One of the main purposes of the area reviews into Further Education, according to the Government’s Reviewing post-16 education and training institutions document, is to ensure that colleges are contributing to productivity and growth by “better responsiveness to local employer needs and economic priorities.” As we stated in the first part of this series, one of the things this means is that colleges must be able to show that they really understand the needs of their labour market.
Stated another way, the college, as a supplier of skills to its local labour market, must meet the demands of that labour market, but this can only be done properly if it first understands what the demands actually are. We would suggest that this implies five things:
1. Access to relevant economic data is a must
2. This data must be able to give the college an understanding of the structural trends of the economy
3. It must be able to help the college to understand the future direction of the economy
4. It must be able to reveal details right down to the most specific industry and occupation levels
5. It must be able to do all of this right down to the level of the local economy in which the college is situated
Getting access to economic data is the least of the problems. There are plenty of publicly available data sources out there, such as Working Futures (WF); Business Register Employment Survey (BRES); and Labour Force Survey (LFS). However, the problem with these sources is that none of them gives a complete picture of the labour market. Some (such as BRES and LFS) provide suitably granular data, but they only describe historic and not future trends. Others (such as WF) do provide forecasts, but do not provide the granularity at the industrial or occupational level, or at geographies below the LEP level.
The bottom line is this: there are no publicly available datasets out there that can tick all five boxes above, and so any college that really wants to demonstrate responsiveness to the needs of their local labour market cannot do so adequately by relying either on the publicly available datasets, or on solutions which take these datasets and make assumptions of what is going on at the specific industry, occupation and geographical levels based on the higher level data (see here for some bizarre examples of where such an approach can lead).
Having said all this, our purpose is not to dismiss those datasets. Far from it. As we hinted above, each one of them has strengths and weaknesses, and so if it were possible to keep the former and omit the latter we might well be able to build a comprehensive and robust dataset that does tick all the boxes.
Think of it as a bit like someone building a car out of parts. He might take a new gearbox out of one car, but leave everything else. In another car, the gearbox might be worn out, but he might save the engine and the carburetor which have recently been replaced. The mechanic that knows what he’s doing and what he’s looking for assesses the strengths and weaknesses of each part, taking what he needs and discarding others.
The analogy may be somewhat simplistic, but this is essentially the principle we use when building our dataset. By using a technique we have developed over a number of years, we are able to model public datasets together — some of which have a good “engine” but a poor “gearbox”, others of which have a fantastic “carburetor” but worn out “brakes” — retaining the strengths and discard the weaknesses of each source. What we end up with is a dataset that gives a detailed, robust and accurate picture not only of regional labour markets, but also of the sub-geographies within, right down to the most specific industries and occupations.
But just as a collection of working car parts are not much use unless they are fitted into a car body, so too the complete dataset we end up with after modelling all the datasets together would not be much use unless we put it into a system capable of showcasing the data in a simple and easy-to-use format. Enter Analyst, our highly accessible and intuitive online tool, which houses our complete dataset. Analyst can give your college a real understanding of the structural trends and future direction of your local economy, right the way down to the most specific industry (SIC 4) and occupation levels (SOC 4), all at the touch of a button.
The area reviews demand that your college shows “better responsiveness to local employer needs and economic priorities”, and this first and foremost means that you will have to demonstrate a good understanding of what those needs and priorities are. With its unparalleled and comprehensive data, Analyst ticks all the boxes mentioned above, and so provides you with an essential tool for understanding your economy and so helping you prepare for your area review.
If you would like to book a demonstration of Analyst, please contact Andy Durman (email@example.com)