1. society
2. welfare
3. healthcare
4. hospital

Competition and Quality Choice in Hospital Markets*
M ATTHEW S. L EWIS AND K EVIN E. P FLUM†
October 24, 2014
Hospital quality is typically measured through outcomes (such as mortality rates), but other hospital attributes are also important when competing for patients. Moreover, given the prevalence of
managed care organizations (MCOs), hospitals must compete on multiple margins: to be included
in managed care networks and to attract patients. We examine how competition impacts hospitals’
level of both clinical quality and demand-shifting quality using a control function approach to account for the endogeneity of market structure. We find strong evidence that demand-shifting quality
declines when hospitals face a more competitive environment and that, although patient demand is
not particularly sensitive to differences in clinical quality, clinical quality improves with competition
for some managed care patients while declining significantly with competition for traditional Medicare patients. Together these findings suggest that clinical quality is more important for competition
at the managed care level than in the direct competition for patients.
I.
I NTRODUCTION
Hospital markets have been in a state of constant and rapid change over the last few
decades. On the payer side there has been enormous growth in managed care from Health
Maintenance Organizations (HMOs) and Preferred Provider Organizations (PPOs) at the expense of traditional indemnity insurance. Today indemnity plans represent less than 1% of
the employer sponsored insurance market (Kaiser Family Foundation and Health Research
and Education Trust, 2012) shifting hospitals’ focus away from competition over patients towards securing favorable reimbursement rates with managed care plans (Dranove, Shanley,
and White, 1993). At the same time, the provider side of the market has experienced an unprecedented wave of consolidation as thousands of hospitals have been acquired by systems
substantially altering the competitive landscape. Nearly 60% of all acute-care hospitals belong
to a system now.
* The authors thank Daniel Henderson, Le Wang, and participants at workshops at both Clemson University and the
University of Alabama, the 2014 International Industrial Organization Conference, and 2014 ASHEcon for helpful comments
and discussion. The authors also thank Ming Meng for excellent research assistance.
† Lewis: Lewis: Clemson University, John E. Walker Department of Economics, 228 Sirrine Hall, Clemson, SC 29634,
[email protected]: University of Alabama, Department of Economics, Finance and Legal Studies, Box 870224,
Tuscaloosa, AL, 35406, [email protected].
1
The impact of these changes on the prices charged by hospitals for their services has
been extensively studied in the literature;1 however, the impact of these changes on the quality
of hospital services is much less understood. The majority of the literature has focused on clinical outcomes such as in-hospital and 30-day mortality rates and have found mixed evidence
for the impact of competition on health outcomes. For example Gowrisankaran and Town
(2003), Shen (2003) and Propper, Burgess, and Green (2004) find evidence that competition
may increase mortality for certain groups of patients while Kessler and McClellan (2000)
and Kessler and Geppert (2005) uncover evidence that competition may improve quality by
decreasing mortality rates.2 Furthermore, instead of examining just one or two measures of
clinical quality, Mutter, Wong, and Goldfarb (2008) examined 38 distinct measures and found
that competition increases the value for some measures while it decreases the value for others.3
One reason for these mixed and inconclusive findings may be that the importance of
managed care organizations (MCOs) forces hospitals to compete on several margins. At one
level hospitals must compete to be included in an MCO’s provider network, and particularly
selective MCOs, such as HMOs, may seek out providers with lower treatment costs. This
incentive to keep costs low could lead hospitals to provide a lower quality of care or it may encourage hospitals to provide more effective treatment and improve health outcomes in an effort
to lower the overall long-run cost of care for the MCOs enrollees. However, hospitals must
also compete for patients. When a hospital is more attractive to patients it becomes more valuable to the MCOs, both increasing their likelihood of network inclusion and allowing them to
secure higher reimbursements when they are included (Town and Vistnes, 2001; Capps et al.,
2003; Ho, 2009). If marginal patients are sufficiently profitable, hospitals will find it beneficial to invest more in those dimensions of quality that impact hospital choice. Similarly, when
it comes to patients insured by Medicare, which does not form exclusive provider networks,
competition over patients will likely represent the primary competitive margin.
Although clinical outcomes studied by much of the previous literature are of primary
interest to policy makers, hospitals may instead compete in dimensions of quality that, at best,
1
Examples of the literature examining the impact of hospital consolidation on prices include Dranove et al. (1993); Lynk
(1995); Melnick et al. (1992); Brooks et al. (1997); Connor et al. (1998); Simpson and Shin (1998); Dranove and Ludwick
(1999); Keeler et al. (1999); Town and Vistnes (2001); Capps et al. (2003); Gaynor and Vogt (2003); Cuellar and Gertler
(2005); Melnick and Keeler (2007); Ho (2009); Lewis and Pflum (2014).
2
Gowrisankaran and Town (2003) find that competition for HMO patients increases clinical quality (decreases mortality)
while competition for Medicare patients decreases clinical quality.
3
See Gaynor (2006) for a detailed review of the literature on competition and quality choice in hospital markets.
2
indirectly impact clinical outcomes. For example, hospitals may make investments in providing better hotel services such as single-bed recovery rooms in order to attract patients or they
may compete by trying to attract those physicians who are the most sought after by patients
(Pope, 1989; Mukamel et al., 2002; Pauly, 2004; Gaynor, 2006; Mutter et al., 2008). If the use
of single-occupancy recovery rooms decreases the likelihood of a hospital-acquired infection,
or if the most sought after physicians also tend to have the best clinical outcomes, then these
enhancements will ultimately result in better clinical outcomes.4 However, hospitals may
alternatively compete in dimensions of quality that are not strongly correlated with clinical
outcomes. Romley and Goldman (2011) find evidence consistent with this view by observing
that revealed quality is higher at hospitals having more quality responsive demand while also
finding that revealed quality and risk-adjusted mortality are only moderately correlated.
In this paper we examine the degree to which hospitals alter their demand-shifting (or
revealed quality) and their clinical quality in response to competitive pressures to gain insight
into the relative importance of these competitive margins and how the incentives generated by
HMOs in contrast to those generated by Medicare impact hospital choice of quality. To identify how hospitals alter revealed quality we estimate the relative utility that patients receive
from hospitals as a function of the level of market competition and managed care penetration
using a panel of discharges for California from 2000 to 2010. We utilize the HerfindahlHirschman Index (HHI) as our measure of market competitiveness and control for the endogeneity of market structure using a control function approach. This allows us to identify
whether hospitals alter their demand-shifting quality in response to a change in the amount of
competition they face or whether the incentives created by managed care plans for network
inclusion, in particular, have caused hospitals to compete in other ways. We then assess how
a change in the degree of competition impacts mortality for four diagnoses commonly used in
the literature: acute myocardial infarction (AMI), heart failure, pneumonia, and acute stroke.
In addition to including the HHIs for HMO and Medicare patients, we separately perform the
estimation using discharges for patients enrolled in either one of the five largest HMOs or in
traditional Medicare as patients having different types of insurer may have important differences in preferences while the payment and network inclusion requirements of the different
4
Single-occupancy rooms do in fact reduce the likelihood of hospital acquired infection and result in lower average
length of stays. See Chaudhury et al. (2005) for a review of the research on the impacts of single- versus multiple-occupancy
recovery rooms.
3
types of insurer may generate different incentives to provide quality. Additionally, as some
quality enhancements may impact patient choice more when a patient’s diagnosis belongs to a
particular major diagnostic category (MDC) we also perform the analysis utilizing discharges
belonging to each of the top three MDCs (excluding child birth, which is the most frequent
MDC for HMO patients but essentially non-existent among Medicare patients) plus diagnoses
belonging to the nervous system, which includes acute stroke.
This study builds on the previous empirical research in several important ways. First,
with our approach we are able to consider all of the dimensions of quality that could be affected by the different competitive margins that hospitals face, which allows us to draw some
conclusions regarding the importance of these margins. Second, instead of focusing on one
or two clinical outcomes that are associated with a specific patient population or type of diagnosis, we examine the impact of competition for several of the largest diagnostic categories
using different patient populations defined by their type of insurance. Third, rather than using a measure of competitiveness based on predicted market shares under the counter-factual
experiment that all hospitals provide the same level of quality (e.g., Kessler and McClellan,
2000; Gowrisankaran and Town, 2003) we take a control function approach and utilize several
arguably exogenous instruments for concentration to identify the impact of the actual market
HHI on revealed quality. Finally, we utilize more recent data, which is important given the
ever changing state of health care markets.
We find that revealed quality increases as markets become more concentrated. For example, a hospital having an HHI 250 points higher than an otherwise equivalent hospital is
about 10% to 14% more likely to be chosen by the average HMO patient having that hospital
in its choice set as a result of the higher indirect utility it provides.5,6 Furthermore, we find that
patients belonging to different types of insurer have distinct preferences requiring hospitals to
make quality trade-offs. That is, the dimensions of quality that appeal to patients enrolled in a
private managed care plan (HMO) appear to differ from those enrolled with Medicare as HMO
patients receive higher utility at hospitals facing high HMO penetration rates while Medicare
5
In hospital markets each hospital will have a slightly different HHI reflecting the difference in the hospitals’ markets
that come from the spatial distribution of patients and hospitals. See Section III.B for an explanation of how we calculate
HHIs.
6
Note that this effect is independent of the fact that hospitals in areas with higher HHI are likely to have higher market
shares because there are fewer hospitals. The comparative static does not involve a change in the set of hospitals in consumers?
choice sets, so the effect represents only the differences in revealed quality that are observed at hospitals that have higher
HHIs.
4
patients find these hospitals to be less attractive.
Though competition lowers revealed quality, we find that it can be beneficial to clinical
quality depending on the type of insurer. Clinical quality improves slightly when hospitals
face a more competitive environment for HMO patients; however, competition for Medicare
patients has a detrimental effect on clinical quality. That clinical quality improves with increased competition for HMO patients while revealed quality is worsened suggests two things.
First, it suggests that patients are not particularly responsive to differences in hospital clinical
quality, perhaps because patients do not have sufficient information regarding the differences
in hospital clinical quality; and second, it suggests that competition for inclusion in HMO
provider networks is particularly important as improving clinical quality likely better positions a hospital for inclusion in an insurer’s network (or to secure better rates). In contrast,
as there is no need to compete for network inclusion, traditional Medicare does not provide
sufficient incentives for hospitals to improve either clinical or revealed quality.
The remainder of the paper is organized as follows. Section II provides some background on the theoretical predictions and the approach taken in the empirical literature. We
outline our empirical approach in section III. Section IV provides details for the data used.
The evidence for the impact of competition on revealed quality and the responsiveness of patients to differences in hospital clinical quality are presented in section V while Section VII
examines the direct impact of competition on health outcomes. Lastly, section VIII concludes
with some final remarks.
II.
BACKGROUND
There exists a broad literature examining firms’ optimal choice of quality.7 Much of that
literature is interested in how a firm’s choice of quality differs from the socially optimal level
or how a firm may be induced to provide the socially efficient level. Although the welfare implications of quality choice are important, our interest is more basic: how do firms adjust their
quality as a result of competition? Dorfman and Steiner (1954) provide some guidance here.
In a model of advertising, which is conceptually the same as demand-shifting quality, Dorfman
and Steiner show that a monopolist firm will increase (decrease) quality if the quality elasticity
of demand increases (decreases). Naturally this follows because a higher demand elasticity of
7
Gaynor (2006) provides a detailed review of the literature most relevant to the hospital market.
5
quality increases the marginal benefit of providing quality. Dranove and Satterthwaite (1992)
analyze the optimal quality and price when hospitals are profit maximizing and consumers
receive noisy signals for quality and price. Similar to Dorfman and Steiner (1954) they find
that the effect of competition on quality choice depends on how it alters the price elasticity of
demand compared to the quality elasticity of demand. The impact on quality is ambiguous,
for example, if competition increases the elasticity for both; however, if only the elasticity of
demand for quality is increased, then hospitals will provide higher quality. Although demand
may be quite price inelastic at the patient level, the selective contracting practices of managed
care has the potential to make demand price elastic as hospitals compete to be a part of the
payer’s provider networks (Dranove et al., 1993).
Hospitals also receive a significant proportion of their total revenue from Medicare,
which utilizes administrative rates. When hospitals cannot adjust their prices then the only
dimension over which they can compete is through quality. In consequence, as long as it is
profitable to attract more patients (i.e., the payment for treatment exceeds the cost of treating
the marginal patient), hospitals will increase their quality in response to additional competition (Pope, 1989). The marginal patient, however, may not be profitable or the disutility
of traveling to a further hospital may be sufficiently low as a result of the increased substitution opportunities that result from increased competition causing competition to lower
revealed quality (Brekke et al., 2011). As a further complication to the hospital’s optimization problem, they typically receive revenues simultaneously from both publicly and privately
insured patients. For some dimensions of quality, a hospital may be able to target its quality efforts so that one patient group receives higher quality than the other. For example, a
hospital may try to decrease the amount of time a Medicare patient remains at the hospital
compared to an equivalent privately insured patient. However, for some dimensions, such as
single-occupancy recovery rooms, both groups will benefit from a quality enhancement. Ma
and McGuire (1993) and Glazer and McGuire (2002) consider how having two types of payers
alters a hospital’s incentives to provide quality.8 They show that a hospital will compromise
on its quality choice by providing a level of quality that differs from what it would provide if
it faced either payer alone. They find that the compromised level of quality can be beneficial
8
Rogerson (1994) develops a model in which a non-profit hospital must select a vector of treatment intensities that can
be applied to scenario in which the hospital faces two kinds of payers. Although the treatment intensities (qualities) affect the
marginal costs, however, the choice of treatment intensity is distinct for patients having that payer.
6
when the public payer’s payments are too low, however, it also creates incentive for the public
payer to free-ride on the private payer.
The theoretical literature provides many insights into the level of quality that hospitals
will choose; however, it also indicates that competition could either increase or decrease the
quality that hospitals provide depending on the relative demand elasticities, payment levels,
degree of hospital altruism, or substitutability of hospitals by location (i.e., how the relative
disutility of travel compares to the utility of quality). Whether hospitals compete over quality given the existing market structure then is ultimately an empirical question. To address
this question, much of the literature on hospital competition and quality compares clinical
outcomes to market competitiveness as measured by the Herfindahl-Hirschman Index (HHI).9
Most of these studies ignore the fact that the HHI is potentially endogenous since it includes
a hospital’s own market share so will generate biased estimates.10 Kessler and McClellan
(2000), Gowrisankaran and Town (2003) and Kessler and Geppert (2005) avoid the endogeneity of HHI by predicting hospital market shares utilizing a utility model based on distances
and using the predicted HHI derived from these predicted market shares as their measure of
competitiveness.11 The endogeneity of HHI is particularly problematic for our identification
strategy since higher quality hospitals will necessarily have a higher market share. Without
instrumenting for HHI, any increase in quality that is not driven by competition will be incorrectly attributed to competition and the effect of competition on quality will be overestimated.
As the introduction points out, previous studies have generated contradictory or ambiguous results when examining the impact of competition on clinical outcomes. This may reflect
the differences in methodologies, differences in the data used, differences in the hospitals’ reimbursements relative to marginal cost or differences in demand elasticities across diagnoses.
However, another reason for the conflicting results could simply be that hospitals do not compete via clinical outcomes, and the dimensions of quality over which they compete are only
loosely correlated with the outcomes used. Romley and Goldman (2011) find evidence con9
Shen (2003) and Mutter et al. (2008) also use the number of hospitals within a fixed distance as a measure of competition. Ho and Hamilton (2000) examined the impact of mergers and acquisitions of hospitals by systems on mortality, 90-day
readmissions for heart attack patients and discharge times for normal newborns.
10
The use of HHI in hospital markets also presents an added problem with respect to how the market is defined. We take a
similar approach to that taken by Kessler and McClellan (2000), Gowrisankaran and Town (2003), Tay (2003), among others
in defining hospital markets. The details are provided in section III.A.
11
Kessler and McClellan (2000) and Kessler and Geppert (2005) both utilize information on the distance to suitable
substitute hospitals to avoid assuming independence of irrelevant alternatives to predict hospital choice while Gowrisankaran
and Town (2003) utilize the direct distance between a patient and hospital.
7
sistent with this view in a study examining the cost of hospital quality. Specifically, Romley
and Goldman identify the demand-responsiveness of patients by calculating the change in demand with respect to a change in revealed quality evaluated at a common quality level for all
hospitals. They find that quality (as perceived by patients diagnosed with pneumonia or AMI)
is higher at hospitals that have more elastic demand but that risk-adjusted mortality was only
moderately correlated with revealed quality. Taking a similar approach, we utilize panel data
to ascertain how perceived quality changes in response to a change in the competitiveness of
the market and examine the relationship between perceived quality and clinical outcomes. The
following section outlines our approach.
III.
A.
E MPIRICAL A PPROACH
Hospital Choice
As our focus is on how competition impacts a hospital’s choice of demand-shifting
quality we begin with the patients’ hospital choice problem. In choosing from which hospital
to receive treatment, patients balance the relative value of the hospitals’ quality characteristics
with how convenient it is to travel to the different hospitals. Patient i’s utility from receiving
treatment at hospital h at time t can thus be specified as
Uiht = Xit ADiht + BQht − ηOP Ciht + iht ,
(1)
where Xit is a vector of patient characteristics (age, race, gender, income level), Diht is a
vector of distance and relative location metrics for patient i and hospital h; Qht is a vector of
various measures or dimensions of hospital h’s quality; OP Ciht are patient i’s out-of-pocket
costs for treatment at hospital h; and iht is the idiosyncratic patient-hospital error all at time
t. The parameter matrices A and B represent the patients’ tastes for the location and quality
characteristics.
Ideally the vector Qht includes all aspects of quality that affect a patient’s choice of
hospital (either through a patient’s direct choice based on hospital quality or by a physician
steering the patient to a particular hospital based on hospital quality characteristics).12 These
12
Many studies have provided evidence suggesting that patients actively make a choice over hospitals (e.g., Town and
Vistnes, 2001; Capps et al., 2003; Tay, 2003; Ho, 2006). That the choice predominantly rests with the patient or the patient’s
physician is not important since hospitals will be aware of what factors influence that choice and, if they compete for patients
8
characteristics likely include service quality, availability of technology and equipment, hospital amenities, and expected health outcomes. Some of these dimensions of quality will be
observable by the econometrician, but many are likely not observable, or at least not perfectly
observable. This does not pose a problem, however, as we are not interested in identifying the
precise dimensions of quality. Instead we want to identify a hospital’s total “revealed” quality
index, which represents the quality isoquant achieved using some mix of inputs and relate that
to market characteristics that impact a hospital’s quality choice. For example, the competitiveness of the market will affect the hospital’s residual demand curve and, thus, alter the elasticity
of demand to quality. In addition, it may further alter the marginal benefit to providing quality
by altering the reimbursement price that it is able to negotiate with insurers since hospitals that
stand out from their competitors because of their quality will be able to secure higher prices.
Hospitals that treat a large proportion of publicly insured patients will not be able to secure
higher reimbursement prices by increasing their quality so have less incentive to make quality
investments. Likewise, hospitals operating in markets with higher HMO penetration may find
less benefit to quality if HMOs generate sufficient pressure to keep costs low via competition
over network inclusion. To identify how these factors impact revealed quality we parametrize
Qht as
(2)
Qht = ΓFht + ∆Cht + µh ,
where Fht is a vector of noncompetitive market characteristics that could affect a hospital’s
choice of quality (by altering the marginal benefit of quality) and Cht is a vector of measures
of market competitiveness that could also affect a hospital’s choice of quality; all for hospital
h at time t. We also include a hospital fixed effect, µh , to capture any remaining, unobserved
hospital quality that is independent of market characteristics. Plugging (2) into the patient’s
utility function yields the parameterized utility function:
(3)
Uiht = Xit ADiht + ΓFht + ∆Cht + µh − ηOP Ciht + iht .
In this representation of utility the parameters in Γ indicate the amount the patients’ utility
over quality, will increase the quality in those relevant dimensions. In a survey on patient perception of hospital choice,
Wolinsky and Kurz (1984) report that patients view the choice of hospital as theirs.
9
changes when the hospital adjusts its quality in response to non-competitive market characteristics Fht . Similarly the parameters in ∆ indicate the amount the patients’ utility changes
when the hospital adjusts its quality based on the competitive pressures indicated by Cht . As
the objectives and constraints of government and privately owned hospitals likely cause them
to respond to market characteristics differently we allow the parameters to differ based on
whether the hospital is publicly or privately owned. Note that in this specification one cannot
interpret the parameters of Γ and ∆ as i’s taste for quality as could be done in a standard utility
model since they capture both i’s taste and the amount of quality adjustment by the hospital
that results from the corresponding characteristic.
We include in D both the travel time and travel time squared to the hospital and allow for
separate coefficients on travel time for discharges that originate in a hospital’s emergency room
versus those that do not. These are all interacted with patient characteristics age, gender, race,
and income level. To soften the assumption of independence of irrelevant alternatives (IIA),
we also include an indicator identifying whether the hospital is one of the five closest hospitals
among those in i’s choice set and an indicator identifying whether hospital h is among the 5th
to 15th closest hospitals within i’s choice set, all at time t. The vector D additionally includes
an indicator identifying if the hospital is the closest to a patient when the patient is admitted
through the emergency room. There is typically very little difference in the travel times to
the five closest hospitals in California;13 consequently, we found no significance in the value
of the closest hospital for non-emergency discharges in comparison to the next five closest
hospitals. The relative distance indicators are not interacted with patient characteristics.
The vector Fht includes several market characteristics that are likely associated with the
marginal benefit for quality. The first is the number of patients that have that hospital in their
choice set as a measure of the size of the market. Larger markets increase the potential returns
to investing in quality and hospitals that treat more patients may be able to provide higher
quality through learning-by-doing. Larger markets can also potentially support more hospitals
generating systematic differences in the concentration of larger versus small markets. The
second and third characteristics are the proportion of all patients in the hospital’s market that
are insured by an HMO or PPO. Both types of MCO form provider networks and HMOs in
13
This is because of the large number of hospitals in the San Francisco/Oakland, San Diego, and Los Angeles metropolitan
areas.
10
particular are typically able to negotiate lower reimbursement prices compared to other forms
of insurance by making hospitals compete over inclusion in their network. A large number
of patients insured by managed care could put pressure on a hospital to keep costs low causing it to select lower overall quality than it otherwise would if quality enhancements result in
higher costs. We additionally include the proportion of patients in the hospital’s market that
are insured by Medicare and the proportion insured by Medicaid. Again, the reimbursements
by Medicare, and especially Medicaid, are typically lower than those from private insurance
companies. Moreover, as these payments are not negotiated but administratively set based
on the average cost of care adjusted for case severity and geographic factors these reimbursements are not influenced by the relative value of a hospital to patients. In consequence, the
only benefit to quality enhancements for these patients comes from their demand responsiveness and not from changes in reimbursement rates. All of these proportions are calculated
as weighted averages by zip code (details of the weighting are provided later in subsection
III.B). We utilize the HHI as our measure of competitive pressure, Cht . Because the impact
of competition for privately insured patients (whose reimbursements can vary with the degree
of competition) and publicly insured patients (whose reimbursements will not vary) could be
different we include the HHI for Medicare patients, and if using HMO or Medicare patients
to estimate the model, the HHI for HMO patients, and if PPO patients are used to estimate the
model, the HHI for PPO patients.
In addition to selecting hospitals based on their quality, many insurance plans require
patients to pay co-insurance; i.e., a share of the price for treatment. For example, a plan could
require a patient to make a co-payment of $50-$100 as well as pay a co-insurance rate of
around 20% up to some annual cap. Since the negotiated prices for a single MCO will vary
from hospital to hospital these out-of-pocket costs that patients are responsible for can vary
and consequently impact their choice of hospital. Unfortunately we do not observe out-ofpocket costs or the amount paid by a patient’s insurer.14 We do, however, observe the average
amount paid for a discharge by insurance type so use the difference in a hospital’s average
discounted charge in a particular MDC and the average discounted charge for all hospitals
that would be in the same choice set of a patient as the index hospital and use this as a proxy
for relative out-of-pocket costs. More details of the price derivation is provided in Section IV
14
The discharge abstracts report the amount billed by the hospital, which reflects the list price only.
11
where we describe the data used.
Patients select hospitals based on their preference for location and travel time characteristics compared to hospital quality; therefore patients are unlikely to choose hospitals that
are too far away as the difference in value between hospitals h and k (B[Diht − Dikt ]) can
potentially be quite large. Examining the data supports this notion as nearly 98 percent of all
discharges that originate from in-state are from zip codes that are less than a 75 minute drive
from the hospital.15 With this in mind, for tractability we restrict choice sets to include every
hospital within a 75 minute drive of a patient’s zip code centroid.16,17
Since all patients in our data choose a hospital we assume that there is no outside option
and that iht is drawn from a type-1 extreme value distribution and are i.i.d. across patients
but not necessarily independent for a patient across time.18 We further assume that patients
choose the hospital that maximizes their utility. With these assumptions, following McFadden
(1974), the probability that a patient chooses hospital h takes the conditional logit form
,
πiht = Pr[h = 1 | Hit ] = exp{Uiht }
(4)
X
exp{Uikt } ,
k∈Hit
where Hit is the set of hospitals in i’s choice set at time t. Eq. (4) is estimated via maximum
likelihood. The model is estimated separately for insurance type and major diagnostic category
(see Section IV for details on the specific insurance types and MDCs used).
B.
Hospital HHI: Measurement and Instruments
The Herfindahl-Hirschman Index is commonly used as a measure of market competi-
tiveness throughout the industrial organization literature. When market shares are represented
as decimal values between 0 and 1 the index takes a value of 0 to 1 as well and more competitive markets will have a lower HHI compared to less competitive markets. The advantage of
the index is that it incorporates both the number of hospitals in the market as well as the market shares of those hospitals. However, although the HHI is a convenient measure of competi15
16
The average and median travel times to chosen hospitals are about 20 and 15 minutes, respectively.
Travel time is calculated using the Google Maps API which takes into account traffic patterns, speed limits, and stop
lights
17
Kessler and McClellan (2000) define choice sets as all hospitals within 35 miles and all teaching hospitals within
100 miles. Tay (2003) defines choice sets as all hospitals within 50 miles from the patents’ home zip code centroid and
alternatively as the 50 closest hospitals. Romley and Goldman (2011) also restrict patient choice sets to the nearest 50.
18
We do not observe individual patients, but allow for the to be correlated within a zip code across time.
12
tion, defining hospital markets in order to calculate market shares is not as straightforward in
hospital markets as in other product markets. One difference is that hospitals are spatially distributed, generally drawing patients from slightly different populations that are better thought
of as overlapping markets rather than one large market.19 A second difference is that hospitals
may offer different services or specializations that effectively represent separate markets.
To account for the spacial distribution of hospitals we calculate a hospital-specific HHI
based on patient flows by taking the average of zip code-level HHIs weighted by the proportion
of total patients that a particular zip code represents. In this way farther zip codes contribute
much less to a hospital’s HHI than nearby zip codes and the HHI is not limited to any fixed
distance measure. This is in contrast to Kessler and McClellan (2000) and Gowrisankaran
and Town (2003) who use predicted market shares based on hospital and patient locations.
Using actual market shares is better in this application because we want to capture the actual
competitiveness of the market and not the potential competitiveness that the HHI based on
predicted market shares represents.20 If some hospital is particularly strong (or weak) and has
a large (small) market share, then the index hospital faces a different competitive environment
compared to when all of the competitor hospitals are of similar quality. This variation in
market competitiveness can only be captured by using realized market shares. To help mitigate
the service mix differences of hospitals we estimate HHIs separately by MDC.
In addition to the challenges associated with defining hospital markets, the HHI for a
hospital’s market is endogenous since it includes the hospital’s own market share. As our
objective is to identify changes in quality that are a result of market competitiveness, the
endogeneity of own market share is particularly problematic. Hospitals that are perceived to
be higher quality by patients will necessarily have a higher market share since market shares
are used to identify quality in the logit model of utility. Consequently, any increase in quality
that occurs for reasons unrelated to competition will increase market share. If, for example, the
increase in own-market share increases the HHI for that hospital’s market, then any increase
in quality that is not driven by competition will be incorrectly attributed to competition and
the effect of competition on quality will be overestimated.
19
As we discuss in the following subsection, this is illustrated by the fact that all hospitals in the LA and San Diego
regions can be linked via overlapping choice sets, which represent only the hospitals within a 75 minute drive of the patient,
yet hospitals in San Diego would arguably not consider patients in LA as part of their market.
20
Kessler and McClellan (2000) and Gowrisankaran and Town (2003) use HHI based off predicted shares as an alternative
measure of market competitiveness as this measure does not include the actual market share of the index so is exogenous.
13
To correct for the endogeneity of own market share we must instrument for HHI using
other market characteristics that are exogenous; however, as the hospital choice model is not
linear, we cannot use standard 2SLS IV techniques. Instead we take the analogous control
function approach to condition on the part of the HHIht that depends on iht (Wooldridge,
2010). By doing this the remaining variation in HHIht is independent of the error and the
estimates will be consistent. Specifically, we start by expressing HHIht as a linear function of
the exogenous hospital characteristics, Fht in (3), that affect utility, other exogenous variables
Zht that do not affect utility but impact HHIht , and a single unobservable term ηht ; i.e., we
express HHIht as
HHIht = ΘFht + ΩZht + ηht .
(5)
As Fht and Zht represent exogenous instruments, they are independent of both ηht and iht ,
which are not independent of one another. To capture the relationship between ηht and iht we
decompose the iht into the parts that can be explained by the ηht and a residual yielding
iht = CF(ηht ) + ˜iht ,
where CF(ηht ) is the control function. The simplest approximation of this control function
that is the linear function ηht , CF(ηht ) = γηht , in which case patient utility from choosing
hospital h can be expressed as
(6)
Uiht = Xit ADiht + ΓFht + ∆Cht + µh − ηOP Ciht + γηht + ˜iht ,
where the ˜iht are distributed i.i.d. type 1 extreme value and the ηht are normally distributed.
By including the residuals ηˆht from a regression of HHIht against exogenous hospital characteristics Fht and excluded instruments Zht as estimates for ηht in (6) we can recover a consistent estimator for HHIht .
As with IV, the control function approach requires instruments that are correlated with
HHIht but uncorrelated with iht . We utilize three instruments that arguably satisfy these
requirements. The first is the HHI based on the counterfactual experiment that all hospitals in
14
the market supply the same level of quality.21 That is, we assume that patients only consider
the distances of the hospitals in their choice set by estimating
Uiht = Xit ADiht + iht ,
(7)
where the variables are the same as in eq. 1. Let ρˆhkt denote the predicted proportion of
hospital h’s patients that it receives from zip code k at time t; Kh denote the set of all zip
P
codes in which hospital h receives some patients where k∈Kh ρˆhk = 1; Hk denote the set
of hospitals in the choicest of a patient residing in zip code k; and πkht denote the predicted
market share (or choice probability) for hospital h in zip code k at time t. The predicted HHI
P 2
(denoted as
sˆ so as not to be confused with the fitted HHI from eq. 5) for hospital h at
time t is defined as
X
(8)
X
sˆ2ht =
ρˆhkt ·
P
2
πkjt
.
j∈Hk
k∈Kh
While
X
sˆ2ht will be correlated with changes in competitiveness resulting from entry and
exit and changes in the distribution of patients, it will not capture differences that arise because
of particularly strong or weak rivals. To capture these differences our second instrument is
based on the competitiveness of the residual market for an index hospital, which we measure
using the sum of squared market shares for all other rival hospitals based on all of the patients
that do not choose the index hospital. We call this index the Rivals’ HHI denoted as rHHI.
Algebraically, the rHHI for hospital h in zip code k at time t is defined as
"
(9)
rHHIhkt =
X
i6=h
d
P ikt
j6=h djkt
#2
,
where dikt is the total number of discharges for hospital (or same system members) i in zip
code k. We consider hospitals in the same system as a single firm when calculating market
shares, which has the added benefit of creating additional variation in the measure as a consequence of system acquisitions. This measure is independent of hospital h’s market share
so if h increases its quality, gaining market share from one year to the next, while the other
21
Versions of this instrument are used directly as measures of market competitiveness in Kessler and McClellan (2000);
Gowrisankaran and Town (2003).
15
hospitals maintain their same level of quality, then the rHHI will not change.
We tabulate the rHHI for every zip code using the actual market shares but weight each
P
zip code based on predicted market shares as done with sˆ2 . The rHHI for hospital h at time
t is this defined as
"
(10)
rHHIht =
X
X
ρˆhkt · rHHIhkt =
k∈Kh
ρˆhkt ·
k∈Kh
X
i6=h
d
P ikt
j6=h djkt
#2
.
Variation in the hospital level rHHI is similar to the hospital level HHI and is driven by variation in the zip code level rHHIs—which is driven by hospital entry and exit, hospital consolidations, and changes in the market shares of existing hospitals—as well as from variation in
the distributions of patients that result from population changes across zip codes.
Our last instrument is the weighted average of the travel time to the closest hospital
that is not the index hospital to control for differences in hospital density across markets.
Specifically, the instrument wTT is defined as
(11)
wT Tht =
X
ρˆhkt · min{T Tkj }.
j6=h
k∈Kh
Controlling for the presence of close competitors to patients helps to uncover the presence of
P
close substitute hospitals otherwise not identified by sˆ2 and rHHI.
P 2
In addition to the excluded instruments
sˆ , rHHI, and wTT, we include the fraction
of patients in the hospital’s market that are insured by an HMO, PPO, traditional Medicare,
and Medicaid, as well as the number of patients within a 10 and 25 mile radius, which arethe
instruments included in the choice model. All of these instruments but the number of patients
within 10 and 25 miles are weighted by zip codes based on predicted market shares as described above while the number of patients is unweighted so that it represents a measure of
the size of the total market. Larger patient populations will allow for more entry resulting in a
lower HHI, independent of quality.22
22
Larger markets also allow hospitals to make additional investments in service offerings; consequently hospitals in a
growing market may improve their quality while new hospitals are simultaneously entering the market. Dranove et al. (1992)
discuss how this simultaneity makes it difficult to identify quality investment that is a result of competition and investment
that is a result of market growth. We include a measure of the market size in the utility model to capture quality changes
that come from the extent of the market while the competition term captures quality changes that come from the degree of
competition.
16
C.
Hospital Fixed Effects Estimation
With the inclusion of hospital fixed effects the parameter estimates for the financial and
competition terms are identified off of variation for a hospital over time. However, the lack of
an outside option complicates the identification of the hospital fixed effects. The problem is
that, although relative hospital fixed effects can be estimated simply by excluding one of the
hospitals from the choice sets, an individual patient’s choice set (a zip code choice set) will not
necessarily be the same as the choice set for a patient in another zip code. Identification of the
included hospitals can only be accomplished if there is substantial overlap between zip code
choice sets. To illustrate, consider the case of two hospitals which do not belong to a common
choice set, but the choice sets to which these hospitals belong all include a third hospital.
Because the quality differences of both hospitals that belong to different choice sets and the
third hospital will be identified, the relative quality difference between the two hospitals will
also be identified.23
In principle then, the relative fixed effect for every hospital in the data will be identified
if the choice sets are sufficiently large to ensure that every hospital can be linked to every other
hospital via overlapping choice sets. However, as our data are from California and there are
regions of the state which are thinly populated, any estimation may be particularly sensitive to
the selection patterns of patients in the rural regions. Moreover, linking the hospitals in the San
Francisco Bay area, for example, with the hospitals in the Los Angeles (LA) basin in this way
can only be accomplished if the choice sets include every hospital within a two and a half hour
drive, which, an addition to being well beyond the 75 minute threshold, will likely cause the
estimates to be very sensitive to the functional form of travel time.24 To mitigate this problem
we remove the hospitals in the central valley (Health Service Area 9) from the estimation so
that there is no overlap between the central-north and southern regions of California. Hospital
fixed effects are then identified by removing one hospital from the central-north region and
one hospital from the southern region of California.25
23
This problem is avoided with the availability of a common outside option since all hospital fixed effects would be
relative to it.
24
The estimates could still be sensitive to the functional form of travel time. To verify that they are not, we also estimate
the model using a vector of dummies measuring the travel time; i.e., we include a dummy indicating if the travel time is less
than 10 minutes, between 10 and 20 minutes, between 20 and 30 minutes, between 30 and 40 minutes, and greater than 40
minutes. The results are very similar and provided in Appendix Table A5.
25
For the central-north region the excluded hospital is Salinas Valley Memorial Hospital and for the southern region the
excluded hospital is Marian Medical Center.
17
By excluding one hospital from each region, the hospital fixed effects represent an index
of the relative difference in utils from the excluded hospitals within each region where a util
represents the overall value of choosing that hospital. Since the relative difference in utils
of the two excluded hospitals cannot be identified the levels for the fixed effects between
two hospitals where one is in each region (e.g., San Francisco and Los Angeles) cannot be
compared. However, this does not pose a problem as the units are the same and the estimates
for hospital characteristics that vary over time are all identified off of changes within a hospital.
Ideally we would also want to interact patient characteristics with the hospital fixed effects to account for patient differences in tastes. However, each patient characteristic requires
an additional set of hospital parameters, which would be computationally impractical since
there are typically well over 200 hospitals in the samples. Instead we only allow preferences
for travel time to vary by patient characteristics (age, income, and gender) while assuming
patient preferences for quality within a diagnostic category are more homogeneous.26 We also
separately estimate the model by insurance type and MDC. This allows for different preferences for hospital quality and location attributes between each patient group, who, to some
extent, have revealed that they have different preferences by selecting different forms of insurance.
IV.
DATA
The data used primarily come from the American Hospital Association’s (AHA’s) annual survey of hospitals and the California Office of Statewide Health Planning and Development (OSHPD) patient discharge abstracts and hospital financial disclosure reports. Data is
collected for the even years between 2000 and 2010, inclusive. The AHA and OSHPD data
are linked using the hospitals’ CMS Medicare provider number.
Hospital system status, which is used in estimating market shares, come from the AHA
annual survey of hospitals. The AHA annually surveys all of the approximately 6,000 hospitals within the U.S. and its territories soliciting information such as system membership,
ownership type, and service offerings. We exclude from the analysis Kaiser hospitals, which
are integrated HMOs, Veteran’s Administration and military hospitals, Shriner’s hospitals for
crippled children as well as psychiatric, chemical dependency, and long-term care hospitals.
26
Income represents the average income level of the zip code as reported in the U.S. census area resource file.
18
TABLE 1—D ISCHARGE C OUNTS
Major Diagnostic
Category
Top 5 HMO
Total
118,929
113,772
67,694
48,125
Circulatory System
Digestive System
Respiratory System
Nervous System
Mean S.D.
95
87
56
45
(97)
(86)
(55)
(43)
Medicare
Total
Mean S.D.
312,642 212 (177)
142,685 97 (77)
172,589 116 (84)
99,157 72 (57)
Notes: Summary statistics are at the hospital-year level except totals, which reflect the entire sample.
The AHA survey has data for 297 of the 302 California hospitals in the sample over the 2000
to 2010 time period.
The OSHPD discharge abstracts contain just under 4 million discharges per year for
all acute care hospitals in the state of California for these years. For each hospital discharge,
these reports identify characteristics of the patient such as gender, age, and insurer type (e.g.,
private, Medicare fee-for-service, Medi-Cal, etc.), characteristics of the patient’s illness (e.g.,
Major Diagnostic Category (MDC), Diagnosis Related Group (DRG), diagnostic and procedure codes), and the reports identify the hospital from which the patient was discharged and
source of admission (e.g., home, transfer from another hospital, etc.). We include only patients
whose source of admission is from home and who are 75 years of age and younger because
older patients may have significantly different preferences from the typical patient under 75
who is likely not receiving extraordinary end-of-life treatment. We also exclude patients who
are either self-pay, or covered by workers compensation, Medi-Cal, or other government program (excluding Medicare) as these patients may have restricted choice-sets or preferences
that differ significantly from the general privately insured population. All discharges for intermediate and skilled nursing, psychiatric, chemical dependency and recovery, and physical
rehabilitation care are removed from the sample since hospitals may face competition from
other non-hospital providers for these services. We also exclude discharges for infants under
24 hours of age and other unknown types of admission, discharges for patients not originating
from the state of California, and discharges from hospitals over 75 minutes from the patient’s
zip code.
We estimate the model separately using patients insured with traditional Medicare and
patients insured with a private HMO since patients belonging to these insurers may have dif19
ferent preferences over hospitals as well. Medicare patients have the option of visiting any
hospital that accepts Medicare while both HMO and PPO plans typically offer restrictive hospital networks. As a low quality hospital may also be low cost it could be more attractive to
an HMO or PPO. In consequence, if low quality hospitals are more likely to be in provider
networks than high quality hospitals while both are erroneously included in a patient’s choice
set, then the low quality hospital will have a higher market share and appear to be higher quality by the econometrician. Although we lack data on actual hospital-MCO contracts, we do
observe to which HMO an HMO patient belongs and use this information to piece together
an HMO’s likely network by observing which hospitals treat patients from a particular HMO.
To reduce the possibility of misidentifying HMO networks we restrict the sample of HMO
patients to those enrolled with one of the five largest HMOs, which together account for about
85 percent of all HMO patients in the data.27
Table 1 provides summary statistics for the discharge data. The table reports the total
number of discharges included in the patient group as well as hospital-level summary statistics for the number of discharges. We conduct the analyses using patients having a diagnoses
of the circulatory, respiratory, and digestive systems (the top 3 MDCs excluding child birth
and delivery28 ) and for diagnoses of the nervous system. As the table indicates, there is considerable heterogeneity in the number of patients treated by hospitals with the largest treating
thousands of patients from a particular MDC-patient group pair while others only treat a handful. The number of patients for the four MDCs also drops considerably with less than half as
many patients treated for a diagnosis of the nervous system as are treated for a diagnosis of
the circulatory system. Medicare is the largest of the three insurance groups for all of the
diagnostic categories.
We use data from the OSHPD Discharge and Financial Reports paired with the discharge abstracts to estimate the average discounted price per inpatient day for each type of
insurer used in the analysis (top five HMO and traditional Medicare). Because OSHPD reports both the gross charges (the list price for the services offered) and the net revenues (the
27
We include a hospital in an HMO’s provider network (a patient’s choice set) if it has at least 50 non-ER discharges
from that HMO or at least 25 percent of its non-ER HMO discharges come from that HMO. ER patients are excluded from
identifying networks since patients admitted through the ER may not have had a choice of hospital. Using other cut-off values
to identify networks did not alter the results in any meaningful way.
28
Child birth and delivery is the top MDC for HMO patients, however, for easier comparison we selected the top three
MDCs for Medicare patients, which are in the top four for HMO patients.
20
TABLE 2—H OSPITAL D ISCHARGE S UMMARY S TATISTICS
Major Diagnostic Category
Circulatory System Digestive System Respiratory System
HMO
Mcare
∆ ln(Avg. Price) −0.007 −0.031
(0.748) (1.209)
HHI
−0.516 −0.475
(0.164) (0.175)
P 2
sˆ
−0.131 −0.145
(0.173) (0.172)
rHHI
0.392
0.393
(0.212) (0.203)
wTT
0.091
0.095
(0.045) (0.045)
% HMO
0.223
0.223
(0.138) (0.138)
% PPO
0.079
0.079
(0.074) (0.074)
% Medicare
0.486
0.486
(0.166) (0.166)
% Medicaid
0.121
0.121
(0.101) (0.101)
% ER
0.609
0.609
(0.209) (0.209)
HMO
Mcare
HMO
Mcare
−0.007 −0.046 −0.002 −0.020
(0.545) (1.230) (0.590) (1.195)
−0.502 −0.498 −0.512 −0.520
(0.178) (0.194) (0.173) (0.197)
−0.126 −0.148 −0.126 −0.165
(0.168) (0.187) (0.174) (0.197)
0.374 0.380
0.369
0.402
(0.214) (0.209) (0.214) (0.212)
0.091 0.092
0.089
0.095
(0.045) (0.046) (0.045) (0.046)
0.249 0.249
0.224
0.224
(0.144) (0.144) (0.137) (0.137)
0.119 0.119
0.074
0.074
(0.103) (0.103) (0.076) (0.076)
0.361 0.361
0.457
0.457
(0.146) (0.146) (0.174) (0.174)
0.144 0.144
0.175
0.175
(0.115) (0.115) (0.138) (0.138)
0.572 0.572
0.641
0.641
(0.198) (0.198) (0.211) (0.211)
Notes: Summary statistics are at the hospital-year level. The variables
P
Nervous System
HMO
Mcare
−0.000 −0.022
(0.618) (1.220)
−0.499 −0.496
(0.163) (0.178)
−0.112 −0.140
(0.157) (0.178)
0.331 0.369
(0.182) (0.200)
0.085 0.089
(0.041) (0.045)
0.216 0.216
(0.129) (0.129)
0.085 0.085
(0.080) (0.080)
0.429 0.429
(0.163) (0.163)
0.126 0.126
(0.104) (0.104)
0.605 0.605
(0.194) (0.194)
sˆ2 , rHHI, and wTT are defined in Section III.B.
actual amount received reflecting contractual discounts) as aggregates for each payer, the reimbursements are calculated by multiplying the deduction ratio, which is the net revenues
by payer group (traditional Medicare, private managed care) divided by the gross charges by
payer group, against the total gross charges for all discharges belonging to the specific insurer
type.29 Next, we calculate the difference in a hospital’s average discounted price with the average discounted price of the other hospitals that are in the same choice set for a patient having
29
For example, the average price of a HMO patient discharge is calculated as:
Avg.Price/HMO Discharge =
Net Rev. for Private Managed Care
Total Charges for HMO Discharges
×
.
Gross Chrg. for Private Managed Care
# of HMO Discharges
21
the index hospital in his choice set; i.e. we calculate ∆ ln( Avg. Price) as
∆ ln(Avg. Price)h =
1 X
Ph − P H i ,
Nh i
where i indexes patients, Nh are the total number of patients having hospital h in their choice
set, P is the natural log of the discounted price, and Hi are the set of hospitals in i’s choice
set.
Table 2 provides summary statistics for the various hospital characteristics used in the
choice model based on each patient sample analyzed, again broken down by patient group.
The shares %HMO, %PPO, %Medicare, and %Medicaid are all tabulated at the zip code-level
and weighted based on the individual hospital demand proportions in the same manner as the
P 2
instruments
sˆ , rHHI and wTT. The statistics show that the concentration measures are
very similar across insurer types and MDCs. As the majority of hospitals treat patients from
all four included MDCs the competition measures are similar, but the patients belonging to
the MDCs differ slightly. For example a slightly larger proportion of Medicare patients are
admitted through the ER and Medicare patients represent a larger proportion of the discharges
relating to the circulatory system than the digestive system.
V.
R EVEALED Q UALITY R ESULTS
Table 3 reports selected coefficient estimates from the hospital utility model for each of
the diagnostic categories and insurance types. At the bottom of each specification we report
the first-stage Angrist-Pischke F statistics for a single endogenous regressor (Angrist and Pischke, 2009). These F-values can be compared to the Stock-Yogo critical values reported at
the bottom of the table to assess the strength of the excluded instruments (Stock and Yogo,
2005). The Stock-Yogo maximal IV relative bias critical values indicate the F value at which
the bias of the IV estimate relative to an OLS estimate is at most the reported threshold.30
Based on these values, most of the HHIs will exhibit between 10% and 20% of the bias of the
endogenous variable without the included control function. In a couple cases the instruments
are fairly weak (e.g., HHIMedicare for diagnoses of the respiratory system); however, as our
interest is in the sign and general magnitude of the effect, these biases are reasonably small.
30
Note that the relative bias is based on a linear second stage so are only suggestive of the strength of the instruments.
22
TABLE 3—R EVEALED Q UALITY BY M AJOR D IAGNOSTIC C ATEGORY
Major Diagnostic Category
Circulatory
β
Top 5 HMO
∆ ln(Avg. Price)
% HMO
% PPO
% Medicare
% Medicaid
1 − HHIHMO
1 − HHIMedicare
# Hospitals
(s.e.)
−0.039a
0.744c
−0.565c
−1.135c
−1.297c
−5.545c
−0.689b
(0.020)
(0.178)
(0.176)
(0.168)
(0.274)
(0.570)
(0.309)
β
(s.e.)
−0.045b
1.277c
−0.065
−1.377c
−0.519a
−3.951c
−0.490
(0.019)
(0.179)
(0.150)
(0.160)
(0.274)
(0.465)
(0.323)
Respiratory
β
(s.e.)
−0.066b
1.026c
−0.627c
−2.034c
−1.262c
−3.747c
−0.007
(0.030)
(0.217)
(0.211)
(0.208)
(0.260)
(0.495)
(0.232)
Nervous
β
(s.e.)
−0.061
0.880c
−0.862c
−1.284c
−0.711b
−3.981c
0.705b
(0.042)
(0.256)
(0.233)
(0.238)
(0.361)
(0.552)
(0.325)
203
197
188
Angrist-Pischke F statistics for excluded instruments
HHIHMO
7.12
8.08
HHIMedicare
6.10
7.31
8.84
4.56
6.22
10.47
Medicare
∆ ln(Avg. Price)
% HMO
% PPO
% Medicare
% Medicaid
1 − HHIHMO
1 − HHIMedicare
# Hospitals
201
Digestive
−0.022c
−0.877c
−0.359a
0.394c
−0.009
−0.149a
−2.058c
255
(0.007)
(0.121)
(0.188)
(0.109)
(0.199)
(0.082)
(0.414)
−0.014a
−0.841c
−0.273a
0.473c
0.130
−0.162a
−1.147c
(0.008)
(0.123)
(0.159)
(0.144)
(0.190)
(0.098)
(0.329)
−0.011
−0.954c
−0.157
0.405c
0.397b
−0.306c
−1.848c
(0.009)
(0.149)
(0.190)
(0.157)
(0.186)
(0.065)
(0.463)
−0.006
−0.829c
−0.115
0.393b
0.281
−0.306c
−1.281c
(0.009)
(0.161)
(0.200)
(0.174)
(0.243)
(0.084)
(0.292)
246
257
235
Angrist-Pischke F statistics for excluded instruments
HHIHMO
7.00
8.40
HHIMedicare
6.10
7.52
9.32
5.01
6.19
11.25
Stock-Yogo weak ID critical values for single regressor:
10% maximal IV relative bias:
10.83
20% maximal IV relative bias:
6.77
30% maximal IV relative bias:
5.25
Notes: All variables are based on patients belonging to the indicated diagnostic category and insurer type. Standard errors are clustered by
zip code and corrected to reflect the noise from the first stage estimation following Karaca-Mandic and Train (2003). Significance levels:
c : p < 0.01, b : p < 0.05, a : p < 0.10.
We include in Table 3 the coefficient estimates for the hospital and market characteristics
only (see the appendix for the coefficient estimates of the various travel-time and relative
location variables). The standard errors are clustered by zip code and corrected to account
23
for the noise in the first-stage residual following Karaca-Mandic and Train (2003).31 The
first characteristic reported in the table is the parameter estimate for the difference between
the log of the average discharge price and the choice set average. The price elasticities are
an integral part of a hospital’s quality decision since a higher elasticity of demand to price
than quality could tip a hospital’s incentives in favor of lowering quality in order to lower
prices when facing a more competitive environment. Although the data do not allow us to
calculate the true price elasticities, consistent with previous research the estimates indicate
that patients are quite price inelastic with with HMO patients not surprisingly exhibiting a
much higher price sensitivity than Medicare patients. When evaluated at the mean choice
probability (approximately 0.02) the estimates imply that an interquartile increase in price
will result in a reduction of about 1 to 2 percent in demand for HMO patients. That Medicare
patients exhibit any price sensitivity could be due to differences in the propensity of some
hospitals to more frequently up-code patients or otherwise over treat increasing the out-ofpocket costs for those patients or be the result of some correlation between physician hospital
preference and that hospital’s average prices. Regardless of the underlying driver, they are
quite price inelastic.
Each market characteristic is allowed to vary based on the ownership type: private or
non-Federal government (state, city, university). However, we only report the results for the
private hospitals.32 The first four market characteristics represent the non-competitive characteristics that could impact hospital quality choice and include the population shares for patients
insured by an HMO, a PPO, Medicare, and Medicaid, respectively. The last market characteristics are the HHIs based on patients insured by an HMO and patients insured by traditional
Medicare.
The estimates indicate that patients generally have higher utility for hospitals located in
areas that proportionally have more patients with the same insurer type. That is, HMO patients
prefer hospitals in markets with higher HMO penetration rates while Medicare patients prefer
hospitals in markets with higher proportions of Medicare patients. This finding likely reflects
the incentive hospitals have to cater to specific types of patient when they represent a larger
31
Karaca-Mandic and Train (2003) develop a correction for standard errors in a two-step estimation that follows Murphy
and Topel (1985) but allows for a nested data structure.
32
We have also allowed the estimates to vary based on the profit status of the private hospital. However, the estimates
were extremely similar so we pooled the two types of hospital together. These additional estimates are available upon request.
24
TABLE 4—D IFFERENCE IN C HOICE P ROBABILITY FOR A H OSPITAL HAVING AN HHI 250 P OINTS H IGHER
T HAN AN OTHERWISE I DENTICAL H OSPITAL
Major Diagnostic Category
Circulatory Digestive Respiratory Nervous
Top 5 HMO
1 − HHIHMO
1 − HHIMedicare
13.56
1.69
9.67
1.20
9.16
0.02
9.73
−1.72
Medicare
1 − HHIHMO
1 − HHIMedicare
0.37
5.06
0.39
2.91
0.75
4.62
0.75
3.15
Notes: All variables are based on patients belonging to the indicated diagnostic category and insurer type. The percentage change in choice
probability is evaluated at the mean of the data and the average choice probability is approximately 0.02 or 2% for each of the MDCs and
insurer types.
share of all patients it treats. The magnitude of the estimates are slightly higher for HMO patients. This could indicate that HMOs generate stronger incentives for hospitals for investing
in demand shifting quality or that HMO patients are responsive to quality differences (which
would create more incentive to invest in quality as well). It is also possible that this reflects
some error in the choice sets. A hospital is much more likely to be an an HMO provider
network in markets having higher HMO penetration possibly lowering the incidence of false
inclusion in the data. The choice sets for Medicare patients do not suffer from this bias, however, and estimates based on even more conservative inclusion criteria also indicate a positive
effect on patients. Interestingly the results also suggest that HMO and Medicare patients have
significantly different preferences. HMO patients experience a considerable drop in utility
from hospitals that treat proportionally more Medicare patients while Medicare patients similarly experience a loss in utility from those hospitals that have higher HMO penetration rates.
Together this suggests that hospitals face some clear trade-offs between these two types of
patient when investing in demand shifting quality.
The estimates for the HHIs are similarly consistent across insurance types and MDCs
and in all cases increased competition negatively impacts the utility of those patients. As
with the market proportion variables, there is little impact on hospital revealed quality from
increased competition for Medicare patients when estimated using HMO patients and there is
little impact on hospital revealed quality from increased competition for HMO patients when
estimated using Medicare patients, reinforcing the notion that HMO and Medicare patients
25
have significantly different preferences.
To provide some sense of importance for the impact that competition has we evaluated
how much a difference in HHI of 250 points impacts the probability that a hospital is chosen. Specifically we evaluated the marginal effect of a change in HHI of 250 points at the
mean of the data.33 Table 4 reports the changes in choice probabilities for each of the MDCs
and insurer types. Competition for HMO patients having a diagnosis belonging to the circulatory system has the largest impact on the choice probability. The estimates indicate that
a hospital having an HHI (for HMO patients) 250 points higher than an otherwise identical
hospital would be about 13.5% more likely to be chosen for treatment by the average HMO
patient. For the other MDCs, a hospital having an HHI 250 points higher would be about 9 to
10% more likely to be chosen over an otherwise equivalent hospital. The quality differences
stemming from competition for Medicare patients has a much smaller impact on the choice
probabilities. Again, the largest effect is found with diagnoses of the circulatory system and
the estimates indicate that a hospital having an HHI (for Medicare patients) 250 points higher
than an otherwise identical hospital would be about 5% more likely to be chosen for treatment
by the average Medicare patient. The difference in choice probabilities for the other MDCs
are only around 3% with diagnoses of the respiratory system exhibiting a difference in choice
probability of 4.62%.
The utility differences implied by these results are highest for HMO patients suggesting that changes in reimbursement rates could be driving some of the differences in revealed
quality across insurer types. However, the utility of Medicare patients are also impacted by
competition for Medicare patients while competition for HMO patients has very little impact on the utility of Medicare patients indicating that the changes in reimbursements that
may result from increased competition cannot be the only driver of the reductions in revealed
quality. For example, this result could indicate that there is an important reduction in total
reimbursements impacting a hospital’s ability to make fixed cost quality investments (though
competition for HMO and Medicare patients should have a similar effect on revealed quality
for both patient groups) or that marginal patients are unprofitable prompting hospitals to re33
Recall that the marginal effect of the logit is mj = βj p(1 − p) where p is the choice probability and βj is the coefficient
for explanatory variable j. As a result, the marginal effect represents a partial derivative and the change in choice probability
resulting from a change in HHI is not a consequence of a change in the number of hospitals available to a patient nor does it
reflect the fact that if one hospital’s HHI were to go up the HHIs for nearby hospitals would also likely change.
26
duce quality to specific patient groups when facing a more competitive environment (Brekke
et al., 2011).
VI.
H EALTH O UTCOMES A S D EMAND S HIFTERS
By modeling patient indirect utility as a function of the HHIs for privately and publicly insured patients we identify how revealed quality changes with competition. While this
approach allows us to cleanly identify the impact of competition on demand-shifting quality
through its impact on utility, it does not allow us to identify what dimensions of quality impact
patient hospital choice. We can get some sense of those dimensions, however, by additionally
including quality characteristics of hospitals and observing their effect on the revealed quality
directly associated with competition. To illustrate, suppose that the dimension of quality over
which hospitals compete is simply the number of nurses per patient that a hospital staffs. In
this case we will no longer find a relationship between HHI and revealed quality if we include
this measure in the demand estimations. Indeed, given the results of the previous section, if
we regress the number of nurses per patient against 1-HHI, we should find a strong, negative
relationship. If instead, the number of nurses per patient is either one of several dimensions
of quality that patients use to choose a hospital for treatment or has no relationship to hospital
choice at all, then we will still observe a strong, statistically significant relationship between
HHI and revealed quality.
Before assessing the relationship between clinical quality and competition we want to
ascertain whether hospital mortality represents a demand shifter. Following this literature
we consider the mortality rates for four diagnoses frequently considered in the literature—
AMI, heart failure, pneumonia, and acute stroke—and include the mortality measure that is
relevant for the MDC in the choice model. That is, we include the mortality rates for AMI and
heart failure when estimating the model using diagnoses relating to the circulatory system;
the mortality rate for pneumonia when estimating the model using diagnoses relating to the
respiratory system; and the mortality rate for acute stroke when estimating the model using
diagnoses relating to the nervous system. We calculate the risk-adjusted mortality rate using
version 4.5 of the Agency for Healthcare Research and Quality’s (AHRQ) QI SAS software.
Not all hospitals that have discharges belonging to the MDCs used have a sufficient number
of observations relating to the mortality measures to generate a risk-adjusted mortality rate so
27
TABLE 5—R EVEALED P REFERENCES FOR C LINICAL O UTCOMES BY M AJOR D IAGNOSTIC C ATEGORY
Major Diagnostic Category
Top 5 HMO
1 − HHIHMO
1 − HHIMedicare
AMI/Pneumonia/Stroke
Value Missing
Heart Failure
Value Missing
Medicare
1 − HHIHMO
1 − HHIMedicare
AMI/Pneumonia/Stroke
Value Missing
Heart Failure
Value Missing
Circulatory
Respiratory
Nervous
−5.545c −5.537c
(0.570) (0.569)
−0.689b −0.675b
(0.309) (0.317)
−0.173a
(0.088)
−0.081
(0.107)
0.302c
(0.092)
0.151
(0.547)
−3.747c −3.658c
(0.495) (0.495)
−0.007 −0.051
(0.232) (0.231)
−0.232a
(0.120)
−0.147
(0.143)
−3.981c −3.971c
(0.552) (0.554)
0.705b 0.714b
(0.325) (0.330)
−0.298c
(0.114)
−0.212
(0.193)
−0.149a −0.146
(0.082) (0.095)
−2.058c −2.136c
(0.414) (0.424)
0.059
(0.071)
−0.191c
(0.051)
0.026
(0.047)
0.035
(0.103)
−0.306c −0.305c
(0.065) (0.065)
−1.848c −1.851c
(0.463) (0.456)
−0.205c
(0.074)
0.083
(0.169)
−0.306c −0.299c
(0.084) (0.079)
−1.281c −1.284c
(0.292) (0.294)
−0.158b
(0.071)
−0.088
(0.069)
Notes: Standard errors are clustered by zip code and corrected to reflect the noise from the first stage estimation following Karaca-Mandic
and Train (2003). Significance levels: c : p < 0.01, b : p < 0.05, a : p < 0.10.
we additionally include a dummy indicating whether or not the hospital has a risk-adjusted
mortality rate and assign a value of 0 for the risk-adjusted mortality rate for that hospitalyear when it does not. Table 5 reports the estimation results. All specifications are identical to
those reported in Table 3, but only the estimates for 1-HHI and mortality are reported. For each
MDC the table reports the original estimation results when the mortality rate is not included
and the new results with the inclusion of the mortality measure.
The results show that the mortality measures effectively have no impact on the revealed
quality associated with market competition. Although the estimates for HHI are generally
28
smaller in magnitude from when mortalities are not included, the differences are exceedingly
small and certainly not statistically significant at any conventional level. Most of the mortality rate estimates have the expected sign (higher mortality lowers utility) and utility is also
generally lower at hospitals that treat too few patients having the specific diagnosis to generate a risk-adjusted mortality rate.34 Significant estimates for the mortality rates suggest that
they represent demand shifters (i.e., they affect hospital choice), or are correlated with some
hospital characteristics that are the true demand shifters, while their near zero impact on competition suggests that they do not represent the primary dimensions of revealed quality that are
affected by hospital competition. Finally, even though estimates indicate that mortality measures function as demand shifters, the implied elasticities are extremely small. For example,
when evaluated at the mean of the data, the implied elasticity for AMI mortality is -0.07; i.e.,
a 10% increase in mortality for AMI will reduce demand by about 0.7%. In consequence,
changes in competition can still have sizable impacts on a hospital’s clinical quality without
having much of an impact on its revealed quality. In the next section we examine directly how
clinical quality is affected by competition.
VII.
C OMPETITION AND H EALTH O UTCOMES
The results of the previous sections indicate that competition for patients has a detrimental affect on hospital revealed quality, but that the dimensions of revealed quality that are
impacted by competition do not include the mortality rates considered. Although a loss in
revealed quality suggests a loss in consumer surplus, policy makers are of course ultimately
concerned with how competition impacts clinical outcomes, which may be a better measure
of the true ex post surplus. This is the question that much of the previous literature has attempted to answer and which has generated ambiguous and sometimes conflicting answers.
In addition, competition at the insurer level may not affect patient demand (conditioned on
the hospital belonging to the provider network already as is the case in our sample of HMO
patients). We therefore examine the direct impact of competition on the mortality rates for the
four diagnoses considered in the previous section—AMI, heart failure, pneumonia, and acute
stroke.
To identify the relationship between hospital competition and mortality we assume that
34
The estimate is never statistically significant whenever it is positive.
29
a patient having observable personal and disease attributes Xi , unobservable attributes i , and
chooses hospital h having clinical quality µht at time t will die if y ∗ > 0 where
∗
yiht
= α + βXit + µht + iht .
The vector of observable patient and disease characteristics Xi includes the patient’s travel
time and travel time squared to the treating hospital, that patient’s age, gender, ethnicity, and
primary payer type;35 as well as several observable characteristics of the discharge: the log
of the number of procedures performed, the percentage of the diagnoses that were present
on admission, a dummy indicating if the discharge included palliative care, the Charlson comorbidity index (Charlson et al., 1987; Deyo et al., 1992), and the APR-DRG mortality risk
code.36 We assume that the unobservable disease characteristics hit are i.i.d. type I extreme
value.
We follow a similar strategy used when estimating hospitals’ revealed quality and decompose µht as
µht = ΓCht + µh ,
where Cht is a vector of competitive measures and µh is hospital h’s fixed contribution to
mortality. With this decomposition the estimates for Γ capture the relationship between clinical quality and competition while µh captures the relative, time-invarient quality level of a
hospital. As before we use the HHI as our measure of market competitiveness. With this
decomposition the latent variable y ∗ can be expressed as
(12)
∗
yiht
= α + βXit + ΓCht + µh + iht ,
and given the distribution of the probability of dying during treatment takes the logit form:
(13)
h
i−1
Pr(y ∗ > 0) = 1 + exp −α − βXit − ΓCht − µh
.
An important issue with (12) is that the unobservable characteristics of a patient’s illness
35
Controlling for payer type is important because patients may select one form of insurance over another due to their
overall level of health prior to experiencing an illness.
36
The APR-DRG morality risk is a categorical variable taking an integer value between 0 and 4 and is generated using QI
SAS, version 4.5, provided by AHRQ and are more generally used to generate risk-adjusted quality statistics for hospitals.
30
severity may be correlated with the patient’s choice of hospital. For example, a more severely
ill patient will likely choose a higher quality hospital. Indeed, Gowrisankaran and Town (1999)
examine this possibility and conclude that GLS estimates of hospital quality are inconsistent
because of just such a correlation between illness severity, mortality, and hospital choice.
Moreover, as with revealed quality, HHIht is endogenous since hospitals that are of higher
clinical quality may have higher market shares, reversing the direction of causality in (12). To
control for the endogeneity of both hospital choice and HHI we again utilize a control function
approach. As before, the control function will simply be the residual of a linear regression,
however, instead of estimating the HHI based on the exogenous instruments we regress the
HHI of the chosen hospital against the expected value of the exogenous instruments where
expectations are over hospital choice based on (7). That is, we estimate
(14)
HHIiht = α + βXit +
X
X 2
πit (h) γ1
sˆkt + γ2 rHHIkt + γ3 wTTkt + νiht ,
k∈Hit
where the Xit are the patient and disease characteristics from (12); πit (h) is the probability
that patient i chooses treatment at hospital h at time t based on (7); and νi is an i.i.d. normally
P 2
distributed disturbance term. We use the exogenous instruments
sˆ , rHHI, and wTT to
control for the endogeneity of a hospital’s HHI and take expectations over the instruments to
control for the endogeneity of hospital choice. We then include the fitted residual νˆ in (13) as
the control function to condition on the part of the HHIiht that depends on i . We treat µh as a
nuisance parameter and condition it out by estimating (13) as a conditional logit.
Table 6 reports the results of this estimation. All of the non-Federal hospitals used in
the revealed quality estimation of the previous section are included and, as before, we allow
the impact of competition to differ based on whether the hospital is privately owned or a state
hospital, but only the estimates for the private hospitals are reported. Note that both insurance
types are pooled together since the patient and disease characteristics are likely to impact
patient mortality the same (indicators for insurer type are included to further control for any
treatment differences that may occur).37
Similar to the findings of Gowrisankaran and Town (2003), the estimates indicate that
mortality declines as the market becomes more competitive for HMO patients but generally
37
Supporting this notion, we recover very similar estimates when the mortality model is estimated separately for each
patient group.
31
TABLE 6—T HE I MPACT OF C OMPETITION ON M ORTALITY R ATES
AMI
β
1 − HHIHMO
1 − HHIMcar.
# Discharges
# Hospitals
Avg. Mortality
Heart Failure
(s.e)
−0.867c
7.402c
(0.279)
(2.676)
22,241
253
0.433
β
Pneumonia
β
(s.e)
−0.942c
9.733c
(0.219)
(2.252)
(s.e)
−0.511c
45,252
271
0.195
8.548c
β
(s.e)
(0.177) −0.180
(1.633) −5.027b
40,656
276
0.318
% Difference in mortality associated with 250 point increase in HHI
HHIHMO
−1.325
−1.835
−0.862
HHIMcare
10.344
19.628
14.585
Angrist-Pischke Test Statistic for Weak Identification F(1,N)
HHIHMO
2,042
4,658
HHIMcare
132
214
Acute Stroke
4,681
255
(0.233)
(2.277)
25,839
218
0.456
−0.225
−6.914
2,579
121
Stock-Yogo weak ID critical values for single endogenous regressor
5% maximal IV relative bias
15.72
Notes: The data include discharges for both HMO and Medicare patients pooled together. Standard errors in parentheses are clustered by
hospital but not corrected to reflect estimated data. Significance levels: c : p < 0.01, b : p < 0.05, a : p < 0.10.
increases as the market becomes more competitive for Medicare patients. The magnitudes of
the effects can be quite large, especially competition for Medicare patients. For instance, the
effects are largest for heart failure, which has an unconditional mortality rate of 19.5%. At
this rate, a 250 point increase in HHIHMO will reduce the probability of dying by about 1.8
percent while a 250 point increase in HHIMcar. will increase the probability of dying by 19.6
percent. The impact of competition on mortality for AMI and pneumonia are similar, while
higher competition for Medicare patients decreases acute stroke mortality.
The results show that competition for Medicare patients reduces both revealed and clinical quality. However, the impact of competition for HMO patients varies with competition
decreasing revealed quality but increasing clinical quality. This contradicting impact of competition indicates that hospitals do not improve clinical outcomes as a means of attracting
additional patients, but as a way to ensure inclusion in a provider network. Illustrating this, in
an article discussing how hospitals can gain leverage vis-`a-vis insurers, Janie Patterson, senior
vice president at Conifer Health Solutions says that “...payors are placing more emphasis on
quality of care, and if a hospital doesn’t meet a certain standard a payor may not even want a
contract with that hospital (Oh, 2010).” This conclusion is further supported by the fact that
32
competition for traditional Medicare patients results in significantly lower clinical quality as
Medicare does not form restrictive provider networks.
VIII.
C ONCLUSIONS AND F INAL R EMARKS
In this study we utilized panel data and a control function approach to develop new
insights into how hospitals compete for patients. Our results show that competition has a
detrimental impact on a hospital’s demand-shifting quality. Regardless of the type of insurer
and diagnostic class of the patient’s illness, revealed quality is lowered in more competitive
environments. The results also indicate that there are important differences in the preferences
of patients depending on their type of insurer. For example, Medicare patients find hospitals
that have higher HMO penetration rates to be less attractive, while HMO patients similarly
find hospitals that treat a high proportion of Medicare patients to be less attractive.
Importantly the results also indicate that although a more competitive market is associated with improved clinical outcomes for HMO patients, this is not the result of competition
to attract patients. That is, hospitals do not appear to compete for and attract patients by improving mortality, yet mortality falls with more competition. That hospitals do not compete
for patients via clinical quality should not be too surprising. After all most of the evidence on
hospital report cards indicates that if patients do respond to report cards, which try to provide
clear, comparable information on hospital clinical quality, that response occurs when there
are substantial differences in patients’ priors and the report card rating (Dranove and Sfekas,
2008). Supporting this we found that patients are extremely demand inelastic to differences
in clinical quality. Nevertheless, a hospital’s relative clinical quality appears to represent an
important dimension of competition for managed care patients. This follows because a small
change in clinical quality can mean the difference between being in an HMO’s provider network or not, effectively generating large elasticities even if individual patients are not particularly sensitive to differences in clinical quality. As Medicare does not form restrictive provider
networks there is much less incentive to improve clinical quality and as a result, it worsens
with increased competition.
The results raise a couple important policy questions. What are the welfare implications
of the reductions in revealed quality? The benefit of our approach is that we did not need
to identify the specific dimensions of quality that hospitals compete over, many of which are
33
likely not observable to the econometrician. The downside to this approach is that it remains
unclear what dimensions of quality are impacted by competition and, in consequence, what
the drawbacks of competition are. Related to this, how are hospitals’ cost affected by these
dimensions of revealed quality; i.e., what are the tradeoffs that hospital face when making
their quality investment choices? We leave these questions for future research.
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37
A DDITIONAL E STIMATIONS
TABLE A1—R EVEALED Q UALITY T RAVEL T IME PARAMETER E STIMATES FOR HMO PATIENTS
Major Diagnostic Category
Circulatory
β
Travel Time
× 1 – 17 yrs
× 18 – 34 yrs
× 35 – 64 yrs
× ≥65 yrs
×Male
×Income
Travel Time2
× 1 – 17 yrs
× 18 – 34 yrs
× 35 – 64 yrs
× ≥65 yrs
×Male
×Income
ER×Travel Time
× 1 – 17 yrs
× 18 – 34 yrs
× 35 – 64 yrs
× ≥65 yrs
×Male
×Income
ER×Travel Time2
× 1 – 17 yrs
× 18 – 34 yrs
× 35 – 64 yrs
× ≥65 yrs
×Male
×Income
Closest 5
6th to 10th closest
ER×Closest
#Pat. within 10 mi.
#Pat. within 25 mi.
(s.e.)
Digestive
β
−0.118c
−0.254c
−0.266c
−0.277c
−0.006
0.149c
(0.022)
(0.021)
(0.019)
(0.019)
(0.005)
(0.033)
−0.264c
−0.312c
−0.305c
−0.295c
−0.023c
0.189c
0.001b
0.002c
0.002c
0.002c
0.000c
−0.002c
(0.000)
0.002c
(0.000)
0.002c
(0.000)
0.002c
(0.000)
0.001c
(0.000)
0.000c
(0.001) −0.002c
−0.043
−0.069c
−0.057c
−0.047b
−0.002
−0.073b
(0.028)
(0.023)
(0.020)
(0.021)
(0.006)
(0.035)
0.000
0.000
−0.000
−0.000
−0.000
0.001
1.400c
0.912c
0.103a
−0.059
0.012c
(0.000)
0.000
(0.000) −0.000
(0.000)
0.000
(0.000)
0.000
(0.000)
0.000
(0.001)
0.000
(0.094)
1.122c
(0.071)
0.730c
(0.055)
0.105b
(0.047) −0.078
(0.004)
0.030c
−0.009
−0.006
−0.012
−0.028
−0.014c
−0.067c
(s.e.)
Respiratory
β
(s.e.)
(0.019)
(0.019)
(0.019)
(0.023)
(0.007)
(0.028)
(0.000)
0.002c
(0.000)
0.002c
(0.000)
0.002c
(0.000)
0.002c
(0.000) −0.000
(0.000) −0.002c
(0.000)
0.001c
(0.000)
0.002c
(0.000)
0.002c
(0.000)
0.001c
(0.000)
0.000
(0.001) −0.002c
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.017)
(0.017)
(0.015)
(0.018)
(0.005)
(0.026)
(0.022)
(0.026)
(0.022)
(0.028)
(0.009)
(0.037)
−0.053b
−0.062c
−0.064c
−0.071c
0.001
−0.048
(0.022)
(0.022)
(0.021)
(0.025)
(0.009)
(0.034)
(0.000)
0.001b
(0.000)
0.001
(0.000)
0.000
(0.001)
0.001
(0.000) −0.000
(0.001)
0.000
(0.089)
1.199c
(0.073)
0.817c
(0.051)
0.141c
(0.100)
0.152
(0.009)
0.042c
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.001)
(0.088)
(0.071)
(0.049)
(0.163)
(0.013)
0.031
−0.031
−0.021
0.005
−0.018b
−0.094b
(0.000) −0.000
(0.000)
0.000
(0.000)
0.000
(0.000) −0.000
(0.000)
0.000
(0.000)
0.001
(0.079)
1.248c
(0.063)
0.782c
(0.048)
0.146c
(0.090) −0.250b
(0.007)
0.044c
(0.021)
(0.025)
(0.021)
(0.026)
(0.007)
(0.037)
Notes: Standard errors are clustered by zip code. Significance levels: c : p < 0.01, b : p < 0.05, a : p < 0.10.
A-1
β
−0.181c
−0.257c
−0.267c
−0.276c
−0.005
0.143c
(0.018)
(0.018)
(0.016)
(0.019)
(0.004)
(0.028)
−0.279c
−0.311c
−0.309c
−0.341c
0.004
0.193c
(s.e.)
Nervous
TABLE A2—R EVEALED Q UALITY T RAVEL T IME PARAMETER E STIMATES FOR M EDICARE PATIENTS
Major Diagnostic Category
Circulatory
β
Travel Time
× 1 – 34 yrs
× 35 – 64 yrs
× ≥65 yrs
×Female
×Income
Travel Time2
× 1 – 34 yrs
× 35 – 64 yrs
× ≥65 yrs
×Female
×Income
ER×Travel Time
× 1 – 34 yrs
× 35 – 64 yrs
× ≥65 yrs
×Female
×Income
ER×Travel Time2
× 1 – 34 yrs
× 35 – 64 yrs
× ≥65 yrs
×Female
×Income
Closest 5
6th to 10th closest
ER×Closest
#Pat. within 10 mi.
#Pat. within 25 mi.
(s.e.)
Digestive
β
(s.e.)
Respiratory
β
(s.e.)
Nervous
β
(s.e.)
−0.219c
−0.262c
−0.276c
0.016c
0.097c
(0.019) −0.191c
(0.015) −0.250c
(0.015) −0.273c
(0.003)
0.007a
(0.027)
0.068c
(0.019) −0.188c
(0.014) −0.237c
(0.015) −0.271c
(0.004)
0.020c
(0.024)
0.056a
(0.025) −0.201c
(0.017) −0.229c
(0.017) −0.270c
(0.006)
0.002
(0.031)
0.112c
(0.022)
(0.017)
(0.018)
(0.005)
(0.029)
0.002c
0.002c
0.002c
−0.000
−0.001c
(0.000)
0.001c
(0.000)
0.002c
(0.000)
0.002c
(0.000) −0.000
(0.000) −0.001a
(0.000)
0.001c
(0.000)
0.001c
(0.000)
0.002c
(0.000)
0.000
(0.000) −0.001
(0.000)
0.001c
(0.000)
0.001c
(0.000)
0.002c
(0.000) −0.000
(0.000) −0.001b
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
−0.064c
−0.068c
−0.073c
−0.009b
0.012
(0.022)
(0.016)
(0.016)
(0.004)
(0.032)
−0.119c
−0.106c
−0.086c
−0.003
0.011
(0.024)
(0.017)
(0.017)
(0.007)
(0.031)
0.000
0.000
−0.000
0.000
−0.000
1.306c
0.849c
0.215c
0.109c
0.005c
(0.000)
0.000
(0.000)
0.000
(0.000)
0.000
(0.000)
0.000
(0.001) −0.000
(0.077)
1.154c
(0.058)
0.645c
(0.044)
0.176c
(0.021)
0.536c
(0.002) −0.003
(0.000)
0.001b
(0.000)
0.001b
(0.000)
0.000
(0.000)
0.000
(0.001) −0.000
(0.081)
1.327c
(0.065)
0.748c
(0.044)
0.220c
(0.056)
0.865c
(0.004) −0.018c
(0.000)
(0.000)
(0.000)
(0.000)
(0.001)
(0.086)
(0.067)
(0.044)
(0.084)
(0.005)
−0.088c
−0.063c
−0.052c
−0.016c
0.030
(0.021)
(0.014)
(0.014)
(0.005)
(0.026)
−0.125c
−0.090c
−0.071c
−0.033c
0.051
(0.000)
0.001
(0.000)
0.000
(0.000)
0.000
(0.000)
0.000c
(0.001) −0.000
(0.078)
1.136c
(0.058)
0.608c
(0.044)
0.178c
(0.071)
0.386c
(0.005)
0.001
(0.025)
(0.017)
(0.017)
(0.006)
(0.031)
Notes: Standard errors are clustered by zip code. Significance levels: c : p < 0.01, b : p < 0.05, a : p < 0.10.
A-2
TABLE A3—R EVEALED Q UALITY BY M AJOR D IAGNOSTIC C ATEGORY U SING NON -ER A DMITTED PA TIENTS O NLY
Major Diagnostic Category
Circulatory
β
HMO
∆ ln(Avg. Price)
% HMO
% PPO
% Medicare
% Medicaid
1 − HHIHMO
1 − HHIMedicare
# Hospitals
Medicare
∆ ln(Avg. Price)
% HMO
% PPO
% Medicare
% Medicaid
1 − HHIHMO
1 − HHIMedicare
# Hospitals
(s.e.)
−0.088c
1.639c
0.006
−1.469c
−1.228c
−4.868c
−1.698c
(0.033)
(0.289)
(0.240)
(0.246)
(0.412)
(0.852)
(0.483)
198
−0.041c
−1.108c
−0.069
0.911c
0.078
−0.179a
−2.288c
254
Digestive
β
(s.e.)
−0.025
1.647c
0.256
−1.566c
−1.064c
−5.196c
−0.420
(0.031)
(0.256)
(0.235)
(0.256)
(0.404)
(0.668)
(0.347)
200
(0.012)
(0.191)
(0.272)
(0.193)
(0.286)
(0.096)
(0.819)
−0.034c
−0.659c
−0.261
0.741c
−0.612b
0.260a
−0.805a
246
Respiratory
β
(s.e.)
−0.118c
1.703c
−0.508
−2.096c
−1.076c
−3.316c
−1.377c
(0.045)
(0.403)
(0.348)
(0.355)
(0.395)
(0.772)
(0.469)
195
(0.011)
(0.180)
(0.235)
(0.198)
(0.293)
(0.139)
(0.480)
−0.015
−0.583b
0.807b
0.348
0.998c
−0.108
−3.134c
256
Nervous
β
(s.e.)
−0.146c
1.036c
−1.059c
−1.409c
−0.628
−4.081c
0.619
(0.053)
(0.400)
(0.328)
(0.350)
(0.512)
(0.735)
(0.589)
184
(0.017)
(0.267)
(0.364)
(0.284)
(0.347)
(0.103)
(1.025)
−0.016
−1.314c
0.046
0.780c
−0.382
−0.171
−1.402b
(0.017)
(0.288)
(0.358)
(0.296)
(0.413)
(0.126)
(0.587)
233
Notes: All variables are based on patients belonging to the indicated diagnostic category and insurer type. Standard errors are clustered by
zip code and corrected to reflect the noise from the first stage estimation following Karaca-Mandic and Train (2003). Significance levels:
c : p < 0.01, b : p < 0.05, a : p < 0.10.
A-3
TABLE A4—R EVEALED Q UALITY BY M AJOR D IAGNOSTIC C ATEGORY U SING NON -ER A DMITTED PA TIENTS O NLY
Major Diagnostic Category
Circulatory
β
HMO
∆ ln(Avg. Price)
% HMO
% PPO
% Medicare
% Medicaid
1 − HHIHMO
1 − HHIMedicare
# Hospitals
Medicare
∆ ln(Avg. Price)
% HMO
% PPO
% Medicare
% Medicaid
1 − HHIHMO
1 − HHIMedicare
# Hospitals
(s.e.)
−0.041a
0.342
−1.069c
−1.004c
−1.011c
−6.570c
0.256
(0.024)
(0.216)
(0.218)
(0.191)
(0.341)
(0.698)
(0.366)
198
−0.032c
−0.743c
−0.632c
0.156
−0.043
−0.089
−1.920c
254
Digestive
β
(s.e.)
−0.049b
1.127c
−0.289a
−1.367c
0.211
−3.776c
0.190
(0.023)
(0.211)
(0.170)
(0.182)
(0.337)
(0.519)
(0.371)
200
(0.009)
(0.137)
(0.188)
(0.134)
(0.255)
(0.082)
(0.437)
−0.019b
−0.844c
−0.369b
0.361b
0.789c
−0.343c
−1.809c
246
Respiratory
β
(s.e.)
−0.059a
0.789c
−0.565b
−2.020c
−0.932c
−4.190c
0.385
(0.033)
(0.247)
(0.228)
(0.229)
(0.319)
(0.631)
(0.278)
195
(0.010)
(0.160)
(0.186)
(0.178)
(0.243)
(0.123)
(0.463)
−0.018b
−0.962c
−0.353a
0.322b
0.271
−0.312c
−1.243c
256
Nervous
β
(s.e.)
−0.052a
0.901c
−0.733c
−1.239c
−0.548
−4.072c
0.740b
(0.032)
(0.293)
(0.273)
(0.224)
(0.398)
(0.640)
(0.356)
184
(0.009)
(0.139)
(0.181)
(0.143)
(0.191)
(0.077)
(0.368)
−0.022b
−0.654c
−0.200
0.389b
0.748c
−0.348c
−1.093c
(0.010)
(0.177)
(0.270)
(0.190)
(0.289)
(0.087)
(0.340)
233
Notes: All variables are based on patients belonging to the indicated diagnostic category and insurer type. Standard errors are clustered by
zip code and corrected to reflect the noise from the first stage estimation following Karaca-Mandic and Train (2003). Significance levels:
c : p < 0.01, b : p < 0.05, a : p < 0.10.
A-4
TABLE A5—R EVEALED Q UALITY BY M AJOR D IAGNOSTIC C ATEGORY U SING C ATEGORICAL M EASURE
OF T RAVEL T IME
Major Diagnostic Category
Circulatory
β
HMO
∆ ln(Avg. Price)
% HMO
% PPO
% Medicare
% Medicaid
1 − HHIPrvt
1 − HHIMedicare
# Hospitals
Medicare
∆ ln(Avg. Price)
% HMO
% PPO
% Medicare
% Medicaid
1 − HHIPrvt
1 − HHIMedicare
# Hospitals
(s.e.)
−0.039a
0.744c
−0.565c
−1.135c
−1.297c
−5.545c
−0.689b
(0.020)
(0.178)
(0.176)
(0.168)
(0.274)
(0.570)
(0.309)
201
−0.022c
−0.877c
−0.359a
0.394c
−0.009
−0.149a
−2.058c
255
Digestive
β
(s.e.)
−0.045b
1.277c
−0.065
−1.377c
−0.519a
−3.951c
−0.490
(0.019)
(0.179)
(0.150)
(0.160)
(0.274)
(0.465)
(0.323)
203
(0.007)
(0.121)
(0.188)
(0.109)
(0.199)
(0.082)
(0.414)
−0.014a
−0.841c
−0.273a
0.473c
0.130
−0.162a
−1.147c
246
Respiratory
β
(s.e.)
−0.066b
1.026c
−0.627c
−2.034c
−1.262c
−3.747c
−0.007
(0.030)
(0.217)
(0.211)
(0.208)
(0.260)
(0.495)
(0.232)
197
(0.008)
(0.123)
(0.159)
(0.144)
(0.190)
(0.098)
(0.329)
−0.011
−0.954c
−0.157
0.405c
0.397b
−0.306c
−1.848c
257
Nervous
β
(s.e.)
−0.061
0.880c
−0.862c
−1.284c
−0.711b
−3.981c
0.705b
(0.042)
(0.256)
(0.233)
(0.238)
(0.361)
(0.552)
(0.325)
188
(0.009)
(0.149)
(0.190)
(0.157)
(0.186)
(0.065)
(0.463)
−0.006
−0.829c
−0.115
0.393b
0.281
−0.306c
−1.281c
(0.009)
(0.161)
(0.200)
(0.174)
(0.243)
(0.084)
(0.292)
235
Notes: All variables are based on patients belonging to the indicated diagnostic category and insurer type. Standard errors are clustered by
zip code and corrected to reflect the noise from the first stage estimation following Karaca-Mandic and Train (2003). Significance levels:
c : p < 0.01, b : p < 0.05, a : p < 0.10.
A-5
TABLE A6—R EVEALED Q UALITY BY M AJOR D IAGNOSTIC C ATEGORY, LA BASIN H OSPITALS O NLY
Major Diagnostic Category
Circulatory
β
HMO
∆ ln(Avg. Price)
% HMO
% PPO
% Medicare
% Medicaid
1 − HHIPrvt
1 − HHIMedicare
# Hospitals
Medicare
∆ ln(Avg. Price)
% HMO
% PPO
% Medicare
% Medicaid
1 − HHIPrvt
1 − HHIMedicare
# Hospitals
(s.e.)
−0.069b
0.959c
−0.675c
−0.656b
−1.649c
−6.841c
0.629
(0.034)
(0.284)
(0.232)
(0.257)
(0.404)
(0.966)
(0.500)
130
−0.025c
−0.990c
−0.451b
0.688c
0.125
−0.385c
−1.225a
165
Digestive
β
(s.e.)
−0.035
2.262c
0.514b
−1.102c
0.045
−4.595c
−1.091c
(0.030)
(0.270)
(0.216)
(0.247)
(0.382)
(0.812)
(0.415)
134
(0.009)
(0.150)
(0.198)
(0.126)
(0.243)
(0.126)
(0.665)
−0.003
−1.143c
−0.614c
0.619c
0.058
−0.086
−1.141b
160
Respiratory
β
(s.e.)
−0.042
1.257c
−0.674b
−1.720c
−1.240c
−3.860c
−0.199
(0.040)
(0.366)
(0.314)
(0.290)
(0.351)
(0.805)
(0.661)
129
(0.011)
(0.185)
(0.213)
(0.192)
(0.242)
(0.162)
(0.503)
−0.016
−0.975c
−0.222
0.609c
0.418a
−0.271b
−2.489c
165
Nervous
β
(s.e.)
0.018
1.685c
−1.000c
−1.010c
−1.317b
−4.480c
0.899b
(0.045)
(0.419)
(0.332)
(0.280)
(0.527)
(0.819)
(0.444)
124
(0.013)
(0.203)
(0.250)
(0.205)
(0.238)
(0.107)
(0.766)
0.001
−1.163c
−0.595b
0.730c
0.571a
−0.240a
−1.092c
(0.012)
(0.225)
(0.302)
(0.217)
(0.306)
(0.129)
(0.394)
152
Notes: All variables are based on patients belonging to the indicated diagnostic category and insurer type. Standard errors are clustered by
zip code and corrected to reflect the noise from the first stage estimation following Karaca-Mandic and Train (2003). Significance levels:
c : p < 0.01, b : p < 0.05, a : p < 0.10.
A-6
TABLE A7—R EVEALED Q UALITY BY M AJOR D IAGNOSTIC C ATEGORY, N ORTH /C ENTRAL CA H OSPITALS
O NLY
Major Diagnostic Category
Circulatory
β
HMO
∆ ln(Avg. Price)
% HMO
% PPO
% Medicare
% Medicaid
1 − HHIHMO
1 − HHIMedicare
# Hospitals
Medicare
∆ ln(Avg. Price)
% HMO
% PPO
% Medicare
% Medicaid
1 − HHIHMO
1 − HHIMedicare
# Hospitals
(s.e.)
−0.016
0.767b
−1.958c
−2.047c
−1.602b
−4.339c
0.357
(0.030)
(0.367)
(0.472)
(0.254)
(0.638)
(0.999)
(0.418)
72
−0.007
−0.968c
−0.749c
−0.110
0.230
−0.039
−2.930c
91
Digestive
β
(s.e.)
−0.004
1.524c
−1.216c
−1.885c
−1.398b
−2.586c
−0.054
(0.031)
(0.346)
(0.334)
(0.279)
(0.600)
(0.832)
(0.388)
70
(0.012)
(0.162)
(0.288)
(0.174)
(0.389)
(0.086)
(0.603)
−0.032b
−0.876c
−0.677b
−0.298
−0.010
−0.185
−0.350
87
Respiratory
β
(s.e.)
−0.019
1.913c
1.078a
−2.205c
−0.626
−3.304c
−0.723b
(0.050)
(0.410)
(0.593)
(0.420)
(0.704)
(0.887)
(0.338)
69
(0.015)
(0.198)
(0.280)
(0.296)
(0.462)
(0.251)
(0.540)
−0.004
−1.209c
−0.703b
−0.489a
0.511
−0.202b
−1.633c
93
Nervous
β
(s.e.)
−0.114b
1.291c
0.539
−1.685c
−1.621b
−4.170c
−0.411
(0.051)
(0.490)
(0.610)
(0.320)
(0.714)
(0.938)
(0.458)
65
(0.015)
(0.227)
(0.310)
(0.255)
(0.368)
(0.089)
(0.509)
−0.002
−0.873c
−0.732a
−0.300
−0.145
−0.199b
−1.887c
(0.016)
(0.277)
(0.402)
(0.249)
(0.422)
(0.093)
(0.436)
84
Notes: All variables are based on patients belonging to the indicated diagnostic category and insurer type. Standard errors are clustered by
zip code and corrected to reflect the noise from the first stage estimation following Karaca-Mandic and Train (2003). Significance levels:
c : p < 0.01, b : p < 0.05, a : p < 0.10.
A-7