Open jobs and the case for making the labour market more intelligent
How data and new tools can empower job seekers
Open jobs and the case for making the labour market more intelligent
What’s the need?
The UK labour market works pretty well by some measures: the most recent statistics show an employment rate at 75.3 per cent, the highest for decades.
But there are many other signs that the labour market is far less efficient than it could be; stagnant social mobility; stagnant pay; stagnant productivity; and major failures of transition to work for many young people.
In and around labour markets lots of innovation is happening. The tech giants have started engaging - for example with the recent Google for jobs - and larger companies now benefit from highly sophisticated integrated HR, talent and learning management systems and digital tools for talent acquisition like the Ideal platform.
But the benefits of these new innovations are having little or no impact on much of the workforce. Many job-seekers still lack basic information about their career prospects – what skills they need to thrive in different sectors, what jobs they might be able to secure and how to plan their career path to stay relevant in the light of likely changes over the next decades.
Despite recent moves to galvanise the careers system, many children in schools get careers advice largely untouched by new data analytic tools. Universities still do relatively little to guide students and many rely on career-development opinion pieces published online and contacts.
There’s been a proliferation of websites and apps. But, as a recent survey showed, most are not well linked and don’t use the best available data sets and the net result for many job seekers is confusion rather than clarity.
So, although labour markets are often rich in data, its potential is not being realised
We believe there is a solution to this problem – the deliberate cultivation of an open commons of real time labour market data, available to everyone: let’s call it Open Jobs for short. That would allow UK cities and regions to achieve a far more intelligent jobs market, guided by accurate real time data and analytic tools to help decision makers at every level – from Whitehall departments to school leavers.
The promise would be to achieve the combination of observation, analysis, prediction and action that we have called a ‘collective intelligence assembly’ – equivalents of which exist in other fields ranging from cancer care to the environment – in this case, with the aim of empowering job seekers and employers to do better.
Achieving this would require serious leadership and a genuine partnership of government, business, LEPs and others, but it would not be expensive relative to potential impact.
The elements of Open Jobs and a smart labour market commons
In what follows, I set out the building blocks of a smart labour market commons and show the existing tools, some developed by Nesta, which already point the way to how this could work.
1. Observation – accurate and reliable information on the state of the labour market, accessible in real time
The first priority is a labour market that has detailed and real time information about what is happening: what skills are being looked for and where; what courses are on offer; what’s happening to pay; what’s happening to location.
Much of this data exists. But to be made useful it needs to be combined and offered as a commons. This can be achieved through combining public data, commercial data and web scraping to determine what jobs exist in which sectors, what is their geographical distribution, at what pay and what are the key skill requirements.
Nesta has shown this through the method developed for the TechNation maps of the UK’s digital landscape, including jobs, pay and productivity. These provide some of the most advanced descriptions of an economy in real time anywhere in the world.
Nesta’s new Skills Profiler goes a step further. It draws on all online job advertisements in the UK to power a tool that allows individuals to assess potential job opportunities, pay potential, skills requirements and gaps in education that must be filled to be more employable.
We should also soon be able to benefit from new data sources and linking. Universities already track labour market outcomes. Schools will soon be able to do the same, linking into HMRC data about earning outcomes.
This isn’t yet accessible to the public and is some years away from offering detailed value added measures. But it will make an important contribution to a smarter system, as well as encouraging schools to reprioritise employability which has fallen sharply as a government priority in recent years.
2. Analysis – making sense of the data to empower students, job seekers and employers with accurate information
The next task is to make sense of the patterns. For individuals, there will be a need for intermediaries to interpret the data - careers advisers, personal coaches (as in the studio school model), and sectoral bodies.
At a more macro level this will be a task for researchers. Nesta is part of the new centre for economic statistics (ESCoE), and is currently producing a working paper on using online job ads to classify STEM and Creative Occupations. We should expect more detailed sectoral surveys and better use of ESCO-like (European Skills/Competences qualifications and Occupations) classifiers.
There have been some previous attempts to better mine the data to make sense of emerging patterns. Platforms such as CISCO’s Pathways online academy develop study plans and match careers with the skills gained from their courses. Nesta and ODI’s Open Data Challenge Series supported the Skills Route by MIME Consulting, that uses open data to give students personalised education guidance.
Choosing the right employer to help career progression, or offer meaningful work experiences and apprenticeships, is equally important
There are some websites providing feedback on employer quality, like Glassdoor, but these remain patchy.
Google’s Hire recruitment process navigation initiative, on the other hand, is aimed at the recruiters. The app uses machine learning to visualise applicants’ data and make talent acquisition more efficient.
Any new initiatives can build on the many existing offerings that attempt to interpret data - like SIGI Plus for students or myIDP that helps graduates and postdocs in the sciences create step-by-step plans for identifying and reaching their career goal; Magellan that uses automated assessments to measure a student’s academic skills, physical skills, temperament, people skills, data skills, interests and time required for training; personality-based job matching engines like CareerMaze, Sokanu and Me3.
Other tools include a searchable online database Career Coach that enables students to locate information on jobs in their local area, and the site provided in the UK by the National Careers Services. For other examples see the CES's LMI For All and the employment advisory aimed at 14-17 year olds at the Jobcentre Plus in Birmingham.
3. Prediction – of likely future labour market demand
To understand choices in the present, it’s vital to have a sense of the direction of change. Nesta’s collaboration with Pearson, published in September 2017, is one of the biggest exercises to map likely future skills needs in the UK and US, showing how automation and other factors are likely to shrink some jobs categories and grow others.
It shows that around a fifth of jobs are at serious risk of automation, including many low and medium skilled jobs in factories, offices and services. But it also shows that around a tenth of job categories are highly likely to grow, and that we should expect expansion in many fields, from health and education to food and sports, hospitality and engineering. The study also shows which kinds of skill are likely to be in greater demand in 10-15 years time, including problem-solving, critical thinking, systems skills and others.
This kind of work can guide big employers, LEPs, further education, skills providers, universities, schools, migration policymakers and HEFCE/TEF. It can also be tailored to be useful in making individual predictions – what and how potential skills could affect job prospects.
There are many gaps, including fine-grained help for specific career paths. Another obvious gap is availability of predictive algorithms around law that could help people predict their prospects in industrial tribunals or court cases, drawing on data already held by CAB and open data on law. But we’re beginning to see far more powerful tools for prediction and planning.
4. Creativity and evaluation – using data and experiment to find out what works
A crucial additional step will be to understand how digital and other tools are made most useful to job seekers and others. This sort of behavioural analysis can itself be experimental, finding what combinations of information, advice and guidance have most impact. Our assumption is that there needs to be both a ‘wholesale’ approach to new data and information, ensuring that there is comprehensive, reliable data available across all areas and industries, and a ‘retail’ capacity to tailor this to likely patterns of use, whether by job seekers or SMEs. But this needs to be tested with different groups.
We have long advocated a ‘what works’ centre for labour market policies, and have done some work to map out how it could function based on our experience in helping set up other ones through the Alliance for Useful Evidence.
A consistent problem in this field is that, although there are many innovative projects, there is no institution dedicated to mapping and monitoring the innovations, and making sense of the evidence
There are many promising initiatives that combine job search, one-to-one advice and help with both formal and informal skills. For instance, the Fastlaners programme run by Uprising - a UK youth leadership development organisation - builds employability skills over eight days and offers professional mentoring for unemployed graduates.
At a larger scale, the Movement to Work programme links hundreds of employers providing work experience. But these, and other initiatives of this kind, lack sufficiently hard evidence of impact, and the disappearance of the Council for Employment and Skills has weakened the UK’s capacity to think more systematically in this space.
A related task is to help young people to be creative problem solvers of their own career challenges. Much activity is underway to include more problem-solving into the curriculum, despite the scepticism from the current education ministers in the UK government.
Collaborative problem solving consistently comes up as a top future skill for personal and professional development in OECD rankings. Nesta has studied evidence on what works and is running a series of programmes in schools to support these skills. But, as many employers have commented, there is a disconnect with the DfE, which has a more traditional view of priorities for schools.
To get the full benefit of a smarter, more data-driven labour market, there is a need for progressive national policies that will help people adapt, and make use of the many information sources described above. Singapore is arguably leading the way, and although its policies can’t be directly replicated they’re a good role model, showing what a serious-minded government looks like.
Here are a few of the elements. SkillsFuture is a national movement to provide Singaporeans with the opportunities to develop their fullest potential throughout life, regardless of their starting points. The programme features Education and Career Guidance (ECG), enhanced Internships to learn through meaningful work assignments and industry exposure, MySkillsFuture an online guidance portal to plan career, education and training into the working life, and Young Talent Programme (YTP) to provide opportunities for young talents to gain global exposure and market immersion experience.
Some SkillsFuture initiatives target adults at the beginning of their careers. P-Max is an enhanced Place-and-Train programme that is placing thousands of Professionals, Managers and Executives (PMEs) in SMEs by December 2017, helped by Career Centres and the governments’ CaliberLink. SkillsFuture Credit encourages individuals to take ownership of their skills development and lifelong learning. All Singaporeans aged 25 and above receive an opening credit of S$500 from January 2016 that can be used on top of existing government course subsidies to pay for a wide range of approved skills-related courses.
SkillsFuture Earn and Learn Programme is a work-study programme designed to give polytechnics and ITEs graduates a head start in their careers. Participating employers can recruit local fresh talent and prepare them to take up suitable job roles. This creates a smoother transition into the workforce.
These are just a few of the policies. What’s interesting is the way they are organised as a movement as much as a programme, a coalition that brings together employers and government with a sense of both urgency and ambition.
Such radical new policies may be unlikely in the near future in the UK. But the creation of an intelligent labour market – an Open Jobs inititative - organised around a commons of data and interpretation, would be relatively easy.
We believe that the UK could aim to be the world’s first truly intelligent labour market, drawing on what already exists, to offer a backbone that is organised as a commons, around which a thriving market of new services can be organised.
That will require engagement by government and employers to pull together the elements listed above into a genuine collective intelligence. This could then be organised at the level of a sector, industry, or a city; like Manchester Digital, the Mayor’s Digital Talent Programme Skills for Londoners, or Google’s Digital Skills Academy.
There are many institutions with a stake in this – from the public sector to business and civil society. Philanthropy can also play a role – as, for example, in this programme on digital tools for Africa from Rockefeller. The only missing elements now are leadership and vision.