The increasing power of processors and the advent of Open Data provides us information in many areas of society including about the Labour Market. Labour Market data has many uses, including for research in understandings society, for economic and social planning and for helping young people and older people in planning and managing their occupation and career.
Yet data on its own is not enough. We have to make sense and meanings from the data and that is often not simple. Gender pay gap figures released by the UK Office of National Statistics last week reveal widespread inequality across British businesses as every industry continues to pay men more on average than women. This video by Guardian newspaper journalist Leah Green looks at the figures and busts some of the common myths surrounding the gender pay gap.
“Skill shortages are costly and can hamper growth, but we don’t currently measure these shortages in a detailed or timely way. To address this challenge, we have developed the first data-driven skills taxonomy for the UK that is publicly available. A skills taxonomy provides a consistent way of measuring the demand and supply of skills. It can also help workers and students learn more about the skills that they need, and the value of those skills.” NESTA
It should help with careers guidance and is ideal for people looking at the return to differing career choices and how you get there. NESTA began with a list of just over 10,500 unique skills that had been mentioned within the descriptions of 41 million UK job adverts, collected between 2012 and 2017 and provided by Burning Glass Technologies. Machine learning was used to hierarchically cluster the skills. The more frequently two skills appeared in the same advert, the more likely it is that they ended up in the same branch of the taxonomy. The taxonomy therefore captures ‘the clusters of skills that we need for our jobs’.
The final taxonomy can be seen here and has a tree-like structure with three layers. The first layer contains 6 broad clusters of skills; these split into 35 groups, and then split once more to give 143 clusters of specific skills. Each of the approximately 10,500 skills lives within one of these 143 skill groups.
The skills taxonomy was enriched to provide estimates of the demand for each skill cluster (based on the number of mentions within adverts), the change in demand over recent years and the value of each skill cluster (based on advertised salaries). The estimates of demand get us halfway to measuring skill shortages. Most importantly, a user can search on the taxonomy by job title, and discover the skills needed for a wide range of jobs.
The ten clusters (at the third layer) containing the most demanded skills are:
MPs on the UK House of Commons Education Committee have released a report titled “Value for Money in Higher Education.” They draw attention to figures from the Office for National Statistics (ONS) that indicated 49 percent of recent graduates (within five years of achieving their degree) were in non-graduate roles in 2017.
This is a significant increase over the proportion at the start of 2009, just after the 2008 financial crash, when 41 percent of recent graduates were in that position. It is matched by a very similar rise even among the population of graduates taken as a whole—including mature students—from 31 percent to 37 percent in the same years.
The report stated: “Higher education institutions must be more transparent about the labour market returns of their courses.” It came with the warning that “too many universities are not providing value for money, and … students are not getting good outcomes from the degrees for which so many of them rack up debt.”
As the title of the report implies, much of the attention on graduate employment is due to the political controversy over the funding of Higher Education in the UK and the cost of participation in degree courses.
But there is another issue which has received less attention: how graduate (and non graduate) jobs are defined.
1.The skill level groups are created by grouping jobs together based on their occupation according to the Standard Occupation Classification (SOC) 2010 lower level groups. The occupation group is not available for some workers, these have been excluded from the total.
Occupations were grouped by the skill level required according to the following guidelines:
2,1. High – This skill level is normally acquired through a degree or an equivalent period of work experience. Occupations at this level are generally termed ‘professional’ or managerial positions, and are found in corporate enterprises or governments. Examples include senior government officials, financial managers, scientists, engineers, medical doctors, teachers and accountants.
2,2. Upper-middle – This skill level equates to competence acquired through post-compulsory education but not to degree level. Occupations found at this level include a variety of technical and trades occupations, and proprietors of small business. For the latter, significant work experience may be typical. Examples of occupations at this level include catering managers, building inspectors, nurses, police officers (sergeant and below), electricians and plumbers.
2,3. Lower-middle – This skill level covers occupations that require the same competence acquired through compulsory education, but involve a longer period of work-related training and experience. Examples of occupations at this level include machine operation, driving, caring occupations, retailing, and clerical and secretarial occupations.
2,4. Low – This skill level equates to the competence acquired through compulsory education. Job-related competence involves knowledge of relevant health and safety regulations and may be acquired through a short period of training. Examples of occupations at this level include postal workers, hotel porters, cleaners and catering assistants.
The sentence “Occupations at this level are generally termed ‘professional’ or managerial positions, and are found in corporate enterprises or governments.” Arguably this ignores ongoing changes in the economy with high skilled technical jobs being created by Small and Medium Enterprises rather than large corporations. As Malcolm Todd, Provost (Academic) of the University of Derby, points out in an article in WonkHE: “The current government methodology of using traditional Standard Occupational Codes (SOC) to declare which roles are graduate level is dated. It’s not reflective of the current employment market and is not ready for the future job market. Codes are based on traditional views of careers and highly skilled roles, not the whole requirements of a role.”
He draws attention to Teaching Assistants working with pupils that have special education needs and disabilities, and emerging jobs in the growing retail, social care and hospitality, many of which require high skills but are classified as non graduate jobs. At the same time, jobs presently classified as requiring a degree such as accountants are like to decline due to automation and the use of Artificial Intelligence.
To some degree, the debate is clouded by a perception that graduate level jobs should command a higher salary (an argument used by the Government to justify high university tuition fees). Yet wage growth in the UK has been low across all sectors since the onset of the recession in 2008.
But with growing skills required in a range of different jobs, maybe it is time for a new look at how graduate jobs are classified or even whether dividing employment into graduate or non graduate occupations is relevant any more.
This year, the Annual Meeting of the European Network on Regional Labour Market Monitoring is being organised in co-operation with the Marchmont Employment and Skills Observatory at the University of Exeter. The conference explores the transformation of skills needs in European regions and localities. On 10 September, it focuses on the specific needs of different target groups, such as young people entering the labour market, low and unskilled workers or unemployed. The emphasis is on the availability of data for monitoring such developments and different concepts for operationalising and measuring skills at regional level. On 11 September, the focus is on the use of Big Data for studying the changes in the regional and local labour markets and the governance of (vocational) education and training systems in the light of these new insights.
The conference takes place on the 10 and 11 of September at Exeter University and is free to attend. For more details see the LEP network webpage.