Results from TALIS 2024

Reader’s guide

Countries and territories

There are five territories participating in TALIS 2024. They are italicised in figures and referred to in the following manner:

• The province of Alberta, in Canada, is referred to as Alberta (Canada).

• The Flemish Community of Belgium is referred to as Flemish Comm. (Belgium) in tables and figures.

• The French Community of Belgium is referred to as French Comm. (Belgium) in tables and figures.

• Kosovo.

• The municipality of Shanghai, in the People’s Republic of China, is referred to as Shanghai (China).

In tables, countries and territories are sorted in alphabetical order. There are two exceptions:

• The Flemish Community and the French Community of Belgium are indented, italicised and appear below Belgium, for ISCED level 2 teacher and principal data. The French Community of Belgium is indented, italicised and appear below Belgium, for ISCED level 1 teacher and principal data.

• Countries and territories that did not meet the TALIS technical standards with respect to participation rates are placed at the bottom of the tables by ISCED level.

Cyprus did not participate directly in TALIS 2024: its data collection and processing were managed exclusively by the international research consortium. Its data are reported in the result tables listed in Annex C. Two notes are added to the information on Cyprus:

• Note by Türkiye: The information in this document with reference to “Cyprus” relates to the southern part of the Island. There is no single authority representing both Turkish and Greek Cypriot people on the Island. Türkiye recognises the Turkish Republic of Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context of the United Nations, Türkiye shall preserve its position concerning the “Cyprus issue”.

• Note by all the European Union Member States of the OECD and the European Union: The Republic of Cyprus is recognised by all members of the United Nations with the exception of Türkiye. The information in this document relates to the area under the effective control of the Government of the Republic of Cyprus.

One note is added to the information on the data for Israel:

• The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.

One note is added with an asterisk to the first mention of Kosovo:

• This designation is without prejudice to positions on status, and is in line with United Nations Security Council Resolution 1244/99 and the Advisory Opinion of the International Court of Justice on Kosovo’s declaration of independence.

Throughout the report, “education systems” is used a shorthand for countries and territories participating in TALIS with available data.

High exclusion rates

For reasons of practicality, safety or economy (e.g. remote schools, unique demographic groups, school types, areas under civil unrest, natural catastrophe), the national survey population could be reduced by no more than 5% of the international target population of ISCED level 2 teachers. However, not all countries/territories could adhere to this rule:

• Israel: ultra-Orthodox schools (20.4% of teachers) and schools teaching in English or French (0.1% of teachers) excluded.

• Brazil: schools in Rio Grande do Sul (10.5%) excluded due to flooding.

In trend analyses, substantial differences in exclusion rates across TALIS cycles warrant caution:

• Brazil: increase in exclusion rate from 2% in 2018 to 11% in 2024.

• Saudi Arabia: decrease in exclusion rate from 17% in 2018 to 0% in 2024.

Low participation rates

Data for education systems that did not meet the TALIS technical standards with respect to participation rates are annotated (*) in tables, figures and the text:

• ISCED level 1 (teacher and principal data): the Flemish Community of Belgium, the Netherlands and New Zealand.

• ISCED level 2 (teacher and principal data): Alberta (Canada), the Netherlands, New Zealand and Norway.

• ISCED level 3 (principal data): the Flemish Community of Belgium.

Data for education systems that did not meet the TALIS technical standards in TALIS 2018 with respect to participation rates are annotated (^a) in tables, figures and the text referring to TALIS 2018 results or change between TALIS 2018 and TALIS 2024:

• ISCED level 1 (teacher and principal data): Australia and the Netherlands.

• ISCED level 2 (principal data): Australia.

Caution is required when interpreting estimates for countries and territories that did not meet the TALIS technical standards with respect to participation rates due to higher risk of non-response bias. See Annex A for more detail.

Reporting conventions

Teachers

The report uses “teachers” as shorthand for the TALIS target population of lower secondary (ISCED level 2) teachers. “Primary teachers” refer to teachers providing instruction in programmes at ISCED level 1 (primary education). “Upper secondary teachers” refer to teachers providing instruction in programmes at ISCED level 3 (upper secondary education).

Results from the report focus on lower secondary (ISCED 2) teachers. Results related to primary (ISCED 1) and upper secondary (ISCED 3) teachers are presented in two types of boxes throughout the report:

• Section-specific boxes covering one topic for both primary (ISCED 1) and upper secondary (ISCED 3) teachers.

• Chapter-specific boxes covering multiple indicators for either primary (ISCED 1) or upper secondary (ISCED 3) teachers.

This report refers to findings that are based on responses of teachers as a “share of teachers”, “percentage of teachers”, etc. For example, 35% of teachers in an education system enjoy teaching.

Principals

The report uses “principals” as shorthand for the TALIS target population of lower secondary (ISCED level 2) principals. Principals provided information on their schools’ characteristics and their own work and working conditions by completing a principal questionnaire. Responses from principals are usually treated in this publication as attributes of the teachers’ personal factors, working conditions, practices, etc. In which case, principals’ answers are analysed at the teacher level and weighted by teacher weights.

Where a principal provided data linked to the teachers at that school (i.e. data provided by one principal applies to several teachers, such as the size of the school), this report refers to findings as a “share of teachers in schools”, a “percentage of teachers in schools”, etc. For example, 35% of teachers in privately managed schools in an education system enjoy teaching.

Self-reported data

TALIS results are based exclusively on self-reports from teachers and principals and, therefore, represent their opinions, perceptions, beliefs and accounts of their activities. As with any self-reported data, this information is subjective and may, therefore, differ from data collected through other means (e.g. administrative data or video observations). The same is true of principals’ reports about school characteristics and practices, which may differ from descriptions provided by administrative data at a national or local government level.

While self-reported data allow respondents to share their beliefs and perceptions, there are also limitations when interpreting findings based on self-reported information.

• Cultural response patterns: Social and cultural backgrounds might systematically affect how individuals respond to questions (e.g. tending to respond in moderate or extreme ways) (van de Vijver and Leung, 1997[2]).

• Social desirability bias: Individuals respond in ways that they think are viewed favourably but do not represent their true beliefs or actions (Krumpal, 2011[3]).

• Validity concerns: Respondents might have to remember something that occurred a long time in the past, or their interpretation of a question is not consistent (e.g. what one person believes to be “critical thinking” could be different from another person’s opinion).

As a result, direct comparisons across countries using self-reported measures warrant caution as they can be misleading.

Classification of levels of education

The classification of levels of education is based on the International Standard Classification of Education (ISCED). ISCED is an instrument for compiling statistics on education internationally. The latest version, which was formally adopted in November 2011, distinguishes between nine levels of education:

• early childhood education (ISCED level 0)

• primary education (ISCED level 1)

• lower secondary education (ISCED level 2)

• upper secondary education (ISCED level 3)

• post-secondary non-tertiary education (ISCED level 4)

• short-cycle tertiary education (ISCED level 5)

• bachelor’s or equivalent level (ISCED level 6)

• master’s or equivalent level (ISCED level 7)

• doctoral or equivalent level (ISCED level 8).

International averages

The OECD and TALIS averages correspond to the arithmetic mean of the respective country estimates. They are calculated for most indicators presented in this report. The European Union average, called “EU total”, takes the European Union (EU) member states as a single entity, to which each country contributes in proportion of the estimated size of the target population. It can be used to assess how a country compares with the European Union as a whole.

The system-level estimates of the Flemish Community of Belgium and the French Community of Belgium are not included in the international averages, as these subnational entities already contribute to the international averages through Belgium as a whole.

The system-level estimates of countries that did not meet the TALIS technical standards with respect to participation rates are excluded from the international averages.

In the case of some countries, data may not be available for specific indicators, or specific categories may not apply. Readers should, therefore, keep in mind that the terms “OECD average”, “TALIS average” and “EU total” refer to the countries with available data that are included in the respective averages. Each of these averages may not necessarily be consistent across all columns of a table.

Throughout the report, “on average” is used as a shorthand for on average across OECD countries and territories with available data.

The number of education systems included in an international average is indicated next to that average:

• OECD average-27: arithmetic average based on ISCED level 2 teacher or principal data across 27 OECD education systems with a data adjudication rating of “good”, “fair” or “poor”. The report refers to the average teacher as equivalent shorthand for the average teacher “across the 27 OECD education systems participating in TALIS”.

• OECD average-25: arithmetic average based on ISCED level 2 teacher data across 25 OECD education systems with a data adjudication rating of “good”, “fair” or “poor” that participated in both the TALIS 2018 and TALIS 2024 cycles.

• OECD average-24: arithmetic average based on ISCED level 2 principal data across 24 OECD education systems with a data adjudication rating of “good”, “fair” or “poor” that participated in both the TALIS 2018 and TALIS 2024 cycles.

• OECD PIAAC average-29: arithmetic average based on data across the 29 OECD countries and territories that participated in the Survey of Adult Skills 2023.

• TALIS average-49: arithmetic average based on ISCED level 2 teacher or principal data across 49 TALIS 2024 education systems with a data adjudication rating of “good”, “fair” or “poor”.

• EU total-22: weighted average based on ISCED level 2 teacher or principal data across all EU member states that participate in TALIS with a data adjudication rating of “good”, “fair” or “poor”.

• TALIS ISCED 1 average-12: arithmetic average based on ISCED 1 teacher or principal data across 12 TALIS 2024 education systems with a data adjudication rating of “good”, “fair” or “poor”.

• TALIS ISCED 3 average-8: arithmetic average based on ISCED 3 teacher data across eight TALIS 2024 education systems with a data adjudication rating of “good,” “fair,” or “poor.”

• TALIS ISCED 3 average-7: arithmetic average based on ISCED 3 principal data across seven TALIS 2024 education systems with a data adjudication rating of “good,” “fair,” or “poor.”

PIAAC data

This report uses data from the second cycle of the Survey of Adult Skills (a product of the Programme for the International Assessment of Adult Competencies, hereafter “PIAAC”) to compare teachers with adults working in other professions.

The second cycle of PIAAC has been conducted in 2022/23 in 31 countries/economies. In each country/economy, a representative sample of about 4 000-5 000 adults aged 16-65 participated in the survey. As part of the survey, respondents are asked to name their job title and their most important responsibilities on the job, which are used to derive their occupation, coded in accordance with the International Standard Classification of Occupations ISCO‑08 (OECD, 2024[4]).

The analysis conducted in this report focuses on school teachers, including in a single group primary school teachers (ISCO code 2341), secondary education teachers (ISCO code 233) and vocational education teachers (ISCO code 232). This ensures that there is a sufficient number of teachers in each national sample for the analysis to be sufficiently precise. This choice is also justified by the fact that primary and secondary school teachers share similar working environment and conditions; on the other hand, early childhood educators, other teaching professional and university and higher education teachers are excluded from the analysis.

Teachers are then compared to adults working in other professions. To make such comparisons more meaningful, the comparison group is restricted to adults whose highest level of education is at ISCED 6 (equivalent to a bachelor’s degree) or ISCED 7 (equivalent to a master’s degree). These are by far the two most common levels of education attained by teachers in the PIAAC sample.

Glossary

• Novice teacher: Teacher with up to five years of teaching experience.

• Experienced teacher: Teacher with more than ten years of teaching experience.

• Second-career teacher: Teacher with at least ten years of work experience in non-education roles for whom teaching was not a first career choice.

• Advantaged school: School with 10% or fewer students from socio-economically disadvantaged homes (those that lack the basic necessities or advantages of life, such as adequate housing, nutrition or medical care).

• Disadvantaged school: School with more than 30% of students from socio-economically disadvantaged homes (those that lack the basic necessities or advantages of life, such as adequate housing, nutrition or medical care).

• Urban school: School located in a community with a population of over 100 000 people.

• Rural school: School located in rural area or village (with a population of up to 3 000 people). To note, in some countries and territories, schools labelled as rural may actually be located in urbanised villages.

• Publicly managed school: School whose principal reported that it is managed by a public education authority, government agency, municipality or governing board appointed by government or elected by public franchise. To note, a publicly managed school may not necessarily be publicly funded.

• Privately managed school: School whose principal reported that it is managed by a non‑governmental organisation (e.g. a church, trade union, business or other private institution). To note, a privately managed school may not necessarily be privately funded.

• Students with special education needs: Students for whom a special education need has been formally identified.

• Target class: Lessons taught over the week preceding the survey to a class randomly selected from teachers’ current weekly timetables.

Abbreviations

AI	Artificial intelligence
Dif.	Difference
ICT	Information and communication technology
ISCED	International Standard Classification of Education
PIAAC	Programme for the International Assessment of Adult Competencies
PISA	Programme for International Student Assessment
PPP	Purchasing power parity
S.D.	Standard deviation
S.E.	Standard error
%pt. dif.	Percentage point difference

Technical information

Causality

The design of TALIS does not allow to establish causal relationships between two variables. All analysis presented in this report should be interpreted as uncovering the presence (or absence) of statistical association, i.e. whether or not variations in one variable (for instance, teachers’ participation in professional development) is systematically associated to variation in another variable (for instance, self‑efficacy in teaching). It is not possible, however, to assess causality between two variables, i.e. whether making teachers participate in professional development, all other things being kept equal, would cause a change in self-efficacy.

Data underlying the figures

Four symbols are used to denote non-reported estimates:

• a: The category does not apply to the country/territory concerned, data were not collected by the country/territory, or there was no observation in the sample.

• c: There are too few or no observations to provide reliable estimates and/or to ensure the confidentiality of respondents (i.e. there are fewer than 30 teachers or 10 schools/principals with valid data; and/or the item non-response rate [i.e. ratio of missing or invalid responses to the number of participants for whom the question was applicable] is above 50%).

• m: Data were collected but subsequently removed, or not reported on, for technical reasons.

• w: Data were withdrawn or were not collected at the request of the country/territory concerned.

Rounding figures

Because of rounding, some figures in tables may not add up exactly to the totals. Totals, differences and averages are always calculated on the basis of exact numbers and are rounded only after calculation.

All standard errors in this publication have been rounded to one, two or three decimal places. Where the value 0.0, 0.00 or 0.000 is shown, this does not imply that the standard error is zero, but that it is smaller than 0.05, 0.005 or 0.0005, respectively.

Focusing on statistically significant differences

This volume only comments on statistically significant differences or changes. These are denoted by using bold font in tables and filled shapes in figures. See Annex B for further information.

Further technical documentation

For further information on TALIS documentation, instruments and methodology, see the TALIS 2024 Technical Report (OECD, forthcoming) and TALIS 2024 and TALIS Starting Strong 2024 User Guide (OECD, forthcoming).

This report uses the OECD StatLinks service. All tables and charts are assigned a URL leading to a corresponding Excel^TM workbook containing the underlying data. These URLs are stable and will remain unchanged over time. In addition, readers of the e-books will be able to click directly on these links and the workbook will open in a separate window if their Internet browser is open and running.

Content of this report

Teacher success depends not only on individual effort but also on supportive working environments. Teachers are more likely to be effective, maintain well-being, be satisfied with their jobs and remain in the profession when they have the support needed to face the challenges at hand (Admiraal and Kittelsen Røberg, 2023[5]; Awwad-Tabry et al., 2023[6]; Babb, Sokal and Trudel, 2022[7]; Collie and Mansfield, 2022[8]; Granziera, Collie and Martin, 2020[9]; Harmsen et al., 2019[10]). Education authorities can play a key role in removing barriers and providing relevant support. Thus, the central theme of this report is: supportive systems for thriving teachers.

Analytical framework

The TALIS 2024 initial report is guided by literature on the job demands-resources (JD-R) model, which is the basis for understanding teachers’ occupational perceptions in the TALIS 2024 Conceptual Framework (OECD, 2025[1]). The JD-R model has often been used to study the relationships between employees’ working conditions and their job performance, occupational well-being and engagement (Bakker and Demerouti, 2017[11]).

Within the JD-R framework, all job characteristics can be classified either as demands or resources.

• Demands encompass all the physical, social or organisational aspects of a job that “require sustained physical or mental effort and are, therefore, associated with certain physiological and psychological costs” (Demerouti et al., 2001, p. 501[12]).

• Resources are job characteristics that may “(a) be functional in achieving work goals; (b) reduce job demands at [sic] the associated physiological and psychological costs; (c) stimulate personal growth and development” (Demerouti et al., 2001, p. 501[12]).

At its core, the JD-R model suggests that job demands deplete employees’ physical and mental energy, which can increase the strain experienced by employees and negatively impact their health and job performance – although this relationship depends on whether demands are perceived as challenges or hindrances (Crawford, LePine and Rich, 2010[13]; LePine, Podsakoff and LePine, 2005[14]). Meanwhile, job resources positively affect job performance by mitigating the negative effect of job demands and by increasing employees’ engagement.

Recognising that employees’ outcomes at work are not only affected by job characteristics, the JD-R model has also been expanded to include personal resources. These refer to the individual characteristics that support employees in achieving positive occupational outcomes, such as their beliefs, motivation or self‑efficacy (Bakker and Demerouti, 2017[11]; Collie et al., 2020[15]).

The analytical framework is based on the notion that teachers’ professional outcomes, alongside their career intentions, are influenced by their personal resources, and the resources and demands present in their working environments. Education systems can deploy policies and regulations to influence the demands and resources facing teachers. However, personal and contextual factors can also affect the level of demands and resources encountered by teachers and their influence on teachers’ outcomes. Figure 1 outlines the conceptual model that underpins this report.

Figure 1. Conceptual model for TALIS 2024 initial report

Sources: Adapted from Bakker, A. and E. Demerouti (2017[11]), “Job demands-resources theory: Taking stock and looking forward”, https://doi.org/10.1037/ocp0000056; and Granziera, H., R. Collie and A. Martin (2020[9]), “Understanding teacher wellbeing through job demands-resources theory”, https://doi.org/10.1007/978-981-15-5963-1_14.

Professional outcomes

This report primarily focuses on the following professional outcomes, outlined in Figure 1, which give an indication of whether teachers are thriving: teachers’ fulfilment of their lesson aims, their well-being and job satisfaction. Closely tied to teachers’ well-being and job satisfaction are career intentions, which are key to understanding teacher retention and attrition.

Demands and resources

Job resources explored in this report include: pre- and in-service learning opportunities; teacher leadership and autonomy; support from school leaders, colleagues and parents; teachers’ terms of employment, such as job remuneration, stability and flexibility; and social status (Siegrist, 2008[16]; Vansteenkiste et al., 2007[17]) (Figure 1).

Personal resources include self-efficacy, growth mindset and motivation (Figure 1).

Regarding job demands, the report considers the following factors linked to the sources of stress covered by the teacher questionnaire: workload (regarding the volume and variety of tasks); work pressure (regarding accountability and performance evaluation); student behaviour and discipline; having a mix of learning needs within a group; uncertainty and change (regarding curricula and requirements) (Figure 1).

Personal and contextual factors

Given the same demands and resources, outcomes can vary depending on personal and contextual factors (Figure 1). Personal and contextual factors are included as breakdown variables and as control variables in regression analyses.

Teachers’ personal factors include demographics, such as age and gender, years of teaching experience and career pathways into teaching. Regarding school and classroom characteristics, school location, governance type and the composition of the student body are considered as contextual variables where relevant (Figure 1).

Policies and regulations

Beyond personal and contextual factors, the demands facing teachers and their resources are also influenced by the policies and regulations in place within their education systems. It is often the very purpose of policies and regulations to increase the resources available to teachers, counterbalance particular demands and improve teachers’ outcomes. Where possible, details of countries’ education policies are presented in boxes throughout the report.

Outline

This report is organised in the following way:

• Chapter 1 provides an overview of the teaching workforce and how it has evolved in response to changing contexts.

• Chapter 2 takes stock of teachers’ professional outcomes, examines which teachers are more likely to be thriving professionally, and explores how the demands on teachers are related to their professional outcomes.

• Chapter 3 examines the demands that teachers’ face, often in response to their changing contexts.

• Chapter 4 looks at teachers’ learning opportunities, which are crucial resources that can influence teachers’ professional outcomes.

• Chapter 5 examines the autonomy and leadership opportunities experienced by teachers, and how these resources relate to teachers’ reported professional outcomes.

• Chapter 6 explores teachers’ professional relationships, including their interactions with school leaders, colleagues, students, and parents or guardians. It explores how collaborative leadership, collegiality and engagement with families contribute to supportive school communities, and how these relationships are linked to teachers’ professional outcomes.

• Chapter 7 describes teachers’ career intentions, which are closely linked to their professional outcomes. It examines personal and job resources, such as motivation, employment terms and social status, and how they relate to patterns of teachers staying or leaving the profession.

• Annex A contains information about the TALIS target populations, the TALIS samples and a summary of the adjudication outcomes for each sample, along with cautionary notes about the interpretations of results, whenever necessary.

• Annex B contains information about complex variables derived from the teacher and principal questionnaires analysed in the volume, and statistical methods used to analyse TALIS data.

• Annex C contains the full list of online result tables.

• Annex D lists the members of the TALIS Governing Board, managers in the TALIS national centres, members of the OECD Secretariat, members of the TALIS Consortium and members of TALIS expert groups that contributed to the TALIS 2024 cycle.

References

[5] Admiraal, W. and K. Kittelsen Røberg (2023), “Teachers’ job demands, resources and their job satisfaction: Satisfaction with school, career choice and teaching profession of teachers in different career stages”, Teaching and Teacher Education, Vol. 125, p. 104063, https://doi.org/10.1016/J.TATE.2023.104063.

[6] Awwad-Tabry, S. et al. (2023), “Arab teachers’ well-being upon school reopening during COVID-19: Applying the job demands-resources model”, Education Sciences, Vol. 13/4, p. 418, https://doi.org/10.3390/EDUCSCI13040418.

[7] Babb, J., L. Sokal and L. Trudel (2022), “This is us: Latent profile analysis of Canadian teachers’ burnout during the COVID-19 pandemic”, Canadian Journal of Education/Revue canadienne de l’éducation, Vol. 45/2, pp. 555-585, https://doi.org/10.53967/cje-rce.v45i2.5057.

[11] Bakker, A. and E. Demerouti (2017), “Job demands-resources theory: Taking stock and looking forward”, Journal of Occupational Health Psychology, Vol. 22/3, pp. 273-285, https://doi.org/10.1037/ocp0000056.

[15] Collie, R. et al. (2020), “A multilevel person-centered examination of teachers’ workplace demands and resources: Links with work-related well-being”, Frontiers in Psychology, Vol. 11, p. 519757, https://doi.org/10.3389/fpsyg.2020.00626.

[8] Collie, R. and C. Mansfield (2022), “Teacher and school stress profiles: A multilevel examination and associations with work-related outcomes”, Teaching and Teacher Education, Vol. 116, p. 103759, https://doi.org/10.1016/J.TATE.2022.103759.

[13] Crawford, E., J. LePine and B. Rich (2010), “Linking job demands and resources to employee engagement and burnout: A theoretical extension and meta-analytic test”, Journal of Applied Psychology, Vol. 95/5, pp. 834-848, https://doi.org/10.1037/A0019364.

[12] Demerouti, E. et al. (2001), “The job demands-resources model of burnout”, Journal of Applied Psychology, Vol. 86/3, pp. 499-512, https://doi.org/10.1037/0021-9010.86.3.499.

[9] Granziera, H., R. Collie and A. Martin (2020), “Understanding teacher wellbeing through job demands-resources theory”, in Cultivating Teacher Resilience: International Approaches, Applications and Impact, Springer Singapore, https://doi.org/10.1007/978-981-15-5963-1_14.

[10] Harmsen, R. et al. (2019), “The longitudinal effects of induction on beginning teachers’ stress”, British Journal of Educational Psychology, Vol. 89/2, pp. 259-287, https://doi.org/10.1111/bjep.12238.

[3] Krumpal, I. (2011), “Determinants of social desirability bias in sensitive surveys: A literature review”, Quality and Quantity, Vol. 47/4, pp. 2025-2047, https://doi.org/10.1007/S11135-011-9640-9.

[14] LePine, J., N. Podsakoff and M. LePine (2005), “A meta-analytic test of the challenge stressor-hindrance stressor framework: An explanation for inconsistent relationships among stressors and performance”, Academy of Management Journal, Vol. 48/5, pp. 764-775, https://www.jstor.org/stable/20159696.

[1] OECD (2025), Teaching and Learning International Survey (TALIS) 2024 Conceptual Framework, OECD Publishing, Paris, https://doi.org/10.1787/7b8f85d4-en.

[4] OECD (2024), Survey of Adult Skills – Reader’s Companion: 2023, OECD Skills Studies, OECD Publishing, Paris, https://doi.org/10.1787/3639d1e2-en.

[16] Siegrist, J. (2008), “Effort-reward imbalance and health in a globalized economy”, SJWEH Supplements 6.

[2] van de Vijver, F. and K. Leung (1997), Methods and Data Analysis for Cross-cultural Research, Sage, Newbury Park, CA, https://research.tilburguniversity.edu/en/publications/methods-and-data-analysis-for-cross-cultural-research.

[17] Vansteenkiste, M. et al. (2007), “On the relations among work value orientations, psychological need satisfaction and job outcomes: A self-determination theory approach”, Journal of Occupational and Organizational Psychology, Vol. 80/2, pp. 251-277, https://doi.org/10.1348/096317906X111024.