Antonio Fatas discusses inherent bias in economics given our reference point – an important issue, and one that economists need to think on (Note: James wrote on this as well – we did our posts separately, so are focusing on different points). Specifically:
This subtle (or not so subtle) bias in economic analysis is my biggest source of frustration with my profession. Not being able to predict crisis, the stock market or exchange rates does not bother me, it is just a reflection of the limits of our knowledge and I can live with it. But using the same naive predictions of models that refer to a fictitious world as the reference and only moving away from them when someone produces an unquestionable piece of empirical evidence is in my mind the true cost of our profession to society.
His post raises good points, but I suspect that what he is laying out runs into the same problem many of us run into when using models to think about policy and the impact of policy – we are not being clear on where the “burden of proof” lies or what we think about assumptions.
Now I like his writing, and this is a good post, but I have a bit of a different view on what economists do with reference to this. Perhaps New Zealand economists are a bit different? Essentially, the question of burden of proof is usually treated as a central part of how we frame and discuss policy questions in New Zealand, so it becomes part of the way we discuss models.
Let me give an example. In New Zealand our central bank has pushed towards macroprudential policy, and more broadly we have pushed towards an idea of having financial institutions accept “compulsory insurance”. This comes from a view that the “burden of proof” has shifted from showing the actions of financial institutions will do harm to regulate, to a situation where we have to justify why certain actions in the current institutional structure will not harm.
So why the change? Well the “benchmark model” of the appropriate institutional factors in this area changed. It is as simple as that.
How does a benchmark model/models change? This makes more sense when we move away from talking about “increasing realism” and just think about the appropriate scope of assumptions in the economic method.
To motivate his post, Fatas links to a post by Greg Mankiw where he discusses an “implausibly strong assumption”. However, Mankiw was doing something very specific with his view on the model. He was trying to identify core assumptions, and then asking if they were appropriate. Economists, and scientists generally, make plenty of unrealistic assumptions – but the core assumptions of our model (the fundamental causal factors behind the result) need to be realistic. Not all assumptions are created equal, and discussing and comparing core assumptions is part of what we do.
Models are conditional, and one of the elements they are conditional on are that our “set of core assumptions” is both realistic and appropriate (namely that assumptions we identify as peripheral are in fact peripheral). So we can critique each others models on that basis, and use a variety of models that enlighten the way some assumptions translate into conclusions. This ain’t pretty, but it is the best we can do. We can view this as a way of “updating our beliefs“, something we can only do in a rational manner if our argument (the core assumptions and their link to our conclusion) is transparent.
In that environment what Mankiw did (calling out that a core assumption was entirely unreasonable. Note: Not saying I agree it was unreasonable 🙂 ) was appropriate.
Now do not get me wrong, if we had the stock of data and knowledge to make models massively more realistic this is grand – but we don’t. As data availability improves, and our ability to isolate causes rises, this will change … but not today! With this more limited set of knowledge, clarity about core assumptions, and a focus on them specifically when making policy related claims, is essential. Given that conclusion I quoted earlier seems like a non-sequitur to me:
But using the same naive predictions of models that refer to a fictitious world as the reference and only moving away from them when someone produces an unquestionable piece of empirical evidence is in my mind the true cost of our profession to society.
The worlds we build with theory are fictitious worlds, and always will be. What is important to understand is what assumptions matter and their relation to reality. In this context what Mankiw did was completely appropriate – if we are going to look at policy, we don’t need to defend the realism of all assumptions, but we do need to be able to justify our core assumptions.
That discipline is not a “cost to society” – it is a benefit. Economists just need to be sure they remain mindful of these lessons, as without care about expressing and testing core assumptions the discipline becomes little more than folk economics 🙂