Uncertainty is a hot topic in economics. This is perhaps no surprise. The economic forecasting profession lost a lot of credibility following the Financial Crisis. So the idea what economists would want to subtly remind their audience that they are not soothsayers with divine foresight is entirely rational – and economists love rationality.

There are broadly three types of uncertainty in macroeconomic forecasting. Uncertainty about the state of the world, uncertainty about one’s ability to predict economic variables when the state of the world is known, and the uncertainty that influences businesses/consumers decision-making process. The first two lead to wider or narrow error bands around a forecast, while the third influences the forecast itself.

Economists understand these differences, indeed an entire nomenclature has been developed to distinguish different types of uncertainty (the name Frank Knight is imprinted in the minds of all economists from a young age). But the way that economic forecasts are typically used seemingly ignores these distinctions, and leads to unconscious biases in forecasts to compensate.

Most ‘medium-term’ economic forecasts are modal. That is, they predict what the economy might look like IF a series of assumptions comes true, such as who will win a forthcoming election and that said party will stick to their promises. Give an economist a perfectly accurate set of assumptions and they are likely to be able to provide a forecast of the economy to a reasonable degree of accuracy.

Yet economists and the users of forecasts typically disagree about ‘a reasonable degree of accuracy’. The typical user of an economic forecast (for business planning) treat economists’ conditional forecasts as pure fact, in much the same way as actual historic data. Either that, or it is entirely ignored.

This is entirely inappropriate. Economic variables are inherently uncertain. If there are any true laws that govern the economy in the same way as the Laws of Physics, they are yet to be discovered. Economists rely on a mixture of ‘rules of thumb’ and historic relationships to take educated guesses at the likely impact of one event on another. Not only may that period of history not be appropriate, but the data relating to that period of history on which the relationships are observed are often unreliable. Although econometricians attempt to control for these, rules of thumb and generalised historic relationships will never provide precise results.

Yet it is this treatment of economic forecasts as factual inputs that leads economists to the introduction of bias on the part of forecasters, be it conscious or not. If warnings around the inability to precisely predict economic variables are essentially ignored then the factors which should lead to wider or narrower ranges around forecasts are gradually factored into the point forecasts themselves. In essence, modal forecasts become mean forecasts – at least partially.

This is a mistake. Providing decision-makers forecasts that do not accurately reflect like most likely outcome, but presenting it as such, will almost certainly lead to an inefficient allocation of resources. And if there is one thing that economists hate, it is an inefficient allocation of resources.

Switching to ‘mean’ forecasts is not the answer for business planning. If there is a path that will either lead to a booming economy or a cataclysmic recession, forecasting an economy bumbling along will lead to entirely the wrong decisions for either outcome.

Until business planning adequately accounts for the inherent uncertainty in economic forecasts, this bias is likely to persist. To change this, economists should take responsibility for better educating stakeholders in their product.