This paper studies the design and impact of auto-insurance monitoring programs, in which insurers incentivize consumers to have their driving behavior monitored for a short period of time. We acquire proprietary datasets from a major U.S. auto insurer, matched with price menus of the firm’s main competitors. We first estimate structural parameters for consumers’ monitoring opt-in choice and for their insurance demand using rich data variation in insurance claims, prices, contract space, and monitoring status. We then conduct counterfactual simulations using a dynamic pricing model that endogenizes the firm’s information set. We find three main results. (i) Data collection changes consumer behavior. Drivers become 30% safer when monitored, which boosts total surplus and alters the informativeness of the data. (ii) Safer drivers are more likely to opt in. But monitoring take-up is low due to both consumers’ innate disutility for being monitored and attractive outside options from other insurers. Nonetheless, introducing monitoring raises both consumer welfare and total surplus. (iii) Proprietary data facilitate higher markups but protect the firm’s ex-ante incentives to produce the data. A counterfactual equilibrium in which the firm must share monitoring data with competitors harms both profit and consumer welfare. This is because the firm offers smaller upfront incentives for monitoring opt-in, so that fewer drivers are monitored in equilibrium.
This paper studies the impact of uncertainty on market competition. Michigan requires auto insurers to cover all expenses related to injuries from auto accidents. However, prices are unregulated as long as competition exists. Starting in the early 2000s, long-term medical costs have ballooned, leading to rapidly increasing injury coverage premiums. Detroit, for example, had an average auto insurance rate more than five times the national average in 2017. However, insurer profit also increased during this period, largely fueled by rising markups on non-injury and smaller coverages. Using publicly available data on insurance quotes and firm-level cost, I show that uncertainty played a key role in mitigating market competition. Specifically, I propose a model of insurance pricing that incorporates firm learning and risk aversion in a market with changing fundamentals. Comparing similar neighborhoods with different realizations of catastrophic claims over time, the model can explain why larger unexpected loss developments lead to higher markups ex-post, despite already exacerbated adverse selection.
We provide evidence on online firms' decision to acquire information and on the impact of data access on firm strategy and performance. We take advantage of proprietary data from a large e-commerce platform that offers a data analytics product for stores operating on the platform. The product provides detailed and real-time information on traffic sources, customer characteristics, aggregate demand (transaction and search), and competitor strategies. These data are largely proprietary to the platform, and firms need to pay lump-sum fees to access some data. Our empirical investigation relies on several high-frequency panel datasets and makes use of back-end changes in pricing and bundling of different pieces of information. Focusing on several consumer electronics and peripherals markets, we show that firms are very sensitive to the cost of acquiring information, especially for small ones. Meanwhile, acquiring information is primarily tied to, and enhances the effect of, product and marketing strategies. We find no evidence that prices are impacted by the information gathered.