What is the data about?
The data used in this post relates to information brought together by Eurostat in their Experimental statistics on ‘Skills’. Specifically, the “over-qualification rate by economic activity for the period 2008 to 2016”.
Rather than paraphrase the Eurostat definition, you can read it here. However, to briefly set out why we thought it was an interesting dataset for #VisualisingHE to have a quick play with, we’ve taken a few sentences from the Eurostat preamble about why we may need indicators about skills:
The European Commission communication ‘A New Skills Agenda for Europe‘ defines among others policy priorities and sets out actions to be undertaken with the aim to make better use of people’s existing skills. It should be ensured that the skills available in the labour market match the needs of businesses and the economy.
This undertaking includes finding ways to improve the matching between skills and labour market needs as well as bridging the gap between education and work. Skills mismatch indicators should measure the gap between demand and supply of skills (macro-level) as well as conditions of workers, jobs or vacancies (micro-level).
Where does the data come from?
The indicators are derived by combining available figures using EU Labour Force Survey (EU-LFS).
The use of a single source ensures consistency, comparability and reliability, and the methodology behind is based on the literature in the field (‘The skill matching challenge: Analysing skill mismatch and policy implications’ – Cedefop 2010) and on existing empirical exercises (‘Employment and labour demand’ – Eurostat 2016 and EU-LFS ad hoc module 2000).
What does the original data look like?
The image below is an example of one tab of the 8 occupational job categories, 2008-2016 by European country: Wholesale and retail trade, repair of motor vehicles and motorcycles
How might we present this data for better consumption?
Having brought the data together using Easymorph, Adam wanted to understand the distribution in proportions of over-qualified roles by occupation types for a single year (2016) to establish which sectors where hotbeds for skills mismatches across Europe and whether the data presented any areas with differing deviations.
The following chart highlights Transportation and storage as the occupation type with the highest median proportion surveyed, with Spain the country with the highest rate @ over 70%.
This visualisation of the data provides the ability to grasp all of the proportions for all of the countries, for all of the occupation groups in one visual. The interactive viz also enables you to delve into where countries appear in the distribution of proportions.
How might we present the idea of change over time?
Wanting to recreate elements of the original data, a focus on the percentage point change in proportion over qualified between 2008 and 2016 followed:
Again, presenting the change for all countries and all occupations using the box plots enables you to contextualise a particular countries change against that of the other countries.
Focus on a single occupational grouping
Could we create a simple highlight table only, the success of this is wholly down to Chris Love of the Information Lab, he helped me work through the calculations needed to rank by a single year, which turned out to be a little more complicated than initially thought. blog post here.
How could we present all the data in one visual?
Could we go one further and present all of the year data, all of the country data and all of the occupational data in one chart? And what would you get out of it?
Having a bit of fun with presenting change with Radials? This view does draw out the changes so that you can see that wholesale and retail have had an increase in virtually all countries where as something like construction has had some big decreases and a fair few more moderate increases.
What about trend over time?
Elena took a more focused approach and only produced one visualisation that expands on the idea of change over time.
By utilising a slope graph and Tableau’s latest out-of-the-box style if step line charts, Elena’s dashboard allows the user to see how a country’s overqualification rate changed from 2008 to 2016 in each of the eight occupational areas whilst also allowing the reader to see the fluctuation over time. The colour indicates whether the rate for the country in the given year was above or below the median.
So….. Did we find a story?
The data threw up plenty of small nuggets but no overall narrative so for the moment this post will be a demonstration of what data is there and how you can visualise the data in a few ways to present the proportions and change in proportions in more ‘graspable’ ways. This also allows the viewer to identify some of the outliers and find trends in either a particular occupation type, or country.
Thanks for reading!
Team #VisualisingHE
(doing a post on something a little wider this month – Education, moreover over education!)
VisualisingHE 