About this toolkit


This toolkit aims to help organisations create and use Labour Market Intelligence in order to guide local employment policy effectively. It presents UK and international case studies and best practices across the six key steps in local LMI development.

read more

step 1:
Design - Identifying the aims and scope of LMI


Identify parameters, including:

  • spatial boundaries
  • the level of detail required for sectoral analysis
  • the different stages of education and training

Identify stakeholders, intermediaries and partners, including:

  • career advisors
  • employment support officers
  • end users
Identifying partners both within the functional labour market and beyond will minimise duplication, and provide opportunities to share information and costs of data collection and analysis.
case study A
Greater Manchester skills and employment partnership - aligning further education (FE) funding with identified local skill needs
full study

step 2:
Data collection and collation


Draw on a wide set of robust data sources that are timely, accurate and relevant. This may include:

  • national surveys
  • real-time data and qualitative data collected from interviews and regular meetings with stakeholders

Develop a comprehensive core set of data that can be readily updated to provide time series data

case study B
Leeds City Region - increasing employer engagement in education and training
full study

Ensure standard definitions are used for sectors, occupations, and so on, in order to allow for later benchmarking against other geographies.

step 3:
Analysis, contextualisation and supplementary evidence gathering


Synthesise key messages, drawing out the relationship between different metrics and data sources. Include graphic presentations of data.

case study C
North East Scotland College (NESCOL) - promoting an educational institution’s relevance to the local labour market
full study

To confirm that your data accurately captures local labour market characteristics, engage with stakeholders, including employers and labour market experts.

case study D
Sheffield City Region LEP - upskilling existing employees
full study

Present trends in the data to partners to test relevance and usefulness before final product is published.

This will help identify gaps in research and indicate where more information is needed to answer remaining queries

May require the collection of new data to bridge gaps.

step 4:
Translating to action


Identify priorities for action through a range of processes, including discussions with partners and policy makers.

case study E
The Regional Learning and Skills Observatory (RLSO) – developing new course/training programmes to meet identified needs
full study

Identify what has been successful in policy implementation and what has worked less well. From this develop policies/programme to overcome identified labour market issues.

case study F
New Philanthropy Capital (NPC) data labs - evaluation
full study

Use pilot studies to test ideas before full implementation.

step 5:
Communication and dissemination


Promote awareness and use of data, key trends and recommendations by engaging labour market intermediaries, including:

  • Job Centres
  • career advisers
  • app developers
case study G
West of England LEP - assisting young people with career planning
full study

Establish opportunities to share and build understanding of LMI through user support. This may take the form of:

  • peer learning networks and user forums
  • FAQs, documents and written guides
  • organised workshops, presentations, hack days and training days

Develop data platforms – simple to use web delivery systems providing a range of data, analysis and graphics in one place.

step 6:
Keeping LMI up-to-date


Monitor changes in markets, information requirements and methodologies to ensure LMI is fit for purpose.

Update to reflect specific economic shocks such as large firm closures.

Test and continually improve products on the basis of feedback gathered.

case study I
Oregon Employment Department - embedding local knowledge firmly into analysis
full study