Ex-post analysis of Dutch hospital mergers1
Ron Kemp2 and Astrid Severijnen 3

Key words: competition law enforcement, merger control, ex-post studies, health care
JEL code(s):D49, L40, L50, L80

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Summary
Ex-ante assessments of hospital mergers are complicated and often lead to a lot of discussion,
particularly about defining the geographical market. Therefore, ex-post assessments of
hospital mergers are gaining more attention. In this study, we perform an ex-post analysis of
two mergers that involve Dutch hospitals: the merger of Ziekenhuis Hilversum and
Ziekenhuis Gooi-Noord, and the merger of Erasmus MC ziekenhuis and Havenziekenhuis
Rotterdam. We investigate whether or not the merging hospitals have used their increased
market power to raise their prices for one specific type of treatment, i.e. hip surgery.
Moreover, pre-merger and post-merger travel behavior of patients is also examined.
For the Ziekenhuis Hilversum – Ziekenhuis Gooi-Noord merger, we found a significant price
increase, whereas for the Erasmus MC ziekenhuis – Havenziekenhuis Rotterdam merger, we
did not find a significant price increase. For both mergers, travel behavior of patients prior
and after the merger show no significant changes. The observed price increase combined with
the unaltered travel behavior could be an indication that the competition authority has defined
too small a geographical market for the merger of Ziekenhuis Hilversum and Ziekenhuis
Gooi-Noord. However, more information is needed before any definite conclusions can be
drawn, information such as investigation into additional types of treatments, additional
information about quality, as well as additional qualitative research.

1 Introduction
There is a lot of attention for hospital mergers and for the assessment of these mergers by
competition authorities. Ex-ante assessments of mergers are challenging due to specific
features of hospital markets, such as the presence of third-party payers, differentiated products
and asymmetric information. Despite these issues, most attention is given to the definition of
the geographical market.
In the United States (U.S.), there have been over 900 hospital mergers during the period of
1995 – 2002. Competition authorities challenged only seven of these cases. In court, they lost
all of these cases, and most of these losses were because of the definition of the geographical

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market. The courts usually accepted the broad market definition that the parties put forward.
Nevertheless, studies that performed ex-post assessments of hospital mergers showed that
several hospital mergers did have anticompetitive effects. Moreover, in 2005, competition
authorities challenged a hospital merger ex-post. In this case, the court accepted the limited
geographical market put forward by the competition authorities. This was the first time since
the 1980s that the courts ruled in the competition authorities’ favor with regard to challenging
a hospital merger (Varkevisser and Schut, 2008)4.
Since the gradual introduction of managed competition in the Dutch hospital market in 2004,
the NMa has ex-ante assessed eight hospital mergers. Apart from an intended merger that was
prematurely cancelled by the merging parties, the NMa approved all of the other mergers,
mainly because there would be enough competition left on the market after each merger.
Some of these decisions have led to a lively debate between policymakers, scholars, and
politicians.5 The geographical market definition in particular turned out to be one of the focal
points of this debate (Janssen et al., 2009). Unlike in the US, hospital mergers in the
Netherlands have yet to be empirically investigated ex-post.
In this study, we perform an ex-post analysis of two mergers involving Dutch hospitals that
were approved by the NMa: the Ziekenhuis Hilversum – Ziekenhuis Gooi-Noord merger6
(hereafter: Gooi-hospital merger) and Erasmus MC ziekenhuis – Havenziekenhuis Rotterdam7
(hereafter: Rotterdam-hospital merger). The former merger in particular was a controversial
one and has led to much debate and discussion, whereas the latter merger has not led to any
debate. In our analysis, we investigate whether or not the merging hospitals have used their
increased market power to raise prices for hip surgery. Moreover, in order to gain a clearer
understanding of what the actual geographical markets could have been, the travel behavior of
patients is examined as well.
Section 2 provides an overview of the related literature. In Section 3, we describe the process
of (hospital) merger control in the Netherlands, and the merger background. The methodology
is described in Section 4, and Section 5 contains a description of the data. The results are
discussed in Section 6, followed by the conclusions and discussion in Section 7.

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2 Related literature
Ex-post evaluation of competition authorities’ enforcement activities consists of comparing
the effect of the outcome of either an antitrust intervention or antitrust abstention with the
estimated effect of a counterfactual (alternative decision). These kinds of studies are often
carried out by the authorities themselves (Van Sinderen and Kemp (2008). These studies have
an internal and an external objective (Neven and Zenger, 2008). The external objective
focuses on the substantive decisions of the competition authority: do the decisions contribute
to its ultimate goal? 8 Answers to this question could, for example, be used to improve future
ex-ante assessment of mergers. The internal objective focuses on the improvement of the
competition authority’s internal procedures, i.e. using better managerial and organization
techniques to increase the likelihood that decisions of the authority contribute to the ultimate
goal (Kovacic, 2006).
Qualitative ex-post investigation is one of the methods that are used to investigate the price
effect of mergers. For example, in Great Britain, the Office of Fair Trading (OFT), the
Department of Trade and Industry (DTI) and the Competition Commission (CC) have
commissioned PricewaterhouseCoopers (PwC) to perform an ex-post evaluation of mergers
that had been approved by the CC between 1991 and 2002 (PwC, 2005). PwC concluded that
there is effective competition in all of these cases at the moment of research, although in some
cases there were some short-term competition concerns as a consequence of the merger.
Recently, the OFT commissioned external researchers to conduct a review of eight merger
decisions in the period of 2004-2006 (Deloitte, 2009). They concluded that, in most of the
cases, post-merger market developments have not led to considerable concerns about the
soundness of the decision.
In the Netherlands, ECORYS, commissioned by the Dutch Ministry of Economic Affairs,
performed a qualitative ex-post analysis of ten case studies which included five merger cases
(ECORYS, 2002). It concluded that in none of the four approved mergers (three with
remedies) the level of competition had been negatively influenced. In the investigated blocked
merger, it found ex-post that the level of competition would have been lower had the merger
been approved.

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According to Weinberg (2008), a quantitative analysis of prices pre-merger and post-merger
is the most credible way of assessing the price effects of mergers. He surveyed 22 mergers in
various sectors of the U.S. and showed that most of the mergers led to higher prices for the
merging parties, at least in the short run. Most of these ex-post studies were conducted in
sectors that were in three historically regulated industries where pricing data are publicly
available: airlines, banking and hospitals (Pautler (2003), Ashenfelter and Hosken (2008)).

2.1 Ex­post evaluation of hospital mergers
There are many quantitative ex-post studies of mergers in the health care sector available, in
part driven by the wide availability of data in this sector. Most ex-post evaluations of hospital
mergers originate from the U.S, as competition in the health care sector is quite common
there. In Europe on the other hand, for instance in Germany and the Netherlands, the health
care sector is currently in a transition in order to become competitive in the future.9
Accordingly, in these countries, hospital mergers can be assessed ex-post since a couple of
years now.
Until the beginning of the 2000s, most of the studies that investigated the effects of hospital
mergers were variants of the structure-conduct-performance paradigm. In these studies, the
correlation between market concentration and price is employed to assess a merger. Since the
mid-1980s, the studies typically found a positive relationship between concentration and price
(see e.g. Dranove et al., 1993, Pautler and Vita, 1994), suggesting that hospital mergers would
lead to higher prices after the mergers have gone through. Although quite informative, these
studies, however, did not reveal any direct evidence of the effects of mergers. Furthermore,
the results of these studies depend heavily on the market definition, which in itself is very
challenging (Varkevisser et al., 2008). Consequently, in these studies, inaccurate market
definitions may have led to incorrect conclusions about the effects of mergers.
Since the 2000s, comparing pre-merger prices with post-merger prices has been the most
common methodology, particularly using the difference-in-differences (DID) approach. One
of the major advantages of this methodology is that it does not require any market definition.
Accordingly, erroneous conclusions about the effect of a merger, as a consequence of
inaccurately defining the market, are thus avoided. One of the first studies that employed this

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methodology in the hospital sector is the work of Vita and Sacher (2001). Their analysis
showed that the merger led to significant price increases (around 30 and 15 per cent). They
also demonstrated that the change in cost did not provide a justification for the price increases,
and that the market share of the merging hospitals in its relevant market has declined.
Connor et al. (1998) and Krishnan (2001) also used DID approaches to assess multiple
hospital mergers ex-post. Connor et al. analyzed the change in total patient revenue for all of
the 122 hospitals in the U.S. that merged during the period 1986-1994. They found a price
decrease of 5 per cent for the merging hospitals relative to the control group of non-merging
hospitals. They also analyzed costs, and found a decrease in costs of 5 per cent. The decrease
in costs is converted into lower prices and the mergers have thus been pro-competitive.
Krishnan (2001) examined 22 hospital mergers in Ohio and 15 hospital mergers in California.
His analysis took place at the level of case (treatment) types and he showed that, for all case
types, the price increase for the merging hospitals was higher than for the control group.
Moreover, he demonstrated that the price increase is larger for the case types for which the
merging hospital obtained a larger market share.
Recently, the Federal Trade Commission (FTC) produced three working papers that provide
case studies of hospital mergers that took place in the beginning of the 2000s, using a DID
approach. Tenn (2008) investigated the pre-merger prices and post-merger prices for one
hospital merger, for three large insurers. He used control variables for observable hospital
characteristics, like the type of hospital, the number of beds and the for-profit status of the
hospital. One of the merging hospitals had relatively low pre-merger prices, while the other
hospital had relatively high pre-merger prices. Post-merger, the prices converged to the higher
price level. Regression analysis confirmed that the price change of the hospital with lower
prices was significantly larger than the average price change, while the price change of the
hospital with higher prices was not statistically different from the control group. This
conclusion held for all insurers.
Haas-Wilson and Garmon (2009) investigated two hospital mergers. For the first merger,
regression analysis showed that for four of the five managed-care organizations (MCOs), the
price increase was large and significant.10 In the second merger, regression analysis showed a
relative price decrease for three MCOs, a not-significant price increase for one MCO and a

6

significant price increase for another MCO. On average, there was a price increase of 4 per
cent in the period 1999-2002.
Finally, the results of the evaluation of a hospital merger by Thompson (2009) were mixed.
Regression analysis demonstrated that some insurers experienced a significant price increase
(> 50 per cent), one insurer had a significant price decrease (-29 per cent), whereas for the
others the price increase was the same as for the control group.
The results of the discussed studies are mixed: price increases, price decreases, as well as
stable prices. In particular, these different results become visible when studying at the
hospital-insurer level. Therefore, the conclusions cannot be easily generalized, and the results
may even depend on the specific circumstances of a hospital merger. Next to that, it should be
noted that the hospitals that have been investigated are not a random sample of all of the
mergers that took place. Therefore, Carlton (2007) is in strong favor of systematical and
quantitative analyses of all mergers, or of a sample thereof, as he argues that a mistake in one
case could be a random error and would not necessarily have to point at a systematic error in
the policy. Finally, the institutional framework of a country with respect to the health care
sector plays an important role as well.

3 Ex­ante assessment of hospital mergers in the Netherlands
The NMa, established in 1998, enforces fair competition in all sectors of the Dutch economy.
A part of its responsibility is ex-ante assessment of mergers. During the period of 1998-2003,
before the major reforms in the health care sector had been introduced, the NMa was already
asked to assess several hospital mergers. The conclusion of the subsequent investigations by
the NMa was that actual competition between hospitals was not yet possible due to price and
supply regulation (Varkevisser et al., 2008). Therefore, the NMa decided to approve these
hospital mergers without carrying out substantive assessments.
However, in 2004, after extensive investigations, the NMa concluded that, given the
legislation at that time, hospitals could compete with respect to quality, service and supply.11
From then onwards, the NMa started to assess hospital mergers with respect to the content.

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The NMa has assessed eight hospital mergers since 2004.12 In most cases, the merging
hospitals were close competitors in a geographical sense. Except for an intended merger that
was prematurely cancelled by the merging parties, the NMa approved all of these mergers,
primarily because of the fact that there would be enough competition left on the market after
the merger.13 In the assessment of hospital mergers, the definition of the relevant product and
of the geographical market are central issues.

3.1 Product market definition
In all relevant merger cases14, the NMa has considered inpatient care and outpatient care as
two separate product markets in all hospital merger cases.15 One reason for this distinction is
that both supply substitution and demand substitution can be different for inpatient and
outpatient care. Blank and Van Hulst (2005) confirm this observation for the supply
substitution. Moreover, competition circumstances differ, since independent treatment centers
are only allowed to supply outpatient hospital care. Also, in the United States and in New
Zealand, inpatient care and outpatient care are considered to be two separate product markets
in hospital merger control. There is no public debate about this product market delineation. In
contrast, a geographical market definition in hospital merger cases is more challenging.

3.2 Geographical market definition
For hospital mergers, geographical market definitions are exceptionally challenging, since the
normal tests that are applied to define markets are not useful. This is due to the specific
characteristics of hospital markets, namely the presence of third-party payers, differentiated
products, asymmetric information, uncertainty, and entry and exit barriers (Varkevisser and
Schut, 2008).
Normally, competition authorities use the standard ‘Small but Significant Non-transitory
Increase in Price’ (SSNIP) test in order to define the relevant geographical market. A SSNIP
market is a group of products for which there are no close substitutes outside the group, but
for which there are good substitutes within the group (Gaynor and Vogt, 2007). There are two
main problems with the application of the SSNIP test in hospital markets. The first problem is
that, in the non-competitive segment, price is no competition parameter, since prices are
regulated. Second, the prices in the competitive segment do not have to be paid by the patients
themselves, since all patients have mandatory insurance. Therefore, patients are not price8

sensitive. Due to these problems, price elasticity of demand of the hospital cannot be
calculated. There are various alternatives for the SSNIP test that can be used to define the
geographical markets in hospital merger cases. We will further examine the Elzinga Hogarty
(EH) test, since this test is commonly used by competition authorities worldwide, including
those in the U.S., Germany and the Netherlands.16 Moreover, the NMa used the EH test in the
ex-ante assessments of the two hospital mergers that are investigated in this paper.
The EH test is a shipment-based approach to geographical market definition and is based on
historical patient-flow data. The rationale behind the method is that if a certain geographic
area is the supposed relevant market, there is little export of hospital services (consumers
outside the relevant market do not make use of many hospital services from within the
relevant market, LOFI, little in from outside) and there is little import of hospital services
(consumers within the relevant market do not make use of many hospital services from
outside the relevant market, LIFO, little out from inside). The test starts with a narrowly
defined market, and that market is enlarged until the thresholds are met. If both LIFO and
LOFI are at least 75 per cent, Elzinga and Hogarty consider such a market to be a ‘weak’
market and if both LIFO and LOFI are at least 90 per cent, they define such a market as a
‘strong’ market.
Although the EH test is transparent and easy to understand, it also has some important
shortcomings (Frech III et al., 2004). First of all, it is a static test: it uses patient-flow data premerger to conclude what will happen post-merger. But current behavior is not necessarily a
correct measure of future behavior – particularly not for sectors that are in a transition, like
the health care sector in the Netherlands. Furthermore, the threshold values of 75 and 90 per
cent are chosen arbitrarily. Next to that, heterogeneity of the patients in the geographic area
could lead to markets that are too broadly defined: this problem is called ‘silent majority
fallacy’. If a certain subgroup of patients is willing to travel to a more distant hospital, this
does not automatically mean that this holds true for the whole population. Hence, the
existence of such a subgroup is no reason to define the geographical market broader. In other
words, the presence of a group of patients that travels to more distant hospitals does not
discipline merging hospitals from abusing its increased market power, since there is a nontraveling silent majority (Varkevisser et al., 2008). The results are also sensitive to alternative
implementations of the EH test (Frech III et al., 2004). Finally, the EH test can lead to either

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too large a geographical market, in case of horizontally differentiated products, or too small a
geographical market, in case of very close substitutes.17
As a consequence, the EH-test is considered to be unreliable in defining the relevant
geographical hospital markets. Therefore, most of the time, the EH test is used in combination
with other techniques for market definitions. However, all other methods have their pros and
cons, and therefore, there is no ideal method. This makes market definition in hospital
markets very challenging.

3.3 Merger Background
In this Section, we describe the institutional framework of the Dutch health care sector, since
it is important to take this into account when performing ex-ante and ex-post assessments of
hospital mergers. Then, the two mergers under investigation are described.

3.3.1 Reforms in the Dutch health care system
During the last five years, a number of major reforms have taken place in the Dutch health
care sector. These reforms’ objectives are to keep health care affordable and of high quality.
This is done by a gradual introduction of a system of managed competition.
With regard to hospitals, the first steps are the introduction of a system of so-called diagnosistreatment combinations (DBCs), as well as the introduction of a competitive segment of
hospital care. A DBC describes the care for a patient in four codes: care question, care type,
diagnosis and treatment. In this way, a DBC is the entire treatment process a patient goes
through, from the first appointment to the last check. The DBC diagnosis codes are coupled to
the international ICD10-codes. The DBC system was introduced in 2005, and it forms a
univocal negotiation and declaration language: DBCs are the same in all Dutch hospitals and
can be seen as relatively homogeneous treatments. For a carefully selected small group of
DBCs, namely DBCs that deal with uncomplicated, elective (non-acute) outpatient hospital
care, prices, quantities and quality have become subject to bargaining between insurers and
hospitals. This competitive segment of hospital care is called the B-segment. All other
treatments are part of the non-competitive segment (A-segment). In 2005, 8 per cent of the

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hospital care is part of the competitive segment. The competitive segment expanded to 20 per
cent of the hospital care in 2008 and to 34 per cent in 2009.18
Moreover, in 2006, the Netherlands Healthcare Authority (NZa) was established, which is the
sector-specific regulator for all health care markets in the Netherlands. The NZa promotes
quality, attainability and affordability in the health care sector and provides the NMa with
opinions in hospital merger cases with respect to these three aspects of health care.19 Since
January 1st, 2006, the new Health Insurance Act has been in effect. Thereunder, all residents
of the Netherlands are legally required to take out health care insurance and to get at least a
basic health insurance package. This package consists of primary medical and hospital care.
What this package covers is the same for each resident, although the fee can differ per insurer.
Health care insurers are required to accept all applicants for the basic package, and they have
to charge the same price to each insured person. As a result, price discrimination and risk
selection are not allowed. The hospitals are not allowed to refuse patients.

3.3.2 The cases
In this study, the price effect of two Dutch hospital mergers is investigated: the Gooi hospital
merger and the Rotterdam hospital merger. We chose these two mergers because they were
finalized at around the same time in 2005. Moreover, it is interesting to compare the two
mergers: the decision to approve the Gooi hospital merger has led to much debate and
criticism, whereas the decision in the Rotterdam hospital merger was hardly an issue.
In the Gooi hospital merger, both hospitals are of comparable size. In the first-phase
investigation, the NMa used the EH test to define the relevant geographical market. The EH
test pointed at a small geographical market in which the combined market share of the two
hospitals was high. More extensive studies in the second-phase investigation on the relevant
geographical market, with a more dynamic character and more focused on the patients’
willingness to travel, did not lead to a univocal picture. Due to these ambiguous results and
given that the hospital market was in transition, whereby the availability of transparent
information on quality was expected to increase in the near future, the NMa decided that there
were ‘insufficient grounds’ for defining a small geographical market. In the broader
geographical market, the combined market shares of the merging parties would be low.

11

Therefore, the NMa approved the merger. This decision led to a lot of debate and criticism
from policymakers, scholars and politicians (see e.g. Janssen et al., 2009). The focus of the
debate was the geographical market definition. According to these critics, the NMa should, in
case of doubt, have chosen for the smaller geographical market instead of for the larger
geographical market20.
In the Rotterdam hospital merger, two hospitals of different size wanted to merge. The EH
test showed that the LOFI and LIFO scores were below 80 per cent for the zip code areas in
which the hospitals are located. This is an indication that the geographical market would
probably be larger than this area. In the end, the NMa has not defined the relevant market in
detail. Even for the smallest possible geographical market, it was not likely that the merger
would lead to the creation or to a strengthening of a dominant position, since there would be
sufficient competition left on the market.

4 Methodology
In this Section, the methodology that is applied to estimate the effect of the two mergers on
prices is described. We use the commonly used difference-in-differences (DID) approach,
which is based on a ‘before-after’ comparison (Hunter et al., 2008). This method is also used
in previous studies on the price effect of hospital mergers (Tenn, 2008, Thompson, 2009). In
order to distinguish the effect of the mergers, it is necessary to control for other observable
economic factors that can cause a price change, such as hospital size and the competition
circumstances.
Unobservable economic factors, like technological developments and changes in the
regulatory framework are also controlled for in the DID approach. We include other hospitals
in other geographic areas in the regression, in which the price is affected by the same
unobservable economic factors, but not by the merger. The assumption is that the merging
hospitals are, for example, influenced by the regulatory reforms to the same degree as the
other hospitals in the Netherlands are.
Typically, ex-post merger studies use a model of the form (Tenn, 2008):

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ln pi = X i β + γ h + αPostMergeri + θ ( PostMergeri * M i ) + ωi

(1)

The dependent variable pi is the price for individual i, Xi represents the set of patient
characteristics and γh is the fixed effect for hospital h. The variable PostMergeri is a dummy
variable that has value one if a patient enters a hospital in the post merger period. Dummy
variable Mi is equal to one if the hospital is one of the merging hospitals. The coefficient θ is
the DID parameter and reflects the difference between the price change of the merging
hospitals and the price change of the control group, after controlling for the observable
characteristics (X).
Tenn (2008) and Thompson (2009) use a two-step approach to avoid downward-biased
standard errors (Bertrand et al., 2004). In the first stage, the average price change for each
hospital is estimated, while controlling for hospital and patient characteristics. In the second
stage, the difference between the price change of the merging hospitals and the price change
of the control group is estimated. In our analysis, we can aggregate the patients’ level data to
insurer-hospital level data without a loss of information as the prices are the same for all
patients that have the same insurer and that enter the same hospital. Therefore, it is not
necessary to control for patient characteristics, as our level of analysis is the hospital-insurer
level.21
We determine the price effect of the merger after controlling for hospital characteristics. In
other words, an OLS regression will be performed to control for both the observable hospital
characteristics by means of including these in the regression model and the unobservable
hospital characteristics by means of including a control group22.
So we estimate a model of the form:23
∆p hi = α + β X h + γI i + φM hi + ε hi

(2)

where, ∆phi is the price change per hospital-insurer combination, α is the constant, Xh reflects
the observable hospital characteristics and Ii is an insurer fixed effect for insurer i. The

13

coefficient φ is the DID parameter and Mhi is a dummy variable that is equal to one if hospital
hi is one of the merging hospitals.

5 Data
The analysis is based on a unique NZa data set of treatment, prices, quantities and patient
characteristics for all hospitals in the Netherlands. The analysis is done for hip surgery in the
competitive segment, and for one specific disorder in particular, i.e. the abrasion of the hip,
arthrosis. This is a disorder that is very common among seniors and is a homogeneous
treatment. Other closely-related treatments have DBC-codes. The treatment includes more
than 95 per cent of total hip treatments in the competitive segment in the period of
investigation. We concentrate on this treatment because hip treatments have a large share of
the total revenue in the competitive segment of hospital care in the Netherlands (20 per cent in
2005).24 Furthermore, hip surgery is performed in almost all hospitals in the Netherlands, so a
large control group can be constructed. Finally, hip surgery is typically performed in hospitals
and not in independent treatment centers.25
The level of analysis is the hospital-insurer combination level, since prices are the same for all
patients that have the same insurer and enter the same hospital. We focus on two years: the
year before the merger took place (2005) and the year after the merger had been finalized
(2007).26 The dependent variable in our analysis is the price increase between 2005 and 2007,
as a percentage of the price in 2005.
The control group consists of all other hospitals in the Netherlands that perform hip surgery in
both 2005 and 2007. In total, we have 387 observations, of which 27 are related to one of the
two mergers. We control for observable differences between the hospitals by including
several control variables (see Table 1).27 The variables for which no source is mentioned are
obtained from the NZa.
Table 1 about here
Type of hospital, Number of beds and Change in number of medicals specialists control for
the influence that the type and size of hospitals may have on prices. For example, academic
14

hospitals and medical teaching hospitals may have a higher expertise and quality, which can
lead to higher price increases. The number of beds indicates the size of the hospital. A change
in the number of medical specialists demonstrates to what extent the hospitals have grown
during this period.28
Change of outpatient cases and Orthopedists control for the focus and expertise of the hospital
with respect to the competitive segment in general and to hip surgery in particular. The
change of outpatient cases shows to what extent hospitals focus on the competitive segment,
relative to the non-competitive segment during this period.29 Orthopedics is the medical
specialism that deals with hip surgery. An increase of the number of orthopedists may imply,
for example, lower price increases (economies of scale), or higher price increases (more
expertise and higher quality).
The variables Regions and Urbanization control for the area in which the hospital is active, as
prices may differ per region in the Netherlands. HHI hospital market30, HHI insurers and the
nearness of an independent treatment centre control for the competition and negotiation
circumstances. A higher HHI of the hospital market can lead to higher price increases, since
hospitals can use their market power. A higher HHI of insurers can lead to lower price
increases, due to stronger bargaining power of insurers. Fierce competition in the market, e.g.
as the result of the nearness of an independent treatment centre, can lead to lower price
increases as well.
Finally, we control for quality developments. Increased quality may lead to higher price
increases, while decreased quality may lead to lower price increases. Quality is measured by
the change of the score in the annual survey of Dutch newspaper Algemeen Dagblad (AD) on
quality, which consists of twenty six quality indicators that are formulated by, amongst others,
the Netherlands Health Care Inspectorate (IGZ).31 Examples of these quality indicators are
undernourishment, pain after surgery, cancelled operations and patient satisfaction.32

15

6 Results
6.1 Price development
Figure 1 shows the average price increase for the Netherlands and for the merging hospitals,
between 2005 and 2007, per insurer.33
Figure 1 about here
For the Netherlands, there is an average price increase that varies between 0.2 and 3.9 per
cent. For hospital 1, there is a price increase for all insurers (approximately 10 per cent),
except for insurer 5, for which there is a price decrease of 1.4 per cent. For hospital 2, there is
a price increase for all insurers between 5 and 10.5 per cent. Hospital 3 has a fixed price
increase for all insurers of 2.4 per cent, for hospital 4, the price increase is between 1.7 and
4.6 per cent. The weighted average price increase in the Netherlands is 2.5 per cent, the
weighted average price increase for hospital 1 is 3.6 per cent, for hospital 2 7.1 per cent, for
hospital 3 2.4 per cent and for hospital 4 3.0 per cent.

6.2 Pre­merger and post­merger prices in comparison to the national
average
For the controversial merger, there are substantial price increases for this specific treatment,
compared to the average price increase in the Netherlands. In order to put this price increase
in a wider perspective, we compare the absolute price of each merging hospital in 2005 to the
national average price, per insurer (see Table 2). The national average price is set at 100.
Table 2 about here
In 2005, for Ziekenhuis Gooi-Noord and Ziekenhuis Hilversum, most prices were below the
national average. In 2007, these merging hospitals use one uniform price, which, in most
cases, is above the national average in 2007. So between 2005 and 2007, the prices of the
merging hospital have, on average, changed from below the national average into above the
national average level, except for insurer E, which is the largest insurer in the region (market
share > 50 per cent). This may be a sign that negotiation power is effective here.

16

For the Rotterdam merger, both hospitals use their own price after the merger. For
Havenziekenhuis Rotterdam, the prices were above the national average in 2005. In 2007, its
prices were again above the national average with approximately about the same percentages
as in 2005. For Erasmus MC ziekenhuis in 2005, the prices were around the national average
for insurer A, B and D. For insurer G and H, the prices were above the national average. After
the merger, this pattern does not change much. So for this merger, the price development is in
line with the average price development in the Netherlands.

6.3 Regression analysis for the two mergers simultaneously
In this section, we perform a regression analysis in which we control for the relevant
economic factors. We have estimated equation (2) for both mergers simultaneously (see Table
3)34.
Table 3 about here
Several control variables are statistically significant. The number of beds is statistically
significant and positive, indicating that the larger the number of beds, the higher the price
increase. All variables that control for the location of the hospitals (urbanization and dummies
for the regions) are statistically significant. The price increases are higher in rural areas. A
reason for this could be that there is less competition in rural areas. Price increases also vary
between regions in the Netherlands.
Moreover, the number of outpatient cases appears to be statistically significant and positive.
Apparently, an increased focus on the competitive segment leads to higher price increases for
hip surgery. A possible explanation is that, as a consequence of a more specific competition
strategy in the period under investigation, these hospitals may have higher capacity for
treatments in the competitive segment. As a result, these hospitals have shorter waiting lists
and can achieve higher price increases.
The ∆ HHI insurers and the nearness of an independent treatment centre both have a
statistically significant negative effect on the price increase. This matches our expectations,
since this leads respectively to a worse negotiation position and fiercer competition for

17

hospitals. The own HHI of hospitals is also significant and has a negative sign. Although the
coefficient is small, this is an unexpected result. A possible explanation is that a hospital in a
high HHI market has to compete with one or more large hospitals. This means that fierce
competition may explain the negative price increases. The price increase also differs per
insurer, as several insurer dummies are statistically significant.
The dummy variables for Ziekenhuis Gooi-Noord and Ziekenhuis Hilversum are significant.
This means that the effect of the Gooi hospital merger is statistically significant and positive.
The regression analysis shows that, even if we control for other economic factors that can
cause a price increase, price increases of 3.5 per cent for Ziekenhuis Gooi-Noord and 5.1 per
cent for Ziekenhuis Hilversum can be attributed to the merger35.
The dummy variables for the Havenziekenhuis Rotterdam and the Erasmus MC ziekenhuis
are both statistically insignificant36. This matches our expectations, since there is no price
increase that deviates considerably from the national average for this Rotterdam merger.

6.4 Analysis of travel behavior of patients
In this section, the aim is to link the price effect of the mergers with the travel behavior of
patients.
For the Gooi hospital merger, the NMa chose for a broader geographical market in its decision
to approve this merger.37 If this is the correct market definition, one would expect to see a
change in travel behavior as a consequence of the increase in prices. In other words, one
would expect that some patients that went to the merging hospitals pre-merger, went to other
hospitals in the broader geographical market post-merger, in order to avoid the price
increase.38 With the purpose of testing this hypothesis, an additional analysis is performed.
We have calculated the average travel time of patients that have undergone hip surgery in one
of the merging hospitals (see Table 4).
Table 4 about here
The results indicate that although the average travel time to the merging hospitals has changed
only slightly, it has not changed significantly during this period. Furthermore, the number of

18

surgeries that the Gooi hospital merging hospitals have performed decreased during this
period, while its market share in the small relevant market increased with 3 per cent.39 This
indicates that less people from outside the small relevant market have come to the merging
hospitals. This indicates that the actual relevant market may have been smaller. Namely, if the
market would have been larger, also patients in the smaller market would have gone to
another hospital in order to avoid the price increase, instead of bearing the price increase. For
the Rotterdam hospital merger, we do not expect to find a change in travel behavior in the
post merger period. The average travel time did also slightly change, but not significantly
during this period.

7 Conclusions and discussion
7.1 Conclusions
In this paper, we have investigated the price effect of two hospital mergers in the Netherlands.
We focused on one specific treatment, hip surgery. The results were quite appealing: for the
Gooi hospital merger, we observed a substantial price increase. Part of this price increase may
be explained by the fact that the prices were below the national average before the merger.
But this does not explain why the prices are well above the national average post-merger. For
the Rotterdam hospital merger, we did not find a statistically significant price increase. In
addition, for both mergers we found that the travel time did not significantly change after the
merger. In the Gooi hospital merger, the patients did not react to the price increase by
choosing another hospital. This may indicate that the relevant geographical market as defined
by the NMa may have been smaller than assumed. Namely, if the relevant market would have
been larger, patients would have travelled to other hospitals within the relevant market in
order to avoid the price increase, instead of bearing the price increase. In the large relevant
market, the price increase would not have been profitable. In the small relevant market, it
apparently was.
The decision is partially based on the expectation that patients would travel if prices would
increase. In hindsight, we see that patients did not change their travel behavior in both
mergers. This result underlines the observation that is seen more often in the Netherlands:
patients do not travel to distant hospitals.40 Patients tend to go to a hospital that is located

19

close to their homes. This result points out that the NMa should be cautious if it bases a
decision on the expectation that patients are going to travel in the future.
However, it is possible that patients are going to travel in the future, when quality becomes
more transparent and selective contracting and channeling by insurers becomes common
practice. We further examine both of these developments. At the moment, quality is not yet
transparent in the Netherlands, since it is very difficult to measure. The quality measure is not
significant in the regression either. Currently, there are a number of attempts to make more
information available on quality, and especially on the quality of specific treatments.41
Moreover, the Netherlands Health Care Inspectorate has started a project, called Zichtbare
Zorg, which, within a couple of years, has to lead to a uniform set of quality information that
is useful for consumers.
Nowadays, most insurers have contracts with almost all hospitals in the Netherlands.
Moreover, most patients can choose any hospital they want, as insurers virtually do not
channel patients to hospitals that they prefer out of price or quality motives. Our results
indicate that channeling by insurers has indeed not taken place in the period of study. If
channeling would have taken place, patients within the smaller geographical market would
have been channeled to hospitals outside the smaller geographical market and the market
share of the merging hospital in the smaller market would have gone down. But there are
signs that selective contracting is possible and that channeling will take place in the future.
One insurer already introduced such an insurance package, and, according to a survey of the
NZa, 52 per cent of health care insurers indicated that they pay ‘much more’ or ‘more’
attention to channeling in 2009, relative to the year before.42 Furthermore, the possibility
alone to channel patients will give insurers more bargaining power in their negotiations with
the hospitals.

7.2 Discussion and future research
Just like the results of some recent studies of the FTC, the results of this study show that there
is a substantial post-merger price increase that can be attributed to the merger. However, it is
important to be careful when drawing conclusions. First of all, the mergers took place one
year after a major market reform, i.e. the introduction of competition in the health care sector.

20

At least a part of the price increase may be explained by the fact that the prices of the merging
hospitals were below the national average before the merger, i.e. a learning effect. For some
hospitals, it took a few years after the opening of the market before the negotiators were used
to the bargaining process for the competitive segment. High price increases can accordingly
indicate improved negotiation skills. The price increase could also be explained by an increase
in quality, although our quality indicator is not significant in the regression. Recently, the
NMa approved a hospital merger with remedies.43 These remedies imply, among other things,
a price cap for the merging hospitals. So, for this recent merger, possible quality
improvements or improved negotiation skills can by no means be paired with price increases
that raise anti-competitive doubts.
In addition, it is important to be aware of the limitations of this study. First of all, we have
focused on only one type of surgery: we did not take into account all other treatments in the
competitive segment. Investigating the price change of all treatments in the competitive
segment may provide a different picture. It is, for example, quite common that insurers and
hospitals agree on a total budget that hospitals annually receive. Within this budget, the
amounts of funds that are allocated to treatments can be somewhat arbitrary: for some
treatment, a relatively high price can be agreed on, while for other treatments, a relatively low
price can be agreed on. Secondly, as long as quality is not transparent, it is hard to draw any
conclusions from a price increase. A price increase may (partially) be justified by a quality
increase (Argue, 2009).
Moreover, in order to interpret the results of a study that only investigates quantitative
information, like this study, it is worthwhile to perform complementary qualitative research.
This qualitative research can, for example, consist of interviews with the involved hospitals,
insurers, patients and other stakeholders. The combination of quantitative and qualitative
information will provide the most insight into what really happened after the finalization of a
merger. For future research, we suggest that this combined research could be done for this
merger and preferably systematically for all Dutch hospital mergers, like Carlton (2007)
already recommended.
Finally, in this study, we mainly investigated the price-effect of the merger. In order to
evaluate the decision of the NMa, it is necessary to go into detail in the original case and the

21

original decision of the NMa. To be more precise, it is important to find out which
information was or could have been available at the moment of the decision. Based on that, it
can be determined whether or not the NMa made a type 2 error in approving the Gooi hospital
merger.

22

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23

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24

Figure 1 Average price increase in the Netherlands and the merging hospitals, per insurer
12
10
8
6
4
2
0
-2
Insurer A Insurer B Insurer C Insurer D Insurer E Insurer F Insurer G Insurer H
Average price increase The Netherlands
Average price increase Ziekenhuis Gooi-Noord
Average price increase Ziekenhuis Hilversum
Average price increase Havenziekenhuis Rotterdam
Average price increase Erasmus MC Ziekenhuis

25

Insurer I

Table 1 Control variables for observable hospital characteristics
Control variable
Type of hospital
Number of beds
∆ Medical specialists
∆ Outpatient cases
∆ Orthopedists
Urbanization
Region1
∆ HHI insurers2
HHI hospital market
Independent treatment centre
∆ Quality AD3

Description
A dummy variable for a general hospital, medical teaching
(topklinisch) hospital or academic hospital
The number of beds (absolute value)
Change in the number of medical specialists working in the hospital
Change in the number of outpatient cases in the hospital
Change in the number of orthopedists working in the hospital
The extent to which the area in which the hospital is located is
urbanized (scale of 1 to 5; 1 is most urbanized)
A dummy variable for the region in which the hospital is located
(north, east, south, west)
Change in the HHI of insurers, per province
The HHI of its relevant market, per hospital
The extent to which a independent treatment centre is located nearby
(scale of 1 to 3; 1 is most nearby located)
Change in the yearly Algemeen Dagblad (AD, a Dutch newspaper)
survey on hospital quality

1

This information is obtained from Statistics Netherlands (www.cbs.nl)
This information is obtained from Monitor Zorgverzekeringsmarkt 2008, by NZa.
3
This information is obtained from Algemeen Dagblad, AD Ziekenhuis Top 100 (www.ad.nl/ziekenhuistop100)
2

26

Table 2 Price index of merging hospitals, pre and post merger, per insurer

Gooi Merger

Rotterdam Merger

Erasmus
Haven‐
Erasmus
Haven‐
Gooi
National National Ziekenhuis
Ziekenhuis hospital ziekenhuis MC
ziekenhuis MC
average average Gooi‐
Hilversum merger Rotterdam Ziekenhuis Rotterdam Ziekenhuis
Noord
2007
2005

Insurer A
Insurer B
Insurer C
Insurer D
Insurer E
Insurer F
Insurer G
Insurer H
Insurer I

100
100
100
100
100
100
100
100
100

pre
pre
post
pre
pre
post
post
merger
merger
merger merger
merger
merger
merger
102.2
96.4
98.6
106.9
103.4
99.6
105.9
104.1
103.9
98.7
98.5
109.0
104.9
100.0
107.4
101.7
100.1
95.1
101.4
103.4
96.0
98.2
105.6
103.1
99.2
105.5
103.6
102.6
102.9
96.7
101.4
101.7
97.1
105.1
101.1
103.5
103.9
97.7
97.5
107.8
103.8
104.3
106.3
107.4
103.4
97.4
107.5
103.5
104.0
106.0
107.1
103.9
98.0
97.8
108.1
104.1
106.6

27

Table 3 Regression analysis for the two mergers simultaneously
Relative price increase

Coef.

General hospital
Medical teaching hospital
Number of beds
Urbanization
Dummy region 2
Dummy region 3
Dummy region 4
Δ Medical specialists
Δ Orthopedists
Δ Outpatient cases
Δ HHI insurers
Δ Quality AD
Independent treatment centre
HHI relevant market
Insurer A
Insurer C
Insurer D
Insurer E
Insurer F
Insurer G
Insurer H
Insurer I
Dummy Ziekenhuis Gooi‐Noord
Dummy Ziekenhuis Hilversum
Dummy Havenziekenhuis Rotterdam
Dummy Erasmus MC Ziekenhuis
Constant

0.754
0.076
0.001
0.755
‐1.928
2.588
‐2.011
‐0.002
0.149
0.157
‐0.002
0.021
‐1.779
‐0.000
‐1.641
‐3.231
‐0.297
‐1.102
‐1.748
‐0.624
‐0.308
‐0.174
3.500
5.130
‐0.278
‐0.629
4.673

R2
N

0.3486
387

Std.Err
0.791
0.724
0.001
0.268
1.145
0.585
0.858
0.054
0.369
0.041
0.001
0.018
0.595
0.000
0.507
0.780
0.503
0.560
0.567
0.747
0.533
0.638
1.243
1.055
1.139
1.424
2.396

*
***
*
***
**

***
***
***
***
***
***
**
***

***
***

*

Note: Significance levels are defined as * = 10%, ** = 5% and *** = 1%.

28

Table 4 Average travel time
Ziekenhuis Gooi‐Noord
Ziekenhuis Hilversum

2005
16.32
17.33

Hospital Gooi hospital merger
Havenziekenhuis Rotterdam
Erasmus MC Ziekenhuis

14.29
23.04

2007

18.57
16.63
24.98

29

Appendix
Table 5 Regression analysis Gooi hospital merger, per insurer
Insurer A

Insurer B

Insurer D

Insurer H

Relative price increase

Coef.

Std. Err.

Coef.

Std. Err.

Coef.

Std. Err.

Coef.

Std. Err.

General hospital
Medical teaching
hospital
Number of beds
Urbanization
Dummy region 2
Dummy region 3
Dummy region 4
∆ Medical specialists
∆ Orthopedists
∆ Outpatient cases
∆ HHI insurers
∆ Quality AD
Indep. treatment centre
HHI relevant market
Dummy Ziekenhuis
Gooi-Noord
Dummy Ziekenhuis
Hilversum
Constant

‐0.171
‐0.628

1.795
1.508

1.034
1.879

1.494
1.372

‐0.320
‐0.328

1.584
1.374

3.090
2.780

1.281
1.045

0.001
‐0.214
0.369
0.309
‐6.308
‐0.128
0.823
0.155
‐0.001
0.055
‐1.295
‐0.000
8.802

0.001
0.692
3.616
1.426
2.394
0.131
0.911
0.098
0.001
0.046
1.837
.000
3.174

***

0.000
0.835
‐6.648
‐0.141
‐4.553
‐0.006
0.454
0.102
0.001
0.025
‐3.358
‐0.000
4.329

0.001
0.444
2.149
1.135
1.662
0.110
0.784
0.075
0.001
0.036
1.152
0.000
2.413

***
**
*

0.002
1.770
‐2.106
3.386
‐3.662
‐0.104
‐0.176
0.120
‐0.004
0.018
‐2.238
‐0.000
2.652

0.002
0.628
2.846
1.366
2.020
0.117
0.938
0.095
0.001
0.042
1.423
0.000
2.946

0.001
0.440
‐4.059
‐1.519
‐3.438
0.154
‐0.761
0.224
‐0.003
0.006
‐3.811
‐0.000
6.340

0.001
0.416
2.128
1.397
1.885
0.109
0.596
0.067
0.001
0.030
1.085
0.000
1.908

6.004

2.813

**

6.233

2.253

***

3.628

2.633

(dropped)

5.997

6.403

11.375

4.627

**

4.072

5.327

9.766

R2
N

0.583
60

**

*
***
***

0.519
61

0.505
62

Note: significance levels are defined as * = 10%, ** = 5% and *** = 1%.

30

***
**
*

***

0.655
50

4.342

**
**

*
*

***
**
***
**
***

**

Endnotes
1

We would like to thank the Dutch Health Authority for giving access to the data. We thank Jarig van Sinderen, Ilaria Mosca, Rein Halbersma, Willem Kerstholt, Krijn
Schep, Gerard la Bastide, Frank Pellikaan, Tako Vermeulen, Dennis Fok, the participants of the symposium on the Ex-post Evaluation of Competition Policy in Mannheim
(2009) and two anonymous reviewers for their useful comments and suggestions.
2
Senior Member of Staff at the Office of the Chief Economist of the Netherlands Competition Authority and assistant professor at Wageningen University
Netherlands Competition Authority, P.O. Box 16326, 2500 BH The Hague, The Netherlands, R.G.M.Kemp@nmanet.nl
3
Former Junior Member of Staff at the Office of the Chief Economist of the Netherlands Competition Authority, currently a Trainee at the Ministry of Economic Affairs.
4

This case is FTC – Evanston Northwestern Healthcare Corporation, No. 9315 (FTC May 17, 2005).

5

See for example a special issue of the journal Markt & Mededinging, No. 2: April 2009, that was entirely devoted to competition in the health care sector. Other
examples are Kalbfleisch (2009), Reerink (2009), Canoy (2008) and Van Sinderen (2008).
6
See http://www.nmanet.nl/engels/home/News_and_publications/News_and_press_releases/2005/05_17.asp
7
See http://www.nmanet.nl/nederlands/home/Besluiten/Besluiten_2005/5047BCM.asp (in Dutch)
8
The ultimate goal of competition enforcement in the EU and in the Netherlands is to protect consumer welfare (Buccirossi et al., 2006).
9
Comparatively speaking, Germany is a bit further with respect to the introduction of competition in the health care sector than the Netherlands.
10
A MCO is a health organization that finances and delivers health care using a specific provider network and specific services and products.
11
After the introduction of the competitive segment in 2005, competition also became possible with respect to price.
12
For more extensive descriptions of the cases, see for example Janssen et al. (2009).
13
Recently, the parties of the withdrawn merger applied again for a license to merge and this time, the merger was approved with remedies (Besluit 6424/ Ziekenhuis
Walcheren – Oosterscheldeziekenhuizen, 25th March 2009).
14
Besluit 3524/Juliana Kinderziekenhuis/Rode Kruis Ziekenhuis – Ziekenhuis Leyenburg, 28th January 2004, points 46-48
15
Inpatient care concerns treatments whereby patients are admitted to a hospital for more than 24 hours; outpatient care concerns treatments whereby patients are admitted to a
hospital for 24 hours or less .
16
There are other alternatives for the SSNIP test, under which the critical loss analysis, the time-elasticity approach, the competitor share approach, the LOCI and the
option-demand approach. See Varkevisser et al. (2008) and Halbersma et al. (2009) for extensive descriptions of these methods. Competition authorities also make use
of analyses of travel time for market definition.
17
For a more extensive description of the EH test, see for example Gaynor and Vogt (2007).
18
Monitor Ziekenhuiszorg 2008, by NZa.
19
Before the establishment of the NZa, its predecessor, the College Tarieven Gezondheidszorg, advised the NMa on these aspects.

31

20

This is a discussion about Type I and Type II errors. The policy of the NMa appears to be concentrated on the avoidance of Type ! errors: to prohibit a merger that
would actually be pro-competitive. In the US and Germany the policies appear to be more focussed on the avoidance of type II errors: avoiding approving a merger that
would actually be anticompetitive.
21
In contrast, Tenn (2008) and Thompson (2009) do need to control for patient characteristics, like age, sex and type of insurance. This is due to the differences
between the American health insurance system and the Dutch system. We have also employed an analysis in which we corrected the actual prices for patient
characteristics (age and sex), but the results were not different from the results reported in this paper. Moreover, the variable PostMergeri is only useful in the first stage,
in which the price change is estimated. Since we can calculate the price change directly from our dataset, we can exclude PostMergeri.
22
Moreover, our control group is relatively large (387 hospital-insurer combinations), so we do not have the problem that the obtained estimates are imprecise as a
consequence of a small control group (see e.g. Tenn, 2008, p. 13).
23
The variable PostMergeri is only useful in the first stage, in which the price change is estimated. Since we can calculate the price change directly from our dataset, we
can exclude PostMergeri.
24
See Monitor Ziekenhuiszorg 2004, Monitor Ziekenhuiszorg 2005, Monitor Ziekenhuiszorg 2006, Monitor Ziekenhuiszorg 2007, by NZa.
25
Independent treatment centers are small outpatient treatment centers that are allowed to enter the market since 1998. These independent treatment centers are only
allowed to provide elective (no acute) hospital care (Halbersma et al., 2007).
26
We exclude the year in which the merger took place, since we consider this to be a transition year in which the effect of the merger is not yet correctly measurable (see
e.g. Thompson, 2009).
27
We wanted to include the length of the waiting lists as a control variable as well, but this was not possible due to a lack of reliable information.
28
The change of the number of beds may also indicate to what extent the size of hospitals has changed, but this measure only takes into account inpatient cases.
Conversely, the change of the number of medical specialists takes into account both inpatient and outpatient cases. Moreover, the number of beds of the hospitals has
virtually been constant during the period of our investigation.
29
The major part of the treatments in the competitive segment is outpatient cases.
30
To calculate the HHI’s we need to determine the relevant geographical markets. We used the EH-test on patients flows to define the relevant market. For these
markets we calculated the market shares of the hospitals. These resulting market shares are used to calculate the HHI’s for each geographical market (see also
Halbersma, et al. 2007).
31
Inspectie voor de Volksgezondheid (www.igz.nl).
32
This quality measure is at the hospital level. There is no measure of quality of hip surgery available for the whole period under investigation.
33
Only the insurers that have actually insured patients that have undergone hip surgery in one of the merging hospitals in the relevant period are included. Moreover, the
numbers of the insurers are randomly assigned.
34
We also performed the regression for both mergers separately. The results are the same, therefore we feel confident to pool the results.

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The effect of the merger differs per insurer. The results can be found in the Appendix (Table 5). We only include the four insurers for which the equation is statistically
significant. Because the ratio observations to number of variables is relatively small, we have to be careful with drawing strong conclusions based on these results.
36
We also performed the analysis per insurer and this provided us with the same picture: the dummy variables are insignificant for all insurers. In other words, for none
of the insurers, there is a price increase that can be attributed to the merger.
37
Although the NMa did not define the market exactly.
38
Although patients are not price sensitive, we would expect to find an effect on travel behavior, for instance as a consequence of channeling of patients by insurers.
39
In order to calculate the market shares, we have made use of the definition of the small market that the NMa has made in the original decision.
40
See e.g. Roland Berger (2008) and http://www.volkskrant.nl/archief_gratis/article865116.ece/Patient_wisselt_niet_van_ziekenhuis_door_wachtlijst
41
Examples are www.kiesbeter.nl, www.independer.nl and http://rivm.openrepository.com/rivm/bitstream/10029/16490/1/260101003.pdf (for hip surgery).
42
Monitor Ziekenhuiszorg 2009.
43
Besluit 6424/ Ziekenhuis Walcheren – Oosterscheldeziekenhuizen, 25th March 2009.

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