On average across OECD countries, adults with a short-cycle tertiary degree earn 17% more than those with upper secondary attainment. This earnings advantage rises to 39% for those with a bachelor’s degree and 83% for those with a master’s or doctoral degree.
For all countries with available data, the private net financial returns for a man or a woman obtaining a bachelor's, master's, doctoral or equivalent degree are greater than from obtaining a short-cycle tertiary degree. On average, the highest private returns for a man and a woman attaining a bachelor’s, master’s or doctoral tertiary qualification are observed in Chile.
On average, among adults with upper secondary or post-secondary non-tertiary attainment, those scoring at or above Level 4 (where 5 is the highest) in numeracy proficiency in the Survey of Adult Skills (PIAAC) – meaning that they can complete tasks requiring advanced mathematical concepts – earn 31% more than those scoring at Level 2 (who are only able to apply basic mathematical concepts). This skills premium rises to 40% among tertiary-educated adults.
Education at a Glance 2025
Context
Higher levels of education are strongly associated with better employment opportunities (see Chapter A3) and higher earnings. The expectation of greater financial returns – alongside broader social benefits – motivates individuals to invest in education and training throughout their lives.
However, the earnings advantage of higher educational attainment is not uniform. For each country, it varies by age, gender, programme type and field of study. Labour-market participation also plays a key role: individuals working part time generally earn less, both in total and per hour, than their full-time counterparts. Likewise, those with more work experience tend to earn more. Despite gains in education, gender pay gaps persist across all levels of attainment and programme types.
Today, more young adults than ever before hold tertiary qualifications (see Chapter A1), and the expansion of tertiary education continues. Although the labour markets in most countries have absorbed this growing supply of highly educated workers, large differences in earnings remain, depending on the field of study. These differences may reflect varying levels of demand for specific skills across sectors, as well as structural and cultural factors. As economies evolve, education systems must ensure that graduates are equipped with knowledge and competencies aligned with both labour-market needs and broader societal goals.
Earnings disparities are also shaped by broader economic and institutional factors. In some countries with a smaller share of tertiary-educated adults, high earnings are more concentrated among this group, contributing to wider income inequality and raising concerns about social mobility. Wage outcomes are also influenced by the interplay of supply and demand for skills, minimum wage policies, labour-market regulation and institutional characteristics such as the presence of trade unions, collective bargaining arrangements and the overall quality of working conditions.
25-64 year-old full-time full-year workers, upper secondary education = 100
1. Year of reference differs from 2023.
2. Index 100 refers to the combined levels of upper secondary or post-secondary non-tertiary education
3. Includes part-time and part-year workers.
For data, see Table A4.1. For a link to download the data, see Tables and Notes section.
Other findings
The returns for a man attaining a short-cycle tertiary qualification are highest in Austria and the Netherlands while for a woman they are highest in France and Luxembourg (over USD 250 000).
A few countries have a small share of tertiary-educated adults who enjoy high relative earnings on average while in others tertiary attainment is more widespread and the differences in relative earnings are smaller. A third group of countries have both a small share of tertiary-educated adults and a low earnings premium, highlighting that there is room to improve the attractiveness of tertiary education.
On average, women earn less than men and this is true for any educational attainment level and field of study. Tertiary-educated women who studied business, administration and law earn between 10% and 33% less than their male peers, depending on the country. The gender gap across countries can reach between less than 1% and to 38% among those who studied science, technology, engineering and mathematics (STEM) fields, and between 9% and 43% for health and welfare.
Note
The analysis presents three types of relative earnings: 1) using the earnings of workers with upper secondary education as the baseline; 2) using male workers’ earnings as the baseline; and 3) using earnings of tertiary-educated workers from all fields of study as the baseline. In all cases, given the focus on relative earnings, any increase or decrease in the results could reflect a change in the interest group (numerator) or in the baseline group (denominator). Readers are advised to consider actual earnings in Tables A.A4.4 and A.A4.5 from Education at a Glance 2025 Sources, Methodologies and Technical Notes when interpreting relative earnings (https://doi.org/10.1787/fcfaf2d1-en).
Due to the difference in survey methods used to gather data from countries, the analysis of relative earnings is based on full-time full-year workers to ensure better comparability across countries. Refer to Education at a Glance 2025 Sources, Methodologies and Technical Notes (https://doi.org/10.1787/fcfaf2d1-en) for more information on the survey methods. Data on relative earnings for all workers (full- and part-time) are available for consultation on line (http://data-explorer.oecd.org/s/4s).
Earnings relative to those of workers with upper secondary attainment
Higher levels of educational attainment in general lead to higher earnings. The foundational skills, knowledge and competencies provided by upper secondary education are essential in the labour market and ensure that individuals have achieved a minimum level of literacy and numeracy, which are fundamental for most jobs. Without these basic skills, individuals are often limited to low-paying jobs, although vocational education and training pathways can also lead to stronger labour market outcomes, particularly when they are well aligned with employer needs and provide access to quality jobs.
Tertiary education is key to achieving upward economic and social mobility, enabling individuals to improve their socio-economic status through higher earnings. The in-depth knowledge and specialised skills provided by tertiary programmes make individuals more competitive in the job market. A tertiary degree also opens up a wider range of job opportunities, including those in professional and managerial roles, which typically offer higher salaries. Universities and colleges also provide opportunities for students to network with their peers, professors and industry professionals, which can lead to better job prospects and higher earnings.
The average earnings of tertiary-educated full-time full-year workers are substantially higher than those of workers with only upper secondary attainment. This earnings premium for completing a tertiary degree is 54% on average across OECD countries but individual countries have larger differences. The earnings advantage for tertiary-educated workers is 25% or less in Denmark, Norway and Sweden, but over 100% in Chile and Colombia among OECD countries and over 140% in Brazil and South Africa (Table A4.1).
Among tertiary-educated workers, the earnings advantage tends to increase with the level of attainment. In most OECD and partner countries, full-time full-year workers with a master’s or doctoral or equivalent degree earn more than those with a bachelor’s degree, who in turn earn more than those with a short-cycle tertiary degree. On average across OECD countries, adults with a short-cycle tertiary degree earn 17% more than those with upper secondary attainment, rising to 39% more for those with a bachelor’s degree and 83% more for those with a master’s or doctoral or equivalent degree. Among OECD countries, the greatest earnings advantage over upper secondary attainment for adults with a long tertiary degree is in Chile (128% more than the earnings of adults with upper secondary attainment for a bachelor’s degree and 340% more for a master’s or doctoral degree) while for a short-cycle tertiary qualification the greatest advantage is in Ireland (41% more) (Figure A4.1). The largest earnings premium among partner countries is observed at bachelor’s level in South Africa.
Earnings advantages by educational attainment tend to increase among older workers. On average across OECD countries, tertiary-educated 25-34 year-olds earn 39% more than their peers with upper secondary attainment while 45-54 year-olds earn 67% more. Within the levels of tertiary attainment, the earnings advantage of a short-cycle tertiary qualification is 10% among 25-34 year-olds, compared to 20% for 45-54 year-olds on average across OECD countries. For master’s or higher attainment, the advantage is 53% for the younger age group and 96% more for the older one (Table A4.1. ).
Investing in education has a significant impact on earning potential and employment outcomes (see Chapter A3), particularly when considering the type and level of tertiary qualifications attained. As more individuals pursue higher education, understanding the economic returns of different tertiary pathways becomes increasingly important. Box A4.1 examines the financial implications of pursuing various levels of tertiary education and highlights how these choices shape individuals’ economic trajectories.
Investing time and money in education is an investment in human capital. Better employment prospects (see Chapter A3) and higher earnings are strong incentives for adults to pursue education and postpone employment. Returns to education, however, are not limited to academic tertiary degrees. Vocational and professional programmes at the upper secondary or post-secondary level can also provide strong financial incentives, especially when aligned with labour-market needs and offering pathways to further learning or specialisation (OECD, 2020[1]).
This box provides information on the incentives for an individual to invest in education by considering three measures: private net financial returns, internal rates of return and the benefit-cost ratio. It examines the financial consequences for individuals from investing in tertiary education rather than entering the labour market with an upper secondary qualification. Specifically, the benefits to tertiary education are the difference in tertiary-educated workers’ estimated lifetime earnings from employment after paying income taxes and social contributions compared to those of individuals who enter the labour force at the typical age for completing upper secondary education. While this analysis focuses on returns to tertiary education, it does not capture the potentially high returns from other forms of human capital investment, such as professional certifications or advanced vocational programmes. This analysis also accounts for the costs of tertiary education as well as the forgone earnings while completing tertiary education (see Definitions section). It estimates the financial returns on investment in education only up to a theoretical retirement age of 64 and therefore does not take pensions into account (OECD, 2021[2]). Nor does it take into account either student loans or part-time or part-year employment. In order to account for the fact that money earned tomorrow is worth less than money today, this analysis computes the net present value (NPV) of estimated future financial flows. In the results presented below, future financial flows are discounted at 2%.
On average across the OECD, the private net financial returns to tertiary education from a full-time full-year job are USD 364 200 for a man and USD 300 900 for a woman. The private net financial returns to tertiary education are higher for men than for women in most OECD countries with available data (Table A4.5, available on line). Despite these lower returns, young women are more likely than young men to complete tertiary education (see Chapter A1). This is partially related to the fact that the differences in earnings and employment between upper secondary and tertiary educational attainment are higher for women than they are for men.
The highest returns for both men and women for all levels of tertiary attainment combined are in the United States, although Chile has the highest benefit-cost ratio and internal rate of return (i.e. the discount rate that would equalise the NPV of benefits and costs) (Table A4.5, available on line).
The returns for tertiary education can be broken down into short-cycle tertiary attainment, and bachelor's, master's and doctoral or equivalent level. The composition of the population with qualifications at each tertiary level differs between countries (see Chapter A1), and the mix of qualifications can have a significant effect on the financial returns to education for tertiary education overall. For nearly all countries with available data, the private net financial returns from obtaining a bachelor's, master's, doctoral or equivalent degree are greater than from obtaining a short-cycle tertiary degree. Although the total costs of a higher degree tend to be larger than for a short-cycle tertiary qualification, the total benefits accrued during individuals’ working lives compensate for the higher initial costs (Figure A4.2 and Table A4.5, available on line).
The returns for a man attaining a short-cycle tertiary qualification are highest in Austria and the Netherlands while for a woman they are highest in France and Luxembourg (over USD 250 000). Sometimes the earnings, employment and cost data breakdowns by level of tertiary education are misaligned and the different data sources may suffer from small sample sizes, especially for short-cycle tertiary attainment. This may explain the negative average figures in some countries, for example for men in Sweden and for women in the United Kingdom. The average returns of attaining a long tertiary qualification (bachelor’s, master’s or doctoral) are USD 394 000 for a man and USD 320 500 for a woman, with the highest returns observed in Chile for both genders (Figure A4.2 and Table A4.5, available on line).
As compared with returns to a woman attaining upper secondary education; in equivalent USD converted using PPPs for GDP; future costs and benefits are discounted at a rate of 2%
1. Year of reference differs from 2022. Refer to the OECD Data Explorer (http://data-explorer.oecd.org/s/4s) for more details.
2. Only net earnings are available, therefore calculations use these values as if they were gross earnings.
For data, see Table A4.5. For a link to download the data, see Tables and Notes section.
Calculating the financial returns of education means choosing a specific discount rate to estimate the current worth of future financial flows. The choice of discount rate is challenging, and it makes a considerable difference when analysing the returns to long-term investments, as is the case with investment in education. Table A4.6, available on line, shows how the private financial returns for men and women attaining tertiary education change when three different discount rates are used. Changing from a discount rate of 2% (assumed in the analysis above) to a rate of 3.75% reduces the NPV for men by at least 33% in all countries with available data. If a discount rate of 8% is used, the NPV falls by over 72% in all countries. These comparisons highlight the sensitivity of the NPV results to changes in the discount rate.
Distribution of earnings among workers by educational attainment
Relative earnings by educational attainment level are not only a measure of how much the labour market rewards further education, but also reflect broader patterns of income distribution and social inequality (OECD, 2024[3]). Higher relative earnings for tertiary-educated adults indicate strong individual incentives to pursue education, but they can also signal wider income inequalities, or wage dispersion – particularly when wages at the lower end of the attainment scale remain stagnant. Although education can be a powerful equaliser, unequal access and outcomes may reinforce existing socio-economic disparities (UNESCO, 2020[4]).
This trade-off is evident in countries where high earnings premiums coexist with greater income inequality. For example, Chile, Colombia and Costa Rica are among the OECD countries with the highest earnings premiums for tertiary-educated adults, as well as the highest levels of wage dispersion. Conversely, in countries with more compressed wage structures, such as the Nordic countries, the earnings advantage of tertiary education is smaller, but overall income inequality is also lower (Table A4.1).
A key indicator of education-related labour-market inequality is the proportion of individuals at each attainment level who earn significantly more or less than the median. On average across OECD countries, 28% of workers with below upper secondary attainment earn at or below half the median wage, compared to 17% of those with upper secondary or post-secondary non-tertiary education and just 10% of tertiary-educated workers. Conversely, only 26% of workers with below upper secondary attainment earn more than the median, compared to 42% of those with upper secondary or post-secondary non-tertiary attainment and 68% of tertiary-educated workers (Table A4.2).
These disparities are even more pronounced at the top of the earnings distribution. On average across OECD countries, just 3% of workers with below upper secondary attainment earn more than twice the median wage, compared to 6% of those with upper secondary or post-secondary non-tertiary attainment and 22% of tertiary-educated workers. Among OECD and partner countries, more than 40% of tertiary-educated 25-64 year-olds earn more than twice the median in Brazil, Chile, Colombia, Costa Rica and South Africa (Table A4.2).
Figure A4.3 compares relative earnings for tertiary graduates with tertiary attainment rates. In Brazil, Colombia and Costa Rica, where less than 30% of adults hold a tertiary qualification, they enjoy high relative earnings. In these countries, investing in education yields strong labour-market returns, with an earnings premium over upper secondary attainment of 99% or more. At the other end of the spectrum are countries where tertiary attainment is more widespread and the wage dispersion is lower, resulting in smaller relative earnings differences. This is the case in Australia, Canada, Korea and the United Kingdom, where more than half of adults hold a tertiary qualification, but the earnings premium is below 40% – and similarly only a small share earn over twice the median (Table A4.2). In these countries, tertiary education has become the norm and is associated with a more equitable income distribution. In contrast, in countries such as Italy and Romania, less than one-quarter of adults have completed tertiary education yet the earnings premium is no more than 41%. These countries need to make efforts to strengthen the value of tertiary qualifications in the labour market and ensure that economic conditions encourage individuals to attain a tertiary education.
25-64 year-old adults and full-time full-year workers
1. Year of reference differs from 2023.
2. Includes part-time and part-year workers.
3. Year of reference for educational attainment: 2022.
For data, see Table A4.1 and OECD Data Explorer: https://data-explorer.oecd.org/. For a link to download the data, see Tables and Notes section.
Gender disparities in earnings
Although increasing educational attainment narrows gender differences in employment rates (see Chapter A3), the gender gap in earnings does not vary much across educational attainment levels. On average across OECD countries, tertiary-educated women working full time and for the full year earn 23% less than their male peers, while those with upper secondary or post-secondary non-tertiary attainment earn 20% less and those with below upper secondary attainment earn 21% less (Table A4.3). As women are more likely to work part time or only for part of the year than men, the gender differences in earnings are wider among all workers than among full-time full-year workers (OECD, 2025[5]).
For all education levels, the gender gap in earnings widens with age up until age 54. Among full-time full-year 25-34 year-old workers, young women earn between 17% and 18% less than their male peers, depending on the level of educational attainment, while 45-54 year-old women earn between 20% and 24% less. On average, the gender gap is between 2 and 7 percentage points wider for 45-54 year-old women than for 25-34 year-old ones. However, differences across educational attainment levels vary by country and are relatively small on average (Table A4.3).
There is no single explanation for why the gender pay gap persists despite women outpacing men in educational attainment (see Chapter A1). It reflects various complex factors including occupational segregation, biased hiring practices and unequal opportunities for career advancement (World Economic Forum, 2024[6]). Women are less likely than men to be promoted or to get substantial wage increases when they change employers. Moreover, career breaks for women around the age of childbirth remain an important contributor to wage differences between men and women in many OECD countries (OECD, 2022[7]) (Rabaté et al., 2021[8]). Women are more likely to seek less competitive paths and greater flexibility at work in order to deal with their family commitments. This leads to lower earnings than men with the same educational attainment. As a result, although there have been improvements in gender pay equality, significant disparities still exist globally, with women often earning less than men for similar work due to ongoing discrimination and structural biases (ILO, 2022[9]).
Differences in earnings by field of study
A tertiary degree yields better earnings, but as Figure A4.4 shows, there are substantial differences depending on the field of study. Among the OECD countries with available data, STEM fields are most commonly associated with the highest earnings. In the United States, having a tertiary qualification in a STEM field can be associated with earnings that are up to 20% higher than the average. In other countries, different broad fields attract the highest relative earnings. Denmark and Sweden have the highest earnings premium for business, administration and law compared to other fields (19% more) while Slovenia is the country where health and welfare offers the greatest relative increase (28% more). The lowest earnings tend to be associated with qualifications in the fields of arts and humanities and of education (Table A4.4. ).
Figures on relative earnings by field of study provide important insights into the labour-market outcomes of graduates, but they should be interpreted with caution. One key limitation is that these figures do not necessarily reflect earnings within the same field of work. Although graduates from STEM fields are more likely to work in STEM-related occupations, this is less true for other disciplines. For example, education-related jobs are often filled by individuals with degrees in a wide range of subjects – including humanities, social sciences and languages – especially teachers in primary and lower secondary (OECD, 2022[10]).
The high relative earnings associated with some fields of study may relate to a potential mismatch between the supply of current graduates and labour-market needs. With rapid digitalisation, the relatively high earnings associated with an information and communication technologies (ICT) degree may reflect the imbalance between strong labour-market demand for ICT workers and the very small share of graduates who studied this field (see Chapter A1). However, labour-market demand could be met by exploring other skills that may substitute for the lack of an ICT degree. For example, using job posting data, a recent study suggests that tertiary-educated workers who had studied engineering or business management have technical skills that are suitable for filling vacancies in some ICT occupations (Brüning and Mangeol, 2020[11]).
25-64 year-old full-time full-year workers, percentage difference from average earnings (all fields)
1. Year of reference differs from 2023.
For data, see Table A4.4. For a link to download the data, see Tables and Notes section.
Disaggregating earnings advantages by narrower fields of study helps to highlight the differences that may exist within a broader field. In the OECD countries with available data, the differences in earnings across the individual STEM fields are quite small except in Estonia and Latvia, where they are primarily driven by higher employment rates for those with a degree in information and communication technologies (ICT). However, there are wide differences within the broad field of health and welfare. Although average relative earnings overall are often modest, this masks significant variation between the subfields of medical and dental, and nursing and associate health fields (Table A4.4).
Moreover, relative earnings by field of study are closely intertwined with gender patterns in higher education and the labour market. Medical degrees typically lead to high-earning careers as medical doctors, while nursing degrees, more commonly pursued by women, often lead to lower-paid positions ( (OECD, 2021[2]); (OECD, 2023[12])). In countries with strong occupational segregation, such differences may amplify gender wage gaps and influence perceptions of the value of certain fields.
Across OECD countries with available data, tertiary-educated women who studied business, administration and law earn between 10% (Costa Rica) and 33% (Latvia) less than their male peers (Figure A4.5). The gender gap ranges between less than 1% (Costa Rica) to 38% (Germany) for those with a STEM background and between 9% (the United Kingdom) and 43% (Estonia) for those who studied health and welfare. Women who studied arts, humanities, social sciences, journalism and information earn less than their male peers (up to 29% less in Portugal) in all OECD countries with the exception of Costa Rica, where they earn 27% more).
25-64 year-old full-time full-year workers; percentage difference between women's and men's earnings
1. Year of reference differs from 2023.
For a link to download the data, see Tables and Notes section.
Differences in relative earnings by level of educational attainment or field of study are metrics based on that both attainment and fields of study are proxies for skill levels and, in this case, for how well people with different skills do on the labour market. The newly-published data from the Survey of Adult Skills, a product of the OECD Programme for the International Assessment of Adult Competencies (PIAAC) (OECD, 2024[13]) sheds light on earnings differences by skill levels (Box A4.2).
Skills enable adults to perform tasks more efficiently, contributing to higher productivity and, in turn, higher wages. The link between skills and earnings is well established in economic theory and supported by empirical evidence. According to standard microeconomic theory, wages reflect workers’ productivity; individuals with higher skills are therefore expected to earn more. The first cycle of the Survey of Adult Skills (administered in 2012-17) confirmed this relationship, showing that proficiency in literacy and numeracy is positively associated with wages, even after accounting for formal educational attainment.
Although education and skills are correlated – education develops skills and individuals with greater skills tend to pursue more education – attainment in formal education fails to capture differences in programme quality and individual skill differences within levels. Moreover, returns to education may reflect not only skills but also other factors such as signalling, screening or access to restricted opportunities (OECD, 2024[13]).
Further analysis of PIAAC data has shown that while the earnings premium associated with formal education tends to decline with educational expansion, the association between skills and wages remains robust (Araki, 2020[14]). Skills also become more important later in life, as employers shift from relying on educational credentials to observing actual performance – a process known as employer learning (Hanushek et al., 2015[15]).
Findings from the 2023 Survey of Adult Skills suggests that the effects of educational attainment are greater than those of information-processing skills, although both remain positively associated with wages. This may be because formal qualifications reflect not just cognitive skills but also a broader set of competencies, including social and emotional attributes like perseverance and conscientiousness (OECD, 2024[13]).
The OECD average monthly earnings for 25-64 year-old adults with below upper secondary education range from USD 3 100 for those scoring at or below Level 1 in numeracy proficiency (those who are not able to tasks involving the application of basic mathematical concepts) to USD 4 200 for those at Level 3 (those who are able to complete tasks involving more advanced mathematical reasoning). For those with upper secondary or post-secondary non-tertiary education, average earnings range from about USD 3 300 (at or below Level 1) to USD 4 900 (at or above Level 4, which includes those who are able to complete tasks involving problem-solving with intricate mathematical information) while for tertiary-educated adults the range is between USD 4 000 and USD 6 400 by skill level. Compared with 25-64 year-olds with upper secondary or post-secondary non-tertiary attainment, adults with below upper secondary education earn between 4% less (Level 3) and 8% less (at or below Level 2), while tertiary-educated adults earn between 23% more (at or below Level 1) and 34% more (at or above Level 4) (Table A4.7, available on line).
On average across OECD countries and economies with available data, high performers in numeracy (at or above Level 4) earn 31% more than those with proficiency Level 2 among those with upper secondary or post-secondary non-tertiary attainment and 40% more among those with tertiary education. Among tertiary-educated adults with high proficiency levels, the highest relative earnings are in Estonia and Japan, with an earnings premium of at least 70%. The lowest are recorded in Korea, the Slovak Republic and the United States, with 20% or less (Figure A4.6).
At the other end of the spectrum, low performers in numeracy (at or below Level 1) earn on average 14% less than those with Level 2 proficiency among those with a tertiary education, 13% less among those with upper secondary or post-secondary non-tertiary attainment and 10% less among those with below upper secondary education (Figure A4.6).
Survey of Adult Skills; earnings of adults with proficiency skill Level 2 = 100; 25-64 year-olds
For data, see Table A4.7, available on line. For a link to download the data, see Tables and Notes section.
Adults refer to 25-64 year-olds; young adults refer to 25-34 year-olds. The analysis on financial returns to education considers the net present value of earnings over the lifetime of an individual limited to ages 16-64.
The benefit-cost ratio is total benefits relative to total costs, representing the financial benefits of attaining an additional level of education for each USD invested in it.
Direct costs are the direct expenditure on education per student during the time spent in school. Direct costs of education do not include student loans. Private direct costs are the total expenditure by households on education. They include net payments to educational institutions as well as payments for educational goods and services outside of educational institutions (school supplies, tutoring, etc.). Forgone earnings are the net earnings an individual not in education can expect.
Earnings include annual money earnings as direct payment for labour services provided, before taxes, plus work-related payments such as annual bonuses, result-related bonuses, extra pay for holidays and sick-leave pay from employer(s). Earnings do not include income from other sources, such as government social transfers, investment income, net increase in value of an owner operated business and any other income not directly related to work.
Educational attainment refers to the highest level of education successfully completed by an individual.
Fields of study are categorised according to the ISCED fields of education and training (ISCED-F 2013). See the Reader’s Guide for full listing of the ISCED fields used in this report.
Individuals with zero earnings refer to individuals who have earnings, but the result of their business activities is exactly zero.
Individuals with negative earnings refer to individuals who reported deficits in their business activities.
Gross earnings benefits are the discounted sum of earnings premiums over the course of a working-age life associated with a higher level of education. The income tax effect is the discounted sum of additional levels of income tax paid by the private individual over the course of a working-age life associated with a higher level of education. The social contribution effect is the discounted sum of additional employee social contributions paid by the private individual over the course of a working-age life and associated with a higher level of education.
The internal rate of return is the (hypothetical) real interest rate equalising the costs and benefits related to the educational investment. It can be interpreted as the interest rate an individual can expect to receive every year during a working-age life on the investment made on a higher level of education.
Levels of education: See the Reader’s Guide at the beginning of this publication for a presentation of all International Standard Classification of Education (ISCED) 2011 levels.
Net financial returns are the net present value of the financial investment in education, the difference between the discounted financial benefits and the discounted financial cost of education, representing the additional value that education produces over and above the 2% real interest that is charged on these cash flows.
The analysis of relative earnings of the population with specific educational attainment and of the distribution of earnings does not control for hours worked, although the number of hours worked is likely to influence earnings in general and the distribution in particular. For the definition of full-time earnings, countries were asked whether they had applied a self-designated full-time status or a threshold value for the typical number of hours worked per week.
Earnings data are based on an annual, monthly or weekly reference period, depending on the country. This chapter presents annual data, and earnings data with a reference period shorter than a year are adjusted. Please refer to Table A.A4.1 in Education at a Glance 2025 Sources, Methodologies and Technical Notes, for more information on the adjustment methods (https://doi.org/10.1787/fcfaf2d1-en). Data on earnings are before income tax for most countries. Earnings of self-employed people are excluded for many countries and, in general, there is no simple and comparable method to separate earnings from employment and returns to capital invested in a business.
This chapter does not take into consideration the impact of effective income from free government services. Therefore, although incomes could be lower in some countries than in others, the state could be providing both free health care and free schooling, for example. The total average for earnings (men plus women) is not the simple average of the earnings figures for men and women. Instead, it is the average based on earnings of the total population. This overall average weights the average earnings separately for men and women by the share of men and women with different levels of educational attainment.
In the earnings data, individuals with zero and/or negative earnings should be reported as earners. Individuals with negative earnings should also be considered in the calculation of the overall median earnings. However, data on individuals with zero and/or negative earnings are not available for all countries. Individuals with zero earnings are included for Belgium, Brazil, Canada, Germany, Ireland, New Zealand, Norway, Sweden, Switzerland, the Republic of Türkiye and the United States. Individuals with negative earnings are included for Belgium, Canada, Denmark, Italy, New Zealand, Norway, Spain, Sweden and the United States. Refer to the Definitions section for the definition of individuals with zero and negative earnings. Note that the share of both zero and negative earners are very low among full-time full-year workers in countries with available data, and this finding holds true when looking at the breakdown by educational attainment levels. The impact of the inclusion/exclusion of zero and/or negative earners is negligible on the relative earnings and the distribution of earnings.
For more information see the OECD Handbook for Internationally Comparative Education Statistics (OECD, 2018[16]) and Education at a Glance 2025 Sources, Methodologies and Technical Notes ((https://doi.org/10.1787/fcfaf2d1-en).
In calculating the returns to education in Box A4.1, the approach taken here is the net present value (NPV) of the investment. To allow direct comparisons of costs and benefits, the NPV expresses the present value for cash transfers happening at different times. In this framework, costs and benefits during a working-age life are transferred back to the start of the investment. This is done by discounting all cash flows back to the beginning of the investment with a fixed interest rate (discount rate). The model assumes that tax rates and social contribution rates remain at today's values. Similarly, earnings and employment rates by age and educational attainment are assumed to remain at the most recent observed values.
This chapter is based on the data collection on education and earnings by the OECD Network for data development on labour market, economic and social outcomes of education (LSO Network). The data collection takes account of earnings for individuals working full time and for the full year, as well as part time or part of the year, during the reference period. This database contains data on dispersion of earnings from work and on student earnings versus non-student earnings. The source for most countries is national household surveys such as Labour Force Surveys, the European Union Statistics on Income and Living Conditions (EU-SILC), or other dedicated surveys collecting data on earnings. About one-quarter of countries use data from tax or other registers. See Education at a Glance 2025 Sources, Methodologies and Technical Notes, for country-specific notes on national sources ((https://doi.org/10.1787/fcfaf2d1-en). Various sources have been used for Box A4.1 on financial returns to education:
The source for the direct costs of education is the joint data collection by UNESCO, the OECD and Eurostat (UOE) on finance (year of reference 2022 unless otherwise specified in the tables). The data on gross earnings are based on the earnings data collection by the OECD Network for data development on labour market, economic and social outcomes of education (LSO Network), which compiles data from national Labour Force Surveys (LFS), the EU Statistics on Income and Living Conditions (EU-SILC), Structure of Earnings Surveys, and other national registers and surveys. Earnings are age, gender and attainment-level specific.
Income tax data are computed using the OECD Taxing Wages model, which determines the level of taxes based on a given level of income. This model computes the level of the tax wedge on income for several household composition scenarios. For this indicator, a single worker with no children is used. For country-specific details on income tax in this model, see Taxing Wages 2025 (OECD, 2025[17]).
Employee social contributions are computed using the OECD Taxing Wages model scenario of a single worker aged 40 with no children. For country-specific details on employee social contributions in this model, see Taxing Wages 2025 (OECD, 2025[17]).
Data on proficiency levels and mean scores are based on the Survey of Adult Skills (PIAAC) (2012 and 2023). PIAAC is the OECD Programme for the International Assessment of Adult Competencies.
References
[14] Araki, S. (2020), “Educational expansion, skills diffusion, and the economic value of credentials and skills”, American Sociological Review, Vol. 85/1, pp. 128-175, https://doi.org/10.1177/0003122419897873.
[11] Brüning, N. and P. Mangeol (2020), “What skills do employers seek in graduates?: Using online job posting data to support policy and practice in higher education”, OECD Education Working Papers, No. 231, OECD Publishing, Paris, https://doi.org/10.1787/bf533d35-en.
[15] Hanushek, E. et al. (2015), “Returns to skills around the world: Evidence from PIAAC”, European Economic Review, Vol. 73, pp. 103-130, https://doi.org/10.1016/j.euroecorev.2014.10.006.
[9] ILO (2022), Global Wage Report 2022-23: The Impact of Inflation and COVID-19 on Wages and Purchasing Power, International Labour Organization, Geneva, https://doi.org/10.54394/ZLFG5119.
[5] OECD (2025), Education and earnings, OECD Data Explorer, http://data-explorer.oecd.org/s/4s.
[17] OECD (2025), Taxing Wages 2025: Decomposition of Personal Income Taxes and the Role of Tax Reliefs, OECD Publishing, Paris, https://doi.org/10.1787/b3a95829-en.
[13] OECD (2024), Do Adults Have the Skills They Need to Thrive in a Changing World?: Survey of Adult Skills 2023, OECD Skills Studies, OECD Publishing, Paris, https://doi.org/10.1787/b263dc5d-en.
[3] OECD (2024), Education at a Glance 2024: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/c00cad36-en.
[12] OECD (2023), Health at a Glance 2023: OECD Indicators, https://doi.org/10.1787/7a7afb35-en.
[10] OECD (2022), Education at a Glance 2022: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/3197152b-en.
[7] OECD (2022), “Same skills, different pay: Tackling gender inequalities at firm level”, Policy Brief, OECD, Paris, https://www.oecd.org/en/publications/same-skills-different-pay_a4d18506-en.html.
[2] OECD (2021), Education at a Glance 2021: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/b35a14e5-en.
[1] OECD (2020), Education at a Glance 2020: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/69096873-en.
[16] OECD (2018), OECD Handbook for Internationally Comparative Education Statistics 2018: Concepts, Standards, Definitions and Classifications, OECD Publishing, Paris, https://doi.org/10.1787/9789264304444-en.
[8] Rabaté, S. et al. (2021), “The Child Penalty in the Netherlands and its Determinants The Child Penalty in the Netherlands and its Determinants *”, https://doi.org/10.34932/trkz-qh66.
[4] UNESCO (2020), Global Education Monitoring Report 2020: Inclusion and Education: All Means All, United Nations Educational, Scientific and Cultural Organization, Paris, https://doi.org/10.54676/jjnk6989.
[6] World Economic Forum (2024), Global Gender Gap Report 2024, World Economic Forum, https://www.weforum.org/publications/global-gender-gap-report-2024/ (accessed on 10 June 2025).
To download the data for the figures and tables in this chapter, click StatLink above.
To access further data and/or other education indicators, please visit the OECD Data Explorer: https://data-explorer.oecd.org/.
Data cut-off for the print publication 13 June 2025. Please note that the Data Explorer contains the most recent data.
Table A4.1. Relative earnings of workers compared to those with upper secondary attainment, by educational attainment and age group (2023)
Note: There are cross-country differences in the inclusion/exclusion of zero and negative earners. Columns showing data on relative earnings for workers with upper secondary attainment, and for 45-54 year-olds are available for consultation on line.
1. Year of reference 2022.
2. Index 100 refers to the combined levels of upper secondary or post-secondary non-tertiary education (levels 3 and 4 in the ISCED 2011 classification).
3. Includes part-time and part-year workers.
4. Earnings net of income tax for Türkiye and a combination of gross (self-employed) and net (employees) earnings for Argentina.
Table A4.2. Distribution of workers by educational attainment and level of earnings relative to the median (2023)
Note: There are cross-country differences in the inclusion/exclusion of zero and negative earners. For a given level of educational attainment, the figures by level of earnings relative to median earnings may not add up to 100% because of missing data. Columns showing data broken down by gender are available for consultation on line.
1. Year of reference: 2022.
2. Earnings net of income tax for Türkiye and a combination of gross (self-employed) and net (employees) earnings for Argentina.
Table A4.3. Women’s earnings as a percentage of men's earnings, by educational attainment and age group (2023)
Note: There are cross-country differences in the inclusion/exclusion of zero and negative earners. Columns showing data for other age groups are available for consultation on line.
1. Year of reference: 2022.
2. Includes part-time and part-year workers.
3. Earnings net of income tax for Türkiye and a combination of gross (self-employed) and net (employees) earnings for Argentina.
Table A4.4. Relative earnings of tertiary-educated adults, by field of study (2023)
Note: Cross-country differences in the inclusion/exclusion of zero and negative earners. See Methodology section for more information. Columns showing data for the categories Total and more data breakdowns are available for consultation on line.
1. Year of reference differs from 2023: 2022 for Finland, 2020 for Australia, Canada, Germany, Portugal, Slovenia and the United Kingdom.
2. Earnings refer to academic programmes only.
3. Arts and humanities, social sciences, journalism and information does not include the subfield of Languages.
a – category not applicable; b – break in series; c – there are too few observations to provide reliable estimates; d – contains data from another column; m – missing data; r – values are below a certain reliability threshold and should be interpreted with caution x – contained in another column (indicated in brackets). For further control codes, see the Reader’s Guide.
For further methodological information, see Education at a Glance 2025: Sources, Methodologies and Technical Notes (https://doi.org/10.1787/fcfaf2d1-en)
Adults with income from employment (full-time full-year workers); upper secondary attainment for each age group = 100
Note: For notes on this table and a link to download the data, see Tables and Notes section above.
Median earnings from work for 25-64 year-olds with income from employment (full- and part-time workers)
Note: For notes on this table and a link to download the data, see Tables and Notes section above.
Average earnings of adults with income from employment (full-time full-year workers)
Note: For notes on this table and a link to download the data, see Tables and Notes section above.
25-64 year-olds with income from employment (full-time full-year workers); all fields = 100
Note: For notes on this table and a link to download the data, see Tables and Notes section above.