Understanding GDP : Output Method (Part 1)

As percursor to further work, for example on making links between economic outcomes and the nature of activity and behaviour at the coalface e.g. lean-type behaviour, this blog begins to look at how GDP is put together.  It’s based on some brief research I did in 2010, when after having put a notional National RONOA model together (see menu pages) I realised that I didn’t really know how GDP was calculated. Initially I used a document on the ONS website outlining the three different ways of calculating GDP [“UK GROSS NATIONAL INCOME (ESA95) INVENTORY”, new link needed here].

GDP History snippet: I understand that the GDP method was originally concieved to encourage growth in anticipation of WWII (the “war effort”), and was not supposed to be used further after the event. (In Business, R4, 201o).

At this stage I don’t want to open the Neo-Mathusian can-of-worms on whether GDP, or more critically “GDP Growth”,  is an appropriate “key performance indicator” and driver of behaviour for an economy as a whole, because it’s the one we currently use and there’s a wealth of useful historical data and constructs used to arrive at this single number. Essentially I’m trying to understand the AS-IS  or “current state”.

I visualised the components of the output, income & expenditure methods of GDP for use in discussion. So, let’s cut to the chase and look at the Output or Production Method – GDP(O).

This method is the one that’s used most for updates every quarter, as the sample data is more readily available from organisation’s financial output. When more data comes in or it is compared with other methods at a later stage (which ultimately should give the same result). Adjustments are made and then re-published. So, you get the”initial estimate” and subsequent “revisions” posted on the ONS website. GDP proper is not usually confirmed for a particular quarter until several months down the line (and then in the Blue Book), which is why the time series tend to be at least a couple of quarters in arrears. We are truely looking at a Lag Indicator here .

Anyway, the first figure (below) shows the output GDP by industry source. It’s a reflection of what is produced, not what is necessarily sold, so prices are assigned in the assumption that these goods (or services) will be sold at some point (in principle captured in the Expenditure Method). At it’s highest level GDP(O) is the sum of Gross Value Added (GVA), which tends to be the dominant number, plus Taxes on Products minus Subsidies on Products. GVA is defined as  Output of Production of Goods and Services (at basic prices) minus Intermediate Consumption (at purchase prices). These “assumed” prices change through time, so to compare one year with another fixed prices from a particular sample year are used. For example, the GDP data I presented in a previous blog had prices using 2006 data.

GVA information and data is collected from the various industry sectors, on a sample basis. The methods of calculation for each sector vary. In many cases assumptions need to be made, where specific types of data are not applicable to that industry, so that bulk comparisons can be made. The figure below outlines the components of the Energy and Manufacturing method for calculating GVA. This seems to be the most comprehensive for understanding the lower levels of data. The components here also start to look familiar to those involved in dealing with company financial information. I’ve added some other items (in dotted boxes) to show links to P&L type items and where the value streams feed in.

Because this method is looking to see what value had been “added” in monetary value to the product / service, the intermediate costs need to be stripped out i.e. minus “Intermediate Consumption”, such as materials, energy, repairs etc. What’s left in GVA are items such as Employment Costs, which are assumed to be covered by the “value add” or margin. Importantly, the data collected for GVA calculations are mostly essential components of the business-level data set and could be tracked as such (see Economic Dashboards)

They might also be used as vehicles to delve deeper into the mechanisms at the “coalface” and identify some key-drivers of business “outcomes”, at least from the company accountant’s point of view.

Further thoughts on the Output method, and diagrams of the Income and Expenditure methods will be looked at in subseqent blogs.

At some point, I want to produce a revised economic causal diagram to investigate it’s system behaviour and drivers (mostly to see if anything interesting pops out). It could (or should) at some point be expanded into a wider socio-enviro-economic model (SEE), primarily to take a view on the global “big picture”.

That’s it for now. If there are any comments or corrections you want to make (or ideas on funding!) please leave a message.

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About Dr_JAH

Independent Researcher
This entry was posted in Analysis, GDP, Visual Economy. Bookmark the permalink.

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