SALAMI | Shared Aggregation of LAbour Market Information July 2010 to July 2011 JISC-funded under the Flexible Services Delivery Programme www.nottingham.ac.uk/eportfolio/salami

Re-presenting Labour Market Information data sources: what we have learned from the SALAMI project

Roger Clark, August 2011

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Contents

INTRODUCTION ..................................................................................................................................... 2 IAG USE CASES: LINKING JOB PROFILES TO LMI THROUGH OCCUPATIONAL CODING ............................ 3

JOB ROLE PROFILES: THE CONNEXIONS RESOURCE CENTRE INDEX ......................................................................... 4 JOB ROLE PROFILES: OTHER SOURCES .............................................................................................................. 5

DATA SOURCES USED OR CONSIDERED FOR USE BY THE PROJECT ........................................................ 6 TABLE OF DATA SETS USED IN THE SALAMI DEMONSTRATOR ............................................................................... 7 CORE LMI DATA SETS .................................................................................................................................. 8 Jobseeker Allowance Claimants .............................................................................................................. 8 Job Centre Plus vacancies ..................................................................................................................... 10 Annual Survey of Hours and Earnings (ASHE) ....................................................................................... 11

ISSUES, PROBLEMS, OPPORTUNITIES .................................................................................................. 11 WORKS CITED ...................................................................................................................................... 12 APPENDICES ........................................................................................................................................ 13

SAMPLE OF JOBS4U JOB PROFILES CODED TO SOC 2000 ................................................................................. 13

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Introduction

In 2010 members of the SALAMI team published a report, “Labour Market Information to support learners making course choices” (Clark & Coolin, June 2011) examining sources of LMI in order to assess the potential for a project such as SALAMI. Since that time UKCES have commissioned and published a number of substantial consultation papers on technology and LMI for careers IAG. Before looking at the SALAMI work in detail, it is timely to review briefly current knowledge about the state of labour market information sources with respect to the project’s stakeholder needs.

The earlier paper looked in detail at some potential sources of data that could be made more accessible for IAG, and these later UKCES documents entirely support its original conclusions that the available LMI is not ideally suited to the needs of individuals. For example, in “Improving individual choice in career direction and learning” (UKCES Consultation paper, November 2010) it is noted that:

“LMI is collected by a wide range of public and private sector bodies. In regard to the public sector, substantive data are collected through a range of surveys, and other research, regarding the current operation of, and future trends in, the labour market. However, these have frequently been collected for economic/workforce development purposes and the use of these data by individuals has not been a prime focus in the past. Furthermore, data collected has limitations, including the extent to which it can reliably provide information at a local enough level to meet individual requirements.”

This is a problem that the project has encountered throughout, and one that stakeholders are very aware of.

These recent publications have been useful to the project in confirming the approach taken by SALAMI, extending knowledge and establishing intellectual frameworks to work within. In “The Use of LMI in Online Career Direction and Learning” ( UK Commission for Employment and Skills, December 2010) a useful tripartite classification is made which helps to clarify the discourse:

Core LMI: Encompassing the bedrock labour market and employment information that is rarely generated primarily for careers purposes, but which provides undoubted value in informing careers decisions (e.g. demand for labour; employment trends; vacancy information).

Careers intelligence: More directly careers-related information, which is often quite qualitative in nature (compared to core LMI) and includes skills intelligence as well as information about employment practice (job/role profiles; recruitment requirements; career progression). Indirectly, it could also be argued to include various skills diagnostic tools available within the careers market, as these are presumed to be based on some form of underpinning LMI (e.g. skill requirements for employment in a given field).

Skills development and training: A third element that is relevant when thinking about LMI in a careers context relates to training provision, development opportunities and qualifications. This brings into scope a much wider body of information about courses and institutions that is arguably best-considered in its own right (and is the focus of other work, such as that noted earlier on course labelling).

The SALAMI project deals with, and links all these three types of LMI. It has linked job profiles (careers intelligence) to LMI statistical data (core LMI, primarily from NOMIS), and it also links to locally available courses relevant to the selected career through an XCRI course repository (skills development and training). Missing from this analysis is a feature found on some job search sites

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where job role/profile information is directly linked to currently advertised positions. This kind of feature implies that there could be a section of the labour force able to “repurpose” their existing skill profile to a new career.

The difficulties of linking to “Core LMI” sources are alluded to in the same report:

“...... the requirement for sources to be updated regularly over a period of time means that there are relatively few public LMI sources that that could feasibly be incorporated by the online careers market.”

To summarise, a coherent account is emerging of the possibilities for individualised LMI (from an employee rather than employer perspective), and the major challenge is seen to be in how relevant and interpretable the information can be for the individual.

While bearing in mind this challenge of relevance and interpretation, there are now a huge number of new data sets available because of the open data initiatives. The open data site data.gov.uk claims 85 data sources relating to “labour-market”, though in reality few of these are usable and some are duplications of sources already available through ONS. Nevertheless there are datasets of use in the SALAMI project context, for example data about travel or crime levels may have some bearing on decisions about where to work or study.

IAG use cases: Linking Job profiles to LMI through occupational coding

One of the first tasks of the project was to ascertain from stakeholders what uses they made of LMI in their organisations. After a consultation event three areas of application emerged:-

Area 1: LMI for external funding applications/bids

Area 2: LMI for curriculum development

Area 3: LMI for Information, Advice and Guidance

Looking at who would be involved in wanting the LMI, why they want the LMI and for what audience, and what medium the LMI would be required in, we can refine these areas into six use cases as follows:

a) Use Case Title: Using LMI for funding applications and bid writing

Who: Bid writers, University/College Staff Why: Evidence to funding bodies to strengthen the bid Medium: They need the LMI to support documents, graphical representations

b) Use Case Title: Using LMI for changes in curriculum (new courses)

Who: Marketing, employer engagement, academic staff Why: Supporting arguments to peers/management within the institution to win approval for new courses/programmes Medium: To support documents, graphical representations

c) Use Case Title: Using LMI for Prospective Students

Who: Marketing, student recruitment, guidance

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Why: to present the career development merits of course to groups of students Medium: Web Interface, presenting to groups of students

d) Use Case Title: Using LMI for Individual Careers Advice Guidance

Who: IAG specialists Why: Assist students in choosing a career pathway Medium: one on one, using web interface to support advice given

e) Use Case Title: Using LMI for Professional Development for IAG

Who: IAG Staff Why: Improve awareness of data available on web, trend awareness Medium: web interface

f) Use Case Title: Using LMI for Individual Learner

Who: Student Why: Self motivated discovery of possible career options/avenues Medium: Web interface, graphical data - comparisons

Covering all of these use cases with a single demonstrator was not feasible given the resources and timescale of the project, so the project focussed on those use cases that involved an IAG element. Since the starting point of IAG is normally thinking about possible career paths, this meant that the project needed to find a means to link career-focussed consultations to relevant LMI.

Job Role profiles: The Connexions Resource Centre Index

“There are a large number of role profiles available in various formats (both in terms of numbers of roles and numbers of sites providing information about roles). These profiles can provide a clear approach to clustering a range of information about jobs – such as qualification and skills requirements, progression routes and salary information. There is arguably a degree of proliferation in terms of this material, but given its relevance for career learning, that is not necessarily a major problem. The most comprehensive information in this area is publicly-funded.” (UKCES Consultation paper, November 2010)

A Job Profile is a description of what someone with that job does, the skills and qualifications they need, and other information such as salary levels. They are useful if you are thinking about what career you might want in the future. A number of different organisations have Job Profiles, including the government (Nextstep), Sector Skills Councils (SSCs), Prospects, professional bodies and job sites.

From conversations with stakeholders it appeared that one particular set of job profiles was widely used particularly in schools. This was a set of about 800 job roles, arranged in 23 “families”, provided by the Connexions service through the Jobs4U website. This set of profiles was also referred to as the “Connexions Resource Centre Index” (CRCI) which were available freely along with other useful resources. Because the CRCI was documented and widely used the project decided to map these job profiles to Standard Occupational Coding as used in official statistics.

Initially the coding was done by looking up each job role title in the ONS-provided SOC coding guide, but subsequently the online tool CASCOT (http://www2.warwick.ac.uk/fac/soc/ier/software/cascot/) provided by the Warwick Institute for Employment Research was used for the bulk of the entries.

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Having a SOC code against each of the Connexions job titles allowed a user interface where a job role is selected and then referenced to relevant LMI statistics already coded with SOC. Some datasets, particularly Job Centre Plus and Job Seeker Allowance Claimants, have data coded to the full 4 levels. The example below shows how “plasterer” is coded and shares a code with 6 closely related jobs, which is fairly typical.

Job Role Profiles: other sources

Following measures taken by the incoming government, the job profiles maintained by Connexions and previously available at the Jobs4U site have been archived and will no longer be maintained. This means that this particular set of profiles cannot now be used to link careers discovery with LMI.

The project has responded to this development by producing a database of all job profile sets known to the project . The database contains the job title, the web address of each profile and the organisation that maintains them. It has 4581 entries as below:

Sector Skills Councils (SSCs)

532 Building Trades 5321 Plasterers 5322 Floorers and wall tilers 5323 Painters and decorators

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Asset Skills 14

Construction Skills 74

Creative & Cultural Skills 154

Cogent 159

e-skills UK 12

Energy & Utility Skills 29

Financial Services Skills Council 50

Go skills 62

Improve Ltd 10

Institute of the Motor Industry 152

lantra 534

uksp 128

Skills for Care and Development 62

Skills for Logistics 25

Skillsactive 14

Skillset 453

Other Organisations

Nextstep 794

jobs4u (archived) 804

Prospects 617

Totaljobs 172

Caterer 29

Local Government Careers 233

The other SSCs not listed either have no job profiles, or have technologies such as Flash which prevent the profiles being individually accessible. There are overlaps, particularly between the universal lists such as Nextstep and the specialised lists. A Google custom search over all the profiles is available at www.scottclarkconsultants.co.uk/jobProfiles.htm

The objective of this piece of work has been to discover what resources are available, and the conclusion is that there are substantial sets of Job Role profiles available that give more coverage, depth and detail than any single set, e.g. Nextstep. Having collected this data, the next step would be to add SOC codes, group together duplicates and closely related profiles in preparation for creating a web application to link LMI to these profiles.

Data sources used or considered for use by the project

SALAMI produced a demonstrator during the project, the purpose of which was to show stakeholders the kind of service that was possible. Because the purpose was to elicit comment and suggestions from stakeholders, the demonstrator featured many different kinds of data beyond core LMI data. Subsequent stakeholder events showed enthusiasm for the kind of service demonstrated, but limited clarification of the particular data services that were needed. Derby College and New College both took the demonstrator and worked to embed it in their live student services, and their conclusions are presented elsewhere.

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The demonstrator can be accessed at http://salami.samson-portal.org/default.aspx and the data sets and services explored and/or used in the demonstrator are listed in the table below:

Table of data sets used in the SALAMI demonstrator Description Location Access

XCRI – Course information from learning providers

East Mids Web Service http://yourfuture-eastmidlands.co.uk/services/service.asmx

Individual institutions at http://xcri.co.uk/organisations-using-xcri.html

SOAP web service for YFEM or HTTP XML

NOMIS – Official labour market statistics from ONS

www.nomisweb.co.uk, (log in to access API) JSA claimant count, Job Centre + vacancies

API

Google Graphs – convert stats to motion graphs

http://code.google.com/apis/visualization/documentation/

API

OpenStreetMap - view, edit and use geographical data in a collaborative way from anywhere on Earth

http://www.openstreetmap.org/ API

Maps (Various) www.maps.google.co.uk

www.ordnancesurvey.co.uk

API

DBPedia – Open data for subset of online encyclopedia wikipedia.

http://www.dbpedia.org Various

Police – Local crime and policing website. See the crime levels for individual postcode

http://www.police.uk/ API on request

iCould – Careers advice, 1100 videos of professional sharing experiences

http://www.icould.com/api API on request

Google Places – Business directory (coming soon)

http://www.google.co.uk/places API on request

Linked In – Professional networking – Profile API grants access to user info using oAuth

http://developer.linkedin.com/docs/DOC-1002 API

Companies House, Gov – Company information/address/status/accounts

http://xmlgw.companieshouse.gov.uk/ HTTP XML on request

National Public Transport Access Node (NaPTAN) database is a UK nationwide system for uniquely identifying all the points of access to public transport in the UK

http://www.dft.gov.uk/naptan/

XML Download

Openly Local http://www.openlylocal.com

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Core LMI data sets

The project relied on the office for national statistics (ONS) for core LMI data, particularly the specialised LMI data service NOMIS which is provided by Durham University under contract to ONS. In discussion with the NOMIS team it was confirmed that there was a well developed REST style API to their data which was not widely used or promoted. The NOMIS team encouraged the project to use the API and data. NOMIS are currently reviewing their service and it is not clear whether the API will be available in future and what data sets will be available.

For this project three main ONS data sources were considered because they were coded with SOC at the full code level:

Jobseeker Allowance Claimants

This is a potentially valuable group of datasets described by ONS thus:

“The Jobseeker's Allowance (JSA) claimant count, which is often referred to just as the claimant

count, is a prominent indicator which is published monthly by the Office for National Statistics

(ONS). The claimant count normally refers to the second Thursday of each month. The data on

which the count is based are derived from the Jobcentre Plus administrative system for paying

JSA. They relate to all JSA claimants, including people who do not receive any benefit payment

but who sign on for National Insurance credits only to preserve their eligibility for state pension.

Claimants must declare that they are out of work, capable of, available for and actively seeking

work during the week in which their claim is made. They enter into a Jobseeker's agreement

setting out the action they will take to find work and improve their prospects of finding

employment.”

The data is available for localities down to district LA level and for occupations down to the 4-digit

code level of SOC. The occupations are coded for usual occupation and for sought occupation.

The series starts in 2005 and are available monthly. As well as numbers of claimants “on the

books” there is data referred to as onflows and outflows i.e. claimants signing on for the first time

and claimants leaving.

Below is an example analysis using the most detailed level of occupational classification (usual

occupations).

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Nottingham & Derby regions. JSA claimant numbers for SOC 2132 (software professionals) compared to all SOC2 professionals.

The comparison line is a total of all the professionals and so is a much larger number. In order to show it on the same axis, and to show changes in a comparable visual way, the values in each series have this time been multiplied by a constant to give the same starting point and to reflect proportionate rather than absolute changes. The effect of the recession is clearly visible, but software professionals have not been hit as hard as the group as a whole.

The graph is an exploration of a different dataset in this group which show onflows and off-flows, i.e. people signing on and signing off the register. These figures can be broken down by occupation (usual or sought). One might expect that an occupation that was declining would show higher onflow than off-flow and vice-versa. Fig. 3 supports this indirectly by the clear effect of the recession on the separation of the two graphs. The data in the graphs is monthly and is not seasonally adjusted, which causes some problems of interpretation but it seems a rich source for exploration.

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The examples given above show interesting and interpretable features, but they are the result of an analysis and transformation of the raw data. For the project the demonstrator simply showed raw data in a graphical form as we had no steer from stakeholders at that point on the kind of analysis required. It is also important to note that the interpretative layer is actually required for meaningful presentation of data.

Job Centre Plus vacancies

This data seems to have a lot of potential for providing up to date (almost real time) information about the job market. It contains the data from the Job Centre Plus vacancies management computers showing all the vacancies notified by employers to JC+. It is classified to the highest levels of details by geography, occupation and industry and so has potential for personalisation to the enquirers needs. The datasets are as follows

Summary analysis:- Overview of the number of notified, filled and outflows by area.

Notified vacancies:- Monthly numbers of newly notified vacancies to Jobcentre Plus.

Unfilled vacancies:-A monthly snapshot of the number of unfilled vacancies held by Jobcentre Plus.

Vacancy outflow.:-The count of vacancies that have either been filled by Jobcentre Plus or withdrawn during the specified month.

The SALAMI team looked at this data set in detail, attempting to provide interpretations that might be of use. These analyses threw up strange anomalies and after discussion with NOMIS it was decided that there was a high probability that use of the data would be misleading. This is because of the way the vacancies are added/removed from the database, differences between JC+ centres in data management and in vacancy advertising use by local employers, and the bias towards non-professional jobs. What remains are the bare facts about how many jobs with a

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onflow

off flow

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particular SOC code came onto the database in a particular period, and this is what the demonstrator used.

Annual Survey of Hours and Earnings (ASHE)

The representation of this data held at NOMIS is not classified by SOC code, but there is an excel spreadsheet held on the ONS site that is classified in detail http://www.statistics.gov.uk/StatBase/Product.asp?vlnk=15313

The project team looked at this (2009) data to see if it could provide useful information about salaries. On detailed examination, one problem is that the data is only coded to 2-digit level so that categories are broad e.g. “sales occupations”. Another problem is that there is no differentiation between starting salaries and salaries of those well established in their roles. The data presents salaries in percentiles but combined effect is that there is a big range between lowest and highest salaries for a particular SOC code, and there isn’t much obvious difference between different codings. This is an example of how national statistics are collected for purposes different to the needs of advice and guidance professionals. The data was not used in the demonstrator.

Issues, problems, opportunities

A survey referred to in one of the UKCES reports (UK Commission for Employment and Skills, June 2010) provides some important information for the SALAMI project. Their respondents indicate the following:

The most useful LMI:

identification of skills shortages;

LMI relating to new entrants (like conditions for new entrants and new entrant schemes);

regional labour market information; and

local labour market information (including up-to-date vacancies). The least useful LMI:

statistical information (i.e. raw statistics without any interpretation);

LMI that is ‘out-of-date’;

information on employees who have been in an industry for a number of years (because enquiries for information are mainly from new entrants); and

information on the training needs of sectors (rather than training routes for individuals).

What do people want to know:

the demand for labour (how easy is it to get a job in this occupation, industry, role?);

progression routes, career structure and earnings (what are the prospects?);

geographical availability (how available is this in my travel-to-work area?);

overall trends (is employment on the increase in this occupation or industry?);

transferability (will I be able to transfer the competences and skills developed in this industry, should job opportunities decrease?);

recruitment and selection methods (where and how do people get jobs in this industry?).

(Gothard, Mignot, Offer, & Ruff, 2001)

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It is clear that the core LMI data available through national statistics is not capable of satisfying all of these requirements. Even where data does seem useful the problem of interpretation looms large:

“Facts do not always ‘speak for themselves’. If LMI is to be helpful to individuals in informing choice it has to be:

Accurate: Based on robust evidence.

Accessible: Available in appropriate formats and be understandable (including necessary levels of interpretation).

Relevant: Able to answer questions individuals have.

Transparent: Clear about sources used.”

(UKCES Consultation paper, November 2010)

Data needs to be interpreted, and this interpretation needs to be done for the user so that they can draw valid conclusions to help their decision making. The main conclusion is then:

Conclusion 1: There has to be an interpretative layer between the raw statistics and the user

Just what data can be used and how it can be interpreted to provide valid and useful information is a matter for research. When established, methods for processing and presentation in a service can be readily found.

The other main issue that SALAMI has struggled with is the lack of a stable, coherent, accessible, documented and appropriately coded set of job profiles. From our research SSCs have done a good job of liaising with their sectors to provide up to date and detailed job profiles (we identified 1,932). However these vary widely in structure and are difficult to find and do not carry useful codes to connect them to other information. SALAMI has drafted a data standard for job profiles as one potential solution. If all SSCs provided their information within a flexible standard and accessible by standard interfaces this would build into an even more valuable resource for advice and guidance.

Conclusion 2: Harmonisation of job role information through standardisation would benefit users and allow linking of LM intelligence

Finally, it is worth noting the high degree of support and enthusiasm shown by stakeholders during the project. There is clearly a felt need for similar work to continue.

Works Cited

UK Commission for Employment and Skills. (December 2010). The Use of LMI in Online Career Direction and Learning.

Clark, R., & Coolin, K. (June 2011). Labour Market Information to support learners making course choices. Nottingham: Leap Ahead LLN.

Gothard, B., Mignot, P., Offer, M., & Ruff, M. (. (2001). Offer, M. (2001) The Discourse of the Labour Market, in Careers Guidance in Context. London: Sage.

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UK Commission for Employment and Skills. (June 2010). Labour Market Information (LMI), Information Communications and Technologies (ICT) and Information, Advice and Guidance (IAG) The way forward?

UKCES Consultation paper. (November 2010). Improving individual choice in career direction and learning. The potential of new technology within a world-class careers system.

Appendices

Sample of Jobs4U Job profiles coded to SOC 2000

CRCI Job Role(Connexions) SOC2000 4 digit code

Leisure, Sport and Tourism

Betting Shop Cashier/Manager 4123 Counter clerks

Bingo Caller 9229 Elementary personal services occupations n.e.c.

Cinema Projectionist 3434 Photographers and audio-visual equipment operators

Cinema/Theatre Attendant 9226 Leisure and theme park attendants

Cricketer 3441 Sports players

Croupier 6211 Sports and leisure assistants

Entertainment Manager 1225 Leisure and sports managers

Footballer 3441 Sports players

Health and Fitness Instructor 3443 Fitness instructors

Holiday Centre Worker/Manager 1221 Hotel and accommodation managers

Holiday Representative 6213 Travel and tour guides

Horse Riding Holiday Centre Manager 1225 Leisure and sports managers

Horse Riding Holiday Centre Ride Leader 6139 Animal care occupations n.e.c.

Leisure Centre Assistant 3449 Sports and fitness occupations n.e.c.

Leisure Centre Manager 1225 Leisure and sports managers

Outdoor Activities Instructor 3449 Sports and fitness occupations n.e.c.

Pool/Beach Lifeguard 6211 Sports and leisure assistants

Professional Jockey 3441 Sports players

Rugby Player 3441 Sports players

Sport and Exercise Scientist 3442 Sports coaches, instructors and officials

Sports Coach 3442 Sports coaches, instructors and officials

Sports Development Officer 3442 Sports coaches, instructors and officials

Sports Professional 3441 Sports players

Racing Groom/Jockey (Apprentice/Conditional)

6139 Animal care occupations n.e.c.

Theme Park/Fairground Worker 9226 Leisure and theme park attendants

Tour Manager 1226 Travel agency managers

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Tourist Guide 6213 Travel and tour guides

Tourist Information Centre Assistant 6213 Travel and tour guides

Travel Agent 6212 Travel agents

Yoga Teacher 3443 Fitness instructors