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1. Using the standard international classifications of occupation (ISCO-08) at two-digit level and sector (NACE Rev 2.0) at one-digit level, a matrix of jobs is created in each country. Each job is an occupation in a sector. In total, there are 43 two-digit occupations and 23 one-digit sectors, which generates 989 job cells. In practice, many of the theoretical job cells do not contain employment; there are unlikely to be many skilled agricultural workers in financial services, for example. The country total of job cells with employment varies between around 400 and just over 800 and is largely determined by country size and the European Union Labour Force Survey (EU-LFS) sample size. The bigger the workforce, the greater is the variety of possible job combinations that can be identified using LFS data.
2. The jobs in each country are ranked, based on some ranking criterion, mainly the mean hourly wage. The job–wage rankings for each country used in the most recent analysis are based on combining data from the EU-LFS annual data files for 2017–2019 and aggregated data from the Structure of Earnings Survey (SES) for 2018. These sources allowed the creation of country job–wage rankings for the 27 EU Member States.
3. Jobs were allocated to quintiles in each country (or terciles in each region for the 2019 regional analysis) based on the job–wage ranking for that country. The best-paid jobs are assigned to quintile 5, the lowest-paid to quintile 1. Each quintile in each country should represent as close as possible to 20% of employment in the starting period, i.e. jobs are assigned to quintiles based on their employment weights. Hereafter, the job-to-quintile assignments remain fixed for each country so that in all of the charts that follow a given quintile in a given country (however decomposed) always refers to employment data in a specific group of jobs exclusive to that quintile. For presentation purposes, the focus then is shifted to the change in the stock of employment at quintile level during a given period in each country, e.g. 2019–2022.
The chart below illustrates in simplified format the three steps outlined above, using some of the top-paid and lowest-paid jobs that employ large numbers at EU level as examples. (While the jobs are correctly assigned in terms of EU quintile, the individual job–wage ranks, 1–4 and 1105–1108, are for illustrative purposes only.)
Job rankings and quintile assignments carried out for each country
4. Net employment change between starting and concluding periods (in persons employed) for each quintile in each country is summed to establish whether net job growth has been concentrated in the top, middle or bottom of the employment structure. Except where otherwise indicated, all charts in the report describe net employment change by quintile for the indicated country or for the EU as a whole. The EU aggregate charts are based on applying a common EU job–wage ranking (based on the weighted average of the standardised national job–wage rankings).
5. The resulting quintile charts give a simple, graphical representation of the extent of employment change in a given period, as well as an indication of how that change has been distributed across jobs at different pay levels. A similar classification of jobs can be done using jobholders’ skills or a broad-based, multidimensional indicator of job quality as a ranking criterion. The quintile chart below, for example, illustrates employment change for the EU27 during 2019–2022. The figure should be read from the leftmost bar cluster (quintile 1, representing the lowest-paid jobs) to the rightmost cluster (quintile 5, representing highest-paid jobs). Net employment change is represented on the vertical axis, generally in thousands but sometimes in annual percentage change. The dominant features of the chart are the loss of nearly 3.4 million low-paid jobs (bottom quintile) as well as the simultaneous addition of around 2.3 million well-paid jobs (top quintile) over the pandemic and post-pandemic period.
6. This method also offers further possibilities of breaking down these net employment changes by such categories as gender, employment or professional status, and working time category: full time or part time. For a more extensive description of the data-processing involved, please refer to the extensive material in the annexes of previous EJM annual reports where the same jobs-based approach was used.