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.”

 

Are job algorithms good enough?

We’ve all made jokes about the jobs that various ‘professional’ social networks recommend for us.  This morning I had a message from ResearchGate:

LinkedIn is no better. Here are tworesearchgate jobs it recently found for me:

linkedin

Goodness knows how they  vaguely thought I was qualified for these jobs. But never mind – it is only the usual nonsense form free social networks, we think. But it does matter. These reconsiderations come through algorithms. And nearly every Public Employment Service I have talked to is either trialling or considering trialling software which matches applicants to jobs. OK, the algorithms may be better written. And probably the employment services have more data on both applicants and jobs that has the likes of ReseachGate and LinkedIn. But in seeking to provide a better service at less cost through the use of technology the employment services are ignoring that many people need guidance and support when seeking employment form qualified professionals. Taking a job is not like ticking a like on a social website.  It involves serious decisions which can affect peoples futures and the future of their family.  And, at the moment, Artifical Intelligence is not enough for helping in those decisions.

Happy birthday, Graham Attwell

Today the fellow-bloggers on Pontydysgu site can congratulate Graham Attwell on his birthday. I hope there is no home-made rule that would prevent us from celebrating this day via his own website.  Cheers, Graham!

Years and more …

Happy birthday, Graham Attwell

Today the fellow-bloggers on Pontydysgu site can congratulate Graham Attwell on his birthday. I hope there is no home-made rule that would prevent us from celebrating this day via his own website.  Cheers, Graham!

Years and more …

Why is there such a big gender difference in graduate employment

salaries grad

In our work on Labour Market Information Systems, we frequently talk about the differences between labour market information and labour market intelligence in terms of making sense and meanings from statistical data. The graph above is a case in point. It is one of the outcomes of a survey on Graduate Employment, undertaken by the UK Higher Education Statistics Agency (HESA).

Like many such studies, the data is not complete. Yet, looking at the pay by gender reveals what WONKHE call “a shocking picture of the extent of the pay gap even straight out of university, and how different subject areas result in a diverse range of pay differences.”

Understanding why there is such a gap is harder. One reason could be that even with equal pay legislation, employers simply prefer to employ male staff for higher paid and more senior jobs. Also, the graph shows the subject in which the students graduated, not the occupational area in which they are employed. Thus the strikingly higher pay for mean who undertook nursing degrees may be due to them gaining highly paid jobs outside nursing. Another probable factor in explaining some of the pay gap is that the figures include both full and part time workers. Nationally far more women are employed part time, than men. However, that explanation itself raises new questions.

The data from HESA shows the value of data and at the same time the limitations of just statistical information. The job now is to find out why there is such a stark gender pay gap and what can be done about it. Such ‘intelligence’ will require qualitative research to go beyond the bald figures.