Why Economic Models are Bullshit (Part II)

Hello readers!

Previously, I wrote a post entitled “Why Economic Models are Bullshit (Part I)”. Therein, I covered one of the problematic areas of macroeconomics: namely, that students are badly taught the subject. But that does not tell us enough about the other side of the coin—the reason why macroeconomic models are, by themselves, problematic (aka “bullshit”). In this post, I explain just this.

Before I begin, and in case you are wondering: yes, I am progressing with Fallen Love. I am about halfway through the revision process; I will post an update later on. For the time being, I am extremely busy both with the book and with my university studies. Consider this my final update for the month.

Anyway, onto the topic of today’s post...

The Follies of Macroeconomic Models

I am not the first to criticise an economic model, and in particular, I am not the first to criticise the discipline of economics as a whole. Some critics speak from a position of ignorance; they sometimes make good points, but cannot articulate their criticisms beyond relatively vague generalities. (A few examples: economic models don’t work because you can’t put people in an equation. Or, economics is not a science. Both hold a grain of truth, but are not extended upon beyond platitudes.)

Some critics, however, are economists. Thomas Piketty and Ha-Joon Chang are good examples of the latter, though the venerable Steve Keen is my personal favourite among the rebels. If you have had read these economists, you may detect some of their criticisms among my own; though I quite fancy that I am original. Anyway, vanity is a vice, so allow me to get to the meat of the arguments...

Problem No. 1: economic models are not dynamic. To clarify, by “dynamic” I mean that the models do not explicitly refer to time—either graphically, mathematically, or even in argument. Some economists (especially those who have never done a proper science, like physics or chemistry) seem to think that economic models are dynamic because... time is in there somewhere.

Of course time is present in these models, in some way—they wouldn’t make any sense otherwise. (They make little enough sense as it is!) The problem is that this relationship is dreadfully ambiguous; there is absolutely no clarity about what happens when, and this leads to a number of conceptual errors and oversights.

The above graph is a clumsily drawn example, representing an IS–LM model of a small open economy, with e (nominal exchange rate) on the vertical axis and Y (national income) on the horizontal axis. The two sloping lines are IS-curves, while the vertical line is an LM curve. The shift of the IS curve outwards represents a ceteris paribus fall in taxes.

The moral of this story is that, in a small open economy with perfect capital mobility, fiscal policy doesn’t work: you can’t change national income with fiscal policy measures. Even if we assume this is true (there are economists who do not agree with this assessment), the problem is that the graph is extremely obtuse.

There is a whole time dynamic involved here. First, a fall in taxes leads to an increase in Y, which in turn leads to an increase in r (interest rate) as the money supply is fixed. The increase in r leads to a situation where r>r* (the domestic interest rate is higher than the international); this leads to an influx of capital, which in turn drives up the exchange rate e. The appreciation of e leads to a fall in NX, which brings Y back down to the initial Y.

None of that is shown on the graph. We only see a new equilibrium point at e2 and Y. Great explanation there!

Problem No2: economic models confuse cause and effect. If you look at the IS–LM graph aforementioned, you might be forgiven for thinking that somehow Y (national income) affects e (the nominal exchange rate). In the sciences, we put the independent variable on the x-axis, and the dependent variable on the y-axis.

To peruse one of many examples from physics:

The story here is pretty straightforward: you apply a force, and the material stretches in a particular way dependent on its material properties.

Occasionally in physics, some graphs don’t follow this convention, usually for reasons of convenience.

The problem is that nearly all economics models have it backwards: they put the independent variable (the causation, the mover) on the Y-axis, and the independent variable (the observed change) on the X-axis. This small change makes economic graphs unnecessarily confusing. In the IS–LM model, e affects Y because e affects NX and Y is dependent on NX; however, there is no clear relationship going the other way round.

In formal logic notation,

(x → y) ≠ (x ↔ y)

This says that (x implies y) is not the same as (x and y imply each other). Or to put it in more comprehensible terms: if I sleep through my alarm I will be late; but if I am late, that doesn’t necessarily mean I slept through my alarm (I could have been stuck in traffic!)

Problem No3: economic models make overly idealised assumptions. This is a big one. Economists say that the art of economic modelling is choosing good assumptions; but if so, economists must be terrible at their job.

Let’s look at the previous model I showed: the IS–LM model under conditions of a small open economy with perfect capital mobility. You may now observe that, actually, well—capital isn’t perfectly mobile. You can’t do a runner with a house. What’s more, houses take time to sell (again, dynamic systems!) and the resale value is not always high (risk element). In many parts of the world, there are restrictions on foreigners buying houses.

Because the assumption of perfect capital mobility is wrong, the aforementioned conclusion is wrong as well. Fiscal policy does have an effect on national income—just look at the UK under austerity. It is thought by many economists that Osborne’s economic policy cost the UK a lot of lost income growth. The Sterling did not depreciate and net exports remained pretty dismal (the former stayed high and the latter stayed negative).

A more useful assumption would have been: assume that some capital assets are mobile while others are not. Determine the share of mobile-assets for the economy you are looking at. This way, you get a much better grasp for what’s actually going on.

Problem No4: vagueness. This is a problem that I have rarely seen mentioned, perhaps because it is of a slightly more philosophical nature. Essentially, what I have noticed in economic models is that they can be quite unclear as to what a concept or variable is referring to.

Take the example of r*, which represents the going interest rate across the globe. Or even just r, which represents the going interest rate in a national economy. My question is: which interest rate does it represent, exactly? Investments have many rates of return. We all know that some investors make a fortune on the stock market; others make a loss. Bonds have different returns based on their maturity period.

If we just take a weighted mean of all these different interest rates, we risk missing some important constituent details.

If we look at the globe, we... observe that there are many interest rates, across both private sector and government investments. Even if we confine ourselves to only government bonds, we see that there are large discrepancies based on the countries’ riskiness (Argentina or South Africa have higher interest rates on their bonds than Germany or the US).

At this point, economists just thought: “Aha! We can model interests rates as being r* + P, where P is the risk premium.”

Except it’s not that simple; the concept of risk premium is itself vague. How do you quantify a risk premium? No one knows. Investors make investment decisions based on their perception of that risk, but the risk itself is uncertain; the interest rate we observe is just the expression of a social belief, not some neat numerical correction.

To put it in philosophical language, the ontological status of the risk premium (and numerous other macroeconomic concepts) is misunderstood. And the consequences are not just philosophical; they can lead to a number of conceptual errors with serious policymaking implications. One prominent example is in neoliberal economics, and its belief in the divine importance of the market price.

In a debate about rent prices in London, the neoliberal economists might say: “All these social housing schemes are nonsense. Why should the state interfere and distort the housing market price?” The use of the word distort is very important—it suggests that the market price is almost like a physical quantity, a reality that should not be meddled with. In reality, of course, the housing prices of London are really just a reflection of the (deluded) expectations of property owners on future prices, among other things.

Problem No5: the role of risk, uncertainty, and expectations. This is another area of economics that is under active research, and in which we are starting to see improvements. I’ve decided not to go detail here; the topic is quite technical, and anyway, I’m doing research on it right now. Perhaps I will cover it in a future post. Until then, I will (again) recommend reading the venerable Steve Keen, along with various other economists such as Frank Knight and Gunnar Myrdal.

Concluding Remarks

What I have written ultimately only scratches the surface; there are much more fundamental questions to be asked about macroeconomics and its ability to accurately model and predict real world economies. Nevertheless, I think the five key problems I have highlighted constitute a good set of methodological problems with macroeconomics—and they are problems that can be feasibly solved.

My conclusion for students, policymakers, and other economists is this: presently, economic models are pretty rubbish. They are in urgent need of improvement—or else economists will find themselves stuck in the credibility crisis they are now in. But better models will demand the work of newer, wiser, and better educated thinkers.

In other words, we need a twin revolution; a revolution in the way we teach economics, to attract stronger students from a wider variety of fields, and a revolution in the way we do economics. Will the field rise up to this challenge? Perhaps. People like Steve Keen give me hope. On the other hand: there are a lot of economists who prefer to keep their head in the sand. What can I say? I hope they die quickly.