Medicine and the microfoundations hegemony in macroeconomics

Mainly for economists

I’m beginning to think I should have made much more of analogies between economics and medicine in discussing what I call the microfoundations hegemony: the idea that the only ‘proper’ macroeconomic models are those that have all their equations consistently derived from microeconomic theory. The analogy I have in mind is that biology represents microfoundations, and statistical analysis linking, say, smoking to lung cancer are the non-microfounded models. (I've used the analogy in other contexts.)

I was thinking about this in the context of a paper I have just finished which uses a diagram that Adrian Pagan used to describe different types of macromodels. The diagram, which you can find in an earlier post, has ‘degree of empirical coherence’ and ‘degree of theoretical coherence’ on the two axes. Particular macromodels can be placed within this space. At one extreme involving the highest theoretical coherence but weaker empirical coherence are microfounded DSGE models. At the other are VARs: statistical correlations between a group of macro variables with no theory-based theoretical restrictions imposed. In the middle are what I call Structural Econometric Models and Blanchard calls Policy Models, which use an eclectic mix of theory and econometric evidence.

If you have a simple view of the hard sciences, this diagram looks very odd. Theories either fit the facts or they do not. But I think a medic could make sense of this diagram by thinking about medical practice based on biology (for example how cells work and interact with various chemicals) and practice based on epidemiological studies. Ideally the two should work together, but at any particular moment in time some medical ideas may borrow more from one side or the other. In particular, statistical studies could throw up links which do not have a clear and well established biological explanation.

Now imagine the microfoundations hegemony in macroeconomics applied to medicine. Statistical longitudinal studies in the 1950s showed a link between smoking and lung cancer, but the biological mechanisms were unclear. The microfoundations hegemony applied to this example would mean that medics would argue that until those biological mechanisms are clearly established they should ignore these statistical results. The investigation of such mechanisms should remain a top research priority, but for the moment advice to patients should be to carry on smoking.

OK, that is perhaps a little harsh, but only a little. That some macroeconomists (I call them microfoundations purists) can argue that you should model and give policy advice based not on what you see but on what you can microfound represents something that I cannot imagine any philosopher of science taking seriously (after they had stopped laughing).        

Economists as medics

I got some stick on twitter the other day for my (longstanding) view that economics is in many respects like medicine. It is of course not exactly like medicine: as the man said, economics is an inexact and separate science. But think about what most doctors spend their time doing. They are in the business of problem solving in a highly uncertain environment in which they only have a limited number of clues to go on. They have solutions to a subset of problems that work with varying degrees of reliability.

If you read Dani Rodrik’s book Economics Rules (which if you have not you should, and can the person who borrowed my copy return it please!), you will see that economists have a large number of distinct models, and the problem that many economists spend their time solving is which model is most applicable to the problem they have been asked to solve. Where doctors have biology as the underlying science behind what they do, they also rely on historical correlations to see if the science is appropriate. Think about solving the problem of why there had been an increase in lung cancer in the middle of the last century.

The science for economists is microeconomic theory, now enriched by behavioural economics. Most of the models economists use are derived from this theory. But as Rodrik emphasises, the trick is to know which model is applicable to the problem you have been asked to solve. To help solve that problem, economists, like doctors, want data. Many have observed how journal articles are now more likely to be about investigating data than establishing theoretical results. Economists have recently started adopting the terminology of medicine in economic studies, talking about treatment effects for example. We both do controlled trials (for economists, mainly in development economics).

Sometimes the paths of the two disciplines cross (as they do all the time, of course, in health economics). One of the big empirical discoveries of recent years has been by Case and Deaton, looking at mortality rates of the US white population. Here is a key figure from their 2015 study.

Mortality has been falling steadily almost everywhere, except since just before 2000 among US whites. Focusing just on the US, the problem seems to be mainly for non-college educated whites (this graphic comes from here).

As with anything to do with race and class in the US, this work has been controversial, but some excellent analysis from Noah Smith shows that the problem suggested by the data is real enough.

Case and Deaton have a new study which tries to understand why this is happening. They describe it as evidence of ‘deaths of despair’. In each age cohort among this group, deaths from suicide, drug overdose or alcohol have been steadily rising. Some useful data is shown here. The interpretation the authors give for the despair is the decline in economic circumstances and status of the white working class in the US.

One of the factors that they describe as an ‘accelerant’ in this development has been the overprescription of opioids drugs that provide short term pain relief, but which have negative consequences in the longer term. US policy over the last 20 years has led to what some describe as the

“worst drug epidemic in U.S. history. Enough opioids are prescribed in the United States each year to keep every man, woman and child on them around the clock for one month.”

They go on

“It is hard to believe that medicine, which prides itself on empiricism, could have taken such a wrong turn.”

Of course individual doctors make mistakes all the time, but the profession as a whole can make major mistakes. It is of course subject to pressures from individuals and large organisations (drug companies). In this, again, it is like economics.

Consider this chart, taken from Alan M. Taylor, ‘The Great Leveraging’, NBER WP 18290.

The blue line shows the percentage of high income countries experiencing a financial crisis each year. Crises were endemic until after WWII, when it appeared for two decades or more that they were a thing of the past. In the 1980s they returned, but without any major impact on high income countries. Then there was Japan’s lost decade, and plenty of papers were written about how that was a particularly Japanese problem. The 2000s seemed quiet, and some called it the Great Moderation, until the global financial crisis arrived.

Looking at this chart, it is hard to believe that economics, that prides itself on its empiricism, could have made the mistake of believing that now things were different. But economics, like medicine, can make big as well as small mistakes. The point I want to make here is the different nature of the response to these mistakes from outside these disciplines. No one says that medicine has failed us, and we need to find fresh voices. No one will say that ‘mainstream medicine’ is in crisis, and we need to look at alternatives.

They do not say that because it would be stupid to do so. With the opioid epidemic something has gone very wrong and it needs to be corrected, and the same is true for economics and the financial crisis. So why the overreaction when it comes to academic economics? One reason is that doctors are not generally asked how long people will live, and even when they do their forecasts are not published almost every day in the press. Most economists are as honest as doctors would be about that kind of unconditional forecasting, but it suits the media to appear shocked and surprised when things go wrong. Another reason is that ordinary people can see doctors doing good things all the time to themselves, their friends and families, but the work of economists is felt less directly. It also seems intuitive that medics are in some sense better than economists, although how you could measure that I do not know. Both factors may explain why medicine is internally policed to a large degree (doctors can be stopped from practicing), whereas economics is not.

Another big difference involves politics. Economists bring unwelcome news to both left and right, so it suits both sides to occasionally bash the discipline that brings the message. We have seen a great deal of that from the right over Brexit. For the left more than the right there are also non-mainstream economists who have an interest in arguing that the mainstream has been corrupted by ideology. Quite why so many on the left choose to attack mainstream economics rather than use the mainstream to attack the right I do not know. All I do know is that they have been doing it for 40+ years, as I remember being told by many economists that the mainstream was fatally flawed back in Cambridge in the early 1970s, which was before Thatcher and Reagan.

But these differences should not obscure the similarities between economics and medicine. We both deal with people, and their mind and body can be pretty complicated whether as individuals, or as a society. In some areas we have developed quite detailed degrees of quantitative understanding that allow us to make successful interventions (more so than in other social sciences I suspect). In other areas we do things that work most of the time but sometimes fail, but there are many important areas where if we are honest we do not have any real idea of what is going on. So we make mistakes, which can sometimes be extremely costly for huge numbers of people, but we also learn from these mistakes.

Should economics be democratised?

In the continuing fallout from the Brexit vote comes a call to democratise economics. I tend to think about these issues by drawing an analogy between economics and medicine. The reason I like this analogy is that both are stochastic sciences: people are unpredictable in terms of their behaviour and biology, at least in terms of the current state of knowledge. There remains a great deal that is mysterious. Both can use theory to a considerable degree, but both also rely on statistical analysis and experiments/trials. I am happy to acknowledge that medicine is ‘better’ in some sense than economics (although I do not really know, or know how that could be ascertained), but I would argue that any difference is of degree rather than kind.

One other similarity that is worth mentioning because it always comes up: both are hopeless at forecasting. Your doctor will not tell you how long you have to live, and can often only give you a rough idea even if you have a fatal disease. Economists get involved in macroeconomic forecasting not because users think it is accurate, but because it is marginally better than guesswork. But while doctors cannot tell you how long you will live, they can tell you that smoking will be very likely to shorten your life. Equally an inability to do good macro forecasts does nothing to refute the claim that if we make trade with our neighbours more difficult we will do less of it and this will reduce people’s welfare and incomes.

The two subjects are also similar in that key decisions are often delegated to expert committees: in the UK the MPC and NICE, for example. But when it comes to other policy decisions, the two subjects differ. Occasionally government or policymakers clash with medical experts on medical matters, but that is rare. In contrast politicians quite routinely ignore economic expertise, or choose minority views over the consensus. The difference is not hard to explain of course: political interests and economic decisions are often intertwined. This can in turn influence the discipline itself. But if you accept my analogy, this is not good for society. Those who voted for Brexit were told it would produce positive results for them in the long term, and will almost certainly be disappointed.

Is the solution to this to democratise economics? I cannot think of anyone, or at least no economist, who would object to the public knowing more economics. Some might go further, and suggest that knowledge of economics among policy makers is dangerously deficient. I would also agree that sometimes economists can learn from interactions with policymakers or even the public. But when it comes to medicine people generally do not want to know about medical science. What they want to know is what medical opinion is on key issues, and they want policymakers to make decisions that embody that knowledge.

I think the same is true of economics. Most people do not want to know the theoretical basis for why fiscal consolidation when interest rates are at their lower bound is bad for the economy, let alone the arguments that a few make against that consensus opinion. (If you read this blog, you may be an exception to this generalisation.) Instead they want to know what the consensus opinion is and how strong that consensus is. If the economics conflicts with their intuition, they might want to check that economists are answering the same question as they are. This the broadcast media generally fails to do, and the tabloids only do if it suits their political line. There are reasons for this in the way the media works, which I have discussed many times, but it would be negligent for economists to imagine it was not their problem as well.

For example in medicine I suspect you could rely on medics to be able to tell you what the consensus opinion on issues was. Unfortunately that would be less true in economics. But that is partly economists own collective fault, because the number working on subject areas can be quite large and not as well connected as they might be. To take just one example, there seemed to be a widespread perception among macroeconomists that many of the top schools taught little Keynesian economics at graduate level. It turns out according to survey data I and Andre Moriera collected that most schools do teach quite a bit of Keynesian economics.

Which leads to my punchline. Economists need to act more as a collective. We need to regularly survey economists (all economists, not just selected groups) about what they think on key policy issues, recording at the same time whether this is their area of expertise. We need spokespeople to explain any consensus in the media. When policymakers, City economists or think tanks depart from this consensus, these spokespeople need to be aggressive as a discipline in pointing this out, and not leave this to individual academics. Much as the medical profession does when rogue claims become popular. We do not so much need to democratise economics, but to organise it.

Conditional and Unconditional Forecasting

Sometimes I wonder how others manage to write short posts. In my earlier post about forecasting, I used an analogy with medicine to make the point that an inability to predict the future does not invalidate a science. This was not the focus of the post, so it was a single sentence, but some comments suggest I should have said more. So here is an extended version.

The level of output depends on a huge number of things: demand in the rest of the world, fiscal policy, oil prices etc. It also depends on interest rates. We can distinguish between a conditional and an unconditional forecast. An unconditional forecast says what output will be at some date. A conditional forecast says what will happen to output if interest rates, and only interest rates, change. An unconditional forecast is clearly much more difficult, because you need to get a whole host of things right. A conditional forecast is easier to get right.

Paul Krugman is rightly fond of saying that Keynesian economists got a number of things right following the recession: additional debt did not lead to higher interest rates, Quantitative Easing did not lead to hyperinflation, and austerity did reduce output. These are all conditional forecasts. If X changes, how will Y change? An unconditional forecast says what Y will be, which depends on forecasts of all the X variables that can influence Y.

We can immediately see why the failure of unconditional forecasts tells us very little about how good a model is at conditional forecasting. A macroeconomic model may be reasonably good at saying how a change in interest rates will influence output, but it can still be pretty poor at predicting what output growth will be next year because it is bad at predicting oil prices, technological progress or whatever.

This is why I use the analogy with medicine. Medicine can tell us that if we eat our 5 (or 7) a day our health will tend to be better, just as macroeconomists now believe explicit inflation targets (or something similar) help stabilise the economy. Medicine can in many cases tell us what we can do to recover more quickly from illness, just as macroeconomics can tell us we need to cut interest rates in a recession. Medicine is not a precise enough science to tell each of us how our health will change year to year, yet no one says that because it cannot make these unconditional predictions it is not a science.

This tells us why central banks will use macroeconomic models even if they did not forecast, because they want to know what impact their policy changes will have, and models give them a reasonable idea about this. This is just one reason why Lars Syll, in a post inevitably disagreeing with me, is talking nonsense when he says: “These forecasting models and the organization and persons around them do cost society billions of pounds, euros and dollars every year.” If central banks would have models anyway, then the cost of using them to forecast is probably no more than half a dozen economists at most, maybe less. Even if you double that to allow for the part time involvement of others, and also allow for the fact that economists in central banks are much better paid than most academics, you cannot get to billions!   

This also helps tell us why policymakers like to use macroeconomic models to do unconditional forecasting, even if they are no better than intelligent guesswork, but I’ll elaborate on that in a later post.