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.

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.

Jobs in cyber security

In a new fact sheet the Tech Partnership reveals that UK cyber workforce has grown by 160% in the five years to 2016. 58,000 people now work in cyber security, up from 22,000 in 2011, and they command an average salary of over £57,000 a year – 15% higher than tech specialists as a whole, and up 7% on last year. Just under half of the cyber workforce is employed in the digital industries, while banking accounts for one in five, and the public sector for 12%.

Productivity and vocational education and training

apprenticesInterest in Vocational Education and Training (VET) seems to go in cycles. Its always around but some times it is much more to the forefront than others as a debate over policy and practice. Given the pervasively high levels of youth unemployment, at least in south Europe, and the growing fears over future jobs, it is perhaps not surprising that the debate around VET is once more in the ascendancy. And the debates over how VET is structured, the relation of VET to higher education, the development of new curricula, the uses of technology for learning, the fostering of informal learning, relations between companies and VET schools, the provision of high quality careers counselling and guidance, training the trainers – I could go on – are always welcome.

Whilst in some countries like the UK deregulation seems to have created many jobs, most of these are low paid and insecure.

Higher productivity requires innovation and innovation is in turn dependent on the skills and knowledge of the workforce. But in a time of deregulation there is little incentive for employers to invest in workforce training.

There are signs that some companies are beginning to realise they have a problem. There has been a notable interest from a number of large companies in supporting new apprenticeship programmes and not just in the German speaking countries. In Spain the recently launched Alliance for FP Dual is making slow but steady progress in persuading companies to support the FP Dual alternance or apprenticeship programme. There remain many obstacles, not least the continuing austerity programme, political instability and the perilous financial position of many small and medium enterprises. I will talk more about some of these issues in forthcoming articles on this web site, coming out of the findings of a  small research project in Valencia sponsored by the International Network on Innovative Apprenticeship (INAP).

But to be successful initiatives like the Spanish FP Dual and the wider EU backed Alliance for Apprenticeships have to be linked to wider programmes to promote innovation. Without some degree of labour market regulation this is going to be hard to achieve.

Jobs of the Future

There is a lot of speculation at the moment as to the jobs of the future. On the one hand, it is said that we are educating young people for jobs which do not yet exist; on the other hand there are dire predictions that up to of existing 55 per cent of jobs may disappear to automation in the next five years.

If it is hard as a researcher who works with labour market data to make sense of all this, imagine what it is like for young people trying to plan a career (and if doing a degree in the UK, running up major debt).

However, there is beginning to appear some more nuanced research on the future of jobs. Michael Chui, James Manyika, and Mehdi Miremadi have just published the initial report on a research project looking at how automation will affect future employment. The report, entitled ‘Where machines could replace humans—and where they can’t (yet)’, is based on detailed analysis of 2,000-plus work activities for more than 800 occupations. Using data from the US Bureau of Labor Statistics and O*Net, they have quantified both the amount of time spent on these activities across the economy of the United States and the technical feasibility of automating each of them.

Their overall finding is that while automation will eliminate very few occupations entirely in the next decade, it will affect portions of almost all jobs to a greater or lesser degree, depending on the type of work they entail.
automation
Each whole occupation is made up of multiple types of activities, each with varying degrees of technical feasibility. In practice, they explain, automation will depend on more than just technical feasibility. Five factors are involved: technical feasibility, costs to automate, the relative scarcity, skills and costs of workers who might otherwise do the activity, benefits (e.g. superior performance) of automation beyond labour costs substitution and regulatory and social acceptance considerations.
The likelihood and ease of automation depends on the types of activities organised on a continuum of less susceptible to automation to more susceptible to automation: managing others, applying expertise,  stakeholder interactions, unpredictable physical work, data collection, processing data, predictable physical work. Thus occupations like accommodation, food service and manufacturing which include a large amount of predictable physical work are likely to be automated, similarly work in finance and insurance which involves much processing of data. On the other hand jobs in construction and in agriculture which comprise predominantly unpredictable physical work are unlikely to be automated, at least at present. And there is good news for teachers: “the importance of human interaction is evident in two sectors that, so far, have a relatively low technical potential for automation: healthcare and education.”