The Consequences of a Public Health Insurance Option: Evidence From Medicare Part D Prescription Drug Markets Daniel P. Miller Clemson University Jungwon Yeo Singapore Management University∗ November 2011
Abstract Policy makers in the US have debated the merits of including a government sponsored public health insurance option as a part of health care reform. Under the proposed legislation, the government would offer a basic coverage plan with premiums set at cost that competes against plans offered by private insurers. The government’s ability to negotiate favorable prices with health care providers and lack of profit taking could increase competition and perhaps crowd out a significant amount of private competition. We consider the existing Medicare Part D prescription drug market which resembles the new health insurance exchanges that will be coming online in 2014. We use data from the 2006-2009 Part D market to estimate a random coefficient discrete choice demand system and a supply side model for Part D plans. Using our estimates, we conduct policy counterfactuals that include a public option as proposed by legislators in the 2009 “Medicare Prescription Drug Coverage Improvement Act.” Our results indicate a small increase in competition if the government plan operates with a cost structure similar to private plans. But, if the government has up to a 25% cost advantage over private plans, perhaps achieved through a strong bargaining position with drug manufacturers, the government plan dominates the market and crowds out a significant fraction (25%) of private plans’ market share. There are consumer surplus gains; they increase in the size of the public option’s cost advantage. But, no matter the cost advantage, all total surplus gains are eliminated after factoring in the decline in insurer profits and the implicit subsidies in the Part D program. ∗
We thank Leemore Dafny, Amanda Kowalski, Greg Lewis, Claudio Lucarelli, Tom Mroz, Robert Town and seminar participants at Clemson, University of Georgia, and University of South Carolina for helpful comments. First Draft February 2011. Author correspondence: [email protected]
and [email protected]
In 2010 US legislators passed a major health insurance reform bill. A controversial provision that was ultimately struck from the bill is the “Community Health Insurance Option”1 , colloquially known as a “Public Option.”2 When fully enacted in 2014, the reformed health care system will continue to rely on private health insurance markets, but under the “Community Option” the government would have offered a zero-profit basic coverage health insurance plan that sells alongside private plans. The legislation stipulates that the government plan compete on a level playing field with private plans. It will not be subsidized, is subject to the same regulations as private plans, has no special mandates on coverage, and negotiates provider fees without statutory mandate. There are pros and cons of a public option. Supporters advocate the benefits of more choice and increased competition. They also hope the government will further drive down cost by leveraging a strong negotiating position over fees with providers (physicians, hospitals, drug manufacturers). Critics argue that the public option could significantly crowd out private plan enrollment without any competitive benefits. Moreover, they are concerned that greater negotiating power will distort providers’ supply-side incentives: for example, pharmaceutical manufacturers incentives to develop new drugs (Lakdawalla and Sood, 2009). Although the plan is explicitly budget neutral, there are concerns that the public option could have an budgetary impact through other subsidies and tax-provisions tied to the reform. To another set of critics, who believe that government provision of services is less cost-efficient than private provision, the government option would simply be a fringe plan that has little impact on competition. In this paper, we consider the existing Medicare Part D prescription drug insurance market—which closely resembles the reformed insurance markets—to quantitatively evaluate the competitive consequences of introducing a public option. As part of the 2003 “Medicare Modernization Act” (MMA), Medicare introduced “Part D,” that for the first time would cover prescription drugs. Unlike the original Medicare program, where the government is the sole insurer, Part D is a regulated insurance exchange. Senior citizens enroll in plans subsidized and regulated by the government, but sold by competing private insurers. The Part D market has never had a public option; we cannot conduct a retrospective 1
House of Representatives Bill H.R. 3590 Section 1323, struck from bill Sec 10104. The language in an earlier bill proposal (H.R. 3962) proposed by Nancy Pelosi, used the term “Public Health Insurance Option,” which was was changed to “Community Health Insurance Option” in the passed bill. The descriptions in the two bills are nearly verbatim identical. The key difference is that in the passed legislation states may opt-out of the community option whereas in the earlier bill states could not opt-out. 2
program evaluation. Instead, we estimate an equilibrium supply/demand model for insurance plans and then conduct a policy counterfactual that recomputes the market equilibrium with the inclusion of a public option. We assess many competitive outcomes such as the effect on enrollment for the government plan and private plans, monthly premiums, consumer welfare, industry profits, and subsidies linked to the Part D program. Two stylized facts about Part D markets fuel the debate over a public option. On one hand, individuals have lots of choices. The typical enrollee can choose from over 40 plans offered by about 20 insurers. On the other hand, it is a concentrated industry. Between 2006 and 2009, the Herfindahl-Hirschman concentration index (HHI) for the average market is 2376, in the range the Department of Justice labels “moderately concentrated,” just shy of the “highly concentrated” threshold of 2500. At the national level, the two largest insurers (United Healthcare, and Humana) together have a 50% market share. Taking these two facts together, it’s not immediately obvious whether a public option would have a minor or major impact on competition. We model plans as differentiated products. Per regulation, private insurers must offer at least one plan meeting a basic, minimum coverage standard, but they are also allowed to offer enhanced plans with more generous coverage, the so-called “Cadillac” plans. Coverage characteristics such as deductibles, drug copays, and drug formularies (the list of covered drugs) differentiate plans. We use a flexible random coefficients discrete choice model of plan demand. Our model captures heterogeneity in consumer’s preferences for plans, driven by factors such as enrollees’ health status and idiosyncratic differences in enrollees’ drug regimens. We model the supply of plans as a Bertrand multiproduct firm oligopoly model. We pay particular attention to the rules regarding premium subsidies. They distort the residual demand elasticities faced by insurers and hence effect insurer markups. Moreover, the subsidies are the channel by which the public option impacts the government budget. Using data from 2006 to 2009 on aggregate plan enrollment, pricing, and plan characteristics, we estimate the model using the method in Berry (1994) and Berry, Levinsohn, and Pakes (1995) (BLP). To evaluate the consequences of introducing a public option, we recompute the model equilibrium under the counterfactual market structure that includes a public option. As in Petrin (2002), BLP models are tailored for counterfactuals that introduce new products. A public option for the Part D market has been under consideration long before the 2010 health insurance reform debates. Every session of congress since the MMA legislation passed
in 2003 proposed a public option. The proposals detail exactly how the public option would be introduced.3 The government would only offer a basic plan—no enhanced plans. The plan competes on a level playing field as if it were just another private plan. Like private plans, the public option would construct a drug formulary, set copay and coinsurance rates, and negotiate discounts with drug manufacturers. The negotiating rules are fully flexible; there are no statutory pricing directives set in law such as reference pricing and the government (and manufacturers for that matter) may exclude drugs from the formulary.4 Unlike private plans, it foregoes profits by setting monthly premiums at cost. Thus, the public option is explicitly budget neutral. But, its introduction will have an indirect effect on the government budget through the existing Part D subsidy rules. We construct the public option’s plan characteristics to match the basic benefit structure, and, as our baseline, assume that it sells at the marginal cost equal to that of the average private plan. We explore the possibility that the government could offer a more or less desirable plan by varying the plan’s cost. For instance, the government plan could have a low cost if it has the ability to negotiate deep discounts with drug manufacturers. We consider a case where the government plan has a 25% cost advantage, comparable to the drug discounts negotiated by Canadian provinces and the Veterans Affairs program.5 We also consider the case that the government has a cost disadvantage, representing either poor drug price negotiations or inefficient management. Our results show that if the government plan operates with a cost similar to the average private plan it becomes an average plan. It ranks as a top 10 to 15 plan (out of about 40) with a 1.4% market share. It gains most of its share by crowding out private plans’ share. There is little effect on premiums; private plans lower them by just a couple cents. Nationwide consumer surplus increases by about $78 million, which is offset by a decrease in industry profits of $53 million. The competitive effects are more pronounced if the government has a 25% cost advantage. The government plan is number 1 with a 8.6% market share. Basic plans–the closest in product space to the government plan—respond with slightly lower premiums, yet lose 15% of their enrollees. Enhanced plans are affected modestly, losing 3
The 2009 “Medicare Prescription Drug Coverage Improvement Act.” is the most explicit. The proposed Pelosi bill (H.R. 3962) included reference pricing clauses, but the passed law H.R. 3590 does not. 5 Danzon and Furukawa (2008) constructs a price index comparing Canadian and American brand name drugs and finds 20% to 40% lower prices in Canada. The advocacy group, Families USA, compared Part D drug prices to the Veterans Affairs (VA) negotiated Federal Supply Schedule (FFS) prices for the top 20 drugs in 2007. They found the VA negotiates a median 58% lower price than the lowest price that a Part D plan negotiated. source: www.familiesusa.org/assets/pdfs/rhetoric-vs-reality.PDF (accessed 8/12/10). 4
about 4% of their enrollees. The different response by basic and enhanced plans is due to the estimated higher cross price elasticity amongst basic plans, than between basic and enhanced plans. Nationwide consumer surplus increases $565 million while industry profits fall $311 million. If the government has a 10% cost disadvantage, there is negligible competitive effect. Despite the government forgoing profits, this result follows because the market is already highly competitive and has low markups: estimated markups average just 7% to 9% over cost. If we factor in the subsidies in the Part D program, all total surplus gains are wiped out (regardless of the government plan’s cost advantage) because more enrollees are brought in to the highly subsidized Part D program from other options with lower subsidies. The remainder of the paper is organized as follows. In section 2 we relate our work to the existing literature. Section 3 provides background and institutional details of the Medicare part D market. Section 4 introduces the demand and supply model. Section 5 describes the data. Section 6 reports our supply and demand estimates. Section 7 conducts the counterfactual exercise. Section 8 concludes.
Contribution to Existing Literature
There is an emerging literature about the Medicare Part D prescription drug program. Much of this literature focuses on the behavioral economics of senior citizens choosing plans. In particular, the literature examines whether senior citizens rationally choose plans. Abaluck and Gruber (2009) use micro level consumer data on plan choices and drug expenditures and find that senior citizens don’t necessarily pick the plan that would minimize total out of pocket expenditures on premiums, deductibles, and copays. Enrollees tend to overvalue plan characteristics such as the monthly premium and deductible, when in fact there exists a plan with a higher deductible or premium that results in lower expected out of pocket expenditures, even after factoring risk consideration. In a series of work Heiss, McFadden, and Winter (2006, 2010), document cases of sub-optimal plan choice with regards to plan coverage and a penalty for late enrollment. They find people delay enrollment who, looking towards future years, would otherwise be better off enrolling despite a short term loss. Lucarelli, Prince, and Simon (2008) estimate a similar model to ours and run a counterfactual experiment that limits the number of plans insurers may offer. These papers suggest limiting plan choice could be welfare improving if enrollees are not fully rational or face high search costs. Our study differs in several dimensions. First, we consider an equilibrium model of the
market. Our focus is not only on consumer demand, but also the supply side of the market. We are able to analyze both sides of the market because we have enrollment data on the entire market, not just a selected sample of consumers. We also have the necessary data on the subsidy rules that allows us to correctly model and estimate the supply side. By analyzing all sides of the market, we can conduct a comprehensive welfare evaluation of consumers, insurers, and (as a subsidized market) the government’s budget. Second, we construct plan characteristics using highly detailed data on drug coverage for the the entire universe of prescription drugs for every plan. In short, we observe every financially relevant plan characteristic that consumers observe. Although the micro data available to Abaluck and Gruber (2009) allows them to estimate a richer demand system, we are nonetheless able to estimate a very flexible random coefficient demand system. It is robust to the possibility that enrollees overvalue certain characteristics thus ensuring we get unbiased estimates of demand elasticities and the supply-side. The US healthcare system has historically relied on the private provision of health insurance. But, when the government becomes a supplier, crowding out of private competition becomes a concern. Examples include Medicaid, (Cutler and Gruber, 1996), the Supplemental Children’s Health Insurance Program, (Lo Sasso and Buchmueller, 2004), and Medicaid’s long-term care insurance (Brown and Finkelstein, 2008). These papers use both program evaluation and calibration techniques to measure crowd out effects using data from after the enactment of the government program. In contrast, our structural approach has predictive power that allows us to make pre-enactment forecasts, and we can evaluate welfare effects, not just market share outcomes. This is also one of the first papers to address the competitive consequences of proposals in the recent health care legislation. Others contributions include Dafny, Ho, and Varela (2010) that quantifies the benefits of switching from an employer based health insurance system (with a limited choice of plans) to an individual health insurance exchange (with broader choice) and Avraham, Dafny, and Schanzenbach (2009) that evaluate the impact of tort reform on insurance premiums. Our work is also related to the IO health literature about competition in insurance markets. A recent example is the reduced form work in Dafny, Duggan, and Ramanarayanan (2009) that examines the effect of mergers on health insurance premiums. They find consolidation that results in much higher concentration only causes a modest increase in premiums. There are other papers using that use a structural approach including Dranove, Gron, and Mazzeo (2003); Ho (2009), who, respectively, estimate the competitive effects product differ-
entiation in insurance markets and the role of bargaining between insurers and health care providers. Our model is static on both the supply and demand side which brings up two qualifications. On the supply side, we do not model plan’s entry and exit to determine if a government option would cause plans to exit. We cannot identify this effect in a meaningful way because there is no entry or exit by major firms in the data. Entry and exit only occurs on the extreme competitive fringe, and we believe ignoring this churn will have negligible effect on our results. Second, our model does not account for dynamic features of the demand for health insurance plans. Carlin and Town (2009) document strong persistence in demand for health insurance across time. Miller and Yeo (2011) estimate a dynamic demand system for Part D plans with switching costs. Finally, our work relates to the adverse selection literature, in particular Lustig (2010). We do not have micro level data that would allow us to correct for adverse selection, but we sign the potential bias in our estimates following the intuition in Lustig (2010).
Medicare is the United State’s entitlement program that provides health insurance to all people over age 65 and to some categories of disabled people. It started in 1965 and is funded by a payroll tax. The original Medicare programs (Part A and Part B) cover hospital and doctor services, but lack coverage for prescription drugs. Under Part A and Part B, there is only 1 insurance plan that is provided by the government. The government negotiates fees with hospitals and doctors according to rules mandated in legislation. Medicare reforms have introduced a privatization of insurance. In 1997, Part C, currently called Medicare Advantage (MA), created a health insurance exchange that gave Medicare beneficiaries the option to purchase plans offered by competing private insurers. MA plans set coverage levels and freely negotiate fees with providers. Medicare regulates coverage standards and provides subsidies. Enrollment in a MA plan is voluntary and substitutes for Part A and Part B coverage. As part of the legislation in the 2003 Medicare Modernization Act (MMA), Medicare introduced Part D to offer prescription drug coverage. Like Part C, Medicare beneficiaries choose from a menu of plans sold on an insurance exchange by competing private insurers. Part D plans set coverage levels and freely negotiate drug prices with manufacturers. Medicare regulates coverage by setting a minimum standard, and subsidizes monthly premiums.
Insurers may offer enhanced coverage that exceeds the minimum standards, but the enhancements are not subsidized. Roughly speaking, the subsidy is 2/3 of the average premium set by the plans. In the model of the supply-side, we elaborate and formally model the subsidy rules. Medicare beneficiaries are penalized for not having drug coverage.6 They can obtain coverage from a Part D plan sold in the exchange, a group policy offered by a current employer or union, a group policy offered by a former employer or union as part of the Retiree Drug Subsidy Program (RDS), or another government program such as Veterans Affair insurance. The policies in the Part D market are sold as either stand-alone plans or bundled with a MA plan (MA+Part D). We only endogenize the market for stand-alone Part D plans, but we use enrollment data on MA+Part D plans and the RDS program to account for the subsidy rules in our supply-side estimates and counterfactuals. The duration of a Part D contract is one year, beginning January 1st. Once the contract begins, enrollees are not allowed to switch plans. In November and December there is an open enrollment period when enrollees are allowed to switch plans for the upcoming year. Plans cannot adjust premiums or make major changes to coverage characteristics throughout the year. Enrollees can only select plans offered in their geographic region which are drawn around state boundaries. Plans must charge a uniform premium to all enrollees in a region; they cannot engage in 3rd degree price discrimination based on health status or prior experience. Enrollees can access many channels to evaluate and select plans. Medicare publishes plan information online through its “Plan Finder” tool and in print forms distributed through the mail. Third parties, such as pharmacies and consumer advocacy groups offer assistance. Insurers are allowed to market their plans through many types of media. Enrollees sign-up by contacting Medicare or directly with the insurer. These are complicated financial contracts; these channels are intended to make the plan selection process as transparent as possible. Low income households are eligible for additional subsidies. All enrollees receive a premium subsidy, but Medicare pays all premiums and drug copays/coinsurance for households below 100% of the poverty level. The low income subsidy gradually phases out up until 150% of the poverty level. Low income subsidy enrollees may choose any plan, but they receive less than 100% premium and copay subsidies for enhanced plans and basic plans with premiums above a market determined threshold. The lowest income households that 6
For every month a beneficiary lacks coverage, premiums increase by 1% for the rest of the person’s life. For example, delaying enrollment by 2 years, results in a 24% premium penalty.
do not actively sign-up are randomly assigned by Medicare to a plan that qualifies for the full low income subsidy. A large fraction of the Medicare population, over 20%, receives a low income subsidy.
Plan’s are differentiated along several dimensions of coverage characteristics (deductibles, coinsurance/copay rates, drug formularies) and other non-pecuniary characteristics. The regulations set minimum standards for cost sharing rules. Under the minimum Part D benefit structure enrollees’ out of pocket expenses follow a 5 part tariff. Table 5 and figure 1 depict the benefit structure and the names of the 5 regions of the tariff schedule. Enrollees pay a premium regardless of their drug expenditures. For the first $250 of drug expenditures (deductible) they pay 100% of their expenditures, and then between $250 and $2250 they are in the initial coverage zone and pay 25% of their drug expenditures. Then, between $2250 and $5100 of expenditures they are in the donut hole and pay 100%. Beyond $5100, they are in the catastrophic range and pay 5%. These are the 2006 thresholds; to keep pace with drug price inflation and to keep the Part D program on budget, the thresholds have increased in later years. Plan’s classified as “basis” satisfy the minimum standard. They can use either coinsurance rates that cover a percentage of drug expenditures or copay rates denominated in fixed dollar amounts. For example, a plan may set a $30 copay for brand name drugs and $4 for generic drugs. Across all drugs on the formulary, copay rates must be approved by Medicare as being “Actuarially Equivalent” to a benefit structure using coinsurance rates. Plans with more generous coverage are classified as “Enhanced.” They typically offer more generous coverage by reducing the deductible and/or reducing out of pocket expenditures in the initial coverage and donut hole regions. The portion of premiums attributable to enhancements are not subsidized. On a drug-by-drug basis, plans have a lot of scope selecting drugs for their formulary and setting copay/coinsurance rates. The regulations require plans to cover at least two drugs from the major therapeutic classes. Within a class they can pick and choose which drugs to cover with a few exceptions for protected drugs, such as some cancer treatments, that must be included on the formulary. Plans can cover “elective” medications, such as prescription sleep medications, but that coverage is considered an enhancement and not subsidized. Enrollees receive coverage for any on-formulary drug, but there may be restrictions in the form
Figure 1: Part D Basic Benefit Structure
of quantity limits, prior authorizations, and step therapies.7 Off-formulary drugs are not covered; to purchase an off-formulary drug, an enrollee would have to pay full retail price or go through an appeals process with the plan. Plans do not have to set 25% cost sharing rates for every drug on the formulary. They set higher or lower copay/coinsurance rates by placing drugs on tiers. For example, the tiers might be ranked as preferred, non-preferred, specialty. Medicare must approve the tiers as being “actuarially equivalent.” Despite this requirement, we observe a lot variation at the aggregate level, particulary amongst the most popular drugs. Drug prices are determined through a bargaining process between insurers, manufacturers, wholesalers, and pharmacies. Per regulation, plans are required to pass along all discounts and rebates to enrollees. Medicare monitors every drug transaction to ensure compliance. Negotiated prices matter for enrollees. Even if plans have identical cost sharing rules, an enrollee would have a stronger preference for the plan with lower negotiated drug prices. While the regulations ensure copay/coinsurance rates remain fixed, negotiated drugs prices fluctuate throughout the year. As shown in Duggan and Scott-Morton (2010), restrictive formularies, tiered copay/coinsurance rates, and bargaining were important considerations in the design of the Medicare Part D market. Plans contract with pharmacies to construct a pharmacy network. Contracts are not exclusive; pharmacies can accept many plans, and plans can have several pharmacies. Plans designate network pharmacies as either preferred, non-preferred, or mail-order. Drug prices and copay/coinsurance tiers may differ across pharmacies, and are usually, but not always, lower at preferred and mail-order pharmacies. There are other important non-pecuniary plan characteristics. As in Starc (2010), marketing and advertising activities influences consumer’s choices. As an example, the market leader United Healthcare pays royalties to AARP to market and endorse their plans. Humana, the second largest Part D insurer, has a marketing arrangement with Walmart. While marketing increases an insurer’s visibility, it is also signals to enrollees that they are dealing with a sound and reputable company. Case in point, Medicare recently terminated the plans offered by a fringe insurer, Fox Insurance, citing several regulatory violations. Finally, service quality characteristics such as the accuracy and ease of understanding documentation, leniency in appeals procedures, and customer service also differentiate plans. 7
Prior authorization requires the plan’s approval before filling a prescription at the pharmacy. Step therapy routines requires enrollees to try another drug first, and if it is ineffective then the plan provides coverage.
We model the supply and demand system for plans using the discrete choice framework of Berry (1994); Berry et al. (1995). We separately introduce the demand and supply side.
Every year t, a consumer, indexed by i, can enroll in one prescription drug plan. Consumers choose amongst the j = 1, . . . , Jmt differentiated plans offered in market m in year t. Markets are geographically separated by Medicare regions drawn around state borders. Residency requirements and the annual enrollment period admit a very clean market definition; consumers cannot enroll in plans outside their region, nor switch plans within a year. They may also choose an outside option, j = 0. Following the convention in the demand estimation literature, we normalize the utility from the outside option to zero. The outside option includes going without drug coverage, enrolling in an MA+Part D plan, or getting it from another source, such as a current employer, another government program, or a Retiree Drug Subsidy program plan. Each year, enrollees pay a premium pjmt set by the plan. They derive utility from plan characteristics and income left over after paying the premium. Define the conditional indirect utility of person i choosing plan j in market m as: Ui (Xjmt , pjmt ) = −αi pjmt + X0jmt βi + ξjmt + ijmt
where Xjmt is a vector of observable plan characteristics, ξjmt represents an index of unobservable (to the econometrician) plan characteristics, and ijmt is a term capturing idiosyncratic differences in consumers’ preferences over plans. The terms αi , and βi are random coefficients that represent consumer i’s marginal utility over income and over product characteristics. The random coefficients are distributed iid normal across consumers and markets with mean α ¯ and β¯ and variance Σ. Consumers choose the plan yielding the highest conditional indirect utility in equation 1. After describing the supply-side model, we introduce our measures of plan characteristics in the data section and further discuss factors driving heterogeneity in preferences.
We model the supply side by closely following the regulations in the Medicare Modernization Act of 2003. A set of F multiproduct insurers compete in a Bertrand-Nash fashion. In year t, each plan j offered in market m submits a bid bjmt to Medicare. Insurers submit separate bids in each market, even if the plans offered in different markets are otherwise similar. For each enrollee, the plan receives a monthly payment equal to its bid. Part of that payment is made by enrollees in the form of the premium pjmt , and the remainder is subsidized by the government. We model plan’s marginal costs mcjmt of enrolling a customer as a constant. As multiproduct firms that can offer plans in many regions, profits for firm f are given by, X X Πf t = Mmt (bjmt − mcjmt )sjmt (2) mt
where Mmt is the number of potential enrollees in market m and Jf mt indexes the set of plans offered by firm f in market m. The first order conditions with respect to a bid bjmt are given by, sjmt +
(br − mcr )
∂sr =0 ∂bjmt
for all plans across all markets. Notice, we are explicit about summing across all markets; r 6= 0 if r is in a different market than because of the subsidy rule, the cross derivatives ∂b∂sjmt j. The system of first order conditions can be inverted to solve for marginal cost, mct = bt + ∆−1 st
where ∆m is a matrix of own and cross price share derivatives appropriately defined for a ∂s ∂s multiproduct firm. It has elements, ∂bjj for own share derivatives along the diagonals; ∂brj as off diagonal terms if the same firm offers plans r and j, otherwise zero. The boldface terms, mcmt and bmt , are vectors of marginal costs and bids. Under the assumption that the market is in equilibrium, the inversion allows us to estimate marginal cost without any data on cost. We only need data on bids, market shares, and demand elasticity estimates from the demand model. Because the demand model is expressed in terms of premiums and the supply model, in terms of bids, we need to account for the subsidy rules. The regulation sets the rule for determining the size of the subsidy. The government 13
subsidizes a fixed proportion, λt , of the enrollment weighted average bid of all plans in the country (λt ≈ 65%). The enrollee pays the balance as its premium. Thus, each enrollee gets the same subsidy amount regardless of plan choice. Enrollees realize savings from choosing cheaper than average plans, or pay extra to pick one that is more expensive than average. The determination of the weighted average bid is complicated by the distinction between basic and enhanced plans. Only the portion of the bid attributable to basic coverage is included in calculation of the weighted average bid. The portion attributed to enhanced coverage is not subsidized. A bid bjmt separates into a basic component bbasic jmt , and enhanced enhanced component bjmt , enhanced bjmt = bbasic jmt + bjmt Plans choose bids but do have discretion allocating between the basic and enhanced component. The allocation is based on an actuarial calculation that takes into consideration the plan’s coverage characteristics. We assume a fixed ratio γjmt between the two components. γjmt
benhanced jmt = bbasic jmt
For example, γjmt is zero for basic plans and is larger for an enhanced plan that eliminates the deductible and provides donut hole coverage than for an enhanced plan that only eliminates the deductible. The formula to map a bid bjmt to a premium pjmt is: pjmt = bjmt − λt¯bt
The weighted average bid ¯bt is based on bids of all stand-alone part D plans and select MA+part D plans in the entire nation. ¯bt =
s˜jt−1 bbasic jt
The weights s˜jt−1 are based on the previous year’s enrollment Ejt−1 , Ejt−1 s˜jt−1 = P jt Ejt−1 The weight is zero for plans that are new entrants to the market. Premiums are bounded below at zero, but it has never been a binding constraint. In the first year, 2006, the weights were equal for all plans. The shift from a simple average to the weighted average method
was gradually phased in through 2008.8
Supply-Side with Adverse Selection
Adverse selection may bias our marginal cost estimates. Marginal cost depends on the drug consumption (or risk profile) of a plan’s pool of enrollees; “sicker” consumers are more costly to enroll than “healthier” ones. Adverse selection occurs because a plan’s risk profile depends on its premium and coverage characteristics relative to competing plans. Adjusting bids shuffles the sorting of enrollee risk-profiles across plans. Our model’s constant marginal cost is implicitly invariant to the risk profile of the pool of enrollees and thus potentially misspecified. Medicare uses risk adjustment payments to combat adverse selection. They gather highly detailed enrollee demographic and health information from Medicare records9 to compensate plans that attract high risk pools. With perfect risk adjustments, our model of constant marginal cost is correctly specified; even with less than perfect risk adjustments, the bias is mitigated. See Fong and Schwarz (2009) and McAdams and Schwarz (2007) for further discussion. But, it has been documented that Medicare’s risk adjustments are not perfect (Lustig, 2010), and we believe adverse selection is a legitimate concern. Without individual level data we cannot account for adverse selection, and thus it is beyond the scope of this paper. Nonetheless, it is useful to understand how adverse selection would bias our marginal cost estimates. In the appendix, we expand our supply-side model and follow the intuition in Lustig (2010) to heuristically sign the bias for basic and enhanced plans. The bottom line is that our marginal cost estimates for basic plans may be overstated, and understated for enhanced plans.
Elasticities with Subsidy Distortion
To perform the inversion that solves for marginal cost, it is necessary to express demand elasticities in terms of bids, not premiums. For ease of notation, we use a non-random coefficient specification to illustrate how the subsidy rule distorts insurer’s residual demand 8
The “Medicare Demonstration to Limit Annual Changes in Part D Premiums Due to Beneficiary Choice of Low-Cost Plans” act, passed in mid-2006, amended the original legislation to phase-in the weighted average bid calculation method. 9 Healthcare Effectiveness Data and Information Set (HEDIS)
elasticities. The market share for plan j in region m in year t is given by: sjmt =
M Pjmt 1 + k Mkmt
where Mjmt = exp −α bjmt − λt
bkmt 1 + γjmt
+ X0jmt β + ξjmt .
We substituted bids in for premiums using the subsidy rule given in equation 5. There are three relevant price elasticities given in equation 7: own price, cross price with a plan offered in the same market m, and cross price with a plan offered in a different market m0 .10 ηjjmt
ηkjm0 t =
∂sjmt bjmt ∂bjmt sjmt ∂skmt bjmt ∂bjmt skmt ∂skmt bjm0 t ∂bjm0 t skmt
h i λt = −αbjmt (1 − sjmt ) − 1+γjmt s˜jmt−1 (1 − s0mt ) h i = −αbjmt −sjmt − 1+γλtjmt s˜jmt−1 (1 − s0mt ) h i λt 0 0 = −αbjm t − 1+γ 0 s˜jm t−1 (1 − s0mt )
The second term inside the brackets captures the distortion in residual demand elasticities cause by the subsidy. The distortion makes own-price elasticities more inelastic and cross price elasticities larger relative to a market with no subsidy. The intuition is that when plan j in market m increases it’s bid, the subsidy increases for all plans across the nation. With a larger subsidy, inside goods become more attractive relative to the outside option. Insurers internalize the subsidy distortion and will have higher markups, more so for large national insurers with high enrollments (hence high weights s˜jmt−1 ) that offer plans in many markets. Also notice the subsidy distortion would be more severe if the subsidy fraction λt were higher or if Medicare subsidized the enhanced component of bids (γjmt =0 for enhanced plans). In our results, we quantify the impact of the subsidy rule on markups by comparing our estimated markups to estimates from a model where insurers treat the subsidy amount as lump sum.
We collected publicly available data from the Center for Medicare and Medicaid Services (CMS) on plan level enrollment and bids for all stand alone part D plans offered since the the programs inception in 2006 through 2009. We also purchased detailed data on plan 10
Because the weights s˜jmt−1 are based on lagged enrollment, we could also calculate cross price elasticities across time. We don’t because our model is static.
characteristics from CMS. There are four files. The formulary file lists all drugs on a plan’s formulary, the beneficiary cost file describes cost sharing rules, the pharmacy network file lists all preferred and non-preferred pharmacies, and the pricing file reports average drug transaction prices for every drug and plan. The pricing file was first published in 2009, the other files are available in all years. Specifically, prices are the average transaction price net of all rebates for a 30 day supply filed at the plan’s preferred pharmacies in Q3 2009.11 They are used to calculate enrollee drug expenditures. It’s worth noting that enrollees may not know the exact drug price during the enrollment period because drug prices and rebates vary throughout the year, and prices reported by the ”Plan Finder” tool on Medicare’s website are not necessarily accurate.12
Enrollment and Premiums
Across the four years and 39 regions, 75 insurers offer stand alone Part D plans. The market penetration of insurers is quite skewed; 18 national firms offer plans in all states,13 while 44 regional insurers offer plans in just one market. Insurers offer an average of 2.5 plans in a market, with most offering 1, 2, or 3 plans.14 At least one must be a basic plan. Enrollees have lots of choices. A typical enrollee can choose from over 40 plans offered by about 20 insurers. Table 6 shows the average number of insurers and plan offerings in a market. The entries are broken down by year and plan segment: Enhanced and Basic. Despite the large number of insurers, the Part D market is highly concentrated. Table 7 reports various measures of firm concentration (1-firm concentration, c1, 2-firm c2, 4-firm c4, and Herfindahl-Hirschman index) averaged across markets. The top firm (United Healthcare for most markets) commands an average 36% market share, while the top 4 firms, 73%. The 11
Plan’s report all transactions, called Prescription Drug Events (PDE) to CMS. A PDE includes information on prices and all rebates/discounts with manufacturers, wholesalers, and pharmacies. Rebate information is proprietary, only the net price is available to researchers. Some pharmacies charge a dispensing fee, on the order of a couple dollars, that may or may not be included in the net drug price. Our data are based on PDE records. 12 Every two weeks plans are required to submit a separate pricing file to CMS that is used in the plan finder database. The database is not constructed from PDE records. If the plan never submits a price, the finder reports a price 30% below the average wholesale price for generic drugs, 10% below for brand name drugs. Even if a plans submits a price, it may not get updated every 2 weeks, so the prices can be outdated. Recently, Medicare began reporting survey results that ask enrollees to rate the accuracy of drug prices paid at the pharmacy compared to price reported on the plan finder. Many plans get very poor ratings. 13 excluding 5 markets for US territories with very small penetration 14 There are a few exceptions where insurers offer more than 3 plans, up to a maximum of 9. The most notable is in 2006 when United Healthcare, the market leader, offered 5 plans. Two of these plans were sponsored by Pacificare, which was acquired by United Healthcare. United Healthcare consolidated these 5 plans into 3 in 2007.
HHI averages 2376, which according to the Department of Justice guidelines falls into the “moderately concentrated” category.15 Overtime, the markets have become less concentrated but still fall into moderately concentrated levels. Table 8 reports national enrollment as a percentage of all eligible Medicare beneficiaries (≈ 42 million) and monthly premiums. The table divides shares into three categories: stand alone part D plans, bundled MA+Part D, and stand alone Medicare Advantage plans. Stand alone Part D enrollment has been stagnant since program inception in 2006, while monthly premiums have risen dramatically; average premiums rose about 30% between 2006 and 2009. Meanwhile, both stand alone MA and bundled MA+PartD, plans have experienced an increase in enrollment. Tables 9 reports enrollment and premium statistics separate by basic and enhanced plans. Basic plans attract about 3 times as many enrollees and charge 30% lower premiums as compared to enhanced plans. Table 10 reports more detailed summary statistics for both basic and enhanced plans at the market level. Note the large variation in premiums for both plan segments as well as the variation and skew in enrollment figures. The average basic plans enrolls 1% of Medicare beneficiaries, while the largest, upwards of 18%. The average enhanced plan has a smaller share, .4%, but the distribution also exhibits a large skew. and average preEvery year Medicare announces the official average bid amount ¯bbasic t basic basic ¯ ¯ − λt bt . These figures, along with the calculated subsidy fraction λt , are mium bt reported in table 11.16 Table 12 reports summary statistics on plan bids.
Our primary plan characteristic variables measure the generosity of plans coverage. Our first variable is the deductible. The second and third are intended to measure the generosity of coverage in the initial coverage and donut hole regions. The challenge is taking our rich drug-level data and converting it into a meaningful plan-level characteristic. We construct price indices for the top 100 most popular drugs ranked by prescriptions 15
A market with a HHI between 1800 and 2500 is considered moderately concentrated. The official average bid and average premium are reported by Medicare. We also observe the basic and enhanced component of the bid for all stand alone Part D plans. We do not have complete data on MA+Part D bids due to further complications in the rules for subsidizing MA+Part D plans. Instead, we use enrollment figures from MA+Part D plans and the subsidy formula (equation 6) to calculate an average basic bid for MA+Part D plans. Since MA+Part D plans are included in the outside option, the average bid is a sufficient statistic for us to properly calculate marginal cost and perform counterfactuals. Because of the phase-in of the weighted average bid method, we are missing data that would allow us to calculate marginal cost in 2006 through 2008. 16
filled.17 Our first price index reflects the out of pocket cost for an enrollee to fill a 30 day supply for a basket of the 100 drugs when they are in the initial coverage zone. Our second price index reflects out of pocket costs in the donut hole. The basket of drugs evenly weights each drug (1/100th). While there may be drug-by-drug idiosyncratic variation in a plan’s out of pocket prescription drug cost, this measure captures a plan’s average cost across drugs. Constructing out of pocket costs is straightforward for drugs covered by a copay. For drugs covered by coinsurance, it is necessary to know the price of the drug. We use the 2009 pricing file. For off-formulary drugs, enrollees do not receive coverage, therefore the out of pocket cost is the full retail price. We set the retail price to the average price in the region.18 We do not construct a price index for the catastrophic region because there is virtually no variation across plans.19 There are three sources of variation in the price indices: copay and coinsurance rates, negotiated drug prices, and formulary composition. Table 14 reports statistics on out of pocket price indices for the top 100 drugs and separate indices for brand and generic. Most of the variation in the donut hole is between enhanced plans that fill the donut hole and basic plans with no coverage. There is more variation in the initial coverage zone than in the donut hole. The source of this variation comes from differences in copay rates. Comparing brand and generic drugs, the variation is larger for brand name drugs. Its also interesting to note trends across time. Average donut hole prices remain steady, while out of pocket prices in the initial coverage zone fall across years. Figure 3 shows a histogram of negotiated prices for all drugs in 2009. To compare across drugs, we record prices as percent deviations from the drug’s average price. Notice there is a lot of price dispersion; it contributes to the variation in our price indices. To give a sense of magnitude, 10% of drugs are priced 25% below the average, and 10% are priced 15% above average. The dispersion is quite remarkable considering these are perfectly homogenous 17 CMS published a report ranking the top 100 drugs by number of prescriptions filled by Part D enrollees in 2006. Rankings by cost are quite different. For example, the generic drug FUROSEMIDE is number 1 by prescriptions filled and 98 by cost. 18 Since the base price includes rebates and discounts, we are probably understating retail prices by using negotiated prices. We use the average national price in rare cases where a region price does not exists. For the years 2006 to 2008 we construct the price indices in the same manner using 2009 prices. For plans that did not exists in 2009, we use average regional prices. Drug prices, coinsurance rates, and copays differ across preferred, non-preferred, and mail order pharmacies. All of our calculations are based on preferred pharmacies. 19 The Part D regulations do not allow plans to use a tiered copay/coinsurance structure in the catastrophic region. Out of pocket payments are capped at $5 per prescription or 5% of drug cost. There is little variation across plans. Moreover, few enrollees, only 8% in 2006 reached the final tier, and of that group, they are over-represented by the low income subsidy enrollees who pay zero in the catastrophic region.
products. We measure formulary comprehensiveness by counting the number of top 100 drugs included on a formulary. We also break this list down by brand and generic medication; there are 42 brand name medications and 58 generic. Table 13 reports statistics on formulary comprehensive. On average plans cover most of the drugs (more than 90%), but there is significant variation that appears to have grown over the years, indicating the plans are more differentiated now than in 2006. There is little difference between enhanced and basic plans. Insurers typically share formularies for their plans. Across all four years and regions there are 6679 plans and 400 formularies. Th only intended to illustrate a source of variation in the price index; we do not includes these drug counts as a separate plan characteristic. Across the universe of Part D drugs, over 5400, there is a lot of idiosyncratic variation in formularies. Figure 2 depicts a snapshot of formularies in 2009. Gaps in the formularies show that less comprehensive formularies are not strict subsets of more comprehensive formularies. With so many non-overlapping formularies, each enrollee is likely to find a plan tailored his individual drug regimen. This suggests enrollees sacrifice very little in terms of choice when plans use formulary restrictions as a bargaining chip with drug manufacturers. From our data on pharmacy networks, we construct a measure of network coverage by counting the number of in-region network pharmacies per eligible Medicare beneficiary in the region. We group preferred and non-preferred pharmacies because many plans do not make a distinction.
Basic vs Enhanced Plan Characteristics
Figures 4, 5, and 6 display side-by-side histograms of coverage characteristics for basic and enhanced plans in 2009. They illustrate the relative location in characteristic space for the two categories. The differences are relevant because they drive the substitution patterns in our counterfactual that introduces a basic government option. The plans are highly differentiated with respect to the monthly deductible. Most basic plans have the maximum deductible ($295 in 2009), while most enhanced plans have a $0 deductible. No systematic difference emerges in the histograms for the price index in the initial coverage zone, but there are differences in the donut hole price index. Almost no basic plans have a price index below 100, while about 50% of the enhanced plans fall below the $100 thresholds. The price index is lower for these enhanced plans because they provide some coverage in the donut hole.
Heterogeneity in Preferences: Random Coefficients
Our most flexible demand specification includes random coefficients on the monthly premium, deductible, and out of pocket price indices. As in a typical demand system, the random coefficient αi captures heterogeneity in consumers’ marginal utility over income, driven by differences in income and price sensitivity. Two other factors affect the distribution of αi : the low income subsidy and late enrollment penalty. The low income subsidy truncates αi . For example, it is zero for those receiving a 100% subsidy and half of what it would otherwise be for an individual receiving a 50% subsidy. For an enrollee subject to the late enrollment, penalty—1% per month not enrolled— αi increases in proportion to the duration of late enrollment. Combined, the low income subsidy and late enrollment penalty increase the variance on the distribution of αi . They may also skew the distribution, but we lack data on these populations to estimate higher order moments of the distribution. Heterogeneity in preferences for the deductible and out of pocket price indices are driven by heterogeneity in enrollee’s health status and risk aversion. All enrollees (weakly) prefer a lower deductible, but “Healthy” enrollees have a relatively low (in magnitude) marginal utility with respect to the deductible because their drug expenditures are unlikely to exceed the deductible. “Sick” enrollees have a higher marginal utility because they would expect to spend through the deductible with certainty. By the same reasoning, health status affects preferences over out of pocket prices in the initial coverage and donut hole regions. It’s worth noting, enrollees care not only about the cost sharing rates in the marginal tier of the tariff schedule they fall into (deductible, initial coverage, donut hole, or catastrophic), but also tiers they surpassed. Marginal utility only diminishes for the higher tiers that they are unlikely to enter. More risk averse enrollees place a higher preference on the deductible and price indices. Like the premium, the low income subsidy truncates the distributions towards zero. We use a parsimonious Normal distribution over the random coefficients with a block diagonal covariance matrix. However, this distribution may not be appropriate for three reasons. First, in a model of demand for drugs with a kinked tariff schedule, the distribution of drug expenditures will exhibit bunching and gaps around the kinks (Marsh, 2010). In our model of plan demand, this translate into gaps and mass points in the distribution of random coefficients. Second, we would expect correlation in the random coefficients across the tiers of the tariff schedule because they all relate to expenditures of money. For a risk neutral enrollee, a dollar spent on the premium is worth exactly the same as an (expected) dollar spent in the later tiers. This implies an enrollee’s marginal utility over characteristics 21
monotonically decreases moving down the tariff schedule. The premium should have the highest coefficient because it is paid with certainty, and the donut hole should have the lowest coefficient since it is not necessarily reached with certainty. Third, if consumers are risk averse, a risk premium is built into their preferences which further complicates the correlation structure of random coefficients. The distribution over the marginal tier that an enrollee enters matters for the risk premium. For example, a consumer that exceeds the deductible with certainty places a risk premium on the coefficient in later tiers and no risk premium on the deductible coefficient. For estimation, we experimented with specifications that accommodate these three qualifications (correlation in random coefficients, discrete type models (Berry and Jia, 2010), and mixtures models with Normal and discrete distributions) but could not obtain sensible results. In principle these richer models may be identified with aggregate data (?), but accessing consumer level data, as in Abaluck and Gruber (2009), would help with identification. We leave this as future work. Nonetheless, the focus of this paper is on the the supply-side for which the demand model is a means to obtain reasonable price elasticity estimates. With our more parsimonious random coefficient model, we estimate sensible elasticities and ameliorate the standard criticisms of non-random coefficient discrete choice models.
Heterogeneity in Preferences: Idiosyncratic Preferences
The idiosyncratic logit error terms, ij , reflect unobserved heterogeneity in preferences that are not otherwise captured by random coefficients. There are several reasons we believe they should enter our demand specification. Drug purchasing patterns are likely the primary source of idiosyncratic preferences. Enrollees have stronger preferences for plans that cover their specific drug regimen at low out of pocket prices. Thus, drug-by-drug idiosyncratic differences in formulary composition and copay/coinsurance rates generate idiosyncratic preferences. Figure 2 illustrates idiosyncratic differences in formulary composition; a similar visual representation of copay/coinsurance rates shows the same pattern. Marketing activities and pharmacy networks may also contribute to idiosyncratic preferences. Using the examples from before, AARP members might have stronger preferences for AARP endorsed plans, and Walmart customers might prefer plans that contract with Walmart. It is also worth noting low income subsidy enrollees who accept random assignment to plans. Their behavior can be rationalized in our model by attributing the random assignment to draws from the distribution of ij .
? and Berry and Pakes (2007) have critiqued this model because the dimensionality of ij increases as products are added. The added term ensures consumers benefit from the introduction of a public option, even if it has a high premium, undesirable average characteristics, and there is no competitive response by existing plans. We justify the extra ij term for the government plan by assuming its formulary and copay/coinsurance rates will exhibit the same sort of idiosyncratic differences found amongst the privately offered plans. These difference are a likely outcome because the legislation permits the government to bargain with drug manufacturers using restrictive formularies and tiered copays, just like private plans.
In this section, we report our demand and supply-side marginal cost estimates. Our most flexible demand specification includes random coefficients on the monthly premium, deductible, and out of pocket price indices. We also report estimates for non-random coefficients specifications estimated by OLS and IV, and a more parsimonious random coefficient specifications. We include formulary fixed effects. They control for unobserved differences in the mean quality of formularies that would otherwise be difficult to measure with a few observable coverage characteristics. Formulary fixed effects have two advantages. First, they capture the desirability of the entire formulary, not just the top 100 drugs that are included in the out of pocket price indices. Second, without fixed effects, the price index variables may be correlated with mean unobserved quality in ξ. Specifically, the price index could be high because the formulary composition includes many high-cost, high quality drugs. Formulary fixed effects purge this correlation. The remaining characteristics in ξ reflect marketing activities and service characteristics. We use the instruments proposed in BLP to instrument for the endogenous premium variable: the sum of the other exogenous observable product characteristics offered by rival firms and by the firm’s other plans. It is necessary to justify the validity of the instruments: that is, the exogeneity of the characteristics. The price index variables are exogenous for several reasons. First, the benefit design is regulated by Medicare, not chosen by plans. Second, the underlying drug prices are set by pharmaceutical manufacturers, not insurers. Part D enrollment may affect insurers’ bargaining power in drug price negotiations which brings into question the validity of the instruments. In defense, enrollment does not change
drastically across years, and bargaining power is only partially affected by the Part D market. Enrollment in other plan offerings outside Part D affect bargaining power. See Lakdawalla and Yin (2010) for more about the interaction of drug prices negotiations in Part D with non-part D markets. Pharmacy networks are typically set through long term contracts, often in the form of joint marketing agreements. It is more difficult to justify the exogeneity of the formulary composition. But in defense, year-by-year formulary changes are minimal, large adjustments are made infrequently in a lumpy manner, and the upcoming year’s formularies are likely constructed months before plans submit bids. With aggregate market share data, the random coefficients are identified off of variation in choice sets across markets. Unfortunately, there is not a lot of variation because most markets have about the same number of plans, and, with so many plans, the product space is fairly dense. We overcome this limitation by using a large number of markets pooled across the years 2006 through 2009. This may have also contributed to the difficulties we had estimating more flexible random coefficient specifications. Table 1 reports estimates across several specifications.20 In all specifications the signs on the coefficients are as expected. It is clear that formulary fixed effects as can be seen by comparing specifications. There are three points to notice for the random coefficient specification in the last column. First, the deductible captures the most heterogeneity in preferences as indicated by the large standard deviation estimate. As basic plans have the full deductible and most enhanced plans have zero deductible, the heterogeneity contributes to cross price elasticities that are higher amongst plans in the same category, than between plans of different categories. Second, there is very little heterogeneity in the sensitivity to the premium. The standard deviation is insignificantly different from zero. Third, we find moderately high heterogeneity in preferences for the price index in the initial coverage zone and little in the donut hole. But it is important to include random coefficients on both variables. A Wald joint hypothesis test that both are statistically zero is rejected at the 5% significance level (p-value=0.036). It is useful to examine demand elasticities under the preferred random coefficient specifi20
The estimation method uses simulated GMM. We use 70 simulated consumers drawn identically across markets. In the inner loop contraction mapping, we set a loose convergence tolerance when far from the optimal parameters in the other loop, and then increase the tolerance when near the optimal. This saves a significant amount of computational time. We experimented with tolerance levels until we were confident the contraction mapping convergence error did not propagate to the outer loop. We minimized the GMM objective function using a simplex minimization algorithm. The algorithm converges too soon for some starting values. We used 50 random starting values which converged to 6 different parameters. Of those, we selected the one yielding the lowest value of the objective function. See Nevo (2000a) for more on the estimation algorithm.
Table 1: Demand Estimates
OLS w. fe -.042 (.002)
IV w.o. fe -.049 (.005)
IV w. fe -.136 (.016)
RC prem deduct -.151 (.056) .010 (.056) -.240 (.121) .273 (.135) -.138 (.044)
1.65 (.09) 6679 — — Y
1.65 (0.11) 6679 — 731.07 N
2.14 (0.11) 6679 — 423.54 Y
3.37 (1.09) 6679 70 227.90 Y
Std Dev(premium) deductible/12 Std Dev(deductible) Initial coverage Price index Std Dev(index) Donut Hole Price index Std Dev(index) pharm per eligible (x1000) N obs N sims Gmm Obj Func formulary fixed effects
RC prem deduct &coverage donut hole -.162 (.058) .013 (.056) -.284 (.174) .355 (.215) -.156 (.056) .038 (.020) -.271 (.110) .015 (.037) 4.40 (1.45) 6679 70 217.20 Y
Standard errors in parentheses. We pool the years 2006 to 2009. The estimates on the 400 formulary fixed effects are suppressed. The IV and Random Coefficient specifications use BLP instruments. See text for further details on the estimation.
Table 2: Select Own and Cross Price Elasticities in South Carolina Firm Humana Humana Humana United United United
Plan Enhanced Enhanced Basic 3 Basic 4 Enhanced Enhanced
Deductible 1 2
0 0 295 295 0 0
1 -7.19 .346 .035 .027 .345 .337
Own and Cross Price Elasticities 2 3 4 5 6 .139 .034 .011 .111 .079 -14.78 .045 .014 .111 .100 .019 -6.18 .306 .009 .010 .021 .730 -4.40 .007 .007 .138 .027 .009 -6.53 .080 .171 .039 .012 .110 -11.08
This table reports select own and cross price elasticities for the largest insurers in the 2009 South Carolina Market. It illustrates that cross price elasticities are higher amongst plans of similar characteristics. In the logit model, the cross price elasticities are constant down the columns and do not depend on product characteristics.
cation in the last column of table 1. Table 2 reports a demand elasticity matrix with respect to premiums (not bids) for the two largest national insurers, Humana and United Healthcare, in the 2009 South Carolina market. This is a snapshot of the elasticities but is representative of all markets and plans. Each company offers two enhanced plans that have zero deductible and one basic plan that has the 2009 deductible of $295. For all plans, own price elasticities are negative—demand curves slope down. Cross price elasticities are more interesting. The red shaded entries are cross price elasticities for both insurers’ basic plans. They are much larger than the cross price elasticities with enhanced plans by a factor over 10 (compare up and down the columns). There is a similar difference in magnitude in enhanced-enhanced vs. enhanced-basic cross price elasticities. Cross price elasticities under the logit specification do not have a dependence on product characteristics: there are constant cross price elasticities down the columns. These elasticities drive the substitution and pricing responses when a basic government plan is introduced. We also experimented with specifications that separate the price indices by brand and generic. Non-random coefficient specifications yield results that are qualitatively similar to the more parsimonious specification. We prefer our random coefficient specification because there are fewer standard deviation terms to estimate.
After obtaining demand estimates, we separately estimate marginal cost using the inversion given in equation 4. Our full results account for the effect of bids on the national average bid, which in turn affects subsidies. We use these estimates for the counterfactuals. We also estimate costs under the assumption that plans take the subsidy as a lump sum. Table 3 reports enrollment weighted averages for marginal cost and markups, measured 26
Table 3: Marginal Cost and Markup Estimates Avg MC ($) Avg Markup (%) Basic Enhanced Basic Enhanced Lump sum subsidy 2006 76.05 86.73 9.32 8.98 2007 69.46 85.12 10.08 9.09 2008 72.08 85.67 9.21 8.72 2009 77.67 91.23 8.72 8.10 Full Model 2009 77.26 91.90 9.20 7.44 This table reports enrollment weighted averages of our estimates for marginal cost and markups b−mc . The Full model mc estimates account for the subsidy rule, and the other estimates treat the subsidy as lump sum. We lack data to estimate the full model in earlier years. Note that adverse selection may bias these estimates; see discussion in model section.
. We report all four years for the case of a lump sum subsidy and only 2009 for the as b−mc b full model.21 Enhanced plans are more costly, by about $15. Despite, being a concentrated industry, markups are modest, on the order of 8 to 10 percent. Our estimates are in-line with Congressional Budget Offices estimates of insurance plan markups.22 Basic plans have higher markups than enhanced plans, in part due to the supplemental component of an enhanced plan’s bid not being subsidized. The 2009 estimates differ for the lump sum and full model, indicating the importance of accounting for the full subsidy rules. Markups and marginal costs are fairly stable across time. These estimates are potentially biased by adverse selection. In our model section, we heuristically derived the sign of the bias in the marginal cost estimates. That derivation suggests our marginal cost estimates are too low for basic plans and too high for enhanced plans. Because it is not entirely satisfying that enhanced plans have lower markups, the bias direction is actually reassuring. Under a 2006 amendment to the MMA legislation, the determination of the average bid ¯b was phased in from a simple average to a lagged enrollment weighted average in 2007 and 2008. 2009 was the first year that the bid was calculated based on lagged enrollment. We lack one critical piece of data that would allow us to account for the phase-in, and thus we cannot estimate the full model for the years 2006 to 2008. The average bid includes select MA+Part D plans. For these plans, we only have enrollment data, not bid data. We estimate the average bid for the select MA+Part D plans using the official average bid data enrollment data. This in turn, allows us to determine the contribution of MA+Part D plans to the weighted average. We need to account for MA+Part D plans to retrieve the correct residual demand elasticities for stand-alone Part D plans. 22 Source: CBO July 22, 2010 letter “Analysis of a Proposal to Offer a Public Plan Through the New Health Insurance Exchanges.” They estimate 5 to 7 percent markups for non-prescription drug health plans. 21
Counterfactuals: Introducing a Government Plan
In our counterfactual, the government introduces a basic plan that sells alongside the existing plans offered by private insurers. We conduct the counterfactual for 2009. We construct the plan characteristics to match those of basic plans. The deductible is set according to the basic plan guidelines: $295 in 2009. The other observable characteristics, initial coverage and donut hole price indices and pharmacy network size, are set equal to the national average of basic plans. The unobservable component of plan quality, ξ and marginal cost are also set equal to the average for basic plans. To justify an additional ijmt for the government plan, we assume there are idiosyncratic differences in characteristics discussed in the model section such as formulary composition, negotiated drug prices, and copay/coinsurance tiers. We vary the government plan’s marginal cost to explore the possibility that the government may have a strong bargaining position with drug manufacturers (low cost), or be inefficiently managed (high cost) Whereas private plans choose bids to maximize profits in equation 2, the government plan sets its bid at marginal cost. The government plan’s premium is subsidized according to the same rules as private plans. In the spirit of the legislation (competing on a level playing field), we assume the government plan gets a weight of zero in the calculation of ¯b, as is the case for all new entrants. To calculate the new equilibrium, we numerically solve for the bid vector that satisfies the system of first order conditions in equation 3. Table 4 summarize our key findings. The columns represent four different assumptions about the government’s cost. They range from a very low cost plan with marginal cost $20 lower than the average privately offered basic plan, to a high cost plan with cost $10 higher. Consider the effect on enrollment. If the government plan has average cost, it captures 1.41% of potential enrollees across the nation. In most markets it would be a top 10 to 15 plan (out of 40 to 50), on the edge of the competitive fringe. National enrollment in individual plans (private + government) increases minimally, 0.32 percentage points. The government gains most of its enrollment by crowding out private competition, it steals 1.09% of potential enrollees from private plans. To address critics of the public option, the government plan is by no means dominant, and the magnitude of the crowd out is small. A high cost government plan would place it as a fringe plan with 0.46% of potential enrollees. Other shifts in enrollment are negligible. If the government plan is low cost, with a $10 cost advantage, it is a top 5 plan in most markets, and national it captures 3.85% of potential enrollees. Enrollment in individual plans increases modestly, 0.64 percentage points. In this case, the government plan is a major player, but the crowding out of private plan competition remains modest. A very low cost government plan, with a $20 cost advantage, is the number 1 plan. Nationally 28
Table 4: Counterfactual Results: Introducing a Government Plan Govt plan’s cost relative to private plans Very Low Low Average High Govt plan MC difference from avg $-20 $-10 $0 $+10 Enrollment (as percentage of potential enrollees in nation) govt plan(%) 8.59 3.85 1.41 0.46 private plans w/ govt (%) 26.60 30.39 32.43 33.32 private plans w/out govt (%) 33.52 33.52 33.52 33.52 ∆ govt+private (%) 1.67 0.64 .32 .16 Basic vs Enhanced Bsc Enh Bsc Enh Bsc Enh Bsc Enh ∆ avg Monthly Bid ($)** -.186 -.016 -.160 -.011 -.076 -.004 -.023 -.001 ∆ Enrollment (%) -15.04 -4.45 -11.98 -1.69 -4.14 -0.64 -1.14 -0.239 Welfare Calculations (at national level, annual basis) CV ($ mil.) 565.7 221.2 78.4 28.0 ∆ Industry profit ($ mil.)* -310.9 -145.8 -53.1 -15.4 ∆ Total Surplus (CV+PS) ($ mil.) 254.8 75.4 25.3 12.6 ∆ monthly per enrollee govt subsidy ($) -.0922 -.0461 -.0208 -.0067 ∆ Total govt Subsidy ($ mil.) 258.5 105.4 47.2 24.8 ∆ Total Surplus (CV+PS+Subsidy) ($ mil.) -3.7 -30.0 -21.9 -12.2 * Enrollment weighted average ** $ 1.4 billion estimated industry profits without govt plan We base the counterfactual on the full model that accounts for the subsidy rules using the random coefficient specification in the last column of table 1.
it captures 8.59% of potential enrollees, which represents an inside market share of 24.4%. Most of the market share gains come from existing plans: total enrollment in individual plans increases 1.67 percentage points, while private plans lose about 7 percentage points of market share. At these levels, critics might find merit in theirs concerns about too big of a government plan and too much crowding out of private competition. If the government plan “plays by the rule”, in which we mean it bargains with drug manufacturers in the same fashion as private plans, it is unlikely the government plan could have a 20% cost advantage over its private rivals. Only about 1% of the private plans have a cost advantage of $20 over the average basic plan. But, if the government “forces” manufacturers to sell their drugs at artificially low prices—as some critics fear—such a cost advantage is entirely conceivable. A common pattern in all of the cases is that there is relatively little gain in overall enrollment in individual plans, but rather market share shifts from private plans to the government plan. The bottom of table 4 breaks down the private plans’ pricing response and enrollment changes for basic and enhanced plan. The primary effect on competition comes not in the form of lower prices, but rather crowding out market shares. On average, plans make minor adjustments to their bids. In the most extreme case of a very low cost government plan, 29
basic plans—the closest in product space to the government plan—reduce their monthly bids by an average of 19 cents. This is a small adjustment considering the average bid is $90. Enhanced plans adjust even less, just over a penny. Most of the adjustment is found in enrollment. Competing against an average cost government plan, basic plans lose 3.5% of their enrollees. Enhanced plans lose just .3%. Against a low and very low cost government plan, basic plans lose 12% and 15%. Enhanced plans lose much fewer, 1.7% and 4.5%. These effects illustrate the importance of modeling a differentiated products market. The market is effectively separated into one for basic plans and one for enhanced plans. Thus, introducing a government plan with a basic benefit structure will have little effect on the market for enhanced plans. Consider the effect on consumer surplus and industry profits. In the logit demand model, we measure consumer surplus using compensating variation: the dollar value of income consumers would have to be compensated to give up the option of a government plan.23 With an average cost government plan, consumer surplus increases by $78.4 million, while industry profits decrease by $53.1 million. Net, total surplus (CV+∆ profits, excluding subsidies) increases just $25.3 million. These effects are tiny. With 45 million eligible Medicare beneficiaries, per capita consumer surplus increases less than $2 a year. Profit losses are about 4% of the estimated $1.4 billion industry profits in 2009. The effects are almost nill with a high cost government plan. The welfare effects are substantial for a low and very low cost government plan. With a low cost plan, consumer surplus increases $221 million, industry profits fall by $146 million. That works out to an average of about $5 per consumer, and a profit decline of about 10%. Total surplus increases by just $75 million. The effects amplify with a very low cost government plan. Consumer surplus increases $565 million, about $12 per person, and industry profits fall $310 million, a 22% drop. This is a significant shift in surplus from industry to consumers, with an appreciable $255 million increase in total surplus. These gains should appeal to consumer advocates and those wanting to crack down on insurer profits. As a word of caution, if the government achieves its cost savings through artificially low drug price concessions, these gains could come at the expense of future drug R&D. Next, we consider government subsidies tied to the Part D program. Although the government plan is budget neutral (bid set at cost), its introduction impacts subsidies by lowering the average bid and through substitution patterns with the outside option. Lower bids by private plans decrease the per enrollee subsidy (1 − λ)¯b by $0.0208 per month with 23
See Nevo (2000b) for the formula and further explanation of calculation.
an average cost government plan and up to 0.0922$ with a very low cost government plan. This is a cost savings channel for the Part D program on the intensive margin. It applies to enrollees in both stand-alone Part D plans and MA+Part D plans. We also need to consider the extensive margin: substitution from the outside option to the inside goods. Three groups in the outside option are relevant: MA+Part D, Retiree Drug Subsidy (RDS) program, and other non-part D subsidized options (non-enrollees and those covered by plans outside the Part D legislation). The per person subsidy amounts differ for the three groups. MA+Part D enrollees receive the largest subsidy (equal to the part D subsidy of 64%); RDS enrollees much less (28%); non-subsidized receive zero. We calculate the subsidy effect using our estimated substitution patterns with the outside option and data on the number of people in each of the three outside options. But, due to some data limitations these calculations should be regarded as somewhat tentative.24 No matter the government’s cost advantage, Part D subsidies increase and completely eliminate all total surplus gains. The subsidy amount is $47.2 million higher with an average cost government plan, and $258.5 million higher with a very low cost government plan. Overall, total surplus (CV+∆profits+∆subsidy) decreases $21.9 million for average cost, and $3.7 million for very low cost government plans. Although these calculations are tentative and may not include all subsidies25 , the purpose of this exercise is to illustrate the importance of accounting for the the many, and somewhat convoluted, subsidies and taxes built into a government health insurance program. Examples in the 2010 health insurance reform include Medicaid, business tax credits, and the non-enrollment penalty in the individual mandate. 24 We are missing data but can make two assumptions that allow us to estimate the effect on subsidies. First, we do not have data on RDS program subsidies, but we can estimate subsidies from the legislative rules. RDS plans are only subsidized for the cost component of the plan that meets the guidelines for a basic Part D plan. The cost of enhanced coverage is not subsidized. We assume an average cost for an RDS plan equal to the the average part D basic bid, ¯b. The subsidy rate is 28% which can be compared to 64% for Part D plans. Second, we do not have market level data on the composition of the outside option for 2009, the year of our counterfactual, but we do for 2010 aggregated at the national level. The 2010 statistics are sufficient for the purposes of our welfare calculation. In the logit model, substitution from the individual components of the outside option to inside goods is proportional to the shares of the components in the outside share. We assume those shares are the same in 2010 and 2009 and are equal across markets. There were 9.93 million in MA+Part D, 6.36 million in RDS, and 7.79 million in non-subsidized. 25 For Part D it is also worth noting the low income subsidy. On the intensive margin, subsidies decrease because premiums fall, but on the extensive margin, subsidies increase because more low income subsidy eligible households switch to inside goods. For the RDS program, corporations receive tax credits if they offer plans. These tax credits were reduced in the 2010 health insurance reform legislation.
In this paper, we quantitatively evaluate the consequences of a public health insurance option. We consider the existing Medicare Part D prescription drug market, which, through its similarities to the health insurance exchanges coming online in 2014, will give a glimpse of the outcomes we could expect if a public option is or would be introduced. We estimate a flexible random coefficient discrete choice demand system for Part D plans and an oligopoly supply side that accounts for the subsidy rules in the Part D program. We find that plan characteristics separating basic plans from enhanced plans, such as deductibles and copay rates, are important demand determinants. In our counterfactual exercise, we introduce a basic government plan to match actual Congressional proposals for Part D. We vary its cost to explore the possibility that the government has a cost advantage over private plans. The results can be summarized as follows. If the government introduces just-another-plan (with average cost), the result is simply that it’s just-another-plan. Consumers and private insurers are hardly affected. If the government has a high cost of running a health plan this whole debate is much-ado-about nothing. It’s a fringe health insurance provider. The debate livens if the government gains a cost advantage, perhaps by leveraging its ability to negotiate with drug manufacturers. The government becomes the number 1 plan by crowding out private competition, while consumer surplus increases and private insurer profits fall. Factoring in the various Part D subsidies, they decrease slightly on the intensive margin, increase a lot on the extensive margin, and net wipe out all gains in total surplus regardless of the government’s cost position. We choose not take a stance on the public option: none of our scenarios represent Pareto improvements over another. But, we can use our results to claim that a public option is not a panacea for health insurance markets. Despite the prospects of heightened competition, the benefits to consumers are either minimal or come with the cost of increased implicit subsidies and extra budgetary pressures. This result highlights the importance of conducting a thorough investigation that accounts for the various subsidies entwined in government programs. There are several avenues for extending this work in the future. First, a similar analysis could be conducted when data becomes available on the health insurance exchanges. One feature that differs between drug insurance and health insurance markets for hospital and physician services is the scale of the market. The latter tend to be more localized than prescription drug markets that operate at the state or national level. The degree of competition may be less at a local level, and perhaps public options tailored to local markets could have 32
more of a competitive effect. It would also be interesting to more explicitly account for selection. While the public option may have similar observable characteristics to private plans, there may selection on unobservables that would draw the less healthy and costly enrollees into the government plan. Another issue is the supply side response. Our scenarios remain agnostic about the source of cost savings for the government plan, whether it is driven by market fundamentals in a bargaining framework, or a political economy story. Future work could aim to more fully understand the bargaining process.
References J. Abaluck and J. Gruber. Choice Inconsistencies Among the Elderly: Evidence from Plan Choice in the Medicare Part D Program. NBER Working Papers #14759, 2009. R. Avraham, L. Dafny, and M. Schanzenbach. The Impact of Tort Reform on Employersponsored Health Insurance Premiums. NBER Working Paper #15371, 2009. S. Berry. Estimating Discrete-choice Models of Product Differentiation. The RAND Journal of Economics, 25(2):242–262, 1994. S. Berry and P. Jia. Tracing the Woes: An Empirical Analysis of the Airline Industry. American Economic Journal: Microeconomics, 2(3):1–43, 2010. ISSN 1945-7669. S. Berry and A. Pakes. The Pure Characteristics Demand Model. International Economic Review, 48(4):1193–1225, 2007. S. Berry, J. Levinsohn, and A. Pakes. Automobile Prices in Market Equilibrium. Econometrica, 63(4):841–890, 1995. J. Brown and A. Finkelstein. The Interaction of Public and Private Insurance: Medicaid and the Long-term Care Insurance Market. The American Economic Review, 98(3):1083–1102, 2008. C. Carlin and R. Town. Adverse Selection, Welfare and the Optimal Pricing of EmployerSponsored Health Plans. University of Minnesota working paper, 2009. D. Cutler and J. Gruber. Does Public Insurance Crowd Out Private Insurance. The Quarterly Journal of Economics, 111(2):391–430, 1996.
L. Dafny, M. Duggan, and S. Ramanarayanan. Paying a Premium on Your Premium? Consolidation in the US Health Insurance Industry. NBER Working Paper #15434, 2009. L. Dafny, K. Ho, and M. Varela. Let them Have Choice: Gains from Shifting away from Employer-Sponsored Health Insurance and Toward an Individual Exchange. Working Paper Northwestern Kellogg, 2010. P. Danzon and M. Furukawa. International Prices and Availability of Pharmaceuticals in 2005. Health Affairs, 27(1):221, 2008. D. Dranove, A. Gron, and M. Mazzeo. Differentiation and Competition in HMO Markets. The Journal of Industrial Economics, 51(4):433–454, 2003. M. Duggan and F. Scott-Morton. The Effect of Medicare Part D on Pharmaceutical Prices and Utilization. The American Economic Review, 100(1):590–607, 2010. K. Fong and M. Schwarz. Towards an Efficient Mechanism for Prescription Drug Procurement. NBER Working Paper #14718, 2009. F. Heiss, D. McFadden, and J. Winter. Who failed to enroll in Medicare Part D, and why? Early results. Health Affairs, 25(5):w344, 2006. F. Heiss, D. McFadden, and J. Winter. Mind the gap! Consumer Perceptions and Choices of Medicare Part D Prescription Drug Plans. Research Findings in the Economics of Aging, pages 413–481, 2010. K. Ho. Insurer-provider Networks in the Medical Care Market. The American Economic Review, 99(1):393–430, 2009. D. Lakdawalla and N. Sood. Innovation and the Welfare Effects of Public Drug Insurance. Journal of Public Economics, 93(3-4):541–548, 2009. D. Lakdawalla and W. Yin. Insurers’ Negotiating Leverage and the External Effects of Medicare Part D. NBER Working Paper #16251, 2010. A. Lo Sasso and T. Buchmueller. The Effect of the State Children’s Health Insurance Program on Health Insurance Coverage. Journal of Health Economics, 23(5):1059–1082, 2004.
C. Lucarelli, J. Prince, and K. Simon. Measuring Welfare and the Effects of Regulation in a Government-Created Market: The Case of Medicare Part D Plans. NBER Working Paper #14296, 2008. J. Lustig. Measuring Welfare Losses from Adverse Selection and Imperfect Competition in Privatized Medicare. Working Paper Boston University, 2010. C. Marsh. Estimating Health Expenditure Elasticities Using Nonlinear Reimbursement. Working Paper University of Georgia, 2010. D. McAdams and M. Schwarz. Perverse Incentives in the Medicare Prescription Drug Benefit. Inquiry, 44(2), 2007. D. Miller and J. Yeo. A Dynamic Discrete Choice Model of Switching Costs in Medicare Part D. Working Paper Clemson University, Singapore Management University, 2011. A. Nevo. A Practitioners Guide to Estimation of Random-Coefficients Logit Models of Demand. Journal of Economics & Management Strategy, 9(4):513–548, 2000a. A. Nevo. Mergers with Differentiated Products: The Case of the Ready-to-eat Cereal Industry. The RAND Journal of Economics, 31(3):395–421, 2000b. A. Petrin. Quantifying the Benefits of New Products: The Case of the Minivan. The Journal of Political Economy, 110(4):705–729, 2002. A. Starc. Insurer Pricing and Consumer Welfare: Evidence from Medigap. Working Paper Harvard University, 2010.
Supply-side with Adverse Selection
The expanded model allows marginal cost to depend on the bids of all plans offered in a market, mcjmt (bmt ). Modifying the first order conditions in equation 3, we get X X ∂mcr ∂sr 1− + (br − mcr ) =0 sjmt ∂b ∂b jmt jmt r∈J r∈J
∂mcr where the difference now is that we include the term ∂b that endogenizes the impact of jmt bidding decisions on marginal cost. Inverting the system of first order conditions marketby-market yields,26
−1 M C mcmt = bmt + ∆−1 mt smt + ∆mt ∆mt smt
where ∆mt is the block of the matrix of own and cross price share derivatives defined in C equation 4 for market m in year t and ∆M mt is a matrix of partial derivatives of marginal cost with respect to bids. In equation 4 the matrix of marginal cost derivatives is the zero matrix. That is, bids don’t affect marginal cost. C To derive the bias, we follow the intuition in Lustig (2010) to illustrate how ∆M mt depends on bids. Consider as a baseline a market with one basic plan and one enhanced plan. Enrollees who are costly to insure are also those with a high marginal utility for insurance coverage. Given their preferences, they select into enhanced plans, while the less costly enrollees select into basic plans. Selection is thus adverse. Consider the effects of the enhanced plan increasing its bid. The plan’s marginal consumers that would switch to the basic plan are those that are the least costly to insure. Only the highest cost enrollees remain, resulting in an increase in marginal cost. With this substitution pattern, the basic plan’s risk profile and marginal cost also increases in response to the enhanced plan’s bid increase. Next consider the effects of the basic plan increasing its bid. The marginal consumers that would switch to the enhanced plan are the most costly to insure. The selection effect decreases the risk profile and marginal cost for both the enhanced and basic plan. Next, consider a market with many plans in the basic and enhanced categories and an outside option. Most substitution occurs amongst plans in the same category, not across categories. If there is heterogeneity in generosity amongst plans within a category, the 26
To account for adverse selection bias we do not need to consider the cross market effects caused by the subsidy, and for the purposes of illustration assume the cross-market cross price elasticities are zero.
selection effects are driven by a product’s position in relation to its closest substitutes. But, for the most part, the generosity of plans within a category are homogenous. Thus, substitution within a category does not alter risk profiles. The biggest generosity differences are found across categories. For example, almost all basic plans have a $250 deductible, and almost all enhanced plans have a $0 deductible. Finally, consider substitution towards nonenrollment. Presumably, the absolute lowest cost individuals are on the margin of selecting non-enrollment versus basic plans. A bid increase by a basic plan, not only causes some of its highest cost enrollees to switch to an enhanced plan, but also causes some if its lowest cost enrollees to switch to non-enrollment. The net selection effect dampens for basic plans. C To summarize, matrix 10 presents our predictions about ∆M mt in the presence of adverse selection. ∂mcbasic ∂mcenhanced ∂mcenhanced j j j < 0 < 0 = 0 basic ∂bbasic ∂benhanced k k ∂bj basic ∂mcenhanced ∂mcj j (10) ∂benhanced > 0 ∂benhanced > 0 j k basic ∂mcj =0 ∂bbasic k
The diagonals entries show the derivative of marginal cost with respect to plan j’s own bid, and, on the off-diagonals, with respect to the bid of another plan k where plans are either categorized as basic or enhanced. Applying the predictions to the inverted FOCs in equations 4 and 9 allows us to sign the bias in our marginal cost estimates. We illustrate for the typical insurer that offers 1 basic plan and 2 enhanced plans. Recall the regulations require all insurers to offer at least 1 basic plan, and most offer at least 1 and sometimes 2 enhanced plans. Basic ! basic basic ∂mc ∂mc ∂s ∂s ∂sj ∂mcbasic j j j j j + + >0 bias = mcj − mc ˆ j = sj enhanced enhanced ∂bj ∂bbasic ∂b ∂b ∂b ∂b k l j k l Enhanced bias = mcj − mc ˆ j = sj
∂sj ∂mcenhanced ∂sj ∂mcenhanced ∂sj ∂mcenhanced j j j + + basic enhanced ∂bj ∂benhanced ∂b ∂b ∂b ∂b k l j k l
! 5100 The marginal cost of drugs represents the marginal out of pocket payment an enrollees pays for each dollar of drug expenditures. These are the 2006 thresholds. They increase each year to keep the program on budget.
Table 6: Number of Insurers and Plan Offerings per Market Year 2006 2007 2008 2009 All
All Plans 37 46 48 43 43
Basic 21 23 24 19 22
Enhanced 21 23 24 19 22
Insurers 14 20 18 18 17
Number of plan and insurer offerings averaged across markets. There are a total of 39 markets.
Table 7: Firm Concentration Measures by Market year 2006 2007 2008 2009 All
c1 37.5 37.7 34.0 34.6 36.0
c2 58.2 57.6 52.0 50.1 54.7
c4 75.7 75.3 70.0 70.2 72.9
Hirf 2587 2558 2154 2196 2376
Firm concentration measures averaged across markets. c1, c2, c4 represent the inside market share of the top 1,2 and 4 insurers in a market. According to DOJ guidelines, a HHI index between 1800-2500 is “moderately concentrated”. Above “2500” highly concentrated.
Table 8: National Enrollment and Premiums Year 2006 2007 2008 2009
National Enrollment (%) Part D MA+Part D MA 46.3 19.4 2.5 43.7 19.8 3.4 41.6 22.7 4.1 46.1 28.2 4.3
Part D Avg. Monthly Premium ($) enrollment weighted Unweighted 26.67 37.36 27.40 36.68 29.95 39.86 34.71 45.27
The enrollment percentage is the ratio of enrollment counts to the total number of eligible Medicare Beneficiaries in the nation (growing from about 40 million in 2006 to 45 million in 2009). Part D and MA+Part D are mutually exclusive groups, but MA enrollees may also enroll in a stand-alone Part D plan. The monthly premium figures are reported as enrollment weighted and nonweighted averages.
Table 9: National Enrollment and Premiums: Basic vs Enhanced Plans Part D National Enrollment (%) Year 2006 2007 2008 2009
Enhanced 9.9 9.0 9.4 11.9
Basic 36.4 34.8 32.2 34.1
Part D Avg. Monthly Premium ($) Basic Enhanced Enrollment Enrollment weighted Unweighted weighted Unweighted 34.98 41.62 24.40 34.27 40.37 45.51 24.05 28.69 40.93 49.49 26.77 30.01 45.86 55.72 30.82 33.51
The enrollment percentage is the ratio of enrollment counts to the total number of eligible Medicare Beneficiaries. The monthly premium figures are reported as enrollment weighted and nonweighted averages.
Table 10: Enrollment and Premium Summary Statistics
2006 2007 2008 2009
mean 33.9 27.6 29.4 33.7
2006 2007 2008 2009
mean 1.3 1.1 1.1 1.1
Monthly Premiums ($) Basic Plans Enhanced Plans s.d. min max obs mean s.d. min max 11.9 1.9 70.8 825 41.6 12.3 4.9 99.9 6.4 1.9 49.0 914 45.4 16.4 17.1 135.7 9.4 2.6 72.0 897 49.5 22.2 12.9 107.5 10.0 1.0 112.7 734 55.6 22.0 10.3 136.8 Region Level Market Shares (%) Basic Plans Enhanced Plans s.d. min max mean s.d. min max 2.2 0 18.0 0.5 0.9 0 10.2 2.1 0 18.1 0.3 0.7 0 6.6 1.9 0 18.5 0.3 0.9 0 10.8 1.9 0 17.1 0.4 0.7 0 8.6
obs 606 890 929 884
The top panel reports summary statistics on monthly premiums for basic and enhanced plans. The bottom panel reports market share statistics, expressed as a ratio of the total number of eligible Medicare Beneficiaries in the plan’s region. These are not inside market shares.
Table 11: Official Average Bid and Premium year 2006 2007 2008 2009
avg premium 32.20 27.35 27.93 30.36
avg bid 92.30 80.43 80.52 84.33
λ 0.651 0.660 0.653 0.640
The average bid and avg premium values are collected from official Medicare data releases. They are not calculated from our bid and enrollment data. For 2009 Medicare used equation 6 to calculate the average bid. It is based on the basic component of bids and includes Part D and select MA+Part D plans. We cannot replicate Medicare’s calculation because we lack data on the basic component of bids for MA+Part D plans. In 2006, the bids of stand-alone Part D plans and new MA+Part D plans were evenly weighted and were based on lagged enrollment for MA plans that existed in 2005 and became MA+Part D plans. The shift to lagged enrollment weighting was phased in for 2007 and 2008. We calculate the subsidy ratio as λ = avgbid−avgpremium . avgbid
Table 12: Bid Summary Statistics
2006 2007 2008 2009
2006 2007 2008 2009
2006 2007 2008 2009
2006 2007 2008 2009
Monthly Bid mean s.d. min max 97.3 12.6 62.0 160.0 89.5 15.3 55.0 188.8 92.2 19.9 55.2 160.1 99.6 20.7 55.0 190.8 Monthly Basic Bid Component mean s.d. min max 92.5 11.2 62.0 127.3 81.5 7.5 55.0 111.4 83.9 12.5 55.1 133.8 90.7 15.0 55.0 166.7 Monthly Enhanced Bid Component mean s.d. min max 4.8 9.1 0 55.3 8.0 11.8 0 96.4 8.3 11.8 0 55.0 8.9 10.7 0 55.8 Enhanced/Basic Bid Ratio mean s.d. min max .056 .118 0 .716 .097 .140 0 1.32 .094 .134 0 .670 .096 .117 0 .764
This table reports summary statistics on bids b, the basic component of bids bbasic , and the enhanced component of bids benhanced , where b = bbasic + benhanced . The bottom panel reports the ratio of the enhanced component to the basic component, corresponding the parameter γ in our model. This table only includes the bids of stand-alone Part D plans.
Table 13: Formulary Comprehensiveness
2006 2007 2008 2009
num plans 1446 1909 1877 1650
top100 avg s.d. 90.5 5.9 92.1 6.0 89.2 7.5 86.8 9.1
Brand(top42) avg s.d. 38.7 3.9 39.0 3.4 37.6 4.1 35.6 5.5
Generic(top58) avg s.d. 51.5 3.2 53.2 3.1 51.6 3.9 51.2 4.2
This table reports statistics on the number of top 100 drugs on a formulary. The top 100 drugs are ranked by prescriptions filled. The table also breaks the statistics down for the 42 brand name drugs and 58 generic drugs composed in the top 100.
Figure 2: 2009: Formulary Snapshot The figure depicts the drugs covered on a formulary for a 1/15 sample of the 5400 drugs and a 1/3 sample of the 315 insurers offering plan’s in 2009. This includes both Part D and MA+Part D insurers. Whitespace indicates off-formulary drugs. Notice the idiosyncratic differences in formularies; that is, less comprehensive formularies are not strict subsets of more comprehensive formularies. Generic drugs, with multiple manufacturers, are counted once. Different packages sizes of the same drug appear as distinct drugs.
Figure 3: 2009 Drug Price Dispersion, Deviation from Avg Negotiated Drug Price This histogram displays the price dispersion in negotiated drug prices. The x-axis is the percent difference in price pjd for a drug d offered by plan j from the average price for that same drug p¯d p −p ¯ ( djp¯ d ). There are 16,781,151 drug-plan observations, 5330 drugs d defined by a unique NDC (national drug code), and 4228 plans (including MA+Part D plans).
Table 14: Out of Pocket Drug Price Indices
2006 2007 2008 2009 2010
num plans 1446 1909 1877 1650 -
2006 2007 2008 2009 2010
num plans 1446 1909 1877 1650 -
Initial Coverage Zone Brand(top42) Generic(top58) avg s.d. avg s.d. 70.6 22.1 52.9 10.7 48.7 15.7 56.7 18.0 56.2 16.3 60.4 16.0 71.9 18.4 50.5 9.4 Donut Hole top100 Brand(top42) Generic(top58) avg s.d. avg s.d. avg s.d. 101.2 5.9 185.3 17.4 68.6 10.7 99.8 6.0 188.1 16.5 86.9 10.2 100.0 5.2 167.6 44.3 84.0 16.3 99.9 5.8 160.4 39.8 64.1 23.9 top100 avg s.d. 67.9 9.8 62.7 9.9 58.6 10.9 55.6 11.0 -
top100 min max 41.0 90.7 44.1 83.5 29.0 78.7 24.0 73.0 top100 min max 54.9 107.6 53.0 113.1 78.9 113.1 82.0 113.1 -
The top panel reports summarary statistics for the out of pocket drug price indices for top 100 drugs in the initial coverage zone. The bottom panel reports for the donut hole. The price is what a consumer pays out of pocket for a 30 day supply. For on-formulary drugs, we first locate the copay or coinsurance rate corresponding to that drug from the beneficiary cost file. The out of pocket price is either the copay or coinsurance rate times the negotiated drug price found in the 2009 Q3 pricing file. For 2006-2008 we match to the plan’s negotiated price in 2009. If a plan did not exist in 2009, we use the average negotiated price in the region, or if there aren’t enough observations, the national average price. For off-formulary drugs, the consumer pays full retail price which we set equal to the regional or national average negotiated price in 2009. All drugs are evenly weighted in the index.
Table 15: Network Pharmacies
2006 2007 2008 2009
# Network Pharmacies per Eligible Beneficiary mean s.d. min max .00141 .00024 0 .00376 .00137 .00021 0 .00193 .00135 .00020 0 .00189 .00136 .00018 0 .00186
Summary statistics about the number of network pharmacies per eligible Medicare Beneficiary. We include both preferred and non-preferred pharmacies because many plans don’t make a distinction. These are brick-and-mortar pharmacies located in the region. We exclude out of region network pharmacies and mail order pharmacies.
Figure 4: 2009: Monthly Deductible Histogram This histogram compares the deductible of basic and enhanced plans. Notice most basic plans have a $295 deductible and most enhanced plans have a $0 deductible.
Figure 5: 2009: Initial Coverage Price Index Histogram This histogram compares the initial coverage zone out of pocket price index of basic and enhanced plans. Notice there is no discernible difference between basic and enhanced plans.
Figure 6: 2009: Donut Hole Price Index Histogram This histogram compares the donut hole out of pocket price index of basic and enhanced plans. Notice very few basic plans have an index below $100, while many enhanced plans are below $100. These enhanced plans provide some coverage in the donut hole.