Category Archives: Data

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

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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.

More ways of understanding the Labour Market

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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.

SMEs are not the same as large firms

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Much of my work at the moment is focused in two different areas – the training and professional development of teachers and trainers for the use of technology for teaching and learning and the use and understanding of labour market data for careers counseling, guidance and advice. However as data increasingly enters the world of education, the two areas are beginning to overlap.

This morning I received an email from the European Network on Regional Labour Market Monitoring. Although the title may seem a little obscure, the network, which has been active over some time, organises serious research at a pan European level. Each year it selects a theme for research, publications and for its annual conference. Over the last year it has focused on informal employment. Next year’s theme is Small and Medium Enterprises (SMEs) which they point out can be viewed as perhaps the most vibrant and innovative area of the European economy. However, when it comes to researching and understanding SMEs it is not so easy

A number of European or national statistics exist to analyse SMEs’ but they generally use the same categories as for large firms and are, in general, constructed from a large firm perspective or in any case not from a framework based on SME characteristics. Many academic papers focusing on SMEs show that they cannot fully be understood using the same categories as with large firms. The general idea is that firstly, SMEs are same as large ones, just smaller. Secondly, the assumption that they will grow up to become Midcaps, then large firms, is incorrect. Torres and Julien (2005) start their article explaining that “Most, if not all, researchers in small business have accepted the idea that small business is specific (the preponderant role of the owner-manager, low level of functional breakdown, intuitive strategy, etc.)”. A 2019 French publication directed by Bentabet and Gadille tackles the issue of SMEs focussing on their specific “social worlds”, their “action models and logics”, while elsewhere the influences of institutional logics and multi-rationalities of SMEs have been considered. The entry of social worlds highlights the great diversity of micro-enterprises and SMEs, which often makes it difficult to analyse them. As a counterpoint, specific knowledge of these companies is required because they are at the heart of the debates on flexibility, labour market dynamics, skilled labour shortage and disruptions in the vocational training system.

SMEs will be the focus for the next Annual Meeting of the Regional Labour Market Monitoring to be held in September 2020 in Sardinia

Travel to university time a factor in student performance

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My summer morning’s work is settling into a routine. First I spend about half an hour learning Spanish on DuoLingo. Then I read the morning newsletters – OLDaily, WONKHE, The Canary and Times Higher Education (THE).

THE is probably the most boring of them. But this morning they led on an interesting and important research report. In an article entitled ‘Long commutes make students more likely to drop out’, Ana McKie says:

Students who have long commutes to their university may be more likely to drop out of their degrees, a study has found.

Researchers who examined undergraduate travel time and progression rates at six London universities found that duration of commute was a significant predictor of continuation at three institutions, even after other factors such as subject choice and entry qualifications were taken into account.

THE reports that the research., commissioned by London Higher, which represents universities in the city found that “at the six institutions in the study, many students had travel times of between 10 and 20 minutes, while many others traveled for between 40 and 90 minutes. Median travel times varied between 40 and 60 minutes.”

At one university, every additional 10 minutes of commuting reduced the likelihood of progression beyond end-of-first-year assessments by 1.5 per cent. At another, the prospect of continuation declined by 0.63 per cent with each additional 10 minutes of travel.

At yet another institution, a one-minute increase in commute was associated with a 0.6 per cent reduction in the chances of a student’s continuing, although at this university it was only journeys of more than 55 minutes that were particularly problematic for younger students, and this might reflect the area these students were traveling from.

I think there are a number of implications from this study. It is highly probable that those students traveling the longest distance are either living with their parents or cannot afford the increasingly expensive accommodation in central London. Thus this is effectively a barrier to less well off students. But it is also worth noting that much work in Learning Analytics has been focused on predicting students likely to drop out. Most reports suggest it is failing to complete or to success in initial assignments that is the most reliable predicate. Yet it may be that Learning Analytics needs to take a wider look at the social, cultural, environmental and financial context of student study with a view to providing more practical support for students.

I work on the LMI for All project which provides an API and open data for Labour Market Information for mainly use in careers counseling advice and guidance and to help young people choose their future carrers or education. We already provide data on travel to work distances, based on the 2010 UK census. But I am wondering if we should also provide data on housing costs,possibly on a zonal basis around universities (although I am not sure if their is reliable data). If distances (and time) traveling to college is so important in student attainment this may be a factor students need to include in their choice of institution and course.

 

Is manufacturing finished in the UK?

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The Guardian newspaper highlights a report by Cambridge University for the Department for Business, Energy and Industrial Strategy (BEIS), showing that Britain’s manufacturing sector is much larger than official figures suggest.

The report argues that official statistics, which estimate that manufacturing output accounts for 9% of national income, are based on “outdated and inaccurate methods of counting” and the figure is much higher.

The report avoids putting a fresh figure on the proportion of GDP accounted for by the sector, but one of its authors said it was nearer 15% once activities tied to the sale of UK-made products, including engineering support and contracted services, were included.

“It is essential that policymakers have accurate information on the size of manufacturing sectors in order to develop an internationally competitive industrial strategy,” said Eoin O’Sullivan, one of the report’s authors.

“In particular, policymakers need to be able to measure manufacturing in a way that better reflects how firms actually organise themselves into value networks.”

While the Guardian news spin on the report focuses on the threat to the manufacturing by tariffs on exports resulting from a no deal Brexit, the report has wider implications. Manufacturing has long been seen as in decline and is accordingly unattractive as a careers option when compared to the growing service sector. Yet the report shows the continuing importance of occupations like engineering.