In our work, we regularly review research – both qualitative and quantitative – on occupational requirements and content, to gain insights into workforce changes both global and local.

One project we are doing right now involves us analysing data in the rich US Occupational Network (O*NET) database archive.  In doing so, we are looking for evidence in the data to qualify (or perhaps refute) the changes that more qualitative employment research is telling us.

Since O*NET is a living archive, with analysts and incumbents in almost 1,000 occupations being regularly (re-)surveyed on a rolling basis, we can be reasonably confident that changes in the data can be regarded as representative of the US labour market at least, and in many cases can be read across to the labour markets in other countries such as the UK.

In our project we are looking in particular at Manufacturing and Engineering occupations and examining ‘hot’ issues like the growth of data science, the ‘greening’ of work, skills for innovation and the merging of duties between, say, engineers and technicians.  Our main question always is, does the data support what we are hearing from the market?

To start our project, we looked for major trends in the O*NET data over the last 8 years, since 20111.  One particular area of interest is the so-called ‘soft skills’.  In our conversations with labour market experts and practitioners, we find increasing emphasis on these skills, and much research exists claiming employers actually care more about soft skills than they do about technical abilities like reading comprehension and mathematics.  One practitioner – a talent head in a major financial institution – said to us recently ‘…we will teach [graduates] the skills they need, but we need to know they have good workplace [soft] skills in the first place.’

What are soft skills?  There appears to be no definitive list, but a typical list might include communication, teamwork, adaptability, problem solving, critical observation, conflict resolution and leadership2.  In O*NET, probably for historical reasons there is no category called ‘soft skills’, but the elements can instead be found across O*NET’s various data ‘domains’.  For example, O*NET lists adaptability/flexibility as an ‘element’ in its work styles3 ‘domain’, while problem solving is split between the ability4 (element: problem sensitivity) and cross-functional skill5 (element: complex problem solving) domains.

The figure is a screen shot from Tableau (our tool of choice for data visualisation) that shows the significance ratios6 of elements in the work styles domain between O*NET v16.0 (March 2011) and v24.0 (Aug 2019). The lower chart shows the significance ratios for each element in the two releases and the upper chart shows the increase/decrease in the ratios in v24.0 compared to v16.0.  We have marked up one soft skill – Analytical Thinking – as an example of the linkage between the two charts.

In a related blog, we will present some analysis on the trends we found using this method of data visualisation and analysis.

Notes:

  1. The current O*NET occupational classification – SOC 2010 – was introduced in O*NET v15.1 in Feb 2011 so gives us an uninterrupted occupational timeline to the present version (v.24.0, Aug 2019).
  2. https://www.monster.com/career-advice/article/soft-skills-you-need
  3. O*NET defines work styles as ‘personal characteristics that can affect how well someone performs a job’.
  4. O*NET defines abilities as ‘enduring attributes of the individual that influence performance’.
  5. O*NET defines cross-functional skills as ‘developed capacities that facilitate performance of activities that occur across jobs’.
  6. We define ‘significance’ as the ratio (in %) of the total score across the occupations for each element in a domain, divided by the total score for all the elements in that domain. So,for example, a score of 10% means that the total score for that element is 10% of the total of the whole domain. By implication therefore, if there are more than 10 elements in the domain, this is an indication that the element is more significant than average.