How is careers labour market information and intelligence being used and making an impact across the world?

It is widely accepted that careers labour market information and intelligence is central to the delivery of good careers guidance practice. With more data becoming available and advances in technology, it is possible to create linkages between data to provide more powerful careers information. How careers information and intelligence is made available, linked and used varies greatly by country, but there are interesting international examples available from which to learn. These examples and the evidence of their impact was discussed at a recent symposium organised by the Social Research and Demonstration Corporation (SRDC) held in Ottawa, Canada , in July 2016. The aim of the symposium was to discuss evidence from a recent international literature review of data linkage initiatives and share learning. LMI for All was identified as one of three innovative approaches, along with the US College Scorecard, and New Zealand’s Integrated Datasets. The findings showed that each of these international exemplars provide highly innovative approaches to improving access to accurate, reliable, and timely learning information and labour market information (LMI).

The US College Scorecard is a web-based tool to provide information on the costs and financial returns of post-secondary education (For more information see the latest report and Scorecard data documentation). It was developed by the US federal Department of Education. Behind the tool is a database comprising linked student-level administrative data from the Integrated Postsecondary Education Data System (IPEDS), the National Student Loan Data System (NSLDS) and graduate earnings information from tax records maintained by the Treasury Department. It is an interesting approach to providing data on career pathways.

New Zealand’s Integrated Datasets has been in operation since the 1990s. The New Zealand government has worked to integrate existing datasets from multiple government departments into a single, individual-level dataset. Policy and legislative frameworks were put in place to protect the privacy of its citizens while encouraging greater data sharing between government departments and agencies. The most exhaustive example can be used to analyse labour market outcomes of post-secondary education graduates, their student loan repayment rates, their migration out of the country, and other social and economic factors.

The US and New Zealand approaches share a common methodology of integrating survey and administrative datasets by linking individual-level data. These include individual education programme selections and outcomes (e.g. graduation rates), income tax data, student loan repayments, and in the New Zealand, labour market participation information to capture the true costs and returns of post-secondary education. Like the other two initiatives, LMI for All combines data from various sources, but is based on a different methodology. It does not link individual-level data, but integrates a wide range of existing national sources of data to provide LMI in a single source.

The three initiatives demonstrate the richness of information that can be produced by combining information and data from existing sources. In the UK, the Building out Industrial Strategy Green Paper (2017) discusses the role of LMI in high quality careers advice:

“…. we need to do more to empower students, parents and employers to make confident and informed choices about their education and careers options, whether they are in schools, technical education or higher education. The quality of careers advice is a particular issue for disadvantaged students who lack the social capital to get advice or work experience opportunities via family members.”

This highlights the importance of access to accurate, reliable, and timely labour market information and learning information. It has a number of important consequences for a variety of stakeholders. From a user’s perspective, this can help students and their families make informed choices about learning and work pathways and understand the labour market demands and outcomes related to their choices. For governments, data linkage and labour market online developments provide new and exciting opportunities to better understand career trajectories now and in the future.

Deirdre Hughes, Jenny Bimrose & Sally-Anne Barnes

Warwick Institute for Employment Research, University of Warwick

Understanding pay data and how to use the change in pay indicator?

Through LMI for All you are able to access detailed pay data by SOC2010 4-digit occupational categories. Similar to other data in LMI for All, pay data are also available for a number of other dimensions: highest qualification held; industry; countries and English regions in the UK; gender; employment status; and age.

Information on weekly pay (average, median and decile) is taken from a combination of two sources: the Annual Survey of Hours and

Earnings (ASHE); and the Labour Force Survey (LFS) (both conducted by the Office for National Statistics (ONS)). ASHE is widely regarded as the most reliable source of information on Pay and Hours, however it does not include information on pay by qualification as well as some other characteristics (such as self-employment). This information is available in the LFS. The ASHE and LFS data are based on too small sample sizes to enable a comprehensive set of estimates of Pay to be extracted at the full SOC 4-digit level. The Warwick Institute for Employment Research at the University of Warwick has produced a full set of detailed estimates based on publicly available published ASHE and LFS data Warwick. These estimates are based on an econometric method (the well-established earning function), combining the data sets to produce a full set of detailed estimates, constrained to match publicly available headline data.

Although we provide estimates of pay for a number of years, detailed comparisons between years will not produce statistically robust results at the SOC 4-digit level. We, therefore, provide a separate ‘change in pay’ indicator for those interested in how pay by occupation is changing over time. Currently, this focuses on the period from 2014-2015 and provides detailed information on changes by 4-digit occupation by country and English region for spatial variations. Please note that there is no cross classification for the ‘change in pay’ indicator by any other dimension (e.g: industry, age, gender, employment status or level of qualification held). For example, it is not possible to look at the change in pay by occupation and gender, as data would not be statistically robust.

Developers interested in changes in pay cross classified by these other dimensions are advised to use the aggregate ‘change in pay’ indicator rather than attempt to develop more detailed measures of change by comparing detailed pay estimates for two years.

For more information on this dataset and others available through the LMI for All API, see the LMI for All data documentation.

If you have any queries, please drop us an email at

Careerometer – updated LMI for All widget

LMI for All has released version two of its popular Careerometer widget. Version one, which was released a year ago, makes it simple to embed a widget in any web site. The first version gave access to pay rates in different jobs

The new version provides extra information. As well as providing hourly, weekly and annual pay figures, for each occupation the widget gives details of present numbers employed in the UK and projected future growth or reduction in numbers employed. It also provides prediction of replacement demand – an important measure showing how many new employees are likely to be needed based on those leaving an occupation or retiring. It provides examples of different industries where those in that occupation are likely to be employed as well as a brief description of the occupation itself.

You can also easily compare different occupations to UK average wages.

The widget can be configured to provide a card for providing information on one occupation at a time, or two or three cards for comparing more occupations. Another simple configuration allows you to preset the occupation the widget displays, instead of leaving it up to the user to search.

At a technical level, the Careerometer widget has been designed to be as simple as possible to install even for those with little technical knowledge. It should also cause no problems with your existing web pages and can easily be installed on a full page or as a sidebar widget.

To get the Careerometer 2 widget copy the code into your own website.

Careerometer 2 widget image

What can you do with LMI for All?

Ever wondered what you can do with LMI for All, how to go about using it and how best to get started? Why not begin by having a look at some  case studies on how others have used this resource to design and create new apps and web interfaces. This includes ways of identifying relevant audiences, using the API to integrate (or ‘mash’) LMI for All data with your own data.

For inspiration, here are four organisations keen to share their LMI for All experience.

  • icould was an early adopter of LMI for All. Their story of using LMI for All describes how they have used and visualised data alongside their careers videos.
  • The story from Prospects explains how they went about designing and developing an app, ‘Help Build London’, to raise awareness of the construction sector in London.
  • The Skills Match London website was developed by London Councils and MIME Consulting. The story of its development describes how LMI for All data is presented alongside local data to provide a visualisation of skills gaps and future trends.
  • The story from Kore Education Systems describes how they are creating KareerHub using data from LMI for All and the Northern Ireland Skills Barometer.

If you are inspired and want to know more, the LMI for All website also provides technical support and guidance on how to query the database, use the API, get an API key and the structure of the LMI for All data.

Finally, why not share your LMI for All experience with us. Contact: Sally-Anne Barnes,

LMI for All symposium – 1 December 2016, London

As LMI for All is becoming embedded in careers guidance and education practice, innovative and creative approaches to making data accessible and available to target groups of users are emerging. A half day symposium will be held on 1 December to showcase how LMI is being used; to share best practice; and promote new developments to the service, which can benefit current and future users. There will be presentations from the Department for Education, current adopters of LMI for All and the LMI for All team. In the current context of government priorities of Brexit, social mobility and productivity, LMI for All is more important than ever to help deliver those three priorities.

The symposium is free to attend, but places are limited so please register with Lynne Marston. Places will be offered on a first-come, first-served basis. The symposium will run from 10.15am-2pm, with registration from 9.45am.

If you are unable to attend the symposium, we are running a number of webinars in early 2017 – register your interest and we will be in touch.