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A last thought. Economic Fluctuations merged with Growth in the mid 1990s. At the time there was a great confluence of method as well as interest. Growth theorists were studying growth with Bellman equations, dynamic general equilibrium models of innovation and transmission of ideas, thinking about where productivity shocks came from. Macroeconomists were using Bellman equations, and studying dynamic general equilibrium models with stochastic technology, along with various frictions and other propagation mechanisms.
That confluence has now diverged. I enjoyed spending an hour or two thinking about how religion has blocked or adapted to ideas over the centuries, and Paul’s view on social norms or neuroeconomics. But I don’t really have any expertise to contribute to that debate. Questions like whether young CEOs head more innovative companies, or whether, like deans, what matters is the age of the faculty are a little closer to home, since I spend a lot of time consuming corporate finance. But the average sticky-price macro type does not. Likewise, when Daron Acemoglu, who seems to know everything about everything, has to preface his comments on macro papers with repeated disclaimers of lack of expertise, it’s clear that the two fields really have gone their separate ways. Perhaps it’s time to merge fluctuations with finance, where we seem to be talking about the same issues and using the same methods, and growth to merge with institutions and political or social economics.
This is similar in flavor to John Seater’s comment that I wrote about here. Has growth economics become different enough from mainstream macro that we should separate them from one another?
I’d argue yes. Growth is about development now – meaning that it’s motivating question is “Why are some countries rich and some poor?”. (See my earlier post on this topic here). The exploration of answers to this question are much more about big static differences in institutions, cultures, technologies, and the like, and less about transition paths and dynamics.
On what growth would look like if it did separate (literally at NBER and intellectually as a field) from macro, Cochrane gave us perhaps a pointer:
I’m not sure in the end though whether Paul[Romer] was approving or bemoaning the shift back towards literature in economic analysis. Certainly his vision for the future of growth theory, centered on values, social norms, biology, and so forth, does not lend itself easily to quantification.
Is this a feature or a bug? Perhaps the big question of “Why are some countries rich and some poor?” is not answerable in any solid empirical way. Perhaps the highest achievement here is “literature” in the sense of some overarching theory that one uses to examine history. Think of Pomeranz’s The Great Divergence or Robert Allen’s The British Industrial Revolution in Global Perspective as examples. While both books certainly appeal to economic intuition and occasionally something approaching formal theory, neither considers anything like a Bellman equation.
The counter would be that we can do better than just “literature” in growth by writing down model (perhaps static models, but no matter) that allow us to quantify the forces that people like Pomeranz and Allen propose as relevant. That is, write down an explicit model, and calibrate or simulate it to assess whether a proposed explanation has a plausibly large quantitative effect on output per worker. The issue here is, as Cochrane says, it’s essentially impossible to quantify religion or values. What is the parameter you stick in your quantitative model that captures the effect of a belief in the afterlife on your willingness to work today? If you cannot possibly hope to measure that parameter, then you cannot quantify it’s effect on output per worker.
So if we’ve entered the world where we think that values (or culture or religion) are fundamental to development, then we may be left with “literature” as the only valid form of research output. My guess is that growth economists will resist this kind of transition, mainly because we’ve invested a lot in knowing fancy dynamic models and calibration techniques, and we don’t want those skills to become worthless.