Economic theory or empirical data?

It is not unusual for defenders of quantitative easing to claim that the massive injections of money by the Fed or the Bank of England have not caused price inflation, and therefore do not have the negative effects that critics supposed.

A similar argument is made by the Fed’s defenders regarding the 1920s. Austrian economists have argued that loose money policies by the Fed during this period was a key contributor to the stock market crash of 1929 and the subsequent Great Depression. Keynsians and others argue that consumer prices did not increase significantly during this period, and so inflation can’t be a cause since it didn’t happen.

In both the modern case, and the 20s, the focus on consumer prices fails to tell the whole story. There are 3 key points here.

1. The consumer price index is not a perfect measure. It includes some goods, and excludes others, and had weightings for each. Such a measure cannot reliably tell us the impact of new money creation on consumer prices. It is folly to think we can aggregate hundreds of prices into one number.
2. There are of course other prices in an economy other than consumer prices. Raw materials, oil, real estate have all seen rising prices. These aren’t part of the CPI. To focus only on consumer prices is to assume that new money would only show up in consumer prices. In order to see the impact of new money we need to focus on all prices in an economy.
3. Finally, prices can fall due to increased production. This may result in stable prices after the impact of new money is added, or it may be that prices still fell overall. In such a case it is possible that prices would have fallen further were it not for the new money.

This leads to a crucial insight from the Austrian school. It is dangerous to look at economic events in the real world, and say that A led to B, or we tried A, and B didn’t happen. This is because an economy is not a stable model to run experiments on. There are always countless factors that affects supply, demand, prices, jobs etc. To point to one period in history as proof of one economic idea or another is not good enough. Instead, we must have a solid economic theory to explain causality. Only then can we apply this to periods in history and the present day.

Examples of such theories are the laws of supply and demand. It is true to say that increasing the supply of a good will decrease its price all other things being equal. It is easy to point to examples where prices rose even though supply increased. This is because other factors are also in play – perhaps that demand also rose.

The same applies to the loose money policies of the Fed during the 20s and the recent QE. Economic theory is in little doubt that such actions will cause some prices to be higher than they otherwise would have been. How this manifests itself depends on the other factors at the time.

Of course this does not mean we ignore empirical data and statistics. Rather, we recognise their limitations and understand the framework to interpret them within. This is especially important when we remember the economy is the sum of the interactions of millions of individuals, and to assume we can represent this in a few ‘aggregates’ is the height of folly.

Using empirical data alone, we could deduce that creating new money can increase prices, do nothing to prices, or decrease prices. This is like the man in Douglas Adams’ Hitchhiker’s Guide to the Galaxy, who proved that black equals white and got run over on the next zebra crossing.

This is just a roundabout way of restating the old adage, correlation does not equal causation. In economics though, such common sense is often forgotten.

If I may argue by analogy, consider the science of epidemiology. This science is one of the reasons why the media report so many contradictory ideas about which foods are good for us, and which are bad. I expect most of us pay little attention to these stories – we’ve heard it all before. The way epidemiology works is to gather statistics on a given sample population, and identify risk factors. Perhaps one study finds that those who drink at least 3 cups of coffee a day are more likely to develop a particular disease. The only problem is that no biological cause for this link has been found – and given the countless other risk factors and variants across the sample population, we’ve no idea if there is actually some other cause that we don’t yet know about. Of course, attempts are made to control for factors like genetics, exercise rates, diet and so forth, but until a clear cause has been identified such links are mere correlations – and should be treated as such. (A major reason for the spurious studies that contradict each other is an abandonment of the principle that a risk factor is only worth considering if it is a relative risk greater than 2. A relative risk less than 2 is not considered reliable. In percentage terms, a RR >2 is > 100%. Hence all reports of risk factors like “an increase of 30%” break this rule).

Economics is quite similar. We cannot hope to eliminate all other variables in our study to focus solely on the ones of interest. Therefore, our understanding of economics must be grounded in solid theory.

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3 Responses to Economic theory or empirical data?

  1. jimgthornton says:

    What a sensible post. It’s good to see so called quantitative economists and epidemiologists get a bashing in the same place.

    • andyfrith2 says:

      Epidemiology just popped into my head whilst I was writing the post. I’m not really sure it deserves to be called a science. I’m certainly happy to put Keynesians and epidemiologists in the same boat!

      • jimgthornton says:

        It’s a bit more of a science than Keynesianism, or at least it could be if its practitioners followed the rules. Simple things like pre-specifying their analysis methods before they peek at the data. But most of them are pushing some sort of political agenda, usually a leftist one – regulate this, ban that. So they don’t care too much about truth.

        BTW you’re very generous in regarding a relative risk of 2 as likely to be genuine. Many sensible epidemiologist would say that you need a RR of 6 or more. Utter nonsense like overhead power lines and mobile phones constantly come up with alleged RRs of 2.

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