In our definition of LMI in Part 1, we stated that its purpose is to help us understand “the structural trends and demands of an economy.” This begs the following question: which economy is the data helping us understand?
Local and Regional LMI
LMI can of course be collated to give us details on the national economy, and this can be useful if we are looking to understand nationwide trends. For example, national data telling us how many people are employed in engineering occupations will have a certain statistical value for anyone seeking to understand the big picture.
However, what is often called “the economy” is in reality the aggregate values of a series of smaller “functional” economies, each of which will be markedly different in nature. Engineering occupations in the South West, for instance, will look very different from engineering occupations in Scotland. So for LMI to really be of use in giving us a proper understanding of labour markets, it must be able to accurately report on industrial and occupational trends at the most specific local and regional levels.
In other words, just as the real value of LMI in relation to different industry and occupation classifications is its ability to give us granular data about specific occupations and industries (see Part 2), so too with geographies the real value of LMI lies in its ability not just to give us general numbers about “the economy,” but rather in its ability to really hone in on specific aspects of local and regional economies. This is a theme we shall be returning to in Part 4.
Structural Trends and Demands
There is another aspect of our definition that we need to delve into a little further at this point, which is that of LMI helping us to understand an economy’s “structural trends and demands.” The important point to note here is that the purpose of LMI is not so much to give us a snapshot of what is happening in a labour market at any given time — although that might be useful in its place — but rather to help us understand what are the drivers behind an economy.
At this point an analogy might help to understand the difference. Which football team was the best in the country in the 2014/2015 season? If someone were to answer that by claiming Stoke City, your reply would probably be rather scornful. However, the person making the claim could point to objective information to back up their case, which is that on the final day of the season, Stoke won 6-1 against Liverpool — the widest margin of victory by any club in the highest league in the country.
But of course nobody in their right mind would see this data by itself — the score on the last day of the season — as nearly comprehensive enough or robust enough to substantiate the claim. Rather, most unbiased people would agree that if you want to know which team was the best in the country, the objective test would be to look at which team attained the most points over the course of the season.
So the real answer is of course that Chelsea were the best, and the reason this claim can be justifiably made is that they amassed more points than any of their rivals over the course of the season (87 points over 38 games), and were therefore awarded the Premier League title. Does this make Stoke’s 6-1 victory irrelevant? No, but whilst it tells us something, it cannot give us the full picture of which team was consistently the best throughout the whole season.
Likewise in the sphere of labour markets. Imagine a region of the country associated with a certain type of manufacturing, and let’s say that one of the companies involved in that industry announces that they will be shedding 50 jobs. This information is very useful, since it gives us a snapshot of the labour market in that region at that one particular time. However, what it doesn’t tell us is anything of the structural trends that are driving that economy. For that, you are going to need data that goes back over a period of years. We’ll pick up this issue again in the section on Real-Time LMI versus Traditional LMI in Part 5.
A Summary of LMI Basics
Before moving on, let’s just recap the main points that have been made so far:
- Publicly available datasets are “raw” data. The challenge of turning these datasets into good LMI lies in retaining strengths, discarding weaknesses and dealing with suppressions
- LMI should be accurate down to the most granular level of industry and occupations
- LMI should be accurate down to the most specific geographies
- LMI ought to give us not just a snapshot of a labour market, but rather the longer term structure and demands
In other words, what we ideally want to end up with is LMI that gives us an accurate and complete picture of the structural trends of the economy, right down to specific geographies and all the way down to the most specific occupations and industries.
In the fourth part of this series we will look at industry and occupation granularity as well as geographical granularity.
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