Evidence from a Payment Update in Medicare Part D∗
Colleen Carey Robert Wood Johnson Foundation Scholar in Health Policy Research
University of Michigan 1415 Washington Heights Ann Arbor, MI 48109-2029
(734)763-0410 careycm@umich.edu
Abstract
This paper explores a revision of the system of diagnosis-specific payments that aimed to neutralize
insurer benefit design incentives in a publicly-subsidized program of private prescription drug insurance,
Medicare Part D. In Medicare Part D, insurers are paid on the basis of enrollee diagnoses; in principle, in-
surers are indifferent between individuals with different diagnoses due to this system of diagnosis-specific
payments. Between 2010 and 2011, the diagnosis-specific payment system was reorganized and recali-
brated, changing an insurer’s incentive to enroll an individual with a particular diagnosis. This research
demonstrates that, consistent with prior theory on how insurers design benefits in environments like Part
D, insurers increased coverage and reduced copays for drugs that treat diagnoses that received positive
payment updates. We then use the payment system revision as an instrumental variable for benefits in
a demand estimation that recovers an overall drug demand elasticity.
JEL codes: I11 (Analysis of Health Care Markets); I18 (Government Policy, Regulation, Public Health);
L51 (Economics of Regulation)
∗This research received support from the Robert Wood Johnson Foundation Scholars in Health Policy Research Program. This research was approved by the Institutional Review Board of the University of Michigan.
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1 Introduction
Public payments to private health insurers are a major market design element in the managed-competition
model of public health insurance. These payments – “risk adjustment” – aim to make insurers indifferent
between enrolling individuals of varying ex ante health status by compensating them for each individual’s
expected cost, thus ensuring an equitable benefit. However, there are significant practical challenges to
designing and calibrating these payment systems, meaning that in practice profit-maximizing insurers seek
to enroll individuals made profitable by inaccurate payments and to deter those whose payments are below
expected costs. In this paper, we explore the response of insurers and enrollees to a revision of the payment
system that changed insurers’ preferred enrollees. The response of insurers identifies the “pass-through”
of public payments to enrollees, while the utilization response of enrollees identifies a demand elasticity.
Together, these parameters serve as indicators of the level of competition in Medicare Part D, an important
example of the managed-competition model.
Medicare Part D uses public payments to implement a prescription drug insurance benefit for twenty
million elderly and disabled. The majority of Federal payments to insurers in Part D are diagnosis-specific,
meaning they aim to pay insurers the marginal cost of treating each of an enrollee’s diagnoses. For example,
an average-premium plan in 2010 enrolling a 66 year old man whose medical claims reflect Multiple Sclerosis
would receive a diagnosis-specific payment of $756. If a similar enrollee’s medical claims instead reflect
HIV/AIDS, the plan would receive $2541. In theory, plans are equally willing to enroll both men because
the diagnosis-specific payments offset the higher expected cost of the HIV/AIDS patient. The Part D
payment system does not distinguish between diagnoses where the variance of treatment costs are large
(Multiple Sclerosis) and diagnoses where they are small (Diabetes Without Complications). The levels of the
diagnosis-specific payments were calibrated using data from the early 2000s and then left in place through
2010, despite new drug entry and the onset of generic competition (technological change) raising or lowering
the costs of treating certain diagnoses. In 2011, the payment system was updated to again set payments equal
to associated treatment costs. For the two men discussed previously, their insurer in 2011 now receives $915
for the Multiple Sclerosis patient (a 21% increase) and $2141 for the HIV/AIDS patient (a 16% decrease).
An insurer’s preferred enrollment mix will change as a result of the payment system revision: an increase
in a diagnosis’s payments between 2010 and 2011 will make insurers want to attract individuals with that
diagnosis, and conversely for diagnoses where payments are reduced. Prior theory suggests that insurers in
this setting, who must accept all enrollees at a uniform premium, will seek to attract preferred enrollees
through benefit design (Frank et al. (2000) among others, reviewed in Section ??), and recent empirical work
finds supporting evidence (Carey, 2014). In Section 5.3, we present further evidence that insurers respond to
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the payment system revision by designing more generous benefits for diagnoses receiving positive payment
updates: increasing the number of drugs covered and reducing copays. The magnitude of the response is an
indicator of “pass-through” of public payments to enrollees.
If benefits for a given diagnosis become more generous as a result of the payment system revision, enrollees
will respond by increasing utilization (days supplied). Therefore, we use the payment system update as an
input cost shock in an instrumental variables demand estimation. By exploiting the revision of the payment
system in a panel data setting, we recover an estimate of overall drug demand elasticity while flexibly
controlling for all time-invariant individual-level preference heterogeneity. Our elasticity estimates range
from 0.05 to 0.11, in line with previous literature.
Payment systems such as Part D’s also underlie Medicare Advantage and the Affordable Care Act health
insurance exchanges. There are significant practical and theoretical challenges to designing payment systems
that truly make insurers indifferent among enrollees. This research demonstrates how economists can exploit
inaccuracies in these payment systems to obtain a useful characterization of market parameters.
In what follows, we first describe how demand, supply, and Federal regulation for Medicare Part D,
focusing on the features that facilitate this analysis. We next review related literature, drawing on the prior
theoretical literature to develop predictions on how the payment system revision will affect Part D. We then
describe an econometric model to test those predictions. Next, we document the response of benefit designs
to the change in incentives provided by the payment system revision. Finally, we obtain the elasticity of
demand by examining its response to the change in copays that results from the payment system revision.
2 Medicare Part D
This section details the design of the Medicare Part D market, with special attention to insurer incentives
and the diagnosis-specific payment system. We first describe how enrollees choose Part D plans and drugs.
We then describe the insurers’ plan benefit design problem and the regulations that constrain their actions.
Finally, we review how Part D plans were paid in their first five years and the nature of the recalibration in
2011.
2.1 Enrollment and Drug Demand
In Medicare Part D, enrollees choose among competing insurance plans on the basis of premium and benefit
design. In this section, we describe the demand side of Part D, and develop evidence that the demand side
is characterized by private information on drug needs.
Medicare Part D implements the managed competition model of public health insurance that underlies
Medicare Advantage, Medicaid managed care, and the Affordable Care Act marketplaces. In the managed
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competition model, individuals choose among competing insurers offering a regulated benefit. Approximately
half of Medicare beneficiaries are in the market for stand-alone Medicare Part D (i.e., no prescription drug
coverage through a retiree benefit and not enrolled in a combined medical-drug Medicare Advantage plan).
In 2010, they chose among on average of 45 insurance plans operating in their market; plans must accept
everyone who applies at a uniform premium (Kaiser Family Foundation, 2009). Plans differentiate themselves
both vertically (overall level of benefit generosity) and horizontally (level of coverage for competing drugs
within a therapeutic class), subject to the regulations described in Section 2.2.
Because Medicare beneficiaries have very persistent drug utilization, choice of insurance plan commonly
incorporates enrollees’ private information on predicted drug demand. There are several pieces of evidence
for enrollees’ private information. Firstly, prior to the onset of Medicare Part D in 2006, no free-standing
prescription drug insurance existed for this population; Pauly and Zeng (2004) and Goldman et al. (2006)
suggest the threat of adverse selection inhibited the development of such a market. Secondly, beneficiaries
who remain uninsured despite eligibility for Part D appear to be positively selected (Yin et al., 2008; Levy and
Weir, 2010); however, the presence of substantial government funding, covering 75% of Part D expenditure
on average, means that most eligible beneficiaries enrolled. Finally, direct evidence on prescription drug
utilization reflects substantial year-over-year persistence in drug needs (Soni, 2008; Hsu et al., 2009). An
analysis by Heiss et al. (2013) finds that basing ones’ choices entirely on last year’s drug needs is the choice
rule that minimizes ex post expenditures in a broad set of heuristics and rational expectations models they
test.1
The presence of private information on drug demand in beneficiaries’ plan choices affects insurers’ incen-
tives because it means that enrollment will respond to insurers’ benefit design decisions. In the next section,
we explain insurers’ strategic choices in Part D as well as applicable benefit design regulation.
2.2 Insurers & Drug Firms
Insurers recognize that Part D enrollees have substantial persistence in drug needs. Since they must accept
all applicants at a uniform preannounced premium, they cannot directly select enrollees. Instead, they must
use their benefit designs –what drugs are covered and at what copays– to attract ex post profitable enrollees
and deter those who will spend more than the payments the insurer receives for them.
Federal regulation constrains both choice of coverage and choice of copay in hopes of providing access
to an equitable benefit for all enrollees. For coverage, insurers must cover two drugs in each United States
Pharamacopeia therapeutic class and all drugs in six “protected” classes (drugs for serious chronic illness).
1A substantial literature has developed around inconsistencies in plan choices among Medicare Part D enrollees(Abaluck and Gruber, 2009; Ketcham et al., 2012). In the theoretical discussion in Section 3.1 and empirical model in Section 4, we assume that such choice inconsistencies are orthogonal to the diagnosis-specific incentives that are our focus. Helpfully, the payment system updates we study in our empirical analysis are not centered in diagnoses that affect cognition.
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This regulation still allows considerable variation in coverage across plans. Goldman et al. (2011) find that
plans vary from covering fewer than 2500 drugs to more than 7500.
Copays are also subject to regulation. Copays are defined in relation to the Part D “Basic Benefit”, which
is the baseline coverage that Federal payments aim to implement. In the Basic Benefit, individuals’ copays
depend on their expenditure so far in the year: individuals pay a deductible, then 25% of drug expenditures
in an “initial coverage zone”, then 100% of drug expenditures in the doughnut hole, and finally 5% of drug
expenditures after a catastrophic threshold. Plans can satisfy copay regulation by either (1) offering the
Basic Benefit copays; (2) raising certain copays and lowering others such that copays still attain the Basic
Benefit percentages on average; or (3) offering “enhanced coverage”, financed fully out of premiums, that
reduces copays below the Basic Benefit percentages in some zones of coverage.
This paper focuses on copays in the initial coverage zone. Approximately 85% of plan outlays results
from claims in the initial coverage zone, meaning that plan profits are much more sensitive to copays in this
zone relative to other zones. In addition, copays in the other zones usually follow the basic benefit while,
copays in the initial coverage zone very often differ from 25% of drug prices.
Enrollees also pay a premium to their chosen plan; since premiums do not vary with diagnosis, we do
not analyze their response to diagnosis-specific incentives created by the payment system. Premiums are
computed from a bid that represents for each plan their expenditure on a “typical” enrollee. The premium
is then set to premi = (bidi− bid) + γbid. In this equation, bid is the national average bid (weighted by last
year’s enrollment) and γ is a fixed percentage (36% in 2010). Plans that cover many drugs at low copays
spend more for a “typical” beneficiary and therefore have a higher bid; their premiums are higher by the full
amount that their bid exceeds the national average bid.
Because plans set coverage and copay for approximately 5000 drugs, they have a relatively fine-grained
tool for attracting or deterring potential enrollees who prefer certain drugs. In the next section, we explore
the diagnosis-specific payments meant to make insurers indifferent between all enrollees.
2.3 Diagnosis-Specific Payments
Diagnosis-specific payments, as well as government payments in general, play a critical role in Part D market
design. In the absence of any subsidization, many individuals who know their (persistent) drug needs are
inexpensive would not wish to pool with those with high expected expenditures. The high degree of govern-
ment subsidies to the Part D market induces the healthy to voluntarily enroll, facilitating a balanced risk pool
and providing financial protection for unexpected drug needs. To see why payments are diagnosis-specific,
suppose Medicare had simply paid each Part D plan the average expenditure for each individual: approxi-
mately $1200. Within the benefit design regulations above, insurers would have aimed to disproportionately
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attract healthy beneficiaries and deter the sick. Instead, Medicare conditions its payments on diagnoses:
payments to plans are higher for enrollees with high-cost diagnoses and lower for those who are relatively
healthy. Payments that vary with individuals’ expected health status are known as “risk adjustment”.
A payment system such as Part D’s contains three distinct elements: diagnostic definitions, weights
representing the relative cost of each diagnosis, and a conversion from weights to payments. The first
diagnosis-specific payment system was calibrated prior to Part D’s beginning in 2006 and is detailed in
Robst et al. (2007). The diagnostic definitions, built up from ICD-9 codes, were borrowed from the payment
system used in Medicare Advantage; in addition to diagnoses, individuals were grouped by demographics:
age, sex, and originally entitled to Medicare due to disability. The payment system designers obtained
a sample of prescription drug and medical claims from Federal retirees (incurred in 2000) and disabled
Medicaid beneficiaries (incurred in 2002). They applied the Part D Basic Benefit to each individual’s claims
to simulate the expenditure of a Part D plan for these individuals.
To set relative cost weights for diagnoses and demographics, they ran the following regression:
Ei/E = ∑ x
ωxDix + ∑ g
ωgDig + εi (1)
In this expression, Ei/E is the simulated Part D expenditure for this Federal retiree or disabled Medicaid
beneficiary, normalized by the sample mean expenditure. Dix and Dig are 0/1 flags for the 84 diagnoses2
or demographic categories, and the coefficients on these flags are the relative weights for each. A fixed
factor increases the weight for low-income or long-term institutionalized individuals, since such individuals
generally have more severe forms of diagnoses. An individual with a weight of one is expected to spend the
sample average E .
The payment a plan receives for an individual is the product of the plan’s bid and the sum of the
individual’s demographic and diagnostic weights. Scaling weights by a plan’s bid allows payments to increase
with the overall generosity of a plan’s benefit design.
To see how the original payment system works, suppose an insurance plan enrolls a 66-year-old man
(never disabled, not low-income, not institutionalized). His medical claims from the previous year reflect an
Infectious Disease. The total weight for this man is the ωx for Infectious Disease, 0.073, and his demographic
weight, 0.355. A plan that bids the national average for 2010 ($1060) would receive $454 for this man. A
more generous plan bidding $1500 would receive $642.
As explored in Carey (2014), technological change in the form of the entry of new molecules and the
onset of generic competition (among other forces) caused actual treatment costs in Part D to drift from
2Robst et al. (2007) refer to 87 diagnoses; we disregard two related to Cystic Fibrosis because of extreme rarity, and we treat as a single diagnosis two that were constrained in Equation 1 to have the same coefficient.
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the payment weights set in the initial calibration. Therefore, Part D revised the payment system for 2011
(detailed in Kautter et al. (2012)).
2.4 The Payment System Revision
The payment system revision altered the diagnostic definitions and recalibrated the weight associated with
each diagnosis (the conversion of weights to payments remained the same). Firstly, diagnostic definitions
were altered by reorganizing the ICD-9 codes. For example, the diagnoses Quadriplegia and Motor Neuron
Disease and Spinal Muscular Atrophy in the old payment system are collapsed into one diagnosis – Spinal
Cord Disorders – in the new system. Chronic Renal Failure, on the other hand, is expanded from one
diagnosis to four subtypes. And various forms of cancer are completely reorganized.
In addition, each diagnosis now comes in five subtypes for disabled × low-income status and long-term
institutionalized. This is because those factors can dramatically change the expenditure associated with
a given diagnosis. In principle, creating a payment weight for each diagnosis-subtype can better align a
diagnosis’s payment and a plan’s expenditures for that diagnosis; this reduces the risk an insurer faces for
that diagnosis.
Finally, Equation 1 was reestimated on Part D enrollees in 2008. The introduction described the change
in payments for two diagnoses – HIV/AIDS and Multiple Sclerosis – which were defined by the same ICD-9
codes in both the new and old systems. The payment update for those two diagnoses suggests that many
diagnoses received much larger or smaller payments in 2011 relative to 2010. Later, we develop evidence
that this is indeed the case.
We have seen that, firstly, beneficiaries’ plan choices are characterized by private information on their
drug needs; secondly, insurers can attract individuals by generous benefit design for drugs that treat their
diagnoses; and, finally, the diagnosis-specific payment system and its recalibration provide variation over
time in the payment a plan receives for each diagnosis. In the next section, we review literature related to
analysis of government payments in environments like Medicare Part D.
3 Related Literature
We review two strands of literature: theoretical models of how government payments affect insurer and
provider behavior, and empirical analyses testing the theories in managed competition programs such as
Medicare Advantage and Medicare Part D.
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3.1 Theoretical Models of Insurer and Provider Behavior
A literature inspired by the managed care era in employer-sponsored insurance provides a useful framework
for analysis of insurer benefit design incentives. The primitives of these models (reviewed in Ellis (2008))
mimic a managed competition health insurance framework: individuals differ in their preferences for medical
services in a way known to them and predictable to insurers, and insurers must accept everyone who applies
at a uniform premium. In this setting, if certain enrollees are more profitable for insurers, they will attempt
to effect selection through designing more generous benefits for the services preferred by those individu-
als. In model developed by Frank et al. (2000), gatekeeper managed care organizations create nonfinancial
disincentives – paperwork barriers, administrative requirements – for services preferred by individuals they
wish to deter. An theoretical extension in Carey (2014) predicts similar patterns for financial disincentives:
insurers cover at higher rates and lower copays the services preferred by individuals they wish to attract.
A recently renewed literature (Layton, 2014) considers how to design diagnosis-specific payments that
fully neutralize insurers’ benefit design incentives. Glazer and McGuire (2002)’s fundamental insight is
that diagnosis-specific payments should reflect the profit-maximizing payments an insurer would set, rather
than the linear coefficients described in Section 2.3. A corollary in Bijlsma et al. (2011) demonstrates that
diagnoses that correlate with consumer inertia must receive higher diagnostic payments that dynamically
compensate the insurer for not exploiting inertia.
The above literature tends to disregard the fact that insurers do not produce medical services directly
and instead must obtain them from an upstream provider. Ignoring upstream medical providers in a model
of comprehensive medical insurance can be justified: since medical services are organized by system of the
body, any given provider will treat individuals with a variety of illnesses. Drugs, however, tend to treat a
single diagnosis, and therefore contracting between insurers and drug firms may be strongly affected by the
payment for a given diagnosis. Theoretical research provides little guidance, however, on how negotiations
would respond to the payment system incentives we study, although there is recent progress by de Fontenay
and Gans (2013) and Douven et al. (2014).
3.2 Empirical Analysis of Government Payments In Managed Competition
Several recent papers explore insurer behavior in a market very similar to Medicare Part D: Medicare
Advantage. Medicare Advantage introduced diagnosis-specific payments in 2004; Brown et al. (forthcoming)
show that after the policy change MA plans successfully raised enrollment among individuals with mild
forms of each diagnosis.3 Brown et al. are silent on how insurers successfully select enrollees; we build on
3An alternative view in McWilliams et al. (2012) and Newhouse et al. (2013) argues that diagnosis-specific payments greatly reduced selection among MA insurers.
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their analysis by directly analyzing the links between increases in an insurer’s incentive to enroll a given
individual and changes in benefit designs.
Payments to Medicare Advantage plans must also accommodate geographic differences in cost of care.
A recent literature exploits policy changes (Duggan et al., 2014; Cabral et al., 2014) or geographic variation
per se (Bhattacharya et al., 2014) to measure the impact of higher payments on MA premiums and benefits.
These papers find a weak relationship between exogenous payment variation and premiums/benefits; these
low rates of pass-through are attributed in these papers to relatively low enrollment elasticities. Our paper
follows their general logic but uses variation in payments across diagnoses rather than across geographies.
Carey (2014) is a direct ancestor to this analysis. That research demonstrates that the original Part D
payment system created strong benefit design incentives for insurers and that insurers responded as predicted
by the Frank et al. (2000) framework. Because diagnosis-specific payments were held fixed over time despite
the entry of new molecules and generic competitors, many diagnoses became very profitable for insurers
while others were very unprofitable. When each diagnosis’s profitability is instrumented by its exposure to
new molecules and generic entrants, insurers clearly design more favorable benefits – covered more drugs
and at lower copays – for drugs that treat profitable diagnoses. This analysis exploits variation over time,
which is crucial for demand estimation: we recover elasticities from the change in demand as a result of the
change in payments, while controlling flexibly for the time-invariant demand factors.
This paper also analyzes how benefit designs respond to changes in the variance of expenditures condi-
tional on payments. While many researchers acknowledge the variance of expenditures as a challenge to risk
adjustment systems (“fit” in the framework of Geruso and McGuire (2014)), to my knowledge no one has
evaluated insurers’ response to this variance.
3.3 Demand Elasticities for Pharmaceuticals
Due to its connection with moral hazard and supplier-induced demand, a large number of studies aim to
recover demand elasticities for medical care in general pharmaceuticals in particular. Chandra et al. (n.d.)
and Jung et al. (2014) provide a useful review. Many of these studies study a nonelderly population, where
disease burden differs and the consequences of foregoing pharmaceuticals may be less immediate. Estimates
are in the ballpark of -0.2 (similar as well to the findings of the RAND Health Insurance Experiment).
Siminski (2014) exploits a reduction in copays for middle-class seniors in Australia, and recovers an overall
estimate of -0.1.
Because copays in Medicare Part D vary as an individual progresses through the zones of coverage
described in Section 2.2, several papers estimate the intrayear elasticity of demand as an enrollee enters the
“coverage gap” where they face full drug costs (Gowrisankaran et al., 2014; Jung et al., 2014; Einav et al.,
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2013). If a reduced coverage gap raises overall utilization, the Affordable Care Act policy that slowly closes
the coverage gap over time will cost more than simply the increase in subsidies for purchases in the gap.
Jung et al. (2014) exploit extra payments given to Medicare Advantage insurers that “pass-through” to the
Part D components of MA plans and recover elasticities between -0.14 and -0.36. Einav et al. (2013) find
larger elasticities. Our estimates are smaller in magnitude, consistent with an elasticity that pertains to
overall annual spending.
Taken together the literature reviewed in this section suggests that insurers indeed compete for desirable
enrollees via benefit design, but that relatively low pass-through might arise. A potential explanation for
low pass-through is that insurers face relatively inelastic service demand. In the next section, we propose
methods for recovering pass-through and elasticity exploiting the Part D payment system revision.
4 Measuring Payment Updates, Pass-Through, and Demand Elas-
ticity
The objective of our research is to demonstrate the demand response to changes in benefit design that
result from the payment system revision. The first step (Section 4.2) measures the change in incentives
that accompanied the payment system revision. Next, we link drugs to the diagnoses they treat, since no
reference work links drugs to diagnoses in the Part D payment system (Section 4.2). We then propose
a panel data model that tests the response of benefit designs to the payment system recalibration while
allowing a diagnosis-specific time trend that affects both Part D outcomes and the payment update (Section
4.4). Finally, a panel of utilization at the individual×diagnosis level recovers demand elasticities using the
payment update as an instrument.
4.1 Data
This research combines Medicare claims data with the publicly-available Part D benefit designs. Our Medi-
care claims dataset provides medical and prescription drug claims for a 5% sample of Part D enrollees between
2009 and 2011 (medical claims include 2008 as well). The medical claims enable us to assign diagnoses to
individuals in the exact same way as Medicare: if an individual has a specified ICD-9 code in an Inpatient,
Outpatient or Carrier (Physician) claim in the previous calendar year, the payment for that diagnosis is
given to their Part D plan in the current year. Diagnoses can only be observed for individuals enrolled in
fee-for-service Medicare (not Medicare Advantage) because claims from Medicare Advantage enrollees are
not released to researchers.
The benefit designs of all Part D plans are contained in the Prescription Drug Plan Formulary files. The
Formulary files contain coverage and, if covered, copay for all drugs and all plans in 2009 through 2012. For
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all covered drugs a negotiated price paid by the plan is also listed in the data, but the price is before an
unobserved rebate.
4.2 Measurement of Payment System Change
The first step is measuring the sign and magnitude of payment system updates for each diagnosis in the
payment system. As discussed in Section 2.3, this step is nontrivial because the recalibration also revised
the mapping of ICD-9 codes to payment system diagnoses. We take advantage of our claims dataset to
estimate the change in payments associated with each diagnosis. Our methodology is straightforward: we
calculate the diagnosis-specific payments for Part D enrollees in 2010 under both the new and old payment
systems. The payments are based on the same medical claims: we simply change the diagnostic definitions
and diagnosis-specific weights. We then predict the difference between the payment under the two systems
using flags for the 84 diagnoses under the old system’s diagnostic definitions.
Pi = Pi11 − Pi10 = ∑ x
UxDix + εi (2)
PN i is the diagnosis-specific payment for individual i under the new system, PO
i is the diagnosis-specific
payment for the same individual in the same year under the old system, and Pi is their difference. The
coefficient Ux on each diagnostic flag Dix is what we refer to as the “payment update” for diagnosis x. The
results of this step are reported in Section 5.1.
As discussed in Section 2.3, the recalibration also flexibly set a diagnosis-specific weight for disabled,
low-income, and institutionalized beneficiaries, in the hopes of better aligning payments and expenditures
for these individuals. To measure the resulting change in variance for each diagnosis, we obtain a measure of
plan expenditure for each individual from the Part D claims. We adjust this measure for other government
payments – demographic payments and reinsurance – besides the diagnosis-specific payments. What is
left is the portion of plan expenditure that the diagnosis-specific payments were meant to offset. For each
individual, we measure the squared deviation between adjusted plan expenditure, Ei, and diagnosis-specific
payments PN i or PO
i . Then, as above, we predict the difference Yi between the squared deviations under
both the new and old systems using flags for 84 diagnoses. The coefficient Vx is what we refer to as the
“variance change” associated with each diagnosis.
Yi = (Ei − Pi11)2 − (Ei − Pi10)2 = ∑ x
VxDix + ηi (3)
4.3 Associating Drugs and Diagnoses
While drugs are relatively closely linked to diagnoses, there is no reference work we can consult that tells us
which drugs treat which diagnoses. Instead, we take advantage of our large claims datasets to estimate the
empirical association of drugs and diagnoses using three years of medical and prescription drug claims (2007
to 2009). In particular, we run a probit model to predict whether an individual takes a given ingredient
combination (I abstract from differences in strength and route of administration) using flags for the 84
diagnoses. Each coefficient gives the marginal increase in the probability of taking the drug associated with
having the given diagnosis. For each ingredient combination, I define it as “treating” the diagnosis with the
largest coefficient in the probit.
4.4 Testing Benefit Design Response
We are now in a position to predict benefit designs as a function of payments. We propose a panel data
model of outcomes for drugs, averaged across plans, as a linear function of a time-invariant drug fixed effect,
a diagnosis-specific time trend, and time-varying diagnosis-specific payments.
mean coverage,
diagnosis payment
clustered on d εdt
The time-invariant fixed effect represents all demand or supply factors that affect a drug’s outcomes in
Part D formularies – unobserved drug efficacy and side effects, marginal cost of production, or market power
of drug firm(s) that produce this drug. The diagnosis-specific time trend is discussed in the next paragraph.
The coefficient α is the relationship between payments and benefit designs.
To understand the diagnosis-specific time trend, consider the various factors that comprise the payment
update. The revision may raise or lower payments for a given diagnosis as a result of various factors:
differences in the original calibration sample (Federal retirees and Medicaid beneficiaries) and the Part D
sample used in recalibration; technological change – new drugs or the onset of generic competition – changing
the costs of treating certain diagnoses; changes in the supply-side environment such as insurer or drug firm
consolidation; or changes in demand parameters. Suppose any of these factors are persistent, meaning that
the factor’s trend between 2000 and 2008 (captured by the payment update) is correlated with its trend in
our sample years 2009 to 2012. Such persistence can generate a spurious correlation between the payment
update and benefit design outcomes that is not via the pass-through of diagnosis-specific payments.4 In
4To see why, suppose each year since 2000 insurers have simply raised the copays for drugs that treat diagnosis x a fixed amount: copayxt = copayx00 +βxt. Medicare’s payment recalibration process finds that the diagnosis-specific costs in 2008 are costsx08 = ρcopayx08 + ωx08, where ωx08 captures all the other features of costs in 2008, and the payment in 2011 is set to
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the presence of the time trend, we are identifying α from the deviation from the overall trend that occurs
between 2010 and 2011.
Yd11 − Yd10 = βx+ α
(4)
Due to the reorganization of diagnoses between the new and old payment system, we do not have a set
of WN x that correspond exactly to the old diagnoses. Instead we use the payment update Ux estimated in
Equation 2.
An advantage of this panel data specification is that we can run placebo tests in which we test for an
effect of the payment update in the year-pairs when it did not happen: 2011/2012 and 2009/2010.
A similar model tests for the impact of the reduction in variance.
As a final note, we weight each observation by the drug’s expenditure in Medicare Advantage. This is
because drugs vary in expenditure by a factor of hundreds of millions. If agents have limited resources, their
decisions will more strongly reflect the drugs that account for the majority of their outlays (beneficiaries)
or profits (insurers or drug firms). In the framework of Solon et al. (2013), expenditure weights recover the
average partial effect of a payment system update in the presence of unmodeled heterogeneity across drug’s
expenditure levels in the response of agents to the change in incentives. We use expenditure in Medicare
Advantage since most payments to Medicare Advantage plans are determined by a different payment system
and therefore are outside of the payment system we are studying.
4.5 Recovering Demand Elasticities
Finally, we proceed to estimating demand as a function of copay instrumented by benefit designs. We propose
a panel data model predicting an individual’s demand – days supplied – for drugs that treat diagnosis x in
year t as a function of a time-invariant individual×diagnosis fixed effect, a diagnosis-specific time trend, a
flag for whether individual i has diagnosis x in year t, and the copay per day supplied as instrumented by
this cost. We will find that the payment update is Wx08 −Wx00 = ρ(8βx) + ωx08 − ωx00. If we then use this payment update to predict the change in copays between 2011 and 2010, βx, we will find that the payment update and copays are correlated through βx. But there is no “pass-through” in this setting – it is simply that a time trend in copays influences the payment update and then later affects the change in benefit designs that interests us.
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ind × diag fixed effect dix +
diag time trend bxt +rx
i has x in t
Xixt +(
diag payment
in t Wxt ) + εixt
In this setting, the individual×diagnosis fixed effect controls for the individual’s illness severity or idiosyn-
cratic taste parameters that lead her to a certain average level of days supplied over time. The diagnosis-
specific time trend allows each diagnosis to be rising or falling in days supplied on average over the sample
period; analogously to the argument above, if days supplied is rising persistently it can affect both the pay-
ment update and the change in days supplied in the sample period. Since naturally having a diagnosis weakly
raises the days supplied of relevant drugs, we include this flag as a health status control. The expression in
parentheses is of course very similar to Equation 4.
Similarly, we run this equation in first differences. We define Xix as four dummies for each combination
of diagnoses in each year-pair (instead of constraining the coefficient to be the same for 0→0 and 1→1
transitions).
Dix11 −Dix10 = bx+ rXix x + (Yix = αWx)+ εixt
Dix10 −Dix09 = bx+ rxXix+ εixt
(5)
5 Results
We now report the results of our analysis of the Part D payment system revision. The first section describes
how payments changed – rose or fell, increased or reduced variance – for each diagnosis. The second section
reports on drug-diagnosis linkage. The third section demonstrates how coverage and copay respond to the
payment system revision and provides a general estimate of the rate of pass-through. The final section
reports the results of the demand estimation.
5.1 Results: Measurement of Payment System Change
We measure the changes in the payment system using the model in Equations 2 and 3 and the medical
and prescription drug claims of 739,950 Part D enrollees. The sample is a random 5% sample of individuals
enrolled in Part D in 2010 (so that their prescription drug claims are observed) and in fee-for-service Medicare
in 2009 (so that their diagnoses can be obtained from medical claims). In Table 1, we report the features of
the distribution of our sample. The first two rows describe the distribution of diagnosis-specific payments
under the old and new payment system, and third row is their difference, Pi, which is the left hand side
14
of Equation 2.5 If the difference in payments is positive, payments for that individual are larger under the
new system compared to the old system. Payments for more than 75% of individuals decrease under the
new system. More importantly, we find that many individuals have very different payments under the new
and old systems, suggesting that the payments for various diagnoses rose or fell significantly.
The next rows represent the variables used in Equation 3: adjusted plan expenditure (Ei), the squared
deviations between the individual’s actual adjusted plan expenditure and their payment under both systems,
and difference in squared deviations between the new and old systems. Positive values in the final row
correspond to an increase in squared deviations under the new payment system relative to the old payment
system. Under the new payment system, most individuals receive a payment that deviates less from their
actual expenditures. This means that the new payment system better aligned payments and expenditures
on average.
Figures 1 and 2 also illustrate the enrollee-level variation we use to measure how the new and old payment
systems differ. Figure 1 shows each individual’s diagnosis-specific payments under the new and old systems;
the overall decline in payments is visual in the presence of more mass under the 45 line. Figure 2 depicts each
individual’s squared deviations from adjusted plan expenditures under the new and old payment systems.
The fact that the new payment system reduces the squared deviations is evident from the presence of greater
mass below the 45 line.
Table 2 reports the diagnosis-specific coefficients from Equations 2 and 3. For each diagnosis, we report
the old payment, the payment update for the diagnosis and its standard error, and the variance change and
its standard error. The diagnoses are sorted by the magnitude of the old payment. Note that standard errors
are quite small relative to coefficients; we nearly always reject the hypothesis that a diagnosis’s payment or
variance is not affected by the transition to the new payment system.
5.2 Results: Associating Drugs and Diagnoses
As described in Section 4.3, we estimate a series of probits in order to obtain a linkage between ingredient
combinations and the payment system diagnoses they treat. We estimate these probits on the prescription
drug and medical claims of Part D enrollees in 2007, 2008, and 2009: nearly 2.5 million in all. We restrict
to 732 ingredient combinations taken by at least 200 beneficiaries in a year.
We define an ingredient combination as “treating” the diagnosis that most strongly predicts taking it. On
average, the largest coefficient (i.e., the one for the treating diagnosis) exceeds the second largest coefficient
by a factor of six. Fifteen of 84 diagnoses are not found to “treat” any ingredient combination we study;
these diagnoses tend to be catch-alls (Other Neurological Conditions, Other Blood Diseases) or diagnoses,
5Note that each row reports the distribution for the stated variable, but an individual at the 5th percentile in one row may appear elsewhere in the distribution in another row.
15
such as Pelvic Fracture, where drugs are used for general symptoms such as pain or infection but not for the
underlying diagnosis.
We check this linkage against the Johns Hopkins Adjusted Clinical Groups Case-Mix System. The ACG
System gives a “prescription drug morbidity group” for any drug. Prescription drug morbidity groups do
not correspond exactly to the diagnoses (and therefore cannot supply our linkage) but many are very similar.
This comparison suggests that this step links drugs to diagnoses fairly accurately. Poor linkage of drugs and
diagnoses will create measurement error in the estimation of Equation 4 and will bias our results towards
zero.
5.3 Results: Testing Benefit Design Response
With our measurement of payment updates and variance changes in place, as well as a linkage between
drugs and diagnoses, we are now ready to estimate Equation 4. We use changes in outcomes for 3523
drugs averaged across continuting plans between 2009 and 2012. First differences are taken across year pairs
2009-2010, 2010-2011, and 2011-2012. Our drug sample is comprised of the universe of drugs present in the
Prescription Drug Formulary Files in any year pair between 2009 and 2012 less (1) drugs with ingredient
combinations that were never taken by at least 200 beneficiaries and therefore were not linked to diagnoses
and (2) drugs that began to face generic competition in a year pair.6 We exclude drugs that begin to
face generic competition since a time-invariant drug fixed effect represents such drugs particularly badly.
Our plan sample is the universe of plans operating continuously in a year pair; if two or more 2010 plans
consolidated for 2011, we take a simple average of outcomes before averaging across plans. Note that change
in coverage is observed for all plan×drug combinations, while change in copay is observed only for plan×drug
combinations where coverage is 1 in both years.
Table 3 reports our baseline results. We find that a positive payment update has no effect on coverage
(positive but insignificant) and significantly lowers copay. The magnitudes here are such that a $1 payment
increase raises rates of coverage by a tiny fraction of a percentage point and lower copays by about ten cents.
This is consistent with our predictions.
Table 4 reports how a drug’s benefit design responds to the change in variance associated with the
diagnosis under the new payment system. We control for the payment update for the diagnosis the drug
treats since we know the payment update affects these outcomes. We find that the variance change affects
these outcomes in the opposite way as payment updates: conditional on payment update, a reduction in
variance causes plans to respond by (insignificantly) raising coverage and lowering copays. These effects
suggest that, as predicted, a reduction in the risk an insurer bears for a diagnosis makes individuals with
6Onset of generic competition can be obtained from the Food and Drug Administration; see Carey (2014) for a discussion of this data source.
16
5.4 Results: Recovering Demand Elasticities
Finally, we arrive at the estimation of Equation 5. We estimate this equation on a fixed cohort of 570,490
individuals continuously enrolled in Part D between 2009 and 2011 (due to data limitations, we cannot
perfectly match the sample period used in the test of benefit design responses.) The unit of observation is
an individual×diagnosis; individuals take drugs for 8.5 diagnoses per year on average. The left hand side is
the total number of days supplied in a year for drugs that treat a given diagnosis. The instrumented right
hand side variable is the total copays paid divided by the total days supplied: copay per day. The right
hand side instrument is the payment update for this diagnosis. Right hand side controls are diagnosis fixed
effects to capture the diagnosis time trend and health status controls for each diagnosis. Within a year-pair,
if an individual took drugs for a diagnosis in one year, a days supplied of zero is imputed for the other year.
The first column of Table 5 simply applies OLS to Equation 5. Consistent with copay and days supply
being simultaneously determined, we recover a small positive association: endogenous increase in copays
tend to co-occur with increases in days supply.
The second column of Table 4 is our preferred specification. Our first stage estimates demonstrate the
expected negative relationship between copay and the payment update, similar to what was shown in Table 3.
When we predict the change in days supply from the change in copay that results from the payment update,
we find a significant negative response. The magnitudes are relatively small: for a payment increase of $100,
days supplied of drugs related for that diagnosis would increase by 0.75 days. The recovered elasticity is
-0.107, near other estimates. The final column simply tests the impact of removing individuals who receive
a low-income subsidy that offsets some of their copays. Perhaps surprisingly, removing individuals who do
not pay full copays reduces the estimated elasticity.
6 Conclusion
In this paper, we explore the effect of a revision to diagnosis-specific payments in Medicare Part D. We
found that many diagnoses received large increases or reductions in payments as a result of the revision;
in addition, the variance of expenditures conditional on diagnosis-specific payments rose or fell. We then
showed that Part D benefit designs responded as predicted by prior theory to the change in incentives that
resulted from the payment system revision. Finally, we exploited this benefit design response to recover an
elasticity of demand for drugs; our baseline estimate of -0.107 is similar to other estimates. We conclude
that, based on the responses of insurers and enrollees to the payment system revision, that Medicare Part
D has low levels of pass-through (although the surplus that is not passed through may be largely captured
17
18
References
Abaluck, Jason T. and Jonathan Gruber, “Choice Inconsistencies Among the Elderly: Ev-
idence from Plan Choice in the Medicare Part D Program,” February 2009. Available at
http://www.nber.org/papers/w14759.
Bhattacharya, Jay, Vilsa Curto, Liran Einav, and Jonathan Levin, “Can Health Insurance Com-
petition Work?: The US Medicare Advantage Program,” April 2014.
Bijlsma, Michiel, Jan Boone, and Gijsbert Zwart, “Competition Leverage: How the Demand Side
Affects Optimal Risk Adjustment,” July 2011. CentER Working Paper Series No. 2011-071 and TILEC
Discussion Paper No. 2011-039.
Brown, Jason, Mark Duggan, Ilyana Kuziemko, and William Woolston, “How Does Risk Selection
Respond to Risk Adjustment? Evidence from the Medicare Advantage Program,” American Economic
Review, forthcoming.
Cabral, Marika, Michael Geruso, and Neale Mahoney, “Does Privatize Medicare Benefit Patients or
Producers? Evidence from the Benefits Improvement and Protection Act,” April 2014.
Carey, Colleen, “Government Payments and Insurer Benefit Design in Medicare Part D,” May 2014. Avail-
able at www-personal.umich.edu/~careycm/Carey%20Gov%20Payments%20Benefit%20Design%20Part%
20D.pdf,.
Chandra, Amitabh, Jonathan Gruber, and Robin McKnight, “Patient Cost-Sharing and Hospital-
ization Offsets in the Elderly,” American Economic Review, 100 (1,).
de Fontenay, Catherine C. and Joshua S. Gans, “Bilateral Bargaining with Externalities,” March
2013. Melbourne Business School Discussion Paper No. 2004-32.
Douven, Rudy, Rein Halbersma, Katalin Katona, , and Victoria Shestalova, “Vertical Integration
and Exclusive Behavior of Insurers and Hospitals,” Journal of Economics and Management Strategy, 2014,
23 (2), 344–368.
Duggan, Mark, Amanda Starc, and Boris Vabson, “Who Benefits when the Government Pays More?
Pass-Through in the Medicare Advantage Program,” March 2014. NBER Working Paper 19989.
Einav, Liran, Amy Finkelstein, and Paul Schrimpf, “The Response of Drug Expenditures to Non-
Linear Contract Design: Evidence from Medicare Part D,” August 2013. NBER Working Paper 19393.
19
Ellis, Randall P., “Risk Adjustment in Health Care Markets: Concepts and Applications,” in Mingshan
Lu and Egon Jonsson, eds., Financing Health Care: New Ideas for a Changing Society, Wiley-VCH Verlag,
2008, pp. 177–222.
Frank, Richard G., Jacob Glazer, and Thomas G. McGuire, “Measuring Adverse Selection in
Managed Health Care,” Journal of Health Economics, 2000, 19, 829–854.
Geruso, Michael and Thomas G. McGuire, “Tradeoffs in Design of Health Plan Payment Systems:
Fit, Power, and Balance,” July 2014.
Glazer, Jacob and Thomas G. McGuire, “Setting Health Plan Premiums to Ensure Efficient Quality
in Health Care: Minimum Variance Optimal Risk Adjustment,” Journal of Public Economics, 2002, 4,
1055–1071.
Goldman, Dana E., Geoffrey Joyce, Pinar Karaca-Mandic, and Neeraj Sood, “Adverse Selection
in Retiree Prescription Drug Plans,” Forum for Health Economics and Policy, 2006, 9 (1).
Goldman, Dana P., Geoffrey F. Joyce, and William B. Vogt, “Part D Formulary and Benefit
Design as a Risk-Steering Mechanism,” American Economic Review: Papers & Proceedings, 2011, 101 (3),
382–386.
Gowrisankaran, Gautam, Christine Marsh, and Robert Town, “Myopia and Complex Dynamic
Incentives: Evidence from Medicare Part D,” April 2014.
Heiss, Florian, Adam Leive, Daniel McFadden, and Joachim Winter, “Plan Selection in Medicare
Part D: Evidence from Administrative Data,” Journal of Health Economics, December 2013, 32, 1325–
1344.
Hsu, John, Jie Huang, Vicki Fung, Mary Price, Richard Brand, Rita Hui, Bruce Fireman,
William Dow, John Bertko, and Joseph P. Newhouse, “Distributing $800 Billion: An Early
Assessment of Medicare Part D Risk Adjustment,” Health Affairs, 2009, 28 (1), 215–225.
Jung, Kyoungrae, Roger Feldman, and A. Marshall McBean, “Demand for prescription drugs under
non-linear pricing in Medicare Part D.,” International Journal of Health Care Finance and Economics,
2014, 14, 19–40.
Kaiser Family Foundation, “Part D Plan Availability in 2010 and Key Changes Since 2006,” November
2009.
20
Kautter, John, Melvin Ingber, Gregory C. Pope, and Sara Freeman, “Improvements in Medicare
Part D Risk Adjustment: Beneficiary Access and Payment Accuracy,” Medical Care, 2012, 50 (12), 1102–
1108.
Ketcham, Jonathan, Claudio Lucarelli, Eugenio J. Miravete, and M. Christopher Roebuck,
“Sinking, Swimming, or Learning to Swim in Medicare Part D,” American Economic Review, 2012, 102
(6), 2639–2673.
Layton, Timothy, “Imperfect Risk Adjustment, Risk Preferences, and Sorting in Competitive Health
Insurance Markets,” February 2014.
Levy, Helen and David Weir, “Take-up of Medicare Part D: Results from the Health and Retirement
Study,” Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 2010, 65 (4), 492–
501.
McWilliams, J. Michael, John Hsu, and Joseph P. Newhouse, “New Risk-Adjustment System Was
Associated With Reduced Favorable Selection In Medicare Advantage,” Health Affairs, 2012, 31 (12),
2630–2640.
Newhouse, Joseph P., J. Michael McWilliams, Mary Price, Jie Huang, Bruce Fireman, and
John Hsu, “Do Medicare Advantage Plans Select Enrollees in Higher Margin Clinical Categories?,”
Journal of Health Economics, 2013, 32, 1278–1288.
Pauly, Mark V. and Yuhui Zeng, “Adverse Selection and the Challenges to Stand-Alone Prescription
Drug Insurance,” in David M. Cutler and Alan M. Garber, eds., Frontiers in Health Policy Research,
Vol. 7, NBER Books, 2004, pp. 55–74.
Robst, John, Jesse Levy, and Melvin Ingber, “Diagnosis-Based Risk Adjustment for Medicare Pre-
scription Drug Plan Payments,” Health Care Financing Review, 2007, 28 (4), 15–30.
Siminski, P., “The price elasticity of demand for pharmaceuticals amongst high-income older Australians:
a natural experiment,” Applied Economics, 2014, 43, 4385–4846.
Solon, Gary, Steven J. Haider, and Jeffrey Wooldridge, “What Are We Weighting For?,” February
2013. NBER Working Paper 18859.
Soni, Anita, “The Top Five Therapeutic Classes of Outpatient Prescription Drugs by Total Expense for
the Medicare Population Age 65 and Older in the U.S. Civilian Noninstitutionalized Population, 2005,”
21
Technical Report, Agency for Healthcare Research and Quality, Rockville, MD 2008. Statistical Brief
#199.
Yin, Wesley, Anirban Basu, James X. Zhang, Atonu Rabbani, David O. Meltzer, and G. Caleb
Alexander, “The Effect of the Medicare Part D Prescription Benefit on Drug Utilization and Expendi-
tures,” Annals of Internal Medicine, 2008, 148 (3).
22
N=739,950 enrollees Percentile of Distribution
5th 25th 50th 75th 95th
Payment: Old System 0 464 773 1103 1689 Payment: New System 0 307 554 862 1503 Difference in Payments -670 -319 -174 -29 340 Adjusted Plan Expenditure 0 183 1011 1836 2639 Squared Deviations: Old System 0 70 353 900 2541 Squared Deviations: Old System 4 99 318 745 2272 Difference in Squared Deviations -912 -267 -33 155 612
This table describes the sample of enrollees used to estimate Equations 2 & 3. The first row shows the distribution in payments in dollars for each individual under the old system (PO
i in Equation 2). The second row shows the distribution in payments for each individual under the new system (PN
i in Equation 2). The third row shows the distribution of the difference in an individual’s payments between the new and old system (positive numbers mean payments increase). The fourth row describes the distribution of plan expenditures for individuals (Ei in Equation 3). The squared deviations between adjusted plan expenditures and payments under the new and old systems are reported in the next rows, followed by their difference (positive numbers mean squared deviations increase). Rows are independent, such that the person at the 5th percentile in the first row may be at a higher or lower percentile in the next row.
23
0 20
00 40
00 D
ia gn
os is
co d
ed u
n d
er b
ot h
th e
20 10
an d
20 11
p ay
m en
t sy
st em
s. T
h e
si ze
o f
th e
0 20
00 40
00 In
di vi
du al
’s S
qu ar
ed D
ev ia
tio n
fr om
O ld
P ay
m en
Table 2: Old Payment, Payment Update, and Variance Change: Diagnosis-Level
Diagnosis Old Payment ($) Payment Update ($) SE Variance Change ($) SE HIV/AIDS 2164 -272 3 -1463 12 Age<65 & Schizophrenia 397 266 2 -90 7 Multiple Sclerosis 379 376 3 -624 13 Parkinson’s Ds 339 -126 2 -30 9 Leukemia 311 147 12 -535 49 Diabetes w/ Comps 273 14 1 22 4 Opportunistic Infections 272 -118 4 -106 18 ADD 269 -41 4 -46 16 Congestive Heart Failure 266 -104 1 22 4 Schizophrenia 265 33 3 -3 13 Hypertension 235 -90 1 11 3 Dementia w/ Depression 234 -294 2 59 10 Kidney Transplant 228 88 5 122 18 Rheumatoid Arthritis 210 -7 2 -33 6 Inflamm. Bowel Ds 193 58 3 35 11 Esophageal Ds 187 -39 1 -36 3 Metastatic Acute Cancers 184 152 2 -433 9 Age<65 & Other Major Psych. Dsrs 175 195 1 28 4 Lipoid Metabolism 173 -15 1 -18 3 Asthma and COPD 173 35 1 30 3 Open-angle Glaucoma 171 -27 1 -6 5 Other Major Psych. Dsr 167 -76 1 -46 4 Motor Neuron Ds/Atrophy 161 -38 10 23 41 Psoriatic Arthropathy 159 233 7 -123 28 Dsr of Spine 149 -151 1 -20 3 Myocardial Infarction/Unstable Angina 148 -35 1 0 3 Seizure Dsr & Convulsions 135 133 1 -14 6 Other Psych. 135 -113 3 -36 10 Osteoporosis 122 -29 1 -51 3 Severe Hematological Dsr 120 26 3 -44 10 Migraines 112 107 2 7 8 Incontinence 108 -111 1 -10 6 Heart Arrhythmias 99 -79 1 -38 4 Polycythemia Vera 98 -101 7 -72 26 Hepatitis 98 122 3 183 12 Muscular Dystrophy 88 -99 10 130 41 Other Upper Respiratory Ds 88 -75 1 -27 3 Other Organ Transplant 84 -99 1 -55 3 Major Organ Transplant 84 402 6 -198 23 Other Endocrine 83 36 1 -7 5 Psoriasis 82 80 3 -14 11 Polyneuropathy exc. Diabetic 82 35 1 -11 5 Chronic Renal Failure 78 39 1 8 5 Infectious Ds 77 -73 2 2 9 Mononeuropathy/Abnormal Movement 75 -55 1 0 5 Glaucoma and Keratoconus 72 -75 1 -19 5 Connective Tissue Dsr 70 79 2 -10 9 Cerebral Hemorrhage/Stroke 67 -31 1 -2 3 Vascular Retinopathy exc. Diabetic 59 -51 2 -7 6 Huntington’s Ds 58 -65 9 35 35 Lung Cancer 53 64 1 3 5 Salivary Gland Ds 53 -53 4 0 16 Other Spec. Endocrine 52 -16 1 -26 3 Quadriplegia 51 -28 2 18 9 Cellulitis & Skin Ds 51 -57 1 4 4 Fecal Incontinence 51 -50 5 -19 20 Pancreatic Ds 51 -23 2 -57 8 Urinary Obstruction 51 -65 1 -1 5 Bullous Dermatoses 51 -76 1 -17 4 Chronic Skin Ulcer exc. Decubitus 51 -67 1 -2 6 Polymyalgia Rheumatica 46 -75 3 -78 14 Empyema, Abscess, & Lung Ds 46 -65 8 28 31 Bronchitis & Congenital Lung Dsr 46 -35 1 0 5 Vascular Disease 37 3 1 18 3 Vaginal & Cervical Ds 35 -2 2 -9 6 Ulcer & Gastro Hemorrhage 35 -49 1 -29 5 Pulmonary Embolism & Thrombosis 29 7 2 -19 7 Larynx/Vocal Ds 25 -22 6 -26 24 Bone Infections 24 -1 3 -17 11
This table reports the results of the estimation of Equation 2 on 739,950 Medicare Part D enrollees in 2010. The first column
reports the diagnosis name. The second column reports the payment for the diagnosis in a plan bidding the national average
bid under the old system. The next columns report the payment update and its standard error. The final columns report
the variance change and its standard error. Only the 69 diagnoses used in later analyses are reported.
26
Coverage (p.p.) Copay ($) Payment Update 0.011 -0.095**
(0.014) (0.003) N 10,491 10,491
PLACEBO TESTS Update imputed to 11-12 0.007 -0.008
(0.013) (0.037) Update imputed to 09-10 -0.018 0.054
(0.010) (0.040)
This table reports the results of estimating Equation 4 on the change in coverage and copay for 3523 drugs averaged across 1017 plans between 2009 and 2012. “Payment update” is the change in payments for the diagnosis the drug treats under the 2010 and 2011 payment systems, as reported in Table 2 (positive values mean payments increase). Each observation is weighted by the drug’s expenditure in Medicare Advantage in 2010. Standard errors are clustered on drugs. * and ** represent significance at the 5% and 1% levels.
Table 4: Benefit Design Response to Variance Change
Coverage (p.p.) Copay ($) Payment Update 0.015 -0.095*
(0.017) (0.039) Variance Change -0.011 0.062*
(0.011) (0.028) N 10,491 10,491
This table reports the results of estimating Equation 4 on the change in coverage and copay for 3523 drugs averaged across 1017 plans between 2009 and 2012. “Payment update” is the change in payments for the diagnosis the drug treats under the 2010 and 2011 payment systems, as reported in Table 2 (positive values mean payments increase). “Variance change” is the change in the variance in plan expenditures for the diagnosis under the 2011 and 2010 payment systems (positive values mean variance increases). Each observa- tion is weighted by the drug’s expenditure in Medicare Advantage in 2010. Standard errors are clustered on drugs. * and ** represent significance at the 5% and 1% levels.
27
Copay 0.43230** -21.50658** -7.63656** (0.00633) (3.50300) (1.83101)
Implied ε 0.002 -0.107 -0.038
N (individual X diag) 8,464,598 8,464,598 4,031,367
First Stage -0.00034** -0.00070** (0.00004) (0.00007)
This table reports the results of estimating Equation 5 on the change in days supplied to each individual for drugs treating a given diagnosis over a year pair between 2009 and 2011. The first column simply applies OLS to Equation ??; the remaining instrument for the change in copay using the payment update. Each first-differences regression controls for an individual×diagnosis fixed effect (implicitly), a diagnosis time trend, and a full set of health status controls. * and ** represent significance at the 5% and 1% levels.
28
Introduction
Empirical Analysis of Government Payments In Managed Competition
Demand Elasticities for Pharmaceuticals
Data
Associating Drugs and Diagnoses
Testing Benefit Design Response
Results: Associating Drugs and Diagnoses
Results: Testing Benefit Design Response
Results: Recovering Demand Elasticities