Benjamin Shiller

Emerging tracking data allow precise predictions of individuals' reservation values. However, firms are reluctant to conspicuously implement personalized pricing because of concerns about consumer and regulatory reprisals. This paper proposes and applies a method which disguises personalized pricing as dynamic pricing. Specifically, a firm can sometimes tailor the ''posted'' price for the arriving consumer but privately commits to change price infrequently. Note this personalized pricing strategy should arise---possibly unintentionally---through algorithmic pricing when some employed variables reflect characteristics of the arriving consumer. I examine outcomes in four contexts: one empirical and three hypothetical distributions of consumer valuations. While one may expect this strategy to be most profitable for low popularity items, I find, counterintuitively, that this strategy raises profits most for medium popularity products. Moreover, typically observable measures of price discrimination suggest it is most intense for these products. Furthermore, improvements in the precision of individual-level demand estimates raise both the popularity-level where absolute profit gains peak and the range of popularities this strategy can be profitably applied to. I conclude that this is an auspicious strategy for online platforms, if not already secretly in use.

Emerging tracking data allow precise predictions of individuals' reservation values. However, firms are reluctant to conspicuously implement personalized pricing because of concerns about consumer and regulatory reprisals. This paper proposes and applies a method which disguises personalized pricing as dynamic pricing. Specifically, a firm can sometimes tailor the ''posted'' price for the arriving consumer but privately commits to change price infrequently. Note this personalized pricing strategy should arise---possibly unintentionally---through algorithmic pricing when some employed variables reflect characteristics of the arriving consumer. I examine outcomes in four contexts: one empirical and three hypothetical distributions of consumer valuations. While one may expect this strategy to be most profitable for low popularity items, I find, counterintuitively, that this strategy raises profits most for medium popularity products. Moreover, typically observable measures of price discrimination suggest it is most intense for these products. Furthermore, improvements in the precision of individual-level demand estimates raise both the popularity-level where absolute profit gains peak and the range of popularities this strategy can be profitably applied to. I conclude that this is an auspicious strategy for online platforms, if not already secretly in use.

Many rating organizations intentionally coarsen ratings before public presentation, for example by using a discrete badge rather than a continuous rating. We investigate the impact of coarsening empirically in the context of automobile crashworthiness ratings. Specifically, we construct a univariate continuous crashworthiness rating from crash test measurements and observed fatality rates. We then estimate a random coeficient model of vehicle demand under status quo coarse ratings and simulate outcomes under counterfactual continuous ratings. We find that consumers alter vehicle choices, thereby reducing fatalities by 7.4%, which implies 1,850 fewer U.S. fatalities annually. Finally, we explore whether incentives to produce crashworthy vehicles are reduced enough to offset benefits of finer information. We conclude that a continuous rating format would reduce fatalities.