Health Plan Switching Among Members of the Federal Employees Health Benefits Program

Transcription

1 Adam Atherly Curtis Florence Kenneth E. Thorpe Health Plan Switching Among Members of the Federal Employees Health Benefits Program This paper examines factors associated with switching health plans in the Federal Employees Health Benefits Program. Switching plans is not uncommon, with 12% of members switching plans annually. Individuals switch out of plans with premium increases and benefit decreases relative to other plans in the market. Switching is negatively associated with age due to increasing switching costs associated with age rather than decreasing premium sensitivity. Individuals in preferred provider organizations are less likely to switch, but are more responsive to premium increases than those in the managed care sector. Those who do switch plans are likely to switch to a different plan in the same sector. Employers and governments that provide health insurance often are caught between a desire to provide high-quality health insurance and the need to control the cost of coverage. One option for controlling cost is to use managed competition and offer a choice of several health plans (Enthoven 1978; 1988). In concept, multiple plan options could reduce costs if competition among plans lowered premiums and consumers selected lower-priced plans. Variants of these models have been used in the United States (Royalty and Solomon 1999; Cutler and Reber 1998; Buchmueller and Feldstein 1997) as well as in Germany, Switzerland, and the Netherlands (Schut, Gress, and Wasem 2003). For an employer offering competing plans to lower overall premiums, two events must occur. First, health insurers must have financial incentives to offer plans for an efficient price. Second, consumers must have incentives to select lowercost plans. If consumers are sensitive to differences in premiums, they will select lower-premium plans, creating incentives for insurers to generate efficient premiums. One potential problem with health plan competition is that offering multiple plans can provide opportunities for risk segmentation. It is often speculated that younger, healthier employees may be more willing to switch to lowerpriced plans. If this is correct, plans may be able to manipulate their risk pools by altering benefits and premiums to attract lower-cost individuals. This would leave higher-cost individuals congregated in plans with more generous benefits and provider networks, as well as higher premiums, undermining the cross-subsidization implicit in Adam Atherly, Ph.D., and Curtis Florence, Ph.D., are assistant professors in the Department of Health Policy and Management; and Kenneth E. Thorpe, Ph.D., is the Robert W. Woodruff Professor and chair of the Department of Health Policy and Management, all at the Rollins School of Public Health, Emory University. The Robert Wood Johnson Foundation funded this project under its Changes in Health Care Financing and Organization initiative. Address correspondence to Prof. Atherly at Department of Health Policy and Management, Rollins School of Public Health, Emory University, 1518 Clifton Road, NE, Atlanta, GA aatherl@sph.emory.edu Inquiry 42: (Fall 2005). Ó 2005 Excellus Health Plan, Inc /05/

2 Inquiry/Volume 42, Fall 2005 health insurance risk pools. In response to this potential problem, Enthoven (1988) called for uniform benefit packages and cost sharing. However, in practice, neither plan benefits nor costsharing provisions are typically uniform across employer plan offerings. A second, and less commonly discussed, problem is the limited competition that usually exists in the way plans structure benefits. Managed competition systems often attempt to reduce the cost of providing insurance by allowing enrollees to pay lower out-of-pocket premiums for less expensive plans (Vistnes, Cooper, and Vistnes 2001). But in many cases, managed care plans compete against a limited choice of plans that have the more flexible fee-for-service (FFS) network arrangements. For example, Buchmueller (2000) studied plan switching in California where retirees chose between one FFS plan and two to four plans styled like health maintenance organizations (HMOs). It is possible that enrollees reluctant to leave FFS medicine would leave their FFS plan for a lower-priced FFS plan if one were offered. However, the published literature to date has examined plan designs with limited competition between FFS plans that are relatively close substitutes for one another. The purpose of this paper is to examine the willingness of employees to switch plans in the U.S. Federal Employees Health Benefits Program (FEHBP). This paper identifies the extent of plan switching in the FEHBP population, and examines plan and individual characteristics associated with switching. We develop estimates of the impact of changes in plan premiums and benefits on plan switching. These estimates are important for understanding the potential that multiple choice plan designs have to control health care spending. This paper offers several new contributions to the existing literature on health plan switching and choice. First, in the FEHBP, we examine switching in a plan design with multiple options in both the FFS and HMO sectors. Having multiple options within each sector allows us to examine whether competition among plans is concentrated within a sector (i.e., FFS plans competing with other FFS plans) or across sectors (FFS plans competing with managed care plans and vice versa). Second, this paper examines the question of the effect of age on health plan premium switching elasticities. Previous studies have reported that premium elasticities decline with age, which implies that competitive models will lead to market segmentation based on age. By parameterizing the plan choice in the context of health plan switching, we are able to distinguish between differences in premium elasticities based on age and differences in switching costs based on age. Finally, studying plan switching by the FEHBP population itself is interesting because FEHBP has been touted as a model for health care reform (McArdle 1995; Feldman, Thorpe, and Gray 2002). The FEHBP is considered a successful program that has managed to offer employees a quality benefit package and a wide variety of options at a reasonable cost. It is often viewed as a potential model for reforming the federal government s other major health care program Medicare that has been criticized for its cost, lack of choices, and limited benefits (Butler and Moffit 1995). Major changes in the FEHBP in recent years, such as the availability of the option to pay health insurance using pretax dollars (Gruber and Washington 2003) and changes in the way the employee premium contribution is calculated (Florence and Thorpe 2003), have been examined by earlier research. Background Other researchers have found that switching health plans is a relatively common occurrence. Cunningham and Kohn (2000) found that in a nationally representative U.S. sample, 17% of employees changed health plans during a oneyear period; however, more than two-thirds of the switches were involuntary in that individuals lost access to their previous plan, either due to a job change or changes in their employer s health plan offerings. This study also found that switching was more likely among the younger age cohorts, whites, and those with more education and salary, although whether this suggests a greater willingness to switch among these groups or greater job mobility is unclear. Andersen and Schwarze (1998) reported a 4.4% switching rate for German sickness funds, where there were no involuntary switches. Beaulieu (2002) reported that approximately 5.3% of Harvard University employees switched voluntarily between 1996 and 1997 and that quality information had a smalleffect on plan switching. Buchmueller and Feldstein (1997) found that approximately 15% of 256

3 Health Plan Switching University of California employees with a choice of health plans in 1993 and 1994 switched. The key result from Buchmueller and Feldstein is the importance of out-of-pocket premiums in switching. Similarly, Gress et al. (2002) found that enrollees in German sickness funds were sensitive to differences in fund contribution rates. Indeed, there is now a significant literature suggesting that individuals are responsive to differences in both out-of-pocket premiums and benefits and are attracted to low-cost, higher-benefit plans (Scanlon, Chernew, and Lave 1997; Cutler and Reber 1998; Dowd and Feldman 1994/95; Feldman et al. 1989). Results of the effect of switching on risk pool formation also suggest that healthier, younger individuals are more responsive to changes in premium (Enthoven 1978; Florence and Thorpe 2003; Strombom, Buchmueller, and Feldstein 2002; Schwartze and Andersen 2003). Similarly, Buchmueller (2000) found a smaller premium effect among Medicare beneficiaries than among active workers. Combined, this suggests that consumers do switch plans in response to differences in health premiums, but that this switching leads to some risk pool segmentation. Conceptual Model When making their initial health plan choice, consumers are assumed to pick the plan that maximizes their individual utility. Utility is negatively affected by plan cost and positively affected by plan benefits. Formally, the utility for individual i of plan j is given by: U ij ¼ x j b þ e ij ð1þ where x j is a vector of plan characteristics (premium and benefits) and e ij is random error. This model is typically estimated by McFadden s conditional logit, and assumes that the individual selects the plan with the greatest expected utility. Once the initial plan choice is made, consumers must decide whether to stay with their chosen plan or change to another plan in each subsequent enrollment period. Consumers again will compare the cost and benefits of the current plan to the other alternatives available. However, when consumers make this comparison after selecting a plan, the difference in cost among the plans is no longer solely given by the difference in plan premiums and benefits, and consumers may experience state dependence. State dependence implies that there are significant switching (transaction) costs associated with switching states, and that consumers may prefer to remain in their current plan (state) even if another plan (state) would have been superior without the transaction costs. As described by Klemperer (1995), there are many possible economic reasons for these switching costs or brand loyalty. The most important for health plans are transaction costs associated with switching providers, costs associated with learning the nuances of new plans, uncertainty regarding quality, and psychological costs. In addition, if individuals switch plans, they are trading a known plan for an unknown plan. Although an employee can look at plan brochures and talk to colleagues to try to glean insights into the true nature of a plan, there is always the danger a new plan will have an unexpected drawback only discovered after an employee has locked into the plan for a full year. This uncertainty contributes to state dependence. Individuals with weaker relationships with providers will have a lower investment in their current plan and therefore lower switching costs. Similarly, individuals better able to assess the benefits of the various plans will also have lower switching costs. Formally, individual i switches from plan j in year t to plan k in year tþ1 if: U ktþ1. U jtþ1 and ð2aþ U kt, U jt ð2bþ (omitting the i subscript for notational convenience). This then implies that: X ktþ1 b þ e ktþ1. X jtþ1 b þ C j þ e jtþ1 and ð3aþ X kt b þ e kt, X jt b þ e jt ð3bþ with x, b and e defined as before and C j representing the switching cost associated with changing plans. C j is unique to plan j because plan j was chosen in time t. Rewriting equations 3a and 3b we find that plan switching occurs if: e ktþ1 e jtþ1. X ktþ1 ðx jtþ1 þ C j Þ and ð4aþ e jt e kt. X jt X kt ð4bþ which implies: ðe ktþ1 e jtþ1 Þþðe jt e kt Þ. ðx ktþ1 X jtþ1 C j ÞþðX jt X kt Þ: ð5þ 257

4 Inquiry/Volume 42, Fall 2005 In a context with two plan choices, plan switching could be modeled by estimating equations 4a and 4b jointly with correlated errors. The error correlation would estimate the switching cost, C j. Where there are more than two choices, the problem becomes more complex. The key element in equation 5 is the difference between year t and year tþ1 in the difference between the plan attributes of plan j and plan k, with plan k representing the next best alternative to plan j. Note that there is no reason to expect that plan k is the same actual plan in year t and year tþ1. To frame this problem in a tractable way, we model plan switching as a latent propensity to change plans, where a change in plans is observed if the propensity crosses a threshold level. The latent propensity to switch plans, S*, is a function of both differences in plan attributes in year tþ1 and changes in the differences in plan attributes from year t to tþ1: S* ¼ b½ðx ktþ1 X kt Þ ðx jtþ1 X jt ÞŠ þ e ð6þ If S*. 0 then S ¼ 1 and the individual switches plans. If S*, 0 then S ¼ 0 and the individual remains in his or her previous plan. We assume then that e i ; N(0,1) and estimate equation 6 as a probit. In earlier work on plan switching among California retirees, Buchmueller and Feldstein (1997) argued that there was a clear-cut choice of the next best alternative in their data: a zero premium plan (i.e., plan k). However, in our data, there is no plan that is clearly the next best alternative for the majority of the sample. As described by Buchmueller and Feldstein (1997), the problem with using a market average as the reference plan is that the relative premium is measured with error because the market average includes both plans which are close substitutes and those which are not. This has the effect of biasing the coefficients toward zero. To address this, we tested the sensitivity of our results to the specification of the reference plans by using a number of different alternatives to define plan k (described in the Data section). One of the key advantages of this framework is the ability to model interactions between individual characteristics and plan characteristics. In conditional choice models, modeling the effect of individual characteristics requires the introduction of a plan-specific indicator variable for each plan in the model. To examine interactions, the plan variable then must be interacted with each indicator variable. Such a model runs a risk of becoming over-parameterized in a setting such as FEHBP, where there are approximately 200 plans available across the country. 1 However, in switching models, such interactions are straightforward extensions of the standard probit model. The switching cost, C j, is omitted from the estimated equation 6 because switching costs for particular plans are not observed. However, both individual and plan level time-invariant characteristics difference out of equation 6. This implies that any time-invariant characteristics which are significant in the model are significant because of an interaction with the unobserved switching costs. Data Our data come from several sources. First, the Office of Personnel Management (OPM) has compiled data on all federal employees and retirees, recording, for each employee, the health plan selected (with separate designations for individual or family coverage) along with the employee s age, place of residence, and annual salary. We collected data on FEHBP premiums via OPM s website ( We combined the OPM data with data from the Checkbook Guide, an independent publication that provides information about the different health plans in the FEHBP. The Checkbook Guide examines each FEHBP plan brochure to determine the benefits offered; it is intended to assist government employees in choosing a health plan during the annual open enrollment. To examine plan switching, we used two separate three-year panels, one from the period and the other from the period (because of changes in the coding scheme for the individual identifiers we could not combine the two panels). The panel included choices for 1.84 million individuals, while the panel had million individuals. We then excluded people age 65 and older because federal retirees 65 and older are eligible for Medicare, which potentially alters their FEHBP plan benefits compared to those 64 and younger. We also excluded from the sample retirees under age 65 and postal employees. Retirees under age 65 were excluded because they might 258

5 Health Plan Switching have different search costs than active workers; postal employees were excluded because they faced a slightly different health plan choice set during the time frame of our study. Finally, for security reasons, employees in several sensitive federal agencies (e.g., the Federal Bureau of Investigation, Secret Service, Central Intelligence Agency) were not included in the data provided by OMB. FEHBP Plan Structure Within FEHBP, there are FFS-style preferred provider organization (PPO) plans that are available nationally, plus HMOs that participate in selected markets. (There are also nationally available plans that are open only to certain groups, such as the Secret Service plan. We do not include these plans in our analysis). Each PPO plan charges a nationally uniform out-ofpocket premium along with nationally uniform benefits, while the managed care plans vary their premiums and benefits by market. The HMO plans are traditional managed care organizations that require the use of a gatekeeper and do not pay for care outside the plan s provider network. Both across and within markets, there is significant variation in both premiums and benefits. The statute governing required benefits for plans participating in the program (5 USC 8904(a)) is vague, and simply requires that plans include benefits for both care associated with a general hospital and for other health services of a catastrophic nature. While there is no formal minimum set of benefits required, OPM often signals its interest in benefit changes (e.g., mental health parity), which leads to some similarity in plan benefits. Federal employees choose among the PPO plans plus any HMOs available in their area during the annual open enrollment period each November. Prior to the open enrollment period, plans make brochures available detailing out-ofpocket premiums and benefits. The choice takes effect the following January. After choosing a plan, employees are locked into their selection until the next open enrollment period. Although we present descriptive statistics showing all switching, we limited the sample to individuals in plans that participated in FEHBP for both years (i.e., individuals who had the option to not switch) in our multivariate analysis; this excluded 150,162 individuals. We also excluded from the multivariate analysis individuals who switched from single-to-family and familyto-single coverage (36,027 individuals); switches of this type often are driven by factors outside our data, such as a change in marital status or the employment decisions of a spouse. Explanatory Variables Explanatory variables in our model included individual and plan-level characteristics associated with switching costs. For the plans, the key characteristics were out-of-pocket premiums, benefits, and plan type (PPO or HMO). Premiums were defined as a difference-in-differences: the change in the premium from year t to year tþ1 in the individual s plan less the change in the premium of the next best alternative (plan k). The premium for the next best alternative was defined in three ways. First, we used the lowest premium plan in the sector (PPO or HMO) for each year. Second, we used an enrollment-weighted average sector-specific premium, with the average calculated using all plans available to the individual, including the plan the individual selected and any plans newly available to the individual. Finally, we used the enrollment-weighted overall average (including both PPO and HMO plans), again with the average calculated using all plans available to the individual. The enrollment weights were specific to the individual s market area. One test of the validity of our specification of plan k is the robustness of the model to these specifications. For a given increase in relative premium, we expected more switching. To control for differences in plan benefits, we used a measure of the plan s actuarial value. The actuarial value variable, drawn from the Checkbook Guide, represents the proportion of the enrollees health care spending that typically would be covered by the plan. Actuarial value is defined as expected plan expenditures divided by total expected health care spending (i.e., plan expenditures plus out-of-pocket spending, including deductibles). Because the actuarial value variable calculation is based on a typical employee in the plan, rather than the individual, it is exogenous to the model. Similarly to the premium variable, the actuarial value variable was defined as a difference-in-differences among the enrollment-weighted overall market average, 259

6 Inquiry/Volume 42, Fall 2005 Table 1. Percentage of enrollees switching out of plans, , by plan type Overall PPO single PPO family HMO single HMO family the sector-specific enrollment-weighted average, and the sector-specific plan with the lowest premium. We expected a negative relationship between actuarial value and plan switching: as plans reduce benefits, the probability of switching increases. We also anticipated switching to be negatively associated with PPO plan type. PPO plans have few restrictions on providers and use of services. This suggests that switching from a PPO plan is more likely to entail reductions in provider networks and increases in administrative restrictions in the use of services than switching out of an HMO plan. This implies that the switching costs associated with switching out of a PPO plan will be greater than the costs of switching from an HMO plan, and that we therefore will observe less switching from PPO plans, all other factors held equal. For individual characteristics, we included age, gender, education, race, and federal salary. We hypothesized that older individuals would have stronger relationships with providers and therefore higher switching costs. For education, we expected that more educated individuals would be better able to evaluate the options and therefore would have lower switching costs. Both of these results have been found in previously cited research. It is not clear that race, gender, or salary would affect switching probability after controlling for premium differences; therefore no sign was hypothesized. Finally, we included mean county income and per capita physicians (from the Area Resource File) and indicator variables for census region. We also interacted several of the plan variables and the individual characteristics. In particular, we interacted the change in the plan premium with age and PPO plan indicators. If the switching premium elasticity is different for PPO plans Table 2. Percentage of enrollees switching out of plans, Overall Family Single Overall Plan type PPO HMO Sex Male Female Age Under Over Race Non-minority Minority than HMO plans, then the PPO-premium interaction will be significant. Finally, if the premium elasticity of demand varies with age, the agepremium interaction will be significant. Results The annual percentage of FEHBP enrollees who switched between 1996 and 2001 is shown in Table 1. On average, approximately 12% of enrollees switched each year. As expected, there is a clear difference between the switching rates among people enrolled in the PPO plans and the HMOs, with HMOs experiencing two to three times higher switching rates. This is true every year and for both single and family plans. Also, family plans had lower switching rates than single plans every year for both PPO and HMO plans. Demographically, switching rates declined markedly by age cohort for both PPO and HMO plans, from a high of 19.2% for enrollees under age 30 with a single plan to a low of 6.8% for enrollees over age 60 in family plans (Table 2). Women and minorities had higher overall switching rates than men and non-minorities. To better understand where switchers go when they change plans, the destination of switchers by plan type (PPO / HMO) and coverage type (family/single) are shown in Table 3. Of those en- 260

7 Health Plan Switching rollees who chose a PPO family plan in 2000, then switched coverage for 2001, 40.3% chose a different PPO family plan. A further 35.6% switched to a PPO single plan, so that 75.9% of those who began in PPO family coverage remained in the PPO sector. Only 24.1% of the switchers changed to an HMO plan, with most selecting an HMO family plan. The same overall pattern is also observed for the PPO single group. Looking at the HMO switchers, for both family and single plans, most switchers changed to a different HMO plan within the same plan coverage type. Overall, of those who began in an HMO family plan, 74.6% ended with an HMO plan, as did more than three-quarters of those who began in an HMO single plan. In short, among those who chose a plan in both years, the overall trend for most switching was between plans in the same sector (HMO / PPO) with a relatively small number of individuals switching between HMO and PPO. The multivariate analysis (Table 4) focuses directly on this within-coverage type (individual or family coverage) decision to switch health plan. The sample for this analysis is limited to individuals who were in FEHBP for two years and were enrolled in plans that were offered in consecutive years and maintained the same coverage type (single or family) during that time. For this sample, we estimated a probit model of switching plans, using data from The models were estimated separately for people in family plans and individual plans, and we excluded those who switched between single and family plans. The coefficients in Table 4 represent marginal probabilities and were calculated from the estimated probit models. Results are presented both with and without interaction terms. The reference premium used in Table 4 represents the enrollment-weighted market average. Individuals were more likely to switch from plans with relatively high premium increases. For singles (focusing on the model without premium interactions), a $100 relative increase in annual premium increased the probability of switching from an HMO plan by 1.9 percentage points. Although members of PPO plans were 14.3 percentage points less likely to switch plans than those in HMOs, the PPO / premium interaction shows they were more premium sensitive. Plans that decreased benefits relative to other Table 3. Where do switchers go? Type of plan chosen by switchers, , stratified by origination plan and coverage type Type of plan employee selected in 2000 Type of plan switched to in 2001 PPO (%) HMO (%) Stay in coverage type Change coverage type Stay in coverage type Change coverage type PPO Family coverage Single coverage HMO Family coverage Single coverage plans in the market were also more likely to experience switching. For every 1% decrease in relative benefits, there was a.136 percentage-point increase in the probability of enrollees switching out of their plan. Turning to individual characteristics (again focusing on the model without premium interactions), all age cohorts were more likely to switch than the reference group (those over age 60), with the probability of switching consistently decreasing with age. Someone in the under-30 age group was 2.63 percentage points more likely to switch than the reference group, while those in the age group were.62 percentage points more likely to switch plans. Yet the age cohort indicators interacted with the changes in the premium were insignificant. The insignificance of the interactions indicates that although older individuals were less likely to switch, they were not less responsive to changes in premiums. Switching also was more likely for minorities and females, although the size of the effect was small. Finally, education was positively associated with switching, but the effect, particularly given the large sample sizes, was either marginally significant or insignificant. Results for people with family coverage were largely consistent with the results for enrollees with single coverage, with two notable differences. First, people with family policies were less premium sensitive (a $100 relative increase in 261

8 Inquiry/Volume 42, Fall 2005 Table 4. Effect of plan and individual characteristics on the probability of switching plans, family and single, Family Without age/ premium interactions Constant *** Change in premium relative to average change in premium in market Change in actuarial value relative to average change in actuarial value in market (257.44) *** (16.41) *** (245.23) Begin in PPO plan *** (270.64) Begin in low premium plan *** (273.84) Age cohort under *** (12.62) Age cohort *** (12.18) Age cohort *** (8.46) Age cohort (3.96) College education (1.53) Non-minority *** (28.78) Female.0036*** (6.00) Salary ($1,000) *** (4.90) Relative premium change *** interacted with PPO (7.77) Physicians per 1, population (.13) County per capita income *** (10.26) Mid-Atlantic region *** (224.01) East North Central region.0080*** (10.59) West North Central region.0042*** (4.27) South Atlantic region (.93) East South Central region.0039*** (3.96) West South Central region.0374*** (57.83) Mountain region.0109*** (14.84) Pacific region *** (256.62) Change in premium * Age cohort under 30 Relative premium change * Age cohort With age/premium interactions *** (256.27) (1.72) *** (245.23) *** (270.56) *** (273.86).0573*** (12.35).0358*** (11.84).0197*** (8.16) *** (3.86) (1.54) *** (28.72).0036*** (5.98) *** (4.94) *** (8.63) (.19) *** (10.29) *** (223.93).0079*** (10.56).0042*** (4.30) (.93).00394*** (3.96).0375*** (57.87).0109*** (14.80) *** (256.71) (1.29) (1.83) Single Without age/ premium interactions *** (237.06).00019*** (25.38) *** (212.52) *** (288.25) *** (247.10).0263*** (7.09).0158*** (5.45).0112*** (4.31).0062* (2.40).0024** (2.64) *** (29.63).0084*** (10.92) *** (23.51).00008*** (7.75) *** (27.54).0007*** (13.19) *** (231.49).0209*** (20.66).0103*** (5.81) (.57).0103*** (6.43).0592*** (61.22) *** (24.93) 2.058*** (253.92) With age/premium interactions *** (236.66).00021*** (6.78) *** (212.55) *** (288.20) *** (247.13).02723*** (7.15).01619*** (5.46).01156*** (4.35).00659* (2.50).00240** (2.63) *** (29.62).00843*** (10.93) *** (23.49).00009*** (7.95) *** (27.52).00069*** (13.19) *** (231.51).02084*** (20.64).01033*** (5.84) (.58).01031*** (6.46).05919*** (61.22) (24.93) *** (253.94) (2.78) (1.09) 262

9 Health Plan Switching Table 4. (continued) Family Without age/ premium interactions With age/premium interactions Single Without age/ premium interactions With age/premium interactions Relative premium change * Age cohort (.06) (2.74) Relative premium change * Age cohort (2.28) (2.78) R 2 maximum likelihood Sample size 603, , , ,215 Note: Coefficients are changes in the marginal probability of switching, with t statistic in parentheses below coefficient. * p,.05. ** p,.01. *** p,.001. premium increased the probability of switching from an HMO plan by.25 percentage points). Second, people with family policies were more sensitive to differences in actuarial value. Table 5 shows the marginal probabilities for the two sectors using the three different reference premium specifications, but without any premium interactions. The estimated marginal probability for premium was consistent across the three models, both in magnitude and in statistical significance. Conclusions We found that people were more likely to switch out of plans with relatively higher premium increases. Switching was progressively less likely as enrollees aged and less likely for nonminorities, but more likely for females. There are several important limitations to this paper. First, our sample for the probit models was limited to individuals in FEHBP for two years who were enrolled in plans that were offered in consecutive years and who maintained the same coverage type (single or family) during that time. Also, our data were administrative enrollment data and we therefore had limited information on some important individual characteristics, such as household composition, employment status of spouses (and therefore, alternative health insurance choices and total family salary), and health status. It is possible that differences in the probability of switching for people in the PPO sector were due to unobserved factors. Several of the findings of this paper have implications for policies intended to increase competition among health plans offered by employers or other types of sponsors. First, we found that enrollees in PPO plans were less likely to switch, but were more premium sensitive and, from the descriptive statistics, were relatively unlikely to switch to HMO plans even if they did switch. This result is similar to that reported by Feldman et al. (1989), who found that enrollees in health plans in the Twin Cities were separated into two nests of choices, one for a PPO and the other for managed care. This suggests that it is not sufficient to simply have a large number of plans competing. Those structuring plan offerings must ensure that there are competing plans within both the HMO and the PPO sectors. Otherwise, a situation could be created where one set of plans within the market is competing and offering efficient benefits, while in the other market segment a small number of plans enjoy substantial market power because of a lack of Table 5. Estimated marginal probability for plan premiums using different specifications for reference plan Model Family plan Single plan Reference plan overall market average (22.097) (42.581) Reference plan overall lowest premium plan in sector (18.232) (40.650) Reference plan overall market average within sector (PPO/HMO) (15.496) (32.497) Note: Coefficients are changes in the marginal probability of switching, with t statistic below coefficient in parentheses. 263

10 Inquiry/Volume 42, Fall 2005 competition within their segment, even in an ostensibly competitive environment. A second finding with implications for competitive models is the effect of age on switching. We found that as individuals aged, they were decreasingly likely to switch out of their current plan. However, premium elasticities were not associated with age. Several previous studies have reported that premium elasticities decline with age (Royalty and Solomon 1999; Florence and Thorpe 2003); our study suggests that these findings may be due to the structure of the estimated plan choice model, where switching costs are omitted. Our results show that older workers are not significantly different from younger workers in how they respond to increases in plan premiums. Instead, older workers switch at lower rates than younger workers because of other factors associated with age, such as the cost of switching providers necessitated by switching health plans. If these other factors can be identified and modified to reduce switching costs for older members, competitive approaches to health insurance may not be undermined by selection problems. For example, one of the hallmarks of managed competition is to minimize overlap in provider networks (Enthoven 1978). If nonoverlapping provider networks increase switching costs disproportionately for less healthy members of the risk pool, our findings suggest this will lead to increased risk pool segmentation. In our data, however, the age effect in the multivariate analysis, where someone under age 30 was 2.6% more likely to switch, is far less than the differences in the descriptive statistics suggest (where the difference was 10.1%). 2 This is due to the correlation between age and other factors associated with decreased switching, such as salary. Although age is a significant predictor of reduced switching, it is significantly less important than other factors, such as the PPO effect (14.3%). On balance, our paper provides further evidence that health plan enrollees are sensitive to differences in premiums and benefits. This suggests that market forces are able to exert discipline on health plans and competitive approaches to controlling health care costs have promise. However, our paper also suggests that improperly managed approaches could lead to difficulties, such as new plan entrants acquiring a disproportionately young (and presumably healthy) mix of enrollees because older members of the risk pool are less willing to switch to the new offering. Notes The authors thank Walton Francis for providing the information on plan benefits and an anonymous but very careful reviewer for comments. Any errors or omissions, however, are solely the responsibility of the authors. 1 Although no employee has a choice of all 200 plans. 2 Note that the results presented in Tables 2 and 4 represent different samples; however the results in Table 2 reflect the descriptive statistics for the sample in Table 4. References Andersen, H., and J. Schwarze GKV 97: Kommt Bewegung in die Landschaft? Eine empirische Analyse der Kassenwahlentscheidungen (Statutory Health Insurance in 1997: Is There Any Movement in the Landscape? An Empirical Analysis of Sickness Fund Choice Decision). Arbeit and Sozialpolitik 9/10: Beaulieu, N Quality Information and Consumer Health Plan Choices. Journal of Health Economics 21(1): Buchmueller, T The Health Plan Choices of Retirees Under Managed Competition. Health Services Research 35(5): Buchmueller, T., and P. Feldstein The Effect of Price on Switching among Health Plans. Journal of Health Economics 16: Butler, S., and R. Moffit The FEHB as a Model for a New Medicare Program. Health Affairs 14(4): Cunningham, P., and L. Kohn Health Plan Switching: Choice or Circumstance? Health Affairs 19(3): Cutler, D., and S. Reber Paying for Health Insurance: The Tradeoff Between Competition and Adverse Selection. Quarterly Journal of Economics 113(2): Dowd, B., and R. Feldman. 1994/95. Premium Elasticities of Health Plan Choice. Inquiry 31(4): Enthoven, A. C Consumer Choice Health Plans. New England Journal of Medicine 298:

11 Health Plan Switching The Theory and Practice of Managed Competition in Health Care Finance. Amsterdam: North-Holland. Feldman, R., M. Finch, B. Dowd, and S. Cassou The Demand for Employer-Based Health Insurance Plans. Journal of Human Resources 24(1): Feldman, R., K. Thorpe, and B. Gray The Federal Employees Health Benefits Plan: A Touchstone for Research and Policy on Employment-Based Health Insurance. Journal of Economic Perspective 16(2): Florence, C., and K. Thorpe How Does the Employer Contribution for the Federal Employees Health Benefits Program Influence Plan Selection? Health Affairs 22(2): Gruber, J., and E. Washington Subsidies to Employee Health Insurance Premiums and the Health Insurance Market. National Bureau of Economic Research (NBER) Working Paper. Available at: Gress, S., P. Groenewegen, J. Kerssens, B. Braun, and J. Wasem Free Choice of Sickness Funds in Regulated Competition: Evidence from Germany and The Netherlands. Health Policy 60(3): Klemperer, P Competition When Consumers Have Switching Costs: An Overview with Applications to Industrial Organization, Macroeconomics and International Trade. Review of Economic Studies 62: McArdle, F Opening Up the Federal Employees Health Benefits Program. Health Affairs 14(1): Royalty, A., and N. Solomon Price Elasticities in a Managed Competition Setting. Journal of Human Resources 34(1): Scanlon, D. P., M. Chernew, and J. R. Lave Consumer Health Plan Choice: Current Knowledge and Future Directions. Annual Review of Public Health 18: Schut, F. T., S. Gress, and J. Wasem Consumer Price Sensitivity and Social Health Insurer Choice in Germany and the Netherlands. International Journal of Health Care Finance and Economics 3: Schwarze, J., and H. Andersen Kassenwechsel in der Gesetzlichen Krankenversicherung: Welche Rolle spielt der Beitagssatz? (Switching Funds in Statutory Health Insurance: What Role is Played by the Contribution Rate?) Discussion paper 267. Berlin: Deutsches Institut fur Wirtschaftsforschung. Strombom, B. A., T. C. Buchmueller, and P. J. Feldstein Switching Costs, Price Sensitivity and Health Plan Choice. Journal of Health Economics 21: Vistnes, J., P. Cooper, and G. Vistnes Employer Contribution Methods and Health Insurance Premiums: Does Managed Competition Work? International Journal of Health Care Finance and Economics 1: