Macroeconomics and Confirmatory Bias
Greg Mankiw has written an outstanding new essay that reviews the history of thought in macroeconomics. I don’t know where he is planning to give this paper, but I wish I could be a discussant. Here is a brief excerpt.
Among the leaders of the new classical school, none (as far as I know) has ever left academia to take a significant job in public policy. By contrast, the new Keynesian movement, like the earlier generation of Keynesians, was filled with people who would trade a few years in the ivory tower for a stay in the nation’s capital. Examples include Stanley Fischer, Larry Summers, Joseph Stiglitz, Janet Yellen, John Taylor, Richard Clarida, Ben Bernanke, and myself. The first four of these economists came to Washington during the Clinton years; the last four during the Bush years. The division of economists between new classicals and new Keynesians is not, fundamentally, between the political right and the political left. To a greater extent, it is a split between pure scientists and economic engineers.
In Mankiw’s view, scientists are those concerned with the logical consistency and empirical verifiability of macroeconomic theory. Engineers are concerned with making economic policy. One point he makes is that dramatic developments and debates took place among the scientists, particularly in the 1970’s and 1980’s, but the engineers are still operating much as they did before all this intellectual ferment took place.
My view of macroeconomics is that it operates under conditions of a chronic and severe data shortage. We don’t have a sample of a thousand Great Depressions to provide a testbed for theories. We just have one.
Because of this data shortage, there is an “observational equivalence” among otherwise incompatible macroeconomic models. Two (or more) models that have very different theoretical underpinnings and policy implications can nonetheless both fit the data.
In such an environment, confirmatory bias rules the day. That is, all it takes to believe in a particular model is a slight tendency to elevate the relevance of confirming evidence and a slight tendency to ignore contrary evidence.
For the engineers, there has in fact been plenty of contrary evidence over the past two decades. For example, in predicting the impact of the Reagan tax cuts on output and inflation, the engineers got the direction wrong–forget about magnitude. The standard prediction in 1981 was that the Reagan tax cuts would be highly inflationary. The engineers say that in retrospect monetary policy offset the tax cuts. But I say that one should keep in mind confirmatory bias.
In the early 1990’s, the engineers under-estimated the capacity of the economy to grow and forecast much larger Budget deficits for the latter part of the decade than actually occurred. Again, the engineers say that they can explain what happened in the 1990’s–after the fact. Again, I worry about confirmatory bias.
I am not saying that I, Arnold Kling, possess the true model of macroeconomics. I am saying that it is impossible to know who, if anyone, is the possessor of that model. I believe that because of confirmatory bias, the engineers trust their models much more than I think is warranted.
For policy purposes, I think that if another Great Depression came along, I would say, “try something Keynesian.” I know that if inflation took off, I would say, “try something monetarist.” In between, I think I would focus fiscal policy on long-term growth and tell the Fed not to do anything too crazy.
Greg is troubled by the fact that the engineers still use pre-1970 approaches to modeling, which might indicate that the scientists have not accomplished much since then. My take is that both groups are deceiving themselves about the value of their work. The scientists over-estimate the practical significance of their innovations, and the engineers over-estimate the reliability of their models.
And the reason that macro issues do not generate as much intellectual excitement as they did a generation ago is that the popular intellectual focus has shifted to a new arena in which protagonists hold highly-charged policy debates in the context of a chronic and severe data shortage: climate change.