By now you will hopefully have seen our piece announcing the introduction of Jobs Postings Analytics (JPA). If you haven’t, it’s worth taking a look just to find out why we’re introducing JPA, when we’re introducing it, and what it can do for you.
At the end of that piece, we promised that we would be putting out a number of teaser pieces over the next few weeks, with each one looking at a different aspect of what JPA can bring. In this first teaser, we want to take a look at Job Title Data, and how this could prove extremely useful by giving users of our data an even more in depth look at the labour market.
Standard Occupational Classifications (SOC Codes)
As you will probably know, the Government categorises occupations under Standard Occupational Classification or SOC codes. SOC codes range from nine high level classifications (1-digit SOC), which includes broad occupation categories such as professional occupations and skilled trades occupations, down to 369 granular classifications (4-digit SOC), which includes much more detailed occupation descriptions such as production managers and directors in manufacturing, and IT project and programme managers.
Drilling down to the most granular level enables us to find out an awful lot about the labour market. For example, we might look at the 4-digit level category “nurses” and then dig up any one of a number of different data points about this occupation, from current job numbers to projected job growth, from expected annual openings to regional variations. The graph below, for example, shows projected job change for nurses from 2016-2021 across the 11 Government Office Regions (NB. It is worth nothing that we are able to drill down much further than this, down to county/unitary level and even local authority level):
However, even at the most detailed 4-digit level, SOC codes can only give information on an occupational grouping. What they can’t do is to distinguish between different types of nurses. For example, a school nurse requires a very different skillset from an endoscopy nurse, and both require a very different skillset than a dialysis nurse, but the SOC classification system does not differentiate between them.
The Benefits of Job Title Data
With the introduction of Emsi JPA, we can now show these differentials. Job Title Data functionality enables users to take a far closer look at the different types of occupations that are being hired, giving a much more in-depth analysis for any given occupation. For instance, again looking at nursing, we can now look not only at the broad level category of nurses, but at which type of nursing positions are being advertised.
Please note that these figures represent jobs postings, not actual jobs, which is why we believe that this data must be used in conjunction with our normal labour market data, which does count actual jobs.
Of course this is only the starting point for this data. We could then do any one of a number of further searches, for instance looking to see which type of nurse is most in demand in a given area, or which skills are most required by each different type of nurse. But to see that in action, you’ll have to wait for our next JPA teaser.
If you would like to find out more about Emsi JPA, email us at email@example.com.