Benjamin Shiller

Emerging tracking data enable precise predictions of individuals' reservation values. However, firms may be reluctant to overtly adopt personalized pricing. This paper proposes a strategy that embeds personalization within a dynamic pricing framework, tailoring prices privately while committing to infrequent adjustments to obscure its use. Simulation analyses based on both theoretical and empirically estimated distributions of consumer valuations reveal that profits rise most when consumer arrivals are moderately frequent. Increasing the precision of individual‐level demand estimates broadens the range of products for which this strategy is profitable. These findings suggest the approach may be an auspicious strategy for online platforms.

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.

Since its founding, Amazon has established a reputation for being consumer friendly by consistently offering lower prices than its market position would seem to allow. However, recent antitrust concerns about dominant online platforms have revived questions about whether Amazon’s growing market share threatens consumer welfare. Given its reputation, regulators have proposed a new focus on conduct unrelated to prices. We ask whether such a move is premature. Using the sudden and unanticipated US exit of Toys“R”Us as a natural experiment, we find that Amazon’s toy prices on its US site increased by almost 5 percent in the wake of the exit relative to similar products and to toys on its Canadian site. Thus, despite Amazon’s long-standing reputation, it may exploit increases in market power in traditional ways as competing retailers cease operating.

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.

A consumer's web‐browsing history, now readily available, may be much more useful than demographics for both targeting advertisements and personalizing prices. Using a method that combines economic modeling and machine learning methods, I find a striking difference. Personalizing prices based on web‐browsing histories increases profits by 12.99%. Using demographics alone to personalize prices raises profits by only 0.25%, suggesting the percent profit gain from personalized pricing has increased 50‐fold. I then investigate whether regulations intended to prevent price gouging increase aggregate consumer surplus. Two feasible regulations considered offer at best modest improvements.

Recent technological innovations allow insurance providers to closely monitor and collect detailed data on their customers’ behaviors. Such innovations offer potential benefits by mitigating moral-hazard problems but may provide the incumbent with a lasting first-mover advantage, which may harm consumers. We investigate these outcomes in the context of pay-how-you-drive (PHYD) auto insurance, which offers tailored discounts to drivers monitored by telematics devices. We exploit the staggered entry of PHYD insurance across states and insurers in a difference-in-differences framework. While innovating firms experience initial profit increases, the profits are eroded by entry, which suggests that this innovation does not raise novel antitrust concerns. Furthermore, we find a meaningful impact of PHYD programs on fatal car accidents. Our findings are consistent with impacts implied by canonical theoretical models.

Ad blocking software allows Internet users to obtain information without generating ad revenue for site owners, potentially undermining investments in content. We explore the impact of site‐level ad blocker usage on website quality, as inferred from traffic. We find that each additional percentage point of site visitors blocking ads reduces its traffic by 0.67% over 35 months. Impacted sites provide less content over time, providing corroboration for the mechanism. Effects on revenue are compounded; ad blocking reduces visits, and remaining visitors blocking ads do not generate revenue. We conclude that ad blocking poses a threat to the ad‐supported web.