DROPPED OUT OR PUSHED OUT? INSURANCE MARKET
EXIT AND PROVIDER MARKET POWER IN MEDICARE
ADVANTAGE
DARIA PELECH
Current affiliation: Congressional Budget Office
Ford House Office Building, Floor 4
Second and D Streets, SW
Washington, DC 20515
202.225.2644
[email protected]
I am grateful to David Cutler, Michael McWilliams, Tom McGuire, Hannah Neprash, Linda Bilheimer,
Jessica Banthin, Tim Layton, Martin Anderson, and Adam Sacarny for making helpful comments on early
drafts. I am deeply grateful to Hannah Neprash and Michael McWilliams for data on physician concentration
and vertical integration, David Cutler for American Hospital Association data, and Michael McWilliams and
Mike Chernew for access to Interstudy data. This work was completed as part of my dissertation research
in the Harvard Health Policy Ph.D. program, which was supported in part by the Agency for Healthcare
Research and Quality Grant no. R36HS023477. The content of this paper and views expressed herein are
solely my responsibility and should not be interpreted as being those of my employer, the Congressional
Budget Office, or my funding agency, the Agency for Healthcare Research and Quality. All mistakes are my
own.
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INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
Abstract.
1
This paper tests whether insurers are less likely to operate in con-
centrated hospital and physician markets. A 2011 policy change required certain insurers in Medicare Advantage to form provider networks from scratch.
In response to this change, insurers cancelled two-thirds of a↵ected plans. Using detailed data on provider and insurer market structure, I compare markets
where insurers built networks to those that they exited. Overall, insurers in
the least competitive hospital and physician markets were 14 percentage points
more likely to exit than those in the most competitive. Conversely, insurers
with the most market power were less likely to exit than those with less market power, and an insurer’s market power had the largest e↵ect on exit in the
least competitive hospital markets. These findings suggest that concentrated
provider markets contribute to insurer exit and that insurers with less market
power are less able to survive in concentrated provider markets.
Keywords: Health economics, insurer competition, hospital competition, physician competition
Highlights:
(1) This paper provides evidence that greater hospital and physician market concentration
increases the likelihood that insurers exit markets.
(2) It also suggests that insurers with greater market power are less likely to exit a market.
(3) In the most hospital concentrated markets, insurers who controlled the least market share
more likely to exit than the those that controlled the most, while in more competitive
hospital markets, they were statistically no more likely to exit.
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US health insurance market concentration is high, with the top two insurers controlling over
half the commercial market in 45 states.(1) High insurance market concentration is problematic, as
evidence suggests that insurers with greater market power increase health plan premiums and reduce
benefit generosity.(2–11) However, the reasons why health insurance markets are so concentrated
are not well understood. This study tests one frequently-mentioned theory: that insurers are less
likely to operate in markets where hospitals and physicians have substantial market power.(12, 13)
Though it is often said that provider market power reduces insurer market participation,
the empirical evidence on this issue is limited. A large body of literature shows that insurers pay
higher prices for providers’ services when providers have substantial market power.1 These findings
suggest that provider market power could suppress insurer market entry by making it difficult for
insurers to enter and operate profitably. However, whether a link between provider prices and
insurer market participation exists has not been empirically established.
Of the substantial research on the determinants of insurer market participation(28–35), only
two studies test whether provider market structure a↵ects insurer entry or exit. Ho (2009) finds that
the difficulty of forming hospital networks is a substantial barrier to entry for vertically-integrated
insurers (i.e., insurers who operate their own provider networks).(34) Dranove et al. (2003) finds
that hospital market power reduces entry among local Health Maintenance Organizations (HMOs),
but has no e↵ect on HMOs that operate nationally or regionally.(36)
These studies are an important step in describing how provider market power a↵ects insurer
market participation, but both have limitations. Both studies focus on specific types of insurance products for which entry might be particularly difficult. Ho’s results pertain specifically to
vertically-integrated insurers, who must simultaneously enter both insurer and provider markets.
Dranove, et. al. specifically examine HMOs, which have narrower provider networks than preferred
provider organizations (PPOs) and enroll less than 25% of the total privately-insured population
1
A large literature has established that greater hospital market power increases service prices,(14–16) and a
smaller but growing body of literature suggests that greater physician market power has similar e↵ects.(17–
19) Several recent papers also suggest that insurers can use their market power to counter providers’ market
power and bargain hospital or physician prices back down.(20–26) However, the interaction between provider
and insurer market power is complicated. For instance, Melnick, et al. (2011) find that insurers can only
exercise downward pressure on hospital prices in very concentrated insurance markets. Alternately, Ho and
Lee (2013) find that the e↵ect of insurer competition on hospital prices varies across hospitals.(27) Although
the most desirable hospitals command higher prices in competitive insurance markets, prices for most other
hospitals are lower in competitive insurance markets. This may be because insurers in competitive insurance
markets are less able to pass higher hospital prices on to consumers.
INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
3
nationwide.(37) Additionally, both studies focus on how hospital market power a↵ects insurer entry,
but do not speak to whether physician market power has any e↵ect on entry. Lastly, both studies
rely on structural assumptions to address the fact that insurer market entry and provider market
power may be simultaneously determined.
This paper adds to prior research by identifying an exogenous source of variation in provider
market power with insurers. Specifically, I examine a natural experiment in which a group of
insurers were forced to form networks from scratch. Currently, most health insurance plans form
limited provider networks in order to control costs. Under these arrangements, hospitals and doctors
discount their service prices in exchange for being in an insurer’s network and getting access to
an insurer’s patients. Insurers use these discounted service prices to reduce premiums and attract
more enrollees. However, when providers have substantial market power, they can demand higher
service prices from insurers or threaten to not join an insurer’s network, which in turn can make it
difficult for insurers to operate profitably.
In the private Medicare insurance market (Medicare Advantage), there was a group of plans
that were not required to form networks or negotiate prices with providers. Instead, these plans
- called private fee for service (PFFS) plans - could pay providers administratively-set Medicare
fee-for-service prices. When providers rendered services to beneficiaries, they were required by law
to accept PFFS plans’ payment rates for those services, provided that they had been informed of
the plan’s terms and conditions in advance. As Medicare payment rates are generally lower than
negotiated rates paid by commercial insurance plans(38, 39), this statutory requirement meant
that PFFS plans could pay substantially discounted service prices, without having to negotiate
with providers or explicitly limit which providers their enrollees could see.
These special statutory conditions likely gave these plans several competitive advantages.
In addition to paying lower service prices, PFFS insurers did not have to form networks before
entering a market. This reduced their fixed costs of entry and allowed them to di↵erentially enter
markets where Medicare’s payments to plan were relatively high.(40, 41) It also meant that many
PFFS plans marketed themselves to beneficiaries as having “unlimited networks”, which may have
made them especially appealing to enrollees.2
2
PFFS plans did not have to form networks, but this did not mean that PFFS enrollees could visit “any
provider”. Providers had to accept PFFS plans’ rates after treating a patient, but they could refuse to treat
PFFS plans’ beneficiaries in advance. However, Medicare reports suggest that many beneficiaries did not
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After an across-the-board increase in Medicare’s payments to Medicare Advantage plans in
2003, the number of insurers o↵ering PFFS plans quadrupled and the number of enrollees in these
plans increased nine-fold (from 220,000 to over 2 million).(41, 43) However, the growth in PFFS
enrollment was concentrated in markets where government payments to plans were high, relative
to costs. It appeared that insurers were di↵erentially entering profitable counties, because they did
not face the fixed costs of forming networks. Di↵erential entry resulted in Medicare paying more
for each PFFS enrollee than for other types of Medicare beneficiaries and led many observers to
conclude that these plans were less efficient or “overpaid.”(40, 44)3
In 2008, Congress responded to the concern that PFFS insurers were exploiting favorable
requirements and di↵erentially enrolling beneficiaries in the most profitable counties. They passed
a law requiring PFFS insurers to form provider networks for their plans and pay negotiated provider
prices in most counties.4 This legislation led to the cancellation of over two-thirds of PFFS plans
over the next four years.(11)
This policy change and subsequent cancellation of PFFS plans provides an opportunity to
test whether insurers are more likely to exit markets where hospitals and doctors have greater
market power. Before the policy change, provider market power had little e↵ect on PFFS plans’
costs because PFFS plans did not have to negotiate with providers. Then, the policy caused an
exogenous shock to insurer-provider bargaining by forcing insurers to form networks and negotiate
prices. The size of this shock and its e↵ect on insurer costs varied across markets based on existing
variation in provider market power.
Insurers responded to this shock by selectively exiting some markets and building networks
in others. To measure the impact of provider market power on exit, I construct indicators for when
an insurer exits a market and regress these exit indicators on measures of provider market power,
controlling for insurers’ market power and a range of other variables that a↵ect plan profitability.
understand that this was the case and some insurers were remiss in making sure that these special payment
conditions were understood.(42)
3
PFFS plans also did not manage enrollees’ care or limit the amount of care hospitals or doctors provide.
This meant that the additional financial benefits they provided enrollees were the only functional di↵erence
between PFFS plans and fee-for-service Medicare, and these benefits were financed by premiums and taxes
paid by all Medicare beneficiaries (not just those enrolled in PFFS plans).
4
Insurers were required to form networks in any county were two or more Medicare Advantage insurers
o↵ered plans with local provider networks (HMOs or PPOs). This meant that the law a↵ected 58% of
counties containing 90% of the US population. Una↵ected counties were largely rural areas where HMOs
and PPOs found it difficult to enter.
INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
5
Results show that greater provider market power increased insurer exit and greater insurer
market power decreased it. Insurers in the most concentrated hospital markets were 9 percentage
points more likely to exit than those in the least concentrated markets, while insurers in the most
concentrated physician markets were 14 percentage points more likely to exit than insurers in
the least concentrated. Conversely, insurers with the most market power were 22-27 percentage
points less likely to exit then insurers with the least. Additionally, insurer market power interacts
significantly with hospital market power. In concentrated hospital markets, insurers with belowmedian market share were 24 percentage points more likely to exit than insurers with above-median
market share. In less concentrated hospital markets, insurers of all sizes exited at statistically similar
rates. Taken together, these results suggest that provider market power decreases insurer market
participation, and insurer market power may o↵set provider market power by helping insurers
continue to operate.
This project has several limitations worth noting. First, PFFS plans were a relatively small
part of the Medicare market, and many PFFS insurers were small and marginal players in Medicare
Advantage markets. However, the sample studied here includes plans o↵ered by some of the largest
insurers operating in commercial health insurance markets (United and Humana). The factors that
a↵ect these insurers’ decisions about PFFS plans are plausibly also important for products in other
markets. Another important limitation is that the policies which led to PFFS plans’ existence
and subsequent exit were unusual and are unlikely to reproduced. However, the idiosyncratic set of
policy choices that led to their creation and cancellation provide a unique opportunity to understand
where insurers would choose to operate in the absence of provider market power.
Despite its limitations, this paper makes several contributions to the literature. First, though
two studies have examined how hospital market power a↵ects insurer market participation, this is,
to the author’s knowledge, the only study to also test how physician market power a↵ects insurer
market participation. It is also the first paper to model how hospital market power a↵ects plans
other than HMOs. Lastly, it documents the results of a unique natural experiment, in which
insurers operated without concern for provider market power before they were suddenly forced to
negotiate. Studying how they respond to this policy change gives an estimate of how large an e↵ect
provider market power has on insurer market participation.
There are several reasons why understanding whether provider market power limits insurer
market participation is important for policy. For instance, many federal and state policies seek
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to increase competition between insurers. Increasing insurer competition without also increasing
provider competition may reduce insurers’ bargaining power with providers. This might, in turn,
reduce insurer market participation, which would decrease competition. Additionally, the proliferation of narrow-network plans in health insurance exchanges has led to an interest in regulating
network adequacy. Though adequate networks are an essential part of health plans’ value, insurers
may use narrow networks to counter provider market power. If provider market power reduces insurer market participation, then there are trade-o↵s to be considered from the e↵ects of regulating
network adequacy.
1. Policy change
This paper studies a policy change that occurred in the Medicare Advantage program, which
is the private health insurance market within Medicare. Medicare is the government-sponsored
health insurance program for the elderly and disabled. Within Medicare, beneficiaries can enroll in
traditional fee-for-service (FFS) Medicare or the Medicare Advantage program. In FFS Medicare,
beneficiaries can visit virtually any provider, and the Medicare program pays a flat fee for each
service they receive. In Medicare Advantage, the government pays private insurers a per-enrollee
fee for enrolling beneficiaries, and insurers are responsible for paying hospitals and doctors for
enrollees’ care.
In FFS Medicare, beneficiaries’ provider networks are essentially unlimited. Beneficiaries
can visit any doctor who accepts Medicare and pay the same level of cost-sharing, regardless of
which provider they visit. In Medicare Advantage, most beneficiaries enroll in health maintenance
organizations (HMOs) or preferred provider organizations (PPOs), which have limited networks.
Insurers encourage enrollees to visit in-network providers through a variety of strategies, including
charging higher cost-sharing for visiting out-of-network providers or requiring referral for specialty
care. Beneficiaries who enroll in Medicare Advantage HMO/PPOs accept narrower provider networks because these plans generally o↵er more generous benefits than FFS Medicare. For instance,
cost-sharing is generally lower in Medicare Advantage plans, and Medicare Advantage plans often
o↵er supplemental benefits, such as drug, dental, or vision coverage. As of 2014, nearly a third of
Medicare beneficiaries were enrolled in a Medicare Advantage plan, and the vast majority of these
beneficiaries (88%) were enrolled in an HMO or PPO.(45)
INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
7
Prior to 2008, Medicare Advantage insurers could also o↵er private fee-for-service (PFFS)
plans, which did not explicitly restrict which providers a beneficiary could see. These plans were
authorized in 1997 by the Balanced Budget Act as a response to concerns about managed care plans
limiting patients’ ability to see providers of their choice. They were intended to give patients both
flexibility of provider choice and the option of purchasing additional benefits not provided in FFS
Medicare.(46) Under the law, plans did not have to form networks or negotiate with providers.
Instead, PFFS enrollees could visit any provider who accepted Medicare, and plans could pay
providers administratively-set Medicare prices for any services rendered, as long as the provider
was informed of the plan’s terms and conditions in advance.
These special conditions gave PFFS plans two key advantages over other plan types. First,
FFS Medicare payment rates are thought to be lower than the negotiated prices paid by private
insurers.(38) Because PFFS insurers had statutory power to pay Medicare prices, this may have
lowered their costs of providing care, relative to other plan types. Second, as they were not required
to form networks, PFFS plans had lower fixed costs of entry. This allowed them to di↵erentially
enter markets where Medicare plan payments were high, relative to costs.(40, 41)5
Before 2003, these plans were available in limited areas and o↵ered only by three or four
insurers. In 2003, the Medicare Prescription Drug, Improvement, and Modernization Act (MMA)
increased payments for Medicare Advantage plans in all areas. PFFS plans, with their lower costs
of entry, capitalized on payment increases by di↵erentially entering and marketing plans in areas
where payment benchmarks were substantially higher than the estimated costs of providing patient
care.(40, 41) The fact that these plans were operating in areas where Medicare Advantage plans
were relatively “overpaid” led observers to question their efficiency and value to the Medicare
program. Additionally, PFFS plans were not engaged in the types of medical management or
quality improvement activities that HMO/PPOs provided, and there were reports that doctors
often refused to treat PFFS patients because payment rates were too low. These concerns, together,
led to an interest in revising the statutes governing PFFS plans.
5
Medicare Advantage plans are paid through a modified bidding system. Medicare sets county-level bidding
targets, called “benchmarks”, and plans submit “bids” that reflect their estimated cost of providing beneficiaries with standard Medicare benefits. Plans that bid above the benchmark must charge enrollees an
additional premium beyond the standard Medicare premium. When plans bid below the benchmark, the
Medicare program “rebates” a portion of the di↵erence between the benchmark and bid back to the plan.
Plans are required to use these rebates to reduce the standard Medicare Part B premium or provide extra
benefits. During the study period, benchmarks were high, relative to costs, so most plans have bid below
the benchmark and passed rebates on to consumers in the form of more generous benefits.(47)
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In July 2008, Congress responded to reports that PFFS plans were overpaid by passing the
Medicare Improvements for Patients and Providers Act. This law allowed insurers to continue to
o↵er PFFS plans, but required them to form provider networks and pay negotiated prices in most
counties by 2011. They lost the statutory power to pay providers Medicare payment rates, they
had to form networks and sign formal contracts with providers, and they needed to be able to prove
that their networks were “adequate” to the Centers for Medicare and Medicaid Services (CMS).6
These rules applied to any county where two or more “networked plans” (HMOs or PPOs) already
operated.(48) By 2011, when they law became e↵ective, it applied to 58% of counties, containing
90% of the US population and 80% of PFFS plans’ 2008 enrollment. The counties that were exempt
were generally in rural areas with small populations.
In principle, PFFS insurers could have formed networks, negotiated payment rates with
providers, and continued to operate. In practice, they faced many barriers to network formation.
PFFS plans were often small or operated by marginal competitors, which likely made it difficult
for them to negotiate sufficient discounts with providers to continue operating. Additionally, PFFS
plans had only limited ways to encourage patients to stay in-network. While the policy change
allowed PFFS plans to charge higher cost-sharing for out-of-network care, they were prohibited by
law from barring patients from seeing out-of-network doctors or requiring that they obtain referrals
before seeking specialty care. Lastly, broad networks were the primary characteristic that had
di↵erentiated PFFS plans from other plan types. On average, PFFS plans had higher premiums
and less generous benefits than other plan types. Though insurers might have survived by forming
narrow networks, the resulting plans would not have compared favorably with HMOs or PPOs
without other changes in plan design.
Insurers’ response to the law shows that the new requirements made PFFS plans significantly
less profitable. Over the next three years, two-thirds of PFFS plans were cancelled, and PFFS
enrollment fell from a little over two million in 2008 to around 250,000 by 2015. As documented
elsewhere, many insurers responded to the law by exiting the PFFS business completely, canceling
6
Plans are required to show that they have a sufficient number of providers for their projected enrollment
and that the providers are within reasonable travel distance for most enrollees in a county. Guidelines on the
number of and travel distance to providers vary by type of county (i.e., rural, urban, or both) and provider
specialty.
INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
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all of their plans.(11) Those that remained continued o↵ering PFFS plans, but on a much more
limited basis. Of the 32 insurers who had o↵ered PFFS plans in 2009, only nine remained by 2012.7
Though only a small number of insurers continued to o↵er PFFS plans, these insurers appear
to have responded to the policy by making strategic exit decisions. Within counties where the policy
applied, they reduced the number of counties in which they operated by two thirds, sometimes
exiting one market and leaving their plans in adjacent markets. Some insurers even capitalized
on the specifics of the policy, introducing new PFFS plans in counties that were exempt from the
network requirement.8 This strategic behavior provides an opportunity to compare characteristics
of markets insurers exited to those where they chose to stay and form networks. As these nine
insurers include some of the largest players in national markets (i.e., United, Humana), who had
a presence in most states, their choices may generalize to the decisions about entry and network
formation that insurers must make in all health insurance markets.
2. Methods and Data
The questions of interest are 1) how provider market power a↵ected insurer exit following the
policy change and 2) how insurer market power interacted with provider market power. To test
whether insurers are more likely to exit markets where providers have substantial market power, I
regress exit indicators on measures of provider market concentration. Past literature suggests that
service prices, and hence insurer profitability, are also a↵ected by insurer market power. To reflect
this, measures of each insurer’s market power are included as additional explanatory variables. A
range of other variables that a↵ect insurer profitability are included as controls.
The baseline specification is:
(1)
7
P r(Exitijm = 1) =
0
+ Mjm + ✓Pm + Cijm + [⌘j + ⌫m + µm ] + "ijm
Several insurers had invested heavily in PFFS, and the policy change led to them cancelling all their PFFS
plans. The most dramatic example is Conventry, which o↵ered PFFS plans in 84% of Medicare Advantage
markets in 2008 and operated HMO or PPO plans in only 5% of Medicare Advantage markets.(44) Following
the announcement of the law, Coventry cancelled all their PFFS plans, and exited the majority of markets
in which they operated. Widespread exit damaged their business, and they were purchased by Aetna two
years later.
8United Healthcare, in particular, introduced new PFFS plans into over 100 additional counties that were
exempt from the policy.
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where Exitijm is an indicator = 1 if insurer j o↵ering plan i exited market m following the policy’s
implementation. Mjm is a vector of baseline insurer characteristics in market m, Pm are providers’
characteristics in market m, and Cijm are plan and market-specific controls. The main analysis
uses linear probability models,9 and to avoid endogeneity, all variables are fixed at pre-policy levels.
Exitijm is defined by comparing where insurers operated before the policy was passed and
after the policy was implemented. Exit is defined in this way using a simple pre-post indicator
because insurers used complex and diverse strategies when exiting. Moreover, when insurers should
be counted as “exiting” is not always clear. For instance, there was a three-year lag between when
the law passed and when insurers were required to form networks. In the interim, some insurers
cancelled their plans immediately, while others gradually reduced the geographic spread of their
PFFS plans. Some insurers even appear to have briefly entered markets to take advantage of
competitors’ exits, only to cancel their plans when the policy became e↵ective. In order to draw
generalizable conclusions about market participation, I use a simple pre-post study design.
Exit indicators and insurance market variables are defined on the county level, because the
Medicare program allows insurers to make county-by-county decisions about where to operate.
The preferred specification includes insurer fixed e↵ects (⌘j ) to capture unobserved variation in
insurer strategy and state fixed e↵ects (⌫m ) to capture unobserved variation in state regulatory
environments.10 Measures of provider market concentration used in this analysis do not vary
within markets, so county fixed e↵ects are only included in specifications that interact provider
concentration and insurer market power. However, as insurers showed substantial consensus about
which counties to exit, standard errors are clustered on the county level.
2.1. Sample. To test whether provider market power a↵ected exit rates, analysis focuses on insurers who appear to have made market-by-market decisions about where to cancel their plans (i.e.,
insurers who continued to o↵er PFFS plans in at least some markets.) As discussed in Section 1,
most insurers cancelled all PFFS plans in response to the policy. However, nine insurers continued
9The
preferred specification uses a linear probability model instead of a logit or probit model, because
some fixed e↵ects perfectly predict exit. This results in di↵erent samples in di↵erent specifications, which
changes logit coefficients and makes comparison across specifications difficult. As a robustness check, main
specifications are replicated using logit models; results are similar to those using linear probability models.
10For instance, state policies such as any-willing-provider laws, which that require insurers contract with
provider interested in serving their patients, may blunt insurers’ ability to negotiate prices and a↵ect
profits.(14)
INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
11
to operate PFFS plans in at least some counties. As Medicare Advantage insurers are allowed to
make county-by-county decisions about where to o↵er plans, insurers who continued to operate
PFFS plans in some counties must have found it profitable to build networks in those counties.
To determine which market characteristics led to insurers building networks vs. exiting, analysis
focuses on these insurers.
To determine which market characteristics encourage exit, the main sample is further restricted to plans in counties where insurers had to build a network from scratch. Specifically, I
exclude observations in counties 1) where the policy did not apply or 2) where an insurer already
o↵ered an HMO/PPO. PFFS plans in counties where an insurer already had an HMO/PPO were
in principle a↵ected by the policy, as insurers had to meet new administrative requirements and
pay providers higher prices. However, insurers who already had an HMO/PPO in a county could
use their existing HMO/PPO network to comply with the law and secure favorable prices. Perhaps
as a result, insurers exited fewer than 4% of the counties where they already had an HMO/PPO.
Each county-plan observation is coded with a binary indicator equal to 1 if the insurer
cancelled all plans in that county after the policy went into e↵ect and 0 if they continued to
o↵er plans.11 Empirically, many insurers replaced their PFFS plans with HMOs or PPOs following
the policy. As both these actions required insurers to build networks in the counties where they
did not already have an HMO/PPO, a plan-county observation is coded as not exiting either when
the insurer either keeps a PFFS plan or replaces their PFFS plan with an HMO/PPO.12
2009 is treated as the baseline year, as this was the first year that insurers could cancel
plans following the passage of policy. (Medicare Advantage plans sign annual contracts, and the
Medicare Improvements for Patients and Providers Act passed in late July 2008, after Medicare’s
2009 contracting deadline has passed.) However, though the majority of cancellations occurred
in 2009-2010 (see Figure 1), plans are frequently cancelled in Medicare Advantage. Because it is
possible that some insurers anticipated the passage of the law, I perform robustness checks using
11Consistent
with prior studies(31, 33), I consider an insurer to be o↵ering a plan when the plan enrolls more
than 11 beneficiaries, the threshold at which Medicare censors enrollment data.
12Insurers might prefer to replace a PFFS plan with an HMO/PPO if it helped them reduce costs by using
capitation or requiring referrals. On the other hand, under Medicare’s rules, they were not permitted to
automatically move their PFFS enrollees into their new HMOs or PPOs. If insurers wanted to keep their
existing enrollees, then they might have preferred to keep their PFFS plans.
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Percent of plan-county observations cancelled by year and type. Cancellation includes
terminations and service area reductions (when an insurer removes a plan from one
county but not another.)
Figure 1. Percent of county-plan observations cancelled by year, by type
an alternate sample that uses 2008 as the baseline year (see Appendix). Results are similar to main
results.
Exit is defined based on 2012 market participation, where 2012 is the year after the policy
came into e↵ect. Using 2012 as the follow-up year allows for insurer learning about policies and
markets. However, other Medicare Advantage policies, such as benchmark payment cuts and quality
bonuses, were also implemented in 2012. For this reason, exit is also defined using insurers’ 2011
market participation in a robustness check. Again, results are similar to main results.
Though the policy applied to 90% of the US population, counties exempt from the policy
provide an opportunity for placebo tests. To construct a placebo sample that is comparable to the
main sample, I construct a sample of plans o↵ered by the same insurers in counties where these
insurers did not have an HMO/PPO at baseline. As insurers in these counties were not required to
form networks, exit decisions in these counties should be unrelated to provider market concentration
(but not insurer market power). However, it should be noted that plans in counties una↵ected by
the policy are not perfect controls for plans in a↵ected counties. Chiefly, these counties were exempt
INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
13
from the policy because fewer than two HMOs or PPOs operated there in 2009. This means that,
by definition, insurance markets in these counties are very di↵erent from those in counties a↵ected
by the policy. As is discussed in the next section, many other characteristics of these counties (i.e.,
population density, provider supply) di↵er from the main sample as well.
2.2. Variables. The primary explanatory variables are measures of provider and insurer market
power. Provider market power is measured using standard market concentration measures. Specifically, both hospital and physician market power are measured using Hirschmann-Herfindahl Indices
(HHI), or the sum of firms’ squared market shares. HHI captures both the number of firms operating in a market and the distribution of shares across those firms. HHI plausibly reflects provider
power, as providers in more concentrated markets can more credibly threaten to refuse insurers’
patients.
The primary measures of insurer market power are constructed on the individual insurer
level, rather than the market level. For PFFS plans, it is important to capture individual insurer’s
market power, because PFFS plans were often not the dominant players in the market.13 Theoretically, higher insurer market concentration should be correlated with increased insurer exit for
marginal insurers and decreased exit for larger insurers, as marginal insurers’ are a less important
part of providers’ business. Additional regressions that include market-level measures of insurer
concentration suggest that this is indeed the case (see Appendix).
Hospital HHI is constructed on the hospital system level using American Hospital Association
data on a hospital system’s shares of total admissions in a market. As past literature has found that
hospital markets do not follow county lines,14 I calculate hospital HHI on the Dartmouth hospital
service area (HSA) level and match HSAs to counties, weighting by the population living in each
county-HSA combination. (Further details are in the Data Appendix.) For robustness, I also test
models that calculate HHI on the county level or use a system’s share of Medicare discharges or
hospital beds in a market. Results are similar across measures of HHI (see Appendix).
Physician HHI is calculated on the county level, using a practice’s share of office, outpatient,
and facility spending in FFS Medicare claims data. Following the approach from McWilliams, et.
13In
the study sample, the median insurer was the third-largest insurer in a county and had a market share
about 5 percentage points smaller than the largest insurer. The study sample insurers had the largest market
share in a county only about 18% of the time.
14See, for instance, Town and Vistnes (2001)(49), or Melnick, Zwanziger, Balemzai (1992).(50)
14
DARIA PELECH
al. (2013) and Baker, et. al. (2014), practices are identified using the tax identification number on
Medicare claims.(17, 51) Tax identification numbers provide a rough way of identifying physician
market structure, because multiple physicians can only bill under the same TIN when they are part
of the same financial entity.
Physician-hospital vertical integration is also included as a measure of provider market concentration, as increased integration may enhance provider bargaining power. The degree of vertical
integration in a market is measured using the percent of physicians in an area who bill all outpatient Medicare claims in a hospital outpatient department. (More details of how this variable is
constructed are described in Neprash, et al. (2015)(52) and in the data appendix.) This statistic is
a reasonable estimate of financial integration between physicians and hospitals, as physicians can
only bill Medicare under hospital outpatient department codes when their practices are owned by
that hospital.15
Insurer market power is measured on the individual insurer level, using each insurer j’s share
of the Medicare market in a county. Market share is a reasonable measure of insurer market power,
as larger insurers may extract favorable price terms by threatening to exclude providers from their
networks.
An insurer’s share of the Medicare market is constructed using publicly-available data from
the Centers for Medicare and Medicaid Services (CMS).(53) The numerator of Medicare shares
includes an insurer’s enrollment in all HMOs, local PPOs, and PFFS plans.16 The denominator
of Medicare share includes all Medicare beneficiaries, rather than just those enrolled in Medicare
Advantage. This measure of market share captures both variation in the size of Medicare Advantage
markets and reflects the fact that providers may consider the number of FFS Medicare patients
they could treat when deciding whether to contract with an insurer.
Though an insurer’s Medicare share a↵ects bargaining with providers, it also reflects insurers’
market power with consumers. Disentangling insurers’ market power with providers from market
15To
reduce noise, neither physician HHI or vertical integration measures include data from any provider
who billed fewer than 10 Medicare claims in a county in a year. This exclusion results in missing data for
2.5% of plan-county observations. For these observations, I construct an indicator to reflect that data are
missing and set both measures to 0. Observations with missing data are also excluded in robustness checks
(see Appendix) without meaningfully changing results.
16In main results, Regional PPOs are excluded from the calculation of insurers’ Medicare shares, because
they have special (and more flexible) rules governing how their networks are formed. When they are included
in supplementary analysis (see Appendix), results are similar.
INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
15
power with consumers is difficult, particularly because market power with consumers may enhance
insurers’ bargaining power with providers. To test whether an insurer’s market share primarily
reflects bargaining power with consumers, I use two strategies. First, I use insurers’ shares of
commercial (under 65) markets as an additional measure of bargaining power. Greater commercial
market share gives insurers greater market power with providers, as hospitals and doctors may be
less willing to bargain aggressively with an insurer that controls large portions of the commercial
market. Commercial market share should be largely unrelated to consumer market power, as
Medicare beneficiaries do not purchase commercial plans.17
Insurers’ commercial market shares are constructed using HealthLeaders Interstudy data,
which contain information on insurers’ enrollment in commercial and Medicare markets for each
county each year.(54) Commercial market shares reflect an insurer’s share of enrollees in the fullyand self-insured market. Commercial market share data are matched to Medicare data based on
insurer name and county.18
The second approach for testing whether insurer bargaining power reduces exit is to interact
insurer and provider market power variables. Insurer bargaining power may be particularly important in concentrated provider markets, and, conversely, providers may be less able to bargain with
insurers who have substantial market power. To test whether this is the case, I create an indicator
for when an insurer is large (above median Medicare market share) and interact this indicator with
provider market variables. If Medicare market share measures bargaining, than this interaction
should be significant; that is, insurer bargaining power should matter most in the markets where
providers have the most bargaining power.
Past literature shows that an insurer’s market share is not the sole determinant of market power. For instance, insurers may increase their market power by steering patients to some
doctors within networks (i.e., cheaper specialists), rather than simply excluding providers from
networks.(21, 22) Channeling is less likely to be an important component of market power for
PFFS plans, as PFFS plans are statutorily prohibited from using many of the tools that might help
them channel, such as gatekeepers, capitation, or referrals for specialty care. However, insurers
17Commercial
market share could reflect an insurer’s market power with Medicare beneficiaries if consumers
have substantial brand loyalty or if inertial Medicare consumers prefer to purchase plans from the insurer
they had before turning 65. However, Medicare and commercial market shares are not closely correlated in
this sample of counties and plans (⇢ = .04), suggesting that this is not the case here.
18
Details of the matching process are described in the Data Appendix.
16
DARIA PELECH
might prefer to replace their PFFS plans with HMO/PPOs in order to channel. This is tested and
discussed further in the appendix, using a model that compares markets where insurers kept PFFS
plans to markets where they replaced them with HMO/PPOs. (Section 3 in the Appendix.)
The policy change primarily a↵ected insurers’ relationships with providers by forcing them
to form networks and pay negotiated prices to providers. However, any variable that a↵ects insurer profitability could also change the likelihood of exit. To control for variation in profitability,
regressions include measures of provider supply, Medicare payments to plans, the costs of care in a
county, economic conditions, and plan-level premiums, benefits, and risk scores. Unless otherwise
noted, variables are fixed at baseline values, so that they are determined before insurers made their
exit decisions.
On the plan level, controls include premiums, benefit generosity, plan age, risk scores, and
enrollment, all calculated from publicly-available CMS files.19 Plan premiums reflect the additional
premiums an insurer charges in addition to the standard Medicare Part B premium. As not all plans
o↵er drug coverage, premiums are divided into the premium that a beneficiary pays for medical
coverage (Part C premium) and the premium for prescription drug coverage (Part D premium).
Plan benefit generosity is measured using expected out-of-pocket cost (OOPC), a standardized
measure of generosity calculated using spending data for a representative cohort of enrollees. (These
measures are described further in the data appendix).
Plan-level enrollment is included as a measure of economies of scale. It is measured on the
plan- (rather than insurer-) level as insurers may find it harder to manage multiple small plans
than one large one. Plan age is measured using the length of time that an insurer has o↵ered
a particular insurance contract. It is included as a control as it might theoretically a↵ect either
consumer demand and/or insurer operating costs.20
I proxy for enrollee health using average, plan-level CMS risk scores. Risk scores capture
expected spending based on demographic and diagnostic categories and are used to compensate
plans for enrolling sicker beneficiaries.(56) In theory, risk scores should not a↵ect profitability, as
19
Contract age and enrollment are generated from Contract-Plan-State-County files.(53) Risk scores were calculated from Medicare Advantage plan payment data.(55) Premiums and benefit generosity were calculated
from out-of-pocket cost files, provided by agreement with Center for Medicare and Medicaid Services.
20
On the demand side, older plans may have better reputations with consumers, or be larger because many
consumers do not switch plans. On the cost side, less costly/better managed plans may survive longer, so
plan age may indicate greater plan efficiency.
INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
17
Medicare adjusts payments to plans based on them. However, recent evidence suggests that plans
are under-compensated for higher levels of risk.(57, 58). Additionally, in univariate regressions of
exit on average plan risk scores, plans with higher risk scores were more likely to exit, suggesting
that risk scores a↵ect profitability. In all regressions, risk scores are standardized to the sample
average, so that coefficients represent the e↵ect of a 1 standard deviation change in a plan’s risk
on exit.
On the market level, controls capture variation in county economic conditions and variation
in medical spending and provider supply. Economic conditions are measured using county-level percapita income, the percent of the county population below poverty (from the Census Small Area
Income Poverty Estimates), and the unemployment rate (from the Bureau of Labor Statistics).
Variation in provider supply is measured using the number of hospital beds (from the AHA data)
and the number of doctors per 10,000 people (from the Area Resource file). I control for variation in
population health using average, county-level risk scores for traditional Medicare beneficiaries and
Medicare Advantage beneficiaries. (As above, risk scores are standardized to the national average,
so that coefficients represent the e↵ect of a change in 1 standard deviation of risk.) Variation
in healthcare utilization is measured using average spending for FFS Medicare beneficiaries in a
county. FFS Medicare spending is standardized using county-level risk scores, so that it reflects
utilization for the representative beneficiary.
Lastly, regressions include controls for market size (the number of county residents over 65),
Medicare Advantage penetration (the percent of all Medicare beneficiaries in a county enrolled in
a Medicare Advantage plan), and variation in Medicare payments to plans. Penetration captures
the relative size of the Medicare Advantage market and can be thought of as measure of Medicare
Advantage’s appeal. Variation in Medicare plan payments is captured using baseline levels of
Medicare benchmarks. As benchmark payments were being reduced over this time period, the
amount by which a county’s benchmark was cut between 2009 and 2012 is also included as a
control. As many of the control variables are correlated with main explanatory variables, models
are presented with and without them.
3. Descriptive Statistics
The main sample includes plan-county observations 1) that are o↵ered by insurers who continued to operate PFFS plans 2) in counties a↵ected by the policy 3) where insurer j o↵ered no
18
DARIA PELECH
HMO/PPO plans at baseline. These restrictions result in a sample of 5,836 plan-county observations, o↵ered by nine insurers in 1,592 counties. The sample spans 87% of the counties a↵ected by
the policy change.
The nine sample insurers are America’s First Choice, Blue Cross Blue Shield of Arkansas and
Tennessee. Humana, Medica, Munich RE, United, Universal American, and Universal HealthCare.
Some of these insurers (i.e., Medica of Minnesota) were marginal regional players, while others
(Humana and United) were the largest insurers operating in Medicare Advantage. Insurers such as
Blue Cross Blue Shield of Arkansas and Tennessee were the dominant insurers in their respective
commercial markets, while Universal American and America’s First Choice primarily operated in
Medicare Advantage and Medigap markets. Munich RE is notable because it is not actually a
health insurer, but rather one of the world’s leading reinsurers that happened to also operate PFFS
plans. As these insurers are a very diverse sample, preferred specifications use insurer fixed e↵ects.
Figures 2 and 3 summarize the actions and characteristics of each insurer in the sample. The
top panel of Figure 2 summarizes exit rates in the sample counties, which di↵ered substantially
across insurers. On average, insurers cancelled 57% of their plan-county observations in sample
counties between 2009 and 2012. However, Blue Cross Blue Shield of Arkansas kept all of their
plans in sample counties, while Medica of Minnesota cancelled all of theirs. (Medica is included
in the sample because they introduced new PFFS plans into other markets.) Exit rates for the
remaining seven insurers vary between 1-89%.
The bottom panel of Figure 2 summarizes the rates at which insurers kept their PFFS plans
or replaced them with HMO/PPOs in counties where they continued to operate. Although insurers
made county-by-county decisions about where to exit, the choice of which product to o↵er appears
to have been made on the insurer level. Specifically, America’s First Choice, Blue Cross Blue
Shield of Arkansas, Universal American, and Universal Healthcare continued to o↵er PFFS plans
in more than 92% of the counties where they did not exit. In contrast, United and BlueCross Blue
Shield of Tennessee replaced every PFFS plan with a PPO/HMO in the sample counties.21 Only
Munich Reinsurance and Humana pursued a mixed strategy, with Humana o↵ering PFFS plans
in 54% of counties where they continued to operate and Munich RE o↵ering them in 81%. The
21These
insurers appear in the sample because they continued to o↵er PFFS plans in counties exempt from
the policy.
INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
19
The top panel shows the number of plan-county observations in the sample where insurers exited
or continued to o↵er plans. The bottom panel shows the number of plan-county observations where
insurers continued o↵ering PFFS plans vs. introduced a new HMO/PPO in the subset of counties
where they continued to o↵er plans.
Figure 2. Insurer actions in sample counties
fact that these patterns suggest that many insurers’ choice of product was driven by a broader,
nationwide strategy, not the characteristics of individual markets, leads to the decision to treat
cancellation of all plans as “exit”. Additional analysis presented in the appendix tests whether the
characteristics of markets di↵er between places where insurers kept their PFFS plans or replaced
them with HMO/PPOs.22
Whether an insurer o↵ered a commercial plan in a county also varied greatly across insurers,
reflecting the diversity of the insurers included in the sample. Specifically, four insurers o↵ered no
22As
many insurers replaced PFFS plans with HMO/PPOs, another robustness check also extends the sample
to include any insurer who swapped HMO/PPOs for PFFS plans in more than 10% of counties where they
had no HMO/PPO at baseline. Results are qualitatively similar. (See Appendix.)
20
DARIA PELECH
commercial plans in any of their markets, and four others o↵ered a commercial plan in all of them
(see Figure 3). Only Humana’s commercial market presence varied within the study sample; 25% of
the sample observations o↵ered by Humana were in a county where they also o↵ered a commercial
plan in 2009. Because of this heterogeneity across insurers in commercial market presence, estimates
of the e↵ects of commercial market share on exit are likely to be more reliable in specifications that
include insurer fixed e↵ects.
Columns 1 and 2 of Table 1 summarize insurer and provider market characteristics in the
main sample. Observations are divided into columns based on whether the insurer did or did
not exit. Several characteristics of the main sample are worth noting. First, although insurers
generally controlled larger shares of the Medicare market in counties where they kept their plans,
Medicare Advantage penetration rates were statistically similar across counties where insurers exited or stayed. This suggests that insurers did not simply leave markets that have historically been
inhospitable to Medicare Advantage insurers.
Additionally, average hospital HHI in the sample is high by any absolute standard. At more
than 8000 points, most markets are monopolies, and concentration is well above the threshold that
the Department of Justice considers “highly concentrated” (2,500). However, HHI in the sample
is not high when compared to the rest of the Medicare Advantage market. The average HSA-level
HHI across all counties in which Medicare Advantage plans operated was 8,434 in 2009.
Columns 3 and 4 of Table 1 summarize characteristics for plan-county observations in counties
exempt from the policy. The sample size is much smaller (n=2,931), and insurers exited a smaller
proportion of plan-county observations (n=921, 31%). The small sample size is consistent with the
fact that (by definition) fewer insurers operated in markets exempt from the policy at baseline.
Lower cancellation rates are consistent with the fact that these plans did not experience the same
large shock to profitability as insurers in the main sample. The sample spans all counties exempt
from the policy where insurers o↵ered PFFS plans in 2009 (n=994).
Summary statistics show that the placebo sample is not comparable to the main sample in
many ways. Chiefly, counties exempt from the policy had much smaller populations than counties
where the policy applied, with an average of 6,000-7,000 residents over age 65 in the exempt sample
versus 21,000-24,000 residents in the main sample. They also had significantly higher physician and
hospital HHIs than the main sample, and there were more markets with insufficient observations
INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
21
Table 1. Plan-county characteristics
Main Sample
Exempt Sample
Did not exit Exited Did not exit Exited
Insurer Market Characteristics
Insurer share of Medicare beneficiaries (%)
Insurer share of Medicare Advantage (%)
Number of Medicare Advantage firms
Medicare Advantage penetration (%)
Commercial market share (%)
Has commercial plan in market (%)
2.75
(2.62)
24.71
(23.89)
6.56
(2.73)
20.94
(10.41)
4.56
(12.51)
23.53
(42.43)
1.36
(1.91)
14.92
(19.93)
6.33
(2.58)
20.46
(11.25)
3.18
(6.76)
32.35
(46.79)
2.41
(2.45)
37.98
(30.59)
4.28
(2.11)
12.19
(6.75)
6.30
(13.21)
42.54
(49.45)
1.53
(2.05)
28.15
(25.77)
4.38
(1.88)
11.30
(5.92)
6.90
(12.36)
47.67
(49.97)
8053
(2305)
2544
(2524)
23.33
(15.16)
1.46
(1.43)
2.69
(2.67)
21.01
(34.87)
8009
(2253)
2415
(2545)
22.93
(14.88)
1.34
(1.28)
2.40
(2.15)
23.25
(50.41)
9048
(1667)
2900
(2541)
22.53
(18.55)
1.15
(1.02)
3.09
(3.66)
6.17
(5.98)
8821
(1741)
3068
(2659)
22.63
(18.49)
1.03
(0.84)
2.91
(3.22)
6.87
(7.45)
2.38
(15.24)
2.50
(15.63)
8.71
(28.20)
5.21
(22.24)
2482
3354
2010
921
Provider Market Characteristics
Hospital HHI (HSA-level)
Physician HHI⇤ (county level)
Vertically integrated physician practices⇤ (%)
Doctors per 1000 people in county
Hospital beds per 1000 people in county
Population over 65 (1000s)
Missing Data
Missing physician data (%)
Observations
Mean characteristics for plan-county observations where insurers did and did not exit. Standard deviations in parentheses. The main sample includes plans in counties a↵ected by the policy and excludes
counties where the parent insurer o↵ered an HMO/PPO at baseline. The exempt sample includes plans
in counties una↵ected by the policy and excludes counties where the parent insurer o↵ered an HMO/PPO
at baseline.
⇤ Averages exclude missing values.
to calculate physician HHI and vertical integration.23 Despite the di↵erences in provider market
competition, the supply of physicians and hospitals did not di↵er as much between sets of counties
23Data
are missing for vertical integration and physician market concentration measures when all physicians
in a county bill fewer than 10 FFS Medicare claims in a year. This restriction results in missing physician
market power measures for 2-2.5% of observations in the main sample and 7.6% of observations in the
22
DARIA PELECH
Figure shows the number of plan-county observations in the sample where the insurer did and did
not have a commercial market plan in 2009. By construction, insurers did not o↵er Medicare
Advantage HMO/PPOs in sample observations at baseline.
Figure 3. Commercial presence, by insurer
as one might expect. In fact, counties exempt from the sample had slightly more hospital beds per
1000 people, due in part to the fact that the population in these counties was much smaller than
the population in counties where the policy applied.
Insurance markets were also smaller and more concentrated in the exempt sample than the
main sample. Medicare Advantage markets in exempt counties were on average eight percentage
points smaller (as measured by Medicare Advantage penetration), and had two fewer insurers.
However, insurers operating in exempt counties controlled more of both the Medicare Advantage
and commercial markets than insurers in counties where the policy applied. All of these statistics
are consistent with the fact that policy only applied to counties where two or more insurers o↵ered
an HMO or a PPO.
exempt sample. Excluding observations with missing data, however, does not substantially a↵ect results
(see Appendix).
INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
23
4. Results
Table 2 shows the results of regressing exit indicators on county and plan characteristics. In
counties a↵ected by the policy, greater provider market power was associated with a higher probability of insurer exit, while greater insurer market power was associated with less exit. Interactions
between insurer and provider bargaining power suggest that insurer market power is most important in concentrated hospitals markets. Moreover, provider market variables had little or no e↵ect
on the probability that insurers exited markets una↵ected by the policy.
Column 1 shows the preferred specification, which includes all controls and insurer and state
fixed e↵ects.24 Greater physician and hospital market power both significantly increased the probability that an insurer exited. Each additional thousand points of hospital HHI increased the
probability an insurer exited by 2 percentage points (Table 2), while each additional 1000 points
of physician HHI increased the probability that an insurer exited by 1 percentage point. These
e↵ect sizes imply that insurers operating in markets in the 90th percentile of hospital concentration
(HHI=10,000) were 9 percentage points more likely to exit than insurers in markets in the 10th percentile (HHI=4,400). Insurers operating in the 90th percentile of physician market concentration
(HHI=10,000) were 13 points more likely to exit than those in the least (HHI=36). Although the
sign of the vertical integration coefficient suggests that insurers were more likely to leave counties
with more vertically integrated physician practices, the e↵ect is not statistically significant.
Measures of insurer market power were significantly associated with a lower probability of
exit. Each additional percentage point of the total Medicare market an insurer enrolled reduced
the probability that insurer exited by 4 percentage points. Similarly, each additional percentage
point of commercial market share reduced their probability of exit by one percentage point. Linear
projections using these coefficients show that insurers in the 90th percentile of Medicare market
share (5.43%) exited 44% of the time, while in the 10th percentile (.15%) exited a 64% of the time.
Similarly, insurers in the 90th percentile of commercial market share (14%) were 9 percentage points
less likely to exit than those in 10th percentile (0%).
24Market
fixed e↵ects are not included in this specification, as provider HHI does not vary within markets.
24
DARIA PELECH
Table 2. Predictors of exit for insurers staying in PFFS (1= Exit)
(1)
(2)
(3)
(4)
(5)
Main Sample Main Sample Main Sample Main Sample Exempt Sample
Controls+FE No controls Interactions Interactions
Controls+FE
VARIABLES
Hospital HHI (1000s)
0.02**
(0.00)
0.01**
(0.00)
0.05
(0.05)
-0.04**
(0.01)
-0.01**
(0.00)
Physician HHI (1000s)
Vertical integration
Insurer Medicare share (%)
Commercial market share (%)
Medicare Share
Median) x Hospital HHI
(Medicare Share
Median) x Physician HHI
Enrollment (100s)
Medicare benchmark (100’s)
Benchmark cuts
FFS cost
Doctors per 10,000
Population below poverty (%)
Contract age (years)
Missing physician HHI
0.02**
(0.01)
0.01**
(0.01)
0.06
(0.05)
-0.02**
(0.00)
-0.08**
(0.02)
0.13
(0.07)
0.07**
(0.02)
-0.00**
(0.00)
0.00**
(0.00)
-0.06**
(0.02)
0.18**
(0.05)
0.19**
(0.06)
-0.01**
(0.00)
-0.00
(0.07)
-0.02**
(0.01)
-0.00
(0.01)
-0.02**
(0.00)
-0.07**
(0.02)
0.09
(0.07)
0.07**
(0.02)
-0.00**
(0.00)
0.00**
(0.00)
-0.06**
(0.02)
0.16**
(0.05)
5,836
0.37
Yes
Yes
Yes
No
5,836
0.10
No
No
No
No
5,836
0.38
Yes
Yes
Yes
No
Median
(Medicare Share
Observations
R-squared
All Controls
Insurer FE
State FE
County FE
0.01**
(0.00)
0.02**
(0.00)
-0.03
(0.07)
-0.07**
(0.01)
-0.00**
(0.00)
-0.01
(0.01)
0.01
(0.01)
0.05
(0.07)
-0.02**
(0.01)
-0.00**
(0.00)
-0.01**
(0.00)
-0.14**
(0.05)
-0.01**
(0.01)
0.01
(0.01)
-0.01**
(0.00)
-0.01
(0.01)
-0.09**
(0.04)
0.14
(0.09)
0.09**
(0.03)
-0.00**
(0.00)
-0.00
(0.00)
0.05
(0.04)
-0.06
(0.05)
-0.08**
(0.01)
5,836
0.71
No
Yes
Yes
Yes
Exit=1 if insurers will removed all plans from a market between 2010-2012 and 0 if not. All variables are 2009 values.
Linear probability models, standard errors clustered on the county level. Sample includes
2,931
0.19
Yes
Yes
Yes
No
⇤⇤
p  .05.
PFFS plans o↵ered by insurers who continued to o↵er PFFS plans in 2012, and excludes counties where the insurer had an HMO/PPO in 2009.
Regressions in Cols 1-4 include plans in counties a↵ected by the policy. Col 5 includes plans o↵ered by the same insurers in una↵ected counties.
Control variables not shown: the number of people over 65, fee-for-service, Medicare Advantage, and plan health risk, hospital beds per 1000,
the unemployment rate, per-capita income, plan benefits, premiums, and indicators for when vertical integration is calculated at the MSA level.
Coefficients on controls are presented in the appendix.
INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
25
Coefficients on most controls are of reasonable signs and magnitudes. On the market level,
insurers were less likely to leave counties with higher benchmark payments and more doctors per
capita. They were more likely to leave counties with higher per capita FFS Medicare costs, more
people below poverty, and those missing physician HHI data.25 On the plan level, insurers were
less likely to cancel older plans and plans with more enrollees, suggesting an important role for
insurer experience and economies of scale. All other controls, including future benchmark payment
cuts, Medicare Advantage penetration, and Medicare market size, had no significant e↵ect on exit.
(Unless otherwise noted, regressions are run with a full set of controls, but for brevity, coefficients
are presented in the Appendix.)
The benchmark coefficient provides a useful way to interpret the e↵ects of provider market
structure on exit. Coefficients on provider market variables predict that insurers in the least
competitive hospital or physician markets (HHI=10,000) were 13 percentage points more likely
to exit than insurers in the most competitive markets (HHI=1,600 for hospital markets, HHI=21
for physician markets). To incentivize an insurer to keep o↵ering plans in the least competitive
provider markets with the same probability that they kept o↵ering them in the most competitive,
benchmark payments would need to be increased by $200 per member per month. As the average
benchmark in the least competitive counties was $770 in 2009, payments to plans would need to
increase by more than 25% to o↵set the e↵ects of provider concentration in these markets.
Column 2 tests an alternate specification that omits controls and fixed e↵ects. (Controls
for missing data are still included.) Coefficients on provider and insurer market variables are
qualitatively similar, although the coefficient on commercial market share is very small without
insurer fixed e↵ects.26 Point estimates on provider market variables and Medicare share are slightly
di↵erent without inclusion on controls, but remain the same in sign and significance. This suggests
that, although some controls may be closely correlated with the main explanatory variables, results
are not driven by their inclusion.
25Markets
have missing physician HHI if there is insufficient data for HHI to be calculated. Insurers in these
markets are much more likely to exit, but data are missing for only 2% of observations in the main sample.
26This di↵erence is likely due to the substantial insurer-level heterogeneity in whether insurers operated in
the commercial market.
26
DARIA PELECH
As noted previously, an insurer’s Medicare share reflects both market power with providers
and market power with consumers. To determine whether an insurer’s market power withe providers
reduced exit, I test whether an insurer’s Medicare share had a larger e↵ect on exit in more concentrated provider markets. Specifically, I construct an indicator for when an insurer enrolls more
than the median share of the Medicare market (
.942%) and interact it with hospital and physi-
cian HHI (Column 3). The coefficient on the interaction between Medicare share and hospital HHI
is significant and negative (
=
.02), suggesting that the insurers with smaller market shares
are more likely to leave concentrated hospital markets. In the most concentrated hospital markets, smaller insurers were 24 percentage points more likely to exit than larger insurers, while in
more competitive hospital markets (HHI<4000), they were statistically no more likely to exit. (See
Figure 4.) The interaction on physician HHI and Medicare share is not significant.
Preferred specifications do not include county fixed e↵ects, as provider market concentration
measures do not vary on the county level. However, models with interactions between provider
and insurer market variables can be used in conjunction with county fixed e↵ects. Coefficients are
identified based on within-county variation in insurer market power and provide an opportunity to
test whether results are driven by unobserved, county-level heterogeneity (Column 4).
Adding county fixed e↵ects greatly improves the fit of the model (R2 = 71% vs 38%), perhaps
because insurers cancelled all plans in 40% of counties (n=630) and kept all plans in another 22%
(n=346).27 Results are qualitatively similar to those in previous regressions. As before, commercial
market share reduces the probability of exit, and insurer Medicare share interacts significantly with
hospital HHI. After adding county fixed e↵ects, insurer market power appears even more important;
insurers with above-median market share were 14 percentage points less likely to exit than those
with below-median market shares. This suggests that, in markets where some insurers exited and
some did not, insurer market power was an important determinant of exit.
The preferred specification (a regression that includes all controls and state and insurer fixed
e↵ects) is run in the placebo sample (plans o↵ered by the same insurers in counties exempt from
the policy). Column 5 shows results. As expected, provider market concentration variables had
27These
include many markets where the sample insurers o↵ered only one plan (n=288).
INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
27
Predicted probabilities from regressing insurer exit on interactions between HSA-level hospital HHI
and indicators for whether an insurer controls an above- or below-median share of the Medicare
market. Regressions control for county characteristics, plan characteristics, and provider market
characteristics. Standard errors clustered on the county level.
Figure 4. Interaction between Medicare share and hospital HHI
no significant e↵ect on exit in this sample of plans. The coefficient on hospital HHI is statistically
insignificant and of the opposite sign, and the coefficient on physician HHI is not statistically
significant at conventional levels. As in the main sample, insurer bargaining power significantly
reduced the probability of exit; however, e↵ect sizes on insurers’ Medicare share are only half as
large as in the main sample and are not robust to di↵erent specifications.28 Coefficients on insurers’
commercial market share (which may more closely reflect insurers’ market power with providers
than market power with consumers) are almost exactly 0. Coefficients on most other controls were
of similar signs and magnitudes in both samples. Most notably, the coefficients on benchmarks
28Results
that add additional controls for insurer market power render the coefficient on Medicare share
insignificant (see Appendix).
28
DARIA PELECH
and Medicare FFS costs were statistically identical in the two samples, suggesting that insurers
responded to similar incentives in both sets of markets.
5. Conclusions
Health insurance markets are highly concentrated(59), but why they are so concentrated is
not well understood. It is often said that high health insurance market concentration is a result
of concentrated provider markets, but this hypothesis has not been widely tested. Understanding
whether provider market power is related to insurer market concentration is important for policy.
Specifically, if insurers become concentrated as a result of provider market power, then policies that
foster insurer competition without increasing provider market competition may be unsuccessful.
This study examines selective exit among Medicare Advantage insurers who were forced to
form provider networks, by focusing on insurers who made market-by-market decisions about where
to cancel their plans. Findings show that greater physician and hospital market power increased the
probability of exit and greater insurer market power decreased it. Each additional 1000 points of
hospital HHI increased the probability that an insurer exited by 2 percentage points, while greater
physician HHI increased exit by 1 percentage point. Conversely, for each additional percentage
point of Medicare or commercial market share, insurers were 4 and 1 percentage points less likely
to exit, respectively. In counties una↵ected by the policy, provider market power had little or no
e↵ect on insurer exit and the e↵ect of insurer market power was greatly reduced. Results also
suggest that insurer market power was particularly important in concentrated provider markets.
Specifically, an insurer’s market share had the largest e↵ect on exit in counties with the least
competition between hospitals.
This study has several limitations to note, beyond those discussed in the introduction. First,
measuring provider market power on the market level may result in measurement error, as past
papers have shown that bargaining power varies within markets for each insurer-provider pair.(21,
22, 27, 39, 60) Unfortunately, bargaining power cannot currently be estimated on the insurerprovider level in this market, as Medicare Advantage claims data are not available. However, if
INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
29
anything, measuring provider bargaining power with error likely leads to conservative estimates of
the importance of provider market power for insurer exit.
Another limitation of the study is that the results cannot speak to whether PFFS plans’
exit was welfare-decreasing. On the one hand, results of a companion study show that insurer exit
reduced competition and was associated with significantly reduced benefit generosity.(11) Moreover,
this study also shows that two thirds of cancelled plans’ beneficiaries re-enrolled in other Medicare
Advantage plans, so government savings from PFFS plans’ exit were not substantial.29 These results
suggest that PFFS plans’ exit was a net welfare loss. On the other hand, having more insurers in a
market may not be welfare-improving, as excessive entry can lead to duplication of fixed costs.(62)
Additionally, there were a significant number of complaints about PFFS plans’ marketing activities
and enrollees’ ability to access care in PFFS plans.(42) These reports suggest that many PFFS
plans may have been of low quality and their cancellation might have led to beneficiaries enrolling
in better-quality plans.
Results have several policy implications. First, it is widely recognized that provider market power increases insurers’ prices. Results of this study suggest that provider market power also
reduces insurer market participation, leading to less consumer choice and greater insurer concentration. These findings should be considered when evaluating mergers in both hospital and physician
markets.
Second, both insurer and provider market structure will a↵ect the success of policies that are
intended to foster insurer competition. For instance, cross-state purchasing laws, in which insurers
licensed in one state are allowed to sell policies in another, have been proposed as a way to increase
insurer competition. The results of this study suggest that such policies may not be particularly
e↵ective in some markets, as the structure of local provider markets deter insurers from operating
in a market. As these laws do nothing to a↵ect that structure or increase insurer bargaining power
with providers, their e↵ect may be limited.30
29Medicare
Advantage plans are paid more for accepting beneficiaries than the same beneficiaries would cost
in FFS Medicare. Moving beneficiaries from PFFS plans to FFS Medicare would save money for the average
beneficiary, while moving beneficiaries from PFFS plans to another MA plan would not.(61)
30Consistent with this, states that have adopted cross-state purchasing policies have not seen out-of-state
insurers enter their markets. Industry experts have stated that this is because local provider markets, not
state regulation, are the chief barrier to insurer entry.(63)
30
DARIA PELECH
The last implication of this study’s findings is that policies that enhance providers’ bargaining
power may cause some insurers to reduce their participation in a↵ected markets. Following the
proliferation of managed care plans in the 1990s, state legislators often sought to limit insurers’
ability to exclude providers from networks through such mechanisms as any-willing-provider and
freedom-of-choice laws.31 The recent proliferation of narrow-network plans has led to renewed
interest in the regulation of insurer-provider contracting. However, designing policies to ensure
network adequacy - such as limiting selective contracting by insurers - while maintaining insurer
participation in those markets may prove challenging.
In summary, results show that greater provider market power increases insurer exit, greater
insurer market power reduces it, and insurer market power is more important in concentrated
provider markets. These findings suggest that, when designing policies to foster insurer competition, giving insurers additional tools for bargaining with providers may be at least as valuable as
guaranteeing that insurers compete amongst themselves, particularly in markets where provider
concentration is high.
References
[1] American Medical Association. Competition in health insurance: a comprehensive study of
U.S. markets. 2013.
[2] Leemore Dafny, Jonathan Gruber, and Christopher Ody. More insurers lower premiums: evidence from intial pricing in the health insurance marketplaces. American Journal of Health
Economics, 1(1):53–81, 2015.
[3] Jose Guardado, David Emmons, and Carol Kane. The price e↵ects of a large merger of health
insurers: a case study of UnitedHealth-Sierra. Health Management, Policy and Innovation,
1(3):1–21, 2013.
[4] Leemore Dafny, Mark Duggan, and Subramanian Ramanarayanan. Paying a premium on your
premium? Consolidation in the US health insurance industry. American Economic Review,
102(2):1161–1185, 2012.
[5] Josh Lustig. Measuring welfare losses from adverse selection and imperfect competition in
privatized Medicare. Working Paper, Boston University, February, 2011.
[6] Leemore Dafny. Are health insurance markets competitive? American Economic Review,
100(3):1399–1431, 2010.
[7] Amanda Starc. Insurer pricing and consumer welfare: evidence from Medigap. Working Paper,
Harvard University, November, 2010.
[8] Robert Town and Su Liu. The welfare impact of Medicare HMOs. The Rand Journal of
Economics, 34(4):719–36, 2003.
31Any-willing-provider
require insurers to contract with any provider willing to accept an insurers’ terms of
network participation, and freedom-of-choice laws allow enrollees to visit providers outside their health plan’s
network. Their e↵ects have not been extensively studied, but all evidence to date suggests they increase
spending and lower insurer market participation.(64, 65)
INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
31
[9] Steven Pizer and Austin Frakt. Payment policy and competition in the Medicare+Choice
program. Health Care Financing Review, 24(1):83–94, 2002.
[10] Robert Town. The welfare impact of HMO mergers. Journal of Health Economics, 20(6):967–
990, 2001.
[11] Daria Pelech. Paying more for less? Insurer competition and health plan generosity in the
Medicare Advantage program. Working Paper, Harvard University, November, 2014.
[12] Government Accountability Office. Private health insurance: concentration of enrollees among
individual, small group, and large group insurers from 2010 through 2013. GAO-15-101, 2014.
[13] Department of Justice and Federal Trade Commission. Improving health care: a dose of
competition. July, 2004.
[14] Martin Gaynor, Katherine Ho, and Robert Town. The industrial organization of health care
markets. National Bureau of Economic Research Working Paper, 19800, 2014.
[15] Martin Gaynor and Robert Town. Competition in health care markets. In Thomas McGuire,
Mark Pauly, and Pedro Barros, editors, Handbook of Health Economics, volume 2, chapter 9,
pages 499–627. Elsevier, Amsterdam: North-Holland., 2011.
[16] Martin Gaynor and Robert Town. The impact of hospital consolidation - update. Robert Wood
Johnson Foundation Policy Brief, June, 2012.
[17] Laurence Baker, Kate Bundorf, Anne Royalty, and Zachary Levin. Physician practice competition and prices paid by private Insurers for office visits. JAMA, 312(16):1653, 2014.
[18] John Schneider, Pengxiang Li, Donald Klepser, N. Andrew Peterson, Timothy Brown, and
Richard Sche✏er. The e↵ect of physician and health plan market concentration on prices
in commercial health insurance markets. International Journal of Health Care Finance and
Economics, 8(1):13–26, mar 2008.
[19] Abe Dunn and Adam Shapiro. Do physicians possess market power? Journal of Law and
Economics, 57(1):159–193, 2014.
[20] Adam Shapiro and Abe Dunn. Physician payments under health care reform. Federal Reserve
Bank of San Francisco Working Paper, (March), 2014.
[21] Alan Sorensen. Insurer-hospital bargaining: Negotiated discounts in post-deregulation Connecticut. Journal of Industrial Economics, 51(4):469–490, 2003.
[22] Vivian Wu. Managed care’s price bargaining with hospitals. Journal of Health Economics,
28(2):350–360, mar 2009.
[23] Glenn Melnick, Yu-Chu Shen, and Vivian Wu. The increased concentration of health plan
markets can benefit consumers through lower hospital prices. Health A↵airs, 30(9):1728–1733,
2011.
[24] Michael McKellar, Sivia Naimer, Mary Beth Landrum, Teresa Gibson, Amitabh Chandra, and
Michael Chernew. Insurer market structure and variation in commercial health care spending.
Health Services Research, 49:878–892, 2014.
[25] Asako Moriya, William Vogt, and Martin Gaynor. Hospital prices and market structure in the
hospital and insurance industries. Health Economics, Policy, and Law, 5(4):459–479, 2010.
[26] Laurie Bates and Rexford Santerre. Do health insurers possess monopsony power in the hospital
services industry? International Journal of Health Care Finance and Economics, 8(1):1–11,
2008.
[27] Katherine Ho and Robin Lee. Insurer competition and negotiated hospital prices. National
Bureau of Economic Research Working Paper, 19401, 2013.
[28] Killard Adamache and Louis Rossiter. The entry of HMOs into the Medicare market: implications for TEFRA’s mandate. Inquiry, 23(4):349–364, 1986.
[29] Frank Porell and Stanley Wallack. Medicare risk contracting: determinants of market entry.
Health Care Financing Review, 12(2):75–85, 1990.
[30] Jean Abraham, Ashish Arora, Martin Gaynor, and Douglas Wholey. Enter at your own risk:
HMO participation and enrollment in the Medicare risk market. Economic Inquiry, 38:385–401,
32
DARIA PELECH
2000.
[31] John Cawley, Michael Chernew, and Catherine McLaughlin. HMO participation in Medicare
+ Choice. Journal of Economics & Management Strategy, 14(3):543–574, 2005.
[32] Shiko Maruyama. Socially optimal subsidies for entry: the case of Medicare payments to
HMOs. International Economic Review, 52(1):105–129, 2011.
[33] Austin Frakt, Steven Pizer, and Roger Feldman. The e↵ects of market structure and payment
rate on the entry of private health plans into the Medicare market. Inquiry, 49(1):15–36, 2012.
[34] Katherine Ho. Barriers to entry of a vertically integrated health insurer: an analysis of welfare
and entry costs. Journal of Economics & Management Strategy, 18(2):487–545, 2009.
[35] Jean Abraham, Roger Feldman, and Kosali Simon. Did they come to the dance? Insurer
participation in exchanges. American Journal of Managed Care, 20(12):1022–1031, 2014.
[36] David Dranove, Anne Gron, and Michael Mazzeo. Di↵erentiation and competition in HMO
markets. The Journal of Industrial Economics, 51(4):433–454, 2003.
[37] The Kaiser Family Foundation State Health Facts. State HMO Penetration Rate. September,
2015.
[38] Je↵rey Clemens and Joshua Gottlieb. Bargaining in the shadow of a giant: Medicare’s influence
on private payment systems. National Bureau of Economic Research Working Paper, 19503,
2013.
[39] Chapin White, Amelia Bond, and James Reschovsky. High and Varying Prices for Privately Insured Patients Underscore Hospital Market Power. Center for Studying Health System Change,
27(September), 2013.
[40] Medicare Payment Advisory Commission. Private fee-for-service plans in Medicare Advantage.
Congressional Testimony before the Subcommittee on Health, Committee on Ways and Means,
2007.
[41] Brian Biles, Emily Adrion, and Stuart Guterman. Medicare Advantage’s private fee-forservice plans: paying for coordinated care without the coordination. The Commonwealth
Fund, 48(1183), 2008.
[42] Block, Abby. Congressional Testimony before the Committee on Ways and Means, Subcommittee on Health U.S. House of Representatives, May, 2007.
[43] Marsha Gold. Medicare Advantage in 2006-2007: what Congress intended? Health A↵airs,
26(4):w445–455, 2007.
[44] Marsha Gold. Private plans in Medicare: a 2007 Update. The Kaiser, (March), 2007.
[45] Gretchen Jacobson, Anthony Damico, Tricia Neuman, and Marsha Gold. Medicare Advantage
2015 spotlight: enrollment market update. The Kaiser Family Foundation, June, 2015.
[46] Carlos Zarabozo and Scott Harrison. Payment policy and the growth of Medicare Advantage.
Health A↵airs, 28(1):w55–67, 2009.
[47] Karen Stockley, Thomas McGuire, Christopher Afendulis, and Michael Chernew. Premium
transparency in the Medicare Advantage market: implications for premiums, benefits, and
efficiency. National Bureau of Economic Research Working Paper, 20208, 2014.
[48] Medicare Improvements for Patients and Providers Act of 2008. Public Law 110-275, 122 Stat
2, 2008.
[49] Robert Town and Gregory Vistnes. Hospital competition in HMO networks. Journal of Health
Economics, 20(5):733–753, 2001.
[50] Glenn Melnick, Jack Zwanziger, Anil Bamezai, and Robert Pattison. The e↵ects of market
structure and bargaining position on hospital prices. Journal of Health Economics, 11(3):217–
233, 1992.
[51] J. Michael McWilliams, Michael Chernew, Alan Zaslavsky, Pasha Hamed, and Bruce Landon.
Delivery system integration and health care spending and quality for Medicare beneficiaries.
JAMA Internal Medicine, 173(15):1447–1456, 2013.
INSURANCE MARKET EXIT AND PROVIDER MARKET POWER
33
[52] Hannah Neprash, Michael Chernew, Andrew Hicks, Teresa Gibson, and J. Michael McWilliams.
Association of financial integration between physicians and hospitals with commercial health
care prices. JAMA Internal Medicine, 175(7):1229–1231, 2015.
[53] Center
for
Medicare
and
Medicaid
Services.
Monthly
Enrollment
by
Contract/Plan/State/County.
Available
at:
https://www.cms.gov/
Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/
MCRAdvPartDEnrolData/Monthly-Enrollment-by-Contract-Plan-State-County.html.
[54] HealthLeaders InterStudy. Health plan data and analysis. Available at http://hl-isy.com/
hpda. Accessed: July 2013.
[55] Center for Medicare and Medicaid Services. Plan Payment Data. Available at: https://www.
cms.gov/Medicare/Medicare-Advantage/Plan-Payment/Plan-Payment-Data.html.
[56] Gregory Pope, John Kautter, Melvin Ingber, Sara Freeman, Rishi Sekar, and Cordon Newhart.
Evaluation of the CMS-HCC risk adjustment model. Technical Report 0209853, RTI International on behalf of Centers for Medicare and Medicaid Services, Research Triangle Park, NC,
2011.
[57] Bianca Frogner, Gerard Anderson, Robb Cohen, and Chad Abrams. Incorporating new research into Medicare risk adjustment. Medical Care, 49(3):295–300, 2011.
[58] Katia Noyes, Hangsheng Liu, and Helena Temkin-Greener. Medicare captitation model, functional status, and multiple comorbidities: model accuracy. The American Journal of Managed
Care, 14(10):679–690, 2008.
[59] Gregory Roumeliotis. Aetna to buy Humana for $37 billion in largest insurance deal. Thomson
Reuters, July:1–4, Jul 2015.
[60] Katherine Ho. Insurer-provider networks in the medical care market. The American Economic
Review, 99(1):393–430, 2009.
[61] Brian Biles. Realizing Health Reform’s Potential. The Commonwealth Fund, 27(October),
2012.
[62] N. Gregory Mankiw and Michael Whinston. Free entry and social inefficiency. The RAND
Journal of Economics, 17(1):48, jan 1986.
[63] Sabrina Corlette, Christine Monahan, Katie Keith, and Kevin Lucia. Selling health insurance
across state lines: an assessment of state laws and implications for improving choice and
a↵ordability of coverage. The Center on Health Insurance Reforms, Georgetown University
Health Policy Institute, October, 2012.
[64] Michael Vita. Regulatory restrictions on selective contracting: An empirical analysis of “anywilling-provider” regulations. Journal of Health Economics, 20(6):955–966, 2001.
[65] Michael Morrisey and Robert Ohsfeldt. Do “any willing provider” and “freedom of choice”
laws a↵ect HMO market share? Inquiry, 40(4):362–374, 2003.
APPENDIX: INSURANCE MARKET EXIT AND PROVIDER
MARKET POWER
DARIA PELECH
1. Data Appendix
1.1. Calculating hospital HHI. Hospital HHI is measured on the hospital system level
using a system’s shares of total admissions in a market. Hospital markets are defined on
the hospital service area (HSA) level, as past research suggests that county boundaries do
not accurately describe hospital markets.1 To associate hospital HHI with insurers’ countylevel exit decisions, HSAs are matched to counties, and HHI measures are aggregated to the
county level.2
89% of the 3,114 counties in the US (excluding Alaska and Puerto Rico) span multiple
HSAs.3 In counties that spans multiple HSAs, HSA-level variables were aggregated to the
county level, weighting by the population living in each county-HSA combination. For
example, residents in Clarke County, Alabama (FIPS code 01025) visited hospitals in three
HSAs: Grove Hill, Thomasville, and Mobile. In 2010, 7,088 people in Clarke county lived in
the zips associated with Grove Hill hospitals, 10,632 people lived in the zips associated with
Mobile hospitals, and 8,113 lived in zips associated with Thomasville hospitals. Grove Hill
and Thomasville each had HSA-level HHIs of 10,000, while Mobile had an HHI of 2928.01.
Date: January 25, 2016.
See, for instance, Town and Vistnes (2001) or Melnick, Zwanziger, Balemzai (1992).(1, 2)
2
In four counties, the associated HSAs contained no hospitals in 2009. (HSAs with no hospitals are maintained for historical reasons.) All four counties were una↵ected by the network requirement and are therefore
not in the main sample. In the sample exempt from the network requirement, HHI fore these observations
is set to 0, and an indicator reflecting that data are missing is created.
3
HSAs were matched to counties based on Census zip code tabulation areas (ZCTAs). Counties were associated with ZCTAs using the 2010 Census FIPS-ZCTA crosswalk, (available here: https://www.census.gov/
geo/maps-data/data/zcta_rel_download.html). 2010 crosswalks were used, as 2010 was the first year
Census produced FIPS-ZCTA crosswalks. ZCTAs were associated with HSAs using the 2009 Dartmouth
Zip-HSA crosswalk (available here: http://www.dartmouthatlas.org/tools/downloads.aspx?tab=39).
1
1
2
DARIA PELECH
Clarke County’s aggregated HHI is therefore a population-weighted average of these three
HHI’s:
HHI =
7088
10632
8113
⇤ 10000 +
⇤ 2928.01 +
⇤ 10000 = 7089.40
25833
25833
25833
In robustness checks, hospital HHI is also defined using a hospital system’s shares of
beds or Medicare discharges, rather than hospital admissions. Other robustness checks define
HHI on the county level, rather than the HSA level. Results are similar, regardless of the
measure used (see Appendix Table 4).
1.2. Commercial insurance market data. An insurer’s commercial market share in a
county was calculated from HealthLeaders Interstudy data and reflects an insurer’s share of
enrollees in the fully- and self-insured commercial (under 65) markets. Medicaid managed
care and Federal Employee Health Benefit Program (FEHBP) enrollees are excluded from
calculations of shares.4
Interstudy commercial market data were matched to Medicare data based on year,
county, and insurer name. Names were matched within a county and year based on the
first “token” word in insurers’ names. Token words were identified by stripping out common
words (e.g., “corporation”, “inc”, “and”, and “the”) and standardizing words that identify
many insurers across both data sets (e.g.,“blue cross blue shield” and “healthcare”).5 This
approach is preferred to a more standard distance algorithm, because of the frequency with
which common names created spurious matches in this data set.
Ambiguous cases were resolved using SEC filings and company websites to determine
ownership. For instance, review of company websites revealed that Blue Cross Blue Shield
of Kansas and Blue Cross Blue Shield of Kansas City have di↵erent parent companies and
should therefore be treated as separate entities. Acronyms were also reviewed to insure
4Medicaid
enrollees are excluded from the calculation of shares because many providers do not view Medicaid
patients as substitutes for Medicare patients (because Medicaid payments are so low). FEHBP enrollees are
excluded because of consistency issues in how insurer names are recorded in Interstudy data.
5Blue Cross Blue Shield plans were recoded based on state names (i.e., “Blue Cross Blue Shield of North
Carolina” became “BCBS NC”). Commonly occurring combinations of words were combined into one word
(i.e., community health group=community health group).
APPENDIX
3
consistency. For instance, Tufts HMO, a prominent insurer in the Massachusetts area, is
listed as “Tufts HMO” in Interstudy and “TAHMO” in Medicare data. These di↵erences
were resolved by hand.
Both datasets contain Medicare enrollment. This allowed me to assess the quality of the
match by testing whether each county-insurer observation in the Medicare data corresponded
to a county-insurer observation with Medicare enrollment in the Interstudy data. In the final
dataset, 94% of observations in the Medicare data were matched to Interstudy observations
that had Medicare enrollment.
1.3. Data on plan benefits. Plan characteristics a↵ect insurer profitability and may therefore a↵ect insurer exit. I address this by controlling for a range of plan characteristics,
including premiums, benefits, plan age, enrollment, and plan health risk (as a measure of
enrollees’ health status). Plan premiums include both the Medicare Part C and Part D
premiums. The Medicare Part C premium is the premium an insurer charges to cover standard Medicare (Parts A and B) benefits, if the insurer predicts that benchmark payments to
plans will not be sufficient. This premium is charged in addition to the standard Medicare
Part B premium charged to all Medicare beneficiaries. As Medicare payments to plans were
generous in 2009, this premium was $0 for many plans (i.e., most plans projected that the
benchmark would cover all costs of providing enrollees with the standard benefit package.)
A small number of plans (⇡3% in all Medicare Advantage, 1% in the study sample) not only
charge $0 for the Medicare Part C premium, but also use Medicare rebates to reduce the
standard Medicare Part B premium. For these plans, the Part C premium is adjusted to
reflect the reduction in the Part B premium and is therefore negative.
The Medicare Part D premium is the premium that an insurer charges to cover prescription drug benefits. Many PFFS plans did not o↵er Part D benefits in 2009.6 For these
plans, the Part D premium is set to 0, and an indicator reflecting that they do not o↵er Part
D benefits is included in the set of control variables.
636%
of plans in the main sample and 34% of plans in the placebo sample did not o↵er Part D benefits.
4
DARIA PELECH
Plan benefit generosity is measured using a statistic called expected out-of-pocket cost
(OOPC), which is calculated by Medicare for beneficiaries’ use in selecting plans. OOPC is a
standardized measure of generosity that is calculated using spending data for a representative
cohort of enrollees. It reflects the amount an average beneficiary could expect to pay out-ofpocket, based on each plan’s copays, deductibles, and covered services. It captures spending
for a variety of services, including inpatient and outpatient hospital services, primary care
and specialist services, lab tests, diagnostics, durable medical equipment, and prescription
drugs. In other research, it has been found to predict beneficiaries’ choice of plan(3) and to
be sensitive to changes in insurer competition and Medicare benchmarks.(4, 5)
1.4. Data on vertical integration. Physician-hospital vertical integration measures capture the percent of doctors in a market whose practices are owned by a hospital. Physicians
are considered to be vertically integrated if they bill all of their Medicare claims in a hospital
outpatient department.7
I follow prior work in defining vertical integration on the metropolitan statistical area
(MSA) level. Using MSAs as markets captures that hospital-physician markets may be
larger than counties (the level on which physician HHI is measured) but are unlikely to
be as large as HSAs (the level on which hospital HHI is measured). Aggregating vertical
integration measures to the HSA also reduces the noise that arises from constructing vertical
integration on the county level.
Approximately 25% of county-plan observations are not in an MSA. For these observations, I use a vertical integration measure that is constructed at the county level and include
an indicator to reflect that markets are defined di↵erently in these counties. Alternate specifications, in which vertical integration is measured at the county level for all observations
or observations that are not in an MSA are omitted, yield similar results (see Table 4).
7This
variable is described more fully in Neprash, et. al. (2015).(6) As there is substantial noise in how
physicians bill site-of-service, the preferred measure counts physicians who bill all of their services in an
outpatient department as being vertically integrated. Results that treat the percent of all physician claims
billed in an outpatient department as vertically integrated are similar to those that use the preferred measure
(see Table 4.)
APPENDIX
5
2. Appendix: Additional results and robustness checks
2.1. Additional Results. Table 1 presents additional summary statistics for control variables. In the main sample (Columns 1 and 2), virtually all control variables di↵ered between
cancelled and non-cancelled plans. Many di↵erences are in intuitive directions. Insurers
cancelled plans in counties with higher FFS Medicare costs, higher FFS and Medicare Advantage risk scores, more doctors and hospitals per capita, higher per capita income, more
of the population in poverty, and higher unemployment. Additionally, the plans insurers
cancelled were smaller (55 enrollees vs. 137), had been in the market for fewer years (4.91
years vs. 5.22), and had higher risk scores (risk score=.94 vs .92). Less intuitively, insurers
were more likely to leave counties with higher benchmark payments.
Many characteristics of observations in counties exempt from the policy (Columns 3
and 4) also di↵ered significantly from those in the main sample. (Exceptions were contract
age, out-of-pocket costs, plan risk, and the percent of the county in poverty.) One particular
di↵erence of note is that the average FFS Medicare costs were higher in counties exempt
from the policy and benchmark payments were lower. This gap between payments and costs
may explain why fewer plans operated in exempt counties at baseline.
Table 2 shows the full results from the linear probability models displayed in the main
text, including coefficients for all controls. Most results are discussed in the main text;
however, several additional results are worth noting. First, coefficients on Medicare Advantage plan premiums and Part D premiums are significant in some regressions. They are
not, however, consistently significant, and when they are, coefficients are almost exactly
0. Second, although the magnitude of coefficients on most controls are similar across the
main and exempt samples, they di↵er for three variables: Medicare Advantage risk scores,
enrollment, and contract age. The average Medicare Advantage risk score increased the
probability that an insurer exited a county exempt from the policy and had no e↵ect in the
main sample. This may suggest that beneficiary health is more important to insurers who
cannot selectively contract with providers. Conversely, plan-level enrollment and contract
age significantly a↵ected exit in the main sample but had no e↵ect in the exempt sample.
6
DARIA PELECH
Table 1. Plan-county characteristics
Main Sample
Exempt Sample
Did not exit Exited Did not exit Exited
Medicare Market
Medicare benchmark payments
Future benchmark cuts
FFS costs (normalized for risk)
FFS Medicare risk scores
County Medicare Advantage risk score
793.15
(60.76)
22.52
(15.57)
652.98
(69.29)
0.96
(0.06)
0.91
(0.09)
801.63
(73.74)
22.27
(16.76)
670.72
(75.76)
0.95
(0.07)
0.92
(0.09)
778.95
(62.77)
18.06
(17.31)
667.47
(72.00)
0.96
(0.06)
0.92
(0.09)
788.57
(64.84)
17.37
(18.19)
691.32
(69.54)
0.96
(0.07)
0.93
(0.10)
137.33
(251.14)
5.22
(1.83)
306.30
(23.35)
24.24
(30.46)
0.92
(0.10)
55.12
(86.13)
4.91
(1.98)
310.14
(20.89)
27.23
(31.78)
0.94
(0.11)
69.22
(118.35)
5.04
(1.63)
309.09
(20.96)
24.82
(33.12)
0.93
(0.10)
42.71
(61.16)
4.92
(1.66)
308.13
(21.52)
30.22
(35.26)
0.94
(0.10)
14.75
(4.94)
33.90
(6.41)
9.14
(2.65)
15.35
(5.28)
33.56
(6.74)
9.48
(2.84)
17.89
(6.21)
31.32
(5.98)
9.48
(3.32)
17.54
(5.87)
31.60
(6.07)
9.44
(3.27)
2482
3354
2010
921
Plan Characteristics
Plan enrollment
Contract age (years)
Out-of-pocket cost
Premium
Plan-level risk
County-level control variables
Population below poverty (%)
Per capita income
Unemployment rate (%)
Observations
Characteristics for plan-county observations where insurer did and did not exit. The main sample
includes plans in counties a↵ected by the policy where the parent insurer did not o↵er an HMO/PPO
at baseline. The exempt sample includes plans in counties una↵ected by the policy where the parent
insurer did not o↵er an HMO/PPO at baseline.
This may suggest that newer and smaller plans were less of a liability in markets una↵ected
by the policy.
APPENDIX
7
2.2. Robustness checks. Table 3, Columns 1-3 present results that use alternate samples.
Column 1 uses 2008 as baseline year instead of 2009.8 The 2008 sample is slightly smaller
than the sample that uses 2009 as a baseline year (n=5362 vs. 5836), and a smaller proportion of plans were cancelled (n=2768 or 49%). These di↵erences are due to the fact
that some sample insurers introduced new PFFS plans between 2008-2009 and were then
more likely to cancel these new plans. However, results are qualitatively similar to those in
the main sample. The coefficient on hospital HHI is statistically the same as in the main
analysis, and the coefficient on physician HHI is slightly larger ( = .02). Coefficients on
insurers’ Medicare and commercial market share are smaller, but this is likely due to the
fact that the insurance market landscape had likely changed by the time these plans were
cancelled. Medicare Advantage penetration significantly reduces the probability of exit in
this regression, suggesting that when insurers cancelled older plans, they cancelled them in
less well-established MA markets.
In Column 2, exit indicators are redefined using 2011 as a follow-up year, rather than
2012. The network policy first became e↵ective in 2011, so using 2012 as the follow-up year
allows for a one-year period of insurer learning. However, other policy changes that occurred
in 2012 (i.e., payment cuts in the A↵ordable Care Act) also a↵ected the likelihood of exit.
Redefining exit indicators based on 2011 market participation results in a larger sample
(n=6648 vs 5836) and adds two additional insurers who withdrew from markets between
2011-2012 (Blue Cross Blue Shield of Idaho and Wellpoint). Coefficients on provider and
insurer market variables are qualitatively similar, though physician HHI is only significant
at the 10% level in this sample and coefficients on insurer market variables are closer to 0.
Coefficients on benchmarks and FFS Medicare costs are also attenuated. This may suggest
that it took insurers time to respond to market conditions.
82009
was chosen as a baseline year in main analysis, because it was the first year in which insurers had
the opportunity to cancel their plans, following the policy change. (Because of the timing of the Medicare
Advantage contracting calendar, plans were locked into their annual contracts by the time the law passed.)
However, some insurers may have anticipated the passage of the policy in forming their 2008 contracts, so I
test models that use 2008 as an alternate baseline year.
8
DARIA PELECH
Column 3 runs the preferred specification in a sample with additional insurers. The
main sample was restricted to insurers who continued to o↵er PFFS plans in at least some
counties, as it is likely that these insurers made county-by-county decisions regarding where
to exit. However, several other insurers replaced many of their PFFS plans with HMO/PPO
plans in counties where they had no HMO/PPO at baseline. This behavior may also suggest
these insurers were making strategic exit decisions. Expanding the sample to insurers who
introduced new HMO/PPOs into more than 10% of counties where they only o↵ered a
PFFS plan at baseline results in five additional insurers and 943 additional plan-county
observations.9 However, results are similar to results that use a narrower sample of insurers.
Columns 4-7 test di↵erent ways to characterize insurer market power. Column 4 includes regional PPOs in the construction of an insurer’s Medicare share. RPPOs were
omitted from the share calculation in main analysis, as the rules governing how they form
networks and where they can choose to operate di↵er substantially from those governing local
PPOs, HMOs, and PFFS plans.10 However, results are almost exactly the same regardless
of whether RPPOs are included in the calculation of an insurer’s Medicare share.
Columns 5-7 test alternate ways of measuring insurer bargaining power. Insurer bargaining power is presumably a↵ected both by the size of an insurer’s share of the market
(absolute bargaining power) and the size of their share relative to other insurers in the market (relative bargaining power). For instance, a large insurer may have substantially less
bargaining power when there is another large insurer in a market. To test this, I add a
variable that measures the di↵erence between insurer j’s share and the share of the largest
insurer in the county (“Medicare di↵erence”).11
Adding the Medicare di↵erence variable to the preferred specification (Column 5) shows
that both absolute and relative bargaining power a↵ected insurer exit. Each additional
9The
five additional insurers are: Blue Cross Blue Shield of Idaho, Blue Cross Blue Shield of Michigan, Blue
Cross Blue Shield of South Carolina, Geisinger, and Wellpoint.
10For instance, if insurers wish to o↵er a plan in one county in a region, they must o↵er them in all counties
that region.
11Nineteen percent of plan-county observations in the sample are o↵ered by the largest insurer in the county.
This number is set to 0 for these observations.
APPENDIX
9
percentage point di↵erence between insurer j’s share and the market share of the largest
insurer increased the probability that insurer j exited by a percentage point. This implies
that the largest insurers (those in the lowest quartile of this statistic) had a 52% chance of
exiting a county, while smaller insurers (those in the highest quartile of this statistic) had
a 59% chance of exiting. An insurer’s share of the Medicare market (absolute bargaining
power) also still significantly reduced the probability of exit, even after controlling for relative
bargaining power.
Adding insurer’s relative bargaining power to regressions in the exempt sample (Column 6) shows that coefficients on an insurer’s Medicare share are not as robust as are they are
in the main sample. Specifically, the coefficients on relative and absolute bargaining power
are insignificant when both variables are included in the regression. Sensitivity to specification in the exempt sample is not a result of relative and absolute bargaining power being
correlated. Though these two variables are correlated in the exempt sample (⇢ =
correlation is stronger in the main sample (⇢ =
.23), the
.36.)
Columns 7 shows the results of including Medicare Advantage insurance market HHI
as an additional independent variable. When both HHI and an insurer’s Medicare share
are included in the same regression, the coefficient on HHI captures the e↵ect of an insurer
operating in a more concentrated insurance market (relative bargaining power), controlling
for an insurer’s own market power (absolute bargaining power). As above, results suggest
that both absolute and relative bargaining power a↵ect the probability an insurer exits. Each
additional 1000 points of MA HHI increases the probability an insurer exits by 3 percentage
points. An insurer’s Medicare share is also still associated with a reduced probability of
exit, with an e↵ect size similar to that in main results ( =
.04). Taken together, these
results suggest both that an insurer’s relative position in a market a↵ects exit and that
market-level measures of insurance market structure have heterogeneous e↵ects on insurers
of di↵erent sizes. Studies that fail to control for an individual insurer’s bargaining power
(i.e., by including only HHI as an independent variable) may lead to erroneous conclusions
about the e↵ect of insurance market structure on market participation.
10
DARIA PELECH
Column 8 tests whether results are driven primarily by the exit of plan-county observations with very low enrollment. Average county-plan level enrollment in the sample is
generally quite small, with median enrollment of around 34 people.12 These small enrollment
numbers suggest that some exit might driven by insurers eliminating smaller plans. If plans
also tended to be smaller in areas where providers were more concentrated, this could bias
coefficients on provider market variables upward.
To test this, Column 8 shows the preferred specification in a sample that is restricted
to all plan-county observations with more than median enrollment. Coefficients on most
variables are almost exactly the same. Interestingly, benchmark cuts have a large and statistically significant e↵ect on exit in this subsample (
= .2), with each additional $10 of
reduced benchmarks increasing the probability of exit by nearly 20 percentage points. (The
mean size of benchmark cuts was small at $22.)
The final column uses the insurer-county as the unit of observation, rather than the
plan-county. In main analysis, I used the plan-county because many variables that might
a↵ect profit (i.e., premiums, benefits, plan risk, enrollment) vary at the plan level. However,
as insurers can o↵er multiple plans in a county, using the plan-county as the unit of analysis
could result in over-weighting insurers who o↵er multiple plans per county.13 To construct a
sample of insurer-county observations, all plan-level variables are aggregated to the insurercounty level, weighting by enrollment.
Results are qualitatively similar. Coefficients on physician market variables are of
similar size to those in main regressions. Point estimates on insurers’ market shares are
slightly smaller and the coefficient on enrollment is slightly larger, but t-tests show that
they are statistically the same to those in the main results. Interestingly, coefficients on
benchmarks and FFS Medicare costs are smaller, which may suggest that insurers who o↵er
only one plan per county are less sensitive to payment variables than those that o↵er multiple
plans per county.
12This
sample is not atypical of PFFS plans in Medicare Advantage. The median enrollment in all PFFS
plans in 2009 was 36 people.
13The average number of plans-per-county was 1.8, but one insurer o↵ered as many as 7 plans in a county.
APPENDIX
11
Table 2. Predictors of exit for insurers staying in PFFS (1= Exit), all coefficients included
(1)
(2)
(3)
(4)
(5)
Main Sample Main Sample Main Sample Main Sample Exempt Sample
Controls+FE No controls Interactions Interactions
Controls+FE
VARIABLES
Hospital HHI (1000s)
Physician HHI (1000s)
Vertical integration
Insurer Medicare share (%)
Commercial market share (%)
Medicare Share
0.01**
(0.00)
0.02**
(0.00)
-0.03
(0.07)
-0.07**
(0.01)
-0.00**
(0.00)
Median) x Hospital HHI
(Medicare Share
Median) x Physician HHI
Enrollment (100s)
MA penetration (%)
Medicare benchmark (100’s)
Benchmark cuts
Population 65+ (10000’s)
FFS cost
County FFS risk
Plan risk
County MA risk
Doctors per 10,000
Hospital beds per 10,000
Population below poverty (%)
Per capita income (1000’s)
County Unemployment
Out-of-pocket cost
Premium
Contract age (years)
Part D Premium
Plan o↵ers Part D
Missing physician HHI
Integration measured on MSA-level
0.02**
(0.01)
0.01**
(0.01)
0.06
(0.05)
-0.02**
(0.00)
-0.00
(0.00)
-0.08**
(0.02)
0.13
(0.07)
-0.00
(0.00)
0.07**
(0.02)
-0.02
(0.01)
0.00
(0.01)
0.02
(0.01)
-0.00**
(0.00)
-0.00
(0.00)
0.00**
(0.00)
-0.00
(0.00)
-0.01
(0.01)
0.00
(0.00)
0.00**
(0.00)
-0.06**
(0.02)
-0.00**
(0.00)
0.01
(0.02)
0.18**
(0.05)
0.03**
(0.01)
0.19**
(0.06)
0.01
(0.01)
-0.01**
(0.00)
-0.00
(0.07)
-0.02**
(0.01)
-0.00
(0.01)
-0.02**
(0.00)
-0.00
(0.00)
-0.07**
(0.02)
0.09
(0.07)
-0.00
(0.00)
0.07**
(0.02)
-0.03
(0.01)
0.01
(0.01)
0.03
(0.01)
-0.00**
(0.00)
-0.00
(0.00)
0.00**
(0.00)
-0.00
(0.00)
-0.01
(0.01)
0.00
(0.00)
0.00
(0.00)
-0.06**
(0.02)
-0.00
(0.00)
0.00
(0.02)
0.16**
(0.05)
0.03**
(0.01)
5,836
0.37
Yes
Yes
No
5,836
0.10
No
No
No
5,836
0.38
Yes
Yes
No
Median
(Medicare Share
Observations
R-squared
Insurer FE
State FE
County FE
0.02**
(0.00)
0.01**
(0.00)
0.05
(0.05)
-0.04**
(0.01)
-0.01**
(0.00)
-0.01
(0.01)
0.01
(0.01)
0.05
(0.07)
-0.02**
(0.01)
-0.00**
(0.00)
-0.01**
(0.00)
-0.14**
(0.05)
-0.01**
(0.01)
0.01
(0.01)
-0.01**
(0.00)
-0.01
(0.01)
-0.00
(0.00)
-0.09**
(0.04)
0.14
(0.09)
-0.01
(0.03)
0.09**
(0.03)
-0.02
(0.02)
-0.01
(0.02)
0.03**
(0.01)
-0.00**
(0.00)
0.00
(0.00)
-0.00
(0.00)
0.00
(0.00)
-0.01
(0.01)
-0.00
(0.00)
0.00**
(0.00)
0.05
(0.04)
-0.00
(0.00)
-0.03
(0.04)
-0.06
(0.05)
-0.02
(0.01)
0.00
(0.00)
0.00
(0.00)
-0.08**
(0.01)
0.00
(0.00)
-0.02
(0.02)
5,836
0.71
Yes
Yes
Yes
Exit=1 if insurers will removed all plans from a market between 2010-2012 and 0 if not. All variables are 2009 values.
Linear probability models, standard errors clustered on the county level. Sample includes
2,931
0.19
Yes
Yes
No
⇤⇤
p  .05.
PFFS plans o↵ered by insurers who continued to o↵er PFFS plans in 2012, and excludes counties where the insurer had an HMO/PPO in 2009.
Regressions in Cols 1-4 include plans in counties a↵ected by the policy. Col 5 includes plans o↵ered by the same insurers in una↵ected counties.
12
DARIA PELECH
Table 3. Alternate samples and insurance market variables
VARIABLES
Hospital HHI (1000s)
Physician HHI (1000s)
Vertical integration
Insurer Medicare share (%)
Commercial market share (%)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
2008
2011
Extended
Share
Ins.Vars
Ins.Vars
Ins.Vars
Baseline Follow-up Sample +RPPOs Main Sample Exmpt Sample Main Sample
(8)
(9)
Enroll
Insurer-county
Median
level
0.02**
(0.00)
0.02**
(0.00)
0.02
(0.05)
-0.03**
(0.00)
-0.00**
(0.00)
0.01**
(0.00)
0.01**
(0.00)
0.05
(0.05)
-0.04**
(0.01)
-0.01**
(0.00)
0.01**
(0.01)
0.02**
(0.00)
0.06
(0.07)
-0.03**
(0.01)
-0.01**
(0.00)
0.01**
(0.00)
0.01**
(0.00)
0.06
(0.04)
-0.03**
(0.01)
-0.00**
(0.00)
0.03**
(0.01)
-0.01**
(0.00)
-0.06**
(0.02)
0.11
(0.07)
-0.00
(0.00)
0.04**
(0.02)
-0.02
(0.01)
0.00
(0.01)
0.02
(0.01)
-0.00
(0.00)
0.00
(0.00)
0.00**
(0.00)
-0.07**
(0.02)
-0.00
(0.00)
0.00
(0.02)
-0.00**
(0.00)
-0.00
(0.00)
0.14**
(0.05)
-0.02**
(0.00)
-0.08**
(0.03)
0.20**
(0.09)
-0.00
(0.00)
0.07**
(0.02)
-0.03
(0.02)
-0.01
(0.01)
0.00
(0.02)
-0.00
(0.00)
0.00
(0.00)
0.00
(0.00)
-0.05**
(0.02)
-0.00
(0.00)
-0.01
(0.04)
-0.00**
(0.00)
0.00
(0.00)
0.21**
(0.08)
-0.03**
(0.01)
-0.05**
(0.02)
0.09
(0.06)
-0.01**
(0.00)
0.05**
(0.02)
-0.01
(0.01)
-0.04
(0.02)
0.02
(0.01)
-0.00
(0.00)
0.00
(0.00)
0.00**
(0.00)
-0.09**
(0.02)
0.00
(0.00)
-0.05
(0.06)
-0.00
(0.00)
-0.00
(0.00)
0.15**
(0.04)
0.03**
(0.01)
0.00**
(0.00)
-0.00
(0.00)
-0.00
(0.01)
0.05**
(0.02)
0.00**
(0.00)
-0.00
(0.00)
-0.01
(0.01)
0.02**
(0.01)
0.00**
(0.00)
-0.00
(0.00)
-0.00
(0.00)
5,836
0.38
3,008
0.37
3,182
0.40
0.02**
(0.00)
0.01
(0.00)
0.03
(0.04)
-0.02**
(0.00)
-0.00**
(0.00)
0.01**
(0.00)
0.01**
(0.00)
0.04
(0.04)
-0.03**
(0.00)
-0.01**
(0.00)
Insurer Medicare share +RPPOs
0.02**
(0.01)
0.01**
(0.00)
0.05
(0.05)
-0.01**
(0.00)
-0.04**
(0.01)
Share di↵erence (%)
0.02**
(0.00)
0.01**
(0.00)
0.06
(0.05)
-0.03**
(0.01)
-0.01**
(0.00)
-0.01
(0.01)
0.01
(0.01)
0.05
(0.07)
-0.01
(0.01)
-0.00**
(0.00)
0.01**
(0.00)
0.01
(0.01)
MA HHI (1000’s)
Enrollment (100s)
Medicare benchmark (100’s)
Benchmark cuts
Population 65+ (10000’s)
FFS cost
County FFS risk
Plan risk
County MA risk
MA penetration (%)
Out-of-pocket cost
Premium
Contract age (years)
Part D Premium
Plan o↵ers Part D
Doctors per 10,000
Hospital beds per 10,000
Missing physician HHI
-0.01**
(0.00)
-0.09**
(0.03)
0.13
(0.07)
-0.00
(0.00)
0.11**
(0.02)
-0.01
(0.01)
0.02
(0.01)
0.02
(0.01)
-0.00**
(0.00)
-0.00
(0.00)
0.00
(0.00)
-0.07**
(0.01)
0.00
(0.00)
-0.12**
(0.03)
-0.00**
(0.00)
-0.00
(0.00)
0.21**
(0.05)
-0.02**
(0.00)
-0.07**
(0.02)
0.10
(0.06)
-0.00
(0.00)
0.06**
(0.02)
-0.01
(0.01)
0.00
(0.01)
0.01
(0.01)
-0.00
(0.00)
0.00
(0.00)
0.00**
(0.00)
-0.04**
(0.01)
-0.00**
(0.00)
-0.02
(0.02)
-0.00
(0.00)
-0.00
(0.00)
0.11**
(0.04)
-0.02**
(0.00)
-0.07**
(0.02)
0.12**
(0.06)
-0.00
(0.00)
0.06**
(0.02)
-0.02
(0.01)
0.01
(0.01)
0.02
(0.01)
-0.00
(0.00)
0.00
(0.00)
0.00**
(0.00)
-0.04**
(0.01)
-0.00**
(0.00)
0.03
(0.02)
-0.00**
(0.00)
-0.00
(0.00)
0.14**
(0.04)
0.00
(0.00)
0.00**
(0.00)
-0.07**
(0.02)
-0.00
(0.00)
0.02
(0.02)
-0.00**
(0.00)
-0.00
(0.00)
0.20**
(0.05)
-0.02**
(0.00)
-0.07**
(0.02)
0.12
(0.07)
-0.00
(0.00)
0.06**
(0.02)
-0.02
(0.01)
0.00
(0.01)
0.02
(0.01)
-0.00**
(0.00)
0.00
(0.00)
0.00**
(0.00)
-0.06**
(0.02)
-0.00
(0.00)
0.01
(0.02)
-0.00**
(0.00)
-0.00
(0.00)
0.19**
(0.05)
0.02
(0.01)
0.00**
(0.00)
-0.00
(0.00)
-0.00
(0.00)
0.02
(0.01)
0.00**
(0.00)
-0.00
(0.00)
-0.01
(0.01)
0.03**
(0.01)
0.00**
(0.00)
-0.00
(0.00)
-0.00
(0.01)
-0.01
(0.01)
-0.09**
(0.04)
0.15
(0.09)
-0.01
(0.03)
0.09**
(0.03)
-0.02
(0.02)
-0.01
(0.02)
0.03**
(0.01)
-0.01
(0.00)
-0.00
(0.00)
0.00**
(0.00)
0.05
(0.04)
-0.00
(0.00)
-0.04
(0.04)
-0.00**
(0.00)
0.00
(0.00)
-0.06
(0.05)
-0.16
(0.12)
-0.02
(0.01)
-0.00
(0.00)
0.00
(0.00)
-0.01
(0.01)
0.03**
(0.01)
0.00**
(0.00)
-0.00
(0.00)
-0.00
(0.01)
0.03**
(0.01)
0.00**
(0.00)
-0.00
(0.00)
-0.00
(0.00)
5,632
0.39
6,648
0.41
6,779
0.37
5,836
0.37
5,836
0.38
2,931
0.19
-0.08**
(0.02)
0.13
(0.07)
-0.01**
(0.00)
0.07**
(0.02)
-0.02
(0.01)
0.00
(0.01)
0.02
(0.01)
Missing hospital HHI
Integration measured on MSA-level
Population below poverty (%)
Per capita income (1000’s)
County Unemployment
Observations
R-squared
Exit=1 if insurers will removed all plans from a market between 2010-2012 and 0 if not. All variables are 2009 values.
⇤⇤
p  .05.
Linear probability models, standard errors clustered on the county level. All regressions include insurer and state fixed e↵ects.
APPENDIX
13
Table 4 presents the results of robustness checks that vary how provider market variables are used and constructed. First, data on physician HHI and vertical integration are
missing for a small number of observations (n=143). Since data are missing when there are
an insufficient number of Medicare claims in a county to reliably construct these statistics,
it cannot be assumed that data are missing at random. To test whether missing data drive
observed results, I repeat the preferred specification from Section 5, omitting observations
with missing data (Column 1). Results do not substantially change, and coefficients on
insurer and provider market variables are statistically similar to those in main regressions.
In Columns 2-4, I test alternate measures of provider market power. Column 2 uses an
alternate version of the vertical integration measure that measures the percent of outpatient
claims billed in a hospital outpatient department, rather than the percent of doctors who
bill the majority of their claims in a hospital outpatient department. Results are similar to
those in main regressions, and the coefficient on vertical integration remains insignificant.
Columns 3 and 4 use alternate definitions of hospital HHI, constructed using a hospital
system’s share of beds or Medicare discharges, rather than admissions. Constructing HHI
using beds might be more appropriate if hospital market power is related to capacity rather
than patient demand. Constructing HHI using Medicare discharges might be more appropriate if providers bargain over Medicare and commercial markets separately. However, results
of regressions using these variables are similar to those in main results.
Columns 5-8 test di↵erent ways of defining markets for vertical integration and hospital
competition measures. Vertical integration measures are constructed on the MSA-level for
most observations and on the county-level for observations that do not fall in an MSA.14
Columns 5 and 6 test whether results are sensitive to constructing measures in this way, first
by running the regression only for observations with non-missing MSA-level observations
and, second, by using the county-level measure for all observations. Results are similar, and
vertical integration coefficients remain insignificant.
14MSA
measures are preferred because vertical integration measures are noisier when aggregated on the
county level.
14
DARIA PELECH
In main results, hospital HHI is calculated on the HSA level, and then HSAs are
associated with counties. Columns 7 and 8 use an alternate HHI variable that defines hospital
markets on the county level. Coefficients on county-level hospital HHI are similar to those
in models using HSA-level hospital HHI ( = .02, Column 7). However, 10% of observations
(n=615) have missing values for county-level HHI, because there are no hospitals in those
counties. Column 8 excludes these observations. Results are similar, but noisier. Specifically,
the coefficient on physician HHI is significant only at the 10% level after excluding counties
that are missing hospital HHI.
APPENDIX
15
Table 4. Provider competition: Robustness checks and alternate variables
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Excl. Missing Alt.Intgr. Alt. Hosp.
Alt.Hosp
Vertical Data Alt.Intgr.
Alt.Hosp. Excl. Missing
Physician HHI Measure Bed HHI Medicare HHI MSAs only County-level County-HHI County-HHI
VARIABLES
Hospital admission HHI (1000s)
0.02**
(0.01)
0.01**
(0.00)
0.05
(0.05)
-0.04**
(0.01)
-0.01**
(0.00)
Physician HHI (1000s)
Vertical integration
Insurer Medicare share (%)
Commercial market share (%)
Claims billed in HOPD
0.02**
(0.00)
0.01**
(0.00)
-0.04**
(0.01)
-0.01**
(0.00)
-0.00
(0.05)
Hospital bed HHI (1000s)
0.01**
(0.00)
0.05
(0.05)
-0.04**
(0.01)
-0.01**
(0.00)
0.01**
(0.00)
0.05
(0.05)
-0.04**
(0.01)
-0.01**
(0.00)
0.01**
(0.01)
0.01**
(0.00)
0.04
(0.08)
-0.04**
(0.01)
-0.01**
(0.00)
0.02**
(0.00)
0.01**
(0.00)
0.05
(0.05)
-0.04**
(0.01)
-0.01**
(0.00)
Medicare benchmark (100’s)
-0.08**
(0.03)
0.13
(0.07)
-0.00
(0.00)
0.07**
(0.02)
-0.02
(0.01)
0.00
(0.01)
0.02
(0.01)
-0.00
(0.00)
-0.00
(0.00)
-0.02**
(0.00)
-0.00
(0.00)
0.00
(0.00)
0.00**
(0.00)
-0.06**
(0.02)
-0.00
(0.00)
0.01
(0.02)
Benchmark cuts
Population 65+ (10000’s)
FFS cost
County FFS risk
Plan risk
County MA risk
Doctors per 10,000
Hospital beds per 10,000
Enrollment (100s)
MA penetration (%)
Out-of-pocket cost
Premium
Contract age (years)
Part D Premium
Plan o↵ers Part D
-0.08**
(0.02)
0.13
(0.07)
-0.00
(0.00)
0.07**
(0.02)
-0.02
(0.01)
0.00
(0.01)
0.02
(0.01)
-0.00**
(0.00)
-0.00
(0.00)
-0.02**
(0.00)
-0.00
(0.00)
0.00
(0.00)
0.00**
(0.00)
-0.06**
(0.02)
-0.00**
(0.00)
0.01
(0.02)
0.18**
(0.05)
0.05**
(0.03)
0.00**
(0.00)
-0.00
(0.00)
-0.01
(0.01)
-0.08**
(0.02)
0.13
(0.07)
-0.00
(0.00)
0.07**
(0.02)
-0.02
(0.01)
0.00
(0.01)
0.02
(0.01)
-0.00**
(0.00)
-0.00
(0.00)
-0.02**
(0.00)
-0.00
(0.00)
0.00
(0.00)
0.00**
(0.00)
-0.06**
(0.02)
-0.00**
(0.00)
0.01
(0.02)
0.18**
(0.05)
0.05**
(0.03)
0.00**
(0.00)
-0.00
(0.00)
-0.01
(0.01)
-0.09**
(0.03)
0.13
(0.09)
-0.00
(0.00)
0.08**
(0.02)
-0.03
(0.02)
0.01
(0.01)
0.03
(0.02)
-0.00
(0.00)
-0.00
(0.00)
-0.01**
(0.00)
-0.00
(0.00)
0.00
(0.00)
0.00**
(0.00)
-0.06**
(0.02)
-0.00**
(0.00)
0.04
(0.03)
0.15
(0.08)
0.05
(0.03)
0.00**
(0.00)
-0.00
(0.00)
-0.01
(0.01)
-0.08**
(0.02)
0.13
(0.07)
-0.00
(0.00)
0.07**
(0.02)
-0.02
(0.01)
0.00
(0.01)
0.02
(0.01)
-0.00
(0.00)
-0.00
(0.00)
-0.02**
(0.00)
-0.00
(0.00)
0.00
(0.00)
0.00**
(0.00)
-0.06**
(0.02)
-0.00**
(0.00)
0.01
(0.02)
0.17**
(0.05)
0.05**
(0.03)
0.00**
(0.00)
-0.00
(0.00)
-0.01
(0.01)
0.00**
(0.00)
-0.00
(0.00)
-0.01
(0.01)
-0.08**
(0.02)
0.13
(0.07)
-0.00
(0.00)
0.07**
(0.02)
-0.02
(0.01)
0.00
(0.01)
0.02
(0.01)
-0.00**
(0.00)
-0.00
(0.00)
-0.02**
(0.00)
-0.00
(0.00)
0.00
(0.00)
0.00**
(0.00)
-0.06**
(0.02)
-0.00**
(0.00)
0.01
(0.02)
0.18**
(0.05)
0.05**
(0.03)
0.00**
(0.00)
-0.00
(0.00)
-0.01
(0.01)
5,693
0.38
5,836
0.37
5,836
0.37
5,836
0.37
4,526
0.36
5,836
0.37
Missing physician HHI
Integration measured on MSA-level
County Unemployment
Missing hospital HHI (county)
Observations
R-squared
0.02**
(0.00)
-0.07**
(0.02)
0.12
(0.07)
-0.00
(0.00)
0.07**
(0.02)
-0.02
(0.01)
0.00
(0.01)
0.02
(0.01)
-0.00**
(0.00)
-0.00
(0.00)
-0.01**
(0.00)
-0.00
(0.00)
0.00
(0.00)
0.00**
(0.00)
-0.06**
(0.02)
-0.00**
(0.00)
0.02
(0.02)
0.17**
(0.06)
0.06**
(0.03)
0.00**
(0.00)
-0.00
(0.00)
-0.00
(0.01)
0.17**
(0.05)
0.02**
(0.00)
-0.08**
(0.03)
0.13
(0.07)
-0.00
(0.00)
0.07**
(0.02)
-0.02
(0.02)
0.00
(0.01)
0.02
(0.02)
-0.00**
(0.00)
0.00
(0.00)
-0.01**
(0.00)
-0.00
(0.00)
0.00
(0.00)
0.00**
(0.00)
-0.06**
(0.02)
-0.00
(0.00)
0.01
(0.03)
0.19**
(0.08)
0.06**
(0.03)
0.00**
(0.00)
-0.00
(0.00)
-0.00
(0.01)
5,836
0.38
5,221
0.38
0.02**
(0.00)
Hospital admission HHI (county)
Per capita income (1000’s)
0.01**
(0.00)
0.07
(0.06)
-0.05**
(0.01)
-0.01**
(0.00)
0.01**
(0.00)
Medicare discharge HHI (1000s)
Population below poverty (%)
0.01**
(0.00)
0.05
(0.05)
-0.04**
(0.01)
-0.01**
(0.00)
Exit=1 if insurers will removed all plans from a market between 2010-2012 and 0 if not. All variables are 2009 values.
⇤⇤
p  .05.
Linear probability models, standard errors clustered on the county level. All regressions include insurer and state fixed e↵ects. Sample includes PFFS plans o↵ered by
insurers who continued to o↵er PFFS plans in 2012, in counties where the insurer had no HMO/PPO in 2009.
16
DARIA PELECH
The final robustness check compares average marginal e↵ects from logistic regressions
to results from linear probability models. As exit is a binary variable, using a logit model
would be appropriate for this analysis. However, state, county, and insurer fixed e↵ects
result in the omission of observations, as some fixed e↵ects perfectly predict exit.15 As logit
coefficients are not comparable across substantially di↵erent samples, this makes it difficult
to compare specifications.
Table 5 shows average marginal e↵ects from logit models that replicate the regressions
from Table 2 of the main text. The marginal e↵ects of insurer- and provider-market variables
are generally similar to the coefficients from linear probability models. The only substantial
di↵erence between specifications occurs for the regressions that include county fixed e↵ects
and interactions between provider and insurer market variables (Column 4). County fixed
e↵ects result in the exclusion of 47% of plan-county observations in the logit model.16 Perhaps as a result, the coefficient on the indicator for above-median Medicare share and the
interaction between this variable and hospital HHI are not statistically significant. However,
coefficients are similar in magnitude to those from the linear probability model. Because
changes in the sample change the magnitude of logit coefficients, it is difficult to say whether
di↵erences in the statistical significance are due to di↵erences in specifications or di↵erences
in the composition of the samples.
15In the main sample,
insurer fixed e↵ects result in the exclusion of one insurer who exits all counties (Medica)
and another who keeps their plans in all counties (Arkansas Blue Cross Blue Shield). State fixed e↵ects result
in the exclusion of five states that insurers exited altogether (New Jersey, Maryland, Massachusetts, DC,
and Delaware). In the exempt sample, state and insurer fixed e↵ects exclude two states where all insurers
exited (Delaware and Massachusetts) and two insurers who continued to o↵er plans in all counties (Blue
Cross Blue Shield of Tennessee and Blue Cross Blue Shield of Arkansas).
16All insurers exited 40% of counties (n=630) and stayed in another 22% (n=346).
APPENDIX
17
Table 5. Average Marginal E↵ects from Logistic Regressions
(1)
(2)
(3)
(4)
(5)
Main Sample Main Sample Main Sample Main Sample Exempt Sample
Controls+FE No controls Interactions Interactions
Controls+FE
VARIABLES
Hospital HHI (1000s)
Physician HHI (1000s)
Vertical integration
Insurer Medicare share (%)
Commercial market share (%)
Medicare Share
0.01**
(0.00)
0.02**
(0.00)
-0.02
(0.07)
-0.07**
(0.01)
-0.00**
(0.00)
Median) x Hospital HHI
(Medicare Share
Median) x Physician HHI
Medicare benchmark (100’s)
Future benchmark cuts (+100’s)
Population 65+ (10000’s)
FFS normalized costs (100’s)
Standardized FFS risk score
Standaridized plan risk
Standardized MA county risk score
Doctors per 10,000
Hospital beds per 10,000
Number of enrollees in plan (100’s)
MA penetration (%)
Population below poverty (%)
Per capita income (1000’s)
County Unemployment
Out-of-pocket cost, no premiums
Premium
Contract age (years)
Part D Premium
Plan o↵ers Part D
Missing physician HHI
Integration measured on MSA-level
0.02**
(0.01)
0.02**
(0.01)
0.05
(0.05)
-0.07**
(0.02)
0.12
(0.07)
-0.00**
(0.00)
0.07**
(0.02)
-0.02
(0.01)
0.01
(0.01)
0.02
(0.01)
-0.00**
(0.00)
-0.00
(0.00)
-0.03**
(0.01)
-0.00
(0.00)
0.00**
(0.00)
-0.00
(0.00)
-0.00
(0.01)
0.00
(0.00)
0.00
(0.00)
-0.07**
(0.03)
-0.00
(0.00)
0.02
(0.03)
0.18**
(0.05)
0.06**
(0.03)
0.20**
(0.07)
0.03
(0.03)
-0.01**
(0.00)
0.03
(0.07)
-0.02**
(0.01)
-0.01
(0.01)
-0.07**
(0.02)
0.09
(0.07)
-0.00
(0.00)
0.07**
(0.02)
-0.03
(0.01)
0.01
(0.01)
0.03
(0.01)
-0.00**
(0.00)
-0.00
(0.00)
-0.03**
(0.01)
-0.00
(0.00)
0.00**
(0.00)
-0.00
(0.00)
-0.01
(0.01)
0.00
(0.00)
0.00
(0.00)
-0.06**
(0.03)
-0.00
(0.00)
0.01
(0.03)
0.14**
(0.05)
0.06**
(0.03)
5,671
Yes
Yes
No
5,836
No
No
No
5,671
Yes
Yes
No
Median
(Medicare Share
Observations
Insurer FE
State FE
County FE
0.02**
(0.00)
0.01**
(0.00)
0.04
(0.05)
-0.03**
(0.01)
-0.01**
(0.00)
-0.01
(0.01)
0.01
(0.01)
0.04
(0.07)
-0.02**
(0.01)
-0.00**
(0.00)
-0.01**
(0.00)
-0.13
(0.07)
-0.02**
(0.01)
0.02
(0.01)
-0.08**
(0.04)
0.14
(0.09)
-0.01
(0.03)
0.09**
(0.03)
-0.02
(0.02)
-0.02
(0.02)
0.03**
(0.01)
-0.00**
(0.00)
0.00
(0.00)
-0.02
(0.02)
-0.00
(0.00)
-0.01**
(0.00)
0.00
(0.00)
0.00
(0.00)
-0.08**
(0.01)
0.00
(0.00)
0.02
(0.03)
3,059
Yes
Yes
Yes
2,884
Yes
Yes
No
Average marginal e↵ects from logistic regression, exit=1. Standard errors clustered on the county level.
All models are replications of Table 2 in the main text. Coefficients on interactions calculated using the Ai-Norton method.(7)
Sample size di↵ers across models because some fixed e↵ects perfectly predict exit.
⇤⇤
p  .05.
18
DARIA PELECH
3. Appendix: Where do insurers replace PFFS plans with HMO/PPOs?
Insurers who continued to operate in sample markets pursued two strategies: they kept
their PFFS plans, or they introduced a new HMO/PPO. In main analysis, these two actions
are treated the same, because both required that the insurer build a network to stay in the
market. (Recall that the sample is limited to observations in counties where insurers did not
already o↵er an HMO/PPO, so insurers could not simply keep their existing HMO/PPOs.)
Here, I present additional analysis that tests whether provider market characteristics varied
between the counties where insurers kept their PFFS plans or introduced an new HMO/PPO.
One reason that insurers might prefer to cancel their PFFS plans and build an HMO/PPO
is that doing so might give them additional bargaining power with providers. For instance,
insurers cannot require referrals for specialty services in PFFS plans but are allowed to
do so in HMO/PPOs. Requiring referrals might increase insurers’ bargaining power with
providers by allowing them to direct patients within network. As past research has shown
that the ability to steer patients to certain providers is an important part of insurer bargaining power(8, 9), insurers might prefer to replace their PFFS plans in markets where
providers have more market power.17
To test whether market characteristics di↵er between places where insurers kept their
PFFS plans or introduced an HMO/PPO, I limit analysis to the subsample of markets where
insurers continued to o↵er plans. I then create an indicator that equals 1 if they cancelled
all PFFS plans and introduced an HMO/PPO to the market and equals 0 if they kept their
PFFS plans. This indicator is regressed on the same set of variables used in main analysis.
Table 6 shows the results. In the main sample, insurers replaced their PFFS plans
with an HMO/PPO in a little over a third of the counties in which they continued operating
(n=868 or 35%) and kept PFFS plans in the rest (n=1614).
17There
are other reasons that insurers might prefer to keep PFFS plans or introduce HMO/PPOs that are
unrelated to provider market power. For instance, insurers might prefer to keep their PFFS plans if they
wanted to keep their existing enrollees. The rules of the policy change did not allow insurers to automatically
re-enroll PFFS beneficiaries in their new HMOs/PPOs, although they could actively market the new plans
to their existing enrollees.
APPENDIX
19
Column 1 shows the results of regressing physician and insurer market variables on this
indicator, without any controls or fixed e↵ects. Results suggest that insurers were more likely
to replace their PFFS plan with an HMO/PPO in counties where physicians and hospitals
had greater market power. Each additional 1000 points of physician or hospital HHI each
increased the probability that the insurer replaced their PFFS with an HMO/PPO by 2
percentage points. An insurer’s commercial market share had a positive and statistically
significant e↵ect on the probability that an insurer replaced their PFFS plans, but the
coefficient is almost exactly 0.
Column 2 adds all controls and fixed e↵ects to this regression. After adding controls and
fixed e↵ects, the coefficients on hospital and physician market power were half as large and
the coefficient on hospital HHI was insignificant. However, the change in size and significance
of coefficients is due almost entirely to the inclusion of insurer fixed e↵ects. After omitting
only the insurer fixed e↵ects (Column 3), the coefficients are identical to those in Column 1.
That coefficients are attenuated by including insurer fixed is consistent with patterns
of insurer behavior discussed in the main text. Specifically, almost all insurers either kept
all their PFFS plans or replaced all PFFS plans with HMO/PPOs. Taken as a whole, these
results have two possible interpretations. One is that insurers were more likely to replace
PFFS plans with HMO/PPOs in places where providers had market power, and that insurers
made the decision to swap their plans based on the aggregate characteristics of the markets
they served. Another interpretation is that insurers who operated in concentrated provider
markets also happened to be those who switched their plans for HMO/PPOs. Unfortunately,
it is difficult to distinguish between these two interpretations in the data.
Coefficients on most controls do not significantly a↵ect the probability that insurers
replaced their PFFS plans with HMO/PPOs. Moreover, the significance of most coefficients
is not robust across specifications. One notable exception is the coefficient on county-level
FFS risk scores. This coefficient suggests that a one-standard deviation increase in risk
20
DARIA PELECH
among FFS beneficiaries decreased the probability that insurers replaced their PFFS plans
with HMO/PPOs by 4-5 percentage points.18
Columns 3 and 4 show the same regressions run in the sample of non-networked counties, with and without insurer fixed e↵ects. Provider and insurer market variables had
no e↵ect on the decision to replace PFFS plans in these counties, and no control variable
was consistently significant across specifications. However, conclusions are hard to draw in
this sample, because very few insurers in these counties replaced their PFFS plans with
HMOs/PPOs (n=174 or 9%).
18There
are several ways to interpret this coefficient. First, risk scores may reflect the health of FFS enrollees
in a county, the diligence with which physicians record diagnoses, or the degree of selection between FFS
Medicare and Medicare Advantage. Each interpretation suggests a di↵erent explanation for the sign of this
coefficient. If risk scores reflect the health of FFS beneficiaries, then a negative coefficient indicates that
insurers are more likely to keep PFFS plans in markets with sicker beneficiaries. Insurers might prefer to
keep PFFS plans in these markets if PFFS plans are more appealing to sicker enrollees than HMO/PPOs.
Alternately, FFS risk scores might be higher in areas where physicians code more intensively, which would
lead to higher per-patient reimbursement. Higher reimbursement might make it easier to operate a PFFS
plan, resulting in more insurers leaving their plans in the market. Lastly, higher FFS risk scores may reflect
greater favorable selection in Medicare Advantage, which might also make PFFS plans more profitable.
However, without more data on the health of beneficiaries in Medicare Advantage plans, these hypotheses
are difficult to test further.
APPENDIX
21
Table 6. Predictors of replacing PFFS plans with HMO/PPOs (1=Replace, 0=Keep PFFS)
(1)
(2)
(3)
(4)
(5)
(6)
Main Sample
Main Sample Main Sample
Exempt Sample Exempt Sample Exempt Sample
No controls or FE Controls+FE No Insurer FE No controls or FE Controls+FE
No Insurer FE
VARIABLES
Hospital HHI (1000s)
Physician HHI (1000s)
Vertical integration
Insurer Medicare share (%)
Commercial market share (%)
0.02**
(0.01)
0.02**
(0.01)
-0.03
(0.08)
-0.00
(0.01)
0.00**
(0.00)
MA penetration (%)
Enrollment (100s)
Medicare benchmark (100’s)
Benchmark cuts
Population 65+ (10000’s)
FFS cost
County FFS risk
Plan risk
County MA risk
Doctors per 10,000
Hospital beds per 10,000
Out-of-pocket cost
Premium
Contract age (years)
Part D Premium
Plan o↵ers Part D
Missing physician HHI
Integration measured on MSA-level
0.14
(0.09)
0.08**
(0.03)
Population below poverty (%)
Per capita income (1000’s)
County Unemployment
Observations
R-squared
Insurer FE
State FE
2,482
0.38
No
No
0.01
(0.01)
0.01**
(0.01)
-0.05
(0.08)
-0.01
(0.01)
-0.00
(0.00)
-0.00
(0.00)
-0.01**
(0.00)
0.03
(0.03)
-0.18
(0.09)
-0.00
(0.00)
-0.02
(0.03)
-0.05**
(0.02)
0.02
(0.01)
0.03
(0.02)
-0.00**
(0.00)
0.00
(0.00)
0.00
(0.00)
-0.00
(0.00)
0.03
(0.01)
0.00
(0.00)
-0.03
(0.03)
0.04
(0.08)
0.08**
(0.03)
0.00
(0.00)
-0.00
(0.00)
0.00
(0.01)
0.02**
(0.01)
0.02**
(0.01)
-0.01
(0.08)
-0.01
(0.01)
0.01**
(0.00)
-0.00
(0.00)
-0.01
(0.00)
0.02
(0.04)
-0.20**
(0.10)
-0.01
(0.00)
-0.03
(0.03)
-0.04**
(0.02)
0.01
(0.01)
0.03
(0.02)
-0.00
(0.00)
0.00
(0.00)
-0.00
(0.00)
-0.00
(0.00)
0.05**
(0.01)
-0.00**
(0.00)
0.10**
(0.04)
0.11
(0.09)
0.08**
(0.04)
0.00
(0.00)
-0.00
(0.00)
0.01
(0.01)
-0.02**
(0.01)
0.01
(0.00)
-0.00
(0.04)
0.01
(0.00)
-0.00
(0.00)
2,482
0.54
Yes
Yes
2,482
0.41
No
Yes
2,010
0.17
No
No
-0.01
(0.03)
0.01
(0.02)
-0.01
(0.01)
0.01
(0.00)
-0.01
(0.04)
-0.00
(0.00)
-0.00**
(0.00)
0.00
(0.00)
-0.00
(0.01)
0.01
(0.02)
-0.04
(0.06)
0.02
(0.03)
-0.01
(0.02)
-0.02
(0.01)
0.02
(0.01)
-0.00
(0.01)
-0.00
(0.00)
-0.00**
(0.00)
-0.00
(0.00)
0.00
(0.00)
-0.02
(0.05)
-0.00
(0.00)
-0.02
(0.03)
-0.02
(0.04)
-0.02
(0.02)
0.00
(0.00)
0.00
(0.00)
-0.00
(0.00)
-0.01
(0.01)
0.01
(0.00)
0.01
(0.04)
0.00
(0.00)
0.00
(0.00)
0.00
(0.00)
0.00
(0.01)
0.02
(0.02)
-0.07
(0.06)
0.01
(0.03)
-0.02
(0.02)
-0.02
(0.01)
0.01
(0.01)
-0.00
(0.01)
-0.00
(0.00)
-0.00
(0.00)
0.00
(0.00)
0.00
(0.00)
0.03**
(0.01)
-0.00
(0.00)
0.05
(0.03)
-0.01
(0.04)
-0.02
(0.02)
0.00
(0.00)
0.00
(0.00)
-0.00
(0.00)
2,010
0.25
Yes
Yes
2,010
0.22
No
Yes
Sample includes only plan-county observations that insurers kept in the market.
Outcome=1 if insurers replaced their PFFS plans with HMO/PPO and 0 if they kept o↵ering a PFFS plan.
All variables are 2009 values.
⇤⇤
p  .05. Linear probability models, standard errors clustered on the county level.
22
DARIA PELECH
References
[1] Robert Town and Gregory Vistnes. Hospital competition in HMO networks. Journal of
Health Economics, 20(5):733–753, 2001.
[2] Glenn Melnick, Jack Zwanziger, Anil Bamezai, and Robert Pattison. The e↵ects of
market structure and bargaining position on hospital prices. Journal of Health Economics,
11(3):217–233, 1992.
[3] Abe Dunn. The value of coverage in the Medicare Advantage insurance market. Journal
of Health Economics, 29:839–855, 2010.
[4] Karen Stockley, Thomas McGuire, Christopher Afendulis, and Michael Chernew. Premium transparency in the Medicare Advantage market: implications for premiums, benefits, and efficiency. National Bureau of Economic Research Working Paper, 20208, 2014.
[5] Daria Pelech. Paying more for less? Insurer competition and health plan generosity in
the Medicare Advantage program. Working Paper, Harvard University, November, 2014.
[6] Hannah Neprash, Michael Chernew, Andrew Hicks, Teresa Gibson, and J. Michael
McWilliams. Association of financial integration between physicians and hospitals with
commercial health care prices. JAMA Internal Medicine, 175(7):1229–1231, 2015.
[7] Pinar Karaca-Mandic, Edward Norton, and Bryan Dowd. Interaction terms in nonlinear
models. Health Services Research, 47(1):255–274, 2012.
[8] Vivian Wu. Managed care’s price bargaining with hospitals. Journal of Health Economics,
28(2):350–360, mar 2009.
[9] Alan Sorensen. Insurer-hospital bargaining: Negotiated discounts in post-deregulation
Connecticut. Journal of Industrial Economics, 51(4):469–490, 2003.