What’s Wrong With Modern Macro? Part 14 A Pretense of Knowledge in Macroeconomics

Part 14 in a series of posts on modern macroeconomics. This post concludes my criticisms of the current state of macroeconomic research by tying all of my previous posts to Hayek’s warning about the pretense of knowledge in economics

Russ Roberts, host of the excellent EconTalk podcast, likes to say that you know macroeconomists have a sense of humor because they use decimal points. His implication is that for an economist to believe that they can predict growth or inflation or any other variable down to a tenth of a percentage point is absolutely ridiculous. And yet economists do it all the time. Whether it’s the CBO analysis of a policy change or the counterfactuals of an academic macroeconomics paper, quantitative analysis has become an almost necessary component of economic research. There’s nothing inherently wrong with this philosophy. Making accurate quantitative predictions would be an incredibly useful role for economics.

Except for one little problem. We’re absolutely terrible at it. Prediction in economics is really hard and we are nowhere near the level of understanding that would allow for our models to deliver accurate quantitative results. Everybody in the profession seems to realize this fact, which means nobody believes many of the results their theories produce.

Let me give two examples. The first, which I have already mentioned in previous posts, is Lucas’s result that the cost of business cycles in theoretical models is tiny – around one tenth of one percent of welfare loss compared to an economy with no fluctuations. Another is the well known puzzle in international economics that trade models have great difficulty producing large gains from trade. A seminal paper by Arkolakis, Costinot, and Rodriguez-Clare evaluates how our understanding of the gains from trade have improved from the theoretical advances of the last 10 years. Their conclusion: “so far, not much.”

How large are the costs of business cycles? Essentially zero. How big are the gains from trade? Too small to matter much. That’s what our theories tell us. Anybody who has lived in this world the last 30 years should be able to immediately take away some useful information from these results: the theories stink. We just lived through the devastation of a large recession. We’ve seen globalization lift millions out of poverty. Nobody believes for a second that business cycles and trade don’t matter. In fact, I’m not sure anybody takes the quantitative results of any macro paper seriously. For some reason we keep writing them anyway.

In the speech that provided the inspiration for the title of this blog, Hayek warned economists of the dangers of the pretense of knowledge. Unlike the physical sciences, where controlled laboratory experiments are possible and therefore quantitative predictions might be feasible, economics is a complex science. He argues,

The difficulties which we encounter in [complex sciences] are not, as one might at first suspect, difficulties about formulating theories for the explanation of the observed events – although they cause also special difficulties about testing proposed explanations and therefore about eliminating bad theories. They are due to the chief problem which arises when we apply our theories to any particular situation in the real world. A theory of essentially complex phenomena must refer to a large number of particular facts; and to derive a prediction from it, or to test it, we have to ascertain all these particular facts. Once we succeeded in this there should be no particular difficulty about deriving testable predictions – with the help of modern computers it should be easy enough to insert these data into the appropriate blanks of the theoretical formulae and to derive a prediction. The real difficulty, to the solution of which science has little to contribute, and which is sometimes indeed insoluble, consists in the ascertainment of the particular facts
Hayek (1989) – The Pretence of Knowledge

In other words, it’s not that developing models to explain economic phenomena is especially challenging, but rather that there is no way to collect sufficient information to apply any theory quantitatively. Preferences, expectations, technology. Any good macroeconomic theory would need to include each of these features, but each is almost impossible to measure.

Instead of admitting ignorance, we make assumptions. Preferences are all the same. Expectations are all rational. Production technologies take only a few inputs and outputs. Fluctuations are driven by a single abstract technology shock. Everyone recognizes that any realistic representation of these features would require knowledge far beyond what is available to any single mind. Hayek saw that this difficulty placed clear restrictions on what economists could do. We can admit that every model needs simplification while also remembering that those simplifications constrain the ability of the model to connect to reality. The default position of modern macroeconomics instead seems to be to pretend the constraints don’t exist.

Many economists would probably agree with many of the points I have made in this series, but it seems that most believe the issues have already been solved. There are a lot of models out there and some of them do attempt to deal directly with some of the problems I have identified. There are models that try to reduce the importance of TFP as a driver of business cycles. There are models that don’t use the HP-Filter, models that have heterogeneous agents, models that introduce financial frictions and other realistic features absent from the baseline models. For any flaw in one model, there is almost certainly another model that attempts to solve it. But in solving that single problem they likely introduce about ten more. Other papers will deal with those problems, but maybe they forget about the original problem. For each problem that arises, we just introduce a new model. And then we take those issues as solved even though they are solved by a set of models that potentially produce conflicting results and with no real way to differentiate which is more useful.

Almost every criticism I have written about in the last 13 posts of this series can be traced back to the same source. Macroeconomists try to do too much. They haven’t heeded Hayek’s plea for humility. Despite incredible simplifying assumptions, they take their models to real data and attempt to make predictions about a world that bears only a superficial resemblance to the model used to represent it. Trying to answer the big questions about macroeconomics with such a limited toolset is like trying to build a skyscraper with only a hammer.