Economics is an inexact science

When I wrote about why the BBC should treat a clear consensus in economics the same way as it now treated climate science, I got a number of comments about why economics is not a science. A common theme was that economics couldn’t prove theories ‘beyond doubt’ the same way as the hard sciences could. A more sophisticated version of this complaint is that most economic theories cannot be disproved in the same way that Popper thought scientific theories could be disproved.

All this ignores a key feature of any social science, which is their inexact nature. Instead we have accumulations of evidence that confirm the applicability of some theories and reject the applicability of others. Economists’ views about what models are applicable change as this evidence accumulates.

A good example involves the minimum wage, as Noah Smith suggests. The basic economic model suggested even a modest minimum wage should significantly reduce employment, but economists discovered that the evidence did not show this. As this evidence accumulated, alternative theories and models (monopsony and search) were thought to be more relevant. It is this response to evidence that makes economics a science.

Jo Michell writes “The scientific method of forming a hypothesis and then testing that hypothesis against reality can never be the final arbiter of knowledge, as it can in the physical sciences.” He is right that no single experiment or regression can kill a theory, but wrong that the accumulation of evidence is not the final arbiter, because no other arbiter is available. He links to a post by Noah Smith which talks about the failures of forecasting. But as that post makes clear, this is not about data rejecting models, but the inability of models to predict the future. We would never dream of condemning medics because they cannot predict the exact time of our death, still less suggest that this failure indicates they are not doing science.

Of course economics involves cases where economists appear too reluctant to give up their favoured models. You can find similar stories in the hard sciences. There will be more such stories in economics because the inexact nature of economics makes it easier to discount any single piece of evidence. What I cannot understand is what leads someone like Russ Roberts to argue against the use of evidence, and instead that “economics is primarily a way of organizing one’s thinking”. Astrology is also a way of organising one’s thinking, but it fails because evidence does not back it up.

That comparison is slightly unfair, because while the theory behind astrology is obviously implausible, the basic principles of microeconomics are not. In a class on economic methodology I once drew a huge tree that showed how most of economics could be derived from principles of rational choice. But go beyond the basics, and add in complications involving information and transactions costs (to name but two) and you very quickly derive competing models. There is no single model that comes from thinking like an economist, so for that reason alone we need data to tell us which models are more applicable.

So thinking like an economist does not tell me at what point raising the minimum wage will reduce employment. But why would anyone want to keep their models from being proved relevant or otherwise by data? The only reason I can think of is that some models give answers that are ideologically convenient. Of course allowing data to establish the relevance of some models over others does not make economics ideology proof. For example people can always select the one study that suggests that fiscal policy does not influence output and ignore the hundreds that show otherwise. That is why the accumulation of evidence, which includes its replicability, is so important. If you think economics has problems in that respect, have a look at psychology.

This is why economists views about the long term impact of Brexit should be treated as knowledge rather than just an opinion. Here knowledge is shorthand for the accumulation of evidence consistent with plausible theory. Sometimes the theories are common sense, like making trade more difficult will reduce trade. Estimates of the size of trade reduction based on evidence are uncertain, but they are better than estimates based on wishful thinking. Empirical gravity equations consistently show that geography still matters a lot in determining how much is traded. Finally there is clear evidence that trade is positively associated with productivity growth. To say that all this has no more worth than some politicians opinion is ultimately to degrade evidence and the science which interprets it.

Being honest about ideological influence in economics

Noah Smith has an article that talks about Paul Romer’s recent critique of macroeconomics. In my view he gets it broadly right, but with one important exception that I want to pursue here. He says the fundamental problem with macroeconomics is lack of data, which is why disputes seem to take so long to resolve. That is not in my view the whole story.

If we look at the rise of Real Business Cycle (RBC) research a few decades ago, that was only made possible because economists chose to ignore evidence about the nature of unemployment in recessions. There is overwhelming evidence that in a recession employment declines because workers are fired rather than choosing not to work, and that the resulting increase in unemployment is involuntary (those fired would have rather retained their job at their previous wage). Both facts are incompatible with the RBC model.

In the RBC model there is no problem with recessions, and no role for policy to attempt to prevent them or bring them to an end. The business cycle fluctuations in employment they generate are entirely voluntary. RBC researchers wanted to build models of business cycles that had nothing to do with sticky prices. Yet here again the evidence was quite clear: for example data on real and nominal exchange rates shows that aggregate prices are slow to adjust. It is true that it took the development of New Keynesian theory to establish robust reasons why prices might be sticky enough to generate business cycles, but normally you do not ignore evidence (that prices are sticky) until you have a good explanation for that evidence.

Why would researchers try to build models of business cycles where these cycles required no policy intervention, and ignore key evidence in doing so? The obvious explanation is ideological. I cannot prove it was ideological, but it is difficult to understand why - in an area which as Noah says suffers from a lack of data - you would choose to develop theories that ignore some of the evidence you have. The fact that, as I argue here, this bias may have expressed itself in the insistence on following a particular methodology at the expense of others does not negate the importance of that bias.

I do not think this is just a problem in macroeconomics. David Card is a very well respected labour economist, who was the first to present detailed empirical evidence that imposing a minimum wage might not reduce employment (as the standard supply and demand model would predict). He gave an interview some time ago (2006), where he said this about the reaction to this work:

“I've subsequently stayed away from the minimum wage literature for a number of reasons. First, it cost me a lot of friends. People that I had known for many years, for instance, some of the ones I met at my first job at the University of Chicago, became very angry or disappointed. They thought that in publishing our work we were being traitors to the cause of economics as a whole.”

As Card points out in the interview his research involved no advocacy, but was simply about examining empirical evidence. So the friends that he lost objected not to the policy position he was taking, but to him uncovering and publishing evidence. Suppressing or distorting evidence because it does not give the answer you want is almost a definition of an illegitimate science.

These ex-friends of David Card are not typical of academic economists. After all, his research was published and became seminal in subsequent work. Theory has evolved (see again his interview) to make sense of his findings, but unlike the case of macro the findings were not ignored until this happened. Even in the case of macro, as Noah says, it was New Keynesian theory that became the consensus theory of business cycles rather than RBC models.

Yet I suspect there is a reluctance among the majority of economists to admit that some among them may not be following the scientific method but may instead be making choices on ideological grounds. This is the essence of Romer’s critique, first in his own area of growth economics and then for business cycle analysis. Denying or marginalising the problem simply invites critics to apply to the whole profession a criticism that only applies to a minority.