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EP:46 Depreciating Licensing Model in NFT Ownership with Anthony Lee Zhang from UChicago Booth
Depreciating Licensing Model.
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Today we will discuss property rights from real life to NFT space with Anthony Lee Zhang. He co-authored a paper with Glen Weyl on Depreciating Licenses (DL). We will go from existing problems with property rights in the physical world to the digital world and how the world is changing to bring more rights and fairness to everyone.
In this discussion, we will refer to DL model including Privatisation, Holdout and Allocation. Next will be the trade-offs of allocation for investment in the short and long term. Finally, we will discuss governance in DL, game theory for DL model and applications in NFT.
Assistant Professor of Finance UChicago Booth, Anthony Zhang
If I could teleport back to two years ago I would tell myself to watch out for this paper. It is exactly what I was looking for back then. Property rights and tokenising property rights as well as finding more efficient ways to be embedded within property rights for efficient allocation is beautiful. We can do that now! Anthony discusses his research paper on property rights.
About Anthony Zhang
Anthony: I'm Anthony Zhang, an assistant professor of finance at the University of Chicago Booth. I mainly work in financial market intermediation and market design. I analyse questions about how we can analyse financial markets, figure out how efficient they are and how we can make them work better. Some of the more classical markets that I study are the Banking industry, Housing markets, and Derivative markets.
This is an earlier paper actually which I've recently revised. We mainly were aiming this paper at analysing questions of natural resource license design but there seems to be an application in the crypto space too. I think the paper predates the NFT boom. When we were seeing all this NFT action going on, we realised that some of the ideas here might also apply to the NFT space, so this is where I’m coming from.
Introduction to Depreciating Licenses Model
The way we often motivate this is that the government has a bunch of natural resources. This can be land but also things like radio spectrum, oil drilling rights, fishing rights, etc. All these things are sources of things that somebody can generate a value from like fishing, building radio towers and selling 5G spectrum. The government wants to decide who should get access to use these resources, and they want to raise revenue from the use of these resources. Privatisation has been the classic way to do this since the Chicago Free Market Revolution.
Property rights in: land but also things like radio spectrum, oil drilling rights, fishing rights Generate a value: like fishing, building radio towers and selling 5G spectrum.
Before this privatisation boom, the government would take these licenses and give them to the best user. This did not work so well because normally the government is constrained regarding how much information it can gain about who will be the most efficient user. Hence, the government decided to privatise and auction off these licenses based on the assumption that the highest bidder for a license would be the best user of the asset. This started happening from the 90s onwards.
Privatisation works fairly well but it generates distortions. One of the biggest distortions is that it generates holdouts — when the government sells a spectrum license or a land-use license, that allocation is often efficient at the time it first sells the license. But 20 years later the same company is stuck with the license and much more efficient companies have come in and they want the license but the old company basically holds onto the license and waits to sell it at the highest price possible.
Pure privatisation does not work perfectly. What we noticed is that there is a way to do slightly better than privatisation in terms of allocating resources efficiently. Resource license design affects the efficiency of use of these resources. Pure privatisation selling really long-term use rights gives buyers security in their assets and gives them incentives to invest. But it generates holdout problems because people own inalienable rights and can hold on to these assets longer than they socially should. It is hard to reallocate them to new entrants.
What Else Did You Miss?
Trade off: Efficient Allocation vs Long-Term Investment
Long Term VS Short Term License
Government Quantitative Easing VS Asset Dilution
Cost Theory VS DL Model
Defining the Fees
Benefits and Trade Off
Application in NFT
Models in the Past
Issue and Solution
Advice to NFT Developers using the DL Model
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In the Depreciating License, Anthony and Glen come up with a simple game theory model of property ownership. Basically, you own a fixed percentage of the asset and the remaining percentage is auctioned or sold every time period. In this way, it combines both a full ownership model and a full rental model.
This is quite attractive because in allocating property rights, we often experience a trade-off between incentive investment (100% equity ownership) and asset allocation (allocating to the best at the time of allocating). The amortization license model combines the two and balances the trade-off.
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