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This paper is an absolute must-read for anyone interested in development and misallocation.
It is one of my favorite papers and can be beautifully summarized in one figure, hence a short thread follow.
Key take-away: a lot of misallocation could be poor measurement.
1/N https://twitter.com/jeffbloem/status/1300827721023533057
It is one of my favorite papers and can be beautifully summarized in one figure, hence a short thread follow.
Key take-away: a lot of misallocation could be poor measurement.
1/N https://twitter.com/jeffbloem/status/1300827721023533057
An extremely successful literature, seeded by the seminal Hsieh and @PeteKlenow paper, argues that reallocating resources within low-income countries could close a sizable share of cross-country income gaps.
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Dispersion of productivity (& of inputs MPs) decreases steeply w/ development. As a results, we can get very large gains from replicating in the low-income countries the same allocation of resources as in the high-income ones.
Stakes are huge, but measurement is tricky.
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Stakes are huge, but measurement is tricky.
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First, a concern on the approach. The optimal allocation in low income countries might differ.
Ex: I show in http://bit.ly/2EEl3Xt that the being far from the frontier leads to larger productivity dispersion as high skilled cluster in few firms that adopt frontier tech.
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Ex: I show in http://bit.ly/2EEl3Xt that the being far from the frontier leads to larger productivity dispersion as high skilled cluster in few firms that adopt frontier tech.
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Second, and at the core of this paper, productivity (& marginal products) might be mis-measured for many reasons. As a result, we might overstate the extent of misallocation.
Question: how to improve measurement and find out the extent of true misallocation?
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Question: how to improve measurement and find out the extent of true misallocation?
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The solution taken by @chrisudry and Doug Gollin is to look at agricultural output.
Agriculture is important per se since it employs a large share of pop in low-income countries. It also allows better measurement than the (available) firm data.
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Agriculture is important per se since it employs a large share of pop in low-income countries. It also allows better measurement than the (available) firm data.
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Most importantly, the authors' great insight is to realize that there is a special feature of agr data: they observe the same farmer choosing how to allocate inputs on different plots.
Allocation across plots must be close to optimal (ex-ante). We have our dream benchmark!
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Allocation across plots must be close to optimal (ex-ante). We have our dream benchmark!
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The picture below show that a large share of the overall productivity dispersion is still present within farmers across plots. As a result, the gains from reallocation are much smaller than previously thought.
Reallocation is relevant, but not a silver-bullet anymore.
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Reallocation is relevant, but not a silver-bullet anymore.
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This paper does not invalidate all the great work showing the large cost of misallocation in low-income countries.
However, it does point out that w/o detailed data is extremely challenging to properly assess the extent of misallocation.
Read the paper! It’s true beauty!
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However, it does point out that w/o detailed data is extremely challenging to properly assess the extent of misallocation.
Read the paper! It’s true beauty!
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