Where do graduates come from and where do they go?

I’ve written too many times about the problems in sense making from data – particularly where the labour market and education are involved. This presentation from the UK Centre for Cities makes an admiral attempt to use the data to tell a story about where students are coming from to study at Glasgow’s Universities and where they go afterwards.

It has its drawbacks – mainly due to the lack of data. For instance most of the slides fail to show movements in and out of the UK. Also, I would have loved to have more detailed data about what jobs students go into after university, but this data just is not available from UCAS at a more disaggregated level. And I am not very sure about the click bait title: “the Great British Brain Drain.” If there is a brain drain, nothing in the analysis points to one.

It is interesting to see that manufacturing still accounts for 44% of new graduate employment is Glasgow, despite manufacturing only constituting 30% of total employment in the city. This is much more that the 19& of new graduate working in the much heralded knowledge intensive business services sector.

One of their conclusions is very important: its not just about the student experience or the quality of nightlife in a city but more importantly “Ultimately it’s the jobs available to graduates which determine if they stay. By offering more, and better, opportunities the city will attract more graduates, both those who have studied in the city and those moving in for the first time from elsewhere.”

Developing a skills taxonomy

This morning’s mailing from the Marchmont Employment and Skills Observatory reports that NESTA have launched an interesting new Tool – a UK skills taxonomy:

“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 provide a rich set of data although requiring some work in interpretation. The six broad clusters of skills are:

The ten clusters (at the third layer) containing the most demanded skills are:

  1. Social work and caregiving
  2. General sales
  3. Software development
  4. Office administration
  5. Driving and automotive maintenance
  6. Business management
  7. Accounting and financial management
  8. Business analysis and IT projects
  9. Accounting administration
  10. Retail

The five skill clusters at the third layer with the highest annual median salaries are:

  1. Data engineering
  2. Securities trading
  3. IT security operations
  4. IT security standards
  5. Mainframe programming

The five clusters with the lowest salaries are:

  1. Premises security
  2. Medical administration
  3. Dental assistance
  4. Office administration
  5. Logistics administration

While the taxonomy is based on web data collected between 2012 and 2017, the approach has teh potential to be developed on the basis of real time data. And it is likely to be only one of a number of tools produced in the next two years using machine learning to analyse large data sets. The use of real-time data from web vacancies is receiving a lot of attention right now.

There is also interest in the idea of skills clusters in the ongoing debate over the impact of Artificial Intelligence on jobs and employment. Rather than whole occupations disappearing (and others surviving) it is more likely that the different skills required within occupations may change

The development of Labour Market Information systems

Over the past few years, part of my work has been involved in the design and development of Labour Market Information Systems. But just as with any facet of using new technologies, there is a socio-technical background to the emergence and use of new systems.

Most countries today have a more or less elaborated Labour Market Information system. In general, we can trace three phases in the development of these systems (Markowitch, 2017). Until the 1990s, Labour Market Information systems, and their attendant classification systems, mainly provided statistics for macroeconomic analysis, policy and planning. Between the 1990s and 2005 they were extended to provide data around the structuring and functioning of the Labour markets.

Mangozho (2003) attributes the change as a move from an industrial society to a post-industrial society (and the move to transition economies in Eastern Europe). Such a definition may be contentious, but he usefully charts changes in Labor market structures which give rise to different information needs. “While previously, the economic situation (especially the job structure) was relatively stable, in the latter phase the need for LMI increases because the demand for skills and qualifications changes fundamentally; the demand for skills / qualifications changes constantly, and because of these changes, Vocational Education and Training (VET) system has to be managed more flexibly (ETF, 1998)’.

He says: “In the industrial/pre-transition periods:

  • The relationship between the education and training system and the Labor market was more direct.
  • Occupational structures changed very slowly and as such, the professional knowledge and skills could easily be transferred.
  • Planning, even for short-term courses, could be done well in advance, and there was no need to make any projections about the future demands of occupations
  • The types of subjects and the vocational content required for specific jobs were easily identifiable.
  • There was little need for flexibility or to design tailor-made courses.
  • The education system concentrated on abstract and theoretical knowledge as opposed to practical knowledge.
  • Steady economic growth made it possible for enterprises to invest in on the job training.
  • There was less necessity to assess the relevance and adequacy of the VET system because it was deemed as adequate.
  • A shortage of skills could easily be translated into an increase of the number of related training institutions or student enrolments without necessarily considering the cost effectiveness of such measures. (Sparreboom, T, 1999).
  • Immediate employment was generally available for those who graduated from the education and training systems.”

Changes in the structure and functioning of Labour markets and the VET systems led to a greater need for comprehensive LMI to aid in the process of interpreting these structural shifts and designing effective HRD policies and programs, which provide for more linkages between the education and training systems and the Labor market.

At the same time, the reduction in the role of the state as a major employment provider and the development of market economies gave impetus to the need for a different approach to manpower planning, where the results of Labor market analysis as well as market based signals of supply and demand for skills are made available to the various economic agents responsible for the formulation and implementation of manpower and employment policies and programmes.

This led to the establishment of formal institutions to co-ordinate the generation of LMI, for instance internet based Labour Market Information Systems and the setting up of Labour Market Observatories and the development of more tangible LMI products, which provide a broad up, dated knowledge of the developments on the Labour market for different users.

Since 2005, Labour Market Information systems have been once more extended to incorporate both matching of jobs to job seekers and matching of supply and demand within Labour markets, particularly related to skills.

Data and the future of universities

I’ve been doing quite a lot of thinking about how we use data in education. In the last few years two things have combined – the computing ability to collect and analyse large datasets, allied to the movement by many governments and administrative bodies towards open data.

Yet despite all the excitement and hype about the potential of using such data in education, it isn’t as easy as it sounds. I have written before about issues with Learning Analytics – in particular that is tends to be used for student management rather than for improving learning.

With others I have been working on how to use data in careers advice, guidance and counselling. I don’t envy young people today in trying to choose and  university or college course and career. Things got pretty tricky with the great recession of 2009. I think just before the banks collapsed we had been putting out data showing how banking was one of the fastest growing jobs in the UK. Add to the unstable economies and labour markets, the increasing impact of new technologies such as AI and robotics on future employment and it is very difficult for anyone to predict the jobs of the future. And the main impact may well be nots o much in new emerging occupations,or occupations disappearing but in the changing skills and knowledge required n different jobs.

One reaction to this from many governments including the UK has been to push the idea of employability. To make their point, they have tried to measure the outcomes of university education. But once more, just as student attainment is used as a proxy for learning in many learning analytics applications, pay is being used as a proxy for employability. Thus the Longitudinal Education Outcomes (LEO) survey, an experimental survey in the UK, users administrative data to measure the pay of graduates after 3, 5 and 0 years, per broad subject grouping per university. The trouble is that the survey does not record the places where graduates are working. And once thing we know for a certainty is that pay in most occupations in the UK is very different in different regions. The LEO survey present a wealth of data. But it is pretty hard to make any sense of it. A few things stand out. First is that UK labour markets look pretty chaotic. Secondly there are consistent gender disparities for graduates of the same subject group form individual universities. The third point is that prior attainment before entering university seems a pretty good predictor of future pay, post graduation. And we already know that prior attainment is closely related to social class.

A lot of this data is excellent for research purposes and it is great that it is being made available. But the collection and release of different data sets may also be ideologically determined in what we want potential students to be able to find out. In the same way by collecting particular data, this is designed to give a strong steer to the directions universities take in planning for the future. It may well be that a broader curriculum and more emphasis on process and learning would most benefits students. Yet the steer towards employability could be seen to encourage a narrower focus on the particular skills and knowledge employers say they want in the short term and inhibit the wider debates we should be having around learning and social inclusion.

 

Learning about Careers: Open data and Labour Market Intelligence

I’ve spent a lot of the last two months writing papers. I am not really sure why – other than people keep asking me to and I really do have a built up load of things which I haven’t written about. But one bad consequence of all this is I seem to have abandoned this blog. So,  time to start catching up here.

This paper – Learning about Careers: Open data and Labour Market Intelligence – is co-written with Deirdre Hughes. It is a preprint and wil be published in RIED – Revista Iboeroamericana de Educación a Distancia (The Iberoamerican Review of Digital Education) some time soon.

The full paper can be found on Research Gate or alternatively you can download it here. The abstract is as follows:

“Decisions about learning and work have to be placed in a particular spatial, labour market and socio-cultural context – individuals are taking decisions within particular ‘opportunity structures’ and their decisions and aspirations are further framed by their understanding of such structures. This article examines ways in which learning about careers using open data and labour market intelligence can be applied. An illustrative case study of the LMI for All project in the UK shows the technical feasibility of designing and developing such systems and a model for dissemination and impact. The movement towards Open Data and increasingly powerful applications for processing and querying data has gathered momentum. This combined with the need for labour market information for decision making in increasingly unstable labour markets have led to the development and piloting of new LMI systems, involving multiple user groups. Universal challenges exist given the increasing use of LMI, especially in job matching and the rapidly expanding use of open source data in differing education and employment settings. We highlight at least six emergent issues that have to be addressed so that open data and labour market intelligence can be applied effectively in differing contexts and settings. We conclude by reflecting on the urgent need to extend the body of research and to develop new methods of co-constructing in innovative collaborative partnerships.”