Category Archives: labour markets

Learning about Careers: Open data and Labour Market Intelligence

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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.”

 

Why is there such a big gender difference in graduate employment

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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.

Supporting start up businesses

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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

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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

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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.