Corrupt Business Practices as a Market Entry Strategy
∗
James Ostler†
February 25, 2016
Abstract
Competition and entrepreneurial activity can drive positive outcomes such as innovation and increased efficiency; however, competition can also lead to unethical behavior. This paper demonstrates
that the competitive disadvantage faced by new entrants can lead to unethical behavior, and in turn, how
opportunities for unethical behavior can enable entrepreneurial activity. Using a policy change to identify
unethical misrepresentation of patient health status to strategically manipulate the allocation of livers to
transplant patients, I find that: misrepresentation was higher in more competitive markets, entrants were
more likely than incumbents to misrepresent patient health status, entry significantly decreased after the
policy change when firms no longer had the opportunity to misrepresent patient status, misrepresenting
patient status increased performance and survival rates of new entrants, and incumbents that did not respond to the threat posed by entering firms by adopting a similar strategy to misrepresent patient status
were more likely to exit.
1
Introduction
Entering a new market is one of the most difficult and risky challenges a firm can face, and new firms
often have to resort to different strategies than incumbents in order to compete (Geroski, 1995). Often this
is in the form of cost competition or trying to innovate their way to higher quality. This is one of the reasons
why entrepreneurship and small firms are often associated with higher levels of innovation (Audretsch, 1991;
Acs and Audretsch, 1988) and price competition (Bresnahan and Reiss, 1991). However, there also exists a
darker side of competition when it drives unethical behavior. (Cai and Liu, 2009; Münster, 2007; Shleifer,
2004; Bennett et al., 2013). Firms that are trying to enter a new market and are at a competitive disadvantage
to incumbents may resort to corrupt business practices (unethical or illegal behavior) in order to improve the
likelihood of success. In these cases, the potential benefit of unethical behavior for new entrants can override
both the moral costs as well as potential financial and reputational risks associated with being caught. Just
as opportunities to innovate can spur entry, can opportunities for illegal and/or unethical behavior enable
entry into highly competitive markets?
While the role of competition in driving unethical behavior of existing firms has been examined by
measuring competition as the number of competitors a firm faces in a given market (Shleifer, 2004; Snyder,
∗I
am grateful to Jason Snyder, Florian Ederer, Marvin Lieberman, Suzanne Shu, Lynne Zucker, Sara Parker and Vanessa
Burbano, as well as seminar participants at the University of Michigan. This research was supported by The Harold and Pauline
Price Center for Entrepreneurial Studies at the UCLA Anderson School of Management.
† Ross School of Business, University of Michigan, 701 Tappan Street, Ann Arbor, MI 48109, [email protected].
2010; Cai and Liu, 2009; Bennett et al., 2013), the effect of competitive pressures on new entrants in leading
to unethical behavior is less developed. As a consequence, the role of unethical behavior in enabling entrants
to enter new markets and the related impact on the performance of both new entrants and incumbents in
these markets has not been studied. The underdevelopment of the relationship between entry and corrupt
business practices is surprising as new entrants not only face higher competitive pressures than incumbents;
they also generally have less to lose than incumbents if caught. Consistent with this logic Branco and
Villas-Boas (2012) theorize that new entrants will not put forth as much effort to comply with market rules
when under competitive pressure, and Bennett et al. (2013) empirically find support for this proposition in
the vehicle emissions testing market finding that entrants respond more strongly to competitive pressures
than incumbents. Thus, the presence of corrupt business strategies as a strategic option for firms to pursue
should disproportionately benefit new entrants who are more likely to use and benefit from these strategies
over incumbents. However, the question remains: can unethical strategies really enable successful entry
under conditions that would otherwise lead to failure? If so, what are the implications for performance of
both the new entrants as well as the incumbents that will now face increased competition?
The questions above are in stark contrast to the way that entrants and entrepreneurs are usually viewed;
that is, as entities that fulfill the role of the “invisible-hand” driving towards economic efficiency and that
promote innovation (Schumpeter, 1994). Instead of bringing about creative destruction, entrepreneurs may
actually introduce socially destructive behavior if the same competitive pressures that drive entrepreneurs to
take risks on new innovations can also drive new entrants to take ethical risks in the form of socially destructive behavior (Ostler, 2012). Perhaps one of the reasons that this potentially darker side of the relationship
between competition and entrepreneurship is less salient is that while firms market and promote their latest
innovations, corrupt behavior is by its very nature buried deep and hidden from sight. This poses obvious
challenges for empirical work, but utilizing a policy change in how livers were allocated to individuals that
needed a liver transplant to identify strategic manipulation of intensive care unit (ICU) enrollment in the
liver transplant market (Snyder, 2010) I am able to identify when liver transplant centers misrepresented
patient health status to manipulate the liver transplant waiting list and increase their own transplant volume
by moving patients to the top of the waiting list.
This paper utilizes this change in policy regarding how livers were allocated to offer strong evidence
that: (1) the competitive pressure faced by new entrants can lead to unethical behavior resulting in new
entrants resorting to unethical behavior more often than incumbents, (2) the availability of an unethical
strategy can lead to increased market entry, (3) entering firms that pursue this unethical strategy experience
increased performance and survival rates, and (4) incumbents that do not adopt similar unethical behavior
have increased risk of exiting from the market compared to incumbents that adopt the unethical behavior.
This paper is the first to my knowledge to empirically demonstrate findings (2), (3) and (4) from above and
re-affirms findings in the vehicle emissions testing market by Bennett et al. (2013) that are similar to finding
(1). These findings make an important contribution to the literature on the relationship between competition
and unethical behavior by demonstrating the important role that entry and the associated competitive pressures can have on unethical behavior. Further, these findings contribute to our understanding of firm entry
strategy with regard to entrepreneurial opportunities. Lastly, this paper demonstrates that, just as competi-
2
tion and entry can improve the efficiency of a market by driving out inefficient firms, if unethical behavior
improves performance then competition and entry can drive out firms that do not adopt unethical practices,
thus lowering the overall ethical composition of the firms in a market.
2
Literature and Theory
The idea that competition may encourage firms to engage in unethical or illegal behavior has been
discussed within various contexts. For example, Shleifer (2004) contends that competition drives firms to
unethical behavior and cited cases of child labor, corruption in the form of bribing officials in developing
countries, high executive pay, earnings manipulation, and commercialization of education to argue his point.
In an earlier study looking more specifically at influences that lead to illegal behavior, Staw and Szwajkowski
(1975) argue that the scarcity-munificence environment that competition creates will drive firms to adopt
illegal practices. Their study identified examples of firms across many industries that performed illegal acts
and found support for their hypothesis.
Empirical support of these arguments has also been found in the organ and heart transplant markets
(Scanlon et al., 2004; Snyder, 2010) as well as the vehicle emissions testing market (Bennett et al., 2013). At
a more aggregate level of analysis Cai and Liu (2009) look at the specific issue of tax avoidance of Chinese
firms, and concluded that competition drives firms to misrepresent earnings. Further, they demonstrated
that firms in more disadvantaged positions are more likely to try to illegally misrepresent profits to avoid
taxation. Similar findings have been found in experimental laboratory work. Schwieren and Weichselbaumer
(2010) found that competition increases cheating, and that participant ability predicts who will cheat, with
those individuals with lower ability for the task are more likely to cheat. In a similar study, Harbring et al.
(2004) looked at sabotage in tournaments, and found that participants at a disadvantage, defined as a less
advantageous cost curve, are more likely to turn to sabotage and a strategy to compete in the tournament.
With the exception of Bennett et al. (2013), the competitive disadvantage of firms or individuals has
been between incumbent firms or the population of individuals in a study and not new entrants. However,
the competitive disadvantage faced by new entrants is one of the most common and discussed aspects of
market entry. Most new entrants do not survive, and even those that do survive generally take years, if ever,
to approach the volume and or profitability of incumbents (Geroski, 1995). Further, while new entrants can
attempt to compete on price (Bresnahan and Reiss, 1991), advertising (Sutton, 1991), or quality (Hung and
Schmitt, 1988), often these options are not available for new entrants. Incumbents are better funded as well
as more experienced, and entrants are left without any traditional dimensions within which they can successfully compete against established incumbents. This often leads to non-traditional strategies such as “judo
economics” (Gelman and Salop, 1983) where entrants try to avoid direct competition with incumbents by
limiting capacity or finding market niches in which incumbents do not compete. In fact, due to the competitive disadvantage new entrants face there is no room for entrepreneurship in markets that are in traditional
economic equilibrium (Eckhardt and Shane, 2003). The traditional role of entrepreneurs is to move markets
back into equilibrium when incumbents are inefficient (Schumpeter, 1994). These inefficiencies can arise
due to developments such as technological changes (Shane, 2000) and changes in the institutional environment (Hiatt et al., 2009). Strategies that allow entrants to successfully exploit these market inefficiencies
3
and enter markets act as entrepreneurial opportunities. However, to take advantage of an opportunity potential entrants must both be aware of the opportunity as well as possess the necessary capabilities to take
advantage of the opportunity (Alvarez et al., 2013).
This paper unites the findings from literature on the effects of competition on corrupt business practices
with literature looking at entry strategies and how new firms compete. The bridge that allows the connection
of these literatures is twofold. First, the same competitive forces that drive firms or individuals in disadvantaged positions to be more likely to perform illegal acts can also drive new entrants to adopt corrupt
practices. Second, unethical and illegal behavior can be an opportunity for entrants to effectively enter and
compete with established incumbents that may not be able, or willing, to adopt the same practices.
By bridging these literatures this paper enrichens what we know about both literature streams and
strengthens previous findings. For example, in a study using the same industry setting as this paper, Snyder (2010) also considered how competition influenced the manipulation of patient health status by liver
transplant centers. Snyder (2010) found that unethical misrepresentation was positively associated with how
competitive the regional market a center was in. This paper builds on that work by looking at competition in
more than a singular dimension regarding the number of competitors a center faces. It also considers the fact
that new entrants are at a disadvantage against established incumbents. This paper complements Snyder’s
findings by affirming support for his conclusion that competition (defined as multiple firms in a market)
drives unethical misrepresentation, as even when controlling for the increased propensity of new entrants
to misrepresent patients health, the absolute number of firms in a market still has a significant impact in
increasing unethical behavior.
In the only study I am aware of to look at new entrants and if they are more likely to perform unethical
or illegal acts due to competition with incumbents, Bennett et al. (2013) find that in the vehicle emissions
testing market that the effect of competition in increasing the likelihood of misrepresenting emissions data
is stronger for new entrants than incumbents. They show that in the absence of competition new entrants
are less likely to misrepresent emissions data, but new entrants react more strongly and are more likely to
increase misrepresentation when under competition. This paper builds on and furthers their analysis being
that they did not look at entry decisions or the impact on survival and the implications on current incumbents
due to new entrants. In contrast, the focus of this paper is the impact of both the availability and the choice
of unethical behavior on the entry decision and performance of these new entrants and incumbents. I show
that the option to use unethical behavior to level the playing field can be an entrepreneurial opportunity
with higher and more successful entry when the regulatory regime allows centers to gain an advantage by
misrepresenting patient status through listing patients in the ICU. Further I demonstrate that performance
of new entrants is improved and increases with the level of patient misrepresentations. Lastly, I show that
the increased competition resulting from new entrants that are willing to cheat increases the exit hazard of
threatened incumbent firms, if they do not react to the new entrants by adopting similar unethical strategies.
3
Empirical Setting: The Liver Organ Transplant Market
There are currently approximately 6,500 liver transplants per year in the US market with the highest
volume centers performing well over 200 liver transplants per year. The liver transplant market is a poten4
Figure 1: Map of Organ Procurement Organization (OPO) boundaries and transplant centers in the US
tially lucrative market for hospitals with transplant centers. Each liver that is transplanted represents around
$600,000 of revenue for the hospital performing the transplant with the most recent estimate of total patient
costs being $739,000 per liver transplant, representing an approximate $4.5 Billion/year market1 . Competition over obtaining viable livers is high and each year thousands of individuals die while on the waiting list
for a new liver. Due to concerns of equity in access to lifesaving livers the allocation of livers for transplant
is highly regulated and the Organ Procurement and Transportation Network (OPTN) has legal authority over
the distribution of available organs.
The OPTN is organized geographically with all hospitals belonging to one, and only one, regional OPO
(organ procurement organization). The boundaries of these OPOs are determined by the OPTN. When an
organ becomes available transplant centers within the OPO that the liver became available have first rights
to the organ. Allocation of the organ is done according to priority on a waiting list within the OPO, and the
potential match based on blood type. Centers have the right to refuse a liver if they feel it is better to wait
for a healthier liver for their patient. Figure 1 shows the boundaries of these OPO’s.2 There is significant
variation in the number of centers within an OPO with OPOs having anywhere from 1 to 7 centers within its
boundaries. Each OPO is nested within an organ transplant region (there are 11 regions in the United States)
and if no match or claim is made on the organ within the OPO then transplant centers within the appropriate
region may have access to the organ. Over 75% of all livers remain within the original OPO and virtually
5
all remain within the region during the time period used in this paper.3
Competition over livers impacts centers in two different ways. First, each liver is competed over so either
a center “wins” the liver or they do not. Second, centers must reach a certain numerical threshold of livers
to be competitive. The reason for this is that due to learning effects a centers ability to perform successful
transplants is largely dependent on the volume of transplants they perform both cumulatively as well as on
a yearly basis. For this reason Medicare requires a center to perform a minimum of 10 transplants per year
in order to receive certification4 , and some state requirements, such as those New Jersey call for 15 or more
per year. In New Jersey a center that fails to meet that threshold within two years after entry will not receive
certification and licensing. Further, this is the minimum volume needed to be certified, it does not guarantee
profitability or survival. Centers with higher volume benefit from lower costs due to learning and high fixed
costs and have lower rates of mortality and severe complications which potential patients will use to select a
center to perform their transplant. In this way centers not only compete over individual livers and patients,
but they must meet a certain level of performance or exit the market. This required level of performance
is obviously most important to new entering centers as established centers (incumbents) generally are able
to meet these performance levels. However, even for large established centers, competition over individual
livers can drive risk taking behavior as each liver represents significant revenue.
The liver transplant market offers a unique opportunity to identify unethical behavior. In 2002 the rules
determining the allocation of livers changed. Before 2002 if an individual was in the intensive care unit
(ICU) they would receive priority and could jump ahead of others on the liver waiting list. Centers could
improperly, yet strategically, place relatively healthy individuals on the ICU list even while they went about
their daily life and “according to the Chicago Tribune, [patients in the ICU] . . . spent weekends at home,
one acted the part of a clown at a blood drive, and another was at a restaurant having dinner when he got
word that a suitable liver had been located. Authorities alleged that one patient on the list was not even
eligible for transplantation” (Murphy, 2004). A high profile case involved the University of Illinois, which
was sued by Medicaid and had to pay a fine of two million dollars for their unethical misrepresentation of
patients on their ICU list 5 . The investigation into this case was initiated by the former head of the abdominal
transplant program at the University of Illinois, Raymond Pollack. According to Dr. Pollack, he attempted
to go public with his concerns, but they were dismissed by the dean of the school. Dr. Pollack claimed
that, “the transplant program falsified health status in order to increase revenue and profits” and that “the
university’s motive was to reach the threshold number of transplants required in order to qualify for Medicare
and Medicaid reimbursement” (Murphy, 2004). However, on March 1st, 2002 the liver allocation process
changed such that livers were allocated according to a patient’s Model for End-Stage Liver Disease (MELD)
score which only relies on clinical indicators of a patient’s sickness such as serum bilirubin and serum
creatinine directly measured from blood tests, and patient ICU status no longer impacted liver allocation
1 http://www.transplantliving.org/before-the-transplant/financing-a-transplant/the-costs/
2 Source
data found at http://www.srtr.org/
the OPTN has tried to allow for sharing across OPOs within regions for sicker patients. Experimentation around this
model has been predominately since 2011, and so does not impact the years our analysis covers.
4 http://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/GuidanceforLawsAndRegulations/- Downloads/TransplantFinalLawandReg.pdf
5 http://www.justice.gov/usao/iln/pr/chicago/2003/pr111703_01.pdf
3 Recently
6
after this point in time. In this paper the new policy for allocating livers is referred to as the MELD policy,
with the time before the policy change being the pre-MELD era and the time after the post-MELD era.
67
The liver transplant market also offers a good setting to look at entry with the number of transplant
centers increasing over time along with the total number of transplants. At the same time, entrants have
to overcome competing at a disadvantage against incumbents with barriers to entry due to the regulatory
regime effectively creating a barrier to entry with a minimum efficient scale and a significant learning curve
that favors incumbents over new entrants (Lieberman, 1987). Further, the liver transplant market offers a
rare opportunity to identify the pool of potential entrants prior to entry decisions. Centers begin performing
simpler organ transplants first and then over time expand into more complicated organs. A transplant center
may perform simpler transplants such as heart, lung, or kidney transplants. In particular, kidney transplants
are a stepping stone to get into the liver transplant market as they are much easier to perform and less risky.
This condition allows considering current kidney transplant centers as the pool of potential liver transplant
center entrants. In addition, all centers may not know about the opportunity to enter, or be willing to take
advantage due to ethical concerns and/or fear of legal repercussions. This is similar to entrepreneurial
opportunities where awareness as well as ability to take advantage of the opportunity are prerequisites to
success.
4
Data
The data for the analysis in this paper come from the UNOS (United Network for Organ Sharing) and
include all liver and kidney transplants for the years of 1990-2009.8 The data contain patient health and
demographic characteristics and identify the center ID of where the transplant was performed. The data
also contain a dummy variable of if the liver transplant occurred before or after the policy change to cleanly
identify differences before and after the policy change. Summary statistics of the data used can be seen in
Table 1.
Some pediatric hospitals will share a transplant center with its associated general hospital. In these
cases this is coded as a single center.9 There are two veterans hospitals that share a transplant center with an
associated general hospital and two hospitals that use an experimental shared services arrangement that are
6 Whether or not patient misrepresentation is actually illegal is not clear. Hospitals have the freedom to diagnose and treat patients
as they judge appropriate and the health profession clearly operates under a code of ethics and trust, but there are also legal actions
that can be taken to ensure compliance to ethical norms as the prevalence of medical related lawsuits can attest to. In the case of
livers this can be in the form of centers losing medicare/medicaid funding, being sanctioned by governing organizations (OPTN), or
by being sued in the courts of law. Penalties can be quite severe. For example, when St. Vincent Hospital in Los Angeles, California
misrepresented a patients identity to give a liver to a patient lower on the waiting list it was forced to shut down its transplant center
and the responsible surgeon was indicted by a federal grand jury. (http://articles.latimes.com/2006/mar/03/local/me-stvincent3,
https://www.fbi.gov/losangeles/press-releases/2010/la010610.htm)
7 It is important to note that I do not take a specific stance on the social welfare of the impact of this behavior in the liver
transplant market. This is both because it is out of the scope of this paper, as well as it is not clear what welfare maximizing would
be in this context. Whether a liver should go to the sickest patient, to healthy patients that have longer to live or to patients that have
others who are dependent upon them is the subject of intense debate. I only highlight what can be identified as strategic decisions
that are contrary to established ethical and/or legal norms.
8 The data reported here have been supplied by the United Network for Organ Sharing as the contractor for the Organ Procurement and Transplantation Network. The interpretation and reporting of these data are the responsibility of the author and in no way
should be seen as an official policy of or interpretation by the OPTN or the U.S. Government.
9 This is similar to how children’s hospitals were handled in Snyder (2010)
7
Table 1: Characteristics of the Liver Transplant Market
Year
# of
Transplants
Performed
# of Liver
Transplant
Centers
# of Active
Entrants
# of Centers
that Exit
# of OPOs
# of OPO’s
with 2+
Centers
# of OPO’s
with 4+
Centers
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2,678
2,953
3,064
3,440
3,652
3,932
4,079
4,182
4,514
4,746
4,990
5,189
5,324
5,662
6,158
6,439
6,647
6,489
6,310
6,291
65
74
81
86
92
90
98
100
102
100
101
98
99
101
100
104
102
105
107
103
0
0
9
11
16
17
24
27
29
30
30
29
30
32
32
36
33
36
38
37
1
1
2
0
4
1
3
3
3
1
4
0
0
2
1
3
1
0
0
1
41
44
46
47
49
49
50
50
50
50
51
50
50
50
50
51
51
52
52
52
15
19
24
24
26
25
25
24
27
26
26
27
27
28
28
29
29
29
29
27
1
2
2
5
6
5
6
6
6
6
6
6
7
7
7
7
6
6
7
7
coded as one center in a similar way as the children’s hospitals10 .
One difficulty faced with respect to the data is deciding on the time window to use for the analysis.
In a previous paper using the same industry context Snyder (2010) used data one year before or after the
policy change, but this short of a horizon is not possible when looking at entry. A bigger window is needed
to capture sufficient entry and exit; further, entrants could easily enter, misrepresent patient status until
they establish enough volume to be viable and then cease unethical behavior before the policy change. To
capture such behavior a larger window is required. I generally use a window of seven years before and
after the policy change as this allows for a time frame with sufficient entry and exit. Using a larger window
introduces the potential for other unrelated events to influence the data. For each analysis I discuss the
particular constraints and potential biases that are specific to that analysis and then discuss the strategies I
use to control for any potential bias. As each analysis faces different potential problems, and subsequently
different strategies to address these issues, I leave the discussion regarding how I deal with these issues to
the related section for that analysis.
Another critical aspect of looking at entry is defining which firms should be considered as entrants in
the context of this market. For example, in a related paper Bennett et al. (2013) consider any firm an entrant
if they have been in business for less than one year. While that definition worked for their context of vehicle
emissions testing centers, it does not work in the context of the liver transplant market due to the time it takes
to get down the learning curve which influences both a firm’s competitive position and regulatory policies
around entry such as how many years a center has to reach certain volume. Using only a time dimension in a
10 For information about the shared services agreement see http://healthleadersmedia.com/content/HR-255499/AHCA-Browards-
Liver-Transplant-Program-Receives-CON##
8
study presents challenges as some centers may struggle for years and never quite make it and end up exiting
after many years, while another center may quickly gain all the volume needed to compete as an incumbent.
Similarly, only looking at volume will not work as it may be that the only reason a center is above a certain
volume threshold is due to the center’s aggressive strategy of misrepresenting patient status, in which case
the true volume driving decisions and the center’s true competitive position is hidden by the very actions the
study is trying to identify. Further, if a center is the first to enter an OPO then due to the structure of the
market they could be competing as a monopolist for that geographic area and not face competitive pressures
from incumbents.
To accommodate the unique characteristics of the liver transplant market this study categorizes centers
as incumbents or entrants in two steps. First any center that exists previous to 1992 or is the first to enter
in their respective OPO is considered an incumbent. Second, this study considers the five years previous to
the policy change and any center that has greater than 66% market share over that time period in a given
OPO is considered an incumbent in 2001. This assures that the categorization of the centers is most accurate
with regard to the policy change. Although this may cause some early entrants to be considered incumbents
it is a conservative categorization that is biased against finding entrant specific behavior differences. The
results are robust to various different interpretations of incumbent such as changing the year and market
share cutoffs and a blind judgment categorizing firms as entrants or incumbents performed by an individual
that had not seen any other related data which could bias the classifications. I use the definition I do for
its consistency with the market context, simplicity in computational transparency and clarity, the fact it is
a conservative measure that would bias against the study’s findings and that it most closely represents the
blind judgment. Some analyses that only examine market entrance and subsequent survival do not rely on
the categorization of centers as an entrant or incumbent. In these cases I take the year of the first liver
transplant a center performs as the entry date.
5
Empirical Approach and Analysis
The empirical analysis consists of several steps to examine how new entrants may use unethical be-
havior to compete with established incumbents. I first test whether or not new entrants are more likely to
misrepresent patient health status by estimating how firms react to the change in policy paralleling the approach used by Snyder (2010), except for the analysis is at the center level instead of the OPO level. I then
estimate a model of entry comparing entry rates before and after the policy change. Next, I examine the
impact of misrepresenting patient health has on performance outcomes of centers. To estimate the impact
on performance I use a two-stage estimation approach. The first step uses the time period after the policy
change to develop an estimation of the likelihood a patient is in the ICU given their current health status as
measured by clinical health measures, demographic and geographic controls. I then use this estimation to
compute the expected ICU use of all centers by year. In the second stage I then use this measure to look
at how deviation from the expected ICU use impacts firm performance as measured by the number of liver
transplants, survival of entering firms and likelihood of exit for any existing center. In these cases, where it
is possible, I compare how the impact of deviations on performance differ before and after the policy change
to control for other systematic reasons why certain centers may deviate from the expected ICU use other
9
than unethical behavior enabled by the specific allocation policy addressed in this paper.
The linear probability model is used as the main specification for the empirical analysis11 . The reasoning
for this is due to various factors. First, interactions and fixed effects are essential to the research design, and
the problems with non-linear models in these cases are well documented (Katz, 2001; Wooldridge, 2010).
Second, in some cases such as entry and exit the smaller number of outcomes often leads to problems
similar to rare event studies (King and Zeng, 2001) as well as to perfectly determined outcomes resulting
in models that will not converge or that are biased. Third, in this context the major concerns with LPM do
not apply: the reported analyses do not make predictions; we only care about average effects; there is low
risk of miss-classified dependent variables (virtually no risk in the case of entry and exit) and asymptotic
inconsistency is irrelevant given the sample sizes. That said, all reported findings were also tested using
non-linear Logit and Probit models where possible and the results are robust and consistent with the reported
LPM results. Non-linear models are also used where appropriate such the cox hazard model to estimate
entering firms’ survival rates. In all models standard errors are clustered by OPO. 12
5.1
Likelihood of Patient Misrepresentation
Figure 2 illustrates that the number of transplant patients coming from the ICU dropped sharply for all
firms after the MELD policy was implemented in 2002, and that the drop was bigger for entrants.13
Table 2 reports the results of the probability that a patient receiving a liver transplant comes from the
ICU. Columns (1) and (2) show that incumbents were significantly less likely to use the ICU before the
policy change, whereas there is no significant difference between incumbents and new entrants after the
policy change. Columns (3) and (4) show the difference between columns (1) and (2) is significant with new
entrants having a larger decrease in ICU use after the policy change (MELD era=1 after the policy change).
Year and center fixed effects are used as well as patient demographic and health controls. Demographic
controls used include race, age and gender. To control for patient health I include controls of whether the
patient is on life support, if the liver came from a live donor, and the patients computed MELD score. I also
include controls for the volume of transplants performed each year.
14
All controls are interacted with the
MELD era dummy whenever both time periods are used in the model.
One concern is that larger firms are simply less likely to manipulate the ICU list. Given that all liver
transplant centers are part of large hospitals and are already operating at a minimum as kidney transplant
centers makes this unlikely. Column (5) confirms that center size does not drive the findings for new entrants.
Next, the difference for entrants could be the result of the markets they are entering having a higher
number of competitors, and that they act no differently than the incumbent firms when faced with a similar
11 This
is consistent with Snyder (2010) and allows for easier comparison of results across the two studies.
do find that the potential issue of serial correlation is a major issue as the analysis is at the center and/or OPO level leading
to all results being much less significant once clustered errors are used. Clustering is done at the OPO level rather than center level
as it is more conservative than clustering at the center level since it is a higher level of clustering with centers nested within OPOs
(Cameron and Miller, 2015).
13 The policy was changed March 1st, 2002. Figure 2 adjusts for this and includes patients in the first 2 months of 2002 with
2001.
14 The meld score was computed following the same procedure as in Snyder (2010).
12 We
10
Figure 2: On average entrants ICU use was higher and dropped more after the MELD policy was implemented in 2002 compared to incumbents
Note: transplants that were flagged as occurring the first two months of 2002 and under the old policy are included in the calculation
of the 2001 percentages.
number of competitors within an OPO. Column (6) shows that while controlling for the number of centers
in an OPO new entrants are still more likely to have patients come from the ICU. This finding supports the
previous work in this setting by Snyder (2010) in that it shows that his results were not driven by the fact
that OPOs with more centers are likely to have more entrants, but rather that competitive pressures arising
from both the number of firms in a market as well as the disadvantage faced by new entrants can lead to
unethical behavior at the same time and in the same market.
Column (6) indicates that on average entrants have 7.3% fewer patients coming from the ICU after the
policy change, and each additional firm leads to 2.01% fewer patients from the ICU after the policy change.
Table 8 in the appendix shows that these results are robust to different time windows and using a nonlinear
logit specification.
5.2
Entry
While the results indicate that entrants are more likely to use the ICU before the policy change, whether
or not firms’ entry decisions were influenced by the opportunity to do so is not clear. As previously noted
the liver transplant market offers a unique setting to evaluate entry decisions as it is possible to identify all
potential entrants. Centers will start by performing more simple organ transplants at first and then over time
expand into more complicated organs. Similar to how Lee and Lieberman (2010) show new entrants can to
use a “stepping stones” strategy to enter a new market by making interim acquisitions into nearby markets as
a way to enter into a target market, performing kidney transplants is a stepping stone to develop the expertise
11
Table 2: ICU use by new entrants and centers in highly competitive OPOs
New Entrant
(1)
ICU
0.075∗∗∗
(0.028)
(2)
ICU
-0.003
(0.013)
MELD era x
new entrant
(3)
ICU
0.094***
(0.028)
(4)
ICU
Absorbed
(5)
ICU
Absorbed
(6)
ICU
Absorbed
-0.100∗∗∗
(0.031)
-0.104∗∗∗
(0.034)
-0.098∗∗
(0.030)
-0.073∗∗∗
(0.028)
0.011
(0.018)
0.016
(0.024)
MELD era x center
volume (x100)
-0.021∗∗
(0.009)
MELD era x
firm count by OPO
MELD era
Year fixed effects
Region fixed effects
Center fixed effects
Demographic controls
Patient health controls
Observations
Clusters
Pre
Yes
Yes
No
No
No
31788
54
Post
Yes
Yes
No
No
No
43923
54
Both
Yes
Yes
No
Yes
Yes
75711
54
Both
Yes
Absorbed
Yes
Yes
Yes
75711
54
Both
Yes
Absorbed
Yes
Yes
Yes
75711
54
Both
Yes
Absorbed
Yes
Yes
Yes
75711
54
All controls are interacted with MELD era, and main effects are included in all specifications
with interactions. Standard errors in parentheses. SEs clustered at the OPO level.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
and credibility to perform the more difficult liver transplants and move into the liver transplant market. In
all cases of entry during the time period represented by the data liver transplant centers entered into kidneys
first.15 The number of kidney centers is relatively stable with 232, 242, and 242 centers in 1992, 2001 and
2009 respectively. This allows us to consider the current kidney transplant centers as the pool of potential
entrants into liver transplants, and to take into account characteristics of the kidney center in evaluating the
likelihood of entry.
One difficulty in identifying entering firms is determining how to deal with centers that exited and
reentered years later. I perform the analysis both looking at only the first entry point of a firm as well as
considering a re-entering firm that does not perform any transplants for two years as a new entrant when
they resume performing transplants. The reported analysis is based off of using only the first entry point,
but the results are robust to either measure of entry.
When firms enter the market it is a forward looking decision. Thus, for this analysis the announcement
date of the new policy, July 16, 2001, was used as the before and after cutoff instead of March 1, 2002, the
date the new policy was implemented16 . The results are robust to either classification.
15 There
were two cases when centers entered without doing kidneys first, but in both cases the “new” center was really an
extension of an existing center. These cases are not coded as entry in my data as they were seen as part of another center. One
was the special “shared services” center mentioned in footnote nine and the other a VA hospital mentioned in the data section. My
results are robust to whether or not I consider these centers as independent or not.
16 http://optn.transplant.hrsa.gov/news/newsDetail.asp?id=15
12
Figure 3: The number of firms entering the liver transplant market drops after the MELD policy is introduced
even though the total number transplants performed continues to increase.
Figure 3 illustrates that the number of entering firms as a percent of potential entrants dropped after the
MELD policy was introduced even though the overall number of transplants was steadily increasing.
A simple test of whether entry significantly changed after the policy announcement is shown in column
(1) of Table 3. It shows that entry was significantly lower after the policy was announced. However, this
result could be biased by omitted variables that would lead better centers to enter early, or that entry changes
due to higher existing concentration of firms in OPOs in later years. To address these concerns and to
see the impact that the policy change had compared to other factors that could influence entry I estimate a
model of entry including both center and market characteristics. To capture relevant characteristics of the
market I create several variables. As measures of the competitive environment within an OPO I calculate
the following for each year: HHI as the Herfindahl–Hirschman Index at the OPO level; OPO count as the
count of the number of centers in the OPO; concentration intensity as HHIxOPO count; OPO volume as
the count of liver transplants performed; average center volume by OPO as OPO volume/OPOcount and
volume increase by OPO as the year over year change in OPO volume. All of these measures with the
exception of volume increase are lagged by one year to determine the conditions when the entry decisions
were made and to avoid issues with the entry influencing these values in the year entered. The variable
volume increase is not impacted by a firm entering as demand is independent of the centers and this variable
is the best representation of the expected future volume for entry decisions. A center specific variable of
kidney volume representing the yearly count of kidney transplants performed by a center is also included.
Other center specific variables such as center prestige, age etc were tried but the volume of transplants
seemed to capture practically all of the information from these other variables; thus in the end only kidney
volume was included.
13
The impact of the policy change increases in both magnitude and significance when market characteristic
variables are included as shown in column (2) and increases even further when including kidney volume in
column (3).
Table 3: Entry into the liver transplant market
(2)
Entry
-0.017∗∗∗
(0.006)
(3)
Entry
-0.019∗∗∗
(0.006)
(4)
Entry
-0.018∗∗∗
(0.006)
(5)
Entry
-0.018∗∗
(0.008)
(6)
Entry
-0.003
(0.005)
(7)
Entry
-0.009
(0.008)
(8)
Entry
-0.020∗∗∗
(0.007)
Lagged HHI
0.073∗∗
(0.029)
0.070∗∗
(0.029)
0.036
(0.030)
0.050
(0.038)
0.054
(0.035)
0.084∗∗
(0.035)
0.056∗∗∗
(0.17)
Lagged average center
volume by OPO (x100)
-0.034
(0.023)
-0.030
(0.023)
0.002
(0.025)
-0.008
(0.029)
-0.022
(0.025)
-0.051∗
(0.031)
-0.008
(0.008)
Lagged OPO count
0.006∗
(0.004)
0.007∗
(0.004)
0.008∗∗
(0.004)
0.011∗∗∗
(0.003)
0.003
(0.004)
0.009∗
(0.005)
0.008∗∗∗
(0.002)
Lagged OPO volume
(x100)
0.017∗
(0.009)
0.014
(0.009)
-0.003
(0.011)
-0.005
(0.011)
0.011
(0.009)
0.017
(0.014)
0.00411
(0.040)
Volume increase
by OPO (x100)
-0.009
(0.013)
-0.006
(0.013)
0.005
(0.015)
0.010
(0.017)
-0.014
(0.012)
-0.014
(0.019)
0.001
(0.011)
Lagged Concentration
Intensity
-0.043∗∗
(0.018)
-0.040∗∗
(0.018)
-0.019
(0.018)
-0.023
(0.025)
-0.035
(0.022)
-0.045∗
(0.024)
-0.029∗∗∗
(0.009)
0.042∗∗∗
(0.013)
0.029∗∗
(0.013)
0.018
(0.016)
0.033∗∗
(0.012)
0.050∗∗
(0.019)
0.028∗∗∗
(0.008)
2001
+/- 7yrs
OLS
OPO
58
2609
2001
+/- 5yrs
OLS
OPO
58
1635
2001
+/- 3yrs
OLS
OPO
58
972
1998
+/- 5yrs
OLS
OPO
58
1554
2005
+/- 5yrs
OLS
OPO
58
1534
2001
+/- 7yrs
Logit
OPO
58
2609
Meld era
(1)
Entry
-0.015∗∗
(0.006)
Lagged kidney volume
(x100)
Meld Start Date
Range
Specification
Clustered SE level
Number of Clusters
Observations
2001
+/- 7yrs
OLS
OPO
58
2609
2001
+/- 7yrs
OLS
OPO
58
2609
Standard errors in parentheses. For logit specifications, marginal effects at the average values are taken.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
The model is highly dependent upon the date of the policy change being the main driver. To ensure that
other time trends are not driving the results I look at other time windows around the policy change as well as
other years for the “policy change”. Columns (4) and (5) show the impact of the policy change is robust to
shorter time windows. Columns (6) and (7) show that setting the before and after date for the policy change
to either 3 years before or after the correct date result in the loss of significance for the impact of the policy
change supporting the finding that it was the policy change and not other events or a time trend that leads
to our finding a significant impact of the policy change on entry. The finding that the policy change had a
significant impact on entry is also robust to non-linear specification as shown by the logit specification in
column (8).
One concern could be that the coefficients from Table 1 indicating an approximate increase in ICU use of
around 7% would not be enough to drive the change in the entry rate. However, that is an average measure,
and we are only concerned about entry at the margin. To get a better grasp of whether strategic manipulation
14
of ICU enrollment could have helped new entrants I looked at those firms that had the biggest changes
between the pre and post policy eras. If firms are ordered by the largest to smallest drop in the percent of
patients coming from the ICU after the new MELD policy was implemented the results demonstrate that
the top 5% (which all happen to be entrants) have a drop of 39%. If the top 10% are considered there is
still an average drop of 33%. If the top 5% are considered individually and the comparison is between a
new entrant’s change in the rate of ICU use and that of the incumbent they were competing against we can
get a better idea of how an new entrant could benefit. The firm with the biggest change entered 4 years
previous to the policy change and average 17.8 transplants per year and had a pre/post ICU rate that was 48
percentage points higher than the incumbent which averaged 192 transplants per year.17 A very crude and
aggressive measure of the potential benefit of misrepresentation would be to assume that the 48 percentage
point difference let to 48% more transplants. If this were true it is easy to see that losing approximately half
of the transplants would have put the new entrant at an average of 8.9 transplants per year which is below
the Medicare threshold. On the other hand, the larger center would probably not notice a difference of 8-9
transplants in a year given that would be well within the normal fluctuation for a center of that size. Further,
the larger center, averaging 192 transplants per year, may not be willing to risk potential sanctions and
lawsuits over 8-9 transplants. On the other hand, if 8-9 transplants are the difference between surviving and
exiting, the potential sanction on the center would be less of a concern. I do not want to imply that this is the
correct interpretation of this specific case, but I do want to help illustrate how strategic misrepresentation
could operate as a mechanism that would enable entering centers to be competitive that otherwise would
not be. Along these lines it is interesting to note that some of these entrants that were in the category of
the top 5% of firms ICU use drop after the MELD policy were among those that exited shortly after the
policy change. The next section explores with more empirical rigor how firm performance was impacted by
gaming the system.
5.3
Improving Firm Performance through Misrepresentation
The analysis so far has been at an industry level of analysis either looking at entrants as a group, or at industry
level patterns of entry. In order to evaluate the impact of misrepresenting patient status on individual firm
performance and to determine anything beyond simple differences of between before and after the policy
change a baseline by firm of what proper ICU use would have been in each year needs to be established. To
accomplish this I take advantage of the rich patient level data to build a model that predicts the likelihood
that a patient is in the ICU at the time of the transplant regressing ICU status in relation to the demographic
and patient health controls used to produce the results in Table 2, using the years after the policy change. To
produce a prediction of what the proper ICU use would have been before the MELD policy I use a k-fold
forecasting method. The details of this procedure and how it was implemented are in the appendix. Figure 4
shows the predicted number of patients aggregated for the entire US market along with the 95% confidence
17 The exact numbers and date entered are altered so the specific centers could not be identified. The changes did not impact the
interpretation of the information. Details about the case can be discussed with the author if desired, and the data with the specific
values are publicly available for anyone signing a privacy agreement for the STAR data from UNOS.
15
Figure 4: Predicted Number of Patients from the ICU vs Actual Patients Listed in the ICU
Note: Predicted values and confidence intervals are derived using the forecasting method explained in the appendix.
interval compared to the actual ICU use.
We can also look at similar figures at the individual firm level.18 Figure 5 show four different entrants
and their use of the ICU that are representative of many other centers in the data. While these figures do not
exhaustively illustrate all patterns seen for actual entrants, they are generally representative and demonstrate
how firms could choose to behave and the impact of the policy change on these firms and their behavior.
Center A shows significantly higher use in the pre-MELD era and was able to survive and become an
established center after obtaining the necessary volume. Center B actually enters twice. The first time they
do not seem to misrepresent patient status and end up exciting after a few years. Later, they re-enter and have
higher than predicted ICU utilization until the policy change and are ultimately able to survive. Center C is
an example of a center that enters, has ICU use almost exactly as the model predicts, and ends up exiting,
presumably due to low volume. Center D is an example of a center that has much higher ICU use than
predicted, but after the policy change they do not seem to be able to maintain volume, and end up exiting a
few years after the policy change.
Moving beyond representative examples, the impact of misrepresentation on performance is analyzed
using a two stage procedure. In the first stage, the years after the policy change are used to build a model
to determine what the expected ICU use was before the policy change to determine how much a center
deviates from the what would be expected under normal conditions. In the second stage this deviation
from predicted ICU use is utilized as a measure of patient misrepresentation. Because this deviation is an
18 In some of the centers in Figure 5 certain information such as year entered or exited has been modified whenever a center could
be identified from the available information. This was done to comply with the UNOS privacy agreement allowing for the use of
the data. The actual shape and behavior has not been changed in a way to alter any qualitative observations of the data.
16
Figure 5: Examples of individual center differences in ICU use.
Note: In some of the centers in Figure 5 certain information such as year entered or exited has been modified whenever a center
could be identified from the available information. This was done to comply with the UNOS privacy agreement allowing for the use
of the data. The actual shape and behavior has not been changed in a way to alter any qualitative observations of the data. Predicted
values and confidence intervals are derived using the forecasting method explained in the appendix.
17
estimate, I adjust the standard errors in the second stage to take into account the variation and potential bias
of this parameter. This is accomplished in two steps. First, the predicted ICU use for the pre-MELD era
is an out-of-sample estimate, so the k-fold forecasting method used to generate figures 4 an 5 is utilized
to determine the potential forecast bias and the variance of the prediction bias.19 In the second step the
standard errors of the second stage parameters are determined via a parametric bootstrap. The details of this
procedure are in the appendix.
5.3.1
Impact of Misrepresentation on Center Volume
The baseline performance metric that misrepresenting patient status could improve is the number of transplants a center performs. To estimate the marginal impact of misrepresenting a patients status the change in
the number of transplants from the previous year, ∆Volume, is regressed on the change in the deviation from
the predicted ICU use compared to the previous year, ∆deviation from predicted use. Table 4 presents the
results of this analysis. The results for before and after the policy change are shown in columns (1) and (2)
respectively. For these baseline regressions both year and center fixed effects are included. The impact of
misrepresentation on center volume is significant before the policy change but not after the change. To see
if the difference between columns (1) and (2) is significant, column (3) interacts the change in the deviation
from the predicted ICU use with the MELD era indicator variable. Column (4) adds OPO level controls.
Two OPO level variables are controlled for. The first is the number of centers in the OPO which is the
same measure used in the previous regressions to measure the level of competition. The second variable,
the change in the number of transplants in the OPO compared to the previous year divided by the number of
centers in the current year, is new and measures what the expected increase in volume for all centers in an
OPO would be if all centers split the growth equally. The coefficients for the impact of misrepresentation in
each of these specifications are significant, and the impact of misrepresentation, ∆deviation from predicted
use, in the full model represented by column (4) finds that for approximately every three patients from the
ICU beyond what would be predicted a center gains one extra transplant patient.
While the economic impact represented in column (4) certainly could be large enough to drive behavior,
it is unclear if this effect could truly enable entry. However, the impact of misrepresentation may actually
be much greater. When reducing the window to only the three years before and after the policy change the
effect essentially doubles in strength. This change is both reassuring and concerning as estimates should be
more reliable the closer they are to the policy change, but it also shows that due to the relatively few number
of centers the effects are not as stable as would be desired. In addition, the main effect of misrepresentation
that matters is its impact on new entrant performance, and the true impact for entrants is much higher than
what would be estimated in Table 4.
The reason for this is due to the steep learning curve, and associated difficulty in establishing early
volume in a center (Everson and Trotter, 2009). Establishing volume early on should have a cumulative
effect over time in helping a center move down the learning curve and become more attractive to potential
transplant patients. Figure 6 shows that this is indeed the case. Using the first three years after a center enters
19 Using
a k-fold method will produce a conservative estimate of forecast error(Arlot and Celisse, 2010).
18
Table 4: Impact of misrepresentation on center volume
∆ Deviation from predicted ICU use
(1)
∆ Volume
0.373∗∗∗
(0.100)
(2)
∆ Volume
0.003
(0.176)
MELD era x
∆ Deviation from predicted ICU use
MELD era
OPO level controls
Year fixed effects
Center fixed effects
Years included
Number of clusters
Observations
Pre
No
Yes
Yes
+/- 7yrs
51
745
Post
No
Yes
Yes
+/- 7yrs
52
699
(3)
∆ Volume
0.464∗∗∗
(0.132)
(4)
∆ Volume
0.249∗∗∗
(0.077)
(5)
∆ Volume
0.193∗∗
(0.077)
-0.431∗∗
(0.195)
-0.343∗∗
(0.137)
-0.595∗∗
(0.229)
Both
No
Yes
Yes
+/- 7yrs
53
1444
Both
Yes
Yes
Yes
+/- 7yrs
53
1444
Both
Yes
Yes
Yes
+/- 3yrs
51
682
All controls are interacted with MELD era, and main effects are included in all specifications with interactions.
Standard errors in parentheses. All coefficient and standard errors are x100.
Standard errors are clustered at the OPO level
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Figure 6: Each patient from the ICU above what would be predicted in the first three years after entry has a
cumulative effect over time.
Note. The predicted increase in volume and associated confidence intervals are derived from regressing the number of center
transplants in a year on the centers total deviation from what would be predicted in the first three years after entering interacted
with the age of the center. Both year fixed effects and fixed effects for year entered are included and standard errors are clustered
by OPO. Only centers entering before 2000 are included.
19
the market and taking the total deviation from predicted ICU use in this time period leads to an increase of
.66 more transplants in the first year year per patient misrepresented which is consistent with the high end
of the estimates from Table 4. However, not only does the impact of misrepresenting patients in the first
three years persist, it grows to where for every patient from the ICU above what would be expected in the
first year a center would expect to have two additional patients in the sixth year of its life. When adding
up all the additional transplants over the first six years of a centers existence the cumulative increase by
the sixth year is 8.6 additional transplants for each “misrepresentation” in the first three years of a centers
life.
20 A
similar analysis of centers entering after the MELD policy change shows no significant effect, and
the cumulative benefit of misrepresentation was not found to exist for centers that were already established
and had significant volume. This asymmetric benefit of misrepresentation supports the proposition that the
ability to misrepresent patient status would disproportionately benefit new entrants.
5.3.2
New Entrant Survival
If entry is enabled by the opportunity for entering firms to pursue unethical strategies then the survival rates
of these entering firms should be improved when pursuing these strategies. To identify if firms that entered
previous to the MELD policy had higher survival rates a Cox proportional hazards model is used to estimate
the hazard rate of exit. The analysis is performed on all firms that entered previous to 2001 and exit events
are captured through the end of the data set and classified as right censored at that time. Any year with
no volume in the following year is considered an exit year, so exit is assumed to occur at the end of the
year.
21 To
measure the extent to which entering firms pursued a strategy to misrepresent patient status the
deviation of entering centers’ ICU use from the predicted use in the first three years after entry is measured
and used in the models.
Two measures evaluating to what extent a center pursued a strategy to misrepresent patients health are
used. The first, Count deviation, is a simple count difference between the actual number of patients and
the predicted number of patients that come from the ICU . The second measure, Fraction deviation, is the
difference in the actual fraction and the predicted fraction of patients that would come from the ICU in the
first three years after entering the market.22 Table 5 shows the summary statistics for each of these variables
as well as total volume of transplants and the number of patients coming from the ICU before and after the
policy change.
Each measure has its strengths and weaknesses. One concern with Count deviation is that if all entrants
pursued a strategy of to misrepresenting patient status with equal likelihood, a higher deviation is only
represents higher volume overall which should naturally indicate a higher likelihood of survival. As can be
20 This effect does not go away after the sixth year. In fact it increases, but so does the error and the results are not as stable
beyond six years after entry. This is due to the exit of some of the entering firms in their sixth year reducing the already small
number of entrants causing instability and greater error in the estimated models.
21 In almost all cases firms that do not do any transplants for a year are out of the market for at least a couple of years. The results
the results are robust to alternate definitions of exit using 2 or 3 years with no production or even only counting exit if the firm never
performs another transplant within the study time period.
22 For example, a firm has 5 transplants with 2 from the ICU in each of the first three years after entering. If the predicted ICU
use would have been 1 patient from the ICU in each year then Count deviation = 3 and Fraction deviation = 0.20.
20
Table 5: Summary statistics for the first three years after entry
Total Transplants
Patients from ICU
Count Deviation
Fraction Deviation
count
38
38
38
38
Firms entering 1992-2001
mean
sd
min
36.61 50.23
1
10.63 16.41
0
5.30
10.80
-1.73
0.123 0.202 -0.154
max
292
86
58.34
0.881
Total Transplants
Patients from ICU
Count Deviation
Fraction Deviation
count
16
16
16
16
Firms entering 2002-2009
mean
sd
min
56.88 41.38
12
5.75
5.09
0
-0.034
2.09
-3.80
0.010 0.056 -0.076
max
135
16
3.09
0.110
seen in table 5 there is significant variation in the rate of misrepresentation, but firms that enter with the
intent to misrepresent patient status but fail to do so at a meaningful level would not show up using this
measure leading to this measure underrepresented firms that do not reach a critical volume.
Conversely, Fraction deviation has the opposite problem in that it overemphasized firms that stay very
small. This measure suffers from highly volatile results since entering firms may only have a few patients
leading to extreme and noisy cases. For example, take two centers that only have one transplant with the
patient coming from the ICU in only one of the two. Claiming the center that had a patient come from the
ICU was pursuing a misrepresentation strategy and that the other center was not is not credible. As both
measures have strengths and weaknesses this paper uses both measures, and tries to control for each of the
the particular weaknesses as best as possible within the given context.
One assumption embedded in both measures is the window of three years. Using three years of data
allows for the best comparable estimate of volume at the beginning of a centers life. This is since a center
may begin offering transplant services at any time during a year, and the starting date will always be earlier
than the date of the first transplant. Using a three year window allows for better comparison of a center in its
first or last year that only operated for one or two months with centers that operated for 11-12 months. Three
years is also the time that centers are generally given to reach a critical volume after entering to qualify for
medicare. Using three years also helps reduce the noise associated with the variable Fraction deviation by
aggregating three years of data reducing some of the problems arising from centers with lower volume.
23
Table 6 presents the survival analysis results.24 Column (1) starts by considering the deviation of entering centers’ ICU use from the predicted in the first three years after entry without any controls. In column
(2) kidney volume of the center and OPO level measures of competition and volume measured at the time
when it enters the liver market are added to the model. The impact of misrepresenting patients above what
is predicted is statistically significant supporting the proposition that pursuing an unethical strategy can be
a viable entry strategy.25 To try and control for the potential correlation between Count deviation and to23 Using
a three year window also helps ensure that the true deviation is captured taking advantage of the higher number of
observations, which is especially important with newly entered centers that may have very low volume. Overall, a three year
window is a more conservative measure requiring consistent behavior over a longer period of time.
24 All models met the constant proportional hazard assumptions.
25 It is possible that centers that do not deviate choose not to do so since they were in a stronger position for some unobserved
21
tal volume, in column (3) I include the total volume for a center in the first three years in the regression,
First three years of volume. Including the total volume is potentially a problem as the mechanism by which
misrepresenting patients helps firms is by increasing the number of transplants performed. Including First
three years of volume in the models would absorb part of this benefit. This could bias against finding that
the frequency of misrepresentation increases survival. However, the coefficient and significance for the
deviation from the predicted ICU use only slightly impacted. However, firms that enter with the intent to
misrepresent patient status but fail to do so at a meaningful level would not show up in this analysis leading
to this measure to underrepresented firms that do not reach a critical volume.
Table 6: Survival analysis of entering liver transplant centers
Count deviation
(In first three years)
(1)
-0.104∗∗
(0.045)
(2)
-0.231∗∗∗
(0.077)
(3)
-0.216∗∗
(0.105)
(4)
-11.083∗∗
(5.396)
Fraction deviation
(In first three years)
-0.272
(0.189)
-0.079
(0.238)
-0.317∗
(0.192)
Kidney volume
-0.023∗∗∗
(0.007)
-0.011∗
(0.007)
-0.027
(0.022)
OPO volume
0.036∗∗∗
(0.012)
0.027∗∗∗
(0.010)
0.042∗∗∗
(0.016)
Average center volume by OPO
-0.072∗∗∗
(0.027)
-0.048∗∗
(0.022)
-0.090∗∗∗
(0.031)
Volume increase by OPO
-0.112∗∗
(0.046)
-0.058
(0.041)
-0.131∗∗
(0.062)
1993-2000
25
38
-0.072∗∗
(0.031)
1993-2000
25
38
1993-2000
20
30
OPO count
Volume in first three years
Entry Years Included
Number of clusters
Observations
1993-2000
25
38
Reported values are model coefficients not hazard rates. Standard errors in parentheses.
Standard errors are clustered at the OPO level.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Conversely, models using Fraction deviation are widely unstable due to overemphasizing a few firms
with low volume. A visual analysis of the data that uncovered the extreme values on both ends of the
distribution were mainly driven by firms with extremely low volume (the most extreme high and low cases
were both from centers that only had one transplant in three years. To try and correct for this issue the
analysis was run after trimming the observations removing observations in the top and bottom 10% of the
distribution for Fraction deviation. While this is not a perfect solution, and the results should be viewed
with caution, it does allow for a stable model to be estimated. The results for this model are in column (4).
reason and they did not need to resort to misrepresentation. This impact would bias against the finding that misrepresentation
increases survival rates.
22
The estimated impact of misrepresentation is relatively consistent and significant across the different
specifications. Taking the coefficient for Count deviation in column (3) the model estimates that for 5,10
and 15 extra patients the hazard of exit would be reduced 66%, 88% and 96% respectively. Similarly, for
Fraction deviation a 10%, 20% and 30% (average for increase from predicted leads to a 67%, 89% and 96%
respective decrease in the exit hazard.
5.3.3
2627
Impact on Exit of Incumbents
While a strategy to misrepresent patients health may work as an entry strategy, strategic behavior in the
form of misrepresenting patient status does not grow the size of the market. In the liver transplant market
each liver that becomes available can only go to one patient and only one center will be able to perform
the transplant. Thus, if some firms are benefiting by unethical behavior, other firms’ performance must be
hurt. This is the case for many markets and strategies where firms compete in zero sum competition. To
identify the impact of misrepresenting patient status has on incumbent firms a model of the probability of
exit for a firm in any given year is estimated. Akin to the previous analysis exit is defined as occurring in
any year where a firm has no transplants in the following year. Similar to the previous analysis a three year
deviation from predicted ICU use is used to measure patient misrepresentation, but now every year has a
new measure looking at the data for the current year and the two previous years. The same OPO and center
level controls used in the entry analysis of table 3 are used, except both the current and lagged values of the
controls are included. The analysis evaluates the probability that the current year will be the last year the
firm performs liver transplants. Table 7 shows the relevant summary statistics and Table 8 looks at whether
misrepresenting patients status lowers the exit rate of incumbents. The results support the proposition that
incumbents are more at risk previous to the policy change when new entrants could pursue an unethical
entry strategy.
Unlike in the hazard analysis, there is a sufficient number of observations both before and after the policy
change to compare the impact of misrepresentation across the two time periods. Columns (1) and (2) look
at the impact that the deviation from predicted ICU use in the previous three years has on incumbent firm
exit before and after the policy change respectively, and column (3) finds the differential impact of Count
deviation across the two time periods to be statistically significant. For these models OPO level controls are
included to control for the impact that the number of centers in the OPO would have on misrepresentation.
To minimize the potential bias the Count deviation measure may have through its correlation with the
total volume of transplants center volume, center volume squared and the total number of kidney transplants
performed at the center are added as center level controls to the model in column (4). Including volume
controls should take care of concerns about correlation between Count deviation and volume in any of the
26 To compute the estimated effect for 5 additional patients coming from the ICU than would be predicted take the exponential
of the coefficient multiplied by the firms value subtracted from one, ie 1 − exp(−.216 ∗ 5) = 66%, so 5 additional patients coming
from the ICU reduces the hazard of exit by 66%.
27 Unfortunately, there are not enough firms that enter and exit after the MELD policy change to compare hazard rates based
on initial conditions across the two time periods. Even expanding the data-set to cover through 2015 does not provide enough
entry/exit after the MELD Policy. Similarly, estimated models for shorter time frames than the one reported are not stable as the
number of observations becomes too small.
23
Table 7: Center summary statistics for the number of transplants and actual vs predicted measures of patients
coming from the ICU
Total Transplants
Patients from ICU
Count Deviation
Fraction Deviation
count
865
865
865
865
Pre-MELD era
mean
sd
min
44.58 45.56
1
10.40 15.09
0
3.14
7.63
-23.87
.083
0.158 -0.345
max
369
142
61.20
0.984
Total Transplants
Patients from ICU
Count Deviation
Fraction Deviation
count
721
721
721
721
Post-MELD era
mean
sd
min
60.82 48.46
1
7.46
11.00
0
-0.006
5.90
-16.96
0.008 0.091 -0.263
max
309
116
81.78
0.914
Note: Each observation is at the center/year level.
models with the MELD era interaction dummy as there is no correlation between the two variables in the
post MELD era. In addition, both to help control for volume effects as well as to estimate a parameter
more meaningful to firms that are in danger of exiting, models only including centers with less than 85
transplants that year are included in the model shown in column (5) and less than 35 transplants that year
in column (6). The results are robust to various other cutoff levels, but these values are used since the
highest number of transplants any center had two years before exiting was 84 and no firms ever exit that
have more than 34 patients in their last year. As you would expect the results show that the estimated impact
of misrepresentation is greater when only looking at those firms that are in danger of exiting as represented
by lower volume. The estimated impacts are a 0.192% and 0.801% respective decrease in an incumbents
exit rate per year for each additional patient coming from the ICU in the previous three years. Table 7 shows
that the average deviation across all firms in the pre-MELD era is three additional patients from the ICU
(or 9 over three years) which leads to an expected decrease in the probability of exit of 8.7% and 32.4%
respectively over a 5 year time period. This is a big impact given incumbents should have an advantage over
new entrants, the overall number of transplants is increasing, significant entry is occurring during this time
period and that actual deviations were often much higher. For example, setting Count deviation = 10.77
(one standard deviation above the mean) leads to an estimated 27.4% and 77.6% decrease in respective exit
probabilities over 5 years.
The estimated effect may increase as much as it does when restricting the analysis to smaller firms
due to the correlation of Count deviation with total volume. The estimated models do include volume and
volume squared as controls, helping alleviate part of this concern, but there may still be an effect. When
restricting the analysis to only incumbent firms the measure Fraction deviation is much less noisy than
when including new entrants due to many more observations and relatively few centers with extremely low
volume.28 Columns (7) and (8) show the results are robust to the alternative measure, Fraction deviation, by
repeating the analysis of columns (5) and (6), but now using Fraction deviation as the regressor instead of
28 For
example, about 20% of the observations for entrants had less than 5 transplants in that year compared to about 5% for
incumbents. The standard deviation of Fraction deviation for entrants for the time period of 1992-2001 was .223 versus .125 for
incumbents.
24
Table 8: Impact of patient misrepresentation on center exit
Count deviation
(1)
exit
-0.118∗∗∗
(0.034)
(2)
exit
0.001
(0.008)
Count deviation x
MELD era
(3)
exit
-0.118∗∗∗
(0.034)
0.119∗∗∗
(0.036)
(4)
exit
-0.099∗∗∗
(0.029)
0.103∗∗∗
(0.029)
(5)
exit
-0.190∗∗∗
(0.047)
0.192∗∗∗
(0.055)
(6)
exit
-0.644∗∗
(0.252)
0.801∗∗
(0.388)
Fraction deviation
Fraction deviation x
MELD era
MELD era
OPO level controls
Center level controls
Year fixed effects
Volume cutoff
Number of clusters
Observations
Pre
Yes
No
No
Post
Yes
No
No
Both
Yes
No
Yes
Both
Yes
Yes
Yes
50
355
48
339
50
694
50
694
Both
Yes
Yes
Yes
<85
48
512
Both
Yes
Yes
Yes
<35
35
208
(7)
exit
(8)
exit
-0.269∗∗∗
(.090)
.280∗∗∗
(.0978)
-0.375∗
(.192)
.520∗∗
(.200)
Both
Yes
Yes
Yes
<85
48
512
Both
Yes
Yes
Yes
<35
35
208
Count deviation includes the current and previous two years. Standard errors in parentheses.
Coefficient and standard errors for columns (1) - (6) are x100. Standard errors are clustered at the OPO level.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Count deviation. The estimated coefficients of Fraction deviation for columns (5) and (6) are .280 and .520
respectively. These coefficients represent an expected decrease in the probability of exit over five years of
11.1% and 19.8% respectively when evaluated at the mean value of Fraction deviation from Table 7 and
29.4% and 48.9% respectively when evaluated one standard deviation above the the mean.
Table 10 in the appendix shows that the results are robust to different time windows around the policy
change as well as to a logit specification. The results are also generally robust to using different time periods
(other than 3 years) to calculate Count deviation and Fraction deviation. General trouble arises when the
current years values are used as the centers could have exited at anytime making the last years volumes have
higher variance and not be completely comparable. Using a one or two year lagged measure helped avoid
some of this issue, but this does not take advantage of the information that is available in that last year of
a centers’ life. The variable Fraction deviation was much less stable under these specifications due to its
sensitivity to smaller values when calculating the fraction from the ICU. Using a thee year window helped
avoid many of these small number issues. In addition, the three year window used in the main analysis
should better capture if firms consistently pursued a strategy of misrepresentation. Using three years to
calculate the measures would bias against finding an impact when misrepresenting an individual patient,
but are more representative of what could be considered a firm strategy to consistently misrepresent patient
health status.
The consistency in results across both measures of deviation increases the strength of the results since
each measure has different strengths and weaknesses. For centers with low volume, Count deviation is potentially biased as it is more likely to measure firms that misrepresent successfully, not just those that misrepresent, leading to potentially not identifying some centers that are intentionally misrepresenting patients
health if they are small. Controlling for center volume ensures that the measure is not only acting proxy for
25
volume, but does not completely alleviate the bias against identifying small firm effects. Conversely, while
Fraction deviation can capture the extent to which centers are utilizing a strategy to misrepresent in a way
that allows comparison across small and large centers, it struggles to capture the true effects for small centers
due to the volatility of the calculation when centers have low volume - especially evident in the analysis of
survival for newly entering centers. While trimming the data could help eliminate some of the volatility and
extreme values, it would disproportionately eliminate observations of the very firms that are at risk due to
their low volume.
5.3.4
Discussion of Empirical Analysis
How should the findings of this paper be interpreted? While the policy changes allows for identification of
unethical gaming of liver allocation, a direct causal link between the entry and unethical behavior can not be
established as there are many exogenous factors that can determine entry. Similarly, misrepresentation may
be correlated with managerial awareness and ability and it is these characteristics that influence performance
rather than misrepresentation. That said, while none of the individual analyses on their own are enough to
establish a clear causal relationship, taken as a whole the analysis presented offers compelling evidence
to support the argument that competition influences unethical behavior. For example, if misrepresentation
is correlated with general managerial ability this would not impact the entry rate when the opportunity to
misrepresent goes away. In addition, this papers findings are consistent with theoretical mechanisms of
previous work (Snyder, 2010; Bennett et al., 2013; Cai and Liu, 2009; Ostler, 2012; Branco and Villas-Boas,
2012), strengthening the plausibility of the findings. These results also support and lend credibility to
the previous theoretical predictions (Branco and Villas-Boas, 2012; Ostler, 2012) and empirical findings
(Cai and Liu, 2009; Bennett et al., 2013; Snyder, 2010)by being the first to demonstrate that competition
between new entrants impacts both the occurrence of unethical behavior and the performance of entrants
and incumbents alike. The magnitude of the effects of performance as measured by: an increase in entry
rates; the number of transplants performed once entered; survival rates of entrants and exit probabilities for
incumbents are all economically significant having a major impact on firm performance.
Implications and Conclusion
The findings in this paper suggest that unethical behavior can be an effective strategy for new entrants to
overcome competitive disadvantages when entering a new market, and that when unethical behavior leads
to improved performance if incumbents do not respond in kind they are at risk of being forced out of the
market. The fact that entry and competition will often drive markets towards greater efficiency by driving
out inefficient firms is well established, and this paper shows that when unethical behavior is the efficient
practice entry and competition can drive out firms that do not pursue unethical strategies and lead markets
towards higher levels of unethical behavior. The rapidly increasing use of the ICU through 2002 in Figure 4
is an illustration of how quickly this can occur under the right circumstances.
26
The results in this paper have critically important implications for policy makers showing that laws
and policies need to simultaneously try to capture the benefits of competition while balancing the potential
downside of increasing unethical behavior (Bennett et al., 2013). Ironically, the industries that may be most
at risk of unethical gaming of the system and offer opportunities for entrepreneurs willing to engage in
ethically dubious behavior are those that are highly regulated due to the the social desire to ensure fairness
and equality like is seen in the liver transplant market (Scanlon et al., 2004; Snyder, 2010) or have regulations
with the intent to promote social welfare, such as reducing vehicle emissions (Hubbard, 1997; Bennett
et al., 2013; Pierce and Snyder, 2008). As regulation in these industries is often necessary to meet socially
desirable goals this paper highlights the important and influential role that organizations such as the Organ
Procurement and Transportation Network have not only on determining whether these goals are achieved,
but also in determining market dynamics and the competitive advantage or disadvantage of both incumbents
and potential entrants.
This paper does not take a stance on whether misrepresenting patient health status hurt or helped social
welfare. Whether organs should go to the sickest patient or a relatively healthy patient that may live longer
and be better able to productively contribute to society is not clear and the subject of much debate. In
unreported analysis I looked at whether a patients age, gender or race influenced if a center was more likely
to misrepresent their health. Unfortunately, the results are not strong or robust enough to warrant discussion,
especially as this question is out of the scope of this paper. However, it may be that centers would only
misrepresent patient health when they could think of a way to rationalize it. You can easily imagine how an
individual would rationalize helping obtain a liver for a young mother with dependents, and it may be that
transplant centers used the ICU to ensure the sickest patients were the ones that received transplants (Snyder,
2010). Perhaps, industries such as the transplant industry are more susceptible to gaming when the policies
do not align with the beliefs of individuals in the position to game the system, and policies need to take
into account the likelihood of rationalization jointly with the potential for competition to influence profit
seeking entities to pursue unethical behavior. Hopefully future research will be able to help explain how
these two factors interact with respect to influencing the likelihood of unethical behavior, as competition
and ambiguous beliefs of what is socially beneficial are present in almost all industries and are present even
in the foundations of financial markets (Dobson, 1997).
The implications of this paper are also central for mangers and entrepreneurs. The results and context
of the study highlight four types of asymmetries that influence the competitive (dis)advantage faced by
entrants and incumbents. The first two are structural asymmetries in the form of either greater benefits or
lower risks (downside) of unethical behavior for entrants. In this paper, due to the barriers to entry and
the learning curve, the benefit of unethical behavior is higher for new entrants trying to get established as
illustrated in the findings from section 5.3.1 and Figure 6. Similarly, the costs of unethical behavior are
lower for new centers since established centers risk sanctions and being forced to shut down an established
and profitable center.29 Both of these asymmetries are driven by the structure of the industry. There also
exist firm (individual) specific differences in how aware or willing firms are to pursue the opportunity to
misrepresent patient status. Building on the idea that entrepreneurship occurs when both awareness of
29 See
footnote 6 for examples
27
and the ability (willingness) to take advantage of an opportunity (Eckhardt and Shane, 2003; Shane and
Venkataraman, 2000), I show that both awareness and the willingness to misrepresent patient status enabled
successful entry into the liver transplant market. In addition, I show that recognizing and being willing to
misrepresent patient status was critical for the survival of incumbents that were threatened by new entrants as
the exit rate of incumbents that did not misrepresent patient status was much higher previous to the MELD
policy change. Interestingly, markets with regulation to promote fairness and ethical behavior may favor
entry by unethical entrepreneurs.
This paper finds shows that not only can competition lead to firms adopting unethical business practices,
but that the presence and subsequent adoption of unethical options by entrants can actually be what enables
entry into this market. This result contrasts with the often espoused stance that competition is good and
will promote social welfare. In particular, instead of entrepreneurs and entrants helping push society to be
more efficient, entrepreneurs may find a competitive advantage through unethical or illegal behavior that
is damaging to society. The often praised nature of entrepreneurs that allows them to find a way around
obstacles can also enable entrepreneurs to find ways around the rules, or to use the rules in their favor,
in ways that would be contrary to the social norms of ethical behavior. Understanding how competition
between incumbents and entrants may lead to unethical behavior can help policy makers and managers
structure their policies and organizations to minimize unwanted behavior as well as highlight situations
where extra vigilance is is necessary to ensure compliance with established norms or policies.
6
6.1
Appendix
Method to calculate predicted ICU use and standard errors.
In this section of the appendix I describe how the predicted ICU use for the first stage of the two stage
analysis was performed and how standard errors were constructed in the second stage.
The first stage consisted of generated a predicted probability that a given patient would be in the ICU
using patient data. Since the predicted values are out of sample for the pre-MELD period, to create the
predicted values a k-fold forecast method was used. This is a cross validation method used in forecasting
that is generally less biased, but has higher variance compared to other potential methodsArlot and Celisse
(2010). For the context of this analysis Using the data from the years 2003-2009 the probability that a patient
would be in the ICU was regressed for all years but one. This was then repeated seven times excluding each
year once. The predicted values were then compared against the actual outcomes in the year that was held
out and the mean squared error (MSE) of each forecast was calculated. The model was then fit using all
seven years of data. To get the variance of this model the variance for the pre-MELD era predictions was
calculated by averaging the seven MSE values to get the variance of the prediction error and then adding
that to the variance of the estimated model.
The next step was to aggregate the prediction and associated errors from the individual to the center
level. The distribution of the sum of binary outcomes with different expectations and variances is a Poison
binomial distribution. Unfortunately, no closed form solution is available. Calculating the errors of the
28
aggregate values was done both via simulation and using a normal approximation. Comparison of the two
methods showed practically no difference in the ultimate impact on the calculated standard errors for the
second stage model so a normal approximation was generally used to conserve computing time.
The predicted values of this model were then used to calculate the deviation of a center from what
would be predicted for the analysis and figures in the paper. As the deviation from the predicted value is an
estimated value standard errors of the second stage were calculated using a parametric bootstrap method.
To calculate the standard errors of the second stage estimation I follow a procedure similar to Mas
and Moretti (2006). I first run the original regression using the predicted values obtains estimates of the
coefficients, β̂ , and their standard errors, se2β . A new dataset was then created with the predicted values
being generated according to the distribution of errors estimated inq
the first stage. This process was then
repeated x times. The new standard error of β̂ was then calculated as se2β + σβ2 where σβ2 = var(βˆ1 ,..., βˆx ).
Setting x = 10 was generally sufficient for stable convergence of the calculated standard errors.
As robustness checks of the process I also tried: using a probit instead of OLS in the first stage, bootstrapped the full analysis starting in the first stage through the second stage to ensure that the estimations
were unbiased, and used 12 years of data (2003-2014) instead of 7 years to produce the forecast for the
predicted values. The results for the standard error calculations were robust across these variations with no
noticeable change in the standard errors beyond 3 significant digits.
6.2
Robustness Check Tables
Table 9: Robustness Checks for Table 2
MELD era x
new entrant
(1)
icu
-0.072∗∗
(0.029)
(2)
icu
-0.076∗∗∗
(0.028)
(3)
icu
-0.088∗∗∗
(0.031)
(4)
icu
-0.059∗∗∗
(0.023)
(5)
icu
-0.050∗∗∗
(0.019)
MELD era x
center volume
0.000
(0.000)
0.000
(0.000)
0.000
(0.000)
0.000
(0.000)
0.000
(0.000)
-0.025∗∗∗
(0.007)
-0.024∗∗∗
(0.008)
-0.017∗∗
(0.008)
-0.014∗∗
(.006)
-0.12∗
(.007)
Yes
Yes
Yes
Yes
+/-3yrs
OLS
51
31169
Yes
Yes
Yes
Yes
+/-5yrs
OLS
52
52030
Yes
Yes
Yes
Yes
1992-2009
OLS
54
83582
Yes
Yes
Yes
Yes
+/-5yrs
Logit
54
52019
Yes
Yes
Yes
Yes
+/-7yrs
Logit
54
71919
MELD era x
firm count by OPO
Year fixed effects
Center fixed effects
Demographic controls
Patient health controls
Range
Specification
Clusters
Observations
Standard errors in parentheses. Main effects are included in all specifications
with interactions. For logit specifications, marginal effects at the average
values are taken
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
29
30
Post
Yes
No
No
5
48
339
OLS
Pre
Yes
No
No
-5
50
355
OLS
50
694
OLS
Both
Yes
No
Yes
+/-5
50
694
OLS
Both
Yes
Yes
Yes
+/-5
Both
Yes
Yes
Yes
+/-3
<85
44
303
OLS
Both
Yes
Yes
Yes
+/-3
<35
28
111
OLS
Standard errors in parentheses
All coefficient and standard errors are x100. Standard errors are clustered at the OPO level
For logit specifications, marginal effects at the average values are reported.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
MELD era
OPO level controls
Center level controls
Year fixed effects
Years included
Volume cutoff
Number of clusters
Observations
Specification
0.827∗∗
(0.327)
0.246∗∗∗
(0.076)
(6)
exit
Count deviation x
MELD era
0.227∗∗∗
(0.083)
0.218∗∗
(0.101)
(5)
exit
-0.827∗∗
(0.327)
(4)
exit
-0.213∗∗∗
(7.917)
(3)
exit
-0.220∗∗
(9.228)
-0.246∗∗∗
(0.076)
(2)
exit
-0.002
(2.163)
Count deviation
Fraction deviation x
MELD era
Fraction deviation
(1)
exit
-0.220∗∗
(9.219)
50
694
Logit
Both
Yes
Yes
No
+/-5
0.365∗∗
(0.165)
-0.265∗∗∗
(0.074)
(7)
exit
Both
Yes
Yes
No
+/-5
<35
35
208
Logit
2.478∗∗∗
(0.796)
-1.174∗∗∗
(0.308)
(8)
exit
Both
Yes
Yes
Yes
+/-3
<85
44
303
OLS
.314∗∗∗
(0.105)
(9)
exit
-0.314∗∗∗
(0.105)
Table 10: Impact of patient misrepresentation on center exit robustness checks
Both
Yes
Yes
Yes
+/-3
<35
28
111
OLS
0.507∗∗
(0.227)
(10)
exit
-.0508∗∗
(0.227)
50
694
Logit
Both
Yes
Yes
No
+/-5
0.179∗∗
(.0781)
(11)
exit
-0.124∗∗
(.055)
Both
Yes
Yes
No
+/-5
<35
35
208
Logit
0.585∗
(0.300)
(12)
exit
-0.377∗
(0.196)
References
Acs, Z. J. and Audretsch, D. B. (1988). Innovation in large and small firms: An empirical analysis. The
American Economic Review, 78(4):678–690.
Alvarez, S. A., Barney, J. B., and Anderson, P. (2013). Forming and exploiting opportunities: The implications of discovery and creation processes for entrepreneurial and organizational research. Organization
Science, 24(1):301–317.
Arlot, S. and Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics
surveys, 4:40–79.
Audretsch, D. B. (1991). New-firm survival and the technological regime. The Review of Economics and
Statistics, 73(3):441–450.
Bennett, V., Pierce, L., Snyder, J., and Toffel, M. (2013). Customer-driven misconduct: How competition
corrupts business practices. Management Science.
Branco, F. and Villas-Boas, J. M. (2012). Competitive Vices*. Journal of Marketing Research.
Bresnahan, T. F. and Reiss, P. C. (1991). Entry and competition in concentrated markets. Journal of Political
Economy, pages 977–1009.
Cai, H. and Liu, Q. (2009). Competition and corporate tax avoidance: Evidence from chinese industrial
firms*. The Economic Journal, 119(537):764–795.
Cameron, A. C. and Miller, D. L. (2015). A practitioner’s guide to cluster-robust inference. Journal of
Human Resources, 50(2):317–372.
Dobson, J. (1997). Finance ethics: The rationality of virtue. Rowman & Littlefield.
Eckhardt, J. T. and Shane, S. A. (2003). Opportunities and Entrepreneurship. Journal of Management,
29(3):333–349.
Everson, G. T. and Trotter, J. F. (2009). Liver Transplantation: Challenging Controversies and Topics.
Springer Science & Business Media.
Gelman, J. R. and Salop, S. C. (1983). Judo economics: capacity limitation and coupon competition. The
Bell Journal of Economics, pages 315–325.
Geroski, P. A. (1995). What do we know about entry? International Journal of Industrial Organization,
13(4):421–440.
Harbring, C., Irlenbusch, B., Kräkel, M., and Selten, R. (2004). Sabotage in asymmetric contests–An
experimental analysis. International Journal of the Economics of Business, forthcoming.
Hiatt, S. R., Sine, W. D., and Tolbert, P. S. (2009). From Pabst to Pepsi: The deinstitutionalization of
social practices and the creation of entrepreneurial opportunities. Administrative Science Quarterly,
54(4):635–667.
Hubbard, T. N. (1997). Using inspection and maintenance programs to regulate vehicle emissions. Contemporary Economic Policy, 15(2):52–62.
31
Hung, N. M. and Schmitt, N. (1988). Quality competition and threat of entry in duopoly. Economics Letters,
27(3):287–292.
Katz, E. (2001). Bias in conditional and unconditional fixed effects logit estimation. Political Analysis,
9(4):379–384.
King, G. and Zeng, L. (2001). Logistic Regression in Rare Events Data. Political Analysis, 9(2):137–163.
Lee, G. K. and Lieberman, M. B. (2010). Acquisition vs. internal development as modes of market entry.
Strategic Management Journal, 31(2):140–158.
Lieberman, M. B. (1987). The learning curve, diffusion, and competitive strategy. Strategic management
journal, 8(5):441–452.
Mas, A. and Moretti, E. (2006). Peers at work. Technical report, National Bureau of Economic Research.
Münster, J. (2007). Selection tournaments, sabotage, and participation. Journal of Economics & Management Strategy, 16(4):943–970.
Murphy, T. F. (2004). Gaming the Transplant System. The American Journal of Bioethics, 4(1):28–28.
Ostler, J. (2012). Swinging for the fences: Strategic risk taking in entrepreneurship. SSRN Scholarly Paper
ID 1850943, Social Science Research Network, Rochester, NY.
Pierce, L. and Snyder, J. (2008). Ethical spillovers in firms: Evidence from vehicle emissions testing.
Management Science, 54(11):1891–1903.
Scanlon, D. P., Hollenbeak, C. S., Lee, W., Loh, E., and Ubel, P. A. (2004). Does Competition For Transplantable Hearts Encourage ‘Gaming’ Of The Waiting List? Health Affairs, 23(2):191–198.
Schumpeter, J. A. (1994). Capitalism, socialism and democracy. Routledge.
Schwieren, C. and Weichselbaumer, D. (2010). Does competition enhance performance or cheating? a
laboratory experiment. Journal of Economic Psychology, 31(3):241–253.
Shane, S. (2000). Prior knowledge and the discovery of entrepreneurial opportunities. Organization science,
11(4):448–469.
Shane, S. and Venkataraman, S. (2000). The Promise of Entrepreneurship as a Field of Research. The
Academy of Management Review, 25(1):217–226.
Shleifer, A. (2004). Does competition destroy ethical behavior?
Economic Research.
Technical report, National Bureau of
Snyder, J. (2010). Gaming the liver transplant market. Journal of Law, Economics, and Organization,
26(3):546 –568.
Staw, B. M. and Szwajkowski, E. (1975). The scarcity-munificence component of organizational environments and the commission of illegal acts. Administrative Science Quarterly, pages 345–354.
Sutton, J. (1991). Sunk costs and market structure: Price competition, advertising, and the evolution of
concentration. The MIT press.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press.
32