Conference Agenda

We model platform competition with endogenous data generation, collection, and sharing, thereby providing a unifying framework to evaluate data-related regulation and antitrust policies. Data are jointly produced from users' economic activities and platforms' investments in data infrastructure. Data improves service quality, causing a feedback loop that tends to concentrate market power. Dispersed users do not internalize the impact of their data contribution on (i) service quality for other users, (ii) market concentration, and (iii) platforms’ incentives to invest in data infrastructure, causing inefficient over- or under-collection of data. Data sharing proposals, user privacy protections, platform commitments, and markets for data cannot fully address these inefficiencies. We introduce and analyze user union, which represents and coordinates users, as a potential solution for antitrust and consumer protection in the digital era.

Money is debt that circulates with no questions asked. Stablecoins are a new form of private money that circulate with many questions asked. We show how stablecoins can maintain a constant price even though they face run risk and pay no interest. Stablecoin holders are indirectly compensated for stablecoin run risk because they can lend the coins to levered traders. Levered traders are willing to pay a premium to borrow stablecoins when speculative demand is strong. Therefore, the stablecoin can support a $1 peg even with higher levels of run risk.

I study credit market outcomes with different competing lending technologies: A fintech lender that learns from data and is able to seize on-platform sales, and a banking sector that relies on physical collateral. Despite flexible information acquisition technology, the endogenous fintech learning is surprisingly coarse---only sets a single threshold to screen out low-quality borrowers. As the fintech lending technology improves, better enforcement harms, while better information technology benefits traditional banking sector profits. Big data technology enables the fintech to leverage data from its early-stage operations in unbanked markets to develop predictive models for expansion into wealthy markets.