Solving the mystery of the benevolent autocrat

UPDATE 4PM: RESPONSE TO COMMENTS (SEE END OF POST) Step 1: You're right, almost all of the biggest growth success stories are autocracies! Step 2: Wait a second, all of the worst growth failures are also autocracies! Step 3: Solving mystery: autocracies have much higher variance of growth rates, so they have both best and worst growth rates

Wonky Moral of the story: focusing on success only in Step 1 created a selection bias that led to the erroneous conclusion that autocracy was good for growth.

Plain English Moral of the Story: autocracy is extremely risky: it could result in high growth, but it could just as well result in a growth collapse -- for every Lee Kuan Yew, there is a Jean-Bédel Bokassa.

Extra credit question: why would arguing that the autocrats under Step 1 are benevolent while Step 2 autocrats are malevolent be logically fallacious?

RESPONSE TO COMMENTS 4PM To answer some questions, the growth rate is the geometric average 1960-2008 of per capita growth per annum. The source is WDI.

The source for the democracy data is Polity IV, which has some problems, but is enough for the kind of illustration here.

The point about causality is well taken, I am just making a point about how what for these data is actually a POSITIVE and SIGNIFICANT correlation between democracy and growth is turned into an apparent NEGATIVE association in Step 1, which is where the "benevolent autocrat" discussion usually stops.

(FOR WONKS ONLY) When I write this up more fully in an eventual paper, I will explain also some exploration of different functional forms for transforming the original POLITY index from -10 to 10, which is an arbitrary scale (autocracy being the negative direction). To illustrate the strongest possible POSITIVE correlation, I chose from 3 alternatives the one with the strongest statistical significance , which was the following function: POLITY/(11-POLITY). I would normally NOT like this kind of "data mining" among several different functions, but again the point of the exercise is to show the fallacy by which a STRONG POSITIVE association appears to be a STRONG NEGATIVE ASSOCIATION.

Red Sea parts, Development data set free

This week, the World Bank unleashed data.worldbank.org, a website that provides free access to 2,000 indicators about development. For years, only those who paid high subscription fees could access much of this data. One of us authors had been meaning for all those years to complain about this -- how could a public organization like the Bank charge high fees despite the obvious case for a free public good of data on development?! I never got around to making this criticism, and now it suddenly happened without any obvious cause.  (Maybe if I had procrastinated on another bit of criticism, the World Bank would now be refusing to finance tyrannical rulers.)

OK,  just teasing, congrats to the World Bankers for making their data wide open to any citizen, journalist, student, researcher, or policymaker with a computer and an internet connection.

The site includes the newly-released 2010 World Development Indicators (WDI), along with other widely-used Bank datasets: the Africa Development Indicators (ADI), the Global Economic Monitor (GEM), Global Development Finance (GDF) and indicators from the Doing Business report. The data covers 209 countries and goes back in some cases as far as 50 years.

Not only that, the Bank is taking some much-needed steps to make the data not just free and available but also user-friendly. For a start, the new visual interface for data exploration is clearly a big improvement over the old Bank statistics sites. Consensus from data and information architecture geeks around the web so far is that the site, created by Development Seed, is both good-looking and intelligently designed.

Getting ever closer to techno-data-utopia, the Bank will host an "Apps for Development" contest later in the year, and you can already download the new World Bank Datafinder app for the iPhone. Aid Watch's crack beta testers swung into action, and within seconds, we had a chart of trends in Rwandan air freight glowing on our iPhones. (Unfortunately, the chart had no data on Rwandan air freight since 1993. Oh and we could only get indicators on the iPhone that start with A, B, or C.  In fact, this app is pretty clunky- stick with the OECD Factbook app for the moment.)

A partnership with Google has made 39 indicators searchable on the experimental and extremely easy to use Google Public Data Explorer.

In the excitement over this very welcome release, we Aid Watchers can’t forget to keep asking the tough questions about where the data comes from, how it is collected, and where more and better data is urgently needed.

For example, regarding the recent controversy on maternal mortality statistics from 1980 to the present on this and other blogs,  we could quickly check for what years the World Bank was willing to stand by the data.  They limited themselves to providing  "modeled estimate" (i.e. made-up) data for 2005.  Apparently,  even the low standards of the Bank on this variable do not allow them to enter data for any other year.

And now, with the new openness, the more eyes on the data, the better.