Adverse selection with a multiple choice among health insurance plans: A simulation analysis

Journal

of Health

Economics

11 (1992)

129-151.

North-Holland

Adverse selection with a multiple choice among health insurance plans: A simulation analysis M. Susan Marquis* RAND Corporation, Washington, DC, USA Received

August

1986, final version

received

March

1992

This study uses simulation methods to quantify the effects of adverse selection. The data used to develop the model provide information about whether families can accurately forecast their risk and whether this forecast affects the purchase of insurance coverage - key conditions for adverse selection to matter. The results suggest that adverse selection is sufficient to eliminate highoption benefit plans in multiple choice markets if insurers charge a single, experience-rated premium. Adverse selection is substantially reduced if premiums are varied according to demographic factors. Adverse selection is also restricted in supplementary insurance markets. In this market, supplementary policies are underpriced because a part of the additional benefits that purchasers can expect is a cost to the base plan and is not reflected in the supplementary premium. As a result, full supplementary coverage is attractive to both low and high risks.

1. Introduction

Finding a solution to the problems of providing basic health insurance to all Americans and containing the rise in health care costs has vexed policymakers for over a decade. A cornerstone of some of the many proposals that recently have been put forth is reform of the tax system that would help low income Americans obtain at least a minimum benefit package and provide incentives for consumers to make cost-conscious decisions in purchasing a health plan to encourage price competition in the insurance market [Butler (1991), Enthoven and Kronick (1991), Pauly et al. (1991)]. The emphasis on consumer choices, however, raises concerns about adverse selection. Adverse selection is a problem of asymmetric information that arises when the purchaser of insurance has information about his or her riskiness that the seller of insurance does not have [Akerlof (1970), Pauly Correspondence to: M. Susan Marquis, RAND Corporation, 2100 M Street, NW, Washington, DC 20037, USA. *This research was supported by the Health Insurance Experiment Grant 016B80 from the Department of Health and Human Services. I am grateful to Emmett Keeler, Joseph Newhouse, Frank Sloan, Robert Valdez, and two anonymous referees for their helpful comments.

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MS. Marquis,

Adverse selection

(1986)]. Because insurers cannot distinguish between good and bad risks, they charge all risks the same premium for any policy. Low risk purchasers may therefore find comprehensive coverage to be unattractive. As a result, those choosing comprehensive coverage will have higher than average expenses. If premiums for each policy are based on the payment experience of the plan, this adverse selection may lead to a premium spiral that drives out comprehensive plans. A number of theoretical studies have considered the nature of market equilibrium when adverse selection exists [Rothschild and Stiglitz (1976), Wilson (1977) Cave (1985)]. In any of these models adverse selection problems arise only if consumers can anticipate future medical expenditures, and plan their insurance purchase accordingly. Information as to whether this condition exists is limited. The lack of empirical information about the importance of adverse selection is largely due to the problem of disentangling it from moral hazard [Pauly (1986)]. In an analysis of the Federal Employees Health Benefits Program, Price and Mays (1985a) found evidence of biased selection, after adjusting for moral hazard (the effect of the generosity of insurance benefits on spending by an individual of given risk). Follow-on work has shown that selection worsens over time [Price and Mays (1985b)]. However, comprehensive plans as a group did not experience adverse selection. Specitic plans were victims of adverse selection, but there was not a significant relationship between the degree of selection and the comprehensiveness of plan benefits. Ellis (1985), on the other hand, concluded that there is a self-selection of high risk individuals into comprehensive benefit plans when employees are offered a choice among plans. This conclusion was based on analyzing one firm’s experience after introducing a multiple choice of plans. Those selecting the comprehensive plan had significantly higher prior year expenditures than those selecting a less comprehensive option. Because prior year expenditures were all subjects to the same benefit provisions, the comparison is not confounded by the effects of moral hazard.’ Short and Taylor (1989) found that older employees are significantly more likely to purchase high option coverage than younger individuals. Since older persons are at greater health risk than the young, this finding too indicates adverse selection into comprehensive plans. However, Short and Taylor did not find that self-reported assessments of the family’s health status influence insurance preferences. This study takes a different approach to quantify the effect of adverse ‘There have also been a number of studies between conventional Fee-for-service insurance found that, prior to joining the prepaid group, than those who remained in the fee-for-service Prihoda (1982), Jackson-Beeck and Kleinman

that have examined selection effects in the choice and a prepaid group practice. These studies have those who select the group had lower utilization system [Buchanan and Cretin (1986), Eggers and (1983)].

M.S. Marquis,

Adverse selection

131

selection. It uses a stochastic simulation methodology to track the effects of adverse selection over time. The analysis builds on earlier analyses from the RAND Health Insurance Experiment of the determinants of health care demand [Manning et al. (1987), Keeler, Buchanan, Rolph et al. (1988)] and of health insurance demand [Marquis and Phelps (1987)]. A unique feature of the Health Insurance Experiment data is that it provides information about a family’s anticipation of what its health care spending will be in the future. Analysis of those data by Marquis and Phelps (1987) demonstrated that those with high anticipation do prefer more generous insurance coverage than those who anticipated lower levels of future spending. Manning and Marquis (1989) and Marquis and Holmer (1986) also found that a family’s actual health care spending is significantly related to its prior anticipations. These analyses show that the key conditions for adverse selection to matter namely, that a family can accurately forecast its risk and that this forecast affects the purchase of insurance coverage - exist. Here, we investigate the magnitude of the adverse selection by simulating how the selection effects the insurance market equilibrium when insurers set premiums for each policy on the actual claims experience of the policy.’ We contrast this solution with a community-rated equilibrium in which the insurer requires only that the set of plans break even and the low coverage plan purchasers subsidize the higher coverage plan purchasers. The solution with experience-rating and community-rating are both compared with that in which insurers and consumers have the same information about risk, and premiums for each family are based on that family’s risk. Our analysis investigates how adverse selection works in a multiple choice situation and in a market where separate, supplementary plans to enhance the coverage of a base plan are offered.

2. The Health Insurance Experiment

data

The data for this study come from the RAND Health Insurance Experiment (HIE), a randomized trial in alternative health insurance plans conducted between 1974 and 1982.3 Families in the study lived in six sites: Dayton, Ohio; Seattle, Washington; Fitchburg and Franklin County, Massachusetts; and Charleston and Georgetown County, South Carolina. The sample was representative of each site’s noninstitutional population but the following groups were not eligible: individuals in families with incomes in ‘Adverse selection may be a factor explaining problems in coverage example, long term care benefits or prescription drugs for the elderly. We adverse selection affects the scope of services because the data we preferences pertain to variations in the generosity of coverage for the services. sFor details about the study design, see Manning et al. 1987.

of certain services, for do not investigate how have about insurance same broad scope of

132

M.S. Marquis, Adverse selection

excess of %56,000 (in 1983 dollars); individuals in families headed by persons age 62 or over; those eligible for the Medicare disability program; and those in the military and their dependents. Families participating in the experiment were randomly assigned to one of 14 different fee-for-service insurance plans. All of the plans had the same comprehensive definition of covered services including hospital services, physician services, services of non-physician providers, dental care, and prescription drugs. The plans varied across two dimensions: the coinsurance rate (the share of the bill the family paid) and a stop-loss or upper limit on annual out-of-pocket expenses. The coinsurance rates were 0, 25, 50, or 95 percent. The stop-loss limit (called the Maximum Dollar Expenditure or MDE) was 5, 10, or 15 percent of the family’s income up to a maximum of $1,000 in then-current dollars. During their participation in the experiment, detailed information about each family’s use of medical and dental services was collected from the insurance claims forms that families submitted to obtain reimbursement from the experimental plan. At the end of their participation in the HIE, we presented each family (excepting families on the zero cost-sharing plan) with hypothetical offers to purchase supplementary insurance to reduce the amount of their stop-loss. The offers stipulated a premium that the family would have to pay for the supplementary insurance, and the family was asked whether it would buy the supplementary plan at the quoted premium. Each family was asked about hypothetical plans to reduce the stop-loss by one-third, by two-thirds, and by 100 percent (full coverage).4 We designed an algorithm to generate premium quotes that were uniformly distributed on the interval ranging from 10 to 100 percent of the offered reduction in the stop-loss so that premiums for a given level of coverage varied from family to family. An important feature of the HIE data set for the analysis of adverse selection is that it provides information about a family’s anticipations of what its health care spending will be in the future, or its perception of its risk.5 Anticipated expenses were asked just prior to enrollment in the study and at the conclusion of the study. We use the relationship between anticipated expenses at the end of the study and the preference for 4The offers were worded as follows: ‘Suppose you were enrolled in a national health insurance plan just like the Family Health Protection Plan, and you had the same Maximum Dollar per year for your family. If you could lower the MDE to Expenditure (MDE), which is % per year, would you do it or not? by paying a fee of $ $_ ‘The anticinated exnense auestion was: ‘Of course, nobody knows what will happen, but we would just like your bkst guess on how much your own personal health care will cost during the next 12 months. Include doctors, dentists, clinics, medical tests or x-rays, prescription drugs the total of all expenses for your own personal health during the next 12 months. Include both what you are likely to pay, and atso what will be paid by insurance, Medicare, Medicaid or others’. Questions were asked for each family member; answers were given in one of 11 tixed interval categories. The family’s anticipated expenditure was obtained by using the mid-point of the response category for each family member and aggregating across all family members.

M.S. Marquis, Adverse selection

133

supplementary coverage reported at that time to simulate whether families adjust their purchase of insurance coverage on the basis of perceived risk. We use the relationship between anticipated expenses prior to enrollment and subsequent observed spending in simulating each family’s health care spending to reflect what is known to the family about its health care needs that the insurer cannot predict based on observable characteristics.

3. Analytic methods 3.1. Overview The simulation model predicts the insurance plan choices made by a sample of families from among a specified set of options at the beginning of the year, the consequent amount of health care spending by each family during the year, and the premiums for each plan in the next year given the actual claims experience of the plan. The insurance plans we study include a full coverage plan and three plans that require a 95 percent cost-sharing up to a stop-loss limit (or family out-of-pocket maximum) of $500, $1,000, or $1,500 (1982 dollars). The plan with a stop-loss of $1,500 is assumed to be the minimum mandated benefit (that is, we do not consider a situation in which the family can forego the purchase of insurance). The choice of insurance plan depends on the premium of the plan, the family’s expectation about what its health care expenditures will be in the year, and demographic characteristics of the family. Health care spending depends on the chosen insurance plan and family attributes. In the model, insurers are assumed to set premiums for each plan that are based on actual claims experience in the preceding period. Thus, the expenditures by families choosing each plan affect premiums in the next year; families may adjust their plan choices accordingly and the insurers acquire new claims experience. This process continues until the premiums remain stable from period to period. The components of the simulation model - the plan choice model, the health expenditure model, the insurer pricing model - and the simulation methods are described in more detail below.

3.2. Plan choice model The simulation of insurance choice is based on an insurance demand model that was estimated from the data on families’ preferences among the hypothetical supplementary insurance plans [see Marquis and Phelps (1987)]. A probit model was used to explain whether the family indicated it would purchase the plan as a function of the amount by which the stop-loss was reduced; the expected value of this benefit; the premium for the

134

M.S. Marquis, Adverse selection

additional coverage; the family’s reported anticipated expenditure as a measure of risk;(j and the family size, family income, and the age and education of the head. The specification of the demand model and a detailed discussion of the results are in Marquis and Phelps (1987), (see also Appendix table 1 for the insurance demand equation).’ Each family’s actual plan choice is simulated using the following methodology: The family’s preference for the full coverage plan, the $500 stop-loss limit plan, and the $1,000 stop-loss limit plan in favor of the minimum benefit plan is predicted using the probit demand model with the difference between the estimated premium for the more generous plan and the minimum benefit plan as the cost of supplementing. The family prefers the more generous plan to the minimum benefit plan if @ (XCI+ w) 2 0.5, where XCI is the prediction from the probit, w is the stochastic term drawn from a multivariate normal distribution, and @ denotes the standard normal cumulative distribution function. The family’s actual choice among the options is taken to be the most generous plan it prefers over the base plan. The stochastic component, w, for the family’s comparison of the minimum plan with the three more generous plans has mean zero, variance of one, and a covariance that represents a family specific preference for insurance that is common to all options. The covariance in the w was estimated by Marquis and Phelps (1987). It is the correlation among the residual errors in explaining the responses to the three insurance hypothetical questions given by each family at exit from the study; the estimated correlation was 0.63. In each period, 50 replicates of the market share in that period and the plan premium for the next period are made by taking repeated drawings from the stochastic components for the plan choice and health expenditure models.’ In the results presented in the next section, we report the average value of estimates over the 50 replicates and the Monte Carlo variation of the estimates.

3.3. The health expenditure

model

The health expenditure component family’s annual health spending (for

of the medical

simulation model and dental care)

predicts a under the

‘Since the role of insurance is to reduce uncertainty, we would expect the variability of the distribution of risks, as well as expected risk, to affect demand for insurance and so adverse selection. We do not have a direct measure of risk variability, but include family characteristics to capture differences between families in the perceived variability of risk. ‘Marquis and Phelps used the loading fee (the percent by which the premium of the plan exceeds the expected benetits) to characterize the premium of the plan exceeds the expected benefits) to characterize the price of the plan. In order to simulate how changes in the premium levels affect choice, we reestimated the parameters of the model for this analysis using premiums and the expected benefit as two explanatory variables, rather than just the ratio. ‘The plan premiums used in simulating plan choice in the next period are the average of these predictions.

MS. Marquis, Adverse selection

135

chosen plan. The simulation is based on a two equation model of the demand for health care. The first equation is a probit equation for the probability that the family has non-zero spending in a year. The second equation is a linear regression for the logarithm of total annual expenditure for families with positive spending. The explanatory variables in each equation include the family’s anticipated expenses; indicator variables for the experimental insurance plans; race, age, education, and sex of the family head; family size and income; and indicator variables for the sites. This two equation model was estimated using expenditure data for all families in the first year of the experiment in each site. Separate models were fit for single person families and for families of size two or more. The estimated equations are given in Marquis and Holmer (1986) and in Appendix tables 2 and 3. From this health care demand model, a family’s expenditure under the full coverage plan, E, is predicted by:

E=O

if

@(xl+u)
E=exp(xB+u)

if

@(xll+u)zOS,

(1)

where x denotes the family’s attributes, 2 and B the parameter estimates from the probit and linear regression, u is drawn from a standard normal distribution, and u is drawn from the observed least squares residuals.’ If the family purchases one of the less generous plans, the required costsharing up to the stop-loss limit will result in a lower demand for services than demand with full coverage. Our estimates of spending under the less generous alternatives assume (1) the incidence of illness is uniform during the period, (2) families’ spending behavior does not anticipate exceeding the stoploss limit, (3) health care spending with 95 percent coinsurance is 55 percent of the spending rate with no cost-sharing. The latter two assumptions are based on the work of Keeler, Buchanan, Rolph et al. (1988) who demonstrated that families behave as if the nominal coinsurance rate will apply throughout the year until they reach the stop-loss limit, and only adjust their spending to the full coverage rate when the limit is reached. The analysis by Keeler et al. also indicated that the rate of spending with 95 percent cost-sharing relative to no cost-sharing is 55 percent. With this assumption, the estimate of each family’s spending (S) under the three cost-sharing plans in the multiple choice situation is:

‘The observed least squares residuals, u, exhibited heteroskedasticity by experimental plan. Therefore, in predicting the amount of spending with the full coverage, V, is drawn from the subset of least squares residuals for families on the experimental full coverage plan.

136

MS.

Marquis,

S=0.55E

if

Q.%
S=L+(f/l2)*E

if

O.SSzL,

Adverse selection

(2)

where E is the rate of spending with 0 coinsurance, 0.55E the rate of spending with 95 percent coinsurance, L the expenditure level at which outof-pocket spending equals the stop-loss limit, and f the number of months remaining in the year at the time the family reaches the stop-loss limit. Time remaining, f; assumes illness and spending occurs uniformly throughout the year, so that the stop-loss limit is reached at month L*12/(0.55E).” 3.4. The model of insurer pricing In the model, insurers are assumed to set premiums for each plan that are based on claims cost plus a return (or loading fee) to cover administrative costs and risk. Given the expenditures of families who enroll in each plan, we compute the average claim payment per enrolled family for each plan and set plan premiums assuming a 15 percent loading fee (return for administration and risk). In the initial period before acquiring claims experience, insurers set premiums assuming that families choosing one plan will be like families choosing any other plan; we will refer to this as the community rated plan. Separate premiums are set for individuals, families of size two, and larger families, but otherwise the same premium is quoted to all who enroll. This is an over simplified model of insurer pricing in its assumptions that the insurer has no prior information about individual risks and that each plan is profitable. However, health insurers seldom use information they do possess to distinguish risk classes.’ 1 On equity grounds, some would object to the use of health variables in setting premiums, and policy reforms may prevent insurers from using health data in pricing insurance.” But there are also easily observable demographic characteristics that correlate with health care use, such as the age and sex of the family head, that insurers do not typically use in quoting premiums, especially for insurance sold in employee “‘Though this procedure will somewhat overestimate the number of non-users on the costsharing plans, the simulated expenditures agreed quite well with observed expenditures. For single person families on the free plan, observed spending during the first year of the experiment was $1,425 (1982 dollars), whereas the simulated average spending if all single person families had full coverage was $1,407. Single person families on the 95 percent cost-sharing plan with the maximum MDE (which was about $1,500 in 1982 dollars) spent on average S975 during year one of the study; the simulated spending was $1,057. For larger families, observed spending with full coverage was $3,196, and with 95 percent cost-sharing and the maximum MDE it was $1,907. The corresponding simulation amounts were $2,918 and $2,061. “In contrast, other insurance premiums, such as for automobile insurance, are usually varied by the age, sex, and accident experience of the policy holder (Newhouse 1986). “For example some reforms would eliminate preexisting conditions exclusions; such clauses are one way of aiproximating individual rates (Newhouse 1984).

M.S. Marquis,

Adverse selection

137

groups [Pauly (1986)]. In some of the simulations, we will examine how the problem of adverse selection is reduced if insurers do use this information about enrollee demographic characteristics to set premiums. Even if insurers cannot or do not distinguish among individual risks, the insurer may have some knowledge that different risks exist and some expectation about what plans would attract different risk classes. Insurers may set initial premiums based on this expectation rather than the expectation that families will sort themselves randomly, as assumed in the model. The model simplification, however, will not affect the end solution unless insurers set premiums for the more generous plans at a level that initially discourages all buyers. The time to reaching a steady state, however, would be reduced if insurers were accurate in their expectations about how families would initially sort among the plans. The second assumption - that each plan must be profitable - is a more critical assumption. While this assumption may characterize the market for individual insurance, the insurer who offers low and high option plans to a single group may require only that the portfolio of plans returns a profit.r3 Cave (1985) has demonstrated theoretically that in this situation equilibrium requires that low option policies subsidize the high option policies. In particular, if the difference between the premiums of the plans offered is held at the difference in the community rates with the total premium of each plan adjusted by the same dollar amount to achieve the required return, then the initial period solution represents an equilibrium solution and there is no adverse selection.14 The difference in the market share of each plan given the community rated pricing and the experience rated pricing is our measure of adverse selection. We also compare the results that obtain with community rating and experience rating to the solution in which there is no asymmetry in information and the premiums charged each family depend on that family’s expected risk. 3.4. The simulation The database processed in the simulation includes the 1,326 families in the HIE who were included in the Marquis and Phelps (1987) analysis of the responses to the hypothetical supplementary offers.15 To set the initial premium, we draw from the distribution of disturbances from the two i3However, the changes in premiums of the Blue Cross high and low option plans offered to Federal workers observed by Price and Mays (1985a) suggests insurers may not operate in this way even in a group market. i4Pauly 1985. This result holds because purchase of the minimum plan is required. “Significant resources were expended to cull the attributes of these families into a single data tile and so for reasons of economy the database used in the simulation was not expanded to include other families. Families participating in the HIE who were omitted from the database used in these simulations include families enrolled in the zero cost-sharing plan, families enrolled in a prepaid group practice, families attriting before completion of the experiment.

138

MS. Marquis, Adverse selection

equation expenditure model for each family to estimate what the average plan payout for each option would be if those enrolling in each option represent the community of families. Given the stochastic nature of the expenditure model, this payout is one possible realization from the distribution of possible payouts, and represents one point of the distribution of expected payouts for the insurer. We generate a distribution of payments for each plan by taking repeated drawings from the distribution of disturbances and calculating a new payout for each new draw. Fifty replicates are generated to create a distribution of expected payouts and corresponding premiums for each plan. The average premium over the 50 replicates is used in predicting plan choice in the initial period. In each period, we take repeated drawings from the stochastic components for the plan choice and health expenditure models to predict market share in the period, health care spending on each plan in the period, and the experience-rated premium in the next period. For each period, 50 replicates of market share and the next period premiums are estimated. In the results presented next, we report the average value over the 50 replicates and the Monte Carlo variance of the estimates. 4. Results Here we investigate the effects of adverse selection in two situations. In the first, each family faces a choice among four plans. If the family chooses a plan that is more generous than the required minimum plan, the family pays out-of-pocket the full cost difference between the minimum benefit plan and the more generous plan. This premium difference, which determines the family’s plan preferences, depends on the cost-sharing requirements of the plans, moral hazard, and adverse selection. This is analogous to a situation in which an employer offers a choice among four insurance plans and contributes a fixed amount and to the market reforms proposed by Pauly et al. (1991) Butler (1991), or Enthoven and Kronick (1991). The second situation is analogous to the Medicare supplementary insurance market. All families are enrolled in a plan providing the minimum mandated benefit. Families can choose to purchase a separate, supplementary plan that will reduce the amount of the stop-loss limit to $1,000, $500, or $0. The supplementary plan pays the cost-sharing required by the minimumbenefit plan above the supplementary plan limit. The premiums for the supplementary plan depend on the benefits paid out by the supplementary plan. This premium differs from the added premium payment for more generous coverage in the situation above because the supplementary plan premium does not include the full amount of additional insurance benefits that purchasers of supplementary insurance can expect to receive. The reduction in cost-sharing from the purchase of supplementary insurance

M.S. Marquis, Adverse selection Table Selection

Stop-loss Individual

Family

Family $1,500 Sl,OOO $500 SO

1

effects in a multiple

choice market.

Additional premium” (1982 dollars)

Family participation (X purchasing)

Community and average individual risk rated

Individual risk rating

Community rated

24 17 20 39

22 19 22 37

34 32 25 9

58 23 8 11

60 25 8 7

100 0 0 0

77 17 3 3

78 17 2 3

100 0 0 0

Uniform

rated Experience rated

in the plans premium Experience rated

plan _

S1,500 $1,000 S500 SO

$1,500 %l,OOQ $500 SO

139

$147 401 916 plan - 1 dependent _ S340 868 1,692

$192 559 1,441

_ $851 1,489 2,341

plan - 2 or more dependents _ _

“Additional plan premium is assumed.

$448 1,077 1,951

$930 1,786 2,633

premium is the family out-of-pocket premium; the difference between and the premium for $1,500 stop-loss limit plan. A 15 percent loading

the fee

induces purchasers to use more covered services. However, the base plan the minimum benefit plan - pays a share of the costs of the additional use; thus the supplementary plan is underpriced. Some analysts believe that this price distortion has led Medicare beneficiaries to purchase more supplementary insurance than is warranted, with consequent increases in the cost of the public program [Ginsburg (1983), Pauly and Langwell (1982)].

4.1. Selection

effects in a multiple

choice market

The simulation results on the effects of adverse selection for the illustrative situation in which the family chooses one of four plans are displayed in table 1. It shows that community and experience rated premiums, and participation in each plan under these pricing schemes and under individual risk rating, in which insurers would know each family’s risk and set premiums that were tailored to the expected claims payout for the family. The community rated premium sets the differential between the minimum required plan and the more generous plans based on the expected additional

140

M.S. Marquis, Adverse selection

plan payout that would occur in the entire group.16 This difference arises because of differences in the generosity of benefits and moral hazard. If premiums are experienced rated, the model sets new premiums for each plan based on the prior period experience. Differences between the premium charged for the minimum benefit plan and the more generous plans in the subsequent periods includes selection effects experienced by both plans. The experience rated premium shown in table 1 is the premium at the equilibrium solution - that in which premiums remain stable from one period to the next, or until the more generous benefit plans are driven from the market because of inadequate demand. In our model this equilibrium occurred after four periods. Those predicted to choose the more generous plans at community rated pricing also were predicted to spend more on health care.l’ For those with family coverage, this adverse selection leads to experience-rated out-of-pocket premiums for the full coverage plan that are almost 40 percent higher than the community rated premium; for the intermediate plans the out-of-pocket premiums almost double. As a result of this upward spiral in premiums, demand for the more generous plans among those with family coverage falls to zero. While the results for those purchasing individual coverage are not quite as extreme, the experience-rated out-of-pocket premium payments for more generous plans increase by 30 to 50 percent relative to community rated premiums because of adverse selection, and this rise causes a significant shift in demand among the alternative plans. The results shown in table 1 assume that insurers have no information, apart from family size, to distinguish between good risks and bad risks. However, there are some easily observable characteristics about the primary insured that insurers can use to adjust premiums.” Table 2 shows plan choices in a market in which premiums are adjusted for the age and sex of the primary insured.” Adjusting p remiums for the age and sex of the primary insured does reduce the degree of adverse selection, but does not eliminate it. For example, 39 percent of those purchasing individual coverage would purchase full coverage if insurers were able to fully adjust premiums r6The community rated uniform premium also equals the average of all the individual risk rated premiums. However, there are small differences in the overall percent purchasing between the situation of perfect risk rating and the community rating. Because the probit is not a linear model, the prediction of the mean of the individual rates - the whole group rate - differs from the mean prediction of the specific rates. 170bserve that although the community-rated premiums for families is higher than the maximum out-of-pocket payment the family would incur under the minimum plan, some families do purchase full-coverage. This suggests that families do account for the additional care they will consume with more generous coverage in making plan choices and that they do value this additional consumption. “Newhouse (1984) observed that health insurance plans often do not make such adjustments. r9The age groups used in the simulation to calculate premiums were less than 30, 30 to 40, 40 to 50, and 50 or older.

141

M.S. Marquis, Adverse selection Table 2 Selection

effects with age/sex adjusted (percent purchasing plan) Age/sex adjusted

stoo-loss Individual $1,500 %l,ooo $500 $0 Family $1,500 %l,OOO $500 $0 Family $1,500 Sl,OOO SSOO $0

premiums

premium

Individual risk rating

Community rated

Experience rated

24 17 20 39

23 19 21 37

32 24 19 25

58 24 8 9

78 16 2 4

plan

plan - 1 dependent 58 23 8 11

plan ~ 2 or more dependents 77 17 3 3

76 17 3 4

96 3 1 1

for differential risk. If a single uniform premium is charged, however, adverse selection causes a premium spiral that drives all but 10 percent of these individuals from the full coverage plan (table 1). With age and sex specific premiums, the outflow is reduced, and about 25 percent of individuals remain with the full coverage plan (table 2). Adverse selection remains a factor because, as noted earlier, families anticipations about their health expenditures predict both the demand for insurance and demand for health care even after accounding for observable family characteristics.

4.2. Adverse selection and population

subgroups

Many, if not most, economists would view a market with perfect risk rating as ideal because it leads to an efficient outcome [(Arrow (1963), Pauly and Langwell (1982), Pauly (1984)]. Others, however, believe that risk rating is inequitable because the sick will pay more than the well for insurance. Uniform rating, by contrast, transfers income to poor-risks (often also considered to be the low income) by taxing the low-risk (high income), which policymakers may consider desirable [Ginsburg (1982), Pauly and Langwell (1982), Phelps (1973)].

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MS. Marquis, Adverse selection

The effects of different pricing strategies and adverse selection on low and high risk families are illustrated in table 3. With community rated premiums, low risks - those with expenditures in the lowest quartile of the distribution given their family size - subsidize high risks - those with expenditures in the higherest quartile. Consequently, the low risk individuals and families whereas the high risk individuals and purchase less generous coverage, families purchase more insurance than they would if premiums were individually risk rated. 2o With experience rated premiums, adverse selection reduces the amount of coverage purchased by each group relative to community rates, even when premiums are adjusted for observable characteristics. With adverse selection, high risk families actually buy less insurance than they would under perfect risk-rating (though this does not hold for individuals purchasing insurance), because adverse selection results in a premium spiral in which the more generous plans are driven from the market; that is the marginal risks continually retreat. Contrary to a common assumption, community rating appears to have little effect on the distribution of income among the rich and poor for those purchasing individual coverage (table 4). Among those with family coverage, however, experience rating and adverse selection have a bigger effect on the amount of coverage held by the poor (relative to perfect risk rating) than on the coverage held by the rich. Adjusting premiums for demographic characteristics modifies this conclusion, because much of the difference in risk class between high and low income families is attributable to differences in the age of the primary insured. 4.3. Selection effects in a supplementary insurance market Summary results from the simulation of plan choice in a supplementary insurance market are given in table 5. In this situation, the supplementary plans reduce the stop-loss limit to $1,000, $500, and $0, and the supplementary plan pays the cost-sharing requirements of the $1,500 stop-loss plan above the supplementary plan limit. The premiums of the plans are based on the supplementary plan payout and, as noted at the beginning of this section, this means that a part of the cost of additional use induced because of the purchase of supplementary insurance will not be included in the premium for the supplementary plan. The effect of this price distortion can be seen by contrasting the results in table 5 with those in table 1. The community-rated supplementary plan premiums are only about 60 to 80 percent of the differences between the full premium for the minimum plan and the more generous plans. Some of the ‘OArrow (1963), however, observes that an argument insurance is thought of in a longer term perspective. against longer term changes in individual risk.

can be made for community rating if Community rating provides protection

8 8 19 65

Individual

$1,500 %l,OOO $500 $0

$1,500 $1,000 $500 $0

Family

$1,500 %l,OOO $500 $0

68 25 4 3

35 27 18 20

82 15 1 2

selection

100 * * *

100 * * *

45 34 21 3

Experience rated

premium

plan - 2 or more dependents

26 36

::

28 23 24 25

Community rated

Uniform

quartile

plan - 1 dependent

plan

Stop-loss

Family

Individual risk rating

Low-risk

Effects of adverse

71 19 5 5

52 29 11 8

24 19 23 34

Table

3

90 7 1 2

65 29 3 3

22 22 29 27

Experience rated

96 4 * *

88 10 1 1

50 26 11 14

Individual risk rating

High-risk

71 21 3 5

3

66 27 4

15 14 19 52

Community rated

Uniform

choice

market.

100 * * *

*

100 * *

26 30 27 17

Experience rated

premium

in a multiple

quartile

by low and high risk groups (percent purchasing)

premium

Community rated

Age/sex adjusted

on purchases

77 17 3 3

7

65 21 7

20 19 19 42

99 1 * *

5

91 4 *

38 28 11 23

Experience rated

premium

Community rated

Age/sex adjusted

E

23 18 19 40

Individual

$1,500 $1,000 $500 $0

$1,500 %l,ooo $500 $0

Family

$1,500 $l,ooo .Wo $0

51 22 12 15

77 I9 2 2

plan - 2 or more dependents

36 22 17 25

62 26 6 6

I8 19 22 41

selection

100 * * *

100 * * *

30 33 26 11

Experience rated

premium

Community rated

Uniform

quartile

plan - I dependent

plan

Stop-loss

Family

Individual risk rating

Low-risk

Effects of adverse

64 23 6 7

44 29 I4 13

18 19 21 42

Table 4

87 9 1 3

58 33 3 6

24 26 22 28

Experience rated

87 10 2 1

75 17 4 4

23 17 20 40

79 16 2 3

58 25 9 8

23 19 22 36

Community rated

choice

100 * * *

100 * * *

35 34 23 8

Experience rated

premium

in a multiple

Uniform

quartile

families

Individual risk rating

High-risk

by low and high income (percent purchasing)

premium

Community rated

Age/sex adjusted

on purchases

83 I3 2 2

69 21 5 5

23 19 22 36

99 1 * *

91 5 1 3

30 24 22 24

Experience rated

premium

Community rated

Age/sex adjusted

market.

E

145

M.S. Marquis, Adverse selection Table 5 Selection

effects in a supplementary

Supplementary (1982 dollars) Community and average individual risk rated

Stop-loss Individual

Family

Family $1,500 $1,000 $500 SO

marketa

Uniform Experience rated

in the plans premium

Individual risk rating

Community rated

Experience rated

13 12 21 54

13 12 21 54

11 14 27 48

31 21 16 32

31 22 15 32

30 27 16 27

42 19 12 26

42 21 11 25

43 24 11 22

plan _%94 283 773

plan - 1 dependent _ $217 595 1,237

S 70 257 844

_ $202 627 1,307

plan - 2 or more dependents _ _

“Premium

insurance

Family participation (% purchasing)

rated

_

$1,500 $1,000 SSOO SO

%1,500 $1,000 $500 SO

plan premiums

$281 715 1,365 includes

a 15 percent

$276 738 1,414 loading

fee.

additional insurance benefits that a purchaser of supplementary insurance can expect to receive will be paid out by the base plan and so are not reflected in the supplementary plan premium. For example, if all persons with individual coverage purchase a full-coverage supplement, their expected insurance benefits average $850 per year over the expected benefits from purchasing the minimum plan. However, only $675 of this is paid out by the supplementary plan; $175 is the increased cost to the minimum plan due to the induced demand resulting from supplementation.21 Because the supplementary insurance is underpriced, demand is strong and purchased coverage is much more comprehensive than if families pay the full cost differential between the comprehensive and minimum benefit plan. The underpricing of supplementary insurance also means that supplementary insurance is attractive to both high and low risks. As a result, there is little adverse selection in the supplementary market; in the equilibrium

‘iThe cost of induced demand to the base plan is 0.85 times the difference in the supplementary plan premium and the full additional premium for a more generous policy. The 0.85 factor accounts for the 0.15 loading fee assumed in the pricing model.

146

MS. Marquis, Adverse selection

solution, there is only a small adjustment to community rated insurance.

in premiums

and demand

relative

5. Conclusions One approach to reforming the health care system emphasizes greater price competition in the insurance market by restoring financial incentives to consumers in choosing among insurance plans and allowing consumer preferences to guide the allocation of resources. However, potential problems of adverse selection raise questions about whether a stable market and choice situation can be maintained. Currently, problems of adverse selection are minimized because many employees have only a single option through the employer group or employers pay more on behalf of more costly plans. But incentive reform strategies would alter this situation. The results of our simulation show potential adverse selection against high-option benefit plans. When insurers charge a single premium differentiated only by the size of the insured unit, adverse selection was sufficient to eliminate some plans that offered more generous benefits than the minimum mandated.” The results also indicated that the degree of adverse selection is substantially reduced, though not eliminated, if premiums are varied according to demographic factors. Some might point to the large fraction of Medicare beneficiaries purchasing supplementary insurance as counter evidence to the simulation findings that adverse selection causes the market for comprehensive coverage to disappear. Though we study only non-aged individuals and so our results apply to Medicare only by analogy, our analysis shows that there is much less adverse selection in a supplementary insurance market. The supplementary insurance policies are underpriced because the premiums do not include the full cost of additional benefits that a purchaser can expect to receive. The reason for this is that part of the additional benefits are outlays by the base plan, but the beneficiary’s premium is not adjusted when he or she purchases a supplementary policy. The simulation results indicate that this price distortion leads to demand for much more generous coverage than would otherwise be the case. In the case of the Medicare supplementary market, some believe that supplementary plan premiums should be taxed so that purchasers of supplementary plans bear the full increased cost they impose on the program to alleviate this problem [Ginsburg (1983)]. Because the supplementary plans are underpriced, they remain attractive to low risk individuals, even with uniform premiums, and so adverse selection is

‘*If the policy objective

is to increase

cost-sharing,

however,

this result might be desirable.

147

MS. Marquis, Adverse selection

restricted. This result suggests that the market for supplementary insurance is not an appropriate place to study the potential problems of adverse selection in a Medicare voucher system. One surprising result from the simulation analysis was the speed with which the effects of adverse selection worked through to a steady state, which took only four periods in our analysis. Adjustments do not occur so rapidly in existing multiple-choice situations. For example, Price and Mays (1985b) found continuing deterioration over a five-year span in the Federal Employees Health Benefit program; they suggest that a few of the plans in the program may not survive much longer but some will survive for many years. Moreover, adverse selection in the Federal Employees Health Benefit program is a recent phenomenon [Pauly (1986)]. One explanation for the speed of adjustment in the simulation that is not mirrored in real choice situations, may be our assumption about how families make coverage adjustments. The simulation analysis assumes that families respond to changes in plan premiums and changes in their health risk by adjusting their coverage immediately at the beginning of the next period. However, several authors have noted a tendency for families to choose the same plan year after year, even when the plan appears to be financially unattractive [Marquis, Kanouse and Brodsley (1985), Neipp and Zeckhauser (1985)]. Because of consumer inertia, it may be years before adjustments occur. ” This inertia may hinder competition and lead to an inefficient allocation. Ironically inertia may also promote efficiency by lessening the problem of adverse selection [Neipp and Zeckhauser (1985), Pauly (1984)]. 23Moreover,

changes

in the population

over time may further

blur the results.

MS. Marquis, Adverse selection

148

Appendix

Table A.1 Probit

equation

for probability

of purchasing

supplementary

insurance.B

Variable

Coeff.

t-stat.

Intercept Supplementary plan characteristics Premium for l/3 reduction in maximum Premium for 2/3 reduction in maximum Premium for full reduction in maximum Offered reduction in maximum Expected value supplementary plan benefits Anticipated expenses Ln (family size) Plan indicators 1 if 25% coinsurance 1 if 50% coinsurance 1 if fixed dollar family maximum Amount of fixed family maximum; 0 if not fixed maximum Maximum as percent of income; 0 if fixed maximum Ln (family income) if income-related maximum plan Ln (family income) if fixed family maximum plan Age head (Age head)’ Education head (years) Site indicators 1 if Seattle 1 if Massachusetts 1 if South Carolina

1.674

1.57

‘Omitted plan indicator is 95% coinsurance; Dayton. Income and expenditures in 1982 dollars.

- 0.005 -0.004 -0.002 - 0.703 0.0009 o.OGO2 0.006

-3.69 -9.49 - 7.48 - 2.46 3.28 3.43 0.06

0.056 -0.129 0.711

0.40 - 1.13 0.54

0.0004

1.07

- 0.028

- 1.71

0.103

1.17

-0.059 -0.071 0.0008 0.002

-0.59 -2.56 2.71 0.11

- 0.009 0.063 0.047

- 0.08 0.58 0.35

omitted

site indicator

is

MS.

Marquis,

149

Adverse selection

Table A.2 Probit

equation

for probability

of non-zero

expenditures.”

Single person family Coeff.

Variable Intercept Ln (anticipated expenses) Ln (family income) Education head (years) Age head Indicator = 1 if male head Indicator = 1 if non-white Ln (family size) Plan indicators 1 if 25% coinsurance 1 if 500/, coinsurance 1 if 25% for medical, 50% for dental and mental 1 if 95% coinsurance 1 if 95% outpatient, 0 inpatient Site indicators 1 if Seattle 1 if Massachusetts 1 if South Carolina “Omitted plan indicator is 0 coinsurance and expenditures in 1982 dollars.

Families of two or more t-stat.

Coeff.

t-stat.

- 1.754 0.387 0.096 0.037 0.001 -0.635 - 0.495 _

- 1.55 3.94 0.84 1.03 0.18 - 3.37 -1.71 _

-0.774 -0.068 0.255 0.035 0.001 0.180 0.776 0.584

- 0.48 0.52 1.68 0.89 1.51 0.68 -3.00 1.99

-0.972 -0.210

-0.32 -0.54

- 0.926 - 1.225

-1.73 - 2.23

-0.049 -0.341 -0.339

-0.14 - 1.38 - 1.42

-1.159 - 1.480 - 1.397

-2.01 -3.08 -2.82

-0.337 0.274 -0.558

- 1.29 1.04 -1.49

0.457 0.715 0.232

1.62 2.31 0.76

is Dayton.

Income

plan; omitted

site indicator

Table A.3 Regression

equation

for positive

expenditure.”

Single person family Variable Intercept Ln (anticipated expenses) Ln (family income) Education head (years) Age head Indicator = 1 if male head Indicator = 1 if non-white Ln (family size) Plan indicators 1 if 25% coinsurance 1 if 50% coinsurance 1 if 25% for medical, 50% for dental and mental 1 if 95% coinsurance 1 if 95% outpatient, 0 inpatient Site indicators 1 if Seattle 1 if Massachusetts 1 if South Carolina

Coeff.

Families of two or more t-stat.

Coeff.

f-stat.

4.325 0.439 -0.009 -0.017 -0.001 - 0.323 0.367 _

4.57 5.72 -0.10 -0.62 -0.11 -2.10 1.26 _

2.599 0.392 0.127 0.006 0.011 - 0.005 - 0.492 0.302

3.92 8.15 1.91 0.56 3.14 -0.04 -3.83 3.10

- 0.462 -1.125

- 1.99 -3.56

-0.438 -0.515

-3.78 -3.57

-0.382 -0.841 -0.781

- 1.38 -4.23 -4.04

-0.278 -0.878 -0.395

- 2.07 -8.85 -4.13

0.294 0.205 -0.193

2.96 2.20 -1.42

0.028 0.039 0.249

0.13 0.19 0.66

“Dependent variable is Ln (Family Expenditures) conditional on expenditures. Omitted plan indicator is 0 coinsurance plan; omitted Dayton. Income and expenditures in 1982 dollars.

having non-zero site indicator is

150

M.S. Marquis, Adverse selection

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M.S. Marquis,

Averse selection

151

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