Information Frictions and Adverse Selection: Policy Interventions in Health Insurance Markets

A central goal of policy in health insurance markets is to set up an environment whereby firms will offer, and consumers can purchase, efficient insurance products that meet consumer demands for risk protection and health care provision. An important concern in accomplishing this goal is that consumers may be far from fully informed about their health plan choices and may have difficulties making decisions under limited information (Abaluck & Gruber, 2011; Ketcham et al., 2012; Kling et al., 2012; Bhargava, Loewenstein, & Sydnor, 2017; Handel & Kolstad, 2015b). Despite this observation, the economic models available to evaluate and design common policies in selection markets are by and large based on assumptions of informed, rational consumers (Chetty & Finkelstein, 2013).

The inability to comprehensively investigate how policy affects health insurance markets when consumers have meaningful choice frictions is problematic. In practice, a range of policy levers are used to overcome adverse selection, a key impediment to insurance market function (Ackerloff, 1970; Rothschild & Stiglitz, 1976).¹ Typically, researchers investigating the positive and normative impacts of these policies ignore the potential role of consumer choice frictions. At the same time, regulators also consider and implement policies to reduce choice frictions such as, information provision, plan recommendations, or smart defaults (Handel & Kolstad, 2015a). These policies are often considered with little or no focus on the impact of adverse selection. Empirical research highlights cases where policies to improve choices can reduce consumer welfare via increased adverse selection (Handel, 2013) as well as cases where such policies improve consumer welfare (Polyakova, 2016). However, there has not previously been a systematic investigation of when one should expect friction-reducing policies to improve (or decrease) welfare.

In this paper, we develop a general yet implementable insurance market model that accounts for consumer choice frictions. Our framework allows for the systematic investigation of both policies to combat adverse selection, in the presence of choice frictions, and policies to combat choice frictions in the presence of selection effects. We derive policy-relevant sufficient statistics that identify the key economic trade-offs and are readily measurable empirically in a wide range of contexts. We demonstrate the applicability of our approach by estimating the relevant primitives in the particular context of employer-provided health insurance and analyzing the positive and normative impacts of different, oft-considered policies in this setting.

In contrast to prior work that has concentrated on the mean value of frictions or inertia (e.g., Handel & Kolstad, 2015b; Baicker, Mullainathan, & Schwartzstein, 2015), we show that the relative distributions (mean and variance) of three model primitives are crucial for policy and welfare analysis: consumers' willingness to pay for insurance, the cost to the insurer, and the impacts of consumer frictions on willingness to pay. We map these foundations into demand, cost, and welfare-relevant value curves, building on Einav et al. (2010) and Spinnewijn (2017).² When policies affect the sorting of individuals into insurance, then demand, cost, and welfare curves will change as well and are no longer sufficient to study positive and normative outcomes, as is assumed in most prior work.

We first use our framework to analyze policies that directly reduce consumer choice frictions and identify the following, potentially opposing effects. First, a friction-reducing policy works like a tax when the targeted frictions were pushing consumers at the margin to demand more coverage on average. For that case, the policy worsens underinsurance in an adversely selected market. In addition to this level effect on willingness to pay, reducing frictions also affects the sorting of consumers, improving the match between consumers and plans conditional on equilibrium prices and increasing the equilibrium prices by increasing the correlation between costs and willingness to pay. We exploit the tractability of our framework to develop surprisingly simple expressions for the marginal impact of a policy change in terms of means and variances of the demand primitives among the marginal consumers. As the mean and variance of surplus in the population rise relative to those of costs (e.g., due to more heterogeneous preferences), friction-reducing policies become more attractive: the benefits of facilitating better matches between consumers and plans in equilibrium begin to outweigh the costs of increased sorting on costs and subsequent adverse selection. We explore these theoretical properties in a series of simulations designed to highlight these key effects.

In addition to characterizing when friction-reducing policies are “good” or “bad” on their own, we study how these policies interact with the supply-side policy of insurer risk-adjustment transfers. These transfers are designed to reverse adverse selection by compensating insurers who enroll ex ante sicker consumers with transfers from insurers that enroll ex ante healthier consumers. Risk-adjustment transfers are present in many different contexts alongside policies to improve consumer choices (e.g., ACA exchanges, Medicare Part D, Medicare Advantage).³ First, we show that in adversely selected markets, increased risk adjustment improves the impact of friction-reducing policies on welfare and can shift them from welfare negative to welfare positive. Second, we demonstrate that as friction-reducing policies become less attractive (e.g., as the potential for adverse selection increases), effective risk adjustment plays a much more important role in increasing welfare. These results illustrate the importance of coordinating demand-side interventions with supply-side policies commonly used in insurance markets.

With these insights in hand, we apply our framework to an empirical context where we can measure the distributions of surplus from risk protection, costs, and the impact of frictions on willingness to pay. The empirical analysis both highlights the impact of the policies we study have in one context, and illustrates how our framework can be applied to study similar policy decisions in other contexts. With fairly typical data on individual-level costs and plan designs, our framework provides insight into whether friction-reducing policies will be welfare increasing or welfare reducing. With more detailed data on key microfoundations, similar to that in our empirical application, our framework can be used to assess the magnitude of the welfare impact of such policies.

Our empirical analysis builds on the model and estimation in Handel and Kolstad (2015b) using proprietary data on the health plan choices and claims of over 35,000 employees (105,000 employees and dependents) at one large firm, linked at the individual level to a comprehensive survey designed by the authors to measure the extent of consumers' potentially limited information on many dimensions relevant to health plan choice. Relying on their estimates of risk preferences, health risk, and friction effects on choices, we use the data to characterize the nonparametric sample joint distributions of consumer costs, consumer surplus from risk protection, and the impact of consumer choice frictions on willingness to pay. Importantly, we are able to characterize not just the average impact of frictions on willingness to pay (the primary focus of Handel and Kolstad, 2015b) but also how they are distributed in the population.

We find that not only is the mean impact of frictions on willingness to pay high (mean of $1,787, pushing consumers toward more generous coverage) but also that the variance in these friction values is substantial (standard deviation of $1,304). Expected costs are high, just over $10,000, as is the variance of costs, implying both high mean and variance of the cost of providing more generous coverage. The mean and variance of estimated surplus from incremental risk protection, however, are both low, reflecting low estimated risk aversion. Given our theoretical results, these foundations suggest that friction-reducing policies on their own will be welfare reducing: mean friction values are positive and large, so reducing their impact would reduce demand and thus equilibrium coverage, and re-sorting into insurance would be substantial when reducing the heterogeneous impact of frictions and further reduce welfare as the mean and variance of costs are high relative to those of surplus. Thus, informing consumers on their underlying value from insurance will increase the role of cost in decision making, exacerbating adverse selection, without substantially enhancing welfare by allocating people to the plan that gives them more surplus. This also indicates an important role for risk-adjustment transfers as a complement to friction-reducing policies.

These predictions based on our theoretical framework are borne out in our counterfactual analysis. Without any policy interventions, 85% of consumers enroll in more generous coverage, with the remaining 15% in just the baseline option. Removing frictions completely, however, leaves only 9% of enrollees in the generous plan, essentially leading to the market fully unraveling, while the surplus of risk protection is positive for all enrollees. Quantifying the welfare impact, we find that the policy that eliminates frictions reduces the share of first-best surplus achieved to 15%. Risk-adjustment transfers are, however, strongly complementary to friction-reducing policies. When there is no policy in place to reduce frictions, risk adjustment transfers that are 50% (100%) effective increase coverage from 84.6% to 87.1% (88.5%), a positive but small impact on coverage. However, when the policy to reduce frictions is fully effective, risk adjustment transfers that are 50% (100%) effective increase coverage from 9.1% to 51.6% (63.5%), with similar increases in the percent of first-best surplus achieved. Though the combined policy of fully reduced frictions and fully effective risk adjustment still reduces welfare slightly relative to the status quo, from a distributional standpoint, there are fewer consumers leaving substantial sums of money on the table given equilibrium prices.

Our paper proceeds as follows. In section II, we present our theoretical framework, characterize market equilibrium and welfare, and demonstrate how both are affected by demand-side and supply-side policy interventions. Section III describes the data and estimates that we use to empirically implement the model and some descriptive statistics related to consumer heterogeneity on important dimensions, and it presents our empirical analysis of market equilibrium, friction-reducing policies, and insurer risk-adjustment policies. Section IV concludes.

Here we develop a stylized model of the insurance market, which can be used to consider available policy options to address adverse selection (e.g., risk adjustment) and information frictions (e.g., consumer choice tools). Focusing on marginal policy changes, we are able to characterize the key trade-offs policymakers are facing and relate them to measurable empirical moments. All proofs are in appendix A.

Our primary model considers a competitive market for one priced insurance plan, following Einav et al. (2010). The plan is offered to all individuals in the market at a uniform price denoted by $P$⁠. Individuals decide whether to buy the insurance plan. An individual $i$'s willingness to pay for the plan is denoted by $wi$⁠. Information frictions enter the model as a distortion to an individual's willingness to pay, following Spinnewijn (2017). The friction, denoted by $fi$⁠, results from, for example, limited information about risks or coverage, or decision biases at the time of purchase. These frictions are assumed to be exogenous, affecting different individuals differently, potentially inducing some to overestimate the insurance value (⁠$f>0$⁠) and others to underestimate the insurance value (⁠$f<0$⁠). The expected cost of providing the coverage depends on the individual's health risk and is denoted by $ci$⁠.

We denote the welfare-relevant value of the plan for individual $i$ by $vi=wi-fi$⁠. An individual buys the plan if her willingness to pay exceeds the premium, $wi≥P$⁠, while her true utility is maximized by buying the plan if and only if $vi≥P$⁠. From a welfare perspective, it is efficient for her to buy insurance only if the surplus from risk protection is positive, $si≡vi-ci≥0$⁠.⁴

Our setup could, for example, reflect a market for supplemental coverage above and beyond a publicly provided government baseline coverage option. The model can also be extended to a market where there are two classes of competitively priced plans (high and low coverage), as studied in Handel et al. (2015) and Weyl and Veiga (2017). (We discuss this distinction further in appendix E). In our context, the comparative statics we study are the same across these distinct setups, though of course actual market outcomes differ. Our setup could also be extended to incorporate issues of moral hazard and imperfect competition, which are empirically relevant in many insurance market contexts.⁵

Individuals with different characteristics will sort into insurance depending on the price. The ordering of individuals, and in particular how individuals differ in their characteristics when ordered according to their willingness to pay, is key for the analysis. Similarly, any policy intervention that changes the ordering of individuals based on their willingness to pay will change the sorting of individuals into insurance and thus affect equilibrium and welfare.

The demand for insurance equals $D(P)=1-G(P)$⁠, where $G$ is the cdf of $w$⁠. We denote the share of buyers by $Q$⁠. We also introduce the notation $EP(·)≡E(·|w=P)$ and $E≥P(·)≡E(·|w≥P)$ to denote the expected value among the marginal buyers (at the margin between buying insurance or not) and the infra-marginal buyers (weakly preferring to buy insurance), respectively.

In line with Einav et al. (2010) and Spinnewijn (2017), the market equilibrium and corresponding welfare have a simple graphical representation. We can plot the demand curve $D(P)$⁠, which orders individuals based on their willingness to pay and the corresponding marginal cost function $MC(P)=EP(c)$⁠, average cost function $AC(P)=E≥P(c)$⁠, and (marginal) value function $V(P)=EP(v)$⁠. In an adversely selected market, individuals who are more costly to insure have a higher willingness to buy insurance. This causes the cost curve to be downward sloping and the average cost curve to lie above the marginal cost curve, as illustrated in figure 1. The competitive equilibrium is simply given by the intersection of the demand curve and the average cost curve. To evaluate welfare, we need the value of insurance relative to its cost and thus compare the value curve (rather than the demand curve) to the marginal cost curve. Information frictions drive a wedge between the demand curve and the value curve.

We consider the impact of oft-discussed insurance market policies that target improving consumer choices and reducing adverse selection. To evaluate a policy intervention, it will be useful to decompose its impact into two effects within our framework; a level effect effect conditional on the sorting of individuals and a sorting effect effect due to the potential re-sorting of individuals. This simple decomposition is useful at both the positive and normative levels. A policy can change the equilibrium coverage directly or through the re-sorting of individuals based on costs. A policy can change welfare through a change in the coverage level $Q$ or by changing sorting into insurance based on surplus.

The welfare impact of changing the level of coverage, conditional on the sorting of individuals, is well understood in the literature and simply relates to whether the market is over- or underinsured to start with. In adversely selected markets, average-cost pricing causes the equilibrium price to be inefficiently high and individuals to be underinsured. This underlies the analysis of price subsidies and mandates in Einav et al. (2010) and Hackmann, Kolstad, and Kowalski (2015). However, these studies only considered the pricing inefficiency coming from the supply side. The presence of information frictions may worsen the supply-side inefficiency but can also reduce this inefficiency and potentially reverse the welfare impact of an increase in equilibrium coverage, as Spinnewijn (2017) argued. Frictions can cause individuals to buy coverage even if their valuation is below the price and vice versa. In particular, if the marginal buyers overestimate the insurance value (⁠$EP(f)>0$⁠), this tends to make the equilibrium coverage inefficiently high. The opposite is true if the marginal buyers underestimate the insurance value (⁠$EP(f)<0$⁠). The specific welfare impact of different scenarios depends on these offsetting effects and which dominates.

The left-hand side equals the marginal surplus at the equilibrium price, $EP(x)(s)=EP(x)(v-c)$ and clearly illustrates the interaction between supply and demand frictions. The marginal surplus equals 0 at the constrained-efficient price.⁷ From the supply side, insurance companies charge prices that are different from the marginal cost in selection markets, $P(x)≠EP(x)(c)$⁠. From the demand side, frictions drive a wedge between value and willingness to pay, $EP(x)(f)≠0$⁠. For example, the underinsurance due to average-cost pricing in an adversely selected market could be fully offset by individuals overestimating the insurance value. But the same friction would further worsen the overinsurance in an advantageously selected market. More generally, it makes clear that policies focused only on the supply side alone may not have their intended effects after accounting for potential demand side frictions. We turn to this later in the context of risk-adjustment transfers.

We first consider the level effect of the intervention, conditional on the sorting of consumers. An information policy increases the demand for insurance, just like a subsidy would, when the average friction among the marginal buyers $EP(α)(f)$ is negative. The policy works like a tax if this marginal friction value is positive. Note that even when the average friction value is positive, the marginal friction value can be negative due to the friction-based sorting of individuals. Whether an information policy increases or decreases insurance demand thus crucially depends on the mean and variance of the frictions (in addition to the other primitives affecting the marginal consumers).

Any policy intervention that induces more individuals at the margin to buy insurance decreases the equilibrium price in an adversely selected market (since average cost exceeds marginal cost). This further increases equilibrium coverage. Conditional on the ordering of individuals, an information policy simply scales the impact on quantity of a uniform subsidy, denoted by $ηc$⁠, depending on the sign and size of the marginal friction value, $EP(α)(f)$⁠.⁸ For a uniform friction, this level effect would be the only impact on the market equilibrium and welfare.

With heterogeneous frictions, an information policy also changes the ordering of individuals' willingness to pay. In particular, the policy reduces the willingness for individuals with positive friction values to buy insurance but increases the willingness for individuals with negative friction values. The impact on the average characteristics of the infra-marginal buyers crucially depends on how those characteristics differ among the marginal buyers with different friction values. For a given share of buyers, the impact of an information policy on $E≥P(z)$ for any variable $z$ is proportional to the covariance between this variable and the friction value among the marginal buyers, $covPz,f$⁠.

To illustrate this key result, let us consider the re-sorting on true values first. Among the marginal buyers, those with large friction values must have lower true values, while those with low friction values must have higher true values. Hence, a simple selection effect is underlying the re-sorting of individuals; an information policy encourages individuals with high true value to buy insurance and discourages individuals with low true value from buying insurance, as illustrated in figure 3. The information policy thus necessarily increases the expected true value $E≥P(α)(v)$ for a given share of buyers. While the re-sorting based on the true insurance value is straightforward, decomposing this sorting effect for costs and surplus is key for positive and normative analysis. The re-sorting based on costs determines the impact on the equilibrium coverage. The re-sorting based on surplus determines the impact on welfare.

In an adversely selected market, individuals with higher true valuation have higher expenses, suggesting that the market becomes even more adversely selected when reducing the role of frictions. This would increase the equilibrium price and thus reduce the equilibrium coverage. In general, the impact of re-sorting on equilibrium coverage is captured by the covariance between costs and frictions among the marginal buyers, $covP(α)(c,f)$⁠. This covariance should be compared to the average friction value among the marginal buyers to assess the impact of the policy intervention on equilibrium coverage.

Regarding welfare, when individuals with higher true valuation have a higher surplus from buying insurance, the average surplus of the individuals buying insurance increases when reducing the frictions (conditional on the share of buyers). The improved matching unambiguously increases welfare regardless of the nature of competition and whether the equilibrium coverage is efficient or not. In general, the sorting effect is captured by the covariance between the friction value and the surplus among the marginal buyers, $covP(α)(s,f)$⁠. The total welfare change then depends on this sorting effect in addition to the welfare impact from the change in coverage.

It is clear that due to the re-sorting of consumers, friction-reducing policies change the demand, value, and cost curves and these changes depend on the underlying microfoundations. Importantly, the original demand, value, and cost curves, considered in Einav et al. (2010) and Spinnewijn (2017), do not provide sufficient information for analyzing the market and welfare impact of such policies. However, the simple formulas in the propositions (exploiting marginal policy changes) clearly indicate the key statistics underlying the overall effects we should anticipate:

To go beyond the local evaluations and provide further insights into how the primitives of the model and the means and variances of the demand primitives in particular, affect positive and normative outcomes under different policies, we present a series of simulations in appendix section D.⁹ The simulations confirm the key insights of this theoretical analysis. First, reducing the mean impact of frictions on willingness to pay for insurance always reduces insurance coverage, but reducing the variance of frictions can increase the demand for insurance when frictions suppress the demand of the marginal buyers. The latter occurs when mean surplus is relatively high such that equilibrium coverage is high as well. Second, reducing the variance of frictions causes incremental adverse selection and reduces coverage more when the variance in costs is relatively high. The welfare implications tend to be in line with the implications for market function in an adversely selected market: equilibrium surplus decreases when equilibrium coverage decreases and vice versa. The exception then holds when, third, the variance of surplus is high relative to the variance of costs. The reason is that the positive matching effect of reduced frictions outweighs the negative equilibrium consequences of any incremental selection on costs, in line with the trade-off highlighted in proposition ².

The impact of demand frictions on equilibrium and welfare indicates their relevance for the evaluation of policies that target supply-side frictions. We explore the importance of this interaction for cost subsidies and risk-adjustment transfers in particular. These policies are key features of U.S. health reform for example, in the state exchanges set up under the ACA, as well as many other efforts to mitigate adverse selection and expand insurance coverage.

An increase in $β$ makes the expected cost of providing insurance less dependent on the individual's risk type but does not affect the ordering of individuals directly.¹¹ In an adversely selected market, the average cost among the infra-marginal individuals unambiguously decreases for a given price. Hence, risk-adjustment transfers unambiguously reduce the equilibrium price and increase equilibrium coverage. Moreover, the more adversely selected the market is, the larger is the impact of risk-adjustment transfers on equilibrium coverage. This indicates a first key interaction with information frictions as they can reduce selection on costs. Risk-adjustment transfers will affect the equilibrium by more the less plan selection is affected by demand frictions.

Since risk-adjustment transfers preserve the ordering of individuals' willingness to pay, the policy affects welfare only through the change in equilibrium coverage. The impact on welfare thus depends on the surplus among the marginal buyers in line with proposition ¹. This indicates a second key interaction with information frictions as the demand and supply frictions jointly determine whether the market is under- or overinsured. In an adversely selected market where information frictions reduce underinsurance, the presence of these frictions reduces not only the effectiveness of risk-adjustment transfers in increasing coverage but also the welfare gain from that increase. The following proposition summarizes the potential effects:

The above analysis highlights the important interaction between demand- and supply-side policies. Information policies can increase the effectiveness of risk-adjustment transfers and increase their impact on welfare. By the same token, the negative consequences of information policies through the increased adverse selection could be directly addressed through risk-adjustment transfers or any other policy that mitigates the increase in the equilibrium price. We confirm this complementarity between information policies and risk adjustment in the simulations in appendix section D. We demonstrate that friction-reducing policies become more tenable and can switch from “bad” to “good” as risk adjustment is more effective. In particular, as the mean and variance of surplus increase relative to the mean and variance of costs in the population, the threshold of risk adjustment necessary to make friction-reducing policies has a positive welfare impact is decreasing.

We now move to our empirical application, which illustrates how the microfoundations related to frictions, surplus, and costs can be measured and used to study policies that have an impact on choice and information frictions and insurer risk-adjustment transfers. We estimate these key microfoundations using detailed proprietary data from a large self-insured employer covering more than 35,000 U.S. employees and 105,000 lives overall. The data include detailed administrative data on enrollee health care claims, demographics, and plan choices, as well as survey data, linked to the administrative data at the individual level, on consumer information and beliefs. The linked survey data allow us to go beyond previous empirical studies and distinguish between choice determinants and preference factors that are typically unobserved to researchers. This in turn permits the positive and normative analysis of both demand-side and supply-side policies. Though our empirical analysis studies one specific environment and population of consumers, it highlights how to connect the theoretical model just presented to data and how to use those data together with an empirical framework to conduct important policy analyses.¹²

The data and estimation of consumer choice parameters we use are the same as those used in Handel and Kolstad (2015b), which performs an in-depth study of consumer frictions and their implications for choice modeling in health insurance markets. That paper describes the data, empirical model, identification, estimation, and structural choice parameter results in significant detail. (See Handel & Kolstad, 2015b, for a full treatment of that material. We include a condensed summary of that content in appendix F.)

The structural estimates from Handel and Kolstad (2015b) provide all the information we need to implement the approach developed in section II. We use the estimates to construct the microfoundations that are key for determining market equilibrium and the impact of potential policy interventions.

Consumers in the empirical environment we study choose between two plan options, denoted $j$⁠. The first option is a generous PPO option with no cost sharing (i.e., maximum risk protection). The second option is a high-deductible health plan (HDHP) with a $3,750 family deductible and $$6,250$ family out-of-pocket maximum that allows access to the same doctors in network as the generous PPO option. The HDHP has an in-sample actuarial value of 78%, implying that of all population expenses, consumers pay 22% of them. The HDHP plan also provides access to a health savings account (HSA) that provides some additional value to consumers by allowing them to make tax-free contributions to that plan that can be used to pay for health spending tax free at any point (and accrue text-free interest over time similar to a 401(k)).

Here, $Ukj$ denotes consumers' constant absolute risk aversion (CARA) utility. $XkA$ denotes observed heterogeneity (e.g., in age and income) for each family $k$⁠. $γ$ denotes the family-specific CARA risk-aversion coefficient, which is estimated as a random coefficient with a normal distribution whose mean depends on $XkA$⁠. $fkj$ denotes the ex ante rational expectations distribution of family out-of-pocket spending for family $k$ and plan $j$⁠, estimated using claims data. $xkj$ reflects a family's monetary equivalent value for each possible out-of-pocket health spending state realization (⁠$z$⁠). $x$ depends on ex ante family wealth $W$⁠, the premium paid $P$⁠, and the amount of out-of-pocket health spending for one realization of uncertainty $z$⁠. In addition, it depends on $Z"kβ^$ which denotes family $k$'s additional willingness to pay for the $HDHP$ relative to the $PPO$ due to a collection of information frictions and perceived hassle costs that are measured with the linked survey in Handel and Kolstad (2015b). $β$ is an estimated vector of coefficients that tells us how much each possible friction in the vector $Zk$ affects consumer willingness to pay. For most frictions measured, $Z$ is a binary indicator of whether the consumer has limited information on a given dimension, though in certain cases, $Z$ is a real number reflecting the extent of a certain friction (e.g., the number of additional hours of hassle costs one incurs when enrolling in the HDHP, relative to the PPO). $εkj$ is a family-specific idiosyncratic preference for each plan $j$⁠.

Here, $CEk,j$ is the certain financial payment that gives family $k$ utility $Ukj$⁠, equivalent to choosing plan $j$ given the present frictions. The relative willingness to pay $wk$ is the empirical analog to $w$ in section II. Figure 2 presents its distribution in the observed environment. This distribution determines the demand curve in our upcoming analysis and is plotted for families (employees covering two or more dependents), who comprise the majority of our primary sample. Consumer willingness to pay for the PPO is high, but there is substantial heterogeneity in willingness to pay across families. To assess the main drivers of the observed heterogeneity, we decompose the willingness to pay into the different demand primitives, following the approach in section II.

The obtained value describes how much the frictions present shift willingness to pay relative to an equivalent frictionless consumer. Figure 2 presents the smoothed distribution of the combined impact of all frictions on willingness to pay for less generous coverage relative to more generous coverage (i.e., $-f$⁠). As the figure illustrates, the information frictions have a high mean impact of shifting consumers toward more generous coverage ($1,787; see table F3), as well as substantial heterogeneity (standard deviation of $$1,304$⁠). Thus, our empirical environment corresponds most closely to the case with high mean friction impact and high friction heterogeneity discussed in section II.

In the context of our theoretical analysis, our empirical environment is one with high mean and variance of frictions, low mean and variance of surplus, and medium to high mean and variance in expected yearly costs. As a result, we expect that friction-reducing policies will lead to substantial unraveling in the absence of complementary risk adjustment.

Table 1 presents the correlations between these microfoundations for families in our primary sample. The first thing to note is that the impact of frictions is relatively uncorrelated with surplus from risk protection, cost, and true value for more generous coverage. It is highly correlated with willingness to pay, since frictions are large in magnitude and feed directly into willingness to pay. Surplus from risk protection is highly correlated with cost and with true plan value but less correlated with willingness to pay due to the presence of frictions. Cost is almost perfectly correlated with true value, because of limited heterogeneity in risk aversion, while frictions are the strongest correlate of willingness to pay. Frictions are thus an important determinant of demand in our environment, as are costs, but costs become much more highly correlated with willingness to pay when frictions are removed.

The primary counterfactual market we consider is, as described in section II, a competitive market for supplemental insurance that moves consumers from universal baseline coverage (represented by the HDHP in our empirical environment) to more generous overall coverage (represented by the PPO). We assume that an individual mandate is enforced, such that individuals enroll in either the public baseline coverage or that coverage plus the supplemental coverage (for this market, this is similar to saying the public coverage is provided for free).

We make the important assumption that the relative information frictions we estimate for our two empirical plans map directly to the relative information frictions that consumers have for supplemental coverage relative to the baseline coverage. This assumption would be violated if, for example, competing insurers worked harder to either provide or obscure information relative to what the firm in our empirical environment does. This analysis should thus be viewed as a stylized analysis that highlights the potentially nuanced implications of friction-reducing policies together with risk-adjustment policies, rather than an analysis that makes specific predictions of what will happen in a particular regulated marketplace.¹⁴

We study a range of demand-side policies that reduce consumer choice frictions and supply-side policies that affect the costs insurers face for different consumers. Using our structural estimates of frictions, surplus, and costs we construct a demand curve, a welfare-relevant value curve, and average and marginal cost curves for each policy scenario.

The insurer cost curves depend on $α$ because costs become a more prominent driver of demand as frictions are reduced. Consequently, the correlation between costs and willingness to pay becomes higher, leading to different costs curves as a function of quantity demanded at a given relative price. The insurer cost curves also depend on $β$⁠, the insurer risk-adjustment transfers, because as risk-adjustment transfers are implemented between insurers, the contribution of a given consumer to plan cost is mitigated by transfers and the curves become flatter. Equilibrium in the market occurs at the lowest value of $P$ such that $P=AC(P;α,β)$ under a set of regularity conditions that we assume hold here.¹⁶

In our empirical application, we first evaluate the positive and normative implications of friction-reducing policies on their own and then discuss the impact of these policies conditional on different levels of risk-adjustment effectiveness. We focus on the Einav et al. (2010)–style market for supplemental coverage, which provides incremental coverage relative to the HDHP baseline plan.¹⁷ We present results only for the family coverage tier, which comprises the majority of our sample and forms a natural population for a community-rated market (since typically, firms can vary premiums with the number of enrollees).¹⁸

Information frictions affect both the number of individuals buying each type of plan and the sorting of individuals across plans. Therefore, we expect both the level and slope of the demand, cost, and value curves to change as $α$ changes. Figure 3 presents these sets of curves graphically for full (⁠$α=0$⁠), half (⁠$α=.5$⁠), and no (⁠$α=1$⁠) choice frictions. Recall that when $β=0$ and there is no risk adjustment, as in this figure, the true marginal cost curve for consumers is the same as the insurer marginal cost curve. Note also that the value and marginal cost curves correspond to the same ordering of individuals as the demand curve for each scenario.

The left-panel in figure 3, which replicates the demand, value, and cost curves as estimated in our environment (with all frictions present), illustrates some key implications of our estimates. First, the frictions present in our environment drive a substantial wedge between the demand curve and welfare-relevant value curve: the demand curve lies well above the value curve, indicating that consumers on average overvalue the more comprehensive $PPO$ plan relative to the $HDHP$⁠. This is true along the entire demand curve, even for consumers with a relatively low willingness to pay for the supplemental coverage. Second, it is clear from the charts that the surplus of the supplemental coverage is quite small, especially relative to the impact of frictions on willingness to pay. In each panel of the figure, surplus is represented by the wedge between the marginal cost curve and the welfare-relevant value curve and corresponds to the risk premiums consumers are willing to pay to be in the $PPO$ as opposed to the $HDHP$⁠. While the average cost curve is downward sloping, a necessary condition for adverse selection, the slope is relatively flat. This indicates that when full frictions are present, marginal enrollee costs to the $PPO$ are not substantially different from those of infra-marginal enrollees and there is limited scope for adverse selection.

Table 2 presents the positive market equilibrium results associated with different policy combinations. The first column, for $β=0$⁠, gives the results for the cases of different friction-reducing policies when there is no insurer risk adjustment (as shown in figure 3). In all cases, since the value curve lies above the consumer marginal cost curve, 100% of consumers should be allocated to the $PPO$ from a social perspective. In our conclusions, we return to these results and discuss unmodeled factors that would change this first-best allocation, such as moral hazard.

For the case of full frictions (⁠$α=0$⁠), the predicted market equilibrium outcome (the one crossing point between the demand and average cost curves) is 84.6% enrolled in the $PPO$ and 15.4% enrolled in the $HDHP$⁠. The price paid for supplemental coverage in equilibrium equals $P=$5,551$⁠. For the case of half frictions (⁠$α=0.5$⁠, figure 3), 73.4% buy the $PPO$ and 26.6% buy the $HDHP$ in equilibrium, with a relative premium difference of $P=$5,741$⁠. When the impact of frictions is reduced by 50%, there is only limited incremental adverse selection against the $PPO$⁠, with market share declining and the relative premium rising.

When all frictions are removed (⁠$α=1$⁠, figure 3), the demand curve and value curve are equivalent, with demand shifting downward relative to the case where frictions are present. In addition, the marginal and average cost curves become steeper, reflecting the sorting effect as consumer marginal costs are much more highly correlated with consumer demand. The market equilibrium reflects an almost complete unraveling of the market due to adverse selection: 9.1% of consumers buy the $PPO$⁠, 90.9% buy the $HDHP$⁠, and the relative premium is $P=$6,250$⁠.

Both the level and sorting effects lead to the unraveling of the market as information frictions are reduced in our environment. The level effect can be seen clearly in figure 3, as the demand shifts down substantially as frictions are reduced (also for the marginal consumers). The sorting effect can be seen clearly in figure 4: as frictions are reduced, the average cost curve becomes steeper, implying that the correlation between consumer costs and demand is increasing. Table 1 shows that this correlation increases from 0.508 to 0.999 as frictions are reduced to nonexistent. In essence, the presence of information frictions drives a gap between demand and welfare-relevant valuation, and the correlation of those frictions with costs determines if removing frictions has a marked sorting effect. In our case, frictions are not particularly highly correlated with costs, so when they are present, they have a substantial impact on the ordering of willingness to pay for more insurance.

The bottom portion of table 2 presents the welfare implications of friction-reducing policies. In our environment, where consumers benefit from more risk protection (assuming no corresponding efficiency loss from increased moral hazard), welfare is generally decreasing as the market unravels and enrollment in the more generous PPO plan goes down (this is not necessarily true because of the improved matching, as discussed before). Our welfare results show that relative to the status quo environment, when frictions are reduced by 50%, consumers are worse off by an average of $16.04 (35% of mean total surplus) per person. When frictions are fully removed and the market unravels, consumers are on average $47.01 (99% of mean total surplus) worse off per person. This is a meaningful drop in welfare for a policy is typically thought to benefit consumers.

One way to counter these welfare losses is a risk-adjustment transfer policy. We demonstrate the impact of these policies spanning $β=0$ to $β=1$ conditional on $α=1$⁠, or when frictions are already fully removed. Figure 5 presents the demand curve for $α=1$ (equivalent to the value curve) and three average cost curves, corresponding to the cases of $β=0$⁠, $β=0.5$⁠, and $β=1$⁠. From the figure, it is clear that as risk adjustment becomes stronger, the average cost curve becomes flatter, becoming completely flat when $β=1$ and all consumers have the same cost from the insurer's perspective. It is also apparent that as risk adjustment becomes more effective, the market share of the $PPO$ plans increases, and the market equilibrium moves toward the first-best of 100% PPO enrollment. Table 2 presents the resulting market shares and premiums. For the cases of $β=0$⁠, $β=0.5$⁠, and $β=1$⁠, the resulting market shares when $α=0$ are 9.1%, 51.6%, and 63.5%, respectively. The relative premiums between the two tiers of plans are $6,250, $5,964, and $5,315, respectively. Thus, conditional on frictions being fully removed, risk adjustment has a substantial impact of reducing premiums in the $PPO$ relative to the $HDHP$ and increasing market share in the $PPO$⁠. Welfare in the market is also increasing as insurer risk-adjustment policies become more effective. When frictions are fully removed, risk adjustment that is 50% effective increases welfare by 19% of mean total surplus ($8.71) per person on average. When risk adjustment is 100% effective, welfare increases by 39% of mean total surplus ($17.67) per person on average.

Figure 5 also presents the same curves for these three risk-adjustment policies, for the case of $α=0$ (our observed environment). Here, though the directional impacts of stronger risk adjustment on plan market shares and relative premiums are the same as when $α=1$⁠, the incremental effect is much weaker because the frictions present in the environment already reduce adverse selection to a large extent. The quantity in the $PPO$ increases from 84.2% to 88.5% as $β$ goes from 0 to 1, with the relative price decreasing from 5,551 to 5,315. The corresponding impact on welfare is again positive but small. Welfare increases by 9% of mean total surplus ($4.30) per person on average.

These findings make clear that the marginal impact of either friction-reducing policies or insurer risk-adjustment transfers depends crucially on the effectiveness of the other policy within any given environment. One important implication of this is that policymakers considering policies to improve consumer decisions may want to simultaneously strengthen insurer risk-adjustment policies in order to prevent incremental adverse selection. This is especially true in cases like our empirical environment, where the mean and variance of surplus are low relative to the mean and variance of costs.

Our analysis of all combinations of policies $α∈[0,1]×β∈[0,1]$ furthermore finds that effective risk adjustment has increasing impact and importance as information frictions are reduced. For low to medium values of $α$⁠, where substantial choice frictions are still present, more effective risk adjustment has only a minimal impact on market outcomes and welfare. This is because the average cost curve is already quite flat for low values of $α$⁠, so there is not much scope for risk adjustment to further change market outcomes by re-sorting consumers and further flattening the cost curve. However, for high values of $α$⁠, where the cost curves are steeper and preferences have been shifted toward the $HDHP$ via the level effect, risk adjustment has an immediate and strong effect by flattening the cost curve, reducing adverse selection, and improving market outcomes. Simply put, if consumer choices are less responsive to a consumer's specific cost, decoupling insurer pricing from individual specific risk has less of an impact. (See table 2 in the main text and figure F3 in the online appendix for detailed results related to this analysis.)

Finally, risk-adjustment policies have a large incremental impact when friction-reducing policies are very effective. When $α=1$⁠, moving $β$ from 0 to 1 improves welfare by $17.67 per person on average, while when $α=0$⁠, the same movement in $β$ improves welfare by $4.30 per person on average. For $α=0.2$⁠, $β=1$ still leads to a welfare improvement relative to the status quo, while for values $α=0.5$ and above, no degree of risk adjustment improves welfare relative to the baseline case.

As a final note, we emphasize that this empirical analysis reflects the case where there is low mean consumer surplus from incremental insurance and low surplus variance, relative to both the degree of frictions in the market and the variance in projected costs. As a result, as frictions are removed, the market unravels relatively quickly because costs feed back into premiums, but lower-cost consumers do not have high enough true surplus to justify the purchase of incremental insurance when frictions are reduced. In different insurance environments, the mean and variance of surplus may be larger (e.g., if there is no out-of-pocket maximum or consumers are more risk averse than those here), which, as our simulations in section II reveal, may lead frictions reducing policies to have positive impacts on their own. In such cases, friction-reducing policies can and should be implemented even if effective risk adjustment is not available.

In this paper, we set up a general framework to study insurance market equilibrium and the welfare that results for environments where limited information distorts consumer plan choices. Understanding the relationship between the key microfoundations—surplus from risk protection, the impact of frictions on willingness to pay, and consumer and insurer costs—is essential for making policy decisions. We use this framework to investigate demand-side policies that reduce consumer information frictions, thereby helping consumers make better plan choices, and insurer risk-adjustment transfers, a supply-side policy designed to mitigate adverse selection by dampening the relationship between consumer costs and insurer costs.

Our theoretical framework and empirical application highlight the subtleties that determine when policies to reduce consumer frictions will be welfare increasing or welfare decreasing. Crucially, the impact of these policies depends not only on the distributions of microfoundations in a market but also on how effective complementary supply-side policies such as insurer risk adjustment transfers are. If insurer risk-adjustment policies are not shown to be highly effective (see, e.g., Brown et al., 2014), then policymakers may want to be more conservative in implementing policies that heavily reduce the impact of information frictions in the market. This is especially true in cases where the mean and variance of costs are high relative to those of consumer surplus. However, when considering more horizontally differentiated markets with strong variation in consumer surplus, the opposite could be true. These insights are important for policymakers thinking about implementing policies such as information provision, plan recommendations, and smart defaults, all of which are being currently considered by different insurance market regulators.

Our empirical example illustrates how our theoretical framework can be implemented empirically in different contexts with distinct microfoundations. Previous work suggests that these microfoundations could be meaningfully different across insurance market environments. For example, a range of papers show meaningful consumer choice frictions in Medicare Part D (see, e.g., Abaluck & Gruber, 2011, or Ketcham et al., 2012), where the mean and variance of costs are lower than in our market (because it insurer only prescription drugs) and risk adjustment may be very effective (because of the predictability of drug use). In that case, our framework suggests that friction-reducing policies are more likely to be welfare improving than in the empirical environment we investigate in this paper. Of course, the relevant microfoundations must be measured in each context to directly apply our framework, though our results demonstrate methods to do so, as well as the feasibility. For parsimony, our discussion focused on the case where frictions push consumers toward more generous coverage, which has been found in several studies of choice in employer-sponsored insurance settings (Handel & Kolstad, 2015b; Handel, 2013; Bhargava et al., 2017). Our framework can also be applied to the reverse case, where frictions push consumers toward purchasing less coverage, which research shows may be relevant in certain contexts such as the subsidized ACA exchanges (Finkelstein, Hendren, & Shepard, 2017).

Our framework contains a range of stylized assumptions that could affect the conclusions in any given context. We assume perfect competition. As Mahoney and Weyl (2017) show, imperfect competition can have subtle implications for policy recommendations in selection markets. In addition, our approach maintains quite stylized assumptions about the potentially endogenous relationship between the extent of competition in the market and consumer information. It is possible that the extent of limited information in any given setting is partially related to the degree of competition or the extent of risk-adjustment policies, an area that we believe is an interesting topic for future work. We also abstracted away from consumer moral hazard to focus clearly on the other microfoundations in the market. Though the relationships we explore would generally be robust to including moral hazard in the model, the mean and variance of that price sensitivity could have important implications for whether increasing coverage is a desirable social goal.