Delong and Logarithms

Brad Delong posted a response to my oped on growth  in the Wall Street Journal. He took issue with my graph, reproduced here,

by making his own graph, here

He characterizes the difference between our graphs with his usual gentlemanly restraint,

"Extraordinarily Unprofessional!!:" "total idiocy" The University of Chicago and the Wall Street Journal Have Very Serious Intellectual Quality Control Problems

and so forth.

If you read Brad, you may wonder what skulduggery I used to make the plot. I will now reveal the dark secret. It's a clever Chicago-school mathematical trick:

Logarithms.

Yes, I plotted log income vs. ease of doing business index.

Now just how much of a sin is this? Well, growth theory is about growth, so it's pretty hard to do without logarithms. If thinking about percentage growth and running regressions with log income on the left hand side is a devious right-wing trick, I'm afraid we're going to have to throw out about 99% of growth theory and empirical economics, including much done by Brad's colleagues at Berkeley.

Furthermore, just look at the graph.  I invite anybody who has sat through a first-year econometrics class where they teach this devious technique to ponder my and Brad's plot, and think whether a level or a log fit is appropriate.

Brad raises one valid concern with all of empirical economics: Endogeneity. The graph is a correlation. How do we know that better ease of doing business causes better business, and not the other way around? In Brad's view, it is equally likely, I guess, that first a contry gets rich, and then it improves its laws and regulations.

I didn't mention this in the Journal, simply for lack of space (try to write anything in 950 words). In a previous blog post, here, I wrote a little bit about it.

One might dismiss the correlation a bit as reverse causation. But look at North vs. South Korea, East vs. West Germany, and the rise of China and India. It seems bad policies really can do a lot of damage. And the US and UK had pretty good institutions when their GDPs were much lower. (Hall and Jones 1999 control for endogeneity in this sort of regression by using instrumental variables.)

(This post isn't hard to find. I linked to from my growth oped post. And if one is curious about "what does John have to say about endogeneity?" -- a rather obvious question, which I ask about twice at every seminar -- it is also possible to email me. )

That post goes on to survey a lot of academic literature on just how important good institutions are to economic growth.

But just think about it. Did North Korea or East Germany first get poor and then get bad institutions? Did the UK and US first get rich, and then develop our rule-of-law and property rights traditions? Is reverse causality at all a plausible explanation for the correlation? Just about every historical episode you can think of goes the other way.

Endogeneity is always an issue in economics, but Brad's case that I am too dumb to have even thought about it, or that this correlation obviously goes the other way,  does not hold up.

But apparently, Brad doesn't know about google, fact checking, or emailing for simple clarifications either. Otherwise he would know that I don't work at Chicago anymore, hardly a secret.

The notion that universities should practice "intellectual quality control" is interesting in this era of declining free speech. Brad, be careful what you wish for.  "Controlling" basic professional ethics may come first.

If anyone is still curious, I posted my data and program to my website, and this post describes it some more. I didn't clean it up well, as I never thought this would be controversial, but at least it documents what I did. Feel free to play with it as you wish.

Update: It's clear from many comments and the twitter storm that many readers, even trained economists, missed this basic point. My graph is an illustration of a conclusion reached by hundreds, if not more, papers in the academic literature. It is not The Evidence, or even particularly novel evidence. Were it so, standard errors, specification search, endogeneity, much better measures of institutions, etc. would be appropriate, as many suggest. My graph is just a quick graphical illustration of the conclusions of much growth economics, including much work by Jones, Acemoglu, Barro, Klenow, and many many others. Institutions matter to economic growth; bad governments have amazing power to ruin economies.  As always in writing, I should have made that clearer; but I thought this literature was familiar to the average economist-blogger.

Update 2: There is, I think, an important mis-specification in a regression of log income on the ease-of-doing business index, which Evan Soltas implicitly points out.  I referred to the index as "simple" and "crude" for this reason, but again it looks like this seemingly obvious point needs expanding.

The World bank's measure is mostly focused on the ease of starting small businesses. When we look at the regulatory sclerosis in the US, it is a much wider phenomenon, encompassing the tax code, social program disincentives, the  recent huge expansion of federal involvement in health and finance, general spread of cronyism, reduction in rule of law, and so forth. These affect large businesses as much or more than small businesses.

Clearly, as we look across countries, the ease of doing business is correlated with these wider legal and regulatory problems. Countries with bad institutions overall also have bad ease of doing business scores. But just as obviously, only fixing the ease of doing business indicators without fixing the larger legal and institutional failures that correlate with those indicators, won't do a whole lot of good, which is what Evan seems to find.

The regulatory program I outlined there and in the longer essay on growth (blog post here, html here,   pdf here) went far beyond ease of doing business indicators, for just this reason.

Update 3: Or, seemingly obvious point #3 that seems to need an answer. A few commenters have questioned  how far "out of sample" one can go. At some point, yes, institutions are perfect and more income will not result from improving them. Where is that? 90? 100? 110? I don't know. But the local derivative is still high, no matter how you fit the "out of sample" points. If you don't think you can draw the line out to 100, going from 82 to 83 still has very large effects.