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

CiCi – the Powerhouse to support your Career

Welcome to CiCi. CiCi is a chatbot for careers information developed by a consortium between DH Hughes Associates and Pontydysgu. The chatbot, now being further developed by a new organisation CareerChat, was developed as part of the Nesta CareerTech Challenge competition. This video forms part of the final report for teh competition.

Introducing CiCi

Some of you will know I have been working with a small team on developing a chatbot to support career education and development. The project is one of the twenty finalists in the UK Nesta supported CareerTech Challenge competition. I another post I will provide more detailed report on the work we have done. But first, as part of the final report on the project, we had to submit a three minute video. We had a lot  of  fun with this – here is our entry.

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.