Using Labour Market Information for Career Development in the Changing World of Work

I was invited to make a presentation earlier this week to the European Union Horizon 2020 HECAT project exploring the use of algorithms within public employment systems. Waterford (Ireland) Institute of Technology is coordinating the project and according to the WaterfordLive web site “HECAT is a sociologically and anthropologically led project to make data trapped in public employment systems (PES) and national statistical offices available to unemployed people and those trying to help them to improve their personal decision-making and visionary future.” Dr Griffin from the Institute said: “Everyone is concerned or should be worried about how algorithms and big data is being used in the labour market, we cannot put the technology genie back in the bottle, rather we need to figure out how to make the output from these novel technologies ethical, fair and transparent. We need to crack them open sociologically and anthropologically so that traditional researchers can fully understand how they operate and communicate that to the public.”

Anyway here are my slides.

LinkedIn says growing job demand for growth hackers!

job, job offer, workplace

geralt (CC0), Pixabay

There is increasing interest in what labour market information job advertisement portals can provide. OK, most sites will have various skews in terms  of what kind of companies advertise on them, but the good side is that they can provide near real time data about labour market supply and demand. In the UK the best known are probably Burning Glass and Emsi. Of course these are both commercial services, charging for their data. LinkedIn also has been collecting and analysing Labour Market jobs adverts and have recently published a list of the UKs fifteen fastest growing job sectors for 2021, including Top jobs, Top skills and Hiring hotspots.

The job sectors are (in rank order):

  1. E-commerce personel
  2. Health care supporting staff
  3. Digital content freelancers
  4. Construction
  5. Creative freelancers
  6. Finance
  7. Specialised medical professionals
  8. Professional coaching
  9. Social, media and digital marketing
  10. Customer service
  11. Education
  12. Mental health professionals
  13. Real estate
  14. Specialized engineering
  15. Artificial intelligence

I’m sure there has been a great deal of work in cleaning and analysing the data. However, I am not quite sure how seriously to take the findings. LinkedIn has presumably a quite heavy skew towards higher qualified professional jobs.

And it is no surprise to find to find the ‘hiring hotspots’ clustered around the major UK cities. In many ways it is teh job titles (or top jobs) that are the most interesting. Job titles are a major problem in trying to clean and analyse data from job adverts. Only recently I had feedback from someone testing a system I am developing that they could not find any jobs for ‘sandwich artists’ on my app.

The top jobs that LinkedIn list for E-Commerce personnel appear neither high paid for requiring high qualification, I am not quite sure what a online specialist is but the rest are driver, supply chain associate, supply chain assistant, warehouse team lead,

And it is pretty obvious why heath workers are in high demand and short supply.

But I am not convinced about high demand for voice over artists and script writers included in the creative freelancers category.  Nor am I sure about a shortage of Life coaches (professional coaching), less still ‘growth hackers’ (Social media and digital marketing), whatever that might be.

I wonder if employers are just getting savvy in how to appeal to younger people with job titles not reflecting the real level of pay or indeed skills. But maybe I am too cynical

More ways of understanding the Labour Market

architecture, skyscraper, glass facades

MichaelGaida (CC0), Pixabay

In most countries we have traditionally relied on official labour market agencies for data for understanding the labour market. From an education and training standpoint, that data has not always been ideal – given the main users are economic planners and policy makers – and the data collected is often difficult to interpret from the viewpoint of careers guidance or education and training provision.

One of the main limitations of national data from official agencies is that the sample is often too small to draw conclusions at a local – or sometimes even regional – level. Yet opportunities for employment vary greatly by region, town and city. In recent years there has been a growth in popularity of scraped data, using big data technologies and techniques to scrape and analyse online job vacancies. This work has mainly been undertaken by US based private sector companies although the EU CEDEFOP agency has also developed a multi national project scraping and analysing data. The job advert data is not better or worse than tradition labour market data. It is another source of data providing another angle from how to understand what is going on. Pontydysgu is part of a consortium in the final of the  UK Nesta CareerTech Challenge prize. Our main word is developing a Chatbot for providing information for people whose jobs are at risk as a result of automation and AI. Of course that includes labour market information as well as possibly scraped data and we have been thinking about other sources of data, not traditionally seen as labour market information.

One organisation which is accessing, visualising and publishing near real time data is the Centre for Cities in the UK. It says its mission is to help the UK’s largest cities and towns realise their economic potential.

We produce rigorous, data-driven research and policy ideas to help cities, large towns and Government address the challenges and opportunities they face – from boosting productivity and wages to preparing for Brexit and the changing world of work.

We also work closely with urban leaders, Whitehall and business to ensure our work is relevant, accessible and of practical use to cities, large towns and policy makers

Since the start of the Covid 19 pandemic the Centre for Cities has been tracking the impact on the labour market. They say:

Luton, Slough and Blackpool have seen the largest increases in unemployment since lockdown began. Meanwhile, cities and towns in predominantly in southern England and The Midlands have seen smaller increases in unemployment. Cambridge, Oxford, Reading, Aberdeen and York have seen some of the smallest increases in unemployment since March.

As of mid-June Crawley, Burnley, Sunderland and Slough have the largest shares of people being paid by the Government’s furlough scheme.

In the medium term, as many as one in five jobs in cities and large towns could be at risk of redundancy or furloughing, and those reliant on the aviation industry, such as Crawley and Derby, are likely to be hardest hit. These areas are also the places most likely to be worst affected if the Job Retention Scheme is withdrawn too soon.

One interesting tool is the high street recovery tracker. This compares the economic performance of city centers since the outset of the Covid 19 crisis. At present they say footfall in the UKs 63 biggest cities has increased by seven percentage points in August and now reaches 63 per cent of pre-lockdown levels.

However, this figure hides great geographic differences: in 14 city centres, footfall in August exceeded pre-lockdown levels; particularly in seaside towns and smaller cities. At the other end of the spectrum, large cities like Manchester and Birmingham have barely recovered half of their pre-lockdown levels of activity.

Instead of relying on traditional surveys for this data, which would take some time to process and analyse, the recovery tracker is based on mobile phone analysis. Another potentially interesting non traditional source of data for understanding labour markets may be travel data, although that data is heavily disrupted by Covid 19. But that disruption in itself may be interesting, given the likelihood that those cities with continuing low travel to work numbers are likely to have a higher percentage of office based work, and possibly a focus on non customer based finance and administration employment. Conversely those cities where travel to work volumes are approaching near normal are probably more concentrated on retail and manufacturing industry.

All in all, there is a lot going on in novel data sources for labour market information. And of course we are also looking at how such data might be accessed:hence our Chatbot project.

Understanding the changing Covid-19 labour market

looking for a job, work, silhouettes

geralt (CC0), Pixabay

Yesterday I attended a webinar organized by the UK Association of Colleges in their Labour Market Observatory Series. The subject of the webinar was Using Job Posting Analytics to understand the changing Covid-19 labour market.

Understanding labour markets is a hard job at the best of time and the Covid-19 pandemic and the resulting lockdown have disrupted the economy with unprecedented speed and scale. As Duncan Brown, Senior Economist from Emsi, explained, raditional labour market statistics take time to emerge, especially to understand what’s going at regional and local level, and real-time indicators become all-important. Duncan Brown, talked through what their Job Posting Analytics – derived from collecting (or scraping) around 200,000 new, unique job postings from job boards across the internet every week — can tell us about where and how the labour market is changing and what to look for as we move into the recovery.

First though he explained how the data is collected using bots before being cleaned and duplication removed, prior to using algorithms to analyse the data. He pointed out that there are limitations to the data derived from job adverts but compared to the time taken for official labour market data to emerge, for instance through the UK National Office of Statistics Labour Force Survey (LFS)job posting analytics can provide an almost real time snapshot view of the labour market, and is easily projected at a local level.

My notes on the webinar are somewhat patchy but here are a few take home points, particularly from a question and answer session that followed Duncan Brown’s presentation.

There was a huge fall in online job adverts in April and May with the lockdown – as high as 80 per cent in some sectors and localities. Since then there has been a steady recovery in the number of jobs being advertised online but this recovery is uneven between different sectors and different cities and regions.

As examples offers of employment in the food and hospitality. Industries remain dire and aerospace is also still badly hit. On the other hand, job advert volumes in manufacturing have substantially recovered and, perhaps understandably there is an increase in jobs adverts in health care.

There is considerable differences as to how far the volume of job adverts has recovered (or otherwise) in different cities. In general, it would appear that those cities with the largest percentage of office work and of commuters are doing worse: London in particular.

One area of the labour market that Emsi is focusing on is skills demand. They have developed their own skills directory, which Duncan Brown said, now contains over 3000 skills and are running a project funded by Nesta to see if these skills can be clustered around different occupations. Yet despite the so-called pivot to skills, he said there few signs that employers were. Moving away from the traditional emphasis on qualifications. However, qualification demands often did not appear in job adverts but rather tended to be assumed by both employers and job applicants. For instance, someone applying for a job as an accountant would presume that they needed formal qualifications.

Although there have long been predictions over the impact of automation and AI on employment, Duncan Brown said there was little evidence of this. His feeling is that, at least in the UK, the existence of relatively cheap labour in many sectors where it would be relatively easy to automate tasks, was a disincentive to the necessary investment. He thought that labour costs may have been kept down by immigration. He pointed to car washes as an example of an area where far from advancing automation had actually gone backwards.

The slides from the presentation and a recording of the webinar will be available from 27 August on the Association of Colleges website.

 

Digitalisation, Artificial Intelligence and Vocational Occupations and Skills

web, network, programming

geralt (CC0), Pixabay

The Taccle AI project on Artificial Intelligence and Vocational Education and Training, has published a preprint  version of a paper which has been submitted of publication to the VET network of the European Research Association.

The paper, entitled  Digitalisation, Artificial Intelligence and Vocational Occupations and Skills: What are the needs for training Teachers and Trainers, seeks to explore the impact AI and automation have on vocational occupations and skills and to examine what that means for teachers and trainers in VET. It looks at how AI can be used to shape learning and teaching processes, through for example, digital assistants which support teachers. It also focuses on the transformative power of AI that promises profound changes in employment and work tasks. The paper is based on research being undertaken through the EU Erasmus+ Taccle AI project. It presents the results of an extensive literature review and of interviews with VET managers, teachers and AI experts in five countries. It asks whether machines will complement or replace humans in the workplace before going to look at developments in using AI for teaching and learning in VET. Finally, it proposes extensions to the EU DigiCompEdu Framework for training teachers and trainers in using technology.

The paper can be downloaded here.