Having arrived at a working definition of what LMI actually is (see Part 1), we now need to delve further to flesh out some of the details of the constituent parts a little more. This is both a necessary and helpful exercise to go through, since it will then help us when we come on to look at some of the issues connected with different LMI solutions in later parts of this series.
Government Data is “Raw” Data
We stated in our definition that LMI is data which is taken from publicly available datasets, and we gave examples of some of the most common datasets out there. However, in and of itself, although such datasets might answer to the general definition we started with — “LMI is data about jobs and industries that help us understand the economy” — they don’t make the grade as far as our more specific definition is concerned: LMI is data taken from publicly available sources, which gives information on jobs and industries, which helps us understand the structural trends and demands of an economy.
The reason for this is that the data which is available from public sources is what we would call “raw data”. What do we mean by this? Chiefly two things: Firstly, no dataset out there gives us a complete picture of the labour market. Indeed, every dataset out there will have both strengths and weaknesses, and anyone relying on a single source will not be able to use it to “understand the structural trends and demands of an economy.” Secondly, within each one of these publicly available datasets some of the data is subject to suppressions. For instance, for specific industries or occupations in a given geography, the data will often be suppressed to avoid revealing sensitive data.
What this means is that in order for these raw datasets to be turned into useful data, two things need to happen:
- The weaknesses of particular datasets need to be discarded and the strengths retained.
- For the data to be really useful, the suppressions need to be dealt with. This is a theme we’ll be returning to in Part 4 of this series.
Industry and Occupation Classifications
As we mentioned in our definition, LMI gives information on jobs and industries, but it is important to note how this information is categorised. The Office of National Statistics uses a 4-digit classification system for both industries and occupations: the Standard Industrial Classification (SIC) system and the Standard Occupational Classification (SOC) system respectively. This 4-digit system is hierarchical in structure, with the 1-digit codes being for general categories of industries or occupations, all the way down to the 4-digit sectors that denote the most specific sectors.
For instance, a 1-digit SIC code would be something like Manufacturing, whereas a 4-digit SIC code would be something very specific, such as Production of meat and poultry meat products. Likewise, with occupations an example of a 1-digit SOC code would be Managers, Directors and Senior Officials, whilst a 4-digit SOC code would be the most specific type of occupation, such as Information technology and telecommunications directors.
The importance of understanding the differences between the various levels of industry and occupation classifications cannot be overstated. The reason for this is because the real value in LMI is not so much in its ability to tell us what is happening at the big picture industry and occupation levels — useful though this might be — but rather to delve down and give us an understanding of what is happening at the more granular level. For instance, knowing that, say, Manufacturing has declined over recent years in Britain is all well and good as far as it goes, however when we delve beneath the generic occupation classification, what we actually find is that not all Manufacturing is in decline. In fact, some types of Manufacturing at the 4-digit level have actually have grown. The real value of LMI is seen in its ability to accurately reveal the figures and trends at these more specific levels and this is a theme we will pick up again in Part 4, when we look at Industry and Occupation Granularity.
In the third part of this series we will look at another couple of LMI basics: Local and regional LMI, and Structural trends and demands.
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