The Economic Journal, 122 (May), 400–417. Doi: 10.1111/j.1468-0297.2012.02496.x. 2012 The Author(s). The Economic Journal 2012 Royal
Economic Society. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.
CHOICE OF NHS-FUNDED HOSPITAL SERVICES
IN ENGLAND*
Walter Beckert, Mette Christensen and Kate Collyer
This article examines the choice of hospital for elective hip replacements amongst patients in
England, using patient-episode level data from the Hospital Episodes Statistics records. The article is
primarily methodological: we estimate a demand model that allows for heterogeneity across observed
patient characteristics and demonstrate how to use this model to simulate the effect of mergers
between hospitals. Our article contributes to the empirical literature on hospital choice and provides
a novel method for simulating mergers between hospitals with regulated prices. Importantly, in an
industry that competes on quality, our demand model estimations also identify important quality
dimensions of choice.
Patient choice of publicly funded health services is an increasingly important public
policy strategy aiming to improve value for money in many European countries.
Countries such as Sweden, France, Italy, the Netherlands and England have all
implemented patient choice in their healthcare systems in one form or another. In the
English National Health Service (the NHS), patients have been able to choose a hospital to attend for all elective (i.e. planned) treatments since 2006. The patient chooses
a hospital, receives treatment and the hospital is then paid a price for this treatment
that is fixed centrally by the Department of Health. This, together with a budget
constraint, creates an incentive for hospitals to compete on various dimensions of
quality in order to attract patients and therefore revenue. The policy intention is that
patient choice thus works as a mechanism for creating competition between hospitals,
which in turn will lead to improvements in hospital care.
This article examines the choice of hospital for hip replacements amongst patients
in England, using patient-episode level data from the Hospital Episode Statistics
(HES) records. The article is primarily methodological: in the context of hospital
choice for elective hip replacements, it presents an empirical demand model to
estimate patient-level valuations of hospital characteristics, allowing for heterogeneity
across patients; and it demonstrates how this model can be used to simulate the effect
of hospital mergers. In line with these objectives, the article has two substantive parts.
The first part uses a conditional logit model to estimate hospital demand. We use
more than 30,000 hip replacement patients from the HES data for 2008–9, combined
with information on the distance from patient to hospital and other patient and
* Corresponding author: Walter Beckert, School of Economics, Mathematics and Statistics, Birkbeck
College, Malet Street, London WC1E 7HX, UK. Email: w.beckert:bbk.ac.uk.
We would like to thank Cory Capps, Zack Cooper, Sean Ennis, Rachel Griffith, Julian Legrand, Bruce
Lyons, Carol Propper, Peter Smith, Michael Waterson, the Health Inequalities National Support Team,
participants at the Royal Economics Society Conference 2011, participants at the Cooperation and Competition PanelÕs conference on choice and competition and two anonymous referees for valuable comments and
discussions, as well as Alistair Brown and Anupa Sahdev for their enormous help in putting the data together.
All errors are ours. The views expressed in this article are those of the authors and not necessarily those of the
Cooperation and Competition Panel.
[ 400 ]
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CHOICE OF NHS-FUNDED HOSPITAL SERVICES
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hospital characteristics. In keeping with the literature, we find that hospital demand
declines with distance, and importantly in an industry that competes on quality, we
also identify important quality dimensions of choice. We find that hospital demand
increases with quality, in particular that it declines with the mortality rate, waiting
times and the MRSA (methicillin-resistant staphylococcus aureus) infection rate. The
second part of the article illustrates how the results from the demand model can be
used to calculate elasticities of demand with respect to quality, and how those elasticities can be used to simulate the effect of a merger between two hospitals on their
ability unilaterally to reduce quality.1 Two hospitals are said to merge when the
Secretary of State for Health takes the decision to transfer responsibility for operating
one hospital and assigns it to another. Hospital mergers come about because of an
aspiration to deliver benefits by exploiting clinical and financial economies of scale
and scope. However, a merger of two close competitor hospitals that significantly
reduces competition between them reduces the competitive constraints on quality
that they face and unless the merger delivers sufficient offsetting benefits, may result
in a reduction in quality. Scrutiny is therefore necessary to ensure that hospital
mergers do not produce adverse effects for patients and taxpayers. Between 1997 and
2006, 112 of the 223 hospitals that existed at the beginning of the period merged
(Gaynor et al., 2011a); it is estimated that up to 20 of the remaining hospitals, or
around 10%, will merge in the next five years (Ham, 2011). We present a methodology to predict the effect of such mergers on hospitalsÕ market power by calculating
the elasticity of demand with respect to quality (mortality rate) before and after the
merger; the greater the change in the elasticity of demand (i.e. the more inelastic the
demand), the greater the offsetting benefits must be to ensure the merger does not
adversely affect quality. We illustrate this methodology for two hypothetical mergers
of neighbouring hospitals.
In the English NHS, patients are entitled to choose a hospital for treatment, when
the treatment is planned. However, they must be referred for treatment by their general practitioner (GP). The choice of which hospital to attend for treatment is supposed to be made by the patient with the support and advice of their GP. In practice,
hospital choice is more complex as some patients may choose by themselves, some are
not aware that they have a choice and their GP may make the choice for them, and
sometimes the GP and the patient make the choice together. In our data, we do not
observe who actually makes the choice, and the current literature suggests that none of
the patient characteristics we observe (such as patient age, the extent of income
deprivation in the area where the patient lives etc.) helps to the GPÕs preferences
separately from those of the patient. For example, The KingÕs Fund (2010) shows that
older patients value choice more than younger patients, whereas GPs believe that
choice is more important to younger patients, hence these GPs may be less likely to
offer choice to older patients. However, from HES data 2006–8, we do, observe referrals
made in the past by the GPs in our sample, so we include these past-referral patterns in
our estimations to control, at least to some degree, the GPÕs influence and find that
1
We use hospital mortality rate as a proxy for quality. This is supported by two points. First, all our
estimations and sensitivity checks show that hospital mortality rate is consistently significant and negative, and
second, other research suggests that hospital competition in the English NHS can lead to improvements in
hospital quality measured by mortality rates (Gaynor et al., 2010; Cooper et al., 2011).
2012 The Author(s). The Economic Journal 2012 Royal Economic Society.
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THE ECONOMIC JOURNAL
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they have a large impact: the higher is the past-referral frequency to a particular
hospital for a particular GP, the higher is the probability that any of that GPÕs patients
choose that hospital.
We have chosen to focus on hip replacements for a number of reasons. First, a hip
replacement is a planned procedure for which the patient can realistically make a
choice and where the choice is more likely to be informed, for example, through
conversations with peers. Secondly, choice has existed for hip replacements since
2006 (whereas choice was implemented later for some other planned procedures), so
patients, GPs and hospitals are more likely to have adjusted to the existence of
patient choice. Thirdly, a hip replacement is less likely to be part of a more complex
health situation, such as long-term conditions that might otherwise affect hospital
choice. Our findings are in principle procedure-specific to hip replacements and
nothing is yet known as to how they generalise to other procedures at hospitals in
England.2 Ho (2006) and Varkevisser and Geest (2007), however, suggest that quality
signals are stronger for more complex procedures. Ho (2006) finds that different
aspects of quality matter to different patients depending on their medical condition
(e.g. that cancer patients have strong preferences for hospitals with a large number of
nurses per bed, whereas patients with neurological conditions have a strong preference for teaching hospitals, reflecting that their condition is complex). Varkevisser
and Geest (2007) find a similar result for the Netherlands: patients who need neurosurgery place greater weight on low waiting times than patients needing orthopaedic care.
Our article contributes to the empirical literature on hospital choice and provides
a novel method for simulating hospital mergers between publicly funded hospitals
that compete on quality. Our method is, to the best of our knowledge, the first that
could be used to predict the competitive effects of mergers in the English NHS.
Gaynor et al. (2011a) examine the effects of hospital mergers in England between
1997 and 2006 on a large set of outcomes including clinical quality and find little
evidence that mergers delivered benefits. A number of articles including (Garmon
and Haas-Wilson, 2011; Haas-Wilson and Vita, 2011) have looked at the impact of
hospital mergers retrospectively, and Capps et al. (2001) have simulated mergers but
all of these studies examine the US and for-profit hospitals, whereas our article is
novel in that it examines mergers of publicly funded hospitals in a regulated
market. Our demand model for hospital choice is estimated using HES data, which
few other studies have done to date. Sivey (forthcoming) and Gaynor et al. (2011b)
examine hospital choice for cataract and coronary bypass patients, respectively. Both
find that distance and waiting times adversely affect hospital demand, and in
addition, Gaynor et al. (2011b) find adverse effects from mortality rates. In line with
these, although for a different procedure, we also find that distance, mortality rate
and waiting times adversely affect hospital choice. In addition, we find that Care
Quality Commission (CQC) ratings, MRSA infections, status as teaching hospital and
size all have significant effects on hospital demand. Our findings are important as
2
Estimating a demand model involving all clinical procedures using HES data is an enormous task, which
nobody has yet attempted. It involves classifying hundreds of clinical procedures into a manageable number,
grouping patients in the data and collecting additional quality measures specific to the different procedures.
2012 The Author(s). The Economic Journal 2012 Royal Economic Society.
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CHOICE OF NHS-FUNDED HOSPITAL SERVICES
403
they show that patients in England do take quality into account when making their
choice of hospital, which is fundamental to the success of the policy of patient
choice.
There is a larger literature on hospital choice in the US. Capps et al. (2003), Capps
et al. (2010) and Ho (2006) all estimate conditional logit models of hospital demand,
including patients with many different medical conditions. Capps et al. (2003) use data
from hospitals in San Diego; Capps et al. (2010) use data from Tampa, Tucson and
Phoenix; Ho (2006) uses insurance plan data for a total of nearly 29,000 admissions
across the US. They all find that distance (or travel time) has a large and negative effect
on hospital demand: the further away the hospital is, the less likely the patient is to
choose it. Tay (2003) considers acute myocardial infarction (heart attack) patients
across three different US states and finds that allowing for a flexible distance-quality
trade-off through a random coefficient shows less of an effect from distance than a
conditional logit would predict. This suggests the importance of including distance and
quality interactions in empirical demand models, either through random coefficients
or by means of interactions of distance and quality measures with socio-demographic
information.
The rest of the article is organised as follows. Section 1 describes the institutional
context of the supply of hospital services and hospital choice for treatment. Section 2
outlines the demand model, Section 3 describes the data and Section 4 presents the
results of the demand model estimations. Section 5 illustrates how the results obtained
can be used for merger simulations and Section 6 concludes.
1. Hospital Choice and the Supply of Hospital Services
1.1. Institutional Context
NHS hospital services in England are funded by the government and the majority are
provided by publicly owned, not-for-profit hospitals. Local government organisations,
Primary Care Trusts (PCTs), are responsible for purchasing hospital services from a
fixed budget for their local population.3 Local and national government organisations
play a role in determining the appropriate configuration of hospital services. Successive
governments have used various forms of competition in an attempt to create incentives
for hospitals to improve the quality. Reforms since 2002 focused on the role of patient
choice in driving hospital competition. From 2006, patients in England have been
offered the choice of hospital and, since 2008, they have had the right to choose any
hospital with a contract to provide NHS-funded services and prepared to accept the
fixed government price for that treatment. Hospitals are reimbursed at a fixed price
per period of care, per patient for groups of clinically similar treatments that use
common levels of healthcare resources (Department of Health, 2011b). Minimum
standards of quality of care are regulated by the CQC, a government organisation. The
government, via the CQC and local purchasing organisations, sets targets for minimum
quality of care including minimum waiting times and MRSA infection rates. During the
3
A PCT is a statutory organisation responsible for purchasing and funding healthcare services on behalf of
its local population. In 2009, there were 152 PCTs in England.
2012 The Author(s). The Economic Journal 2012 Royal Economic Society.
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THE ECONOMIC JOURNAL
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period of our study, the CQC rated hospital clinical and financial quality. These rates
are publicly available.4
1.2. Hospital Choice – Decision Making
The choice of which hospital to attend for treatment is supposed to be made by the
patient with the support and advice of their GP. In practice, hospital choice is
more complex and can be made by either the patient, the GP or by the patient and
the GP together.5 We do not observe who the actual decision maker is and the
current literature suggests that none of the patient characteristics we observe
(such as patient age, the extent of income deprivation in the area where the
patient lives etc.) helps to identify the GPÕs preferences separately from those of the
patient.
Patients are more likely to take an important role in the decision making if they are
already aware of their right to choose a hospital. In 2009, 50% of patients were aware
before they visited their GP that they had a choice of hospitals (Department of
Health, 2009) and awareness of the right to choose has been shown to depend
positively on the degree of education, age, gender (men are more likely to be aware
than women) and location (people living in small towns, villages or rural areas were
more likely to be aware of their right to choose) (The KingÕs Fund, 2010). Patients
are also more likely to influence the decision if they are offered choice by their GP.
The KingÕs Fund (2010) found that overall 47% of patients recalled being offered a
choice. Whether the patient or GP makes the hospital choice decision may also be
affected by the value placed on choice by the patient. Survey evidence from The
KingÕs Fund (2010) shows that 75% of patients said that being offered choice is either
important or very important to them. Choice was important to more of those patients
with no qualification, to more women and to more people from mixed or non-White
backgrounds. Patients aged 51–80 years were significantly more likely than younger
patients aged 16–35 years to think choice is important. However, the decision making
will also depend on the GPÕs perception of the value placed on choice by the patient.
Some GPs believe that the choice of hospital is more relevant to certain types of
patient (younger, more educated and middle class) and to those living in urban areas
(The KingÕs Fund, 2010). Finally, the role of the patient and GP in making the choice
may also differ between the alternative ways of booking the hospital appointment,
which varies between individual GPs, between different GP practices and between
PCT areas. Using Choose and Book, the GP can show the patient the set of hospitals
available to them.6 If the chosen hospital makes appointments available to book
directly, the patient and the GP can book online during the patient GP consultation.
4
Since the availability of quality information is crucial to the success of the patient choice policy, the
government has publicly prioritised the production of measures of hospital quality to inform hospital choice
(Department of Health White Paper, Equity and Excellence: Liberating the NHS, 2010a).
5
There is some evidence that, in 2011, some PCTs constrain patient choice by influencing GP-referral
decisions and restricting hospitalsÕ incentives and ability to compete for patients. It is not clear how prevalent
such behaviour was in 2009, the period of our analysis (Cooperation and Competition Panel, 2011).
6
Available at http://www.chooseandbook.nhs.uk/.
2012 The Author(s). The Economic Journal 2012 Royal Economic Society.
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CHOICE OF NHS-FUNDED HOSPITAL SERVICES
405
Alternatively, the patient receives a letter setting out the options and can then book
for themselves by telephone or online. This all shows that the choice of hospital is a
complex decision procedure but for simplicity hereafter we refer to the patient as the
decision maker.
1.3. The Supply Side
NHS hospitals in England operate subject to a budget constraint and have a statutory
obligation to break even. Certain hospitals, so-called Foundation Trusts (FT), can
retain a surplus to be reinvested in services for patients. We assume that an individual
hospitalÕs objective is to maximise surplus (minimise costs) subject to their budget
constraint. Hospitals are spatially differentiated, can have multiple sites and that the
location of sites is not within their control and cannot be changed by them. We also
assume that hospitals are vertically differentiated. Hospital quality is assumed to be
determined by hospital characteristics, some of which are outside of their control
(such as size and teaching status) and the remainder of which are within their control
(such as the waiting time, the number of doctors per bed, the number of nurses per
bed, the mortality rate, the rate of MRSA infection, CQC quality rate and CQC
financial rate). We assume that each hospital sets the measures of quality within their
control and that the quality chosen affects both the hospitalÕs costs and its demand. In
keeping with the literature, we assume that marginal costs are increasing in quality. An
individual hospitalÕs demand depends on its quality characteristics (both those within
and outside of its control) relative to competitor hospitals and on the degree of spatial
competition it faces, via the potential for patients to choose alternative hospitals. We
assume that a merger between two neighbouring hospitals changes the alternatives
available to patients and that the merging hospitals re-optimise quality characteristics
accordingly.
2. The Demand Model
We model the demand for hospitals by a classical conditional logit model, following
McFadden (1974). Consider patient i and denote i Õs choice set of hospitals by Ji. Let the
indirect, conditional utility that patient i derives from choosing hospital J 2 Ji denote it
uij ,be given by
uij ¼ dij b þ x0j a þ
K
X
k¼1
dij mik gk þ dij x0j c þ
K
X
mik x0j lk þ eij ¼ dij þ eij ;
ð1Þ
k¼1
where xj denote characteristics of hospital j, dij denotes the distance between patient i
and hospital j, and the m ¼ ðmik ; k ¼ 1; . . . ; K Þ denote patient characteristics, so that the
third, fourth and fifth terms amount to interactions between distance and patient
characteristics, distance and hospital characteristics and patient and hospital characteristics, respectively, thereby allowing for heterogeneity in valuations of hospitals
across patients with different socio-demographic characteristics. Here, a0 ; b; c0 ;
ðgk ; k ¼ 1; . . . ; K Þ; ðl0k ; k ¼ 1; . . . ; K Þ are parameters to be estimated, and eij represents
unobservable determinants of iÕs valuation of hospital i.
2012 The Author(s). The Economic Journal 2012 Royal Economic Society.
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[MAY
THE ECONOMIC JOURNAL
The key behavioural assumption is that patient i chooses hospital j if, and only if,
uij >uim for m 2 Ji ; m 6¼ j. Define yi to denote the choice of patient i, i.e. yi ¼ j if patient i
chose hospital j. Under the assumption that the eij are independently and identically
distributed with a type-1 extreme value distribution, the probability that patient i
chooses hospital j, conditional on distance, hospital and patient characteristics, is given
by (McFadden, 1974):
pij ¼ P ½yi ¼ jjðdij ; xj ; j 2 Ji Þ; mi ¼ P
expðdij Þ
;
m2Ji expðdim Þ
j 2 Ji :
ð2Þ
The market share of hospital j, denote it sj , can then be estimated as
sj ¼
^
N
1 X
p^ij ;
Nj i¼1
ð3Þ
where Nj is the number of patients in the sample whose choice sets contain j and where
p^ij ¼ P
expðd^ij Þ
;
expð^
dim Þ
j 2 Ji ;
m2Ji
d^i j ¼dij b^ þ x0j ^
aþ
K
X
k¼1
dij mik ^gk þ dij x0j ^c þ
K
X
^k ;
mik x0j l
j 2 Ji ;
k¼1
^ ^c0 ; ð^
gk ; k ¼ 1; . . . ; K Þ; ðl^k 0 ; k ¼ 1; . . . ; K ÞÞ are the maximum likelihood estiand ð^
a0 ; b;
mators of the model parameters.
3. Data
The analysis in this article is based on HES data that are collected by the NHS
Information Centre for Payment by Results, the case-base payment system by which
hospitals are paid according to the number of patients they treat. We use HES data
for 2008–9 and focus on elective hip replacements.7 HES data are recorded at the
patient-episode level, where a patientÕs stay in hospital may consist of several episodes
(a so-called spell). An episode is defined as a single period of care under one consultant and a spell may consist of several episodes. For example, a patient may be
admitted for the treatment of a broken leg and while still in hospital, diagnosed and
treated for diabetes. In order to ensure that all patients in our sample made a choice
of hospital based on having a hip replacement, we only select the first episode of the
patient spell.
The HES data provide information on, for each patient, at which hospital the patient
was treated, the age of the patient, where the patient lives in form of the super output
lower area of the patientÕs residence, the length of time from the patient was referred to
hospital to the patient was admitted to hospital for treatment, as well as the identity of
7
We select out all emergency admissions. As mentioned in the Introduction, an elective treatment is a
planned, non-emergency treatment, and hospital choice only exists for elective treatments.
2012 The Author(s). The Economic Journal 2012 Royal Economic Society.
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CHOICE OF NHS-FUNDED HOSPITAL SERVICES
407
8
the referring GP. We calculate the distance between each patient and each hospital,
the dij in (1), as the straight line distance (in km) from each patientÕs SOAL of residence
to the exact location of each hospital.9
We combine the HES data with hospital and patient characteristics from several
different sources. Our hospital characteristics are, for each hospital, the size of the
hospital measured by the number of beds, the CQC quality rating and the CQC
financial rating (all from the Department of Health); the number of doctors per 100
bed, the number of nurses per 100 bed and the mortality rate (all from Dr Foster
Intelligence); whether the hospital is an FT or not in 2008;10 whether the hospital is a
teaching hospital as well as total number of clinical staff per 100 bed (both from the
NHS Estates Return Information Collection 2008–9); the number of MRSA infections
in 2007–8 (published by the Health Protection Agency); as well as a variable measuring
whether there was good communication between the hospital and the referring GP
and the patient (from The National Patient Survey 2008–9). In addition to this, we
calculate the waiting time in weeks for each hospital from the information provided in
HES as the average number of weeks from referral to admission across all patients who
had a hip replacement at that hospital.
Our patient characteristics are, in addition to the patientÕs age which is recorded
in HES, an indicator for whether the patient lives in a rural or urban area (using
the Office of National Statistics definition of rural ⁄ urban), as well as income and
health-deprivation measures for the patientÕs area of residence (using the English
Indices of Deprivation 2004 from the Office of the Deputy Prime Minister).
Our final sample consists of 39,060 patients and 216 hospitals. For a complete list of
definitions and summary statistics of all variables, see Appendices A and B.
Finally, to take account of the GPÕs influence on patient choice, we extract all
past referrals for hip replacements made by the GPs in our sample from HES data for
2006–8. For each GP in our sample, we calculate the referral frequency to each
hospital in our sample; i.e. define GPij as patient i Õs GPÕs referral frequency to hospital j, then GPij is the number of patients that patient i Õs GP referred to hospital j
divided by the number of patients that patient i Õs GP referred for a hip replacement
in total.
As described in the previous Section, it is very difficult to determine who is the actual
decision maker: the patient, the GP, or the two together, because none of the factors we
observe (such as patient age, income group etc.) can identify this. We do, however,
8
Output areas are the base unit for Census data releases by the Office of National Statistics. Our data
identifies the lower super output area (LSOA) in which the patient lives. There are 34,378 LSOAs in England
and Wales. An LSOA has an average population of 1,500 households.
9
Hospital postcodes are taken from Connecting for Health, and grid references from the National Postcode Services Directory. Many hospitals in England have several sites with different locations, and the actual
site of treatment is not always recorded in HES. In these cases, we used the National Joint Registry to match as
many patients to the correct site as possible. We were unable to match 4,301 patients with the hospital site
where they were treated. As a result, our sample fell from 62,792 patients who received hip replacements in
their first episode, to 58,491. We further excluded 6,986 patients who were treated at independent sector
hospitals because it has not yet been possible to collect any further information on these hospitals. This leaves
51,505 matched patients in our sample.
10
FTs are not-for-profit, public-benefit corporations that have the freedom to decide their own strategy
and the way services are run. They can retain their surpluses and borrow to invest in new and improved
services. More than half of all hospitals are currently FTs.
2012 The Author(s). The Economic Journal 2012 Royal Economic Society.
408
[MAY
Percentage of Patients
THE ECONOMIC JOURNAL
65
60
55
50
45
40
35
30
25
20
15
10
5
0
1
2
3
4
5
6
7
8
9
10
Fig. 1. The Percentage of Patients Who Went to Their Nth Nearest Hospital
have information on all GP referrals for hip replacements going back to 2003. We
therefore investigate GP-referral patterns through these years to see whether they
change when patient choice is implemented in 2006. If we see a substantive change,
this may be because some patients are exercising their choice.
For each GP practice, we count the number of different hospitals this practice referred patients to for hip replacements over the years. In the years pre-choice (2003–6),
95% of all GPs made referrals to a maximum of three different hospitals. In the years
post-choice (2006–9), the number of different hospitals that GPs refer to goes up: in
2006–7, it goes up to four and, in 2008–9, it goes up to five, with only 75% of all GPs
referring to a maximum of three different hospitals (Appendix A). We take this to
suggest that there has been a significant change in GPsÕ referral patterns after choice
was implemented in 2006.
We conclude this Section with some summary statistics concerning the distance
variable. Figure 1 displays the proportions of patients in our sample that went to their
nearest, second nearest, third nearest and so on, hospital for a hip replacement.
As can be seen, about 40% of the patients in our sample did not choose their
nearest hospital, which could suggest that patients consider other hospital characteristics than just location when they choose a hospital.11 The average distance to the
nearest hospital is 8.7 km; half of all patients in our sample have a hospital located
within 6 km and 90% of patients have a hospital located 20 km within where they
live.12 Furthermore, 20% of patients have a choice of four hospitals or more within
12 km and 60% have a choice of three hospitals or more within 20 km. This shows
that the vast majority of patients in our sample have at least one hospital within a
reasonable distance and a substantial proportion has a choice of several hospitals
within a reasonable distance.
11
If all patients chose their nearest hospital, there would be no variation in the data to explain.
Patients actually travelled on average 12.3 km for a hip replacement; 75% of patients travelled up to
16.7 km and 90% travelled up to 27 km for a hip replacement.
12
2012 The Author(s). The Economic Journal 2012 Royal Economic Society.
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CHOICE OF NHS-FUNDED HOSPITAL SERVICES
409
4. Results
We construct patient-specific choice sets as the set of the 30 nearest hospitals to the
patient. Only 2,111 patients chose a hospital that was not in the set of their 30
nearest hospitals, so only very few choices are excluded by restricting the choice in
this way. We estimate a conditional logit model as described in (1) and (2) in
Section 2 including interaction terms between patient characteristics and distance,
hospital characteristics and distance and between patient and hospital characteristics. Furthermore, we include the GP-referral frequency described in Section 3, as
well as PCT dummies. The parameter estimates of our preferred empirical
Table 1
Hospital Demand Results, MNL Estimation
Interaction terms
Variable
Estimated coefficient
t-value
Distance
Distance squared
Size
CQC rate
CQC financial rate
Number of doctors
Number of nurses
Waiting time
Mortality rate
Teaching hospital
MRSA
GP-referral frequency
0.814
0.000
0.002
0.432
0.275
0.010
0.017
0.076
0.025
0.750
0.306
4.088
(0.02)
(0.00)
(0.00)
(0.05)
(0.06)
(0.01)
(0.00)
(0.01)
(0.00)
(0.11)
(0.07)
(0.06)
32.58
42.39
13.66
8.43
4.35
1.88
8.95
11.73
8.40
6.85
4.37
68.05
Distance
Patient age
Patient in rural area
Patient in income-deprived area
Patient in health-deprived area
CQC rate
CQC financial rate
Size
Number of staff per bed
0.022
0.014
0.187
0.016
0.010
0.025
0.000
0.001
(0.00)
(0.00)
(0.01)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
14.59
6.78
12.78
9.30
11.27
22.77
16.49
1.19
Patient age
Good communication
Teaching hospital
0.009 (0.00)
0.025 (0.00)
29.21
8.26
Patient in
income-deprived area
CQC financial rate
Number of staff per bed
Waiting time
0.055 (0.02)
0.090 (0.02)
0.007 (0.00)
2.22
3.62
1.93
Patient in
health-deprived area
CQC financial rate
Waiting time
MRSA
Teaching hospital
CQC rate
CQC financial rate
Waiting time
MRSA
Teaching hospital
FT status
2.097
0.086
1.657
2.234
0.082
0.252
0.034
0.523
0.562
0.161
9.40
2.55
4.79
4.73
4.61
7.57
8.16
10.75
4.39
8.27
(0.22)
(0.03)
(0.35)
(0.47)
(0.02)
(0.03)
(0.00)
(0.05)
(0.07)
(0.04)
Notes. Maximum likelihood estimation of demand for hospitals using a conditional logit model. Specification
includes 152 PCT dummies and 979,242 observations. Pseudo R2 is 0.7138. Standard errors are provided in
parentheses.
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specification are presented below. Not all possible quality characteristics and possible interactions are included as some are insignificant. The results presented below
are not sensitive to the inclusion of further interaction terms or quality characteristics (Table 1).13
In keeping with the literature (Capps et al., 2001, 2003; Tay, 2003; Ho, 2006; Gaynor
et al., 2011b; Sivey, forthcoming), we find that distance affects patient choice; patients
are more likely to choose a closer hospital. Also in keeping with other studies, we find
that quality matters to patients; the lower is the mortality rate and the shorter the
waiting time, the more likely a patient is to choose a hospital. In addition, we find that
other measures of quality also affect patient choice. We find that patients are more
likely to choose a hospital the higher the CQC rate and the lower the number of MRSA
infections. The results of interactions of patient characteristics with distance suggest
that the effect of distance differs across different patient demographics; older patients,
those living in rural areas and those living in more income-deprived areas are more
likely than average to choose a closer hospital. The interactions of patient and hospital
characteristics suggest that older patients as well as those living in more income or
health-deprived areas may be more sensitive to some quality measures. Finally, the
results yield some insights into the role of the GP and suggest that the higher is the
GPÕs referral frequency to a particular hospital, the more likely the patient is to go to
Table 2
Average Marginal Effects (%)
Marginal effect
Characteristic
Distance
Mortality rate
CQC rate
Waiting time
Proportion of GP referrals
Change in characteristic*
Increase
Increase
Increase
Increase
Increase
by
by
by
by
by
6
5
1
2
4
km
percentage points
unit
weeks
percentage points
All hospitals
43.8
6.9
+15.4
7.3
+8.6
HRy
HO‡
HI§
HH–
34.4
5.4
+11.0
7.8
+8.4
42.9
6.7
+16.2
5.6
+8.3
47.4
7.7
+16.5
9.2
+9.6
44.2
7.2
+14.3
9.1
+8.9
Notes. *The percentage change in hospital demand resulting from changing the characteristic half the
standard deviation in the case of continuous characteristics and one unit in the case of discrete characteristics
(keeping everything else fixed). The marginal effects are calculated for each patient and then averaged across
all patients who chose the hospital to get the marginal effect for hospital j. yHospitals with above-median rural
patients. ‡Hospitals with above-median older patients. §Hospitals with above-median income-deprived
patients. –Hospitals with above-median health-deprived patients.
13
As a sensitivity check, we re-estimated the model as a mixed multinomial logit, with random coefficients
on distance and GP-referral frequency, where the conditional means are allowed to depend on the four
patient characteristics: age, rural residence indicator and income- and health-deprivation measures. We
estimated the mixed MNL on random 5% samples of the data due to computational constraints. These
preliminary estimations suggested the following: the mortality rate consistently stands out as an economically
and statistically significant hospital characteristic that adversely affects hospital choice. The estimated coefficients on the aforementioned four patient characteristics have the expected signs (based on what the
conditional logit model showed) and the effect of age on the sensitivity with respect to distance is relatively
large and negative. However, after controlling for these four patient characteristics, the estimated residual
variances of the random coefficients are negligible. This suggests that the conditional logit with interactions
of patient characteristics and the distance and GP-referral frequency is a pragmatic, parsimonious and good
representation of the variation in choices present in our data.
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CHOICE OF NHS-FUNDED HOSPITAL SERVICES
411
that hospital. That the coefficient is statistically significant suggests the importance of
the role of the GP, as discussed in the Introduction and Section 2.
Table 2 reports the marginal effects of some important characteristics in the model
as the percentage change in hospital demand resulting from changing the characteristic halves the standard deviation in the case of continuous characteristics and one
unit in the case of discrete characteristics (keeping everything else fixed). The marginal effects are calculated for each patient and then averaged across all patients who
chose the hospital to get the marginal effect for hospital j. Finally, we average across
hospitals. Below we report the marginal effects for all hospitals, as well as for hospitals
whose patients live in more rural areas, more income and health-deprived areas and
are older.
5. Merger Simulations
This Section illustrates how the results from the demand model can be used to
calculate elasticities of demand with respect to quality, and how those elasticities can
be used to simulate the effect of a merger between two hospitals on their ability
unilaterally to reduce quality following a merger. Standard merger simulation models
forecast the prices and quantities before and after a merger and allow for the
identification of price effects of a merger, given a set of assumptions. As prices are
fixed in the hospital market in England, we cannot apply standard merger simulations techniques, so we take as our starting point the idea from Capps et al. (2001) of
predicting hospital demand before and after the merger. We analyse the effect on an
individual hospitalÕs demand of a change in its quality, relative to all other hospitals.
To calculate this elasticity of demand, we use the hospital standardised mortality rate
as a proxy for quality. We calculate the elasticity in a ÔbeforeÕ and ÔafterÕ scenario with
respect to relative quality and compare the results to assess the effects of a merger.
The first step is to calculate the market share for each hospital in the pre-merger, or
ÔbeforeÕ, scenario using the market share definition in (3) in Section 2. The next step
is to estimate the pre-merger quality elasticity of demand for each individual hospital.
This is done by decreasing the quality of hospital j by 10%, holding all else constant.
That is, we increase the standardised hospital mortality rate by 10%. We recalculate
the market share and calculate the estimated quality elasticity of demand. If the
hospital has multiple sites, we decrease the quality to all sites simultaneously. This
step is repeated for all hospitals considered. Next, we estimate the quality elasticities
of demand for the scenario ÔafterÕ the merger by decreasing the quality to both
hospital j and hospital k by 10%, holding all else fixed. We then recalculate the
market share of each hospital and recalculate the estimated quality elasticity of demand for each hospital. Finally, we compare the pre and post-merger elasticities of
demand for each hospital. If the two merging hospitals have significantly less elastic
demand after the merger, this suggests that they are close substitutes for patients and
face few other competitors.
We calculate the quality elasticity for each hospital in our sample, which range from
<0 to around 2.5, with a mean of 1.1. It is useful to assess the magnitude of these
elasticities relative to the standard deviations of the corresponding variables. The
average index of mortality rates across all hospitals in our sample is 100 and the
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THE ECONOMIC JOURNAL
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standard deviation is 10, so a 10% increase in mortality rate is equal to the standard
deviation (and is relatively large). The more elastic a hospitalÕs demand with respect to
its mortality rate, the more sensitive its patients are to changes in the mortality rate.
This sensitivity is driven by a combination of both hospital location through the
availability of alternative hospitals and the hospitalÕs quality relative to that of its
competitors. The elasticity of demand with respect to the mortality rate is therefore a
measure of the hospitalÕs market power; the more elastic its demand, the less it is able
to reduce its quality unilaterally. Comparing hospitals with the same level of quality, the
more competition the hospital faces, the more elastic is the demand. Comparing
hospitals in areas with similar competition, the lower the quality of the hospital (higher
mortality rate), the more elastic is the demand. As the elasticity of demand is driven
by both competition (the hospitalÕs location through the availability of alternative
hospitals) and quality (the hospitalÕs quality relative to that of its competitors), a
hospital may have inelastic demand even though it has low quality if it faces little
competition. The vast majority of the 20 hospitals in our sample with the most elastic
demand tend to be in very competitive areas (taken as a count of competitors within a
given distance) or have higher mortality rates. Conversely, the vast majority of the 20
hospitals in our sample with the most inelastic demand tend to be in less competitive
areas or have lower mortality rates.
To illustrate the possible application of this merger simulation, we use this approach to simulate the effect of two hypothetical mergers. The first hypothetical
merger takes place in a densely populated metropolitan area. Hospital 1 (H1) has
two sites providing hip operations. Both sites have a much smaller catchment area
than the average in our sample (bottom 25th percentile). The hospital is much
smaller than average, with far fewer beds than most in our sample (bottom 10th
percentile). H1 has a good CQC rating and its financial rating is fair. It has significantly more doctors and nurses per 100 beds than the average (top 90th percentile).
It has a lower waiting time than the average but the mortality rate is significantly
above average (top 90th percentile). Each of this hospitalÕs sites has very elastic
demand with respect to the mortality rate, perhaps reflecting both the extent of local
competition and the low quality. Hospital 2 (H2) has one site. It has a very small
catchment area compared with the average in our sample. It is also significantly
smaller than average (bottom 10th percentile). It has an excellent CQC rate and an
excellent CQC financial rate. It has significantly more doctors and nurses per 100
beds than the average (top 90th percentile). The hospitalÕs waiting times and mortality rate are below average. H2 has less elastic demand than H1, perhaps reflecting
the higher quality. H1 and H2 are located very close to one another (<10 km). There
are five other hospitals within 10 km of H1 (Figure 2).
Table 3 shows the results of our method for the first hypothetical merger. The
analysis of quality elasticities suggests that these hospitals have relatively elastic demand
before and after the merger meaning that patients are reasonably able to switch away
to a different hospital if quality falls. For example, before the merger, a 10% decrease
in quality at H2 would result in 11% fewer patients choosing this site
(10% 1.12 = 11.2%) and, after the merger, this only falls to 10%. After the
merger, H1 has a significantly above-average elasticity of demand with respect to the
mortality rate and H2Õs is slightly less than the average in our sample.
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CHOICE OF NHS-FUNDED HOSPITAL SERVICES
H3
H1
H1
0
H4
H5
H2
H7
10 km
H6
Fig. 2. Geographic Distribution of Hospitals in Hypothetical Merger 1
Table 3
Pre and Post-merger Elasticities for Hypothetical Merger 1
Hospital
H1 (site 1)
H1 (site 2)
H2
Pre-merger
elasticity
Post-merger
elasticity
Percentage
change in elasticity
2.29
2.32
1.12
2.18
2.21
1.02
5
5
9
The second hypothetical merger is between hospital 1 (H1) and hospital 2 (H2) in a
relatively rural area in England. H1Õs catchment area is wider than the average across
England and the hospital is much smaller than average (bottom 10th percentile). It has
a good CQC rating and its CQC financial rating is fair. H1 has fewer doctors per bed
than average in our sample and more nurses per bed. It has below-average waiting time
but its mortality rate is slightly higher than average. Before the merger, H1 has relatively inelastic demand, perhaps reflecting the degree of local competition. H2 also has
a wider catchment area than average and the hospital is significantly larger than
average (top 25th percentile). It has a good CQC rating and a good CQC financial
rating. It has a very high number of both doctors and nurses per bed compared with
average (more than 90th percentile). It has very short waiting times and its mortality
rate is much lower than average (bottom 10 percentile). This hospital has very inelastic
demand, perhaps reflecting both the local competition and the high quality of the
hospital. H1 and H2 are about 20 km apart. There are three other providers in the
local area, all about 60 km from H1 (Figure 3).
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H3
H1
H2
H4
60 km
H5
Fig. 3. Geographic Distribution of Hospitals in Hypothetical Merger 2
Table 4
Pre and Post-merger Quality Elasticities for Hypothetical Merger 2
Hospital
H1
H2
Pre-merger
elasticity
Post-merger
elasticity
Percentage
change in elasticities
0.77
0.31
0.65
0.14
15
54
Table 4 shows the pre- and post-merger elasticities for this hypothetical merger.
Before the merger, both hospitals have relatively inelastic demand, meaning that few
patients switch away to a different hospital if quality falls. H2 has less elastic demand
than H1, which suggests that pre-merger these hospitals enjoy a degree of market
power as a result of their geographic differentiation and, in the case of H2, likely also
due to some quality differentiation. Before the merger, a 10% decrease in quality of
care at H1 would result in just 8% fewer patients choosing the hospital. The effect of
the merger is particularly significant for H2. Before the merger, a 10% decrease in
quality of care would mean 3% fewer patients choosing it but this is just 1.4% after the
merger, a change of more than 50%. This means that the merger parties are important
competitors to one another and that, after the merger, they will face less competition,
have more market power and their patients will have fewer opportunities to choose an
alternative hospital. After the merger, both H1 and H2, but particularly H2, have
relatively inelastic demand.
Compared with the first hypothetical example, our analysis using this method
indicates that both of these hospitals would have substantially less sensitive demand as a
result of the merger and, after the merger, they have inelastic demand. This suggests
2012 The Author(s). The Economic Journal 2012 Royal Economic Society.
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CHOICE OF NHS-FUNDED HOSPITAL SERVICES
that this merger would lessen competition and have adverse effects on patients and
taxpayers unless there are significant benefits associated with the merger.
6. Conclusions
This article examines the choice of hospital for elective hip replacements amongst
patients in England, using patient-episode level data from the HES records. The article
is primarily methodological: in the context of hospital choice for elective hip
replacements, it presents an empirical demand model to estimate patient-level valuations of hospital characteristics, allowing for heterogeneity across patients; and it
demonstrates how this model can be used to simulate the effect of hospital mergers.
The first part uses a conditional logit model to estimate hospital demand. We find that
hospital demand declines with distance and, importantly in an industry that competes
on quality, we also identify important quality dimensions of choice. We find that hospital demand increases with quality, in particular that it declines with the mortality rate,
waiting times and the MRSA infection rate and increases with the CQC ratings. These
findings are important as they show that patients in England do take quality into
account when making their choice of hospital, which is fundamental to the success of
the policy of patient choice. The second part of the article illustrates how the results
from the demand model can be used to calculate elasticities of demand with respect to
quality and how those elasticities can be used to simulate the effect of a merger between
two hospitals on their ability unilaterally to reduce quality. Our article contributes to
the empirical literature on hospital choice and provides a novel method for simulating
hospital mergers between publicly funded hospitals that compete on quality. Our
method is, to the best of our knowledge, the first that could be used to predict the
competitive effects of mergers in the English NHS.
Appendix A
Table A1
GP Referrals 2003–9
Cumulative % of GP practices
Number of different hospitals a
particular GP practice refers
patients to for hip replacements
2003 ⁄ 4
2004 ⁄ 5
2005 ⁄ 6
2006 ⁄ 7
2007 ⁄ 8
2008 ⁄ 9
1
2
3
4
5
6
7
8
9
10
More than 10 (between 11 and 59)
45.36
81.67
95.03
98.69
99.39
99.52
99.60
99.60
99.60
99.60
100
50.63
85.30
96.42
99.05
99.46
99.53
99.53
99.53
99.53
99.53
100
43.07
79.65
94.29
98.50
99.37
99.43
99.43
99.51
99.51
99.51
100
32.98
70.76
89.83
97.62
99.16
99.55
99.61
99.61
99.61
99.61
100
26.39
61.67
84.69
94.93
98.63
99.64
100
18.97
49.69
74.60
89.17
95.58
98.23
99.05
99.40
99.46
99.46
100
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Appendix B
Table B1
Definitions of All Variables Used in the Demand Model Estimation
Variable
Hospital characteristics
CQC quality rating
CQC financial rating
Size
Doctors
Nurses
Waiting time
Mortality rate
Teaching hospital
MRSA
FT
Good communication
Patient characteristics
Age
Rural–urban
Income-deprived area
Health-deprived area
Hospital and patient
characteristics
Distance
GP-referral rate
Definition
Categories: 1 (weak), 2 (fair), 3
(good), 4 (excellent)
Categories: 1 (weak), 2 (fair), 3
(good), 4 (excellent)
Number of beds
Number of doctors per 100 beds
Number of nurses per 100 beds
Average actual waiting time14
Expected overall mortality rate
index15
Dummy = 1 if hospital has
teaching status
Dummy = 1 if hospital has more
than median MRSA infections16
Dummy =1if hospital is an FT
% age of patients who
experienced good
communication
Summary statistics
% ages: 5.61, 26.64, 45.79, 21.96
% ages: 5.14, 15.42, 41.59, 37.85
Mean
Mean
Mean
Mean
=
=
=
=
855.47, SD = 409.90
68.54, SD = 19.75
178.45, SD = 39.29
12.27, SD = 4.87
Mean = 100.42, SD = 9.94
Dummy = 1 for 15.74%
Dummy = 1 for 59.72%
Dummy = 1 for 38.46%
Mean = 77.35, SD = 3.18
Dummy = 1 if patient is aged
above 70
Dummy = 1 if patient lives in
rural area
Measure of income deprivation
(increasing in deprivation)
Measure of health deprivation
(increasing in deprivation)
Dummy = 1 for 52.63%
Distance from a patientÕs LSOA
to chosen hospital
GP referral frequency:
GPij = (number of patients that
patient i Õs GP referred to hospital
j) ⁄ total number of patients that
patient i Õs GP referred
Mean = 12.3 km; 90
percentile = 27 km
The total number of GPs is 8,237;
33.1% of GPs referred to only 1
hospital; mean = 11.45 hospitals;
SD = 10.33 hospitals
Dummy = 1 for 13.69%
Range = [3.17;3.62],
mean = 0.14, SD = 0.83
Range = [0;0.83], mean = 0.13,
SD = 0.10
Birkbeck College, University of London and Institute for Fiscal Studies
Cooperation and Competition Panel and University of Manchester
Cooperation and Competition Panel
14
For hospital j: the total number of weeks all patients treated at hospital j waited from decision to admit to
admission, averaged across all patients treated at hospital j
15
The expected mortality rate is estimated by Dr Foster Intelligence, using HES data, including factors
such as patientsÕ age, gender and medical history. The expected mortality rate is then compared with the
actual mortality rate; a mortality rate index equal to that expected based on the hospitalÕs patient mix is 100.
For a hospital with 10% greater mortality rate than expected, the index is 110, and for a hospital with 10%
lower mortality rate than expected, the index is 90.
16
The median number of MRSA infections is 21.
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References
Capps, C., Dranove, D., Greenstein, S. and Satterthwaite, M. (2001). ÔThe silent majority fallacy of the ElzingaHogarty criteria: a critique and new approach to analyzing hospital mergersÕ, NBER Working Paper, No. 8216.
Capps, C., Dranove, D. and Lindrooth, R. C. (2010). ÔHospital closure and economic efficiencyÕ, Journal of
Health Economics, vol. 29(1), pp. 87–109.
Capps, C., Dranove, D. and Satterthwaite, M. (2003). ÔCompetition and market power in option demand
marketsÕ, RAND Journal of Economics, vol. 34(4), pp. 737–63.
Cooper, Z., Gibbons, S., Jones, S. and McGurie, A. (2011). ÔDoes hospital competition save lives? Evidence
from the English NHS patient choice reformsÕ, Economic Journal, vol. 121(554), pp. F228–60.
Cooperation and Competition Panel (2011). ÔOperation of ‘‘any willing provider’’ for the provision of routine
elective care under ‘‘free choice’’Õ, available at http://www.ccpanel.org.uk/content/cases/Operation_of_any_willing_provider_for_the_provision_of_routine_elective_care_under_free_choice/280711_
AWP_Review_Final_Report.pdf (last accessed: 13 December 2011).
Department of Health (2009). ÔReport on the National Patient Choice Survey England, JanuaryÕ, available at
http://www.dh.gov.uk/en/Publicationsandstatistics/Publications/PublicationsStatistics/DH_103479
(last accessed: 13 December 2011).
Department of Health (2011a). ÔLiberating the NHS: regulating healthcare providersÕ, available at http://
www.dh.gov.uk/prod_consum_dh/groups/dh_digitalassets/@dh/@en/documents/digitalasset/dh_
117842.pdf (last accessed: 13 December 2011).
Department of Health (2011b). ÔPayment by results guidance 2010-11Õ, available at http://www.dh.gov.
uk/prod_consum_dh/groups/dh_digitalassets/@dh/@en/@ps/documents/digitialasset/dh_112970.pdf
(last accessed: 13 December 2011).
Garmon, C. and Haas-Wilson, D. (2011). ÔHospital mergers and competitive effects: two retrospective analysesÕ, International Journal of the Economics of Business, vol. 18(1), pp. 17–32.
Gaynor, M., Laudicella, M. and Propper, C. (2011a). ÔCan governments do it better? Merger mania and
hospital outcomes in the English NHSÕ, mimeo, the Business School, Imperial College.
Gaynor, M., Moreno-Serra, R. and Propper, C. (2010). ÔDeath by market power. Reform, competition and
patient outcomes in the National Health ServiceÕ, CMPO Working Paper No. 10 ⁄ 242, University of
Bristol.
Gaynor, M., Propper, C. and Seiler, S. (2011b). ÔFree to choose: reform and demand response in the British
National Health ServiceÕ, mimeo, London School of Economics.
Haas-Wilson, D. and Vita, M. (2011). ÔMergers between competing hospitals: lessons from retrospective
analysesÕ, International Journal of the Economics of Business, vol. 18(1), pp. 1–4.
Ham, C. (2011). ÔPoliticians have ducked hard decisions on the NHS for far too longÕ, The Observer, available at
http://www.kingsfund.org.uk/publications/articles/hospital_closures.html (last accessed: 19 June 2011).
Ho, K. (2006). ÔThe welfare effect of restricted hospital choice in the US medical care marketÕ, Journal of
Applied Econometrics, vol. 21(7), pp. 1039–79.
McFadden, D. (1973). ÔConditional logit analysis of qualitative choice behaviorÕ, in (P. Zarembka, ed.),
Frontiers in Econometrics, pp. 105–42, New York: Academic Press.
Sivey, P. (forthcoming). ÔThe effect of waiting time and distance on hospital choice for English cataract
patientsÕ, Health Economics, published online in Wiley Online Library, doi: 10.1002/hec.1720.
Tay, A. (2003). ÔAssessing competition in hospital care markets: the importance of accounting for quality
diffferentiationÕ, The Rand Journal of Economics, vol. 34(4), pp. 786–814.
The KingÕs Fund (2010). ÔPatient choice: how patients choose and how providers respondÕ, available at
http://www.kingsfund.org.uk/publications/patient_choice.html (last accessed: 13 December 2011).
Varkevisser, M. and Geest, S. (2007). ÔWhy do patients bypass the nearest hospital? An empirical analysis for
orthopaedic care and neurosurgery in the NetherlandsÕ, European Journal of Health Economics, vol. 8(3),
pp. 287–95.
2012 The Author(s). The Economic Journal 2012 Royal Economic Society.