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Preliminary: Please Do Not Cite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.

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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.

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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.

 

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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