Content uploaded by Daniel G. Goldstein
Author content
All content in this area was uploaded by Daniel G. Goldstein
Content may be subject to copyright.
Content uploaded by Daniel G. Goldstein
Author content
All content in this area was uploaded by Daniel G. Goldstein
Content may be subject to copyright.
1338
Since 1995, more than 45,000 people in
the United States have died waiting
for a suitable donor organ. Although
an oft-cited poll (1) showed that 85% of
Americans approve of organ donation, less
than half had made a decision about donat-
ing, and fewer still (28%) had granted per-
mission by signing a donor card, a pattern
also observed in Germany, Spain, and
Sweden (2–4). Given the shortage of
donors, the gap between approval and ac-
tion is a matter of life and death.
What drives the decision to become a
potential donor? Within the European
Union, donation rates vary by nearly an or-
der of magnitude across countries and these
differences are stable from year to year.
Even when controlling for variables such as
transplant infrastructure, economic and ed-
ucational status, and religion (5), large dif-
ferences in donation rates persist. Why?
Most public policy choices have a no-
action default, that is, a condition is im-
posed when an individual fails to make a
decision (6,7). In the case of organ dona-
tion, European countries have one of two
default policies. In presumed-consent
states, people are organ donors unless they
register not to be, and in explicit-consent
countries, nobody is an organ donor with-
out registering to be one.
According to a classical economics view,
preferences exist and are available to the de-
cision-maker—people simply find too little
value in organ donation. This view has led
to calls for the establishment of a regulated
market for the organs of the deceased (8,9),
for the payment of donors or donors’ fami-
lies (10,11), and even for suggestions that
organs should become public property upon
death (12). Calls for campaigns to change
public attitudes (13) are widespread. In clas-
sical economics, defaults should have a lim-
ited effect: when defaults are not consistent
with preferences, people would choose an
appropriate alternative.
A different hypothesis arises from re-
search depicting preferences as constructed,
that is, not yet articulated in the minds of
those who have not been asked (14–16). If
preferences for being an organ donor are
constructed, defaults can influence choices
in three ways: First, decision-makers might
believe that defaults are suggestions by the
policy-maker, which imply a recommended
action. Second, making a decision often in-
volves effort, whereas accepting the default
is effortless. Many people would rather
avoid making an active decision about dona-
tion, because it can be unpleasant and stress-
ful (17). Physical effort such as filling out a
form may also increase acceptance of the de-
fault (18). Finally, defaults often represent
the existing state or status quo, and change
usually involves a trade-off. Psychologists
have shown that losses loom larger than the
equivalent gains, a phenomenon known as
loss aversion (19). Thus, changes in the de-
fault may result in a change of choice.
Governments, companies, and public
agencies inadvertently run “natural experi-
ments” testing the power of defaults.
Studies of insurance choice (20), selection
of Internet privacy policies (21, 22), and
the level of pension savings (23) all show
large effects, often with substantial finan-
cial consequences.
Defaults and Organ Donations
We investigated the effect of defaults on
donation agreement rates in three studies.
The first used an online experiment (24):
161 respondents were asked whether they
would be donors on the basis of one of
three questions with varying defaults. In
the opt-in condition, participants were told
to assume that they had just moved to a
new state where the default was not to be
an organ donor, and they were given a
choice to confirm or change that status.
The opt-out condition was identical, except
the default was to be a donor. The third,
neutral condition simply required them to
choose with no prior default. Respondents
could at a mouse click change their choice,
largely eliminating effort explanations.
The form of the question had a dramat-
ic impact (see figure, left): Revealed dona-
tion rates were about twice as high when
opting-out as when opting-in. The opt-out
condition did not differ significantly from
the neutral condition (without a default op-
tion). Only the opt-in condition, the current
practice in the United States, was signifi-
cantly lower.
In the last two decades, a number of
European countries have had opt-in or opt-
out default options for individuals’ deci-
sions to become organ donors. Actual deci-
sions about organ donation may be affected
by governmental educational programs, the
MEDICINE
Do Defaults Save Lives?
Eric J. Johnson* and Daniel Goldstein
POLICY FORUM
The authors are at the Center for Decision Sciences,
Columbia University, New York, NY 10027, USA.
*To whom all correspondence should be addressed:
ejj3@columbia.edu
42
82 79
Opt-In Opt-Out Neutral
Percent consenting
to being donors
0
10
20
30
40
50
60
70
80
90
100
Effective consent rates, online experiment,
as a function of default.
4.25
27.5
17.17 12
99.98 98 99.91 99.97 99.5 99.64
85.9
0
10
20
30
40
50
60
70
80
90
100
Denmark
Netherlands
Effective consent percentage
United Kingdom
Germany
Austria
Belgium
France
Hungary
Poland
Portugal
Sweden
Effective consent rates, by country.Explicit consent (opt-in, gold) and presumed consent (opt-
out, blue).
21 NOVEMBER 2003 VOL 302 SCIENCE www.sciencemag.org
1339
efforts of public health organizations, and
cultural and infrastructural factors. We ex-
amined the rate of agreement to become a
donor across European countries with ex-
plicit and presumed consent laws. We sup-
plemented the data reported in Gäbel (25)
by contacting the central registries for sever-
al countries, which allowed us to estimate
the effective consent rate, that is, the number
of people who had opted in (in explicit-con-
sent countries) or the number who had not
opted out (in presumed-consent countries).
If preferences concerning organ donation
are strong, we would expect defaults to have
little or no effect. However, as can be seen in
the figure (page 1338, bottom), defaults ap-
pear to make a large difference: the four opt-
in countries (gold) had lower rates than the
six opt-out countries (blue). The two distri-
butions have no overlap, and nearly 60 per-
centage points separate the two groups. One
reason these results appear to be greater than
those in our laboratory study is that the cost
of changing from the default is higher; it in-
volves filling out forms, making phone
calls, and sending mail. These low rates of
agreement to become a donor come, in
some cases, despite
marked efforts to in-
crease donation rates. In
the Netherlands, for ex-
ample, the 1998 cre-
ation of a national donor
registry was accompa-
nied by an extensive ed-
ucational campaign and
a mass mailing (of more
than 12 million letters in
a country of 15.8 mil-
lion) asking citizens to
register, which failed to
change the effective
consent rate (26).
Do increases in
agreement rates result
in increased rates of do-
nation? There are many reasons preventing
registered potential donors from actually
donating. These include: families’ objec-
tions to a loved one’s consent, doctors’ hes-
itancy to use a default option, and a mis-
match with potential recipients, as well as
differences in religion, culture, and infra-
structure.
To examine this, we analyzed the actual
number of cadaveric donations made per
million on a slightly larger list of countries,
with data from 1991 to 2001 (27). We ana-
lyzed these data using a multiple regression
analysis with the actual donation rates as de-
pendent measures and the default as a pre-
dictor variable. To control for other differ-
ences in countries’ propensity to donate,
transplant infrastructure, educational level,
and religion, we included variables known to
serve as proxies for these constructs (5) and
an indicator variable representing each year.
This analysis presents a strong conclu-
sion. Although there are no differences
across years, there is a strong effect of the de-
fault: When donation is the default, there is a
16.3% (P< 0.02) increase in donation, in-
creasing the donor rate from 14.1 to 16.4
million (see figure, this page, blue line).
Using similar techniques, but looking only at
1999 for a broader set of European countries,
including many more from Eastern Europe,
Gimbel et al. (5) report an increase in the
rate from 10.8 to 16.9, a 56.5% increase (see
figure, this page, red line). Differences in the
estimates of size may be due to differences in
the countries included in the analysis: Many
of the countries examined by Gimbel et al.
had much lower rates of donation.
Conclusions
How should policy-makers choose defaults?
First, consider that every policy must have a
no-action default, and defaults impose physi-
cal, cognitive, and, in the case of donation,
emotional costs on those who must change
their status. As noted earlier, both national
surveys and the no-de-
fault condition in our ex-
periment suggest that
most Americans favor
organ donation. This im-
plies that explicit con-
sent policies impose the
costs of switching on the
apparent majority (28).
Second, note that de-
faults can lead to two
kinds of misclassifica-
tion: willing donors
who are not identified
or people who become
donors against their
wishes. Balancing these
errors with the good
done by the lives saved
through organ transplantation leads to deli-
cate ethical and psychological questions.
These decisions should be informed by fur-
ther research examining the role of the three
causes of default effects. For example, one
might draw different conclusions if the ef-
fect of defaults on donation rates is due pri-
marily to the physical costs of responding,
than if they were due to loss aversion.
The tradeoff between errors of classifi-
cation and physical, cognitive, and emo-
tional costs must be made with the knowl-
edge that defaults make a large difference
in lives saved through transplantation.
Our data and those of Gimbel et al. sug-
gest changes in defaults could increase do-
nations in the United States of additional
thousands of donors a year. Because each
donor can be used for about three trans-
plants, the consequences are substantial in
lives saved. Our results stand in contrast
with the suggestion that defaults do not
matter (29). Policy-makers performing
analysis in this and other domains should
consider that defaults make a difference.
References and Notes
1. The Gallup Organization, “The American Public’s
Attitude Toward Organ Donation and Trans-
plantation” (Gallup Organization,Princeton, NJ,1993).
2. S. M. Gold, K. Shulz, U. Koch,
The Organ Donation
Process: Causes of the Organ Shortage and
Approaches to a Solution
(Federal Center for Health
Education, Cologne, 2001),
3. H. Gäbel, H. N. Rehnqvist,
Transplant. Proc.
29,3093
(1997).
4. C. Conesa
et al
., ,
Transplant. Proc.
35,1276 (2003).
5. R. W. Gimbel, M. A. Strosberg, S. E. Lehrman, E.
Gefenas, F. Taft,
Progr. Transplant.
13,17 (2003).
6. R. H. Thaler, C. Sunstein,
Univ. Chicago Law Rev.
,in
press.
7. C. Camerer, S. Issacharoff, G. Loewenstein, T.
O’Donoghue, M. Rabin,
Univ. Penn. Law Rev.
151,
2111 (2003).
8. M. Clay, W. Block,
J. Soc. Polit. Econ. Stud.
27,227
(2002).
9. J. Harris, C. Erin,
BMJ
325,114 (2002).
10. C. E. Harris, S. P. Alcorn,
Issues Law Med.
3,213 (2001).
11. D. Josefson,
BMJ
324,1541 (2002).
12. J. Harris,
J. Med. Ethics
29,303 (2003).
13. J. S.Wolf, E. M. Servino, H. N. Nathan,
Transplant. Proc.
29,1477 (1997).
14. J. W. Payne, J. R. Bettman, E. J. Johnson,
Annu. Rev.
Psychol.
43,87 (1992).
15. P. Slovic,
Am. Psychol.
50,364 (1995).
16. D. Kahneman, A. Tversky, Eds.,
Choices, Values, and
Frames
(Cambridge Univ. Press, Cambridge, 2000).
17. J. Baron, I. Ritov,
Org. Behav. Hum. Decision Processes
59,475 (1994).
18. W. Samuelson, R. Zeckhauser,
J. Risk Uncertainty
1,7
(1988).
19. A. Tversky, D. Kahneman,
Q. J. Econ.
106(4), 1039
(1991).
20. E. J. Johnson, J. Hershey, J. Meszaros, H. Kunreuther,
J.
Risk Uncertainty
7,35 (1993).
21. S. Bellman, E. J. Johnson, G. L. Lohse,
Commun. ACM
(
Assoc. Comput. Machin.
) 44,25 (February 2001).
22. E. J. Johnson, S. Bellman, G. L. Lohse,
Marketing Lett.
13,5 (February 2002).
23. B. C. Madrian, D. Shea,
Q. J. Econ.
116(1), 1149 (2001).
24. Methods and details of analysis are available as sup-
porting material on
Science
online.
25. H. Gäbel,“Donor and Non-Donor Registries in Europe”
(on behalf of the committee of experts on the
Organizational Aspects of Co-operation in Organ
Transplantation of the Council of Europe, Brussels,
2002).
26. M. C. Oz
et al.
,
J. Heart Lung Transplant.
22,389
(2003).
27. We used a times series analysis to account for possi-
ble changes in transplant technology and infrastruc-
ture, as well as the effects of continuing public edu-
cation campaigns.
28. An alternative advocated by the American Medical
Association (
30
) is mandated choice, which imposes
the cost of making an active decision on all.This prac-
tice is currently employed in the state of Virginia, but,
consistent with the constructive preferences perspec-
tive, about 24% of the first million Virginians asked
said they were undecided (
31
).
29. A. L. Caplan,
JAMA
272,1708 (1994).
30. American Medical Association, “Strategies for cadav-
eric organ procurement: Mandated choice and pre-
sumed consent” (American Medical Association,
Chicago, 1993).
31. A. C. Klassen, D. K. Klassen,
Ann. Intern. Med.
125,70
(1996).
32. This research has been supported by the Columbia
University Center for Decision Science and the
Columbia Business School Center for Excellence in E-
Business. We thank L. Roels for providing the data on
actual donation rates.
Supporting Online Material
www.sciencemag.org/cgi/content/full/302/5649/1338/
DC1
Opt-in Opt-out
Default
Donation rate per million
9
11
13
15
17
19
Estimated donation rate,opt-in versus
opt-out, as a function of default,
1991–2001. Means ± SEM; this paper,
blue; Gimbel
et al
.(
5
), red.
POLICY FORUM
www.sciencemag.org SCIENCE VOL 302 21 NOVEMBER 2003
