This paper investigates consumer inertia in health insurance markets, where adverse selection is a potential concern. We leverage a major change to insurance provision that occurred at a large firm to identify substantial inertia, and develop and estimate a choice model that also quantifies risk preferences and ex ante health risk. We use these estimates to study the impact of policies that nudge consumers toward better decisions by reducing inertia. When aggregated, these improved individual-level choices substantially exacerbate adverse selection in our setting, leading to an overall reduction in welfare that doubles the existing welfare loss from adverse selection. (JEL D82, G22, I13)

Traditional models of insurance choice are predicated on fully informed and rational consumers protecting themselves from exposure to financial risk. In practice, choosing an insurance plan from a set of complex non-linear contracts is a complicated decision often made without full information on several potentially important dimensions. In this paper we combine new administrative data on health plan choices and claims with unique survey data on consumer information and other typically unobserved preference factors in order to separately identify risk preferences, information frictions, and perceived plan hassle costs. The administrative and survey data are linked at the individual level, allowing in-depth investigations of the links between these micro-foundations in both descriptive and choice-model based analyses. We find that consumers lack information on many important dimensions that they are typically assumed to understand, perceive high plan hassle costs, and make choices that depend on these frictions. Moreover, in the context of an expected utility model, including the additional frictions that we measure has direct implications for risk preference estimates, which are typically assumed to be the only source of persistent unobserved preference heterogeneity in such models. In our setting, we show that incorporating measures of these frictions leads to meaningful reductions in estimated consumer risk aversion. This result has both positive and normative implications since risk aversion generally has different welfare implications than information frictions. We assess the welfare impact of a counterfactual menu design and find that the welfare loss from risk exposure when additional frictions are not taken into account is more than double that when they are, illustrating the potential importance of our analysis for policy decisions.

Measuring consumer responsiveness to medical care prices is a central issue in health economics and a key ingredient in the optimal design and regulation of health insurance markets. We leverage a natural experiment at a large self-insured firm that required all of its employees to switch from an insurance plan that provided free health care to a nonlinear, high-deductible plan. The switch caused a spending reduction between 11.8% and 13.8% of total firm-wide health spending. We decompose this spending reduction into the components of (i) consumer price shopping, (ii) quantity reductions, and (iii) quantity substitutions and find that spending reductions are entirely due to outright reductions in quantity. We find no evidence of consumers learning to price shop after two years in high-deductible coverage. Consumers reduce quantities across the spectrum of health care services, including potentially valuable care (e.g., preventive services) and potentially wasteful care (e.g., imaging services). To better understand these changes, we study how consumers respond to the complex structure of the high-deductible contract. Consumers respond heavily to spot prices at the time of care, reducing their spending by 42% when under the deductible, conditional on their true expected end-of-year price and their prior year end-of-year marginal price. There is no evidence of learning to respond to the true shadow price in the second year post-switch.

Measuring consumer responsiveness to medical care prices is a central issue in health economics and a key ingredient in the optimal design and regulation of health insurance markets. We study consumer responsiveness to medical care prices, leveraging a natural experiment that occurred at a large self-insured firm which required all of its employees to switch from an insurance plan that provided free health care to a non-linear, high deductible plan. The switch caused a spending reduction between 11.79%-13.80% of total firm-wide health spending. We decompose this spending reduction into the components of (i) consumer price shopping (ii) quantity reductions and (iii) quantity substitutions, finding that spending reductions are entirely due to outright reductions in quantity. We find no evidence of consumers learning to price shop after two years in high-deductible coverage. Consumers reduce quantities across the spectrum of health care services, including potentially valuable care (e.g. preventive services) and potentially wasteful care (e.g. imaging services). We then leverage the unique data environment to study how consumers respond to the complex structure of the high-deductible contract. We find that consumers respond heavily to spot prices at the time of care, and reduce their spending by 42% when under the deductible, conditional on their true expected end-of-year shadow price and their prior year end-of-year marginal price. In the first-year post plan change, 90% of all spending reductions occur in months that consumers began under the deductible, with 49% of all reductions coming for the ex ante sickest half of consumers under the deductible, despite the fact that these consumers have quite low shadow prices. There is no evidence of learning to respond to the true shadow price in the second year post-switch.

This Online Appendix has three sections. The first presents details of the choice model estimation algorithm, as well as additional estimates from our primary specification not included in the main text. The second describes our model for consumer self-insurance from savings and borrowing in detail. The third provides additional figures and tables referenced in the main text.