Job Market Paper: The Cryptocurrency Mining Dilemma
This paper studies miner incentives and congestion risks within a new monetarist model of a cryptocurrency. In reward for recording a block of transactions, miners earn per-transaction fees and seigniorage from the creation of new tokens. When a miner earns a fixed fee per transaction in the block it creates, including more transactions raises earnings on that block, if validated, but the risk of invalidation also rises because block size slows transmission to other miners. The settlement rate of cryptocurrency transactions depends positively on the size of blocks produced by miners, hence large enough blocks are needed to make cryptocurrency trade worthwhile. I demonstrate that large blocks are produced only if the ratio of mining fees to seigniorage is high enough. A necessary condition for optimal design is to ensure that seigniorage is moderate relative to transaction fees so that miners have incentive to fulfill their role of record-keepers.
A Theory of Crowdfunding Dynamics (with Matthew Ellman)
This paper develops a dynamic game using endogenous inspection costs to explain empirically salient bidding profiles in crowdfunding. In the baseline, bidders arrive at a constant exogenous rate and face an inspection cost to learn whether they like the crowdfunder’s product. The average funding profile is weakly decreasing over time for any fixed inspection cost, for the uniform distribution and any concave cost distribution. Sufficient cost convexity generates an increasing funding profile. Adding a group of early arriving bidders, such as contacts of the entrepreneur, can lead to a U-shaped funding profile under convexity. Under concavity or uniformity, allowing bidders to delay their bids provides a plausible explanation of U-shaped profiles: least-cost bidders bid early while higher cost bidders delay their inspection towards the end when the project’s prospects are clearer. We also characterize funding profiles when conditioning on project success and failure and develop implications for optimal design.