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Preliminary: Please Do Not Cite
Allocating Scarce Organs: How a Change in Supply Affects Transplant Waiting Lists*
Stacy Dickert-Conlin dickertc@msu.edu
Todd Elder telder@msu.edu
Keith Teltser teltserk@msu.edu
November 12, 2015
Abstract
The shortage of human organs in the United States is vast in the face of legislation that prohibits their purchase or sale. Systems for allocating organs are complex and vary by organ, but they generally begin by generating a list of medically compatible transplant recipients in a geographic area. Because geography plays a key role in allocation, shocks to the local supply of organs will likely affect transplant waitlists. We use data on transplant recipients from the Scientific Registry of Transplant Recipients to assess whether the shift in the supply of organs arising from changes in motorcycle helmet laws affects the behavior and outcomes of transplant candidates. We find that following repeals of statewide motorcycle helmet laws, the local supply of transplantable organs from donors killed in motor vehicle accidents increases by nearly 20 percent. Transplant candidates respond strongly to this supply shock – inflows to local transplant waitlists increase by roughly 12 percent in the years following the repeal. These inflows are especially pronounced among those who live outside the local area, implying that in the absence of a formal pricing mechanism, waiting times for organs are the relevant “price” determining listing decisions. In addition, transplants from living donors decline following these supply shocks, suggesting that the relative prices of transplants from living and deceased donors influence candidates’ decisions to seek organs from living donors.
* The data reported here have been supplied by the Minneapolis Medical Research Foundation (MMRF) as the contractor for the Scientific Registry of Transplant Recipients (SRTR). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy of or interpretation by the SRTR or the U.S. Government. The authors thank Gopi Goda and seminar participants at Montana State University for very valuable input to the project. We are also grateful to Jonathan Siegle for excellent research assistance. All errors are our own.
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I. Introduction The National Organ Transplant Act of 1984 decreed that it is “unlawful for any person to
knowingly acquire, receive, or otherwise transfer any human organ for valuable consideration for
use in human transplantation.” In the absence of a pricing mechanism for this scarce resource,
vast organ shortages have developed, with roughly 122,000 persons awaiting organ transplants in
the U.S.1 This number grows dramatically every year, in spite of numerous efforts to increase
the supply of transplantable organs, including educational campaigns (Siminoff et al., 2009;
Rodriguez et al., 2007), social media outreach (Cameron et al., 2013), and coordination of paired
kidney exchanges (Roth et al., 2004, 2005; Ausabel and Morrill, 2014). Additional reform
proposals include moving to a system of presumed consent for donors (Abadie and Gay, 2006;
Bilgel, 2012), allowing financial exchanges for organs (Becker and Elias, 2007; Lacetera et al.,
2014; Wellington and Sayre, 2011) and altering the organ allocation rules to induce more
donations (Kessler and Roth, 2012; Li et al., 2012). The evidence on the success of these efforts
to increase the supply of organs is limited, and we know very little about how a shift in supply of
organs may affect transplant candidates’ behavior and outcomes.2
Without a pricing mechanism in place, the effect of an increase in the supply of organs
will depend on the nature of the alternative system for allocating the scarce resource. The United
States government oversees a system for allocating organs that attempts to address a balance of
equity and efficiency – as the nationwide Organ Procurement and Transplantation Network
(OPTN) defines it, a balance of “justice (fair consideration of candidates' circumstances and
medical needs), and medical utility (trying to increase the number of transplants performed and
1 http://optn.transplant.hrsa.gov/, accessed 11/09/2015. 2 Fernandez, Howard and Stohr (2013) are an exception, in that they consider the effect of an increase in deceased kidney donors on living kidney donations.
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the length of time patients and organs survive).”3 The system is complex and varies by organ,
but it generally begins by generating a waitlist of medically compatible transplant recipients in a
geographic area. Geographic proximity plays a central role because organs have a limited time
when they are viable between procurement and transplantation.4 As a result, shocks to the local
supply of organs will likely affect the outcomes and composition of local transplant waitlists.
We use data on organ donors and transplant recipients from the Scientific Registry of
Transplant Recipients (SRTR) to consider whether shifts in the supply of transplantable organs
affect the behavior and outcomes of transplant candidates and their physicians. We focus on
shocks to organ supplies generated by variation in state-level motorcycle helmet laws; all else
equal, these shocks might be expected to affect organ shortages and the resulting waiting time for
individuals on transplant waitlists. However, without a price mechanism in place, expected
waiting time serves as a signal of the scarcity of the organs. If supply shocks change expected
waiting times, it is possible that the demand for organs will respond and mitigate some or all of
the effects of changes in supply.
We estimate whether the demand for organs in response to a higher supply of organs
manifests itself in increased inflows onto waitlists following statewide helmet law repeals. In
addition, we consider whether transplant candidates with the option of receiving an organ from a
living donor and more likely to exercise this option when the supply of organs is higher. Finally,
we consider the overall effect of helmet laws on exits from transplant waiting lists, including
both the means of and the timing of exits.
3 http://optn.transplant.hrsa.gov/learn/about-transplantation/how-organ-allocation-works/ 4 OPTN reports the maximum preservation times for hearts and lungs at 4 to 6 hours; liver at 8 to 12 hours; pancreas at 12 to 18 hours and kidney at 24 to 36 hours (from http://optn.transplant.hrsa.gov/learn/about-transplantation/how-organ-allocation-works/).
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We have four main substantive findings. First, repeals of motorcycle helmet laws
substantially increase the supply of transplantable organs. This finding is closely related to
Dickert-Conlin, Elder and Moore (2011; DCEM hereafter), who find that motorcycle helmet
laws generate (presumably unintentional) shocks to the supply of organ donors. Because each
donor can potentially contribute multiple organs to persons on multiple waitlists, we extend
DCEM’s analysis by quantifying how helmet laws affect the supply of individual organs. We
estimate that repeals of statewide helmet laws increase the local supply of transplantable organs
from donors killed in motor vehicle accidents by nearly 20 percent. These shocks are
particularly large for lungs, kidneys, and livers.
Second, we find that transplant candidates respond strongly to local supply shocks, with
inflows to local transplant waitlists increasing by roughly 12 percent in following helmet law
repeals. These inflows are largely driven by those who live outside the local area, rather than by
more candidates signing up for their “home” waitlists. The implication is that transplant
candidates’ decisions of which waitlists to enter are driven, at least in part, by variation in
expected waiting time across the waitlists. Moreover, we find that candidates who are listed on
multiple waitlists have by far the largest response to helmet law repeals, inflows onto waitlists
increasing by over 40 percent relative to baseline. Taken together, these results suggest that in
the absence of a formal pricing mechanism, waiting times for organs are the relevant “price”
determining where candidates choose to list.
Third, we find that donations from living donors decline when the supply of organs from
deceased donors increases due to helmet law repeals. As the relative price – again, as measured
by expected waiting time – of a transplant from a decreased donor declines, some candidates are
induced to opt for a transplant from a deceased donor rather than a living one. These effects are
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most pronounced for potential transplants from living donors who are not blood relatives or
spouses of the candidate, suggesting that these are disproportionately the “marginal” cases where
the relative costs of living and deceased donors are most influential. Increases in the supply of
deceased donors also decrease living donations from parents, children, spouses, and siblings, but
by smaller magnitudes. These findings are consistent with those of Fernandez, Howard and Stohr
(2013), who estimate that an increase in the supply of deceased kidney donors nearly completely
crowds out kidney donations among non-biologically-related living donors.
Our findings on both waitlist inflows and living donors are suggestive that increases in
the supply of transplantable organs generates behavior that at least partially offsets the direct
effects of reduced waiting time. In order to estimate the overall effect on candidate outcomes,
we estimate how increases in organ supply effects health outcomes for transplant candidates.
We focus on time-to-transplant, the probability of exiting the waitlist through various means
(including successful transplant or death prior to receiving a transplant), and, conditional on a
transplant occurring, the probability that it is successful (known as “graft survival”) for one, two,
and five years post-transplant. We find little evidence that an increase in the supply of organs
increases graft survival time conditional on transplant, but we do find evidence for a decline in
the likelihood of dying while waiting for an organ. It is likely that behavioral responses offset at
least some of the beneficial effects of an increase in supply of organs on the outcomes of
transplant candidates, but the offset is not complete.
Finally, our findings raise questions about the balance of justice and medical utility in the
current allocation mechanism, which relies heavily on geographic boundaries. Those transplant
candidates who have informational or financial advantages might be most likely to be able to
capitalize on violations of the law of one price, which in this setting implies that expected
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waiting times for organs should not vary across location. For example, several articles in the
popular press alluded to the lack of “fairness” in the organ allocation mechanism in 2009 when
Steve Jobs, who lived in California at the time, obtained a liver transplant in Memphis,
Tennessee, which had a median wait time roughly 85 shorter than the national average.5
In the following section we explain the setting in which organ donation exists and
describe our data sources on organ donations and transplants. Section III estimates a causal
relationship between helmet laws and organ donations. In Section IV we estimate transplant
candidates responses to the supply shocks estimated in Section III and Section V considers how
the supply and demand for organs combine to affect transplant candidates outcomes. Section VI
concludes.
II. Data and Institutional Details Data on Organ Donations and Transplants
The current system of allocating organs from deceased donors originated with the 1984
National Organ Transplant Act. In addition to prohibiting the exchange of organs for monetary
compensation, the Act created the OPTN as the overseer of a single non-profit organization, the
United Network for Organ Sharing, charged with ensuring an equitable allocation system (see
Leppke et al., 2013, for more details).6
This study uses data from the Scientific Registry of Transplant Recipients (SRTR). The
SRTR data system includes data on all donor, wait-listed candidates, and transplant recipients in
the US, submitted by the members of the Organ Procurement and Transplantation Network
5 A substantial part of the criticism was based on the argument that Jobs used his significant financial means to obtain an organ that might be “better served” by being transplanted to a candidate without metastatic pancreatic cancer, which eventually led to Jobs’ death in 2011. See http://www.cnn.com/2009/HEALTH/06/24/liver.transplant.priority.lists/index.html?iref=24hours for an example of this sort of response to Jobs’ situation. 6 See http://history.nih.gov/research/downloads/PL98-507.pdf for more details on the creation of OPTN and its relationship to UNOS.
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(OPTN). The Health Resources and Services Administration (HRSA), U.S. Department of
Health and Human Services provides oversight to the activities of the OPTN and SRTR
contractors. The SRTR data come from hospitals, OPOs and immunology laboratories, and
include detailed data on persons on the organ transplant waitlists, including time spent on the
waitlist, transplant centers at which each potential recipient is registered, health markers, limited
demographics including zip code of residence, and reason for leaving the waitlist. Each
observation in SRTR represents a registration, so we can also observe individuals who are listed
at multiple transplant centers and who transfer to different transplant centers. These data can be
matched to detailed donation data to view the circumstances of the donor’s death in each
transplant recipient’s case. Patients who receive transplants from living persons were not
required to register on waiting lists, but SRTR data identify living direct transplant recipients and
their outcomes in the data that document all transplants.
Transplant waiting lists
Patients needing a transplant from a deceased donor must register on one or more of
OPTN’s organ waiting lists. To do so, they must obtain authorization from a physician who is
associated with one of roughly 300 transplant centers in the United States.7 A transplant
coordinator at the transplant center oversees the process of medical testing to determine medical
eligibility and lists the candidate on the waiting list for an organ from a deceased donor. Each
transplant center is located in one of 58 donation service areas (DSAs), which are crucial
organizational units in the organ allocation process. An Organ Procurement Organization (OPO)
is the local monopoly within its DSA, exclusively responsible for coordinating and facilitating
donation services between donors and transplant centers. This includes evaluating potential
7 The following lists a directory of transplant centers: http://optn.transplant.hrsa.gov/converge/members/search.asp
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donors and arranging for surgical removal of organs as well as preserving organs and arranging
for their distribution to candidates on organ waiting lists. As Figure 1 shows, the borders of the
DSAs broadly follow state boundaries, although some large states have multiple DSAs and some
DSAs cross borders to include multiple states or portions of states.
Transplant candidates may also register on multiple waiting lists in different areas.
Multilisting allows transplant candidates to be registered in more than one geographic area, in
order to improve the probability of receiving an organ. However, there may be transplant center-
specific rules about the process required for registration – a candidate may have to go through a
separate evaluation for each list, which may not be covered under insurance, and some transplant
centers may refuse persons who are waitlisted at other transplant centers (United Network for
Organ Sharing, 2014).8 Calculations using the SRTR data show that multilisting is not common,
with only 6 percent of all candidates choosing to multilist, but these registrations that are part of
multilisted spells represent approximately 12 percent of all registrations. The highest incidence
is for kidneys and pancreases, in part because we treat persons who are waiting for both kidneys
and pancreases as a multilisting, but variation in multilisting across organ may also reflect the
viability of organs once they are procured and the ability of kidney patients to stay well enough
to travel (see Appendix A for how we identified multilisted candidates and spells in the data).
Table 1 shows aggregate waitlist additions by organ and year, which include
multilistings. In recent years more than 50,000 new candidates joined transplant waitlists, with
kidneys accounting for an increasingly large share of all the inflows – 65 percent in 2012 and
2013. Additions to the waitlist for livers account for 10,000 to 12,000 of the additions in the last
two decades, without much change over this time in comparison to the dramatic growth for
8 A patient’s accrued wait time may or may not transfer to the new listing. See http://optn.transplant.hrsa.gov/learn/about-transplantation/transplant-process/ for more details on the waiting list process.
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kidneys. Waitlist additions for hearts and lungs are also relatively steady over time, while
additions to the pancreas waitlist have fallen since their peak in the year 2000, representing
between 3 and 5 percent of the overall additions. Fewer than 300 persons enter the waitlist for
intestines in most years.
Table 2 shows the number and destinations of waitlist exits per year. As a comparison of
the last column in the table to the last column of Table 2 shows, the number of persons who exit
waitlists each year is consistently below the number of new additions. Receiving a transplant
from a deceased donor is the most common route off the waiting list, accounting for 22,935 of
the 52,480 exits in 2013. When a deceased donor becomes available within a given DSA, a
computer system generates a pool of eligible recipients based on blood type, other compatibility
measures and candidates’ willingness to accept the quality of the organ offered (OPTN, 2015).9
Within the pool of potential matches, the computer generates a ranking of candidates based on
time on the waiting list, urgency status, and distance from the donor organ. The weight given to
each of these characteristics depends on the organ, and other characteristics may also play a role.
For example, a 2005 rule change, called “Share 35”, gave priority to kidney transplant candidates
under age 18 to receive organs from deceased donors under age 35.
In general, the OPO offers the deceased donor’s organ to the candidate with the best
match in the DSA’s pool of matches, making geography, as well as organ type, a key component
of the allocation process.10 If the candidate’s physicians accept the organ, the transplant occurs;
otherwise, the organ is offered to the next person on the list. The next offer may be made within
the DSA or to a good match outside the DSA (to the region first and then nationally, in the case
9 Since 1999, UNet is the computer system that generates potential matches. An additional system entitled DonorNet was added in 2003 and its use was mandated in 2007. 10 There are exceptions to this geographic allocation process: sharing arrangements exist between OPOs inter- or intra-regionally, although OPTN’s Board of Directors must approve such arrangements.
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of kidneys), and the allocation policies change over time along the two dimensions of health and
location.11
A person may also leave the organ transplant waitlist for other reasons. Table 2 shows
that, in combination, death and deteriorating health is the second most common reason for
exiting a waitlist (the category labeled “medically unsuitable” was used prior to 1996 and was
eventually divided into the “deteriorated, too sick” and “improved, transplant not needed”
categories). In other cases, the candidate’s health improves and they are removed from the list
because they are no longer unhealthy enough to qualify for an organ. In the case of kidneys and,
rarely, other organs, a person might leave the waitlist because they received an organ from a
living donor – approximately 10 percent of candidates leave waitlists via this route. Some
candidates exit a waitlist because they transfer to other centers.12 In addition, if a candidate is
awaiting a multi-organ transplant, such as a kidney and pancreas, and receives one of the organs,
they exit from the multi-organ waiting list. Between 1987 and 2014, roughly one-third of all
kidney transplants involve a living donation to a patient who never registered on a waitlist, but
this is only five percent for liver candidates.
The inflows into and outflows from waitlists generate variation over time in the number
of candidates on waitlists. Table 3 shows this variation overall and by organ, illustrating the
scale of the organ shortages. These are counts at a point in time during the year and include
active and inactive members of the waitlist, where inactive waitlist registrants are not currently
eligible for a transplant, typically because of poor health. The number of registrations exceeds
the number of persons waiting for an organ because persons can be multi-listed across transplant
11 In the SRTR data, we estimate that about 2/3 of all organs are transplanted in the same geographic area in which they are procured. This number has grown over time, with the highest share for kidneys and kidney/pancreas transplants. 12 Generally waiting time transfers if a candidate transfers to another transplant hospital, but hospitals are not required to accept existing waiting time. http://optn.transplant.hrsa.gov/about/transplantation/transplantProcess.asp
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centers or for multiple organs. Waiting list lengths vary dramatically across organs, so shifts in
the supply of organs resulting from any shock have potentially very different implications for
affecting the waitlists. While the number of persons waiting for lungs and hearts has been in the
thousands in recent years, more than 10,000 patients were waiting for livers and nearly 100,000
were awaiting a kidney in 2013.
Time on the waitlist also varies dramatically by organ, DSA, and medical factors such as
match probability. The OPTN Annual report highlights the vast differences by geographic
region: “the proportion of adults receiving deceased [liver] donor organs within 5 years of
listing ranged from 30.5% in a DSA in New York to 86.1% in the Arkansas DSA (Figure 1.9).
These differences are striking, and the solution to geographic disparity remains a challenge”
(OPTN, 2012, p. 70). Additionally, “a striking (but not new) observation is the tremendous
difference … in the percentage of wait-listed patients who undergo deceased donor kidney
transplant within 5 years (Figure 1.12),” ranging from 25% in California DSAs to 67% in states
like Wisconsin (OPTN, 2012, p. 13).
In the context of the dramatic growth in waitlists, as well as substantial geographic
disparity in the expected waiting times, we consider how an exogenous shift in the availability of
organs affects behavior and outcomes among transplant candidates. All else equal, an increase in
the supply of deceased organ donors within a DSA decreases the number of times that an
available match is produced and, therefore, may have the direct result of more persons in the
DSA receiving organs from deceased donors. More persons receiving organs from deceased
donors may result in fewer person leaving the waiting lists because of death, all else equal.
However, we hypothesize that all else is not held equal. In particular, transplant candidates have
choices over their waitlist options and may respond to supply shocks along many dimensions,
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including listing and/or multilisting in DSAs where helmet laws are more relaxed or relying on
the waitlist for deceased donors rather than seeking a living donor (see Lindsay and Feigenbaum,
1984, for a formalization of how waitlists function to ration goods that are priced below their
market value).
Helmet laws and waiting lists
We use changes in motorcycle helmet laws to identify an exogenous shift in the supply of
organ donors. Table 4 identifies the timing of law changes between 1988 and 2013, the years
that the SRTR data covers. The proposed mechanism is that that the repeal of a universal helmet
law increases the number of people riding motorcycles and the number of persons riding without
a helmet, which in turn increases the probability of brain death from a motorcycle accident.
Brain death is the principal criteria for becoming a deceased organ donor; as DCEM argue,
almost all deceased organ donors are brain dead at the time of organ recovery, despite brain
death occurring in less than 1 percent of all deaths in the U.S.
Table 5 shows the number of organ donors who died in motor vehicle accidents (MVA)
and in all other circumstances, along with the corresponding number of organs transplanted per
donor, with MVA donors shown in the top panel and non-MVA donors shown in the bottom
panel.13 MVAs account for an average of 1300 organ donors per year, although this number has
fallen since the peak year of 2006, with the share of all deceased donors that died in MVAs
falling from roughly 24 percent in the 1990s to only 16 percent in 2013. Among MVA deaths,
the data do not distinguish between motorcyclists and non-motorcyclists.
13 Motor vehicle accidents were recorded as a circumstance of death for all years in the SRTR data. Before April 1994, circumstance of death was coded as either MVA or “all other circumstances”, but a more complete coding of circumstances of death began in April 1994 (these circumstances include suicide, other accidents, stroke, homicide, and child abuse).
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Using published, state-level OPTN data from 1994 to 2007, DCEM use six state-level
repeals and one enactment of a universal helmet law to estimate that repealing universal helmet
laws increases the supply of organ donors who die in motor vehicle accidents by roughly 10
percent overall and 31 percent among men aged 18 to 34. Moreover, their estimates imply that
every motorcyclist death due to the lack of a universal helmet law produces 0.124 additional
organ donors.14 Because the entire effect is driven by male donors aged 18-34, who are
disproportionately likely to die in motorcycle accidents, their results suggest that response in
MVA donors is due to motorcyclists.
We expect that the relationship between the change in donors and outcomes for transplant
candidates may differ by organ. First, as Table 3 shows, the sizes of waiting lists differ
dramatically across organs, so a shock to the number of organs available will potentially affect
candidates differently depending on the organ that they need. Second, a donor, defined as “a
person from whom at least one organ was procured for the purpose of transplant” (OPTN, 2013),
can contribute multiple organs to the deceased donor waitlist (including two each of kidneys and
lungs), but the probability of a specific organ being transplanted differs dramatically across
organs. Table 5 shows that the number of organs transplanted per donor varies over time, but in
all years it is higher for MVA donors than for non-MVA donors; for example, in 2013, each
MVA donor contributed 3.85 organs that were eventually transplanted, compared to 2.93 organs
for each non-MVA donor. On average, each MVA donor contributes 1.8 kidneys that are
eventually transplanted. Similarly, approximately 80 percent of donors contribute usable livers.
However, fewer than half of the donors contribute hearts and closer to 30 percent of donors
contribute a pancreas or lung. The fraction of donors contributing lungs is strikingly low, given
14 DCEM (2011) show that helmet law repeals induce gradual increases in both motorcyclist death rates and organ donation rates; riders apparently do not abandon their helmets immediately following a repealed helmet law. In contrast, behavior responds essentially immediately to the introduction of helmet laws.
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that each donor typically has the potential to donate two lungs. Medical treatment of brain death
victims can damage some organs more than others, lungs included, but the treatment technology
is changing over time so that the fraction of lungs transplanted from donors is also increasing
over time (Marcelo et al., 2011).
III. The Effects of Motorcycle Helmet Laws on the Supply of Organs
We begin by assessing how helmet laws affect the supply of organs. All our regression
analyses use the DSA at the unit of observation because this is the primary geographic unit when
allocating deceased organs. The Center for Medicare and Medicaid Services (CMS) assigns
counties to DSAs and the OPO in the DSA coordinates all donations and transplants. We use the
most recent county-DSA designation provided by SRTR, which is imperfect, but appears robust
to alternative choices described in Appendix B. We exclude Puerto Rico, resulting in the
inclusion of 57 DSAs in our analysis.
We estimate DSA- and year-specific donation rates as a function of the share of the
DSA’s population living in a state without a universal helmet law in that year using the following
model:
(1) dtdttddt nolawshareD ,
We estimate equation (1) separately for several different dependent variables. In the first
regression, Ddt is a measure of the number of deceased organ donors due to motor vehicle
accidents (MVA). In the second set of regressions, Ddt measures of the number of specific
organs (heart, kidney, lung, liver, intestine, and pancreas) that are transplanted from MVA
donors. In all cases, we measure Ddt per million DSA residents using National Cancer Institute
(2015) county population estimates. In each regression, d indexes the DSA, t indexes the year
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and includes the years 1988 to 2013, and nolawsharedt is the share of the DSA’s population not
covered by a universal helmet law for at least six months in year t.
As an example of our key independent variable, consider the DSA that incorporates
counties in western Pennsylvania, West Virginia and one county in New York. All of those
counties were located in states with universal helmet laws until 2003, when Pennsylvania
repealed their universal helmet law. The law change went into effect in August, so in 2004,
nolawsharedt increases from 0 to about 0.75 percent, which represents the share of the OPO’s
population living in Pennsylvania. All specifications include a full set of DSA and year
indicators (αd and δt, respectively). We weight each observation by the DSA’s population in that
year. Estimates of γ based on (1) capture the association between within-DSA variation over
time in mandated helmet laws and within-DSA variation in the supply of organ donors.
The estimated coefficients from equation (1), reported in Table 6, suggest that repealing
universal helmet laws, which increases the share of persons not covered by a helmet law, is
associated with more MVA donors and transplants of organs recovered from MVA donors.
Column (1) shows that repealing a universal helmet law increases the number of MVA organ
donors by 0.906 per million persons, with a standard error of 0.234 (all standard errors are robust
to within-DSA clustering over time). This represents a 19 percent increase relative to a sample
average of 4.886 donors per million persons (shown in brackets). Column (2) shows that this
increase in the supply of donors results in an average of 3.429 more organs transplanted per
million DSA residents, consistent with the summary statistics in Table 5. Columns (3) and (4)
show analogous results for non-MVA donors and transplants as a placebo test. The estimates in
all cases are statistically insignificant and small relative to the relevant sample means.
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Table 6 also shows that the effects of helmet laws on transplants vary substantially by
organ. Focusing on MVA donors, the most striking estimate is among lung transplants:
repealing helmet laws increases the number of lungs transplanted by 0.434 per million persons,
which is a 33 percent increase relative to the sample mean of 1.299. Kidney donations increase
by 1.577 per million persons (19 percent relative to the sample mean), heart donations increase
by 0.421 per million persons (17 percent), and liver transplants increase by 0.774 per million
persons (21 percent). The estimated effects of helmet law repeals are positive for pancreatic and
intestinal transplants, but not statistically significant at standard levels, perhaps reflecting the low
rate of transplantation per donor among those organs shown in Table 5.
We also estimate “event study” models to highlight the dynamic responses of organ
donors and transplants to repeals of helmet laws. Specifically, we study the responses over time
in MVA organ donors and transplants in DSAs that are headquartered in states with helmet law
repeals during the 1987-2013 period, focusing on how these quantities evolve over time in
comparison to those in DSAs in states that do not repeal helmet laws.15 In all DSAs, the majority
of the population resides in the state where the DSA’s OPO is headquartered, so we use a set of
dummy variables indicating “years since repeal” for 5 years before and after the repeal, with the
omitted category being the year before the repeal:
(2) ,5
5, dtttdtddt rD
where rd,t-τ is a binary variable equaling 1 if a repeal occurred in DSA d in year τ and the
observation occurred in year t, and zero otherwise. In practice, we group observations at least 5
15 Sample sizes are too small to estimate similar event studies for helmet law enactments because all of the enactments (except for Louisiana’s in 2004) occurred in the first few years of our data coverage, precluding reliable estimates of the pre-existing trends in those states.
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years before the repeal into the t – τ = –5 category, and years at least 5 years after into the t – τ =
5 category.
Figure 2 plots the series of γt-τ estimates for MVA donors and MVA transplants, both
aggregating across organs and separately for lungs, kidneys, hearts, intestine, livers and
pancreases (full regression results are available in Appendix C). The estimates show that,
relative to the year before the repeal, both the number of organ donors and the number of total
transplants (both normed by the DSA population, in millions of persons) increased in the year of
the repeal and in the following year. Afterward, the number of donors and transplants do not
appear to increase further, but they also do not return to the baseline level, suggesting that the
repeal of helmet laws results in a new long-run level of both donors and transplants. The shift is
especially dramatic for kidneys and livers. Consistent with the estimates from Table 6, there
does not appear to be any shift for intestines.
Apart from showing the dynamic effects of helmet laws, Figure 2 is useful in illustrating
the key threat to the validity of estimates from models like equation (1) – that DSAs in states
with changes in helmet laws might have different trends in donors and transplants compared to
states with no changes in helmet laws. Our preferred method for showing whether these
differences in trends exist is to present them graphically, as this is the most transparent approach
– and judging from the lines in the figures from 5+ years before repeals to the year before
repeals, there is very little evidence that pre-repeal trends were different in repeal states than in
other states. As an alternative approach, we have also estimated models including DSA-specific
trends in either donors or transplants per capita. The estimates from those models are
remarkably similar to those from specification (1). For example, the inclusion of linear DSA-
specific trends increases the point estimate in the first column of Table 6 from 0.906 to 0.986
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(and increases the standard error from 0.234 to 0.236) and decreases the top point estimate
shown in the second column from 3.429 to 3.409 (again increasing the standard error slightly,
from 0.785 to 0.820). The organ-specific estimates are similarly insensitive to the inclusion of
DSA-specific linear trends. Similarly, including higher-order DSA-specific trends reduces
precision but does not markedly change the point estimates in Table 6. The robustness of the
estimates to these changes in specification provides further suggestive evidence to that given in
Figure 2 – the estimates in Table 6 are not likely to be driven by underlying differences in trends
between DSAs that experienced helmet law changes and DSAs that did not.
IV. Behavioral Responses to the Shift in the Supply of Organs
Demand response to the change in supply
The previous section established that repeals of helmet laws increase the supply of organs
within the local DSA. Because transplant candidates can choose the DSA in which they register,
we hypothesize that repealing a helmet law might also increase inflows onto waitlists due to
perceived or actual increases in the supply of organs. Our approach here mirrors that of our
analysis of donors and transplants, in that we estimate a DSA- and year-specific waitlist inflow
rate as a function of the share of the DSA’s population living in a state without a universal
helmet law in place in that year:
(3) dtdttddt nolawshareAdditions .
In this specification, dtAdditions is a measure of the number of persons added to the
waitlist in DSA d and year t, per million DSA residents. We estimate waitlist additions in the
aggregate and separately by organ (heart, kidney, lung, liver, intestine, and pancreas). Data on
waitlists began in 1987, but the statistics on waitlist sizes are unreliable in the early years as
transplant programs established themselves, so we use data from 1992 to 2013.
19
The results in Table 7 suggest that helmet laws – more specifically, their actual or
perceived effects on the number of transplantable organs – influence the behavior of transplant
candidates. We estimate that repealing a helmet law increases the number of waitlist additions
by 18.665 per million persons, with a standard error of 8.733. This is a 12 percent increase in
waitlist inflows relative to the sample mean of 147.717. The organ-specific estimates show that
repeals increase inflows onto waitlists for lungs (by 27 percent relate to the mean inflow rate of
7.911), kidneys (10 percent), and livers (15 percent), with smaller effects on hearts, intestines,
and pancreases (negative in this case). These organ-specific estimates are borderline significant
at conventional levels. Given the large estimated effects of helmet laws on the supply of lungs
shown in Table 6, perhaps it is not surprising that lung transplant candidates choose to enter
waitlists at least partly based on whether a transplant center resides in a DSA where many
residents are not required to wear helmets.16
Columns 2 and 3 in Table 7 reveal additional information about which candidates
respond to the perceived supply shock induced by changes in helmet laws. Using zip code data
for candidates and the transplant centers at which they have registered, we generate a count of
the number of waitlist additions that come from within the DSA and the number of new additions
involving candidates who reside outside the DSA. From the sample means, we estimate that
roughly 22 percent (= 32.832 / 147.717) of all waitlist inflows consist of candidates who live
inside the DSA’s boundaries. However, inflows induced by repeals of helmet laws are
disproportionately concentrated among those living elsewhere, with 45 percent (= 8.343 /
16 As more evidence that candidates move to register in alternative DSAs, in unreported specifications we include a measure of the share of the DSA’s bordering population living in a state without a universal helmet law as an independent variable. Generally, the magnitude of the coefficient on own population with no law is slightly larger in magnitude. The coefficient on the variable measuring the share of bordering DSA’s without a helmet law is negative, but imprecisely estimated. These results are consistent with transplant candidates seeking DSAs with the least restrictive helmet laws.
20
18.665) of the marginal inflows coming from outside the DSA. The disparity is especially
pronounced for kidneys, for which 64 percent (= 5.395 / 8.481) of the inflows due to repeals
come from outside the DSA, compared to a baseline of roughly 18 percent (= 15.942 / 87.803).17
The geographic patterns of inflows suggest that, in the absence of a formal pricing mechanism,
waiting times for organs are the relevant “price” determining listing decisions. When that price
falls – or when it is perceived to have fallen – candidates from outside the geographic area are
induced to sign up for waitlists that they would not have otherwise joined. We return to this
issue below in the context of multilisting, the practice of listing on multiple waitlists.
In Figure 3, we plot the series of estimates from event-study models of waitlist inflows,
analogous to those shown in Figure 2 for donors and transplants. As was the case in Figure 2,
there does not appear to be any evidence that DSAs in states that repeal their helmet laws have
any differences in pre-existing trends in waitlist additions, as compared to DSAs in states
without repeals. Again, this is most easily seen by comparing additions in time periods 5+ years
before repeals to the year before the repeal, labeled “-1” in the figure. Overall and for kidneys,
inflows rise immediately in the year of the repeal and steadily increase in the following years.
Interestingly, within-DSA inflows increase immediately but then fall almost back to baseline
levels, while outside-DSA inflows adjust more gradually. We have no compelling evidence why
this is the case, but perhaps the gradual increases for outside-DSA inflows stem from gradual
spread of information about waitlist size. For example, if Pennsylvania repeals its helmet law,
individuals living in DSAs headquartered in Pennsylvania might learn that information
immediately, while individuals living in other states might learn with a significant lag. Finally,
17 These estimates do not account for persons who would have registered outside their own DSA when a helmet law is in place, but do register in their own DSA without a helmet law. In related regressions we aggregate additions by DSA of residence and find no evidence that helmet laws encourage persons who would not have registered in their home DSA to do so. This may be because most persons are registered in their home DSA.
21
we note that many of the individual-year estimates shown in the figure are statistically significant
for all organs and for kidneys (for years following the repeal).
The waitlist inflows shown in Table 7 and Figure 3 represent new registrations but not
necessarily new transplant candidates to the extent that candidates are multilisting. Table 8
differentiates the effects of helmet law repeals on waitlist inflows separately for candidates who
multilist and those who do not. The first three columns, under the “No Multilistings” heading,
show estimates for those candidates who only list in one transplant center during their waitlist
spell. These candidates are relatively insensitive to a shift in the supply of organs; the estimate
in the top row of the “All Additions” columns is 6.378, which is roughly 5 percent of the sample
mean of inflows (118.406). In contrast, the analogous estimate for the subsample of
Multilistings is 11.995, which is 29 percent of the sample mean of inflows (41.081). These
differences are particularly pronounced in the “Out-of-DSA” estimates – relative to the sample
mean of 14.114, inflows onto waiting lists increase by 44 percent (6.252) following repeals of
helmet laws.
Table 8 also shows that the response of waitlist additions to changes in the supply of
organs varies dramatically across organs. For kidneys, where multilisting is most common,
annual inflows of multilisters increase by 7.739, which is 30 percent of the mean annual inflow
of 26.144, and again, this effect is most pronounced among those who are coming from outside
the DSA. In contrast, repealing a helmet law has no economic or statistically significant effect
on waitlist inflows among kidney transplant candidates who only register at a single transplant
center. These estimates suggest, perhaps not surprisingly, that shocks to the supply of organs
encourage persons who are already listed in at least one location to list again. That is, candidates
respond to supply shocks on one margin – the decision of where to list, conditional on listing –
22
much more so than another – the decision of whether to list at all. This seems plausible, as it is
difficult to imagine that candidates in need of a life-saving organ would respond along the “list
or not” margin to changes in organ supply generated by variation in helmet laws.
Substitution away from living donor transplants
The previous subsection shows a growth in waitlists in response to a local shock to the
supply of organs. Our evidence on candidates who are multilisted suggests that much of this
growth comes from persons who are already listed in a different DSA (that did not experience
the supply shock). For most organs, the deceased donor waiting list is the only option for those
in need of a transplant. However, kidneys are the most obvious exception, as 34 percent of all
kidney transplants coming from live donors. Most living donors are blood relatives (69 percent),
spouses (11 percent), or friends (16 percent).
The principal mechanism by which a shock to the supply of deceased organs could affect
the prevalence of living donor transplants involves shifting the relative cost of opting for living
versus deceased donor transplants. The principal costs of joining a waitlist, relative to opting to
ask a relative or close friend for a donation, are those associated with having to wait for a
compatible organ. These costs are potentially substantial – as shown above in Table 2, over 10
percent of those exiting waitlist each year do so via their death. However, living donations
impose obvious (and again, substantial) costs to the donor, and to the extent that candidates
internalize these costs, some candidates who were close to the margin of opting for joining the
waitlist will be induced to do so if expected waiting times decrease. As a result, crowd-out of
living donation may occur if the supply of deceased donor organs increases.
In order to investigate the extent of the crowd-out described above, we estimate a variant
of specification (1) above, using transplants from living donors as the dependent variable. Table
23
9 presents the resulting estimates. The top estimate in the table aggregates all living donor
transplants, while the remaining estimates disaggregates by the donor’s relationship to the
intended recipient. Overall, the repeal of a helmet law reduces living donor transplants by 3.445
(with a standard error of 1.403), which is consistent with the hypothesis that an increase in the
supply of deceased donors crowds out living donors. Table 9 further shows that the effect of
repealing a helmet law results in fewer living donations from all relationship types. Siblings,
who donate more organs than any other relationship type, show a large absolute decline in
donations of 0.683 per million persons, representing a 15 percent decline relative to the baseline
donation rate. Similarly, donations from parents, children, other relatives, and spouses decline
by 17, 20, 23, and 29 percent, respectively, relative to baseline. The largest estimate in the table
is for “all other directed donations”, which captures donations in which the donor and recipient
are acquainted but not family members. This category primarily captures donations between
close friends. Helmet law repeals decrease the prevalence of these donations by 1.202 per
million DSA residents, which is slightly over 50 percent of the mean transplantation rate for this
relationship. Finally, anonymous donations, which are relatively rare, fall by 0.125, a dramatic
83 percent of the baseline donation rate.18
Our estimates of the effects of helmet law repeals on waitlist inflows and on the
prevalence of living donation suggest that the local demand for deceased donor organs increases
significantly in response to (real or perceived) increases in the supply of these organs. These two
mechanisms are presumably related – the crowd-out of living organ transplants is part of the
mechanism by which waitlist inflows increase following a positive supply shock. In fact, the
18 Fernandez et al. (2013) disaggregate the crowd-out of living donors using helmet laws as one instrument for a change in the supply of cadaveric donors. At the state-year level, they find no statistically significant effect of an increase in cadaveric donors on blood related donors or anonymous donors. They consistently find statistical significance in their crowd-out estimates only for spouses and friends.
24
estimated overall decline in living donors shown in Table 9 is similar in magnitude to the
estimated increase in waiting list additions from the “within DSA” group among those not
multilisting in Table 8, lending further suggestive evidence that among those at the margin of
listing at all or not, a comparison of the costs of living and deceased donor transplants plays a
role. However, given that the estimated increase in overall inflows in Table 7 greatly exceeds
the estimated decline in living donations in Table 9, variation at the margins of where to list,
rather than the crowd-out of living transplants, is apparently the dominant mechanism at work.
Finally, we note that the shocks to the demand for deceased organs suggested by Tables
7-9 are all at least as large as the magnitude of the supply shock shown in Table 6. For example,
the central estimates imply that a helmet law repeal increases the number of organ transplants by
roughly 3.4 per million DSA residents. However, the ensuing increase in additions to transplant
waitlists of 18.665 is more than 5 times as large as the supply shock itself, raising the question of
whether these local shocks are effective at improving outcomes for candidates on local waitlists.
We turn to this question in the next section.
V. The Effects of a Supply Shock on Waitlist Exits
We next consider how a shock to the supply of transplantable organs – which then
produces a shock to the demand for those organs, as shown by the estimates above – changes
outcomes for candidates on transplant waitlists. To begin, we estimate the following model:
(4) dtdttddt nolawshareWaitlistExits )/( ,
where (Exits / Waitlist)dt equals the number of persons leaving a transplant waitlist per thousand
candidates on the waitlist in DSA d and year t. Table 10 presents estimates of γ from
specification (4) separately by reason for the exit: transplants from a deceased donor, transplants
from a living donor, transfers to another center, and death. Estimates from these specifications
25
can be interpreted as the overall effect of helmet law repeals on the probability of leaving a
waitlist via a specific route, conditional on being on a waitlist. This effect captures repeals’
influence on both the numerator and the denominator of the dependent variable – our estimates
above suggest that repeals increase exits from waitlists via transplants from deceased donors but
also increase inflows onto waitlists, increasing the denominator. The net effect on the overall
probability of exiting is thus ambiguous ex ante.
The top estimate in column (1) shows that helmet law repeals do not appear to increase
the fraction of waitlisted candidates who exit via transplants from deceased donors. In fact, the
point estimate of -8.779 shows that those on the waitlist are 0.8779 percent less likely, per year,
to exit via deceased donor transplant following a repeal than they would be in the absence of the
repeal, although this estimate is insignificantly different from zero. Note that the sample mean
listed in brackets, 197.075, indicates that each year, roughly 19.7 percent of waitlisted candidates
received deceased donor transplants (because the dependent variable measures exits per 1000
waitlisted candidates). The fact that the estimate is negative suggests that the inflow of waitlist
candidates due to helmet law repeals swamps the direct effect of the increase in available organs.
The remaining rows of the table disaggregate the effects by organ. The only statistically
significant estimate in column (1) is for kidneys – the positive supply shock of kidneys due to
helmet law repeals significantly decreases the probability that a waitlisted kidney transplant
candidate will receive a transplant from a deceased donor. This seems counterintuitive at first
glance, but it is actually consistent with our finding above that kidney candidates’ listing
decisions are relatively sensitive to the supply of kidneys. Specifically, kidney candidates are by
far the most likely to multilist among all transplant candidates, and their decisions of where to
list outside their home DSA are by far the most sensitive to the supply of organs (either real or
26
perceived). As a result, the increased inflows onto kidney waitlists more than swamps the
increased outflows due to a larger number of organs, causing outflows per waitlisted candidate to
decline.
Column (2) of the table shows that waitlisted candidates are also less likely to exit via
transplant from a living donor, although these results are statistically insignificant in all cases.
The overall living donor transplant rate is driven primarily by kidney transplants, where persons
who previously might not have entered the waitlist because they identified a living direct donor
do so following the helmet law repeal.19
Column (3) presents estimates for exits via transfer to another transplant center. As
shown in Table 2 above, transfer is the third most common route off of a waitlist. However,
state helmet law repeals appear to neither increase nor decreased the likelihood that a waitlisted
candidate will transfer, either in total or for any specific organ.
Finally, column (4) shows that state helmet law repeals reduce the likelihood of a
waitlisted candidate dying by 0.4402 percentage points per year, which is roughly a 9 percent
decrease relative to the sample mean. When we estimate the effect of helmet law repeals on the
probability of dying on the waitlist for individual organs, the results are consistent with the
discussion above about the importance of the size of waitlists for a particular organ.
Specifically, the largest positive effects on deceased donor transplants are for candidates
awaiting livers, and death rates decrease for this group: repealing a helmet law decreases the
number of patients on waitlists who die awaiting liver transplants by 13.654, a statistically
19 Living donor transplants for organs such as hearts, livers, and pancreases are rare but not impossible. These transplants are known as “domino transplants”, in which a typically young donor with a diagnosed congenital abnormality donates an organ to an older recipient. For example, a young liver donor with amyloidosis, a condition in which the liver produces a protein that gradually destroys the body’s other organs, can donate to an elderly recipient without significant repercussion because the condition takes decades to do significant damage to the other organs. See http://umm.edu/programs/transplant/services/liver/domino-liver-transplant for more details on domino transplants and their dramatic growth in prevalence over time.
27
significant and large effect relative to the sample mean of 65.119. Taken literally, repealing a
helmet law decreases the odds of dying on the waitlist by roughly 21 percent per year. Again,
the estimate for kidneys suggests that the entire effect of an increased supply of kidneys from
deceased donors is offset by an increase in inflows (some of whom are “crowded in” by a
decrease in the prevalence of living donor transplants). As a result, there is no significant effect
on the death rates of those awaiting kidneys. Compared to kidney candidates, liver candidates’
listing decisions are much less sensitive to supply shocks, resulting in the shocks having more
easily-measurable effects on outcomes such as transplants and death.
VI. Conclusions
Repeals of motorcycle helmet laws cause measurable increases in the supply of deceased
organ donors and deceased organ transplants for all organs. The effects are largest for kidneys
and livers, but they are significant for lungs, hearts and pancreas too. In the absence of a formal
pricing mechanism, the increase in the supply of organs appears to act as a signal that waiting
time decreases in those geographical regions. The demand for deceased donor organs responds
in two related ways. First, additional persons choose to enter the waitlists in areas where the
supply increased, and these appear to be largely persons who are already waitlisted in other
geographic regions. Second, waitlist additions for deceased organs appear to be influenced by
the expectation of shorter waiting times via transplant candidates who would have pursued a
living donation from friends or family. The increase in the supply of deceased donors appears to
crowd out living donors, particularly for kidneys, where more than one-third of all transplants are
from living donors. The waitlist responses are as much as five times larger than the increases in
deceased organ donors, which raises the question of how effective an increase in supply is for
28
those on the waitlist if the waitlist grows enough to lower the probability of those on it of exiting
with a positive outcome.
A caveat to our estimates is that the changes in supply induced by changes in motorcycle
helmet laws are likely not representative of the broader pool of organ donors. Organ donors who
die in motor vehicle accidents are generally younger and healthier than the overall pool of donors
(DCEM, 2011). For that reason, shifts in the supply of donors induced by changes in motorcycle
helmet laws may induce larger shifts in transplantable organs – and transplant candidate
responses and outcomes – than an equally-sized shift in the supply of donors who are more
representative of organ donors as a whole.
The ability of transplant candidates to offset supply shocks raises questions about the role
that geographic boundaries play in an allocations system’s goals of balancing “justice (fair
consideration of candidates' circumstances and medical needs), and medical utility (trying to
increase the number of transplants performed and the length of time patients and organs
survive).” The crowd-out of living donors is a real increase in the demand for deceased donors
and reduces the effectiveness of an increase in the supply of deceased organs on the vast
shortages of transplantable organs. Likewise, if persons who are responding to the “price”
signals are those who are multilisting, then it is unclear that the persons with the highest medical
needs are benefiting from the allocation system. Multilisters maintain their place on waitlists
where supply shocks did not occur, raising a broader question of how a supply in one region
broadly affects transplant candidates in all regions.
Evidence that the elasticity of demand with respect to supply shocks is so large,
combined with other changes in the environment, such as improved information about the
“price” in the form of waitlist time changes (see, e.g., http://www.txmultilisting.com/home.htm,
29
a website dedicated to finding the DSAs with the shortest waitlists), bring into question whether
the geographical boundaries for the first round of allocation are efficient. Perhaps a movement
toward a more national allocation system, such as the one used in Spain, would be equity- and
efficiency-improving (Deffains and Ythier, 2010).
30
Appendix A: Identifying transplant candidates who multilist
There is no single code in the data that identifies whether a transplant candidate is
multilisted. Using a unique patient identification variable, we can create a multilisting identifier.
For each patient, we identify all registrations that belong to the same spell for a single-organ
transplant by working backwards from when a spell for ends. A spell ends when the individual
receives a transplant, leaves the waitlist when there are no open registrations, dies without
receiving a transplant, or is still on a waitlist when the data were extracted in 2014.
For each individual, all registrations in which the listing date is the same date or earlier
than the first observed transplant or death are coded as part of the individual’s first spell. A
subsequent spell begins when a registration occurs following the end of a previous spell from
transplant and ends when we observe a transplant or death. All registrations that begin after the
date of the 2nd spell and end before or at the same time as the 2nd spell are coded in the 2nd spell
and so on. Registrations that occur after the most recent transplant are counted as the final spell.
If a patient has never had a transplant, all of their registrations for that given organ are
categorized as a single spell. We code all registrations as part of a multiple listing or not. We
also code the chronological order of the multilisting registrations within spells.
A special case involved candidates who are listed for multi-organ registrations (kidney-
pancreas and heart-lung). We split these into two single-organ observations. For example,
kidney-pancreas registrations are split into one kidney and one pancreas listing. Therefore, we
follow the kidney spell and the pancreas spell. We do this because some of these multiorgan
wait list registrations end when the candidates receives a transplant for one of the two organs.
Observations of transplants without a corresponding waiting list registration are assumed
to not be on the waiting list.
31
Appendix B: Designating Counties to Donation Service Areas
Donation Service Areas (DSAs) are the unit of observation throughout most of our
analysis. As Figure 1 illustrates, the DSAs have their own boundaries. In 2015 there are 58
DSAs; we use only 57, dropping Puerto Rico. According to personal correspondences with
Peggye Wilkerson at the Centers for Medicare & Medicaid Services (CMS), the current county
designations were effective May 31, 2006. The SRTR data provide us with this mapping for the
current counties and DSAs.
We designate each county to its current DSA based on the data provided by SRTR.
SRTR further identifies when counties are split between two DSAs. In particular, there is a
variable in the county level data called “County_fraction: Fraction of county’s referrals to this
OPO in this year,” which is the share of deaths in a county that were handed by a specific OPO.
Only 2 percent of the counties are split between two DSAs, and only 1.4 percent of the counties
are split between two DSAs for more than one year. When the county is split in only one year, it
is because the DSA’s boundaries were changing in that year (see below). The shares do not
change much from year to year. To account for this, we assign, for all years, the entire county to
the DSA where the larger share of the county referrals was made in 2013. Many thanks to Bryn
Thompson at SRTR for helping us sort these issues out.
A second issue for assigning the counties to DSAs is that the DSAs changed over time
and, in some cases, the names of the OPOs that administer the DSAs changed over time.
Therefore, in our individual-level data, we have transplant candidates listed in OPOs/DSAs that
no longer exist and are not available in the current mapping between counties and DSAs. Mark
Paster at the Association of Organ Procurement Organization and Chas MacKenzie at the Life
Choice Donor Services provided valuable information on the history of a Wisconsin OPO name
32
change and a Connecticut name discrepancy in our data. A more substantive issue is that many
of the original DSAs eventually merged into the current set of DSAs. That is, 30 OPOs/DSAs in
the SRTR dataset were in existence at one point but no longer exist. 13 of those were only in
existence between 1987 and 1988. We do not have data on which counties were in the DSAs in
the early years; we only know that the DSAs existed. Peggye Wilkerson of CMS suggested that
the county-DSA concordance from those years is not readily available. The most straightforward
solution, we believe, is to assume that the current county to DSA designation was always in
place. It seems unlikely that this would substantively affect our results since we are simply
treating two DSAs as if they were always one and the DSAs are likely to be affected by the same
state laws, except in few cases where DSAs cross state lines. Most of the 30 DSAs that no
longer exist are in states where the DSAs are wholly contained in a single state.
33
References: Abadie, Alberto and Sebastien Gay. 2006. “The Impact of Presumed Consent Legislation on
Cadaveric Organ Donation: A Cross-Country Study.” Journal of Health Economics, 25: 599-620.
Ausubel, Lawrence M; Morrill, Thayer. 2014. “Sequential Kidney Exchange.” American
Economic Journal: Microeconomics, 6.3: 265-285. Becker, Gary S. and Julio Jorge Elias. 2007. “Introducing Incentives in the Market for Live and
Cadaveric Organ Donations.” Journal of Economic Perspectives, 21(3): 3-24. Bilgel, Firat. 2012. “The Impact of Presumed Consent Laws and Institutions on Deceased Organ
Donation.” European Journal of Health Economics, 13.1: 29-38. Byrne, Margaret M; Thompson, Peter. 2001. “A Positive Analysis of Financial Incentives for
Cadaveric Organ Donation.” Journal of Health Economics. 20.1: 69-83. Cameron, A. M., Massie, A. B., Alexander, C. E., Stewart, B., Montgomery, R. A., Benavides,
N. R., Fleming, G. D. and Segev, D. L. (2013), “Social Media and Organ Donor Registration: The Facebook Effect.” American Journal of Transplantation, 13: 2059–2065.
Clarke, Roberta N. 2007. “Organ Donation A Significant Marketing Challenge.” Health
Marketing Quarterly, 24.3-4: 189-200. Deffains, Bruno and Jean Mercier Ythier. 2010. “Optimal production of transplant care
services.” Journal of Public Economics. 94(9-10): 638-653. Dickert-Conlin, Stacy; Elder, Todd; Moore, Brian. 2011. “Donorcycles: Motorcycle Helmet
Laws and the Supply of Organ Donors.” Journal of Law and Economics, 54.4: 907-935. Fernandez, Jose M; Howard, David H; Stohr Kroese, Lisa. 2013. “The Effect of Cadaveric
Kidney Donations on Living Kidney Donations: An Instrumental Variables Approach.” Economic Inquiry, 51.3: 1696-1714.
Harrison, Tyler R; Morgan, Susan E; Chewning, Lisa V. “The Challenges of Social Marketing
of Organ Donation: News and Entertainment Coverage of Donation and Transplantation.” Health Marketing Quarterly, 25.1-2: 33-65.
Howard DH. 2011. “Waiting time as a price for deceased donor kidneys.” Contemporary
Economic Policy 29(3):295-303. Howard, David H. 2007. “Producing Organ Donors.” Journal of Economic Perspectives, 21.3:
25-36.
34
Kessler, Judd B; Roth, Alvin E. “Loopholes Undermine Donation: An Experiment Motivated by an Organ Donation Priority Loophole in Israel.” Journal of Public Economics, 114: 19-28.
Kessler, Judd B; Roth, Alvin E. 2012. “Organ Allocation Policy and the Decision to Donate.”
American Economic Review, 102.5: 2018-2047. Lacetera, Nicola & Macis, Mario & Stith, Sarah S. 2014. "Removing financial barriers to organ
and bone marrow donation: The effect of leave and tax legislation in the U.S." Journal of Health Economics, 33: 43-56.
Leppke S, Leighton T, Zaun D, et al. 2013. “Scientific Registry of Transplant Recipients:
Collecting, analyzing, and reporting data on transplantation in the United States.” Transplant Rev. 2013, 27(2): 50-56
Li, Danyang; Hawley, Zackary; Schnier, Kurt. 2013. “Increasing Organ Donation via Changes in
the Default Choice or Allocation Rule.” Journal of Health Economics, 32.6: 1117-1129. Lindsay, Cotton M. and Bernard Feigenbaum. 1984. “Rationing by Waiting Lists.” The
American Economic Review. 74(3): 404-17. Marcelo Cypel, M.D., Jonathan C. Yeung, M.D., Mingyao Liu, M.D., Masaki Anraku, M.D.,
Fengshi Chen, M.D., Ph.D., Wojtek Karolak, M.D., Masaaki Sato, M.D., Ph.D., Jane Laratta, R.N., Sassan Azad, C.R.A., Mindy Madonik, C.C.P., Chung-Wai Chow, M.D., Cecilia Chaparro, M.D., Michael Hutcheon, M.D., Lianne G. Singer, M.D., Arthur S. Slutsky, M.D., Kazuhiro Yasufuku, M.D., Ph.D., Marc de Perrot, M.D., Andrew F. Pierre, M.D., Thomas K. Waddell, M.D., Ph.D., and Shaf Keshavjee, M.D. 2011. “Normothermic Ex Vivo Lung Perfusion in Clinical Lung Transplantation” New England Journal of Medicine. 364: 1431-1440.
National Cancer Institute (2015). “Us Population Data 1969-2013.” Release date January 2015.
http://seer.cancer.gov/popdata/ (accessed 8/22/15). OPTN, 2009. “2009 OPTN / SRTR Annual Report: Transplant Data 1999-2008.”
http://www.ustransplant.org/annual_reports/current/ OPTN, 2013. http://srtr.transplant.hrsa.gov/annual_reports/2012/pdf/07_dod_13.pdf OPTN, 2015. “Organ Procurement and Transplantation Network Policies.”
http://optn.transplant.hrsa.gov/ContentDocuments/OPTN_Policies.pdf Rodrigue JR, Cornell DL, Lin JK, Kaplan B, Howard RJ. 2007. “Increasing live donor kidney
transplantation: A randomized evaluation of a home-based educational intervention.” American Journal of Transplantation. 7:394–401.
Roth, Alvin E., Tayfun Sönmez, and M. Utku Ünver. 2004. “Kidney Exchange.” The Quarterly
35
Journal of Economics, 119(2): 457-488. Roth, Alvin E., Tayfun Sönmez, and M. Utku Ünver. 2005. “Pairwise Kidney Exchange.”
Journal of Economic Theory, 125(2): 151-188.
Siminoff LA, Marshall HM, Dumenci L, Bowen G, Swaminathan A, Gordon N. 2009. “Communicating effectively about donation: An educational intervention to increase consent to donation.” Progress in Transplantation. 19(1):35–43.
United Network for Organ Sharing, 2014. “Questions & Answers for Transplant Candidates
about Multiple Lising and Waiting time Transfer.” https://www.unos.org/wp-content/uploads/unos/Multiple_Listing.pdf (accessed 8/15/15).
United States Department of Health and Human Services, 2015. “Medicare Coverage of Kidney
Dialysis and Kidney Transplant Services” https://www.medicare.gov/Pubs/pdf/10128.pdf (accessed 9/30/15).
Wedd, Joel P., Ann M. Harper and Scott W. Biggins (2013) “MELD score, allocation and
distribution in the United States” Clinical Liver Disease. 2(4): 148-51. Wellington, Alison J; Sayre, Edward A. 2011. “An Evaluation of Financial Incentive Policies
for Organ Donations in the United States.” Contemporary Economic Policy, 29.1: 1-13.
36
Listing
Year Heart Intestine Kidney Liver Lung Pancreas All
1988 3,054 12,194 2,182 340 265 18,035
1989 3,159 12,774 2,950 435 550 19,868
1990 3,784 13,418 3,683 694 752 22,331
1991 3,983 1 13,871 4,176 1,105 871 24,007
1992 4,126 15,923 4,807 1,357 1,098 27,311
1993 3,997 59 17,135 5,522 1,520 1,268 29,501
1994 3,884 85 17,747 6,229 1,728 1,417 31,090
1995 4,382 91 19,271 7,329 1,890 1,616 34,579
1996 4,030 88 19,705 8,054 1,990 1,660 35,527
1997 3,897 134 20,460 8,620 2,079 1,739 36,929
1998 4,075 152 21,705 9,537 2,221 1,930 39,620
1999 3,650 149 22,803 10,520 2,099 2,330 41,551
2000 3,565 170 24,290 10,880 2,090 2,794 43,789
2001 3,506 219 24,122 11,126 2,138 2,737 43,848
2002 3,318 203 25,226 9,645 1,975 2,641 43,008
2003 3,008 205 26,053 10,324 2,022 2,561 44,173
2004 2,960 250 28,852 10,856 2,078 2,713 47,709
2005 2,895 284 30,925 10,987 1,623 2,675 49,389
2006 3,114 317 33,171 11,037 1,852 2,551 52,042
2007 3,162 281 34,479 11,083 2,009 2,398 53,412
2008 3,437 267 34,654 11,175 2,058 2,350 53,941
2009 3,579 260 35,658 11,262 2,344 2,231 55,334
2010 3,584 241 36,444 12,010 2,526 2,144 56,949
2011 3,504 184 35,595 11,925 2,520 1,876 55,604
2012 3,706 159 37,058 11,611 2,392 1,935 56,861
2013 4,031 180 38,625 12,020 2,576 1,750 59,182
Table 1: Waiting List Additions, by Organ and Year
Notes: These numbers are calculated at a single point in time in each year. Source: authors’ calculations from SRTR data.
37
Table 2: Waiting List Exits by Year and Reason for Leaving
Year
Deceased Donor
Transplant Medically Unsuitable
Transferred to another center Died Other
Improved, Transplant
Not Needed
Deteriorated, Too Sick
Transplant at Another Center
Living Donor
TransplantAll
Others
Total Waitlist Exits
1987 1,946 104 70 357 631 78 55 102 3,344
1988 9,946 471 282 1,580 1,904 430 314 278 15,217
1989 10,626 547 286 1,784 2,143 635 410 247 16,692
1990 12,473 675 414 2,056 2,280 708 528 298 19,445
1991 13,033 798 482 2,532 2,199 683 634 403 20,775
1992 13,328 1,019 684 2,769 2,137 583 798 300 21,622
1993 14,587 1,189 957 3,140 2,376 693 937 285 24,184
1994 15,098 1,359 762 3,343 2,625 682 1,218 328 25,435
1995 15,834 392 1,047 3,708 2,121 484 703 914 1,497 355 27,055
1996 15,868 971 4,288 1,923 796 1,061 991 1,723 360 27,981
1997 16,170 1,150 4,832 1,933 570 1,149 1,057 1,978 336 29,176
1998 16,904 1,254 5,537 2,061 602 1,228 1,065 2,259 267 31,178
1999 16,919 1,348 6,835 2,094 680 1,369 1,314 2,692 474 33,728
2000 17,240 1,862 6,455 1,680 674 1,473 1,423 3,455 770 35,039
2001 17,554 1,929 7,065 1,413 634 1,584 1,456 4,002 925 36,574
2002 18,188 1,747 7,202 2,143 1,309 1,862 1,556 4,159 1312 39,498
2003 18,561 1,724 7,138 1,836 1,053 1,646 1,568 4,350 1125 39,019
2004 19,949 1,729 7,373 1,903 1,131 1,632 1,973 4,765 1012 41,491
2005 21,117 2,694 7,373 2,067 1,061 1,905 2,073 4,952 1134 44,378
2006 22,135 2,293 7,370 2,725 966 2,119 2,095 5,063 1207 45,974
2007 21,999 2,172 7,135 3,121 1,590 2,463 2,290 4,911 1444 47,129
2008 21,703 1,998 7,170 3,951 1,498 2,947 2,359 5,121 1557 48,305
2009 21,815 1,858 7,158 3,521 1,315 3,427 2,432 5,633 1242 48,403
2010 22,058 1,956 7,049 3,797 1,276 3,880 2,538 5,766 1243 49,564
2011 22,457 1,984 7,301 4,138 1,146 4,441 2,732 5,425 1331 50,955
38
2012 22,141 2,376 6,986 3,667 1,257 4,739 2,670 5,361 1866 51,063
2013 22,935 2,432 6,733 3,761 1,183 5,242 2,741 5,534 1919 52,480
Notes: “All others” includes “removed in error”, “changed to kidney/pancreas”, “deceased donor emergency transplant”, “deceased donor multi-organ transplant”, “inactive program”, “died during transplant”, and “unable to contract transplant and refused transplant”. Note that medically unsuitable became “improved, transplant not needed” and “deteriorated, too sick” in 1995. Blank cells indicate that the count is below 25.
39
Table 3: Number of Persons on Waitlists, by Organ and Year Heart Intestine Kidney Liver Lung Pancreas
1985 1,152
1986 34 3,708 85
1987 617 10,131 431 28 35 1988 969 12,446 553 87 142 1989 1,266 14,975 699 121 282 1990 1,679 16,705 1,020 341 394 1991 2,138 18,449 1,443 655 351 1992 2,625 21,519 2,112 929 138 1993 2,777 43 24,226 2,805 1,201 205 1994 2,832 71 26,761 3,791 1,570 251 1995 3,336 78 30,083 5,288 1,848 315 1996 3,519 78 33,371 6,930 2,201 358 1997 3,664 87 36,665 8,831 2,533 379 1998 3,882 93 39,989 10,936 2,977 454 1999 3,728 100 42,703 13,113 3,227 517 2000 3,713 135 46,095 15,074 3,380 746 2001 3,640 160 48,953 16,615 3,516 1,043 2002 3,468 173 51,469 15,505 3,519 1,143 2003 3,208 162 54,348 15,576 3,586 1,315 2004 2,933 179 58,111 15,405 3,571 1,387 2005 2,689 176 60,994 15,207 2,900 1,372 2006 2,516 198 64,306 14,637 2,599 1,441 2007 2,360 176 67,301 14,106 2,077 1,322 2008 2,490 168 72,087 13,777 1,888 1,259 2009 2,763 181 77,296 13,823 1,760 1,176 2010 2,980 220 82,413 14,262 1,734 1,094 2011 2,958 231 85,819 14,391 1,631 1,003 2012 3,203 218 90,828 14,208 1,560 919 2013 3,512 224 96,520 14,301 1,562 906
Notes: These figures include both active and inactive patients on waitlists for single organs, i.e., they exclude those registered on either the heart-lung or the kidney-pancreas multi-organ waitlists. They are calculated at a single point in time in each year. Source: authors’ calculations from SRTR data. Blank cells indicate that the count is below 25.
40
Table 4 Changes in State Motorcycle Helmet Laws, 1988-2012 Year Universal to Partial Partial to Universal 1988 1989 1990 1991 1992
OR(6) NE(1), TX(9) WA(6) CA(1), MD(10)
… 1997 AR (8), TX (9) 1998 KY (7) 1999 LA (8) 2000 FL (7) 2001 2002 2003 PA (9) 2004 LA (8) … 2012 MI (4)
Note: The month a law changed is listed in parentheses, where “1” denotes January, “2” denotes February, and so on. Source: Insurance Institute for Highway Safety: http://www.iihs.org/laws/default.aspx
41
Table 5: Number of Donors (non-MVA and MVA) and Number of Organs Transplanted from MVA Donors
Donors Organs Transplanted per MVA Donor Year Non-MVA MVA Lung Kidney Heart Intestine Liver Pancreas Total 1988 2749 1404 0.04 1.85 0.49 0.00 0.44 0.06 2.89 1989 2884 1212 0.05 1.81 0.49 0.00 0.58 0.12 3.06 1990 3346 1255 0.08 1.81 0.57 0.00 0.62 0.14 3.22 1991 3405 1165 0.12 1.83 0.57 0.00 0.68 0.15 3.36 1992 3557 999 0.17 1.83 0.58 0.01 0.71 0.15 3.45 1993 3800 1105 0.19 1.83 0.62 0.01 0.75 0.20 3.60 1994 3874 1260 0.18 1.82 0.59 0.00 0.75 0.20 3.54 1995 3975 1410 0.28 1.80 0.58 0.01 0.78 0.26 3.72 1996 4093 1349 0.25 1.80 0.59 0.01 0.79 0.27 3.70 1997 4108 1385 0.29 1.81 0.58 0.01 0.84 0.29 3.82 1998 4406 1400 0.25 1.81 0.58 0.01 0.84 0.31 3.81 1999 4485 1345 0.24 1.83 0.55 0.01 0.85 0.35 3.83 2000 4541 1449 0.29 1.80 0.51 0.02 0.84 0.34 3.81 2001 4706 1377 0.28 1.81 0.55 0.02 0.86 0.39 3.92 2002 4732 1464 0.31 1.83 0.51 0.02 0.88 0.38 3.94 2003 5037 1425 0.31 1.82 0.51 0.02 0.90 0.39 3.94 2004 5631 1521 0.32 1.80 0.47 0.02 0.88 0.37 3.86 2005 6076 1519 0.36 1.80 0.47 0.03 0.89 0.37 3.92 2006 6379 1644 0.34 1.82 0.47 0.03 0.88 0.31 3.84 2007 6511 1583 0.34 1.78 0.46 0.02 0.86 0.32 3.77 2008 6717 1276 0.35 1.82 0.48 0.02 0.85 0.30 3.82 2009 6788 1235 0.39 1.81 0.47 0.03 0.82 0.28 3.80 2010 6690 1256 0.43 1.80 0.48 0.03 0.83 0.28 3.86 2011 6847 1283 0.44 1.82 0.45 0.02 0.81 0.26 3.80 2012 6881 1267 0.42 1.77 0.47 0.02 0.81 0.25 3.74 2013 6974 1300 0.47 1.81 0.50 0.02 0.81 0.24 3.85
42
Table 5 (cont.): Number of Donors (non-MVA and MVA) and Number of Organs Transplanted from non-MVA Donors
Donors Organs Transplanted per non-MVA Donor Year Non-MVA MVA Lung Kidney Heart Intestine Liver Pancreas Total 1988 2749 1404 0.04 1.70 0.38 0.00 0.39 0.06 2.57 1989 2884 1212 0.06 1.70 0.40 0.00 0.50 0.09 2.75 1990 3346 1255 0.07 1.67 0.42 0.00 0.56 0.10 2.83 1991 3405 1165 0.13 1.67 0.44 0.00 0.62 0.11 2.97 1992 3557 999 0.17 1.67 0.46 0.00 0.65 0.11 3.06 1993 3800 1105 0.18 1.65 0.44 0.01 0.67 0.14 3.09 1994 3874 1260 0.20 1.60 0.43 0.00 0.68 0.15 3.07 1995 3975 1410 0.23 1.57 0.40 0.01 0.70 0.16 3.07 1996 4093 1349 0.21 1.55 0.38 0.01 0.72 0.16 3.03 1997 4108 1385 0.25 1.54 0.38 0.01 0.71 0.16 3.05 1998 4406 1400 0.21 1.53 0.36 0.01 0.74 0.18 3.02 1999 4485 1345 0.22 1.50 0.34 0.01 0.75 0.18 3.00 2000 4541 1449 0.23 1.47 0.33 0.01 0.74 0.19 2.97 2001 4706 1377 0.24 1.47 0.32 0.02 0.74 0.18 2.96 2002 4732 1464 0.25 1.48 0.31 0.02 0.78 0.19 3.02 2003 5037 1425 0.25 1.43 0.27 0.02 0.81 0.16 2.93 2004 5631 1521 0.25 1.38 0.24 0.02 0.80 0.17 2.85 2005 6076 1519 0.29 1.38 0.24 0.02 0.79 0.15 2.86 2006 6379 1644 0.28 1.40 0.23 0.02 0.77 0.14 2.84 2007 6511 1583 0.29 1.37 0.24 0.02 0.75 0.13 2.80 2008 6717 1276 0.31 1.39 0.24 0.02 0.74 0.13 2.83 2009 6788 1235 0.34 1.38 0.25 0.02 0.75 0.13 2.87 2010 6690 1256 0.37 1.42 0.27 0.02 0.74 0.12 2.94 2011 6847 1283 0.37 1.43 0.26 0.01 0.74 0.11 2.93 2012 6881 1267 0.35 1.42 0.27 0.01 0.72 0.11 2.88 2013 6974 1300 0.38 1.42 0.27 0.01 0.74 0.10 2.93 Notes: These include both active and inactive patients on waitlists. They are calculated at a single point in time in each year.
43
Table 6: Estimates of the Effect of the Repeal of Helmet Laws on per Capita Organ Donors, Organ Donations, and Organ Transplants, by Organ
MVA Organ Donors
MVA Organ Transplants
Non-MVA Organ Donors
Non-MVA Organ
Transplants
(1) (2) (3) (4) Overall 0.906 3.429 0.336 -0.790
(0.234) (0.785) (0.989) (1.914) [4.886] [17.092] [16.949] [49.974]
By Organ Lung 0.434 0.253
(0.126) (0.400) [1.299] [4.452]
Kidney 1.577 -0.933 (0.392) (1.073) [8.386] [25.295]
Heart 0.421 -0.516 (0.104) (0.262) [2.409] [5.334]
Intestine 0.006 0.105 (0.017) (0.050) [0.072] [0.235]
Liver 0.774 0.361 (0.206) (0.609) [3.665] [12.279]
Pancreas 0.216 -0.060 (0.155) (0.229) [1.261] [2.380]
Notes:
1) All estimation samples consist of 57 DSAs from 1987 to 2013. The unit of observation is a DSA-year. All models include indicators for years and DSAs. 2) Standard errors, in parentheses, are robust to clustering within DSA over time. 3) Sample means for relevant dependent variables are listed in brackets.
44
Table 7: Estimates of the Effect of the Repeal of Helmet Laws on Waiting List Additions by In- Versus Out-of-Area
All Additions
All Additions In-DSA Out-of-DSA
(7) (8) (9)
Overall 18.665 10.322 8.343
(8.733) (6.213) (4.906)
[147.717] [114.885] [32.832]
By Organ
Lung 2.100 1.190 0.910
(1.341) (0.576) (0.923)
[7.911] [4.790] [3.121]
Kidney 8.481 3.087 5.395
(6.887) (5.477) (2.736)
[87.803] [71.861] [15.942]
Heart 0.091 0.355 -0.264
(1.354) (1.041) (0.498)
[12.339] [9.472] [2.867]
Intestine 0.796 0.094 0.703
(0.427) (0.223) (0.305)
[1.668] [0.568] [1.100]
Liver 5.226 3.977 1.249
(3.696) (1.700) (2.479)
[35.257] [25.719] [9.538]
Pancreas -0.014 -0.331 0.317
(0.816) (0.454) (0.435)
[2.423] [1.619] [0.803] Notes: 1) All estimation samples consist of 57 DSAs from 1987 to 2013. The unit of observation is a DSA-year. All models include indicators for years and DSAs. 2) Standard errors, in parentheses, are robust to clustering within DSA over time. 3) Sample means for relevant dependent variables are listed in brackets.
45
Table 8: Estimates of the Effect of Helmet Law Repeals on Waiting List Additions by In- Versus Out-of-Area and by Multilisting Status
No Multilistings Multilistings
All
Additions In-DSA Out-of-DSA
All Additions In-DSA
Out-of-DSA
(7) (8) (9) (7) (8) (9)
Overall 6.378 4.230 2.149 11.995 5.744 6.252
(6.918) (5.445) (3.034) (6.244) (3.231) (3.787)
[118.406] [97.010] [21.396] [41.081] [26.967] [14.114]
By Organ
Lung 2.348 1.278 1.070 0.306 0.180 0.126
(1.234) (0.578) (0.765) (0.304) (0.098) (0.227)
[5.597] [3.522] [2.076] [1.076] [0.509] [0.566]
Kidney -0.283 -0.979 0.696 7.739 3.467 4.272
(5.426) (4.810) (1.399) (3.729) (1.883) (2.410)
[67.597] [59.197] [8.400] [26.144] [17.483] [8.660]
Heart 0.272 0.408 -0.137 -0.044 -0.000 -0.043
(1.104) (0.871) (0.402) (0.251) (0.164) (0.108)
[10.718] [8.329] [2.390] [1.200] [0.822] [0.378]
Intestine 0.144 -0.050 0.193 -0.089 -0.003 -0.086
(0.289) (0.091) (0.227) (0.041) (0.016) (0.042)
[0.479] [0.172] [0.307] [0.126] [0.035] [0.092]
Liver 2.636 2.706 -0.071 2.729 1.400 1.329
(2.572) (1.504) (1.361) (1.870) (0.744) (1.241)
[27.791] [21.043] [6.749] [5.735] [3.415] [2.319]
Pancreas 0.052 -0.202 0.254 -0.175 -0.179 0.004
(0.463) (0.249) (0.255) (0.159) (0.110) (0.078)
[1.284] [0.876] [0.408] [0.755] [0.486] [0.269] Notes: 1) All estimation samples consist of 57 DSAs from 1987 to 2013. The unit of observation is a DSA-year. All models include indicators for years and DSAs. 2) Standard errors, in parentheses, are robust to clustering within DSA over time. 3) Sample means for relevant dependent variables are listed in brackets.
46
Table 9: Estimates of the Effect of Helmet Law Repeals on per Capita Living Organ Donors, by Relation to the Recipient
Overall -3.445
(1.403) [15.200]
By Donor's Relationship to Intended Recipient
Parent -0.366 (0.180) [2.162]
Child -0.481 (0.248) [2.415]
Sibling -0.683 (0.347) [4.725]
Other Relative -0.233 (0.101) [1.022]
Spouse -0.440 (0.193) [1.544]
All Other Directed Donations -1.202 (0.360) [2.373]
Anonymous (Undirected) -0.125 (0.041) [0.152]
Notes: 1) All estimation samples consist of 57 DSAs from 1987 to 2013. The unit of observation is a DSA-year. All models include indicators for years and DSAs. 2) Standard errors, in parentheses, are robust to clustering within DSA over time. 3) Sample means for relevant dependent variables are listed in brackets.
47
Table 10: Estimates of the Effect of Helmet Law Repeals on [Exits / Waitlist], by Reason
Deceased Donor
Transplant Living Donor Transplant Transfer Died
(1) (2) (3) (4) Overall -8.779 -6.915 -1.457 -4.402
(11.693) (6.162) (1.498) (1.623) [197.075] [53.343] [12.737] [49.367]
By Organ Lung 39.524 -3.104 -2.323 10.010
(43.680) (4.153) (3.089) (11.616) [260.164] [3.698] [10.643] [73.167]
Kidney -20.453 -9.638 -1.326 -1.437 (9.173) (6.645) (1.872) (1.313)
[155.130] [73.790] [13.161] [37.990]
Heart 15.460 0.925 1.422 -11.036 (30.088) (0.948) (3.521) (6.641) [359.544] [0.345] [10.666] [83.242]
Intestine -52.605 -3.183 -0.781 -28.577 (33.350) (3.339) (3.696) (11.552) [94.281] [7.242] [5.688] [35.466]
Liver 75.405 -2.377 1.244 -13.654 (45.791) (2.044) (5.035) (5.247) [308.186] [6.800] [11.529] [65.119]
Pancreas 0.125 -1.569 -1.711 9.213 (30.964) (1.175) (2.057) (5.870) [185.466] [0.530] [7.464] [18.121]
Notes: 1) All estimation samples consist of 57 DSAs from 1987 to 2013. The unit of observation is a DSA-year. All models include indicators for years and DSAs. 2) Standard errors, in parentheses, are robust to clustering within DSA over time. 3) Sample means for relevant dependent variables are listed in brackets.
48
Figure 1: Donation Service Area Map of the United States
Source: Wedd, Harper and Biggins (2013)
49
Figure 2 Event Study Estimates of Helmet Law Repeals on Per Capita Organ Donors and Transplants
Notes: Authors calculations from SRTR data. Full estimates available in Appendix C.
‐1
0
1
2
3
4
5
6
‐5 ‐4 ‐3 ‐2 ‐1 Repealyear
1 2 3 4 5
in Donors or Transplants per M
illion
Donors, All Organ and Kidney Transplants
Donors All Organs Kidney
‐0.4
‐0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
‐5 ‐4 ‐3 ‐2 ‐1 Repealyear
1 2 3 4 5
in Transplants Per M
illion
Lung, Heart, Intestine, Liver and Pancreas
Lung Heart Intestine Liver Pancreas
50
Figure 3
Event Study Estimates of Helmet Law Repeals on per capita Waitlist Additions
‐10
‐5
0
5
10
15
20All Organs
All In‐DSA Out of DSA
‐10
‐5
0
5
10
15
20 Kidney
All In‐DSA Out of DSA
‐1
0
1
2
3
4Lungs
All In‐DSA Out of DSA
‐2
‐1
0
1
2Heart
All In‐DSA Out of DSA
‐2
‐1
0
1Intestine
All In‐DSA Out of DSA
‐5
‐3
‐1
1
3
5Liver
All In‐DSA Out of DSA
51
Figure 3 (continued) Event Study Estimates of Helmet Law Repeals on per capita Waitlist Additions
Notes: Authors calculations from SRTR data. Full estimates available in Appendix C.
‐1
0
1
Pancreas
All In‐DSA Out of DSA
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