SCB is working to gradually introduce the disclosure control method Cell Key Method (CKM) in the RMI tables. The method involves adding a small, controlled random variation to statistical values when they are produced. This makes it possible to present detailed statistics without having to suppress values (for example, with dots) or reduce the level of detail in the data. It enables the presentation of RMI statistics at a more detailed level, down to the municipal level, as well as the introduction of additional reporting variables. 

Since last year, the indicator Matching – education (E3) has been available using CKM. This year, three existing indicators are republished using the disclosure control method: 

• Job flows (E2)
• Matching – occupation (E5)
• Education flows (U3)

In all RMI tables that use CKM, statistics are presented for municipalities instead of the previously used, broader functional analysis regions (FA regions). In addition, new, more detailed reporting variables have been introduced, such as five-year age groups. The age range for the four indicators E2, E3, E5, and U3 has also been extended from 65 to 69 years, as an adaptation to changing retirement ages.

Right skills in the right place – how large a share has the appropriate education?

The indicator Matching – occupation (E5) refers to the match between occupation and education among employees aged 20–69. It shows to what extent employees’ current occupations correspond to their education. 

A high share of employees with fully matching education within a given occupation indicates that the education system largely meets the labour market’s demand for skills. This relationship is particularly evident in so-called regulated professions, where there are formal requirements for education, licensing, or other forms of state authorization in order to practice the occupation.

With the exception of elected officials and politicians—where all levels and fields of education are considered fully matched in SCB’s matching assessments—as well as managers in finance and similar fields, the list of occupations with the highest shares of employees with fully matching education is dominated by licensed and other regulated professions.

In practice, however, it is difficult to measure a complete match between occupation and education, even within licensed professions. One reason may be that the employee is working in the occupation as part of their training and therefore has not yet completed their degree. Another important reason is that within all occupational groups there are individuals who do have an education corresponding to the occupation, but who also possess an additional qualification that is longer or at a higher level. In such cases, it is the latter qualification that is used in the matching assessment.

A low share of employees with fully matching education within a given occupation, on the other hand, may be due to the occupation lacking clear or established educational pathways, making it common for people to enter through experience, practical training, or shorter courses. This means that such occupations often have a broad recruitment base, where individuals with different types of backgrounds can work. This is also reflected in the list below of occupations with the lowest share of employees with fully matching education.

At the same time, a low share of matched individuals may also indicate skills shortages or recruitment difficulties. In such situations, employers may need to lower formal requirements or recruit individuals with educational backgrounds different from those normally sought.

Improved regional detail in matching statistics with CKM 

Matching statistics from the indicator Matching – occupation (E5), using the disclosure control method CKM, are now available down to the municipal level. The same applies to all other RMI tables published using CKM. 

The maps below show the number of fully matched individuals in the skilled trades occupations of roofers, floor layers, insulation workers, and glaziers in southern Sweden. The most detailed level of reporting previously available was functional analysis regions (FA regions), but statistics can now be presented at the municipal level without the need to suppress values for confidentiality reasons, which previously meant that some municipalities were shown with values replaced by dots.