Edward asks me to expand on the idea that: "inclusive growth may generate a more prosperous and egalitarian society, whereas redistribution may fail to benefit either inclusion or growth."
Here it goes: many countries spend more money subsidizing gasoline or pensions than in education or infrastructure. As a consequence they redistribute but do not empower people with access to the skills and networks that can make them productive. If you use instead the resources to make people productive they will be able to fend for themselves without further redistribution and their additional productivity will lower inequality.

Hi Jonathan: Nice piece. Thanks for referring me to it. You argue that too many things are assumed to be tacit when they are not (meaning that they are codifiable instead). I don't believe that these two concepts refer to similar ontologies. Knowhow is a wiring in the brain which is fundamentally an induction machine as argued by Jeff Hawkins. You unconsciously see patterns as similar to patterns in memory and respond through actions accordingly. Most of this process does not involve awareness. Codes have a completely different ontology. They may exist on paper, on screen, etc. Codifiable knowledge may facilitate the transfer of knowhow to a brain but as a kind of trick. Like when you learn a second language by trying to memorize the grammar rules of that language. It may make your learning initially easier, but once your induction machine becomes wires your brain never uses the trick again, except when in doubt. So I think that knowhow and codifiable knowledge are just two very different kinds of things and calling both knowledge facilitates the blurring of the fundamental distinction.

Hi Heiner: It is true that China is not a formal democracy and people's rights get often trampled. However, it would be important to describe what are the channels through which information about the the public policy needs of a highly diversified economy are channeled and addressed by the state. That is a description I wish I had.

Hi Eduardo: You make two points. First, we have much less data than the brain. Second, we will keep on making mistakes. To the first point I would say that we already have much more information than we actually use. We can do much more with the Big Data revolution, using government administrative data on taxes, trade, social security and the like, as well as private data from cell phones, credit cards, financial statements, etc. On the second, we will always err because induction is what it is, given that we live in an open non-ergodic changing world. But as David Hume said, induction is our only basis of knowledge about the world.

Hi Joshua: Thanks for your comment. Pattern recognition leads to hierarchies of meaning. The word dog is a useful generalization of many instances of certain kinds of animals. Newton's Law of Universal Gravitation is the recognition of a higher level pattern on a set of motions (the apple and the moon). The overarching framework that you mention is a higher order pattern on patterns in the data. At least, that is what Jeff Hawkins thinks it is. It is pattern recognition turned on itself. The paper "Implied Comparative Advantage" that I link to in the piece tries to show that traditional trade theories (both Ricardian and Heckscher-Ohlin) can provide interpretation and meaning to why the approach I propose actually works in the data, but highlight some possible channels that need to be clarified through future research. (e.g. inter-industry knowledge spillovers).