Archive for the ‘labour markets’ Category

The development of Labour Market Information systems

August 29th, 2018 by Graham Attwell

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

August 2nd, 2018 by Graham Attwell

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

August 1st, 2018 by Graham Attwell

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.”

 

The slow cancellation of the future

March 12th, 2018 by Graham Attwell

leeds-ucu-strike-christianhogsbjergWriting in the Guardian, Becky Gardiner, a senior lecturer at Goldsmiths University in London, explains why she is on strike. Although the strike in the UK universities is now into its fourth week and is ostensibly abut cuts to pensions it raises wider issues’

‭Becky says:

My favourite banner on the picket line reads “Against the slow cancellation of the future”, a phrase popularised by the late cultural theorist, Mark Fisher. In the grip of neoliberalism, we begin to believe that there is no alternative, Fisher told us.

In universities, this slow draining of hope began with the introduction of tuition fees in 1998, and gathered pace when they were tripled in 2010. Successive governments, enthusiastically aided by overpaid senior management drawn from outside the university sector, have turned higher education into a utilitarian and consumer-driven activity that students buy in exchange for skills for the job market.

The latest idea coming from the UK Department for Education (DfE) is to introduce a ratings system would which would allow students to make “consumer-style comparisons of degree courses.” Subjects will be given a gold, silver or bronze award, and details will be made available about post-degree employment prospects, potential earnings and dropout rates, according to the DfE.

The problem for DfE is that for all their efforts educations is not a consumer good. And statistics suggest that the best indicator of potential earnings comes not from which university or indeed which subject is studies but is dependent on the social class that the student comes from. So those courses with more upper class students will have the best post employment prospects, presumably attracting more upper class students and reinforcing their positioning in the consumer style ratings. The slow cancellation of the future seems to be speeding up.

Why is there such a big gender difference in graduate employment

June 16th, 2017 by Graham Attwell

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.

Interpreting and presenting data

March 1st, 2017 by Graham Attwell

I have been working on the contents for week 4 of the free #EmployID MOOC on The Changing World of Work, taking place on the European EMMA platform and starting in late March. Week 4 is all about Labour Market Information – or as I prefer to call it, Labour Market Intelligence – and how we can use labour market data both for job seekers and young people choosing careers and by advisers and other professionals working in the careers and labour market domain.

One of the major challenges is how to represent data. This presentation, Data is beautiful: Techniques, tools and apps for sharing your results by Laura Ennis, provides some good practical advice on how to present data. It come from a talk she did at Leap Into Research 2017.

Making Multimedia for MOOCs

February 22nd, 2017 by Graham Attwell


I’ve been bogged down for the past two weeks writing reports and trying to catch up on a dreadful backlog of work. But that’s another story.

Amongst other things, this week I am producing content for the European EmployID MOOC on the ‘Changing World of Work.‘ As the blurb says:

Do you want to be prepared for the challenges of the changing labour market?

Do you want to better understand and apply skills related to emotional awareness, active listening, reflection, coaching skills, peer coaching and powerful questioning?

Do you want to explore tools for handling Labour Market Information (LMI) and the digital agenda?

This course has been devised as part of the European EmployID project, for Public Employment Services (PES) practitioners and careers professionals. Our 5 lessons will run over a period of 6 weeks with an estimated workload of 3.5 hours per week; the total workload is expected to be 17.5 hours.

I am producing the content for week 5, on Labour Market Information. Its not by any means the first content I have written for on-line courses, but I still feel I am learning.

I find it quite hard to gauge how much content to produce and how long it will take to work through it. I also find it hard switching from writing academic stuff and reports to writing course material and getting the language register right.

One thing I am trying to do, is add more multi media content. The big issue here is work flow and production. I am pretty happy with the video above. OK it only lasts one and a half minutes but I managed to make it from scratch in about two hours.

I made it using the Apple keynote presentation software. All the images come from the brilliant Pixabay website and are in the public domain. And then it was just a question of adding the audio which can now be done inside Keynote, exporting to video and uploading to YouTube. I am planning to make two or three more videos as part of the course. It is much faster than editing video and still produces a reasonabel result I think.

Supporting start up businesses

July 5th, 2016 by Graham Attwell

 

 

 

 

One of the best things about Twitter is the ability to follow links to all kinds of things you probably would never have been to without it. And so I find in my notes somewhere the link to an article in Quartz – an online magazine (?) about which I know nothing. The link is to a loosely researched article about entrepreneurism – making the point that there is not much thing as an entrepreneurial gene but rather propensity to take risk and to set up new businesses is more like to be related to access to money – in other words to class.

The article, attributed to REUTERS/Allison Joyce, quotes University of California, Berkeley economists Ross Levine and Rona Rubinstein who “analyzed the shared traits of entrepreneurs in a 2013 paper, and found that most were white, male, and highly educated. “If one does not have money in the form of a family with money, the chances of becoming an entrepreneur drop quite a bit,” Levine tells Quartz.”

Entrepreneurship is all the trend in Europe at the moment, especially in the recession and austerity hit southern countries, where setting up a business is seen as one of the few ways of getting a job. However the rhetoric seems to overplay the potential of technology (everyone can be the next Steve Jobs!), whilst ignoring sectors of the economy such as tourism which probably represent better opportunities within the existing labour market.

At the same time programmes such as the EU Youth Guarantee fund are being used to set up support agencies for young people wishing to set ups their own business and we are seeing the increasing emergence of co-working spaces for new enterprises. But anecdotal evidence – and some reports although I cannot find them at the moment – suggest that many of these businesses are struggling to survive beyond the first one or two years. In austerity Europe bank capital remains hard to come by and most young people do not have access to their own funds to consolidate and explained their business. Although initiatives like the EU SME programme are very welcome, access to such funding is not simple and anyway the amount of grants on offer are simply insufficient. As European politicians slowly wake up to the disaster austerity policies have wrought, then establishing better support for new businesses should be a priority, tied to easy access to small business start up capital.

Understanding that Brexit vote

July 4th, 2016 by Graham Attwell

Many of my friends from outside the UK have asked me however could people have voted for Brexit. And I have read countless newspaper columnists and analysts asking the smae question (with usually not very profound answers). The best explanation I have come across was posted by Ron Johnston, Kelvyn Jones and Davidn in an article entitled Predicting the Brexit vote: getting the geography right (more or less) on the London School of Economics Politics and Policy blog. Using a large body of polling data collected by YouGov they had earlier this year pointed to “clear evidence suggesting that young people and those with higher-level educational qualifications were much more likely to support Remain, whereas older voters and those with few or no qualifications were much more likely to support Leave.”-And despite they misread the likely outcome of the referendum, their findings largely tie up with a post referendum analysis of the results. Following a detailed analysis they find that:

There are substantial parts of the country where large numbers of people have lost out from the deindustrialisation and globalisation of the last few decades of neo-liberal economic policies, and where the educational system has not helped large proportions of the young to equip themselves for the new labour market. Increasing numbers in these disadvantaged groups were won over during the last few decades by the campaigns in parts of the print media, taken up by UKIP since the 1990s, linking their situations to the impact of immigration – uncontrollable because of the EU freedom of movement of labour principle.

From this they conclude that “class, as expressed through educational achievements, delivered Brexit.”

Linking austerity (which has done nothing good for the vast majority of people in the UK) to the growing inequalities in the education system is important to understanding the Brexit vote. Of course the vote can be seen as an attempt to kick the ruling Tory party toffs. Yet it is very hard to argue for the EU, given that they have been one of the major transnational proponents of austerity.

However, I have some reservations about the idea that “the educational system has not helped large proportions of the young to equip themselves for the new labour market”. On the one hand this is obviously true. But the problem is that the new labour market is largely comprised of low paid and insecure jobs, mainly in the service sector. Many of those who have been able to pay for an increasingly expensive university degree are working in what are classified as non degree jobs. Education and the labour market have to be understood as parts of a symbiotic system. Education alone will not change the reality of lack of opportunity in deindustrialised areas of the UK. Lack of opportunity for meaningful and adequately paid employment and lack of educational opportunity are two sides of the same coin in a currency called austerity.

Making sense of data about education and jobs

June 6th, 2016 by Graham Attwell

restorer
High or low skills? Graduate job or not?

For a number years now I have been working on projects developing the use of open data for careers counselling, advice and guidance. This work has been driven both by the increasing access to open data but also by the realisation of the importance of Labour Market Information (LMI) for those thinking about future education and / or jobs. And of course with high levels of job insecurity, such thinking becomes more urgent and in an unstable economy and labuor market, more tricky.

Yet even if we clean the data, add it to a database, provide and open API for access and develop tools for data visualisation, interpretation is still not easy. Here is one case, taken from this mornings Guardian newspaper.

employment graph

 

Although the article is using the chart to show the rapid growth in knowledge intense occupations, I am not sure it does. Assuming that these are percentage change based on the original job totals, it probably show growth in low skilled jobs is far outstripping high skilled work, especially in the last 12 months. And that is taking into account that (once again probably) most job loss due to technology is focused din low skilled areas – e.g the quoted 70,00 jobs lost in supermarket check outs due to automation.

I am also interested to see from wonkhe that “The Higher Education Statistics Agency (HESA) who have been running the Destination of Leavers Survey (DLHE) and its predecessors for 21 years, are now consulting widely on the future of assessing graduate outcomes.” For some time now there has been disquiet about the numbers of graduates working in ‘non graduate’ jobs. And that raises questions – just like the graph above focusing on high skills occupations – on just what a graduate job is. André Spicer, professor of organisational behaviour at the Cass Business School, City University London has cited “studies suggesting that the jobs which require degree-educated employees have peaked in 2000 and may be going down” and notes that many people apparently employed for their high-level specialist skills end up doing sales and marketing or fairly routine generalist work.

All this of course is highly subversive. Officially we are moving towards a high skilled economy needing more graduates and requiring higher level apprenticeships. My feeling in country slick Spain with high youth unemployment is what we need are apprenticeships in areas like construction and hospitality – both because they are sectors which can provide employment and also where higher skills are desperately needed to improve quality and productivity. Yet for governments there is an awful temptation to launch programmes in new ‘sexy’ areas  like games technologies despite the scarcity of jobs in these fields.

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