Building an Economics for the Real World
Via Lars Syll, a group of distinguished economists recently testified before a Congressional hearing on “Building a Science of Economics for the Real World.”
Robert Solow wasted no time lambasting modern macroeconomics:
We are in desperate need of jobs, and the approach to macroeconomics that dominates the elite universities of the country and many central banks and other influential policy circles, that approach seems to have essentially nothing to say about the problem. It doesn’t offer any guidance or insight and it really seems to have nothing useful to say.
While Solow does not think we need a complete overhaul, he argued that the workhorse DSGE models do not pass the “smell test” because:
They take it for granted that the whole economy can be thought of as if it were a single, consistent person or dynasty carrying out a rationally designed, long-term plan, occasionally disturbed by unexpected shocks but adapting to them in a rational, consistent way.
Sidney Winter expressed similar skepticism:
A distinctive feature of economics among the sciences is the degree to which most economists, especially most theoretical economists, are oblivious to behavioral realities at the levels of the fundamental units of the complex system that they study: the business firms and households.
Scott Page emphasized the importance of complexity in the actual economy (and the lack of it in the DSGE models), explaining that the complexity approach:
…assumes individuals with diverse incomes and abilities who are situated in place and time. These actors don’t necessarily maximize profits of utility. Instead, what they do is they follow rules that have survived or succeeded in the marketplace. So if a financial firm with greater leverage, such as Morgan Stanley, is making higher profits, other firms may follow their lead. Now, note this effect: more leverage leads to greater leverage. This is a ‘‘positive feedback.’’ Positive feedbacks create what we call ‘‘correlated behavior.’’ Hence, systems that contain them can exhibit clustered volatility in large events like stock market crashes and home mortgage crises.
V.V. Chari pointed out that DSGE models have evolved quite a lot over the years and do incorporate a lot of features that were missing in the early models. In his view, the problem was at least partly one of a lack of historical data:
…any model has got to be disciplined by historical data. That is a necessity. Now, modelers of the U.S. economy naturally tend to focus on the experience of the last 60 years, particularly of the United States. What has the experience of the last 60 years been? Well, relative especially to other countries, it has been remarkably stable except for the recent crisis, and so, in that sense, those kinds of models naturally tended to deemphasize these kinds of financial crises.
Finally, David Colander had some good suggestions for how changing NSF funding could help:
I believe NSF can take the lead in changing the institutional incentive structure by implementing two structural changes in NSF programs funding economics, and I think these will change economists’ incentives. The first proposal involves making diversity of the reviewer pool an explicit goal of reviewing process of NSF grants in social sciences. This would involve consciously including what are dissenting economists as part of the peer-reviewing pool, as well as reviewers outside of economics, such as physicists, mathematicians, statisticians and individuals from business and government who have real-world experience. They could put some sense of what Professor Solow said: ‘‘Does it pass the smell test?’’