This chapter uses data from the Survey of Adult Skills to explore the effects of educational background on flexibility in employment and work-life balance.
Across OECD and partner countries, individuals with higher levels of education are significantly more likely to work remotely. On average, tertiary-educated adults report the highest prevalence of working from home (42%), while those with upper secondary or post-secondary non-tertiary attainment are considerably less likely to do so (18%). Workers with below upper secondary attainment show the lowest incidence of remote work across OECD countries (10%).
Adults with higher numeracy proficiency levels are more likely to report flexible working hours. On average across OECD countries, 44% of adults with high numeracy skills (at or above Level 4) report having high or very high flexibility in their working hours, compared to 28% of those with low numeracy skills (at or below Level 1).
In all OECD and partner countries, tertiary-educated men enjoy more flexibility in their working hours than their female peers. On average, 47% of tertiary-educated men report having high or very high flexibility in their working hours in their main job, compared with 35% of tertiary-educated women.
Education at a Glance 2026
Context
Work-life balance is an important aspect of high-quality employment and sustainable labour-market outcomes. It depends not only on the total number of hours worked but also on how work is organised and the extent to which individuals can manage their professional and personal responsibilities effectively. Ensuring adequate balance requires paying attention to the flexibility in working arrangements, the organisation of working time and the risks associated with excessive or irregular hours. Furthermore, as many OECD countries face the challenges of ageing populations, work-life balance policies will need to continue evolving, particularly through more flexible working arrangements, to better support increasing informal caregiving responsibilities (Bernini et al., 2026[1]). Flexibility in working hours such as being able to choose when to start and finish working, organise breaks or schedule holidays has been associated with greater levels of well-being and job satisfaction (OECD, 2022[2]). The increased prevalence of remote work or working from home has expanded opportunities for autonomy and reduced commuting time, but it has also blurred the boundaries between work and personal life and may lead to longer effective working hours (OECD, 2025[3]).
Overtime or work beyond contractual or typical hours remains a key factor affecting work-life balance. Prolonged or regular overtime can increase health risks and limit participation in non-work activities, thereby offsetting the potential advantages of flexible or remote working arrangements (OECD, 2019[4]).
This chapter will address work-life balance as not just a question of reducing hours but of ensuring that work is organised in ways that support workers’ autonomy and well-being while preventing excessive or extended working hours.
In per cent; 25-64 year-olds; Survey of Adult Skills (PIAAC)
For data, see Table A6.1. The data for this figure can be accessed via https://stat.link/otk7yv.
Other findings
In most OECD and partner countries, the share of employees working additional hours in their main job tends to be highest among those with tertiary education, reflecting the greater prevalence of managerial and professional roles and the higher degree of job responsibility and autonomy often associated with such positions.
In most OECD and partner countries, tertiary-educated workers are more likely than those with lower educational attainment to report worrying about work when not at work. On average across OECD countries, 47% of tertiary-educated workers report doing this sometimes or most of the time, compared to 35% of those with upper secondary or post-secondary non-tertiary attainment and 31% of those with below upper secondary attainment.
Note
The analysis included in this chapter using EU Labour Force Survey (EU LFS) does not filter for individuals holding a single full-time job. As a result, information about multiple jobholders is excluded from this analysis. Note that the profiles of multiple jobholders are varied, ranging from highly educated professionals seeking job enrichment to those in elementary occupations driven by financial necessity, and may have an impact on work-life balance that is not explored in this chapter (Conen, 2020[5]).
Care should be taken when interpreting results from different sources, as differences in data collection methods and reference periods can affect comparability. This is especially important when examining data on individuals’ frequency of working from home, autonomy over working hours and the incidence of overtime (Table A6.2 and Figure A6.2) where the timing and geographical coverage of data collection may influence the outcomes reported.
Work-life balance is a critical determinant of overall well-being, influencing both physical and mental health, job satisfaction, and family stability. This chapter analyses three key dimensions of work-life balance: i) flexibility in working hours; ii) the ability to work from home; and iii) the incidence of overtime or extra hours. These dimensions shape how individuals experience and manage their professional and personal responsibilities.
Flexibility in working hours provides workers with greater autonomy to schedule tasks around personal and family needs. This flexibility can support mental wellbeing by reducing stress linked to time conflicts and by improving work satisfaction (OECD, 2022[2]).
The ability to work from home represents another critical dimension of work-life balance as it can enhance work-life integration by eliminating commuting time and allowing for more flexible scheduling. At the same time, it can also blur boundaries between work and personal life, particularly for women who often bear a disproportionate share of household and caregiving responsibilities (Craig and Churchill, 2020[6]). Remote work also results in less time spent collaborating with colleagues and less developed networks in the workplace which may have implications for productivity (Yang et al., 2021[7]).
The incidence of overtime or extra hours presents a more complex picture. Extended hours may reflect greater responsibility or commitment, but can also increase stress levels and the risk of burnout, counteracting the potential well-being gains from flexibility or remote work.
Overall, an individual’s experience of these three dimensions of work-life balance may be influenced by their skill level and education. Highly educated individuals tend to enjoy greater autonomy and flexibility, though often at the cost of longer hours. Meanwhile, lower-educated and lower-skilled workers face more rigid work structures and fewer opportunities for remote work, amplifying challenges in balancing work and family demands. Addressing these disparities requires labour-market and organisational policies that promote flexibility across all occupations, support equitable access to remote work and regulate excessive working hours to protect both productivity and wellbeing (Eurofound, 2022[8]).
Flexibility in working hours
Flexibility in working hours provides an important indicator of how well individuals can balance professional and personal responsibilities, and it reflects the degree of autonomy and trust placed in workers to manage their schedules, as well as industry and occupational requirements. Higher-skilled and more highly educated workers are more likely to benefit from such arrangements, as they are disproportionately represented in managerial occupations or those related to information and communication technologies (ICT) that allow output-oriented work rather than requiring rigid schedules (OECD, 2022[2]; OECD, 2024[9]). In contrast, workers in lower-skilled or manual occupations often face limited control over their schedules, with shift work or fixed hours reducing opportunities for flexibility. Such constraints may contribute to higher rates of work-family conflict and worse health outcomes (Chung and van der Lippe, 2018[10]).
Figure A6.1 shows that on average across the OECD countries and economies participating in the Survey of Adult Skills (PIAAC), 44% of adults with high numeracy skills (proficiency levels at or above Level 4) report high or very high flexibility in their working hours in their main job. This share falls to 38% among adults with intermediate numeracy skills (Level 3), 32% among those with basic numeracy skills (Level 2) and only 28% of those with the lowest numeracy proficiency levels (at or below Level 1). Among adults with low numeracy skills, the greatest levels of flexibility at work are in Finland (49% of employees) and Japan (40%), while the lowest are in Croatia (18%) and Italy (15%). In Finland, the proportion of adults with low numeracy proficiency who report having high or very high flexibility in their working hours is greater than that of adults with intermediate numeracy skills in all other countries, and it is even greater than the share of adults with high numeracy skills in most countries. In Chile, Korea, Poland and Spain, the differences across numeracy proficiency levels are small, below 5 percentage points.
In addition to skill-related differences, there are also gender differences in access to flexible working arrangements among tertiary-educated employed adults. Figure A6.2 shows that on average across OECD countries and economies participating in the Survey of Adult Skills, 47% of tertiary-educated men report high or very high flexibility in their working hours, compared to 35% of similarly educated women, a gender gap of 12 percentage points. This gender gap exists in almost all OECD countries and economies, with the largest differences observed in the Flemish Region of Belgium, Hungary, the Netherlands, Poland and Switzerland, where the gap exceeds 15 percentage points. Conversely, Croatia, Japan and Spain show smaller gender differences with gaps below 7 percentage points.
In percent; 25-64 year-olds; Survey of Adult Skills (PIAAC) or national survey
1. Source for Australia is the Australian Bureau of Statistics Characteristics of Employment Survey (2023)
For data, see Table A6.1. The data for this figure can be accessed via https://stat.link/otk7yv.
Several factors may contribute to this gap. Women are often over-represented in service-sector occupations such as education and care work, fields that offer lower levels of autonomy over schedules, even among tertiary-educated workers (Jacobi, Hamjediers and Naujoks, 2025[11]). In addition, gender differences in job roles, working time preferences and family responsibilities can shape access to and perceptions of flexibility. Where flexibility is available for women, it may also differ in quality or form, such as flexibility from part-time working, which can have implications for income and career progression (Jost and Möser, 2023[12]).
Across OECD countries, the share of employed adults reporting high or very high flexibility in their working hours increases moderately with age. On average, 33% of 25-34 year-olds report high flexibility, compared to 36% among both 35-44 and 45-54 year-olds, and 37% among 55-64 year-olds. In most countries, older workers are slightly more likely to report flexible working hours, although the size of the differences across age groups remains small. Finland, Norway and Switzerland show consistently high levels of flexibility across all age groups, while Italy, Poland and Portugal report comparatively lower levels regardless of age (Table A6.1).
The ability to work from home
Working from home has become a key aspect of modern employment, but access to it is uneven because it depends on two distinct conditions. First, the tasks performed in a job must be suitable for remote work. Many jobs in manufacturing, construction, agriculture, care, retail and other in-person services require physical presence and therefore cannot be carried out from home, regardless of workers’ preferences or the level of digitalisation. Second, where tasks are technically suitable for remote work, actual take-up depends on the broader conditions that enable such arrangements, including digital tools, broadband access, management practices, trust between employers and employees and the regulatory framework (Sostero et al., 2020-07-24[13]; Dingel and Neiman, 2020[14]). Access to remote work therefore varies significantly by education, largely because education is associated with occupation, sector and task content. Tertiary-educated workers are more often employed in knowledge-intensive, professional and managerial occupations whose task can be performed remotely, while workers with lower levels of educational attainment are more likely to hold jobs requiring physical presence. Greater access to remote work can contribute positively to an individual’s job satisfaction and personal well-being (Barrero, Bloom and Davis, 2021[15]). Conversely, lower-educated workers are less likely to have jobs suitable for remote work, which limits their ability to adapt to changing family needs or health circumstances (Chung and van der Lippe, 2018[10]). This structural divide reinforces existing inequalities in well-being and job quality across educational groups.
Figure A6.3 shows that on average across OECD countries that participated in the EU Labour Force Survey (EU-LFS), 42% of tertiary-educated workers report working from home at least occasionally, compared to 18% of those with upper secondary or post-secondary non-tertiary education and only 10% of those with below upper secondary education. This pattern is consistent across almost all countries. The largest share of tertiary-educated workers working from home is in the Netherlands, where the rate exceeds 80%. At the other end of the scale, Bulgaria, Romania and the Republic of Türkiye report considerably lower shares – at or below 10%, across all education levels. The relatively high rate among tertiary-educated workers in the Netherlands likely reflects the country’s larger concentration of knowledge-based and digital service jobs. This is coupled by workplace cultures in the Netherlands that are accepting of hybrid work across all attainment levels, with both employees and employers generally viewing it as positively affecting productivity, motivation, and work-life balance (Conen and Neut, 2026[16]). In contrast, Bulgaria, Romania and Türkiye remain characterized by a high relative importance of manufacturing and other sectors requiring on-site presence that is not compatible with remote work. These structural features might be combined with lower levels of digital adoption among firms and varying teleworking practices that could contribute to less teleworking compared to more service-oriented economies (OECD, 2026[17]; OECD, 2026[18]).
In per cent; 25-64 year-olds; EU Labour Force Survey (EU-LFS) or national survey
Note: The average includes only countries participating in the 2023 EU Labour Force Survey, not all OECD countries.
1. Source for Australia is the Australian Bureau of Statistics Characteristics of Employment survey (2024).
2. Source for Canada is the Canadian Labour Force Survey (2022).
3. Source for Israel is the Social Survey (2024).
For data, see Table A6.2. The data for this figure can be accessed via https://stat.link/otk7yv.
In all countries with available data, the largest differences in remote working are observed between those with tertiary education and those with upper secondary or post-secondary non-tertiary attainment, with two-thirds of countries showing gaps of more than 20 percentage points. In contrast, the differences between individuals with upper and below upper secondary attainment are small, remaining below 20 percentage points in all countries.
Incidence of overtime or extra hours
The incidence of overtime or extra hours worked provides insights into work intensity and labour-market conditions. Although working additional hours in the main job may offer some workers the opportunity to increase their earnings, high levels of overtime can also signal structural pressures.
Patterns of overtime vary across educational attainment levels, with workers who have lower attainment more likely to experience involuntary overtime, where overtime is imposed by their employer due to lack of staff or emergencies, rather than simply being on offer. Such overtime tends to increase stress and physical fatigue and limit opportunities for rest and family life. Among workers with a tertiary education, including managers and professionals, working longer hours may be more of a voluntary decision. This kind of overtime often stems from job responsibilities, performance expectations or career ambitions. However, although these workers generally have greater autonomy over their hours, the fact that overtime is worked voluntarily does not preclude it contributing to work-life imbalance and stress (Kaduk et al., 2022[19]).
Figure A6.4 shows that on average across European countries participating in the EU-LFS, tertiary-educated workers show the highest rates of overtime work (16%), followed by those with upper secondary or post-secondary non-tertiary attainment (12%) and finally those with below upper secondary attainment (10%). This pattern of higher overtime prevalence among individuals with higher educational attainment is consistent across most countries. The highest shares of tertiary-educated workers working overtime are observed in Switzerland, where the rate exceeds 40%. Conversely, Bulgaria, Hungary, Latvia and Romania report considerably lower shares of overtime work across all education levels, generally below 2.5%. Switzerland may show particularly high rates because it has greater levels of high-skilled employment in financial and professional services. Workers in high-skilled sectors such as financial, scientific and professional services are frequently exempt from standard overtime regulations, leading to a higher prevalence of both regular and uncompensated extra hours (Eurofound, 2022[20]). Lower rates in Bulgaria, Hungary, Latvia and Romania may reflect a greater concentration of jobs with fixed schedules, lower shares of high-autonomy professional work and weaker incentives or expectations for unpaid overtime (Eurofound, 2022[20]).
In per cent; share of 25-64 year-olds who reported working overtime or extra hours during the reference week; EU Labour Force Survey (EU-LFS) or national survey
Note: The average includes only countries participating in the 2023 EU Labour Force Survey, not all OECD countries.
1. Source for Australia is the Australian Bureau of Statistics Characteristics of Employment survey (2024).
2. Source for Canada is the Canadian Labour Force Survey (2022).
3. Source for Israel is the Labour Force Survey (2024).
For data, see Table A6.2. .The data for this figure can be accessed via https://stat.link/otk7yv.
Difficulties in maintaining work-life balance
Flexible working arrangements and the ability to work from home can support work-life balance, but they do not necessarily prevent tensions between professional and personal responsibilities. Difficulties in disconnecting from work, fatigue after working hours and conflicts between work and family responsibilities remain important dimensions of job quality and well-being (Sonnentag and Schiffner, 2019[21]). These challenges may vary with educational attainment levels, reflecting differences in working conditions, job demands and occupational responsibilities.
Figure A6.5 shows that tertiary-educated workers are more likely than those with lower levels of educational attainment to report worrying about work when not at work. On average across OECD countries, 47% of tertiary-educated employed adults report that they sometimes or most of the time continue worrying about work outside working hours, compared to 35% of those with upper secondary or post-secondary non-tertiary attainment and 31% of those with below upper secondary attainment. This positive gradient by educational attainment is observed in most countries and is particularly pronounced in Portugal, where around 63% of tertiary-educated workers report worrying about work outside working hours compared to 26% workers with below upper secondary education. There are high shares among tertiary-educated workers in Belgium, Luxembourg, Norway and Sweden. In contrast, Austria, Hungary and Lithuania report comparatively lower shares among tertiary-educated workers, although even in these countries they remain more likely than lower-educated adults to report difficulties disconnecting from work.
In per cent of adults who responded some or most of the time; 25-64 year-olds; European Working Conditions Survey (EWCS)
Note: The average includes only countries participating in the 2024 European Working Conditions Survey (EWCS), not all OECD countries.
For data, see Table A6.3. The data for this figure can be accessed via https://stat.link/otk7yv.
Higher levels of work-related worry among tertiary-educated workers likely reflect differences in job characteristics and work organisation. Adults with higher attainment are more likely to work in managerial, professional and knowledge-intensive occupations associated with stronger managerial expectations and higher levels of psychological or emotional demand (Eurofound and ILO, 2019[22]).
In contrast, the other indicators of difficulties maintaining work-life balance presented in Table A6.3 show weaker and less consistent relationships with attainment. Feelings of being too tired after work to carry out household chores are widespread across all educational groups, with relatively small differences by education level on average across OECD countries. Similarly, the shares of workers reporting that their job prevented them from giving sufficient time to family or that family responsibilities made it difficult to concentrate on work show more mixed patterns across countries and educational groups. In several countries, lower-educated workers report comparable or even greater levels of work-family conflict than tertiary-educated workers. These findings suggest that while higher educational attainment is strongly associated with difficulties disconnecting psychologically from work, this should not be interpreted as indicating that work-related stress is concentrated among tertiary-educated workers. Other dimensions of work-life balance difficulties are widely distributed across the workforce and may reflect different forms of work-related strain across occupations and working conditions, such as time pressure, physical or scheduling constraints and work-related risks.
Age group: the term adult refers to 25-64 year-olds.
Educational attainment refers to the highest level of education successfully completed by an individual.
Flexibility in working hours is defined as the ability of employees to adjust start and end times, take breaks or modify schedules to better accommodate personal needs.
Levels of education: See the Reader’s Guide at the beginning of this publication for a presentation of all ISCED 2011 levels.
Overtime or extra hours is defined as hours worked beyond the regular or contractual working hours established by a labour contract, collective agreement or national legislation.
Proficiency levels: See definitions in Chapter A1.
Working from home is defined as carrying out work responsibilities remotely, whether from one’s home or another suitable location, rather than at the employer’s physical workplace.
Different questions were asked to survey respondents, depending on the data source:
Table A6.1.
1. Survey of Adult Skills (PIAAC) (2023) question: "To what extent could you choose or change your working hours? 1. Not at all 2. Very little 3. To some extent 4. To a high extent 5. To a very high extent".
Table A6.2.
1. European Labour Force Survey (2023) question on work from home: "During those four weeks (in your main job), how much of your working time did you spend working from home, including telework? 1.You worked exclusively from home during the period 2.Half of your working hours or more 3.Less than half of your working hours 4.You did not work from home during the period".
2. European Labour Force Survey (2023) question on autonomy over working hours: "Can you decide for yourself the start and end of your working day? 1.Yes, completely 2.Yes, to some extent 3.No, my working hours are determined by my employer 4.No, my working hours are set by my clients or a third party 5.No, my working hours are determined by other factors (regulatory constraints, weather, etc.)”.
3. European Labour Force Survey (2023) question on overtime/extra hours in the main job: "For the week from Monday … to Sunday … (in your main job), on the days you worked or attended training, did you work more hours than usual / more than your contract stipulates / more than the statutory number of hours? 1. Yes 2. No".
Table A6.3.
1. European Working Conditions Survey (2024) question: “How often in the last 12 months have you:
a. kept worrying about work when you were not working?
b. felt too tired after work to do some of the household jobs which needed to be done?
c. found that your job prevented you from giving the time you wanted to your family?
d. found it difficult to concentrate on your job because of your family responsibilities?
i. 1. Always; 2. Most of the time; 3. Sometimes; 4. Rarely; 5. Never.
2. International Social Survey Programme (2022) question: “How often has each of the following happened to you in the last 3 months?”
a. I have come home from work too tired to do the chores which need to be done.
b. I have found it difficult to concentrate at work because of my family responsibilities.
i. 1. Several times a week; 2. Several times a month; 3. Once or twice; 4. Never
Survey of Adult Skills (PIAAC) Cycle 2 (2023)
The European Labour Force Survey (2023)
The European Working Conditions Survey (2024)
International Social Survey Programme: Family and Changing Gender Roles V – ISSP (2022)
National sources:
Australian Bureau of Statistics Characteristics of Employment Survey – Table A6.1. and Table A6.2.
Canadian Labour Force Survey – Table A6.2.
Israel’s Social Survey and Israel’s Labour Force Survey – Table A6.2.
Japanese Basic Survey on Wage Structure – Table A6.2.
For more information, please refer to Education at a Glance 2026 Sources, Methodologies and Technical Notes (https://doi.org/10.1787/dcf64a14-en).
References
[15] Barrero, J., N. Bloom and S. Davis (2021), “Why working from home will stick”, NBER Working Paper, No. 28731, National Bureau of Economic Research, Cambridge, MA, https://doi.org/10.3386/w28731.
[1] Bernini, A. et al. (2026), “Informal long-term care in the contact of demographic change and intergenerationa fairness in the EU”, Publications Office of the European Union, https://data.europa.eu/doi/10.2760/9689703.
[10] Chung, H. and T. van der Lippe (2018), “Flexible working, work-life balance, and gender equality: Introduction”, Social Indicators Research, Vol. 151, pp. 365-381, https://doi.org/10.1007/s11205-018-2025-x.
[5] Conen, W. (2020), “Multiple Jobholding in Europe”, Economic and Social Science Institute of the Hans-Böckler-Foundation No. 20, https://www.wsi.de/de/15341.htm.
[16] Conen, W. and N. Neut (2026), Juridische en organisatorische vraagstukken bij hybride werken: Eindrapportage, AIAS-HSI (Amsterdams Instituut voor Arbeidsstudies – Hugo Sinzheimer Instituut, Universiteit van Amsterdam).
[6] Craig, L. and B. Churchill (2020), “Dual-earner parent couples’ work and care during COVID-19”, Gender, Work and Organization, Vol. 28/S1, pp. 66-79, https://doi.org/10.1111/gwao.12497.
[14] Dingel, J. and B. Neiman (2020), “How Many Jobs Can be Done at Home?”, Journal of Public Economics, Vol. 189, https://doi.org/10.1016/j.jpubeco.2020.104235.
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[22] Eurofound and ILO (2019), Working Conditions in a Global Perspective, Publications Office of the European Union, Luxembourg, https://doi.org/10.2806/870542.
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[12] Jost, M. and S. Möser (2023), “Salary, flexibility or career opportunity? A choice experiment on gender specific job preferences”, Frontiers in Sociology, Vol. 8, https://doi.org/10.3389/fsoc.2023.1154324.
[19] Kaduk, A. et al. (2022), “Involuntary vs. voluntary flexible work: Insights for scholars and stakeholders”, Community, Work and Family, Vol. 22/4, pp. 412-442, https://doi.org/10.1080/13668803.2019.1616532.
[18] OECD (2026), Foundations for Growth and Competitiveness 2026, OECD Publishing, Paris, https://doi.org/10.1787/40a7532f-en.
[17] OECD (2026), OECD Economic Surveys: Romania 2026, OECD Publishing, Paris, https://doi.org/10.1787/4844067e-en.
[3] OECD (2025), Workforce Insights from Central Governments: Findings of the 2024 OECD/EU Survey of Public Servants, OECD Publishing, Paris, https://doi.org/10.1787/2f9080b1-en.
[9] 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.
[2] OECD (2022), OECD Employment Outlook 2022: Building Back More Inclusive Labour Markets, OECD Publishing, Paris, https://doi.org/10.1787/1bb305a6-en.
[4] OECD (2019), OECD Compendium of Productivity Indicators 2019, OECD Publishing, Paris, https://doi.org/10.1787/b2774f97-en.
[21] Sonnentag, S. and C. Schiffner (2019), “Psychological detachment from work during nonwork time and employee well-being: The role of leader’s detachment”, The Spanish Journal of Psychology, Vol. 22, https://doi.org/10.1017/sjp.2019.2.
[13] Sostero, M. et al. (2020-07-24), Teleworkability and the COVID-19 crisis: a new digital divide, European Commission, https://publications.jrc.ec.europa.eu/repository/handle/JRC121193.
[7] Yang, L. et al. (2021), “The effects of remote work on collaboration among information workers”, Nature Human Behaviour, Vol. 6/1, pp. 43-54, https://doi.org/10.1038/s41562-021-01196-4.
The data for the figures and tables in this chapter can be downloaded via https://stat.link/otk7yv.
Data cut-off for the print publication 17 June 2026. Please note that the Data Explorer, http://data-explorer.oecd.org/s/4s contains the most recent data.
a – category not applicable; b – break in series; d – contains data from another column; m – missing data; 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 2026: Sources, Methodologies and Technical Notes (https://doi.org/10.1787/dcf64a14-en).
In per cent; 25-64 year-olds
Note: Columns showing the standard errors and data broken down by gender and by age group are available for consultation online. Data was sourced from the PIAAC Cycle 2 Database. 1. Reference year 2023. Source for Australia is the Australian Bureau of Statistics Characteristics of Employment survey. Results are not directly comparable to PIAAC as the source questionnaire collects data on “whether workers had/did not have an agreement to work flexible hours”, rather than “high or very high flexibility of working hours” (PIAAC).
The data for this Table can be accessed via https://stat.link/otk7yv.
In per cent; 25-64 year-olds
Note: Columns showing data for all educational levels, the share of adults whose employer, organisation or client(s) decide their working hours, and the average overtime hours worked in the reference week by those working overtime (h) are available for consultation online. Data was sourced from the EU Labour Force Survey Database. The data for this Table can be accessed via https://stat.link/otk7yv.
1. Source for Australia is the Australian Bureau of Statistics Characteristics of Employment survey. The reference year for Australia is 2024. Results are not directly comparable to the EU-LFS as the source questionnaire collects data on whether employed individuals “usually worked/did not work from home” compared to “sometimes or usually working from home” (LFS), “able/not able to work extra hours to take time off” compared to “can fully decide him-/herself” (LFS) and “usually/did not usually work extra hours or overtime” compared to capturing the range of hours of overtime worked (LFS).
2. Source for Canada is the Canadian Labour Force Survey. The reference year for Canada is 2022.
3. Source for Israel for the frequency of working from home is the Social Survey. Results are not directly comparable to the EU-LFS. Please see the Sources, Methodologies and Technical Notes for more information. Source for Israel for working overtime or extra hours is the Labour Force Survey. The reference year for Israel for both indicators is 2024.
4. Source for Japan is the Basic Survey on Wage Structure. For the following reasons, the results are not directly comparable with EU LFS: i) The underlying questionnaire covers the actual number of overtime hours worked during one month period for the population aged 15-70+ years old; ii) Overtime hours are reported by employers, not self-reported by individual employees; iii) Educational attainment categories differ from those used in the EU LFS; iv) The survey primarily aims to measure wage structures and therefore applies specific conditions when compiling data on working hours.
In per cent; share of employed 25-64 year-olds reporting experiencing difficulties sometimes or most of the time
Note: Data was sourced from the European Working Conditions 2024 Survey in May 2026.
1. Source and reference year differs from main table. Reference year is 2022 for Australia, India, Israel, Japan, New Zealand, South Africa and the United States. Survey source is the International Social Survey Programme: Family and Changing Gender Roles V - ISSP 2022. Data from ISSP are not directly comparable with data from EWCS. Due to differences in response scales between ISSP and EWCS, categories have been set to approximate moderate-to-high frequency exposure, grouping ISSP responses of 'several times a week' and 'several times a month' as comparable to EWCS responses of 'sometimes' or 'most of the time'. Following consultations with the country, data on educational attainment for Israel were recoded to improve comparability with the harmonized ISCED variable.
2. The data source for "Felt job prevented them from giving time to family" for Israel is the European Social Survey (ESS). The data refers to 2021. The following response categories were included: “sometimes” and “often”.
The data for this Table can be accessed via https://stat.link/otk7yv.