9. Education, gender, and pay

A clear pattern in the labour market is that pay varies across occupations according to education level. A general pattern, though one with several exceptions, not least related to gender, is that people with longer education can usually expect higher pay than those with shorter education. The normal length of formal education and training within an occupation is a factor in determining the skill level of the occupation in national occupational classifications.

The International Standard Classification of Occupations, ISCO-08, to which national classification systems adhere, organises occupations partly hierarchically by education, based on four broad skill levels:

These levels of education relate to the occupational areas of the ISCO classification as follows. However, professional experience requirements can be used instead of or in addition to educational requirements at all levels.

The various national classification systems have adapted the broad skill classes of ISCO to their own educational systems. The instructions for ISCO-08 (in contrast to previous versions) also emphasise that the typical tasks of the occupation and not education should determine the classification of the occupation, to allow for comparisons of occupations internationally despite the different educational systems in different countries (Tilastokeskus, 2011). As a result, occupational areas 4– 8 in particular may contain very different qualification requirements, and the classification system itself includes little information about formal educational requirements.

However, most job evaluation systems used for pay setting and pay comparisons consider education to be the most important, and thus often determining, factor in defining pay levels. This is also because it often co-varies with the factor of responsibility.

In the Nordic countries, significantly more women than men undertake higher education. In 2021, 61% of Finnish and Swedish students and 57% of Norwegian students were women (Eurostat, 2021). This is the result of a decades-long trend; according to Pekkarinen (2012), even as far back as 1990 the proportion of female students in higher education in the Nordic countries was higher than that of men. Thus, the fact that women more often than men undertake higher education should help reduce the gender pay gap, given the general pattern that those with longer education can be expected to receive higher pay than those with shorter education.

However, the gender pay gap is found to be larger among highly educated and highly paid employees (Måwe, 2019), which in itself increases the overall gender pay gap. The effect of the segregation of higher education is even more important. As women and men choose different study programmes, they end up in different occupations with different pay levels (Hägglund, 2024; Ransmayr & Weichselbaumer, 2024).

There is thus an ‘education gap’ in relation to gender and pay. Instead of women and men who receive the same pay having a similar level of education, as would be expected, it seems instead that they have different levels of education. Table 11 exemplifies this by showing the educational levels for men and women who receive (almost) the same monthly pay. The examples for men’s pay are related to male-dominated fields of education, while the examples for women’s pay relate to female-dominated or gender-equal fields of education.

The examples in Table 11 show that for all three levels of monthly pay compared, women have a significantly higher level of education than men.

Education level alone cannot serve as a direct indicator of job equivalence when comparing different occupations, as responsibility, effort and working conditions must also be taken into account. However, since skill requirements, which are largely a matter of education, factor heavily in the valuation of a job, large differences in pay between individuals with equal levels of education, or large differences in educational level between individuals who receive equal pay, are a reason to reflect on whether differences are justified.

A comparison in which a male-dominated job with lower educational requirements has higher pay than a female-dominated job with higher educational requirements does not, of course, always represent a comparison of jobs regarded as doing work of equal value, but comparing education and pay can reveal potential pay gaps between different occupational areas that do not necessarily come to the surface in an analysis of pay for work of equal value when looking exclusively at different occupations.

Information on education in national statistics is relatively coarse-grained and therefore does not provide detailed knowledge in relation to pay for work of equal value. There are some differences between the Nordic countries when comparing how data on education can be related to data on occupations and pay in national statistics.

Finnish pay statistics are broken down by sector. For the local and central government sectors, the variables of level of education (8 levels) and field of education (99 fields) can be combined. For monthly pay in the private sector, instead of defining the level and field of education, 468 different degrees are specified. Hourly pay in the private sector is organised in the same way as monthly pay in the public sector, while information on education in relation to hourly pay in the public sector is not provided. Some information on educational fields can be linked to specific occupations, however there are also many broad programmes included that serve as a possible background for a variety of occupations. All tables are broken down by gender. However, in addition to the absence of part-time public sector employees in the statistics, comparisons between the private and public sectors are complicated.

In the Norwegian statistics, education is divided into five levels (primary and lower-secondary education, upper-secondary education, specialised education, short university and/or college education of up to 4 years) and long university and/or college education (4 years or more and postgraduate education). In the pay statistics, this data can be refined with information pertaining to educational subject area (179 education groups in total), which can be linked to information on monthly pay but not broken down by gender or directly linked to occupational classifications.

In the Swedish education statistics, information on pay can be obtained for every occupation across seven levels of education, from pre-secondary education of less than 9 years up to postgraduate education, for example the average pay for male and female employment officers with upper-secondary or short or long post-secondary education. Pay statistics are available for about 100 groups pertaining to educational fields, some of which are clearly linked to certain occupations, while others are broader.

Danish statistics provide information on the educational status of the population, e.g. information on gender is available in relation to highest level of education completed based on 93 educational groups or a number of main groups (corresponding to education levels) from primary to postgraduate level. Information on pay by educational group (89 different education groups) is available in relation to sector, type of pay (hourly/fixed), pay recipient group (e.g. employee, manager), different pay components (25 options, e.g. overtime premiums) and gender. However, this data is not linked to occupational classifications.

In Icelandic statistics, detailed information is provided on the number of students in different study programmes in tables covering ‘line of study’ or ‘detailed field of study’. These tables are available for different levels of tertiary education, upper-secondary education and non-tertiary post-secondary education. Several of these lines of study and detailed fields of study correspond to specific occupations. All tables are broken down by gender, and trends can optionally be followed over several years. However, there is no direct link to occupations or pay.

Thus, in some cases, statistical data on education can be linked to a particular occupation, provided individuals work in the area associated with their education – which is not always the case. So, there is an uncertainty factor here too. In the context of work of equal value, gender-disaggregated statistics on education and pay in their current form can mainly be used to draw attention to anomalies in the pay level of occupations in relation to their skill requirements. For organisations, such as trade unions, that are interested in knowing how the pay their members receive compares with other occupational groups facing equal demands in their work, detailed information on educational background and level can be relevant.