Part 4 in a series of short articles exploring labour market information and its role in supporting education planning and economic development. See the whole series here.
How do we bring it all together?
In part 3 of this series, we explored a subset of the vast array of LMI sources. The challenge with LMI in the UK is presented by this fact – how do we make sense of LMI when there is such a disparate range of data all telling slightly different parts of the bigger LMI story? This is where EMSI comes in. Analyst brings all of this data together in an easy to use and highly intuitive web-based tool, saving significant time in the collation of data.
Not only does Analyst make accessing LMI very simple, it also creates new insight through new data formed by the connection of constituent datasets. The Working Futures data provides a picture of employment prospects by industry, occupation, qualification level, gender, and employment status for the UK, its constituent nations, and English regions.
However, although useful at the macro level, Working Futures does not allow analysis of employment trends at a more local level. To achieve this, Working Futures forecasts have been modelled against detailed labour market intelligence at the more granular NUTS3 geographic level from a variety of datasets available through the NOMIS service.
Combining data from these three sources creates something entirely unique, providing employment forecasts at a level of detail that makes labour market data useful to a broad range of local, regional, and national organisations. Linking this with skills and competency intelligence really helps to understand shifts in training priorities associated with a changing labour force.
What’s in a staffing pattern?
One of the unique insights that Analyst provides is the ability to explore staffing patterns. Staffing patterns describe an industry in terms of its constituent occupations, and in Analyst these reports help us to understand key drivers to sectoral change within employment forecasts.
For example, is forecasted growth in the health sector likely to be driven by frontline healthcare occupations, specialist social care jobs, or back office administration and management roles? This insight is fundamental to supporting policy and planning to ensure skills demand is met through the supply of learning and qualifications, and turns interesting LMI into critical insight.
These trends work in reverse – inverse staffing patterns help us to understand trends in the sectors that employ selected occupations and provides insight as to the key drivers to demand for the skills associated with the occupation in question. When training for selected occupations, inverse staffing patterns provide clarity as to the likely destinations for sustainable employment and helps to shape the content of the learning provision.