Lots of people are unhappy with what Lucas et al. invented to replace the "old macro". But few would argue that it needed replacing. Identifying correlations in aggregate data really doesn't tell you a lot about what you can accomplish with policy.
Because of this, I've always been highly skeptical of John Cochrane's claim that if we simply launched a massive deregulatory effort, it would make us many times richer than we are today. Cochrane typically shows a graph of the World Bank's "ease of doing business" rankings vs. GDP, and claims that this graph essentially represents a menu of policy options - that if we boost our World Bank ranking slightly past the (totally hypothetical) "frontier", we can make our country five times as rich as it currently is. This always seemed like the exact same fallacy that Lucas et al. pointed out with respect to the Phillips Curve.
You can't just do a simple curve-fitting exercise and use it to make vast, sweeping changes to national policy.
Brad DeLong, however, has done me one better. In a short yet magisterial blog post, DeLong shows that even if Cochrane is right that countries can move freely around the World Bank ranking graph, the policy conclusions are incredibly sensitive to the choice of functional form.
Here is Cochrane's graph, unpacked from its log form so you can see how speculative it really is:
DeLong notes that this looks more than a little bit crazy, and decides to do his own curve-fitting exercise (which for some reason he buries at the bottom of his post). Instead of a linear model for log GDP, he fits a quadratic polynomial, a cubic polynomial, and a quartic polynomial. Here's what he gets:
Cochrane's conclusion disappears entirely! As soon as you add even a little curvature to the function, the data tell us that the U.S. is actually at or very near the optimal policy frontier. DeLong also posts his R code in case you want to play with it yourself. This is a dramatic pulpification of a type rarely seen these days. (And Greg Mankiw gets caught in the blast wave.)
DeLong shows that even if Cochrane is right that we can use his curve like macroeconomists thought we could use the Phillips Curve back in 1970, he's almost certainly using the wrong curve. You'd think Cochrane would care about this possibility enough to at least play around with slightly different functional forms before declaring in the Wall Street Journal that we can boost our per capita income to $400,000 per person by launching an all-out attack on the regulatory state. I mean, how much effort does it take? Not much.
And this is an important issue. An all-out attack on the regulatory state would inevitably destroy many regulations that have a net social benefit. The cost would be high. Economists shouldn't bend over backwards to try to show that the benefits would be even higher. That's just not good policy advice.
(Also, on a semi-related note, Cochrane's WSJ op-ed (paywalled) uses China's nominal growth as a measure of the rise in China's standard of living. That's just not right - he should have used real growth. If that's just an oversight, it should be corrected.)
Cochrane responds to DeLong. His basic responses are 1) drawing plots with log GDP is perfectly fine, and 2) communist regimes like North Korea prove that the relationship between regulation and growth is causal.
Point 1 is right. Log GDP on the y-axis might mislead 3 or 4 people out there, but those are people who have probably been so very misled by so very many things that this isn't going to make a difference.
Point 2 is not really right. Sure, if you go around shooting businesspeople with submachine guns, you can tank GDP by making it really hard to do business. No one doubts that. But that's a far, far, far cry from being able to boost GDP to $400k per person by slashing regulation and taxes. Cochrane's problem isn't just causality, it's out-of-sample extrapolation. DeLong shows that if you fit a cubic or quartic polynomial to the World Bank data, you find that too much "ease of doing business" is actually bad for your economy, and doing what Cochrane suggests would reduce our GDP substantially. Is that really true? Who knows. Really, what this exercise shows is that curve-fitting-and-extrapolation exercises like the one Cochrane does in the WSJ are silly sauce.
Anyway, if you're interested to read more stuff I wrote about regulation and growth, see this post.