We study the impact of a voluntary monitoring program by a major U.S. auto insurer, in which drivers are tracked for a short period of time in exchange for potential discounts on future premiums. We acquire a detailed proprietary dataset from the insurer and match it with competitor price menus. Our analysis has two steps. First, we quantify the degree to which monitoring incentivizes safer driving and allows more accurate risk-based pricing. Second, we model the demand and supply forces that determine the amount of information revealed in equilibrium. Structural demand parameters are estimated to capture correlations among consumers' monitoring and insurance choices as well as the cost to insure them. A dynamic pricing model makes the firm's information on driver risk endogenous to prices. We can then jointly characterize information and market structures in counterfactual equilibria. Overall, we find large profit and welfare gains from introducing monitoring. Safer drivers self-select into monitoring, with those who opt-in becoming 30% safer when monitored. Accounting for the resource costs of monitoring and price competition, a data-sharing mandate would have reduced short-term profit and welfare.
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.
This paper investigates online retailers' decision to acquire information and the impact of data access on their business strategy and on revenue growth. We take advantage of proprietary data from a large e-commerce platform that sells data analytics products to virtual stores operating on it. The product provides detailed information on customer sources and characteristics, aggregate demand, and competitor strategies. Our empirical investigation relies on several high-frequency panel datasets and makes use of back-end changes in the pricing, variety, and bundling of the data analytics products. Focusing on several consumer electronics and peripherals markets, we find three main results. (i) Data acquisition facilitates growth, but small retailers are very sensitive to the cost of data. (ii) Retailers take marketing and product actions with the data collected but leave prices largely unchanged. (iii) A counterfactual simulation shows that a uniform reduction in the cost of data raises overall platform sales while reducing market concentration on the margin.