The key insight from Hidalgo’s and Hausmann’s work is that “the complexity of an (country) economy is related to the multiplicity of useful knowledge embedded in it, and that hard to transfer, tacit knowledge is what constrains the process of growth and development”. In other words, the present information content of the economy is a predictor of future growth. We have to make adaptions to apply this concept to crypto economies, but since international trade flows and information flows are abstractions with universal properties, the key principles hold surprisingly well.

# Methods and discussion

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## Micro case

### Application of Coase’s Theorem using empirical data

The graph in Figure 3 maps the attention flows from services used by the bitcoin cash and bitcoin communities in the period from 1 month before the hard fork to 1 month after the hard fork. The network clearly shows how audiences interests are sufficiently different to likely support both currencies. The shared space includes common interests (such as wallets that supported both coins). The strength of the links encodes proximity, a measure of affinity between each service and the community — each particular cryptocurrency is a sink, a consumer of attention of the users of a service.
The nodes are vertices of attention, the small ones function as sources and the large ones as sinks. In this example the focus is on the “off-chain” economy, but the same treatment can be applied to on-chain signals.