The Design of Online Markets
Paper Session
Sunday, Jan. 8, 2023 1:00 PM - 3:00 PM (CST)
- Chair: Gauri Subramani, Lehigh University
Digital Privacy
Abstract
We study the incentives of a digital business to collect and protect users’ data. The users' data the business collects improve the service it provides to consumers, but they may also be accessed, at a cost, by strategic third parties in a way that harms users, imposing endogenous users' privacy costs. We characterize how the revenue model of the business shapes its optimal data strategy: collection and protection of users' data. A business with a more 'data-driven' revenue model will collect more users' data and provide more data protection than a similar business that is more 'usage-driven'. Consequently, if users have small direct benefit from data collection, then more usage-driven businesses generate larger consumer surplus than their more data-driven counterparts (the reverse holds if users have large direct benefit from data collection). Relative to the socially desired data strategy, the business may over- or under-collect users' data and may over- or under-protect it. Restoring efficiency requires a two-pronged regulatory policy, covering both data collection and data protection; one such policy combines a minimal data protection requirement with a tax proportional to the amount of collected data. We finally show that existing regulation in the US, which focuses only on data protection, may even harm consumer surplus and overall welfare.Do Incentives to Review Help the Market? Evidence from a Field Experiment on Airbnb
Abstract
Many online reputation systems operate by asking volunteers to write reviews for free. As a result, a large share of buyers do not review, and those who do review are self-selected. This can cause the reputation system to miss important information about seller quality. We study the extent to which a platform can improve market outcomes by attempting to increase the amount and quality of information collected by its reputation system. We do so by analyzing a randomized experiment conducted by Airbnb. In the treatment, buyers were offered a coupon to review listings that had no prior reviews. In the control, buyers were not offered any incentive to review. We find that although the treatment induced additional reviews that were more negative on average, these reviews did not affect the number of nights sold or total revenue. Furthermore, we find that, contrary to the treatment's intended effect, Airbnb's incentivized program caused transaction quality for treated sellers to fall. We examine how the quality of the induced reviews, market conditions, and the design of Airbnb's reputation system can explain our findings.The Welfare Effects of Self-Preferencing: Evidence from Kindle Daily Deals
Abstract
Platforms selling their own products have faced scrutiny for possible self-preferencing. While this is generally hard to study, we examine a transparent context, Amazon's daily rank-ordered ebook promotion. Amazon is both a retailer with 80 percent of the ebook market and a publisher of ebooks. Of the 50 books promoted each day, roughly a fifth are published by Amazon. We document self-preferencing: Amazon gives their books better promotional ranks than their sales prospects warrant. We then estimate the welfare cost of the platform's self-preferencing. Removing the bias in favor of Amazon books would raise revenue by 5.3 percent and consumer surplus by 1.6 percent in our baseline estimates. Replacing Amazon books with non-Amazon books would raise revenue by 3.7 percent and consumer surplus by 4.1 percent, relative to status quo rankings. These changes in revenue and consumer surplus bear relatively constant relationships with the changes available from changed rankings across a wide range of substitution parameters.JEL Classifications
- D8 - Information, Knowledge, and Uncertainty
- L1 - Market Structure, Firm Strategy, and Market Performance