"The Colonial Origins of Comparative Development" is a 2001 article written by Daron Acemoglu, Simon Johnson, and James A. Robinson and published in American Economic Review. It is considered a seminal contribution to development economics through its use of European settler mortality as an instrumental variable of institutional development in former colonies.[1] The theory proposed in the article is that Europeans only set up growth-inducing institutions in areas where the disease environment was favourable so that they could settle. In areas with unfavourable disease environments to Europeans, such as central Africa, they instead set up extractive institutions which persist to the present day and explain much of the variation in income across countries. Other theories explored in the article argue that it is the choices of institutions within the country that result in the effective and efficient use of resources in leading to the successful development of that country.[2]Acemoglu is Armenian-Turkish-American. He does grand studies of gigantic datasets with many authors, and publishes big theories for how the world works.
I have not studied this, but I suspect he is a charlatan.
It is an appealing theory to believe that nations succeed or fail on how well founded their institutions are. If so, then all those failing nations could be fixed by just teaching them to set up better institutions.
But this never happens.
Probably human capital and natural resources are much more important. I think he is rigging the data to tell people what they want to hear. No one wants to hear that some nations fail because they are hopelessly deficicient in some way.
Update: Noahpinion blogger has good criticisms of the prize.
Update: A Statistics professor writes:
I was talking with an economist today about the recent prize given to the authors of the very influential 2001 article, The Colonial Origins of Comparative Development: An Empirical Investigation. According to my colleague, many economists have issues with that paper, with issues regarding data quality, the weakness of the instrument, and problems of selection bias in the analysis. The concern seems to be that those data could be used to show just about anything. Which, as usual, does not mean that their theories are wrong, just that their data are consistent with other theories.
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