Sunday, Jan. 3, 2021 10:00 AM - 12:00 PM (EST)
- Chair: Michael Powell, Northwestern University
Dynamic Privacy Choices
AbstractI study a dynamic model of consumer privacy and platform data collection. In each period, consumers choose their level of platform activity. Greater activity generates more precise information about the consumer, thereby increasing platform profits. Although consumers value privacy, a platform is able to collect much information by gradually lowering the level of privacy protection. In the long-run, consumers become "addicted" to the platform, whereby they lose privacy and receive low payoffs, but continue to choose high activity levels. Competition is unhelpful because consumers have a higher incentive to use a platform to which they have lower privacy.
Consumer (and Driver) Decision-Making under Uncertainty on Digital Platforms
AbstractInspired by the ride-sharing market in New Zealand, with Uber and Zoomy offering respectively a fixed price and an estimated price range per ride, we ask ourselves if competitors in the digital economy could deliberately offer distinct pricing schemes aimed at matching consumers and drivers with different levels of ambiguity tolerance to gain market shares. Our results suggest that in spite of the realistic asymmetric distribution of ambiguity-loving versus ambiguity-averse consumers and drivers - calibrated with the distribution of the attitudes toward ambiguity obtained in a suitable preliminary laboratory experiment - in equilibrium, both platforms offering respectively a fixed price and an estimated price range per ride can coexist in the market: Ambiguity-loving consumers and drivers are attracted toward the price range offers, whereas ambiguity-averse consumers shy away from them. In equilibrium, drivers from both platforms extract rents, so do consumers accepting price range offers; whereas all rents from consumers accepting fixed price offers are successfully extracted away from them.
AbstractThis paper aims to improve the practical applicability of the classic theory of incentive contracts under moral hazard. We establish conditions such that the information provided by an A/B test of incentive contracts is a sufficient statistic for the question of how best to improve a status quo incentive contract, given a priori knowledge of the agent's monetary preferences. We assess the empirical relevance of this result using data from DellaVigna and Pope's (2017) study of a variety of incentive contracts. Finally, we discuss how our framework can be extended to incorporate additional considerations beyond those in the classic theory.
- D4 - Market Structure, Pricing, and Design
- D8 - Information, Knowledge, and Uncertainty