Preliminary and Incomplete Draft. Doctor Switching Costs in Health Insurance. Gordon B. Dahl (UC San Diego and NBER) and

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1 Preliminary and Incomplete Draft Doctor Switching Costs in Health Insurance Gordon B. Dahl (UC San Diego and NBER) and Silke J. Forbes (Case Western Reserve University) Abstract We exploit a targeted change in the health insurance offerings of a large employer to evaluate whether individuals are willing to pay more to keep their existing primary care doctor. Beginning in 2011, employees in one of the health insurance plans were given the option of choosing a new plan which had a lower premium in exchange for a more limited network of doctors. For individuals whose doctors were covered under both the old and new plans, we argue this new option strictly dominated, since all other aspects of insurance coverage remained identical and the option value of the larger network was close to zero. We use the choices of these employees to estimate the amount of non-switching due to inertia. Employees whose existing doctors were not covered under the new option had to decide whether to pay more to keep their current doctor. This setting allows us to identify the costs that individuals associate with switching doctors separately from inertia. We estimate that 22% of employees are initially inattentive and that 16% are inattentive a year later, even though they could save between $638 and $1,851 annually by switching to the new plan. A substantial fraction of individuals are willing to pay higher premiums to keep their doctor, even after controlling for inertia, with 35% of employees choosing to pay an average of over $100 extra per month to retain their doctors. These inertia and doctor switching costs vary by income, age, gender, family composition and prior health care utilization patterns. Our findings have important implications for the design of public and private health insurance offerings.

2 1. Introduction Health care costs have been rising faster than inflation for at least a decade (Kaiser Family Foundation, 2013). A potential solution to rising prices for health care is to increase competition among insurance and health care providers. However, if consumers face costs of switching among providers, then competition will be less effective as a means to contain health care prices (Klemperer, 1987, Farrell and Klemperer, 2007). A central issue in the policy debate over the U.S. Affordable Care Act, as well as other health care reform legislation, has been whether people would be required to switch doctors. 1 In the private market, when firms change insurance providers or when employees change jobs, workers and their families are often unable to keep their current network of doctors. 2 While the magnitude of these switching costs is important for evaluating the costs versus benefits of maintaining access to doctors, little empirical evidence on the topic currently exists. Several challenges make it difficult to estimate the cost of switching doctors. The first is that insurance plans involve a large vector of attributes, including which doctors are in a network, covered medical services, copay amounts and prescription drug coverage; hence, it is difficult to separate out willingness to pay to keep one's doctor from other plan characteristics. A related challenge is that premium changes usually occur either in relatively small increments or in combination with substantial changes in other insurance plan attributes. A final issue is that inattention, inertia or confusion by consumers about the details of insurance options makes it difficult to separate out what might broadly be called "inertia" from "willingness to pay". In this paper, we take advantage of a uniquely targeted change in the health insurance offerings of a large employer to estimate individuals' willingness to pay to keep their existing doctors. The change mirrored a randomized experiment where within the same insurance plan, there were premium increases associated with some doctors (group A) but not others (group B). Prior to the change, employees could choose doctors in group A or B with no price difference. After the change, employees whose existing doctor was in group A were faced with a price increase which could only be avoided if they switched to a doctor from group B. In contrast, employees 1 President Obama outlined the relevant trade off in an interview, say some people are "going to have to make some choices and they might end up having to switch doctors, in part because they're saving money" (WebMD, March 14, 2014). 2 An estimated 55 percent of the U.S. population was covered by employer-sponsored health insurance in 2011 (Current Population Survey, September 2012). 1

3 with existing B-group doctors were not charged more to keep their current doctor, as long as they actively opted in to avoid the price increase. Under the identifying assumption that doctors in groups A and B are substitutable (an assumption we test empirically), one can estimate the fraction of individuals willing to pay a higher premium to avoid switching doctors as well as the amount of inertia. Importantly, other plan attributes remained identical both before and after the change, eliminating confounding factors. The actual details of the natural experiment are as follows. Prior to 2011, employees of the University of California (UC) system could choose from a variety of health insurance plans, with the most popular plan being Health Net. The available set of primary care physicians (PCPs) individuals could choose from in this plan included UC affiliated doctors, as well as doctors in several other provider networks. Starting in 2011, UC expanded its health insurance offerings to include two versions of the Health Net HMO, one with the previously existing network of doctors ( Health Net or HN) and one with a more limited network ( Health Net Blue and Gold or HNBG). The HNBG network included all UC affiliated physician groups and hospitals, but excluded many of the other provider groups. The HNBG option was set up to contain premium rate increases, while the HN plan had premiums rise by as much as 100% depending on family type and income level. Importantly, all other aspects of insurance coverage remained the same as before for both insurance plans. The default option for all employees who were previously enrolled in HN remained HN, even if their PCP was in the HNBG network. The assumption that HN and HNBG doctors are viewed as substitutes by employees is central to our identification strategy. UC was able to instigate the creation of the HNBG insurance plan because it wielded strong pressure on UC affiliated doctors and hospitals to not raise prices beyond the rate of inflation. It did not have the same ability to do this with the other provider networks. We argue the differential bargaining power exerted by UC was institutionally driven, and unrelated to the relative quality of the two groups. When we test this claim empirically by comparing quality ratings, we find no statistical difference between the two groups of doctors. The behavior of employees whose existing doctors were included in the smaller HNBG network can be used to estimate the amount of inertia. For these employees, actively choosing to change insurance from HN to HNBG would result in a substantially lower premium, without the need to change doctors. But given the default option, an employee not paying attention or confused about the new plan would automatically stay enrolled in the now more expensive HN plan. Since 2

4 all other insurance plan characteristics besides doctor networks remained the same, the fraction of these employees who do not switch represents the fraction of inattentive employees. In theory, it is possible these employees do not want to switch to HNBG if they value the option of being able to change from an HNBG doctor to a doctor who is only in the broader HN plan. We argue this option value is close to zero in our setting for several reasons later in the paper, and provide empirical evidence that almost no employees exercise this option when it is available to them. For employees whose existing doctors were not covered under the HNBG option after the change in 2011, they faced a choice: keep their current doctor and pay a higher premium to stay in HN, or switch to a PCP in the HNBG network and save money. Of course, there are two reasons these employees may not switch. They may be willing to accept higher premiums to keep their current doctor, but they may also not be paying attention. Under the identifying assumption that the rate of inertia conditional on observables is the same across both groups, we can separate out these two factors by comparing the switching rates of these individuals to those whose existing PCPs are in the HNBG network (since this group has no switching costs, but only inertia). Empirically, we find evidence of sizable switching costs, even in this relatively simple environment where the only thing changing was physician networks. Over two thirds of employees who were enrolled in HN prior to the change had PCPs who were included in the smaller HNBG network. Twenty-two percent of these employees were inattentive and remained in HN the first year after the change, even though they could have saved between $638 and $1,851 by switching to HNBG. This fraction declines the second year after implementation, with inertia dropping to 16% of employees. Our main result is that a substantial fraction of individuals are willing to pay higher premiums to keep their doctor. In the first year after the change, 35% of employees are willing to pay an average of over $100 extra per month to retain their doctors. Since it takes time to choose and transition to a new PCP, one might have expected a sizable decline in this fraction the second year after the change. However, the fraction drops only slightly, with 32% of employees willing to pay higher premiums to keep their doctor. Note that while these willingness-to-pay fractions are large, one would have erroneously concluded they were even larger without netting out inertia. Willingness to pay to keep a doctor explains why 61% of individuals do not switch insurance plans, while inertia accounts for 39% of non-switchers the first year after implementation. 3

5 Interesting patterns emerge across demographic groups in both inertia and doctor attachment. Multinomial logit models reveal that doctor attachment is a normal good, with high income individuals being much less likely to switch doctors, even after controlling for inertia. For the average earner whose existing doctor is not in the HNBG network, doubling their salary would reduce the likelihood that they would switch from HN to HNBG by 7.4 percentage points. Six percentage points of this effect is attributable to doctor attachment and 1.4 percentage points to inertia. Given the baseline switching rate of 35%, this represents a sizable income effect. We also find that inertia and doctor attachment are strongly influenced by age, gender, family composition and prior health care utilization patterns. Our paper contributes to two strands of the literature on health insurance choice. The first examines switching between health insurance plans as a result of price changes. Buchmueller and Felstein (1997) study the effect of relative price changes on switching between health plans that are close substitutes for each other, and find that even small relative price changes induce substantial switching between plans. 3 Strombom, Buchmueller and Feldstein (2002) examine how price elasticities vary with employee characteristics and find that younger and healthier employees are more likely to switch away from health plans with relative price increases. More recently, Gruber and McKnight (2014) examine switching from broad network (mostly PPO) plans to limited network (mostly HMO) plans among Massachusetts state employees, who were offered one-time financial incentives between $800 and $2300 to switch to a limited network plans. Gruber and McKnight find that, overall, about 10 percent of enrollees switch in response to the financial incentive, and enrollees who can keep their existing primarycare doctor and 60 percent more likely to switch than those who would have to change doctors. The second area of the literature we contribute to is the role of bounded rationality and inertia in health insurance choices. This includes a series of papers examining the suboptimal decisions by retirement-age individuals choosing Medicare Part D plans (Abaluck and Gruber, 2011 and 2013, Ericson, 2014, Heiss, Leive, McFadden and Winter, 2013 and Ketcham, Lucarelli, Miravete and Roebuck, 2012). Examining health insurance decisions of working-age individuals, 3 Buchmueller and Feldstein also use data from the UC system. Since they do not have information on employees doctors before and after the price changes, they do not study this aspect of switching behavior. Moreover, in the time period they study, many doctors were included in multiple plan networks so that employees could commonly keep their doctors when switching health plans. 4

6 Handel (2013) finds substantial inertia in insurance choices, and Handel and Kolstad (2013) demonstrate that information frictions and hassle costs play an important role. To our knowledge, our paper is the first to examine how the ability to keep one s existing doctor affects switching between otherwise identical insurance plans with different prices. We estimate this effect while controlling for inertia, something which has proven difficult to do in most other settings. Our data also allows us to study inertia directly and estimate how inertia varies with employee demographics and health. The findings from our study are important for health care reforms, since such reforms often place restrictions on provider networks to save on costs. Identifying which individuals are willing to pay substantial sums to keep their doctor is important for determining which groups would suffer the largest welfare losses if they were forced into smaller networks that did not include their existing doctor. Our results also suggest that doctor attachment matters for private insurance options and could contribute to job lock. The remainder of this paper proceeds as follows. We first describe the institutional setting and data which make this study possible. In Section 3 we discuss and test the assumptions underlying our identification approach. Sections 4 and 5 present empirical results, followed by Section 6 which adds several extensions to the analysis [still to be written]. The final section concludes [still to be written]. 2. Institutional Background and Data 2.1 Health Insurance Options and Enrollment In 2010, employees at the University of California (UC) could choose between seven different health insurance options. These included a low-deductible and a high-deductible Preferred Provider Organization (PPO) option, a hybrid PPO-HMO, three HMO options and a high-deductible fee-for-service plan. Among the HMO s, Health Net (HN) offered the largest physician network, Kaiser Permanente offered coverage through its own network of physicians directly employed by Kaiser, and Western Health offered only a regional network, primarily in the Davis and Sacramento area. In 2011, the Health Net Blue & Gold (HNBG) option was introduced, with the intention of containing costs and holding the line on premium rate increases. In the same year, the University also switched from Cigna to Anthem as a provider for the high-deductible PPO plan; however, enrollment in these two plans was small (around 1 to 2 percent of all employees). 5

7 The UC system subsidizes insurance premiums based on a combination of income and family status. Employees are grouped into four income tiers, and each tier receives a fixed subsidy amount which is higher for lower incomes. 4 Employees are also differentiated based on whether the insurance covers the employee only, the employee plus children, the employee plus a spouse or the employee plus a spouse and children. Table 1 shows the annual insurance premiums for each plan that an employee in the second income tier would pay if she only insured herself (Panel A) or if she insured herself, a spouse and one or more children (Panel B). 5 The table shows that in 2010 the PPO options were substantially more expensive than the HMO options. Among the HMO s, Health Net was the most expensive option, with an annual premium of $614 for an employee insuring herself only, compared to premiums of $470 for both Kaiser Permanente and Western Health Advantage. Thus, HN would have been the preferred choice for an employee who wanted an HMO with a large network of independent doctors and was willing to pay a modestly higher premium. In 2011, the premium for HN increased substantially for this income group it approximately doubled. The new HNBG option was introduced at a small premium increase relative to what HN had cost in the previous year. Employees whose existing doctors were in the HNBG network could thus keep their doctors and all other features of their existing insurance at close to the same price that they had been paying before. Employees whose existing doctors were not in the HNBG network, however, would have to pay substantially higher premiums if they wanted to stay in Health Net and keep their existing doctors. Alternatively, these employees could enroll in HNBG or another HMO and switch to a new doctor. Table 2 shows the annual premium differences between HN and HNBG. In 2011, the gaps ranged from $638 for an employee insuring herself only to $1,851 for an employee also insuring a spouse and children. In 2012, these numbers increased slightly. The premium differences were independent of the employee s income (in absolute terms). Every year, UC employees can change their insurance plans during a month-long open enrollment period (usually in November for a change effective the following January). Employees who wanted to switch to HNBG had to make an active choice during open enrollment and fill out 4 In 2011, the income tiers were set at $47,000 or less, $47,001-93,000, $93, ,000 and $140,001 or more. 5 Premiums for other income groups and for employees insuring a spouse and no children or children and no spouse are shown in the Appendix [to be added later]. 6

8 a form, either on a website or on paper. The default for employees who made no change during open enrollment was to stay in their existing health care plan. This was true even for individuals who were enrolled in HN in 2010 and whose existing doctor was in the HNBG network. Each employee received information about the HNBG introduction in the form of several s and an annually distributed flyer about UC insurance options. However, some individuals may have chosen not to read this information and thus may have been unaware of the change. Table 3 shows the health plan choices of full-time UC employees under the age of 65 for the years In 2010, Health Net was the most popular insurance option with 44.1 percent of employees choosing this plan. This was followed by Kaiser Permanente (KP) with 30.5 percent. Approximately 23 percent of employees chose one of the PPO options, and less than 3 percent chose the regional HMO, Western Health Advantage (WHA). In 2011, when HNBG was first introduced, 28.4 percent of employees chose this plan and HN s share fell from 44.1 percent to 14.9 percent. In the following year, HNBG s enrollment share increased to 31.7 percent while HN s share fell further to 11.3 percent. Interestingly, the combined share of HN plus HNBG remained fairly close to the pre-change HN share. In both years, the enrollment share of PPO s fell slightly, while KP and WHA experienced small increases in enrollments. Appendix tables A.1 and A.2 show the transition matrices for health plan choices from and from Data Sources and Estimation Sample Our primary data source is administrative records from the University of California, culled from several sources, for the years This includes the health insurance plan chosen by each employee in each year, demographic information about the employee and all insured family members, the employee s salary (in $5,000 bins), a record of each doctor visit and the treating physician (though not the reason for the visit) and, in the case of Health Net enrollees, each family member s primary care physician. All records were anonymized before they were given to us so that we cannot identify any individuals. We augment this data with information from Health Net. In addition to the medical group ratings described above, we received the doctor and hospital directories for the HN and HNBG networks. 6 As we explain below, we exclude employees who live in the zip code for UC Davis or the immediately adjacent zip codes in this and all following tables. 7

9 Our regression sample consists of full-time staff and faculty employees between the ages of who were enrolled in Health Net in In order to be able to focus on the decisions of existing employees, we require that individuals be in our sample for each year from We examine the health plan choices of new employees separately [to be added]. We drop individuals with incomes below $25,000 and above $200,000 because we have very few observations in those ranges. Finally, we drop individuals who live in the zip code for UC Davis and immediately adjacent zip codes. UC Davis employees voiced substantial opposition to the HNBG introduction because the HNBG network excluded the largest doctor network in Davis, Sutter Medical Group and Hospital. While HNBG did include the UC Davis medical center and their affiliated doctors, the hospital is located in Sacramento and therefore less accessible to UC Davis employees. Given these issues, we drop employees in the Davis area because their choices may be systematically different. Our final estimation sample includes 26,359 employees who we observe over three years. We do not include covered family members as separate observations because the insurance plan decision is made at the family level and all family members must be in the same insurance plan. We include family-member attributes, such marital status and the presence of children, as characteristics of the employee's household in our regressions. 3. Identification of Doctor Attachment and Inertia 3.1 Quasi-Experimental Setting As previewed in the introduction, our setting mirrors an experiment where individuals with certain doctors are assigned higher premiums but can keep their doctor (group A), while individuals with other doctors are allowed to keep their doctor without a price increase as long as they make an active choice to do so (group B). For this to be an experiment, the higher premiums need to be randomly assigned to doctors. In this somewhat simplified example, the random assignment of premiums to doctors helps to identify both inertia and doctor attachment. To identify inertia, one can use the fraction of individuals with doctors in group B who do not actively opt in to the lower premium. The fraction of individuals with doctors in group A who do not switch can be used to identify the combination of inertia plus willingness to pay to keep one's 7 Having a doctor in the HNBG network does not predict leaving employment at the university and thus exiting the sample. 8

10 doctor. Since which individuals have group A versus group B doctors is randomly assigned, one can compare the fraction of non-switchers in the two groups to separately identify willingness to pay from inertia. While we do not have an actual experiment, we take advantage of a natural experiment which assigned higher versus lower prices to certain physician groups in a way that appears to be independent of doctor quality. We argue that which provider groups agreed to be part of the HNBG network, which held the line on premium increases, had little to do with relative quality or other demand factors, and more to do with bargaining power. The central administration at UC instigated the creation of the HNBG insurance plan to contain cost and premium increases. It exerted strong pressure on UC affiliated doctors and hospitals to join HNGB and was successful in the attempt. It did not have the same power to do this with the other provider networks, such as Scripps Health (a large provider in Southern California), which did not join HNBG. 8 We argue the differential bargaining power exerted by UC was institutionally driven, and unrelated to the relative quality of the two groups. 9 The fact that no other aspects of insurance coverage changed also helps to cleanly identify the effects. 3.2 Doctor Substitutability across the Two Networks A first reason to believe that doctors in the broader HN network and the more limited HNBG network are substitutes relates to the fact that both sets of doctors were in the same insurance plan prior to Before the change in insurance plan options, employees choosing HN could choose UC doctors as well as a variety of doctors affiliated with other providers without any distinction in cost. In 2010, before the change, 70% of employees choose doctors who would later be in the HNBG network, while 30% choose doctors who would later not be in the HNBG network. Hence, HNBG doctors were not only a popular choice, but there was also a sizable network of doctors to choose from. 8 Many non-uc providers are also in HNBG. It is difficult to know what negotiations led them to be included in the network, but as we show empirically, their inclusion is not related to observed quality. Our results are robust if we focus more narrowly on the UC providers, which all joined HNBG. 9 Grennan (forthcoming) shows that bargaining power matters for how much different hospitals pay for the same product from the same supplier. For medical devices, variation in bargaining ability can explain 79% of the observed price variation and has a large firm-specific component. 9

11 The second piece of evidence for doctor substitutability comes from medical group ratings which are published by Health Net on its website. Each medical group is given a rating from one (lowest quality) to five stars (highest quality) in three broad categories: member satisfaction, clinical care and preventive health. We construct three measures of aggregate quality across these categories. The first two measures are dummies which equal one if the medical group has at least three or at least four stars in each category, respectively. The third measure adds up the number of stars across the three categories. We merge the medical group ratings to the primary care physician for each employee in our sample who was enrolled in Health Net in We then regress each of the three aggregate rating measures on an indicator for the doctor being in the HNBG network in 2011, controlling for fixed effects for the doctor s five-digit zip code. We cluster the standard errors at the medical group level because that is the level of variation in the ratings data. The results of these regressions are presented in Table 4. In each case, we find that the coefficient on the HNBG doctor dummy is not significantly different from zero, indicating that HNBG doctors do not have a systematically different rating than doctors who are only in the larger HN network. As a final piece of evidence that the HNBG network was high quality, consider the U.S. News and World Report rankings of top hospitals for Out of 440 hospitals in the state of California, the five UC campuses with medical centers rank #1 (UCLA), #2 (UCSF), #5 (UCSD), #9 (UCI) and #16 (UCD). Moreover, in the narrower metro area rankings, the UC hospitals are all the #1 hospitals in their respective geographic areas. 10 While an excellent medical center is no guarantee that affiliated UC doctors are also excellent, and while not all HNBG doctors have a UC affiliation, these rankings are certainly suggestive Inertia and Option Value To identify inertia, we take advantage of the non-switching rates of employees whose existing doctors were included in the smaller HNBG network. These employees could save a large sum of money, and still keep their current doctor, by switching from the HN to the HNBG network. 10 UCLA and UCI are both in the same metro area of Los Angeles; UCLA is #1 and UCI is #4 out of 145 hospitals in the LA metro area. UC Riverside established a new medical school in 2008, but only started enrolling its first class in We note that if the HNBG network is actually preferred to the HN network, then our estimates of doctor attachment are biased downward. 10

12 Since employees had to make an active choice to achieve this premium savings, we use the fraction of non-switchers to identify the amount of inertia. However, it is possible that these employees are paying attention, but do not want to switch to HNBG because of the option value of using the larger network of doctors available in HN. In our setting, this option value is likely to be close to zero for three reasons. First, there is a large set of doctors in HNBG (two-thirds of doctors in the broader HN network are also in the HNBG network), so employees can readily change PCPs or see a large set of specialists within the HNBG network. Second, employees can always switch insurance plans during open enrollment, so the option value is limited to desired changes within a calendar year. Finally, we rarely observe individuals exercising the option of switching within the HN network in our dataset, even though it is allowed. 12 For example, almost no employees with HN who had PCPs affiliated with UC medical centers also saw a physician from Scripps Health in the same year. This is partly because few doctors make referrals outside of their narrow provider network and partly because individuals seldom change PCPs outside of the open enrollment period. [add numbers here] While the option value of a larger network is likely to be small, if it is not zero, then what we call our estimate of inertia is an upper bound estimate of inertia. It is important to realize, however, that a non-zero option value only affects the interpretation of the inertia estimates. Estimates of willingness to pay to keep one's doctor are not affected, since any option value will be netted out with our approach. 3.4 Threshold Identification of Willingness to Pay Before proceeding, it is important to make clear what types of price changes we have in our setting, and what these price changes identify. As explained in Section 2.1, insurance premiums vary by four income tiers and by four family types. UC chose to structure the premium differences between HN and HNBG so that the gap in price only varied by family structure and not by income group. Hence, the premiums for HN increased by a larger percentage for employees in the lower-income tiers, with the implication that lower-income employees had to pay a larger fraction of their income in order to stay in HN. It also means that we do not have continuous variation in price differences, but only four price differences in levels by family type. These four 12 A fourth reason is that employees who place the highest value on being able to choose different doctors have already self-selected into a PPO, since PPOs impose the fewest restrictions on doctor choice. 11

13 price differences are perfectly collinear with family type, which is itself an important determinant of doctor attachment. To understand what can be identified in our setting, consider employees with incomes between $47,000 and $93,000 insuring a family (self plus spouse and children) with HN in 2010, whose doctors will not be included in the HNBG network after the change in In 2011, these employees have the option of paying $4,330 to stay in HN and keep their doctors or paying $2,479 by switching to HNBG and choosing new doctors. For simplicity, assume there is no inertia, although this could easily be added in. Since there is only one price difference ($1,851), one cannot estimate a price elasticity. Instead, what is identified without further assumptions is the fraction of employees who are willing to pay $1,851 or more to keep their current doctor. This is illustrated in Figure 1 [to be added]. Since different employees have different preferences over the ability to keep their doctor, there is a distribution in willingness to pay. Some employees would pay a lot while some would pay almost nothing. The shaded area in Figure 1 nonparametrically identifies the fraction of employees who are willing to pay more than a threshold amount to keep their existing doctor. However, the shape of the distribution is not nonparametrically identified. While we do not have many price changes, we do have substantial variation in income. This allows us to estimate the income elasticity of switching for a given price difference. Moreover, there is also variation in how large the price difference is as a fraction of income. If one is willing to parametrically assume unitary income elasticity of demand, the shape of the willingness-to-pay distribution can be estimated, at least over the range of the available data. The reason this works is that the demand for keeping one's doctor is proportional to income, which allows for estimation of the price elasticity. While this is a strong functional form assumption, we use it to provide suggestive evidence regarding the demand elasticity [to be added]. 4. Descriptive Evidence Table 5 shows the 2011 and 2012 health plan choices of employees in our estimation sample. All of these employees were enrolled in Health Net in We show the overall distribution of choices in the first column and then show these choices separately for employees 12

14 without and with existing doctors in the HNBG network (second column and third column, respectively). 13 We find that health plan choices differ strongly depending on whether the employee s (and her family members ) existing doctors were in the HNBG network. More than half of the employees whose doctors are not in the HNBG network stay in the Health Net plan in the first year, while less than a third switch to the cheaper HNBG insurance. There is some additional switching away from Health Net in the second year, but 45 percent of these employees are still in the substantially more expensive Health Net insurance in the second year. Employees who choose neither HN nor HNBG mostly switch to other HMO plans which will also force them to find a new doctor. Among employees who can switch to HNBG and keep their doctor, on the other hand, almost more than 70 percent do so in the first year and this share increases to more than 76 percent in the second year. Only 22 percent stay in Health Net in the first year and this share falls to about 15 percent in the second year. The gap in switching rates to HNBG between employees whose existing doctors are or are not in the HNBG network is 40.4 percentage points in the first year. It falls slightly to 38 percentage points in the second year. The main reason why an employee whose current doctor is in the HNBG network would not enroll in the HNBG insurance plan is inertia. Recall that employees had to make an active choice during the open enrollment period in order to enroll in the HNBG insurance. The default was to keep everyone in their existing plan, which would have been Health Net for the individuals we consider in Table 5. Individuals who either did not read the university s communications about the introduction of HNBG or who failed to act upon it by the open enrollment deadline would thus stay in the more expensive HN plan. It is also possible that some individuals were reluctant to switch from HN to HNBG because of the option value of accessing the doctors in the wider HN network. However, as we argue in Section 3.3, the option value is likely to be small. The data in Table 5 give us an unconditional estimate of how much inertia exists in our setting by showing how many employees could have saved a substantial amount of money on their health care premiums while keeping their doctor. In this first year, this applies to 22.4 percent of 13 Employees who have family members insured are sorted into the second column if the primary care physician of any family member is not in the HNBG network and are sorted into the third column only if all family members have primary care physicians who are in the HNBG network. 13

15 the sample (or 4117 employees) and in the second year it falls to 15.5 percent (or 2,845 employees). Of the 4,117 employees who did not switch to HNBG in the first year, 1,847 had no family members insured, 515 insured themselves and children, 581 insured themselves and a spouse, and 1,174 insured a spouse and children. In total, these 4,117 employees paid an additional $4.7 million in health care premiums during 2011 because they did not enroll in HNBG. Moreover, in 30 percent of these cases not a single covered family member visited a doctor in 2011 and 13 percent only had a single doctor visit for the whole family. Table 6 breaks out this unconditional measure of inertia by insured family members, gender and income. In addition, we also report the share of each group who are willing to change their doctor in order to save on health insurance premiums. A number of patterns emerge from this table. First, employees with children appear to be more attentive than those without children. Second, male employees appear to be less attentive than female employees. However, among those who are attentive, female employees are less often willing to switch to a new doctor in order to save on their health care premiums. Finally, inertia increases with income and so does the willingness to pay higher premiums in order to keep one s doctor. Thus, higher-income employees are more likely to stay with HN than to switch to HNBG. While these patterns are suggestive, they represent unconditional means. In the regression analysis that follows, we control for a number covariates including demographic and geographic controls. 5. Regression Analysis 5.1 Control Variables and Summary Statistics Table 7 presents summary statistics for the control variables that we include in our regression analysis. These values are for the year 2010 because we will test how the employee s 2010 characteristics affect the propensity to switch away from Health Net in 2011 and When we estimate the propensity to switch from Health Net to other insurance options, our main variable of interest is whether all of the family s doctors are included in the HNBG network. This is true for 71 percent of our sample. Another key variable of interest is the employee s income. The mean income in our sample is 64,100 per year, with a standard deviation of 28,800. Recall that we dropped incomes below 25,000 and above 200,000. About 16 percent of our observations are faculty and the other 84 percent are staff. 14

16 The mean age in our sample is 44 and 30 percent of employees are older than percent are male, 48 percent have a spouse insured and 49 have children insured. On average, there were 10 doctor visits per family in 2010, but 15 percent of families had no doctor visits at all. 31 percent of families had between one and five visits, 21 percent had between six and ten visits and 33 percent had more than ten visits. In order to get a measure of the accessibility of HNBG doctors, we compute the share of all Health Net doctors who are also in the HNBG network at the 5-digit zip code level. We merge this to the employee s home zip code. Of course, employees may choose doctors outside the 5- digit zip code in which they reside, so this is only a proxy for HNBG doctor availability. On average, 72 percent of the Health Net doctors in an employee s zip code are in the HNBG network, and the standard deviation is 32 percent. 5.2 Regression Results We estimate multinomial logit models of the insurance choices in 2011 and 2012 for employees who were enrolled in Health Net in We allow for four choices: (i) stay with Health Net, (ii) switch to HNBG, (iii) switch to another insurance that is a PPO, and (iv) switch to another HMO insurance. The level of observation is the employee and our regression sample is as described above. We test for Independence of Irrelevant Alternatives (IIA) using a Hausman test and find that IIA is not rejected. In Table 8, we report results for the employee s health insurance decision in 2011, the first year that HNBG became an option. We show results from three separate specifications. We report marginal effects for continuous variables. For dummy variables, we report the change in choice probability as the value of the dummy changes from zero to one. The base category in our multinomial logit is the choice to stay in Health Net in In the first specification (Columns 1-3), we only include two sets of explanatory variables: a dummy for whether the employee and all insured family members had a doctor in the HNBG network in 2010 and fixed effects for the employee s 3-digit zip code. These fixed effects control for geographic variation in average income and household composition, as well as availability and quality of doctors. We find that the marginal effect of having doctors in the HNBG network on the likelihood of choosing the HNBG insurance plan is (Column 1). This implies that a family whose existing doctors are all in the HNBG network is 35.4 percentage points more likely 15

17 to enroll in the cheaper HNBG insurance than a family who would have to switch their doctors in order to enroll in the HNBG insurance. The effect is a few percentage points smaller than the difference in switching rates that emerges from the descriptive data in Table 5. This suggests that some of the raw difference in switching rates is due to geographic variations which, in the regression, are controlled for by the zip code fixed effects. The results from our first regression specification also show that employees whose existing doctors are in the HNBG network are less likely to switch to other non-health Net insurance plans (Columns 2 and 3). The effects are estimated to be 1.5 percentage points and 4 percentage points, respectively. In our second specification (Columns 4-6), we add controls for income. Specifically, we compute the natural logarithm of the employee s salary, then demean this variable, and then interact it with a dummy for whether all family members have a HNBG doctor in We report this interaction such that there is one coefficient for the effect of logged income of employees with HNBG doctors and another coefficient for employees without HNBG doctors. Because we have demeaned logged income, we can interpret the coefficients on the dummy for having HNBG doctors as the effects for an employee with mean income. This makes it easier to compare these coefficients to the previous specification. In fact, we find almost no change in these coefficients after we add the controls for income. For employees with HNBG doctors, we find that logged income has no statistically significant effect on switching to the HNBG insurance plan (Column 4). Since the primary reason why this group would not switch to HNBG insurance is inertia, we interpret this as inertia not varying significantly with income at least in this specification without additional demographic controls. 14 For employees whose current doctors are not in the HNBG network, we find a statistically significant effect of This means that doubling an employee s salary would reduce the likelihood that this employee switches from Health Net to HNBG insurance by 8.4 percentage points. Given that, on average, only about 31 percent of these employees switch to HNBG insurance, this is a sizable effect. The difference between the point estimates on logged income for employees with and without HNBG doctors is If we assume that the coefficient on logged income for employees with HNBG doctors measures the effect of income on inertia and that the level of inertia is 14 Once we add demographic controls, the coefficient on this variable is significant at the 10 percent level (see Column 7). 16

18 orthogonal to whether or not one s existing doctor is in the HNBG network, then we can decompose the coefficient on logged income for employees without HNBG doctors into two components: The inertia effect (-0.011) and the willingness-to-pay for keeping the family s existing doctors (-0.073). Thus, we would infer that of the 8.4 percentage point reduction in switching to HNBG insurance as a result of doubling an employee s income, 7.3 percentage points are attributable to employees not wanting to change their doctors and the remaining 1.1 percentage points would be due to inertia. The other results for our second specification show how income affects switching to other insurance plans that are PPO s (Column 5) and HMO s (Column 6). We find that the effect of income on switching to PPO s is positive and statistically significant. We find the same point estimate of for employees with and without HNBG doctors. This implies that doubling an employee s income would increase switching to PPO plans by 1.7 percentage points. Since the overall rate of switching to PPO s is 1.9 percent, this suggests that income is a very important driver of the choice to enroll in a PPO. Given the relatively high premiums and co-pays associated with the PPO options, this is not surprising. The fact that the effect of income is the same for employees with and without HNBG doctors suggests that the decision to enroll in a PPO plan is not driven by the desire to keep one s primary care doctor but by the other aspects of PPO plans, such as an even wider network of doctors or the ability to see a specialist without being referred by a primary care physician. The results in Column 6 show that the propensity to enroll in other HMO s declines with income. This is not surprising since the other HMO s (Kaiser Permanente and Western Health Advantage) have lower premiums than HNBG and are thus attractive options for families who want to save on their health insurance. Again, the effects are large relative to the overall rate of switching to HMO s. The point estimate is larger in magnitude for employees whose existing doctors are in the HNBG network. This suggests that employees who could keep their existing doctors when enrolling in the HNBG plan are less likely to switch to the even cheaper HMO s as their income increases, compared to employees who would have to switch doctors to enroll in HNBG as well as another HMO. Our final specification in Table 8 adds our full set of covariates (Columns 7-9). Each variable is interacted with the dummy for whether all of the family s doctors are in the HNBG network, and we report separate coefficients for the group with and without HNBG doctors as in 17

19 the previous specification. Because we add a large number of interacted explanatory variables, the coefficient on the dummy for HNBG doctors is no longer directly comparable to the two previous specifications. With the additional covariates, the effect of logged income for families whose doctors are all in the HNBG network increases slightly in magnitude to , and the coefficient is now significant at the 10 percent level. This suggests that inertia increases with income, since the propensity to enroll in HNBG falls. For families without HNBG doctors, the effect of logged income falls in magnitude to The difference between the two coefficients is now Again assuming that conditional on covariates the level of inertia is the same for both groups of employees, we would infer that the willingness to pay higher premiums in order to keep all of the family s existing doctors increases with income. This effect would reduce the rate of switching to the HNBG insurance from employees without HNBG doctors by 6 percentage points as the employee s income is doubled. The next covariate in this specification is a dummy for whether the employee is at least 50 years old. We find that employees with HNBG doctors are more likely to switch to HNBG insurance if they are in this age category suggesting that inertia is lower for older employees. This may be due to the fact that they tend to have more interactions with the health care system and thus are better informed about their doctor and/or their health care costs. Older employees without HNBG doctors, however, are less likely to switch to HNBG insurance than younger employees. This implies that, while the older employees are more attentive and thus likely more aware of the HNBG option, they are less likely to be willing to change their doctor in exchange for a lower premium. This is therefore one of the groups that would suffer a greater welfare loss if they were forced into a smaller network that did not include their existing doctor. The magnitude of the coefficients implies that among older employees the propensity to enroll in the HNBG insurance is 7.3 percentage points higher if the family s existing doctors are in the HNBG network than when they are not. Thus, the effect of being in the older age group is larger than the effect of doubling the employee s income. For the two other categories ( other PPO and other HMO ), we find that older employees are less likely to enroll in either of these. The fact that older employees whose existing doctors are in the HNBG network are less likely to switch to PPO s than older employees who would have 18

20 to change doctors to enroll in HNBG is consistent with older employees being more attentive to the health care system. Next, we examine the effect of the employee s gender by adding a dummy for males, interacted with whether the family s doctors are in the HNBG network. We find that male employees with existing doctors in the HNBG network are 6.4 percentage points less likely to enroll in the HNBG insurance than female employees with the same observable characteristics. This suggests that male employees are substantially less attentive than female employees. The size of the effect lines up almost exactly with what we found in the descriptive evidence in Table 6. However, gender does not significantly affect HNBG enrollment among employees whose existing doctors are not in the HNBG network. If inertia is the same across employees with both types of doctors, then this implies that female employees are willing to pay higher premiums in order to keep their existing doctors. This again confirms what we found in our descriptive analysis in Table 6. We also find that males with HNBG doctors are less likely than females to switch to PPO s or other HMO s, but that gender has no effect on enrollment in PPO s or other HMO s for employees whose doctors are not in the HNBG network. Marital status has no significant effect on the decision to enroll in the HNBG insurance or in PPO s, regardless of doctor network. Married employees with and without HNBG doctors are more likely than unmarried employees to switch from Health Net to another HMO. Employees with children are more likely to switch to the HNBG insurance, less likely to switch to PPO s and more likely to switch to other HMO s, compared to employees without children. Next, we consider the role of past health status. We proxy for this with the total number of doctor visits which family members had in 2010, the year before the HNBG introduction. Because the distribution of doctor visits has a long tail, we group employees into four categories: (i) no doctor visits, (ii) 1-5 visits, (iii) 6-10 visits and (iv) more than ten visits. We find that, for employees whose existing doctors are in the HNBG network, those with any doctor visits are more likely to enroll in the HNBG insurance. This suggests that attention is higher for those who have interacted with the health care system in the previous year. The coefficients range from to but they are not significantly different from each other. For employees whose existing doctors are not in the HNBG network, we find that those with more than five and especially those with more than ten doctor visits in the previous year are less likely to choose the HNBG insurance. This means that sicker patients are much more willing 19

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