Organ Donation Loopholes Undermine Warm Glow Giving:
An Experiment Motivated By Priority Loopholes in Israel
By Judd B. Kessler† and Alvin E. Roth‡
This Draft: February 28, 2013
ABSTRACT

The authors thank the staff at the Wharton Behavioral Lab at the University of Pennsylvania.
Business Economics and Public Policy Department, The Wharton School, University of
Pennsylvania, Philadelphia, PA 19104; judd.kessler@wharton.upenn.edu
‡
Department of Economics, Stanford University, Stanford, CA 94305; alroth@stanford.edu
†
1
I.
Introduction
There are currently over 117,000 people on waiting lists for life-saving organ
transplant in the United States. These individuals are waiting for an organ from a
deceased donor, an individual whose organs are made available to those who need them
upon the donor’s death. Deceased donors provide the majority of transplanted organs in
United States, nearly 80% of the organs donated in 2012.1 Deceased donors can provide
multiple vital organs and other tissues2 whereas living donors overwhelmingly donate
one kidney. Even though one deceased donor can save the lives of up to eight people and
improve the lives of many more, and registering as an organ donor is relatively easy (it
usually just requires checking a box on a form at the state department of motor vehicles
or filling out a form online) only 43% of Americans over the age of 18 are registered as
organ donors (Donate Life America 2012).
Due to federal legislation, there is no monetary incentive for organ donor
registration (see Roth, 1997). The only benefits a donor receives come from altruism
(Becker 1974) and the warm glow from registering (Andreoni 1988, 1989, 1990), for
example the knowledge that the donor may save the lives of other people. These
motivations alone have not been able to halt the steady increase in the number of people
on organ donor waiting lists. Table 1 lists the number people on the waiting list for a
kidney, which has been growing over the past decade.3
1
Based on OPTN data as of Feb. 25, 2013
(http://optn.transplant.hrsa.gov/latestData/rptData.asp).
2
A deceased donor can provide kidneys, liver, heart, pancreas, lungs, and intestine, as well as
corneas, skin, heart valves, cartilage, bone, tendons, and ligaments
3
It currently stands above 95,000, based on OPTN data as of Feb. 25, 2013
(http://optn.transplant.hrsa.gov/latestData/rptData.asp). The long waiting list for kidneys results
in part from the ability for kidney dialysis to keep patients with kidney failure alive for many
years. No dialysis exists for other organs. Waiting lists for other organs are shorter in part because
many patients on those lists die while waiting.
2
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Table 1: U.S. Kidney Donors, Transplants, and Waiting List
Deceased
New Wait
Deceased
Donor
Living
All Wait
List
Donors
Transplants
Donors
List Patients
Additions
5,638
8,539
6,241
50,301
23,630
5,753
8,668
6,473
53,530
24,680
6,325
6,647
57,168
27,278
6,700
9,913
6,573
61,562
29,141
7,178
10,660
6,436
66,352
32,357
7,240
10,591
6,043
71,862
32,417
7,188
10,553
5,968
76,089
32,579
7,248
10,442
6,387
79,397
33,655
7,241
10,622
6,277
83,919
34,406
7,433
11,043
5,770
86,547
33,568
The data for years 2002–2011 are provided by OPTN as of February 22, 2013. New Wait-list
Additions counts patients (rather than registrants) to eliminate the problems of counting multiple
times people who register in multiple centers. All Wait-list Patients also counts patients rather
than registrants. All Wait-list Patients data are from the 2008, 2009, and 2011 OPTN/SRTR
Annual Reports.
Under the organ allocation system currently employed in the U.S. and most other
nations, priority on organ donor waiting lists is given to those who have been waiting on
the list the longest or those with the most immediate medical need.4 One strategy to
generate an incentive for organ donor registration without monetary payment is to
allocate organs differently — to provide priority on organ donor waiting lists to those
who previously registered as donors. Under such a priority system, individuals who
register as organ donors but end up needing an organ (rather than being in a position to
provide one) are more likely to get an organ, or get one more quickly, than individuals
who did not previously register as organ donors. This policy has been studied
experimentally (Kessler and Roth 2012) and has been implemented in Singapore and,
most recently, in Israel, where it appears to have increased the number of deceased donor
organs and the organ donor registration rate (Lavee et al. 2013) although the research into
its effectiveness is ongoing.
One concern with providing priority on the organ donor waiting list for registered
4
The allocation rules vary by organ. For example, in the United States kidney allocation is
primarily by waiting time while liver allocation is primarily by medical need. As noted above,
while kidney dialysis allows individuals to survive for years without a kidney transplant, an
individual whose liver fails will die very quickly if he or she does not receive a new liver for
transplant.
3
donors, however, is the possibility of gaming or loopholes that would allow individuals to
receive priority if they need an organ but avoid ever donating their organs. One way an
organ allocation system could be gamed is if individuals could wait to register as donors
until after they realize that they need an organ (receiving priority without a chance of
having to donate). Careful implementation of allocation rules can eliminate this scope for
gaming. In Israel, for example, an individual who was not registered by April 1, 2012
must be on the registry for three years before receiving priority on a waiting list.
While the scope for this particular type of gaming has been mitigated in Israel, the
possibility of a loophole in the organ allocation system remains a particular concern
there. One of the reported motivations for the Israeli legislation was widespread concern
over free riding by ultraorthodox religious groups. These groups generally do not
recognize brain death (i.e. when the brain ceases to function) as a valid form of death and
consequently oppose providing deceased donor organs.5 However, members of these
religious groups are not opposed to taking the organs of others, even those recovered
from brain dead donors. It has been argued that this group of explicit free riders — those
who will accept organs but not provide them — is a major factor for the relatively low
rates of organ donation in Israel (Lavee et al 2010, Lavee and Brock 2012).
The Israeli legislation providing priority on organ donor waiting lists for
registered donors has not eliminated the scope for this sort of free riding. Instead,
implementation of the legislation generated a potential loophole in the priority allocation
system. [AL, I REMEMBER THIS BEING CALLED FOR EXPLICITLY BY
RELIGIOUS GROUPS, IS THAT RIGHT?] The Israeli donor card allows a potential
donor to check a box on the registration form to request that a clergyman be consulted
before organ donation occurs (see Figure 1 for the Israeli Donor Card). An individual
who wants priority but does not want to be a donor could check that box with the implicit
or explicit understanding that his clergyman would refuse donation if the supposed
5
Most organ donation follows brain death, since the deceased patient can be left on a respirator,
allowing the organs to be kept alive until they are recovered. Cardiac death (when there is an
irreversible loss of circulation) requires really fast action on the order of a few minutes for organ
recovery to be possible. Data from the New England Organ Bank (NEOB) indicates that in New
England, recovery rates are much higher among potential donors who died from brain death than
cardiac death. Recovery rates were about 20 percentage points higher for registered donors and
about 15 percentage points higher for non-registered donors in 2010, 2011, and 2012. (Personal
communication, Sean Fitzpatrick, NEOB.)
4
“donor” were to die.
Figure 1: Donor Card in Israel
Translated into English the card (emphasis and color in original) reads:
With the hope that I may be of help to another, I hereby order and donate after my death:
() Any organ of my body that another my find of use to save his/her life.
Or: () Kidney () Liver () Cornea () Heart () Skin () Lungs () Bones () Pancreas
[] As long as a clergyman chosen by my family will approve the donation after my death.
Even without an explicit checkbox there is still the potential for a loophole in the
Israeli priority system. Signing the donor card in Israel is not a binding will, so next of
kin of a deceased are still asked about donation and can block the organ donation by a
family member who had signed the donor card (Lavee and Brock 2012). Similarly, in the
United States it is possible for next of kin to refuse donation of an individual who has
previously joined a state registry (Glazier 2006). If next of kin are given a chance to
make a final donation decision or block the donation of a registered donor, then
individuals could register as donors to receive priority but inform their next of kin to
prevent their organs from being donated in the event of death, creating a loophole even if
one is not explicitly available.
What is the potential effect of such loopholes to the priority allocation rules? One
potential downside is that the loophole might eliminate the incentive generated by a
5
priority system, since anyone who wanted priority could simply ask for it and take
advantage of the loophole rather than incur the costs associated with donation. Whether
the loophole completely eliminates the potential increase in donors due to the priority
allocation rule would be a function of how the costs of being an organ donor compared to
the costs of taking advantage of the loophole.
Another potential downside, however, is that the existence of a loophole could
“poison the pool” by making individuals who would have donated in the absence of the
priority allocation rule (who we call warm glow donors) choose not to donate once they
observe people using the loophole to take priority without donating. This concern is
reinforced by the arguments in Lavee et al. (2010) that even before the priority rule some
Israelis did not want to donate because they knew there were free riders that would take
organs but not provide them. It is possible, however, that taking a loophole in a priority
system generates more negative feelings than simply not donating since it explicitly
undermines a system designed to help those who do not free ride. If a priority
system loophole poisoned the pool, then introducing a priority system might lead to fewer
donors as individuals who would have donated otherwise choose not to donate or to take
advantage of the loophole.
In this study, we use a laboratory game modeled on the decision to register as an organ
donor to investigate how a priority allocation system impacts donation rates and how the existence
of a loophole affects the outcome of that system. It will be years before we have field data on actual
donations and actual use of the loophole in Israel, but here we are able to study the loophole,
understand what consequences it can have, and anticipate its effects. 6
We find that a priority system generates a vast increase in the organ donation rate
that increases the number of organs donated and overall efficiency. With our
6
Certainly some hypotheses about organ donation can only be investigated by asking for real
organ donor registrations (see Kessler and Roth 2013). However, a number of important aspects
about the organ donation decision and the organ allocation system cannot be easily manipulated
in practice but can be manipulated and studied in the laboratory. We can use the laboratory to
study the incentive issues involved in organ donation, abstracted away from the important but
complex sentiments associated with actual organs. For example, in practice the costs of
registering as an organ donor are difficult to identify in the field. Costs may include fears about
differential medical care for registered organ donors, fear that organs will be removed at a time or
in a manner that is inconsistent with religious beliefs, or simply discomfort from thinking about
ones death. In the laboratory, we can impose monetary costs to model to some level of
approximation the costs faced by donors and control them, e.g. giving some potential donors low
costs and others high costs.
6
experimental parameters, the priority allocation rule substantially improves outcomes for
low cost donors but harms high cost donors in the process. However, providing a
loophole that allows non-donors to take advantage of priority without paying the cost of
donation completely eliminates the benefit created by the introduction of a priority
system. When a loophole is available, we find that almost all non-donors take advantage
of the loophole — 96% of subjects in the loophole condition have priority.
We also find evidence that providing a loophole can poison the pool, undermining
warm glow giving by inducing individuals who would have given without priority to
withhold donation when there is a loophole. This decrease in warm glow giving occurs
primarily when individuals have information about how many people took advantage of
the loophole and see that other subjects are taking advantage of the loophole.
The results of this study enter a rich literature on the study of public goods and
warm glow motivations for private provision of public goods and other pro-social
behaviors. For example, a closely related literature focuses on the donation of blood and
investigates whether incentives for donation can cause a “crowding out” that might lead
to less donation overall. The work has generally found that incentives increase donations
without leading to a decrease in blood quality (see Mellstrom and Johannesson 2008;
Lacetera and Macis 2010a,b; Lacetera, Macis and Slonim 2012).7 We find that subjects’
decisions are substantially influenced by the choices of other subjects to donate or take
the loophole, particularly when those choices are observed. This relates to a vast
literature on social information and conditional cooperation in public goods games, with
results that span both the laboratory and the field [INSERT CITATIONS HERE.]
While this paper investigates priority systems and how they can be undermined
through loopholes in implementation, there are number of strategies beyond providing
priority on organ donor waiting lists that might be employed to increase the number of
individuals who register as organ donors or make donations of organs while living. One
approach that has been heavily advocated is to change the way individuals are asked to
register, for example by switching from an opt-in protocol — in which individuals check
a box to register and leave it blank not to register — to a mandated choice protocol where
individuals must choose between joining the registry or not joining the registry (see
7
For evidence of crowding out in other contexts, see [INSERT CITATIONS HERE].
7
Thaler and Sunstein 2008).8 Another approach is to facilitate kidney exchange, in which
incompatible patient-donor pairs are matched. This process finds compatible patientdonor pairs where, for example, donor A gives a kidney to donor B’s patient while donor
B gives a kidney to donor A’s patient (Roth, Sonmez and Unver 2004, 2005a,b, 2007;
Roth et al. 2006; Saidman et al. 2006).9
Results from our paper demonstrate that along with these other strategies,
allocation policy may be a powerful tool to increase the number of deceased donor organs
that are made available for transplantation. In particular, providing priority on organ
waiting lists for registered donors has the potential to increase the number of
registrations, but how such allocation rules are implemented can make a significant
difference on their effectiveness. Introducing loopholes that allow individuals to receive
priority without the potential to ever be a donor can undermine the benefits of the priority
system and could actually be worse than no priority allocation system at all.
II.
Experimental Design
In the experiment, subjects played a game modeled on the decision to register as
an organ donor. In the experiment, registering to be an organ donor always makes organ
available when the health outcome allows it, and so we refer to the decision to register in
the experiment as “donating”. In the instructions to subjects, the experiment was
described in abstract terms rather than in terms of organs. Subjects started each round
with one “A unit” (representing a brain)10 and two “B units” (representing two kidneys).11
8
This policy change has been implemented in Great Britain as well as a number of U.S. states,
including Illinois and California (New York State just passed legislation to implement mandated
choice in 2012). Recent research, however, suggests that changing the way individuals are asked
to register can have a perverse effect on total donations, particularly from the next of kin of
unregistered donors. In particular, individuals seem to treat the desire not to join the registry
under mandated choice as more sacrosanct than failing to opt-in to the registry (Kessler and Roth
2013).
9
New institutions have been formed to organize these exchanges and to create chains of donation
that start with a single undirected donor (see Roth et al 2006 and Ashlagi et al 2011). As a
consequence there have been over 2000 transplants due to kidney exchange since 2004 according
to data reported to the Organ Procurement and Transplantation Network (see
http://optn.transplant.hrsa.gov/latestData/rptData.asp).
10
Under laws that require heart death for organs to be recovered, an “A unit” could represent a
heart.
8
Each round of the game, the subject is endowed with $6, an A unit, and two B
units. Each round, the subject must decide whether to pay a cost of donation which makes
their B units available to others if their health outcome allows it (i.e. if they have A-unit
failure). Subjects are randomly assigned a cost of donation — either $0.50 or $4.00, kept
constant for the subject for the entire study — that is paid regardless of whether the
subject has A-unit failure.12
In each round, the subject then observes his health outcome. Each subject either
has B-unit failure (i.e. both B units fail and so the subject needs a B unit) or has A-unit
failure in which case his B units are given to those with B-unit failure if he previously
paid the cost of donation.13 Subjects play in a fixed group of 8 players and are told that in
each round, 2 of the 8 will be randomly selected to have A-unit failure (and thus the
probability of A-unit failure is 25%) and that the other 6 will have B-unit failure (and
thus the probability of B-unit failure is 75%). In each round, 0, 2, or 4 B-units are made
available — depending on whether neither, one, or both subjects who ended up with Aunit failure paid the cost to register as a donor. Consequently, either 0, 2, or 4 of the 6
players with B-unit failure each received one B-unit in a round.
Subjects with B-unit failure who receive a B-unit from another player earn an
additional $4 in the round. Subjects with A-unit failure and subjects with B-unit failure
who did not receive a B unit from another player did not earn any additional money in
that round (for these subjects round earnings were their initial $6 minus their cost of
donation if it had been paid). Since there are always two subjects with A-unit failure and
six subjects with B-unit failure, a subject who pays the cost of donation and has A-unit
failure always provides B units to two individuals who would not otherwise receive a B
11
The design of the game bears similarities to the game in Kessler and Roth (2012) with some
simplifications to the implementation and some different parameters. Consequently, our first
result will be to replicate the results in Kessler and Roth (2012) to show this game generates the
same pattern of behavior.
12
Note that we are modeling the cost of organ donation as a cost of registering to be a donor
rather than of actually donating. Deceased donation occurs after death, when we generally assume
that utility flows stop and an individual no longer incurs costs or benefits. We are implicitly
assuming the costs of registering as an organ donor are psychological costs.
13
As noted above, subjects were always asked for their donation decision before they learned
their health outcome. That is, they had to decide whether to pay the cost of donation before they
know whether they have A-unit failure (in which case their B-units would be given to other
subjects) or B-unit failure (in which case their B-units would be useless).
9
units and that those individuals will each earn $4 from the donation. Consequently, each
subject can calculate that paying the cost of donation generates expected earnings of
$2.00 for other subjects — there is a 25% chance the donor will give away 2 B units that
each generate $4.00 of for another subject in the group.
The experimental design varied the organ allocation rules and the amount of
information provided to subjects. There were three different allocation conditions and
two different information conditions, generating six different treatments in a 3x2 design.
We will first describe the organ allocation conditions and then the information
conditions. The organ allocation conditions differed in how B units were allocated to
subjects with B-unit failure.
In the control condition, any available B units were assigned randomly to the
subjects with B-unit failure, with all subjects with B-unit failure being equally likely to
receive a B unit. The control condition models the first-come first-served waiting list
system in the United States with subjects arriving onto the waiting list in a random order
and a limited number of organs available. Each round, the subject was reminded of his
cost of donation and then asked to choose between two options: “Yes, I want to donate
my B units” or “No, I do not want to donate my B units.”
In the priority condition, subjects who had paid the cost of donation but ended up
needing a B unit received priority for available B units. Subjects who paid the cost of
donation and had B-unit failure were in a priority group, and any available B units were
randomly assigned among subjects in the priority group, with each subject in that priority
group being equally likely to receive a B unit. Only if all of the subjects in this priority
group received a B unit were any B units distributed to the subjects with B-unit failure
who did not pay the cost of donation. In this case, any remaining B units were randomly
assigned to subjects with B-unit failure who did not pay the cost of donation, with each of
these subjects being equally likely to receive one of the remaining B units. This is a very
extreme form of priority in that no subject without priority ever receives a B unit unless
all those subjects with priority who need a B unit have received one. Each round the
subject was reminded of his cost of donation and then asked to choose between two
options: “Yes, I want to donate my B units and receive priority for a B unit if I need one”
or “No, I do not want to donate my B units.”
10
In the loophole condition, organs were assigned as in the priority condition, but
subjects could join the priority group either by paying the cost of donating or by asking to
receive priority without paying the cost of donation. Each round, subjects were reminded
about their cost of donation and chose between three options, the two in the priority
condition along with “No, I do not want to donate my B units, but I do want to receive
priority for a B unit if I need one.” Throughout the paper, we refer to this latter option as
receiving priority by taking advantage of a loophole (or just “taking the loophole”).
In addition to varying the organ allocation conditions, the experiment also varied
the information provided to subjects about the costs and decisions of other subjects in
their group. In the low information conditions, subjects only knew their own cost of
donation and (in each round) whether they had B-unit failure and, if so, whether they
received a B unit. The low information conditions were meant to provide noisy feedback
with regard to the number of registered donors and the number of people taking
advantage of the loophole: participants could only infer this from their own experience of
receiving B units when theirs failed. It is easy to imagine policy makers being opaque
about these statistics, making it difficult for individuals to learn about the true numbers.
In the high information conditions, subjects were also told the costs of the other
subjects in their group and (in each round) the number of group members who paid the
cost of donation and how many took advantage of the loophole when it was available.
The high information conditions were meant to model a world in which policy makers
provide more precise information about the number of people who register as donors and
those who take advantage of the loophole when it is available. Figure 2 shows the six
treatments in the 3x2 design.
Subjects stayed in the same information condition (either low information or high
information) for the entire study but played in two different organ allocation conditions.
Subjects were not told how many rounds of the game they would play, but after their
group had played 15 rounds in one of the condition, subjects were informed that the rules
of the game had changed and changes were explained, and the group played 15 rounds in
another organ allocation condition. Each session had 16 subjects who played in one of
two fixed groups, and both groups in a session played in the same order of conditions so
instructions could be read aloud.
11
Figure 2: The 3x2 Experimental Design
Organ Allocation Condition
Information
Condition
3x2
Design
Control
Priority
Loophole
Low
Control, Low Info
Priority, Low Info
Loophole, Low Info
High
Control, High Info
Priority, High Info
Loophole, High Info
After all rounds had been played, subjects were informed of the round that had
been randomly selected for payment and all subjects were paid their earnings from that
round in cash along with a $10 show-up fee.
III. Experimental Results
This paper reports results from 608 subjects who participated in one of 38
sessions run at the Wharton Behavioral Lab during the fall of 2012. Subjects were college
students who participated for one hour. Each of the subjects played 30 rounds of the
game and made decisions anonymously. Average earnings were $16.62 per subject,
including a $10 show up fee. The experiment was conducted using z-Tree 3.2.8
(Fischbacher 2007). As explained in the previous section, subjects played in either the
low information or high information condition. In that information condition, each group
of 8 subjects first played 15 rounds in one organ allocation condition (either Control,
Priority, or Loophole) followed by 15 rounds in another organ allocation condition.
Since subjects started in one of the three conditions and then switched to one of
the other two, there are six possible organ allocation condition orders. Table 2 shows the
number of sessions, groups, and subjects who participated in each order of the conditions
under low information and under high information.
Table 2: Number of Sessions, Groups, and Subjects in Each Treatment Order
Organ Allocation Condition Order
Control,
Priority
Control,
Loophole
Priority,
Control
Priority,
Loophole
Loophole,
Control
Loophole,
Priority
12
4 sessions
Low
(8 groups,
Information
64 Ss)
4 sessions
High
(8 groups,
Information
64 Ss)
4 sessions
(8 groups,
64 Ss)
3 sessions
(6 groups,
48 Ss)
3 sessions
(6 groups,
48 Ss)
4 sessions
(8 groups,
64 Ss)
3 sessions
(6 groups,
48 Ss)
2 sessions
(4 groups,
32 Ss)
3 sessions
(6 groups,
48 Ss)
2 sessions
(4 groups,
32 Ss)
3 sessions
(6 groups,
48 Ss)
3 sessions
(6 groups,
48 Ss)
The main result of interest is the number of subjects who pay the cost of donation
and make their B units available to others in the event of A-unit failure. Figure 3 displays
the results on the probability that subjects pay the cost of donation across all the
treatments. The top panel, Panel A, displays the data from subjects playing in the low
information conditions. The bottom panel, Panel B, displays the data from subjects
playing in the high information conditions. Notice that in each panel, the data lines are
broken after round 15. This gap is to indicate that different groups comprise the data in
Round 1-15 and Round 16-30 for each organ allocation condition. The following
subsections will analyze the data presented in Figure 3.
13
Figure 3: Probability of Donation by Condition and Round
0.9
Panel A: Low Information Condition
Percent of Subjects Who Donate
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Round
Control (Low Info)
Priority (Low Info)
Loophole (Low Info)
0.9
Panel B: High Information Condition
Percent of Subjects Who Donate
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Round
Control (High Info)
Priority (High Info)
Loophole (High Info)
Lines broken to indicate that different groups make up date for Rounds 1-15 and for 16-30
14
IV.1. Priority
The most striking result visible in Figure 3 is that subjects playing under the
priority allocation rule are much more likely to pay the cost of donation than in the
control condition without an incentive to donate.14 Combining data from the high and low
information conditions, the donation rate across all subjects is 69.3% in the priority
condition and 40.9% in the control condition.15 This 28.4 percentage point difference
represents a 70% increase in the donation rate.
Result 1: The priority allocation rule increases donation rates, the likelihood of
receiving a B unit, and earnings
There are two ways in which the priority allocation rule impacts the donation of
subjects. First, there is an immediate response in terms of the number of individuals who
donate even in the first round that priority is introduced (in either round 1 or round 16,
432 observations and 416 observations respectively, p<0.01 for both tests, data clustered
at group level for round 16 test). In addition, groups playing under a priority allocation
rule are less likely to see a decline in donation rate over the 15 rounds they play in the
priority condition than in the other two conditions. This can be seen in Figure 3 and
confirmed in regression analysis in Table 3.
Across all specifications, Table 3 displays a significant coefficient on Rounds
played in condition, a variable that takes a value of 0 to 14 and denotes the number of
previous rounds subjects have played in that condition. That Rounds played in condition
it is negative demonstrates the standard decrease in contribution routinely observed in
public good games in the control conditions of our experiment.
Additional results from Table 3 demonstrate the two effects of the priority
allocation rule noted above. First, the coefficient on Priority is positive and significant,
14
Here we replicate the result from Kessler and Roth (2012). Even in this slightly different game,
a priority allocation rule substantially increases the probability of donation.
15
There are no statistically significant differences in donation between the information conditions
for any of the allocation conditions, so we pool high and low information conditions for some
analysis.
15
indicating that subjects are much more likely to donate in their first round in the priority
condition than in the control condition. Second, the coefficient on Priority* Rounds
played in condition is positive and significant, indicating that contribution in the priority
condition declines much more slowly than in the control condition. While in control
condition the probability of donation decreases by 1.2% to 1.4% per period, in the
priority condition the decrease is only 0.3% to 0.5% per period. Consequently, the
difference between the donation rate in the priority condition and the donation rate in the
control condition grows as subjects have more experience in the condition.
Regressions (1) and (2) analyze data from the first 15 rounds, when subjects have
only had experience with one condition, and generates the same pattern of results as
Regressions (3) and (4) that analyze data from all 30 rounds. Regressions (2) and (4)
control for Info — indicating that data came from the high information conditions — and
include interactions with Info. None of these are significant, demonstrating that these
results are similar for both the low information and high information conditions.
16
Table 3: Organ Registration By Condition and Round
Donation (0 or 1)
Linear Probability Model (OLS)
Rounds played in condition
Priority
Priority*
Rounds played in condition
Loophole
Loophole*
Rounds played in condition
Info
Info*Priority
Info*Priority*
Rounds played in condition
Info*Loophole
Info*Loophole*
Rounds played in condition
Last 15 rounds
Cost
Constant
Observations
Clusters
R-squared
First 15 Rounds
(1)
(2)
-0.014*** -0.014***
(0.002)
(0.002)
0.215*** 0.199***
(0.035)
(0.046)
0.010*** 0.009***
(0.003)
(0.003)
-0.055
-0.015
(0.036)
(0.043)
-0.001
-0.001
(0.003)
(0.003)
0.020
(0.045)
0.028
(0.066)
0.003
(0.003)
-0.088
(0.066)
0.001
(0.005)
-0.121***
(0.007)
0.713***
(0.028)
-0.121***
(0.007)
0.704***
(0.031)
9120
76
0.23
9120
76
0.24
All 30 Rounds
(3)
(4)
-0.012*** -0.012***
(0.002)
(0.002)
0.228***
0.254***
(0.023)
(0.029)
0.009***
0.008***
(0.002)
(0.002)
-0.053**
-0.035
(0.024)
(0.032)
0.001
0.002
(0.002)
(0.002)
0.020
(0.035)
-0.052
(0.040)
0.001
(0.002)
-0.044
(0.043)
-0.002
(0.004)
-0.071*** -0.072***
(0.015)
(0.015)
-0.129*** -0.129***
(0.006)
(0.006)
0.702***
0.692***
(0.023)
(0.028)
18240
76
0.25
18240
76
0.25
Robust standard errors clustered by group are in parentheses: * significant at 10%; **
significant at 5%, *** significant at 1%. Rounds played in condition takes values 0 to
14 for the number of previous rounds the subject has played in that condition. Priority
and Loophole indicate organ allocation condition and test for differences from the
control condition Info indicates the data is from the high information conditions and
interactions with Info test for differences between the low information conditions and
high information conditions. Cost indicates the subject has the high cost of donation.
Last 15 rounds indicates that the data is from the second set of 15 rounds of the game.
17
Table 4 combines the immediate difference in contribution in the priority
condition and the differential changes in donation rate over play in a condition to test for
the average effect of priority over all rounds. Table 4 controls flexibly for round of the
experiment by including round dummies.
Table 4: Organ Registration By Condition
Donation (0 or 1)
Linear Probability Model (OLS)
Priority
Loophole
Info
Info*Priority
Info*Loophole
Cost
First 15 Rounds
(1)
(2)
0.288*** 0.262***
(0.031)
(0.041)
-0.061*
-0.024
(0.035)
(0.047)
0.020
(0.045)
0.050
(0.061)
-0.082
(0.067)
-0.121*** -0.121***
(0.007)
(0.007)
All 30 Rounds
(3)
(4)
0.291***
0.312***
(0.020)
(0.024)
-0.045**
-0.017
(0.022)
(0.028)
0.020
(0.035)
-0.043
(0.040)
-0.061
(0.043)
-0.129*** -0.129***
(0.006)
(0.006)
Round Dummies
Yes
Yes
Yes
Yes
Observations
Clusters
R-squared
9120
76
0.23
9120
76
0.24
18240
76
0.25
18240
76
0.25
Robust standard errors clustered by group are in parentheses: * significant at 10%; **
significant at 5%, *** significant at 1%. Priority and Loophole indicate organ
allocation condition and test for differences from the control condition. Info indicates
the data is from the high information conditions and interactions with Info test for
differences between the low information conditions and high information conditions.
Cost indicates the subject has the high cost of donation. Round dummies include a
dummy for each round of the game.
Results from Table 4 confirm the magnitude of the priory allocation rule. The
priority rule increases the probability of donation by approximately 29 percentage points
over the control condition. Since two subjects from each group are randomly chosen to
have A-unit failure, the increase in the donation rate from the priority rule has a direct
effect on the number of B-units that are made available. Table 5 pools data from the high
and low information conditions and shows — in addition to the donation rates — the
18
average number of B units that are made available, the percentage of subjects with B-unit
failure who receive a B unit, and average earnings by treatment.
The priority condtion generates 2.80 B units per period, which is a 70% increase
over the 1.65 B units generated in the control condition (the differnece is statistically
significant, p<0.01, data clustered at group level). The same pattern immerges for
probability of receiving a B unit and earnings (p<0.01 for both tests, data clustered at
group level).
Donation Rate
Number of B
Units available
Percent who get B
Unit when needed
Earnings
Table 5: Outcomes by Treatment
Control
Priority
Loophole
40.9%
69.3%***
35.9%**
1.65
2.80***
1.49
27.5%
46.6%***
24.9%
$6.50
$6.87***
$6.46
This table pools data from the high and low information conditions over all 30 rounds. Stars in
Priority and Loophole conditions indicate a statistically significant difference from the Control
condition, robust standard errors clustered at group level: * significant at 10%; ** significant at
5%, *** significant at 1%.
While the priority condition has a large positive effect on donation, the
availability of B units, and earnings, the results in Table 5 mask two countervailing
effects on subject outcomes. The introduction of priority affects the low cost donors
(those who must pay $0.50 to donate) and the high cost donors (those who must pay
$4.00 to donate) differently.
Table 6 pools over information condition and shows the donation rate, the
percentage of subjects with B-unit failure who receive a B unit, and average earnings by
treatment and cost of donation. While the low cost subjects see a large increase in the
probabilty of receiting a B unit and in earnings, the high cost subjects see a decrease in
the percent who receive a B unit and in earnings. These positive effects for low cost
donors and negative effects for high cost donors between the priority condition and the
control condition are statically significant (p<0.01 for all tests, data clustered at group
level).
19
Table 6: Outcomes by Cost and Treatment
Cost = $0.50
Control
Priority Loophole
Cost = $4.00
Control
Priority Loophole
Donation Rate
50.1%
85.5%***
43.8%**
13.1%
20.6%***
12.0%
Percent who get B
Unit when needed
26.2%
55.5%***
24.5%
31.3%
20.1%***
25.9%
Earnings
$6.54
$7.24***
$6.51
$6.40
$5.78***
$6.32
This table pools data from the high and low information conditions. Stars in Priority and
Loophole conditions indicate a statistically significant difference from the Control condition,
robust standard errors clustered at group level: * significant at 10%; ** significant at 5%, ***
significant at 1%.
It is straightforward to see why the priority allocation rule would have a
differential effect on the low cost and high cost donors. The priority allocation rule
rewards individuals who paid the cost of donation with a higher likelihood of receiving a
B unit and the accompanying extra earnings. Since this incentive induces significantly
more donors who are low cost (only 20.6% of high cost subjects donate in the priority as
compared to 85.5% of low cost subjects) high cost donors are more often in the lower
priority class and less likely to receive a B unit. The priority rule, while generating more
donors and increasing overall efficiency generates inequality between low cost donors —
for whom priority makes outcomes substantially better — and high cost donors — for
whom priority makes outcomes substantially worse.
While the net effect is positive, the inequality might still be a concern to policy
makers. A policy maker who wants to mitigate this inequality might be tempted to
provide a way for high cost donors to avoid the harsh outcome associated with the
priority system, for example by providing a loophole for them — a way for high cost
donors to receive priority without donating. In practice, however, the cost of donation is
not observable, and so such a loophole would have to be available to everyone.
In the next section we investigate the effect of introducing the loophole into the
priority allocation system. Of course, a priority allocation system might have loopholes
that were not explicitly designed by policy makers for any particular end. Loopholes may
arise by accident due to the institutional details of the policy. Our loophole condition
20
does not distinguish between the reasons that the loophole exists but rather investigates
how such a loophole affects donation when it is available.
IV.2. Loophole
What is the effect of adding a loophole to the priority allocation rule? We can see
immediately from Figure 3 that having a loophole in the priority allocation system
eliminates the increase in donation induced by priority. These results are statistically
significant as shown in the results in Table 4. The Loophole coefficient has a negative
sign for all specifications, indicating that not only are the donation rates in the loophole
conditions less than in the priority conditions (tests of whether the coefficient on Priority
is equal to the coefficient on Loophole are all rejected with p<0.01, data clustered at
group level) they are also at least directionally less than the donation rates in the control
condition. Consequently, the loophole has completely eliminated the beneficial effect of
priority.
Result 2: The loophole eliminates the increase in donation generated by the priority
allocation rule
Why is the loophole condition is leading to such a vast decrease in decrease in
donation? Table 7 shows the choices subjects make in the loophole condition broken
down by information and cost of donation. Subjects overwhelming take advantage of the
loophole when it is available. Among the low cost subjects, for whom donation only costs
$0.50, the majority of actions are take advantage of the loophole. Only 2.5% of actions of
low cost subject are to not donate and not take the loophole. For high cost donors the vast
majority of actions are to take the loophole without donating (75% under high
information and 83% under low information). Averaging across high and low cost
subjects and both high and low information conditions, only 4% of actions are subjects
who choose neither to donate nor to take priority without donating. Put another way,
averaging across rounds, 96% of subjects in the loophole condition have priority.16
16
While rates of not donating and not asking for priority are quite low, 74 of the 368 subjects
(20%) who play in the loophole allocation condition take that action at least once.
21
Table 7: Choices In the Loophole Condition
Low Information Condition
Cost = $0.50
Cost = $4.00
High Information Condition
Cost = $0.50
Cost = $4.00
Donate
46.03%
12.05%
41.00%
11.83%
Do Not Donate
2.52%
4.74%
2.50%
13.17%
Take Loophole
51.45%
83.21%
56.50%
75.00%
Looking back at Table 5, we can see that introducing the loophole also eliminates
the gains in the B units available and earnings associated with the priority rule. The
percent of subjects with B-unit failure who get a B unit has fallen back to 24.9% and
earnings are down to $6.46. Both of these are statistically smaller than the results from
the priority condition (p<0.01, data clustered at group level) and statistically
indistinguishable from their counterpart results the control condition. The introduction of
the loophole also eliminates the inequality generated by the priority allocation rule.
Looking at Table 6, we see that in the loophole condition the percent of subjects with Bunit failure who get a B unit and earnings are back to control condition levels for both the
low cost ($0.50) and high cost ($4.00) donors.17
Another striking fact that can be seen in results from Tables 4, 5 and 6 is an
additional effect of introducing the loophole to the priority allocation rule. Namely,
donation rates are lower in the loophole condition than in the control condition. The
coefficient on Loophole is negative in Table 4 and the donation rate in the loophole
condition is sometimes significantly different from the donation rate in the control
condition in Tables 5 and 6. The difference is statistically significant when pooling all the
data in the experiment, for example in Regression (3) of Table 4 and in the first row of
Table 5.
Subjects in the loophole condition are significantly less likely to donate than
subjects in the control condition. Breaking down the data by information condition, as in
17
The loophole condition results on percent who get a B unit and earnings are statistically
different from the priority condition results for both the low and high costs donors (p<0.01 for all
tests except the probability of getting a B unit among high cost donors where p<0.05, data
clustered at group level).
22
Table 8, we see that this negative effect of the loophole is statistically significantly
negative for the high information condition and only directionally significant for the low
information condition.
Priority
Loophole
Cost
Round
Dummies
Observations
Clusters
R-squared
Table 8: Organ Registration By Condition
Donation (0 or 1)
Linear Probability Model (OLS)
First 15 Rounds
All 30 Rounds
Low
High
Low
High
Information
Information
Information
Information
(1)
(2)
(4)
(5)
0.262***
0.312***
0.310***
0.271***
(0.041)
(0.045)
(0.025)
(0.031)
-0.024
-0.106**
-0.020
-0.077**
(0.048)
(0.048)
(0.029)
(0.033)
-0.120***
-0.123***
-0.130***
-0.127***
(0.010)
(0.010)
(0.008)
(0.009)
Yes
Yes
Yes
Yes
4800
40
0.21
4320
36
0.27
9600
40
0.26
8640
36
0.26
Robust standard errors clustered by group are in parentheses: * significant at 10%; **
significant at 5%, *** significant at 1%. Priority and Loophole indicate organ allocation
condition and test for differences from the control condition. Info indicates the data is from the
high information conditions and interactions with Info test for differences between the low
information conditions and high information conditions. Cost indicates the subject has the high
cost of donation. Round dummies include a dummy for each round of the game.
Result 3: In the high information condition, subjects are less likely to donate in the
loophole condition than the control condition
It is worth noting that do not get a significant interaction between the high and
low information conditions when comparing the loophole condition to the control
condition, as can be seen in Regressions (2) and (4) of Table 4, in which Info*Loophole is
not statistically significantly less than 0.18 However, we do get an interaction between the
18
One reason that we do not get a significant interaction between high and low information and
the priority and control conditions is that in the presence of low information, the loophole
generates directionally less donation than the control condition.
23
high cost and low cost conditions when comparing the loophole to the priority condition
in the first 15 rounds — before subjects have experienced any other treatments.
As can been seen in Figure 5, which shows donation rates for priority and
loophole by information condition in the first 15 rounds, the interaction is partly driven
by the higher donation rates in priority under high information than under low
information (76.9% in the high information condition and 69.9% in the low information
condition, p<0.1 clustered at group level). In addition, the donation rate in the loophole
condition is directionally lower under high information than under low information.
When combined, this leads to a significantly negative interaction. Adding the loophole to
the priority rule is significantly more damaging under high information than under low
information. Under high information the donation rates drops from 76.9% under priority
to 35.1% under loophole (41.8 percentage points) whereas under low information the
donation rate drops from 69.9% under priority to 41.3% under loophole (28.6 percentage
points). The interaction term reflects a 13.2 difference in difference in donation rates,
p<0.05 clustered at group level).
Figure 5: Priority and Loophole for First 15 Rounds
Percent of Subjects Who Donate
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1
2
3
4
5
Priority (High Info)
Priority (Low Info)
6
7
8
9
Round
10
11
12
13
14
15
Loophole (High Info)
Loophole (Low Info)
24
The lower donation rates in the loophole condition than the control condition
indicate that subjects who would have donated due to warm glow choose not to donate
when there is a loophole in the priority allocation system. This result motivates the title of
our paper, since the introduction of the loophole generates a deterioration of warm glow
giving.
Why is there a decrease in in warm glow giving in the loophole condition and
why is it particularly strong in the high information condition? First, at the start play (i.e.
rounds 1 and 16) donation rates are directionally lower in the loophole condition than in
the control condition, as can be seen in Figure 3. In addition, there are dynamic effects
that reinforce the immediate effects in the donation rates and are more pronounced in the
high information condition.
When provided with extra information in the high information treatments,
subjects are better able to condition their behavior on the behavior of others. In particular,
they respond by withholding donation when they observe others taking advantage of the
loophole in the previous round.
Result 4: In the high information loophole treatment, subjects respond to the
number of people who take advantage of the loophole in the previous round
Table 9 shows regression results investigating the probability of donation within
the loophole conditions. All regressions have fixed effects to control for a subject’s
general proclivity to donte. Regression specifications in Table 9 therefore investigate how
outcomes from the previous round of our game influence a subject’s choice in the current
round. The regressions all include a control for whether a subject received a B-unit in the
previous round. In the low information conditions this is the only signal a subject gets
about the choices of the others in his group.
In the low information condition, the coefficient Others took loopholet-1, the
number of other subjects who took the loophole in the previous round, is small and
insignificant. This result is not suprising given that we have controlled for whether the
subject got a B unit in the previous round, which is the only signal that a subject gets
about the behavior of his group members in the low information conditions.
25
However, the interaction Info*Others took loopholet-1 is large, negative and
significant, demonstrating that in the high information condition, subjects are much less
likely to donate in the current round the more people in their group they observed taking
the loophole in the previous round. This result holds when looking at the first 15 rounds
in Regression (1) or all 30 regressions in Regression (4). Comparing the coefficent on
Info*Others took loopholet-1 to the coefficient on B unitt-1 gives a sense of the magnitude
of the effect. For each other group member who is observed to take the loophole in the
high information condition, the probability an individual donates in the following round
decreases by about 3%. Similarly when an individual receives a B unit in the previous
round, the probability he donates increases by about 3%. Observing one fewer person
take the loophole has an equivalent effect on donation of getting a B-unit in the previous
round.
The negative effect on the number of other group members who took the loophole
persists when we focus on the six members of each group who have low cost of donation
(i.e. $0.50), both in the first 15 rounds in Regression (2) and in all 30 rounds in
Regression (5). This group of subjects is of particular interest since they are more likely
to contribute than the subjects with a high cost of donation and so we are more likely to
observe a response in their donation rates to the actions of others. In addition, by focusing
on this group of subjects, we can test an additional hypothesis: in the high informaton
codntion that these subjects respond disconitnuously to the number of other group
memebrs who take advantage of the loophole. Namely, we expect these subjects to
respond differently when they learn that more than 2 other subjects took advantage of the
loophole.
Why might we expect subjects to behave differently to the first two 2 subjects
who take the loophole? Because there are exactly 2 high cost donors in each group. While
subjects only see the numebr of subjects who took advantage of the loophole, they may
repond more strongly to the third other person who takes advantage of the loophole since
it guarantees that at least one low cost subject — i.e. someone just like them — is taking
advantage of the loophole. Focusing on the first 15 rounds, in Regression (3), and looking
at all 30 rounds in Regression (6), we see that there is a sharp change in the effect of the
loophole when moving from 2 subjects taking the loophole to 3 subjects taking the
26
loophole. In Regression (3), an F-test confirms that the coefficeint on Info*(3 Others took
loopholet-1) is statistically significantly smaller than Info*(2 Others took loopholet-1) with
p<0.05. No other binary comparisons between adjacent coefficients are statistically
significantly different in Regression (3).19
These results suggest that subjects respsond with less doantion when they observe
other subjects taking advantage of the loophole in the high information condition, and the
low cost subjects respond particularly strongly to the knowledge that other low cost
subjects are taking advantage of the loophole.
19
In Regression (5) the magnitude on Info*(3 Others took loopholet-1) is over twice as large as
Info*(2 Others took loopholet-1) although the difference is not statistically significant.
27
Others took
loopholet-1
Info*Others
took loopholet-1
Info*(1 Other
took loopholet-1)
Info*(2 Others
took loopholet-1)
Info*(3 Others
took loopholet-1)
Info*(4 Others
took loopholet-1)
Info*(5 Others
took loopholet-1)
Info*(6 Others
took loopholet-1)
Info*(7 Others
took loopholet-1)
B unitt-1
Info*B unitt-1
Table 9: Donation in the Loophole Condition
Donation (0 or 1)
Linear Probability Model (OLS)
First 15 Rounds
All 30 Rounds
All
Cost = $0.50
All
Cost = $0.50
(1)
(2)
(3)
(4)
(5)
(6)
0.009
0.009
0.009
0.007
0.007
0.007
(0.010)
(0.010)
(0.010)
(0.006)
(0.008)
(0.007)
-0.037***
(0.011)
0.024
(0.022)
0.008
(0.038)
-0.042**
(0.015)
0.025
(0.032)
0.043
(0.044)
-0.034***
(0.009)
-0.002
(0.033)
-0.037
(0.038)
-0.159***
(0.051)
-0.163***
(0.045)
-0.189***
(0.057)
-0.238***
(0.080)
-0.386**
(0.166)
0.025
(0.032)
0.044
(0.045)
0.032**
(0.015)
0.004
(0.028)
-0.039***
(0.011)
0.036*
(0.020)
0.026
(0.031)
-0.040
(0.052)
-0.046
(0.030)
-0.102**
(0.050)
-0.121***
(0.043)
-0.120**
(0.051)
-0.233***
(0.067)
-0.312***
(0.084)
0.036*
(0.020)
0.024
(0.031)
Rounds
Yes
Yes
Yes
Yes
Yes
Yes
Dummies
Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Observations
2464
1848
1848
5152
3864
3864
Subjects
176
132
132
368
276
276
Clusters
22
22
22
46
46
46
R-squared
0.21
0.02
0.02
0.27
0.01
0.01
Robust standard errors clustered by group are in parentheses: * significant at 10%; **
significant at 5%, *** significant at 1%. Others took loopholet-1 is the number of other
subjects who took the loophole in the previous round. Info indicates the data is from the high
information conditions and interactions with Info test for differences between the low information
conditions and high information conditions. B unitt-1 indicates that a subject received a B unit in
the previous round. Round dummies include a dummy for each round of the game. All
regressions exclude round 1 data and Regressions (4), (5), and (6) also exclude round 16
data for which there is no previous round behavior in that condition to observe.
28
IV. Discussion
In our experiment, the introduction of the priority rule generates a significant
increase in the number of donors, which was accompanied by more B units being made
available and higher earnings overall. Adding a loophole to the priority system, as is
allowed in the implementation of the Israeli system, completely undermined the
beneficial effect of priority. In fact, when information about the takeup of the loophole
option was provided, the existence of the loophole had an additional effect of “poisoning
the pool,” undermining warm glow giving and leading to fewer donations under the
loophole condition than in the control condition without a priority rule.
That the priority rule is effective at increasing donation rates above the first-come
first-served policy in the control condition may be somewhat unsurprising given that
donation in the control condition relies completely on pro-social inclination. Subjects in
the control condition do not have a monetary incentive to register as an organ donor since
their donation does not impact their probability of receiving a B unit. Instead, those who
donate may feel an altruistic or warm glow motivation to do so. Under the parameters of
the experiment, registering as an organ donor generates $2.00 in expected earnings for
other subjects (with a 25% probability a donor will have A-unit failure and give a B unit
to two subjects who will earn an addition $4.00). Consequently, we might expect some
subjects to be organ donors in the control condition, particularly those with costs of $0.50
for whom donation is socially efficient. Inspired by Andreoni (1990) we call these donors
warm glow donors.
The priority rule introduces an incentive for an individual to donate as it puts the
donor in a priority class with a higher likelihood of receiving a B unit in the event of Bunit failure. Given the monetary payoffs in the game, there is an equilibrium in which all
low cost subjects donate in the priority condition. Assuming all other low cost subjects
donate, the expected benefit of having priority is $1.52, which is well above the $0.50
cost of donation.20 Of course, there is always an equilibrium in which no one donates,
20
The calculation of the value of priority and the existence of the equilibrium is outlined here.
We assume that all other low-cost subjects donate, and that neither of the high-cost subjects
donate, and determine whether the last low-cost donor wants to donate. With probability 0.25 the
subject has A-unit failure and there is no benefit to having priority. Conditional on having B-unit
failure, there are three possible outcomes that need to be considered: (1) both A-unit failures are
29
since priority has no value to a lone donor — he can never give a B unit to himself. While
providing an incentive for individuals to donate may crowd out intrinsic motivation for
donation from warm glow donors (as has been suggested in other contexts by [INSERT
CITES see Titmuss 1970?, Gneezy and Rustichini 2000, Benabou and Tirole 2006, etc.],
the incentive from the priority rule generates a significant motivation to donate that leads
to more donation.
In our study, the priority allocation rule harms high cost donors while
substantially improving outcomes for the low cost donors. One might think such
inequality is reasonable or even fair. Those with high costs of donation are much less
likely to provide the public good by donating their B units and so they should be less
likely to receive a benefit from the public good themselves (see Lavee and Brock 2012).
Alternatively, one might think that if costs are exogenously imposed, policy makers
should try to eliminate the inequity that comes from the differing costs of donation rather
than exacerbating them. For example, the inequality induced by the priority rule might be
particularly disconcerting if the high cost of donation comes as a result of religious
beliefs about which we do not want to discriminate.21
A policy maker who wanted to mitigate this inequality might imagine giving high
cost donors a way of receiving priority without paying their substantial costs of donation,
for example a loophole that allows them to receive priority without donating. The check
box on the Israeli organ donor card, which asks for the family of the deceased consult a
clergyman before donation, may serve the purpose of providing a loophole for high cost
donors for whom religious beliefs warrant a second option on whether death has
high-cost players, which occurs with probability (2/7*1/6) = 2/42, and generates 0 value to
priority; (2) one A-unit failure is a low-cost player, which occurs with probability (2*2/7*5/6) =
20/42, and generates a value of priority of $4*(2/5 – 0) = $1.60, since having priority puts the
subject in the pool for 2 B units with the 4 other low cost donors; (3) both A-unit failures are lowcost donors, which occurs with probability (5/7*4/6) = 20/42, and generates value to priority of
$4*(1 – 1/3) = $2.67 since having priority secures one of the four B units for sure rather than
having a 1/3 chance of getting the B unit in the non-priority pool. The total expected benefit to
priority is 0.75*(2/42*$0 + 20/42*$1.60 + 20/42*$2.67) = $1.52. Notice that the high-cost
players never donate in equilibrium, since their cost of donation is $4.00, while the benefit of
getting a B unit is $4.00 and so the expected benefit of having priority is always less than $4.00.
21
Whether religious beliefs are a choice or are exogenously imposed (and whether we should let
our opinions on that matter influence how we think about the inequality induced by a priority
rule) are well beyond the scope of this paper.
30
occurred. However, since there is no way to distinguish high and low cost donors, the
loophole (or in this case the check box on the donor card) is available to everyone.
Here we explicitly experiment with a loophole option in a priority allocation rule.
We find that if it is available, those with both low costs and high costs of donation take
advantage of the loophole — only 4% of actions in the loophole condition were subjects
who did not donate and did not take the loophole. This ubiquitous use of the loophole
among non-donors completely eliminated any potential efficiency gain from priority.
In addition, when a loophole is available and individuals are provided with
information about the use of the loophole, donation levels are below what they would
have been in the absence of a priority system. The introduction of a loophole undermines
the warm glow giving that would have taken place otherwise, particularly when subjects
are informed about others donation decisions, leading to significantly worse outcomes
overall. Results from the high information conditions suggest that the one reason for this
decrease in warm glow giving is that low costs subjects respond by withholding donation
when they see people take advantage of the loophole. This effect is particularly strong
when subjects see more than two people take advantage of the loophole, suggesting that
at least one low cost subject was doing so.
This response to the number of group members who took the loophole is
supportive of — but importantly different from — results about conditional cooperation
showing that subjects are more likely to contribute to public goods when others
contribute [INSERT CITES]. It is also supportive of but distinct from the argument in
Lavee et al (2010) that organ donation rates in Israel were historically low because of the
presence of free riders. By looking at use of a loophole in a priority organ allocation
system, we can investigate a special case in which subjects not only observe subjects who
fail to contribute but rather take a actions that specifically undermines a system that is
designed to benefit contributors.
Even though incentives in the loophole condition incentives are identical to the
control condition so long as everyone who does not donate takes the loophole,22 we see
22
To see this note that if everyone in a group either pays the cost of donation or takes advantage
of the loophole, then the loophole condition operates exactly like the control condition. The two
outcomes would be identical since all subjects would have the same likelihood of receiving a B
31
that subjects respond differently to the priority system with the loophole than to the
control condition.
To take advantage of the loophole available in the Israeli system requires an
individual to sign a donor card to receive priority with the specific intention of having
that donation revoked if ever in a position to donate. There are additional issues and
psychological costs that may arise by taking advantage of a loophole in this way, for
example individuals may feel like they have made a promise (see [INSERT CITES, e.g.
Lisa Shu and Max Bazerman]) or agreed to a contract (see Kessler and Leider 2012) that
will generate costs of taking the loophole. By providing the loophole a simple third
option along with donate and do not donate, we likely minimized these costs, although
20% of subjects who played in the loophole condition chose not to donate and not to take
the loophole in at least one round.23
While this paper has focused on the decision to register as an organ donor, and
how priority allocation rules and their loopholes affect donation, we believe our results
speak more broadly to public goods and club goods. Since only one person can use an
individual organ, a transplanted organ is a private good. However, the registry of organ
donors can be thought of as a public good since a larger pool of potential donors benefits
the pool of potential recipients. In other words, registering to be an organ donor
resembles a public good ex ante that may provide private goods ex post.24 The priority
rule we study here turns this ex ante public good into a type of club good, that provides
contributors with preferential access to the ex post private goods. We see that the
allowing for a loophole that provides access to the club good for non-contributors
undermines the effect and turns warm glow donors away from contribution. This latter
result arises when subjects can observe the contributions and loophole selections of
unit whether they donate or not and thus the only incentive to donate is the warm glow a subject
feels from providing B units to subjects who might need one, just as in the control condition.
23
Although among these 20%, this choice was selected on average only 3 times out of the 15
rounds. Only one subject out of 368 made this choice — not donating and not taking the loophole
— in all 15 rounds of the loophole condition.
24
It is worth noting that the pool of potential organ donors is rival (or congestible) since the more
people who take organs from the pool decease the likelihood that others receive an organ — or
force others to wait longer. This feature is common to other non-excludable goods (i.e. public
parks, roads, and bridges) that are commonly thought of as public goods.
32
others and suggests that if loopholes cannot be avoided, it may be counterproductive to
broadcast cases of the use of such a loophole.
V.
References
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Journal of Political Economy
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Donate Life America
Glazier, Alexandria K. Donor rights and registries, Lahey Clinic, Winter, 2006 (found at
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Lavee 2012
see Roth, 1997
Thaler and Sunstein 2008
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