On data

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My comments posted on this blog yesterday on the relationship between GDP and overall tax rates within an economy provoked some furious responses from some people who might best be described as economic trolls.

The accusations were that I did not provide regression data to prove my claims and implied, despite that fact, that it might be possible that there was a positive relationship between these issues i.e. that as GDP rises tax rises, and vice versa. Apparently I was not allowed to do this, and in the eyes of at least one commentator was a charlatan for making such a claim. The same commentator also suggested that Charles Adams, whose work I also referenced, clearly knew nothing about maths. Charles is a professor of physics at Durham, who happens to also study economics.

Apparently neither of us also appreciated that correlation does not prove causation. The fact that this implies that we had established correlation is a point I will ignore. Instead let me address my approach to data.

Some time ago I created the acronym CRAP to describe most data. It stands for ‘completely rubbish approximations' to the truth. I first used it with regard to the Government Expenditure and Revenue Scotland statement (GERS) but it can be used for vast quantities of data.

Virtually every set of accounts is, for example, CRAP. The accounts of large companies are, I suggest, complete works of fiction. That's because there is no company that undertakes the transactions that those financial statements report. And the view presented of the transactions by group companies is deeply selective. That's partly because which companies are in the group is open to abuse, whilst vast numbers of transactions between these companies - where all the tax abuse happens - are hidden from view. In addition, accounting standards are open to serious manipulation. Despite that economists treat them as if they are factual, which is ridiculous.

GDP is also CRAP. It includes completely made-up numbers, like the annual rental value of owner-occupied properties as if rent was paid on them, which it isn't. And as Eurostat once told me, there is massive denial on the size of shadow economies within GDP data.

Tax revenues also ignore tax gaps in most cases, so they do not represent what should happen, but are declared net of abuse, which is not terribly meaningful and means that the data is riddled with distortions.

Add up data these distortions and others throughout the micro and macro economies and the data I compared - GDP and tax revenues - is all about comparing CRAP with CRAP. Applying a spuriously accurate statistical technique to this data just produces garbage in that case.

This is the mistake almost all economists always make. Not only are they not good at maths, but much worse than that they never seem to appreciate that they spend most of their time working on CRAP data to produce meaningless outcomes they then claim to be reliable. They aren't. Bluntly, those economists are no better than unthinking technicians when working in this way.

In contrast, what I do is work as a finance professional, which I have been qualified as for forty years. Note the word professional. It means I profess. That is, I offer an opinion. It's subjective. When forming that opinion I allow for the quality of the data. And I bring my experience to the issue.

Doing so I know that correlation does not prove causation. But I know higher incomes invariably result in higher taxes paid. That's a fact. I also know that increased government spending both provides the means to pay more tax and increases GDP, since it is a component in it.

I do not need regression to confirm my suspicions in that case. Like 99.5% (or more) of all practicing accountants I never did a regression analysis for a client. Nor would they have appreciated me doing so. I used my professional judgement to advise based on the available data, on which I relied to the extent to which I considered it reasonable.

That's what I also do on this blog. I profess opinions here based on experience, knowledge of how markets work and what the available data, for all its weaknesses, implies. Here the data confirms my prior suspicions - and yes, finance professionals are allowed them.

But data is only a component in any sound judgment and spurious accuracy based on unsound data should always be taken as a warning sign of unsound opinion.

I really don't care if economics technicians disagree: until they can prove their data is both suitable for use and is reliable their spurious accuracy is always to be questioned by those with better judgement. I will not be changing my approach to data, in other words.

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