Abstract: We analyze how self-driving vehicles (SDVs) influence commuter behavior and returns to long-lived public transit investments. Using a commuting mode model estimated on detailed home and work location data from Greater Boston, we simulate the widespread entry of SDVs, which offer passive travel similar to transit but use existing road networks. We find that SDVs increase vehicle miles by 40% while decreasing public transit use by about 10%. Transit improvements continue to moderately boost revenues and lower miles driven, but their effects on mileage are small compared to SDVs. These findings highlight planning challenges posed by the emergence of SDVs.

Abstract: We investigate the impact of intentionally coarsening ratings in the context of automobile safety ratings. First, we construct a novel univariate continuous crashworthiness rating from crash test measurements and observed fatality rates. We then estimate a random coefficient 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%—implying 1850 fewer US 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.

Abstract: 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.

Abstract: Since its founding, Amazon has established a reputation for being consumer friendly by consistently offering low prices. However, recent antitrust concerns about dominant online platforms have revived questions about whether Amazon uses its market share to exploit consumers. Using the sudden U.S. exit of Toys R Us as a natural experiment, we find that Amazon's prices increased by almost 5% in the wake of the exit, with larger increases for popular products most likely stocked by Toys R Us. Thus, despite Amazon's long-standing reputation for low prices, it may exploit increases in market power as traditional retailers cease operating.

Abstract: 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.

Abstract: Concerns about anti-competitive effects of proprietary data collection have motivated recent European data portability laws. We investigate such concerns and search for evidence of direct benefits of data collection 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, and we replicate the main findings using state insurance regulations as instruments for entry timing. We find a meaningful impact of PHYD programs on fatal accidents, but we find no evidence of antitrust concerns.

Abstract: 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.

Abstract: An existing theoretical literature finds that frictionless resale markets cannot reduce profits of monopolist producers of perfectly durable goods. This paper starts by presenting logical arguments suggesting this finding does not hold for goods consumers tire of with use, implying the impact of resale is an empirical question. The empirical impact is then estimated in the market for video games, one of many markets in which producers may soon legally prevent resale by distributing their products digitally as downloads or streamed rentals. Estimation proceeds in two steps. First, demand parameters are estimated using a dynamic discrete choice model in a market with allowed resale, using data on new sales and used trade-ins. Then, using these parameter estimates, prices, profits, and consumer welfare are simulated under counterfactual environments. When resale is allowed, firms are unable to prevent their goods from selling for low prices in later periods. The ability to do so by restricting resale outright yields significant profit increases. Renting, however, does not raise profits as much due to a revenue extraction problem.

Abstract: Although bundling can substantially increase profits relative to standalone pricing, particularly for zero-marginal-cost information products, it has one major problem: bundling produces revenue that is not readily attributable to particular pieces of intellectual property, creating a revenue division problem. We evaluate several possible solutions using unique song valuation survey data. We find the Shapley value, a well-motivated theoretical solution, is universally incentive compatible (all bundle elements fare better inside the bundle than under standalone pricing), but revenue-sharing schemes feasible with readily available consumption data are not. Among feasible schemes, Ginsburgh and Zang's modified Shapley value performs best.

Abstract: With digital music as its context, this paper quantifies how much money would be made using alternatives to uniform pricing. Using survey-based data on nearly 1,000 students' valuations of 100 popular songs in early 2008 and early 2009, we find that various alternatives can raise both producer and consumer surplus. Digital music revenue could be raised by between a sixth and a third relative to profit-maximizing uniform pricing. While person-specific uniform pricing can raise revenue by over 50 per cent, none of the non-discriminatory schemes raise revenue's share of surplus above 40 per cent of total surplus.