Managed care and medical expenditures of Medicare beneficiaries.

Journal of Health Economics 27 (2008) 1451–1461
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Journal of Health Economics
journa l homepage: www.e lsev ier .com/ locate /econbase
Managed care and medical expenditures of Medicare beneficiaries
Michael Chernewa,∗, Philip DeCiccab, Robert Townc
a Harvard University and NBER, United States
b McMaster University, Canada
c University of Minnesota and NBER, United States
a r t i c l e i n f o
Article history:
Received 3 August 2007
Received in revised form 29 July 2008
Accepted 31 July 2008
Available online 13 August 2008
JEL classification:
I11
I18
Keywords:
Managed care
Medicare
a b s t r a c t
This paper investigates the impact of Medicare HMO penetration on the medical care
expenditures incurred by Medicare fee-for-service (FFS) enrollees. We find that increasing
penetration leads to reduced spending on FFS beneficiaries. In particular, our estimates sug-
gest that the increase inHMOpenetration during our studyperiod led to approximately a 7%
decline in spending per FFS beneficiary. Similar models for various measures of health care
utilization find penetration-induced reductions consistent with our spending estimates.
Finally, we present evidence that suggests our estimated spending reductions are driven by
beneficiaries who have at least one chronic condition.
© 2008 Elsevier B.V. All rights reserved.
1. Introduction
The 1990s saw a dramatic increase in the percentage of Medicare enrollees who joined an HMO. After the Balanced Budget
Act of 1997, enrollment dropped dramatically, but following the Medicare Modernization Act of 2003, enrollment is again on
the rise. By 2007, 20% of Medicare beneficiaries were enrolled in a privately administered health plan.1 The Congressional
Budget Office predicts further increases in Medicare HMO enrollment, suggesting enrollment in HMOs (excluding Private
Fee-For-Service Plans and regional PPOs) will rise by about 50% by 2017.2 While the rise of a meaningful managed care sector
may affect both the financial health of the program and the physical health of Medicare enrollees, we focus on the former.3 In
particular, we ask the question: Does Medicare HMO penetration affect total health care spending incurred by fee-for-service
beneficiaries? Put differently, do the effects of HMO penetration spill over into fee-for-service Medicare?
Spillover effects refer to changes in the care delivered to fee-for-service enrollees that arise due to changes in HMO enroll-
ment among Medicare beneficiaries, holding the health status of fee-for-service enrollees constant. There are several reasons
to expect spillovers. For example, if physicians tend to practice similarly for all patients, more managed care enrollment may
alter practice patterns for fee-for-service patients. Additionally, managed care enrollment may influence aspects of market
∗ Corresponding author at: Harvard Medical School, Department of Health Care Policy, 180 Longwood Avenue, Boston, MA 02115, United States. Tel.: +1
617 432 0174.
E-mail address: chernew@hcp.med.harvard.edu (M. Chernew).
1 Source: Kaiser Family Foundation, Medicare Advantage Fact Sheet, June 2007.
2 Peter R. Orszag, The Medicare Advantage Program: Trends and Options. CBO Testimony before the Subcommittee on Health Committee on Ways and
Means U.S. House of Representatives, March 21, 2007.
3 In late 2000, the Health Care Financing Administration (HCFA), now called the Center for Medicare and Medicaid Services (CMS), convened a technical
review panel to examine the assumptions used by the Office of Actuaries to assess the financial health of the Medicare Trust Funds. The panel concluded
that these assumptions were in need of revision. One specific area was forecasting the impact of Medicare managed care on total Medicare costs.
0167-6296/$ – see front matter © 2008 Elsevier B.V. All rights reserved.
doi:10.1016/j.jhealeco.2008.07.014

1452 M. Chernew et al. / Journal of Health Economics 27 (2008) 1451–1461
structure such as the number of hospitals, beds or available services over time (Chernew, 1995a; Baker and Brown, 1999).
In turn, these changes could impact practice patterns for all individuals in a given market. Overall, the notion behind the
possibility of spillovers is that an increased managed care presence may change the manner in which fee-for-service patients
are treated.
Accurate assessment of spillovers is important. In the current policy debate, it has been suggested that Medicare managed
careplansareoverpaidand there is somediscussionof reducingpayment rates.4 However, if spilloversare substantial, optimal
payment rates from CMS to HMOs might be higher than they otherwise would be, to encourage greater HMO participation
in the Medicare program. Conceptually, this would reflect some of the externality represented by savings to FFS Medicare
stemming from Medicare managed care enrollment.
Even if CMS adopted a different payment approach for managed care, such as competitive bidding (which is used to
some extent in the current Medicare managed care program) or proxy shopping as outlined by Havighurst (1970) and Rose-
Ackerman (1983), the issue of externalities caused by spillovers is important. Plan bids and proxy prices would not capture
these spillover externalities. Thus, more generally, if spillovers are significant, additional steps to increase enrollment in
HMOs might be warranted.
Assessing the magnitude of spillovers is also important for assessing the fiscal impact of the Medicare managed care
program. The direct fiscal impact of a Medicare beneficiary choosing to enroll in an HMO depends on Medicare’s payment
rates to HMOs, relative to what Medicare would have paid if they remained in the traditional fee-for-service system. Because
payment rates for Medicare HMOs were historically tied to the local average costs in fee-for-service Medicare, and because
HMOs tended to attract a relatively healthier population, analysts have felt that growth in HMO enrollment increases the
total costs of Medicare. Any cost savings obtained by HMOs were either captured by the HMOs or competed away via more
extensive benefit packages. Analysis by MedPAC suggests that spending by Medicare for HMO participants was 12% higher
relative to demographically similar beneficiaries in traditional Medicare (MedPAC, 2008). However, if there are spillover
effects from Medicare HMO penetration, the savings may offset the costs associated with favorable selection.
In this paper, we assess the spillover between Medicare HMO enrollment and expenditures on Medicare fee-for-service
beneficiaries. Our basic approach is to regress spending by fee-for-service Medicare beneficiaries on the share of Medicare
beneficiaries in their county who are enrolled in HMO plans. Because HMO penetration is potentially endogenous, we use
county-level variation in Medicare payment policy as an instrument for Medicare-specific HMO penetration, which we also
measure at the county-level on the assumption that a county geographically represents the relevant market. This approach
has been used successfully in other contexts (c.f., Town and Liu, 2003; Gowrisankaran and Town, 2006). Our identification
comes from longitudinal variation in payment rates over our study period (1994–2001) and reflects, in large part, reforms
instituted in the Balanced Budget Act of 1997 (BBA) and idiosyncrasies in Medicare payment rules.
We find evidence of substantial spillover in a sample of fee-for-service Medicare beneficiaries. In particular, in instru-
mental variables models we find that a 1% point increase in county-level Medicare HMO penetration is associated with a
.9% reduction in individual annual spending on fee-for-service beneficiaries. These estimates are larger in magnitude than
corresponding least squares estimates, which also imply the existence of such spillovers. To investigate the validity of our
findings, we also estimate models which examine the impact of Medicare HMO penetration on various categories of health
care utilization. We find that increases in county-level Medicare HMO penetration reduce both inpatient and outpatient
events, with larger effects found on intensive utilization margins. These estimates are consistent with our main finding
that increased Medicare HMO penetration reduced spending by fee-for-service beneficiaries in that they provide a plausible
mechanism for the spending reductions. Finally, we present evidence that this relationship is driven by individuals, who
report at least one chronic condition. By contrast, we find no evidence of a systematic relationship for beneficiaries without
any reported chronic conditions.
2. Background
The Tax Equity and Fiscal Responsibility Act (TEFRA), passed in 1982, directed the Health Care Financing Administration
(HCFA) to contract with HMOs to provide a managed care option to Medicare enrollees. Under the Medicare HMO program,
Medicare enrollees can forgo the traditional Medicare insurance program and enroll in a qualified HMO. The HMO agrees to
provide health insurance that covers all Medicare-covered services (Parts A and B) in exchange for a per-capita fee, which
varies at the county-level, from CMS. In addition, HMOs may offer benefits beyond those available to fee-for-service Medicare
beneficiaries. The rationale underlying TEFRA is that HMOs may be more efficient at providing care thereby reducing federal
Medicare expenditures.5 Beginning in the early 1990s and extending to the latter part of the decade, there was a surge in the
share of Medicare beneficiaries who took advantage of this option.
A vast literature, including several reviews, documents the effects of managed care plans (Miller and Luft, 1997; Miller and
Luft, 2002). One strand of that literature examines the impact of managed care enrollment on Medicare costs or utilization
4 See, for example, “Private Remedy: Insurers Fight to Defend Lucrative Medicare Business,” Wall Street Journal, April 30, 2007.
5 HMO enrollment may be beneficial for enrollees, themselves, and the Medicare program if Medicare HMOs provide care more efficiently than the
traditional fee-for-service system. More efficient care can manifest itself through lower costs of care, higher quality or through broader benefit coverage. If
savings exist from HMOs, Medicare ultimately may save money and/or enrollees may receive enhanced benefits because of competition among plans.

M. Chernew et al. / Journal of Health Economics 27 (2008) 1451–1461 1453
(Baker and Corts, 1996; Baker, 1997; Baker and Shankarkumar, 1997; Cutler and Sheiner, 1997; Baker and McClellan, 2001;
Cao and McGuire, 2003; Bundorf et al., 2004) as well as the somewhat larger literature examining the impact of overall
HMO activity on the market as a whole (Robinson and Luft, 1988; Robinson, 1991; Melnick and Zwanziger, 1995; Wickizer
and Feldstein, 1995; Robinson, 1996; Gaskin and Hadley, 1997; Hill and Wolfe, 1997). Overall, this research provides strong
support for the general proposition that markets are connected and thus we may reasonably expect activities in the Medicare
HMO market to influence the expenditures associated with treating Medicare fee-for-service beneficiaries.6
Much of the existing literature on spillovers ignores the potential endogeneity of HMO penetration. However, this strategy
may be flawed if, for example, omitted area characteristics are correlated with Medicare HMO penetration and also have
an independent impact on expenditures on fee-for-service enrollees.7 Baker (1997), Cao and McGuire (2003) and Mello et
al. (2002) are exceptions as they report instrumental variables estimates. Baker (1997) and Cao and McGuire (2003) use
cross-sectional models so their identification is fundamentally different from ours. Mello et al. (2002) examine utilization
(not spending) using payment rate changes similar to our approach. They have a short panel from 1993 to 1996, prior to the
BBA.
Our empirical strategy, discussed in detail in the next section, relies on a strong relationship between payment rates,
which are specific to counties, and aggregate enrollment levels. Conceptually, higher payment rates induce more managed
care plans to enter the Medicare market and induce plans to either offer more generous benefits or lower premiums, which
attract beneficiaries. The findings of several studies support this intuition, suggesting that higher payment rates affect HMO
participation in the Medicare program and the benefits offered (Cawley et al., 2002; Town and Liu, 2003; Pizer and Frakt,
2002). However, none of these studies directly measures the impact of payment changes on aggregated HMO enrollment at
the county-level.
Because spillovers likely reflect the impact that managed care plans have on practice patterns, the magnitude of the
spillover effect may depend on the exact mechanism by which plans attract beneficiaries and the characteristics of those
attracted to managed care plans. For example, if managed care plans attract beneficiaries by offering more generous benefits,
as opposed to by lowering premiums, those benefits may influence practice patterns and thereby affect spillovers. Similarly if
managed care plans attract beneficiarieswith certain clinical conditions, the impact on practicemay be concentrated in those
conditions and, depending on the distribution of beneficiaries remaining in the FFS Medicare program, may affect spillovers.
Our analysis should thus be considered a reduced form analysis in which we do not try to disentangle the underlying
mechanisms that drive spillovers.
3. Empirical strategy and related issues
3.1. Basic model
Using a sample of individuals enrolled in traditional fee-for-service Medicare, we estimate models of the form:
LogExpenditure
ict
= ˛
c
+ �
t
+ ˇMC
ct
+ �X
it
+ ε
ict
, (1)
where i indexes the individual fee-for-service beneficiary, c represents county of residence and t represents year of interview.
Expenditure represents total annual medical care spending on fee-for-service beneficiaries enrolled in a given county in a
givenyear.8 In later specifications,we replace spendingwithmeasures of health careutilization (e.g., inpatient andoutpatient
events, doctor visits, etc.) in an attempt to better understand the mechanism driving our spending estimates. MC represents
the fraction of Medicare beneficiaries enrolled in an HMO in a given county in a particular year. Because we include county
fixed effects (˛c) in our specification, we identify the impact of Medicare HMO penetration on spending via within-county
changes in penetration. To the extent that there are unobserved characteristics that are correlated with both penetration and
spending (e.g., county-level health status), this represents an improvement over cross-sectional estimation. In addition, we
also include a vector of year effects (� t) to account for trends that are common across all counties in our sample. The vector
X represents individual covariates that will affect demand for services. These include beneficiary demographic information
as well as additional health status measures and other variables likely correlated with demand. In addition to self-reported
health, additional covariates include experience with sixteen diseases/disorders as well as smoking status and body mass
index.9 In our preferred specification, we add other county-level information including overall commercial HMO penetration
and various measures of county-specific medical resources.
6 Note also that a series of studies by Zwanziger, Melnick and colleagues reach a similar qualitative conclusion using a somewhat different approach,
emphasizing the importance of selective contracting on costs, without explicitly controlling for managed care penetration (Zwanziger and Melnick, 1988;
Melnick et al., 1989a,b; Zwanziger et al., 1994).
7 Baker (1997), Cao and McGuire (2003) and Mello et al. (2002) are exceptions as they report instrumental variables estimates. Baker (1997) and Cao
and McGuire (2003) use cross-sectional models so their identification is fundamentally different from ours. Mello et al. (2002) use payment rate changes,
similar to our approach, using a short panel from 1993 to 1996, prior to the BBA. These latter authors, however, examine utilization and not spending.
8 This specification is similar to those found in the existing literature, though we use individual data.
9 The sixteen disease/disorder indicators are based on a central question which asks respondents if they have ever had: arthritis, rheumatoid arthritis,
emphysema, Alzheimer’s disease, hip fracture, cancer, skin cancer, Parkinson’s disease, at least partial paralysis, psychiatric disorder, coronary heart disease,
hypertension, diabetes, myocardial infarction, stroke or a hearing problem not included in this list.

1454 M. Chernew et al. / Journal of Health Economics 27 (2008) 1451–1461
The disturbance term in Eq. (1) is likely correlated with county-level Medicare HMO penetration. Specifically, there may
be unobserved, time-varying county level traits that are correlated with both Medicare HMO penetration and spending, such
as consolidation in the provider market or changes in employer demand. Assuming that HMOs tend to enter areas with rising
fee-for-service spending (because they have greater potential to achieve savings), we would expect least squares estimates
of ˇ to be biased upwards. If the true effect of penetration on expenditures is negative, this means ˇ will be biased towards
zero.
3.2. Instrumental variables
Wecorrect for this potential bias using an instrumental variables (IV) approach. In particular,weuse county-level payment
rates from CMS to HMOs as instruments to identify the effect of county-level Medicare HMO penetration.10 To the extent
that these payment rates are correlated with county-level penetration, but are orthogonal to current fee-for-service expen-
ditures, our IV estimates represent an improvement over corresponding OLS estimates. Given our expectations regarding
HMO entrance into markets with relatively high cost growth in expenditures, and given our expectation that healthier
enrollees chose HMOs, we expect the IV estimates to be more negative, and hence larger in magnitude, than our OLS
estimates.11
A formal model that highlights the underlying logic for a causal relationship between Medicare HMO penetration and
payment rates canbe found inGowrisankaranandTown (2006). In their frameworkMedicareHMOsenter andmakepremium
andbenefit designdecisions based on thepayment rate knowing that the benefit designmay result in differential selection. In
equilibrium, their simulations show Medicare HMO enrollments are a nonlinear and increasing function of the payment rate.
The intuition is straightforward. All else equal, increasing the payments increases the profitability of the marginal enrollee.
Thus, larger payments make it profitable for plans to incur the costs of entering the market and conditional on operating
offering more generous benefits in order to increase enrollments.
Variation in county-level payment rates comes from two sources. First, prior to the BBA, Medicare based its payment
to HMOs on the per capita costs of the fee-for-service enrollees in counties. This may seem to suggest that payment rates
would be a poor instrument for HMO penetration in our model because of their apparent relationship with fee-for-service
spending. However, payment rates at time t were based on average fee-for-service spending between periods t-8 to t-3.12
The validity of county-level payment rates depends on the degree of autocorrelation in fee-for-service spending over time,
which we explore below.
The other source of payment variation is the BBA of 1997, and subsequent refinements, which broke the link between
payment rates and average local fee-for-service costs. The BBA fundamentally modified Medicare’s payment methodology.
While the changes in thepayment formula are relatively technical, for our purposes, the important feature is that adjustments
to county-level payments are now divorced from the Medicare fee-for-service experience in the county. Specifically, after
the BBA, county rates were set equal to the maximum of three rates: (a) a blended input price which is a combination of an
adjusted national rate and an area-specific rate, (b) a floor payment designed to increase the rates in low-paid counties, and
(c) a minimum increase of 2% per year. Initially, most counties were either ceiling or floor counties, minimizing the variation
in payment changes post-BBA. However, the subsequent refinements to the BBA payment formulas added greater variation
in payments across counties. In most counties the post-BBA payment formula led to a substantial decrease in payment rates
over what HMOs would have received prior to the BBA. It is estimated that the BBA methodology lowered payments to HMOs
by an average of 6%.13 In addition to reducing the level of payments, the BBA also diminished the variance in payment rates
across counties. For example, the standard deviation of the payment rate fell from roughly $89 to $60 from 1994 to 2001 in
constant 1994 dollars. That said, the amount of within-county variation in payment rates is more relevant than the level of
cross-sectional variation, given our identification strategy. To assess this, we regress the payment rate variable on a full set
of county and year fixed effects. We then compute the variance inflation factor which, in this context, is the reciprocal of
one minus the R-squared from this auxiliary regression. Conventionally, it is assumed that if the variance inflation factor is
greater than 10, there is not sufficient independent variation. However, our auxiliary regression yields an R-squared small
enough to imply sufficient variation in payment rates. In particular, the variance inflation factor is about 3.4.
3.3. Tests of the instruments
While the impact of the BBA on payment rates is likely unrelated to the error term in Eq. (1), the payment rate still may
be a “weak” instrument. We test the strength of our instrument set via a standard F-test. As will be seen, all F-tests strongly
reject the hypothesis that our instruments are unrelated to county-level Medicare HMO enrollment rates.14
10 Other potential instruments could be based on the distribution of firm sizes in an area, though this is most likely more relevant to commercial HMO
penetration than Medicare-specific penetration. Baker (1997) advocates the use of such an instrument for commercial HMO penetration.
11 Even with IV estimation, change in the composition of the FFS population remains possible. We discuss this later in this section.
12 More specifically, these are 5-year averages, starting 8 years prior to time t.
13 Source: U.S. Congressional Budget Office (1999).
14 A standard rule-of-thumb is that this F-statistic be greater than 10. All of our F-statistics are greater than 37. In addition, we report the partial R-squared
for each first-stage regression.

M. Chernew et al. / Journal of Health Economics 27 (2008) 1451–1461 1455
Table 1
Pre- and post-BBA growth in mean fee-for-service expenditure, by relative magnitude of payment growth
Ratio of payment growth post-BBA to payment growth pre-BBA
Below median Above median
% growth in mean FFS expenditure
1994–1996 10.0% 9.2%
1998–2001 25.1% 16.7%
Notes: Payment growth pre-BBA is the percent increase in payment from 1994 to 1996 and payment growth post-BBA is the percent increase in payment
from 1998 to 2001. The ratio of these percentages is computed for each sample county and the resulting distribution is divided into two groups—those
counties below the median of this ratio and those counties above it. These two groups are represented in the columns above. Conceptually, counties below
median are those whose payment rates are slowing, while those above median are counties whose payment rates are accelerating over time.
The validity of these county-level payments rates also requires payment changes to be unrelated to existing trends in
spending across counties. For example, our identification strategy would be flawed if the counties that experienced relatively
generous or stingy growth in payments due to the BBA would have had systematically different spending trends not captured
by our covariates.
To examine this possibility, we divided counties in our sample into those whose payment growth was slowed following
the BBA and those whose spending growth was accelerated.15 This taxonomy is based on the ratio of payment growth in
each county post-BBA to growth pre-BBA. The results from this exercise are presented in Table 1. Prior to 1997, counties
which were treated generously following the BBA (i.e., had above median relative payment growth) had roughly the same
percent growth in expenditures as those counties which were treated less generously. In particular, the former counties
experienced growth in spending on fee-for-service beneficiaries of 9.2%, while the latter counties experienced growth of
10%. This suggests spending trends prior to the BBA were similar across counties that later were differentially impacted
by the BBA and subsequent payment regimes. After the BBA, and consistent with results we report below, counties whose
payment growth was slowed following the BBA had higher percentage FFS spending growth (25.1%) relative to those counties
whose payment growth was accelerated following the BBA (16.7%).
We more formally examine the relationship between lagged cost growth (which drives payment changes), and current
cost growth by estimating a first-order autoregression of the residuals from a regression of log spending by fee-for-service
beneficiaries on all of our exogenous variables, including the payment rates.16 The autocorrelation parameter appears to
be sufficiently small to allow this to be a useful source of identifying variation. In particular, the parameter ranges from
0.04 to 0.07 and is not statistically different from zero. Analysis of Dartmouth Atlas data, based on costs measured at the
Healthcare Referral Regions (HRRs) supports this analysis. The correlation between cost increases in FFS Medicare between
1996 and 2000 and cost increases between 2002 to 2003 is minimal (� =−.08) and not statistically significant, suggesting
that correlation between current payment changes (which are based on lagged cost increases) and contemporaneous cost
changes is near zero.
3.4. Selection effects
The measurement of spillovers is complicated by selection concerns. Selection effects refer to the impact of non-random
sorting of beneficiaries into Medicare managed care. A common concern is that relatively healthier individuals will opt
out of fee-for-service Medicare. The concern has fiscal implications. In particular, if healthier beneficiaries systematically
enroll in Medicare HMOs, the costs for those remaining in the fee-for-service sector will rise because that population will
be, on average, less healthy. Conditional on such sorting, costs will be higher in markets with high HMO penetration, even
if care for any given fee-for-service patient is unaffected by managed care penetration. In contrast to the spillover story, if
fee-for-service costs were regressed on Medicare HMO penetration, the estimated coefficient would be positive.
In our IV context, the issue is similar, but we are concerned with whether enrollment shifts induced by payment changes
are systematically related to health status or other enrollee traits that may affect spending. If the FFS beneficiaries who
are induced by payment changes to leave the FFS system for HMOs are healthier than the typical FFS beneficiary, then the
remaining FFS population may become less healthy on average. Such movement would generate estimates that would under-
estimate spillover effects.17 Recent evidence, however, indicates that there is no association between favorable selection into
Medicare HMOs and county-level HMO penetration (Mello et al., 2003), suggesting that at the margin, shifts in HMO pene-
tration associated with payment changes do not substantially alter the health status of fee-for-service enrollees. However,
Cao and McGuire (2003), using service-level variation, find evidence of systematically healthier beneficiaries joining HMOs
in markets with HMO penetration rates below 15%.
15 The figures that follow are generated from our sample of counties. See Section 4.2 for details on our analysis sample, including selection of counties.
16 This required collapsing the residuals to county-year cells, so the residuals used in the autoregression are averaged over all sample individuals in a given
county in a particular year.
17 Of course, if less healthy beneficiaries are induced to leave fee-for-service Medicare for HMOs as payments change, then our measured spillover effect
may overstate the magnitude of the true effect.