Tables section for StatLink and Box 2 for the notes related to this Table.
Indicator SDG 4.1.1
Note: See under Chapter SDG Tables section for StatLink and Box 2 for the notes related to this Table
Indicator SDG 4.4.1
Note: See under Chapter SDG Tables section for StatLink and Box 2 for the notes related to this Table.
Table 1. Selected SDG4 indicators, by gender (2022)
1. Year of reference differs from 2022: 2018 for China, 2021 for Argentina and South Africa.
2. Year of reference differs from 2019: 2018 for Peru.
3. Year of reference differs from 2013: 2016 for Peru.
Table 2 Share of 15-year-olds achieving at least a minimum proficiency in mathematics by the end of lower secondary education, by socio-economic background, gender and immigration status (2022)
1. Caution is required when interpreting estimates because one or more PISA sampling standards were not met (see PISA 2022 Reader’s Guide, Annexes A2 and A4).
Table 3. Share of youth and adults with information and communication technologies (ICT) skills, by gender and locality (2021)
Note: See the United Nations' Principles and Recommendations for Population and Housing Censuses (https://unstats.un.org/unsd/demographic/sconcerns/densurb/densurbmethods.htm) for a definition of urban/rural areas. See Textbox 1 for the definitions and limitations of the SDG indicator.
See Definitions and Methodology sections and Education at a Glance 2024 Sources Methodologies and Technical Notes https://doi.org/10.1787/e7d20315-en for more information.
Data and more breakdowns are available on the OECD Data Explorer (http://data-explorer.oecd.org/s/4s).
Please refer to the Reader's Guide for information concerning symbols for missing data and abbreviations.