Standards and Classification systems (1)

One thing I frequently get asked questions about is classification systems. How are jobs classified and where does the classification system come from? Are classification systems the same in the UK and in other countries? How are job classifications related to courses? And how does LMI for All use classification systems?

In this and a following post I will try to answer some of those questions. In the UK jobs are classified according to the Standard Occupation System 2010 (SOC 2010) which was an updated version of the previous SOC2000 classification. The Standard Occupational Classification, first introduced in 1990, is maintained by the Office for National Statistics (ONS).

Occupations are divided into groups and ONS explains that “the major group structure is a set of broad occupational categories that are designed to be useful in bringing together unit groups which are similar in terms of the qualifications, training, skills and experience commonly associated with the competent performance of work tasks.

SOC 2010 has nine major groups and 25 sub-major groups with 90 minor groups and 369 unit groups. The following table provides a list of the major groups and the general nature of qualifications, training and experience for occupations in the major group.

Major group General nature of qualifications, training and experience for occupations in the major group
Managers, directors and senior officials A significant amount of knowledge and experience of the production processes and service requirements associated with the efficient functioning of organisations and businesses.
Professional occupations A degree or equivalent qualification, with some occupations requiring postgraduate qualifications and/or a formal period of experience-related training.
Associate professional and technical occupations An associated high-level vocational qualification, often involving a substantial period of full-time training or further study.  Some additional task-related training is usually provided through a formal period of induction.
Administrative and secretarial occupations A good standard of general education.  Certain occupations will require further additional vocational training to a well-defined standard (e.g. office skills).
Skilled trades occupations A substantial period of training, often provided by means of a work based training programme.
Caring, leisure and other service occupations A good standard of general education. Certain occupations will require further additional vocational training, often provided by means of a work-based training programme.
Sales and customer service occupations A general education and a programme of work-based training related to Sales procedures. Some occupations require additional specific technical knowledge but are included in this major group because the primary task involves selling.
Process, plant and machine operatives The knowledge and experience necessary to operate vehicles and other mobile and stationary machinery, to operate and monitor industrial plant and equipment, to assemble products from component parts according to strict rules and procedures and subject assembled parts to routine tests. Most occupations in this major group will specify a minimum standard of competence for associated tasks and will have a related period of formal training.
Elementary occupations Occupations classified at this level will usually require a minimum general level of education (that is, that which is acquired by the end of the period of compulsory education). Some occupations at this level will also have short periods of work-related training in areas such as health and safety, food hygiene, and customer service requirements.

The next edition of SOC is expected in 2020 and work is already underway. The revisions are undertaken both to help employers and other users in using SOC and also to reflect the changing use of job descriptions and changes in employment. For instance, many more jobs include manager in the job title than would have been 20 years ago. At the same time there has been a rapid growth of jobs in the service sector and in computer based occupations, with the need for new descriptions to reflect this.

Standard Occupational Classification is sometimes mixed with the Standard Industrial Classification system (SIC). As the names imply while SOC describes and classifies occupations SIC provides a classification system for different industries.

One question that we are frequently asked about with LMI for All is why we cannot provide a breakdown of employment in different occupations at a city or local level. Certainly, it would be useful for job seekers and for people planning their career. But employment figures in occupations are derived from a quarterly survey – the Labour Force Survey – and the sample size is simply too small to provide reliable data at local level.

Also in some occupations we do not have sufficient data to provide numbers at a unit group level. In this case we revert to minor group data.

In the next article I will look at the relation between SOC 2010 and other international classification systems.







Jisc Innovation Lab

The UK JISC organisation which supports universities in the development and use of new technologies has also initiated an innovation Laboratory working with data. They are investigating how the huge and diverse sources of data that are now available be better used to address and inform key policy decisions in education and training, in ways that meet both the requirements of national and regional agencies and also the local nuances and concerns of colleges serving immediate communities (Footring, 2017).

The Jisc College Analytics Lab[1] digital modelling environment provides a means to address complex practice and policy questions using highly diverse sources of data. By engaging both with colleges, with their command of the details of learner data, and regional and national planning agencies, that need to aggregate intelligence across wider areas in order to generate policy recommendations.

In particular, Jisc are focusing on local area data to inform strategic planning and decision making. They say “presenting the information in a visual and interactive way helps leaders to communicate their vision and ideas to their funders, staff teams, students and the wider community.” However, they have found is that there is a wide variation in the effectiveness of the way in which colleges make use of the data available to them and a significant duplication of similar core processes across colleges.

The Analytics Lab environment provides a secure technical, legal and project management framework to enable the creation of new, experimental data dashboards. Participants use a mix of open and secure data from both new and established sources, to create visualisations and dashboards which address key business questions.


What is replacement demand?

Ever wondered what replacement demand is and why you need to know about it ?

Replacement demand is an important part of determining what employment opportunities there may be in the labour market. For those working in careers and the education sector knowing about replacement demand and understanding what opportunities may be available in the future helps with career planning. The recent careers speech by the Robert Halfon, the Apprenticeships and Skills Minister, spoke about the important of meeting the needs of a skills economy:

“We need people of all ages, and those who advise them, to really understand what opportunities are on offer. I want those undertaking apprenticeships or courses in further education to get the same level of information and support to make confident and informed choices when selecting and applying for courses.
We want to ensure that those applying for further education have clear information and support through the process of searching for, choosing and then applying for a particular opportunity. In particular, we want to ensure that they are supported in the same way that higher education applicants are supported through the straightforward and well-understood UCAS system.”

Employment projections are an important to understanding the needs of the economy. These projections are available through LMI for All and are part of the Working Futures dataset.

Employment projections help us understand how levels of employment in an occupation or sector may change over time. These projections are made up by expansion demand (or growth) and replacement demand. Expansion demand is the number of job openings as a result of growth in the sector or occupation. Whilst, replacement demand is the number of openings created by people leaving the labour market on a temporary basis (such as maternity leave or sickness) and those retiring or dying. It is difficult to estimate the number of people who may leave an occupation on an annual basis , as, for example, people retire at different ages. So the replacement demand is often presented over an extended period of time, such as ten years, to provide a projected trend.

As part of the Working Futures projections both expansion and replacement demand are provided as well as the total number of people needed in the occupation (called net requirement). If you want an example of how to use replacement demand and expansion demand data you can get from LMI for Al,l then check out our free widget, Careerometer.

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