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.

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

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.

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.

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.

Rezwan (CC0), Pixabay

I’ve written a lot of articles over the last year about labour market information and what it means both for careers guidance and support and for vocational education and training. But its all too easy to get blase about data, especially when each forecast seems to be worse than the last,

So I thought I would write something more personal, about the impact of Covid 18 and the recession on people around me. This is necessarily an impressionistic account. Since the 14 March when Spanish prime minister Pedro Sánchez declared a state of emergency, I doubt I have ventured more than two kilometers from the flat in Spain where I was at the start on the crisis. How, a sort of new normal is emerging. OK, schools are still closed as are vocational colleges the the universities, most people wear masks, the numbers allowed in bars and restaurants severely restricted and there are still very few flights in and out of the city. But the terraces are open, albeit with social distancing and the traffic appears to be getting back to its usual dreadful density.

But this isn’t a return to the old normal or even a new normal.Every time I goo out I see a new closed shop, bar or restaurant. The UK Centre for Cities has shown how Covid 19 is having a different impact on different cities. Interestingly, many of the cities being hit hardest in terms of employment and industries are those already under pressure from automation and AI. I am not sure what the overall impact will be on Valencia. To an extent it may depend on the future of large companies like Ford which is, I think, the city’s largest employer. But Valencia also has a considerable number of SMEs, mainly involved in manufacturing, in sectors like furniture. And of course Valencia is a major tourist centre, which even though now declared open to most European cities, is not going to really recover this year.

But it may well be that the impact is differentiated on a local as well as city basis. The area near me is a middle class district with most people seemingly able to work from home. Inevitably in teh crisis people have not ventured far for shopping so shops have been dependent on people living nearby. To some extent that has lessened the impact in my district, although small clothes ships have closed, many bars and restaurants are closed or struggling and even the local swimming pool is shuttered with a for sale notice on the facade. But on Tuesday I was in a nearby working class district. Here, where presumably more inhabitants had jobs which could not be undertaken from home, the impact is much starker. It is easier to count the shops that are open than those closed. Here the recession looks grim. I/m waiting to get data which may indicate just what is going on. But impressions may be nearly as useful.

Last week the Taccle AI project organised a workshop at the European Distance Education Network (EDEN) Annual Conference. The conference, which had been scheduled to be held in Romania, was moved online due to the Covid 19 pandemic.There were four short presentations followed by an online discussion.

Graham Attwell introduced the workshop and explained the aims of the Taccle AI project. In the next years, he said “AI will change learning, teaching, and education. The speed of technological change will be very fast, and it will create high pressure to transform educational practices, institutions, and policies.” He was followed by Vidmantas Tulys who focused on AI and human work. He put forward five scenarios for the future of work in the light of AI and the fourth industrial revolution. Ludger Deitmer looked at the changes in the mechatronics occupation due to the impact of AI. He examine dhow training was being redesigned to meet new curriculum and occupational needs and how AI was being introduced in the curriculum. Finally, Sofia Roppertz focused on AI for VET, exploring how AI can support access to education, collaborative environments and intelligent tutoring systems to support teachers and trainers.

MichaelGaida (CC0), Pixabay

I attended an excellent webinar on Thursday from the UK Centre for Cities. The webinar, presented by Elena Begini and entitled ‘How will the recession affect different parts of the country?’, provided an analysis of the latest labour market data, both around the numbers unemployed and those furloughed and receiving government funding.

Elena explained that in the first month between March and April there was very rapid increase in the numbers employed and furloughed but of the main impact Covid 19 crisis was on those cities with weaker economies mostly in the north of England

Between April and May the increase in unemployment was slower. But in this month, it was the stronger city economies in the south of England that were hard hit. London and Basildon both joined the 20 hardest hit cities in terms of unemployment COVID 19 has spread out across most cities. Interestingly those cities hardest hit by unemployment are also those with the most furloughed workers. To a certain extent this depends on whether jobs in a city are able to be undertaken from home. In Wales Cardiff and Swansea have relatively lower rates of both unemployment and furloughed staff whilst in Newport the rates are higher, presumably reflecting a higher percentage of office worker in Cardiff and Swansea while a higher percentage of industrial enterprises in Newport.

These broad trends are also reflected in the percentage of self-employed workers who have applied for government assistance.

Type of employment is important. Crawley and Reading are both major and previously relatively economically strong cities close to London. But while Reading has (only!) 25 percent of jobs hit by the recession, Crawley has 38 per cent reflecting the importance of the aerospace industry (and particularly Gatwick airport) to the city.

It also should be noted that all cities have fast rising unemployment of young people although once more there is wide variation, for instance very high rates in northern cities and relatively low rates in Oxford and Cambridge.

One of the strengths of the Centre for Cities is that they not only analyze the data but put forward policy measures o respond to the research. Elena suggests the scale of the recession means there is an urgent need for active intervention. Austerity is not an option.

But because of the different trajectories in different cities the policy response needs to be differentiated.

For those cities which are relatively strong economically, there is the potential for a quick “bounce back”. This requires enhanced career support both for those seeking jobs for which they are already qualified or for employment in jobs with similar skill requirements.  Secondly there is the need for grants for those seeking retraining or upskilling (and also courses are needed to support this). For places with weaker economies the priority is job creation with a particular focus on the green economy and on infrastructure development.

I have written this from my notes taken during the webinar so apologies for anything I have missed or for misreporting. A recording of the webinar is available for the Centre for Cities web site together with the data and their analysis.

katielwhite91 (CC0), Pixabay

Even before the COVIP 19 crisis and the consequent looming economic recession labour market researchers and employment experts were concerned at the prospects for the future of work due to automation and Artificial Intelligence.

The jury is still out concerning the overall effect of automation and AI on employment numbers. Some commentators have warned of drastic cuts in jobs, more optimistic projections have speculated that although individual occupations may suffer, the end effect may even be an increase in employment as new occupations and tasks emerge.

There is however general agreement on two things. The first is that there will be disruption to may occupations, in some cases leasing to a drastic reduction in the numbers employed and that secondly the tasks involved in different occupations will change.

In such a situation it is necessary to provide pathways for people from jobs at risk due to automation and AI to new and hopefully secure employment. In the UK NESTA are running the CareerTech Challenge programme, aimed at using technology to support the English Government’s National Retraining Scheme. In Canada, the Brookfield Institute has produced a research report ‘Lost and Found, Pathways from Disruption to Employment‘, proposing a framework for identifying and realizing opportunities in areas of growing employment, which, they say “could help guide the design of policies and programs aimed at supporting mid-career transitions.”

The framework is based on using Labour Market Information. But, as the authors point out, “For people experiencing job loss, the exact pathways from shrinking jobs to growing opportunities are not always readily apparent, even with access to labour market information (LMI).”

The methodology is based on the identification of origin occupations and destination occupations. Origin occupations are jobs which are already showing signs of employment. Decline regardless of the source of th disruption. Destination jobs are future orientated jobs into which individuals form an origin occupation can be reasonably expected to transition. They are growing, competitive and relatively resilient to shocks.

Both origin and destination occupations are identified by an analysis of employment data.

They are matched by analysing the underlying skills, abilities, knowledge, and work activities they require. This is based on data from the O*Net program. Basically, the researchers were looking for a high 80 or 90 per cent match. They also were looking for destination occupations which would include an increase in pay – or at least no decrease.

But even then, some qualitative analysis is needed. For instance, even with a strong skills match, a destination occupation might require certification which would require a lengthy or expensive training programme. Thus, it is not enough to rely on the numbers alone. Yet od such pathways can be identified then it could be possible to provide bespoke training programmes to support people in moving between occupations.

The report emphasises that skills are not the only issue and discusses other factors that affect a worker’s journey, thereby, they say “grounding the model in practical realities. We demonstrate that exploring job pathways must go beyond skills requirements to reflect the realities of how people make career transitions.”

These could include personal confidence or willingness or ability to move for a new job. They also include the willingness of employers to look beyond formal certificates as the basis for taking on new staff.

The report emphasises the importance of local labour market information. That automation and AI are impacting very differently in different cities and regions is also shown in research from both Nesta and the Centre for Cities in the UK. Put quite simply in some cities there are many jobs likely to be hard hit by automation and AI, in other cities far less. Of course, such analysis is going to be complicated by COVID 19. Cities, such as Derby in the UK, have a high percentage of jobs in the aerospace industry and these previously seemed relatively secure: this is now not so.

In this respect there is a problem with freely available Labour Market Information. The Brookfield Institute researchers were forced to base their work on the Canadian 2006 and 2016 censuses which as they admit was not ideal. Tn the UK data on occupations and employment from the Office of National Statistics is not available at a city level and it is very difficult to match up qualifications to employment. If similar work is to be undertaken in the UK, there will be a need for more disaggregated local Labour Market Information, some of it which may already be being collected through city governments and Local Economic Partnerships.

mohamed_hassan (CC0), Pixabay

Pontydysgu is very happy to be part of a consortium, led by DMH Associates, selected as a finalist for the CareerTech Challenge Prize!

The project is called CareerChat and the ‘pitch’ video above expalisn the ideas behind the project. CareerChat is a chatbot providing a personalised, guided career journey experience for working adults aged 24 to 65 in low skilled jobs in three major cities: Bristol, Derby and Newcastle. It offers informed, friendly and flexible high-quality, local contextual and national labour market information including specific course/training opportunities, and job vacancies to support adults within ‘at risk’ sectors and occupations

CareerChat incorporates advanced AI technologies, database applications and Natural Language Processing and can be accessed on computers, mobile phones and devices. It allows users to reflect, explore, find out and identify pathways and access to new training and work opportunities.

Nesta is delivering the CareerTech Challenge in partnership with the Department for Education as part of their National Retraining Scheme

• Nesta research suggests that more than six million people in the UK are currently employed in occupations that are likely to radically change or entirely disappear by 2030 due to automation, population aging, urbanisation and the rise of the green economy.
• In the nearer-term, the coronavirus crisis has intensified the importance of this problem. Recent warnings suggest that a prolonged lockdown could result in 6.5 million people losing their jobs. [1] Of these workers, nearly 80% do not have a university degree. [2]
• The solutions being funded through the CareerTech Challenge are designed to support people who will be hit the hardest by an insecure job market over the coming years. This includes those without a degree, and working in sectors such as retail, manufacturing, construction and transport.

You can find out more information about the programme here: https://www.nesta.org.uk/project/careertech-challenge/ and email Graham Attwell directly if you would like to know more about the CareerChat project