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Explaining the "Identifiable Victim Effect."

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It is widely believed that people are willing to expend greater resources to save the lives of identified victims than to save equal numbers of unidentified or statistical victims. There are many possible causes of this disparity which have not been enumerated previously or tested empirically. We discuss four possible causes of the "identifiable victim effect" and present the results of two studies which indicate that the most important cause of the disparity in treatment of identifiable and statistical lives is that, for identifiable victims, a high proportion of those at risk can be saved. Copyright 1997 by Kluwer Academic Publishers
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Journal of Risk and Uncertainty, 14:235–257 (1997)
© 1997 Kluwer Academic Publishers
Explaining the “Identifiable Victim Effect”
KAREN E. JENNI
Department of Engineering and Public Policy, Carnegie Mellon University
GEORGE LOEWENSTEIN
Department of Social and Decision Sciences, Carnegie Mellon University
Abstract
It is widely believed that people are willing to expend greater resources to save the lives of identified victims
than to save equal numbers of unidentified or statistical victims. There are many possible causes of this disparity
which have not been enumerated previously or tested empirically. We discuss four possible causes of the
“identifiable victim effect” and present the results of two studies which indicate that the most important cause
of the disparity in treatment of identifiable and statistical lives is that, for identifiable victims, a high proportion
of those at risk can be saved.
Key words: value of life, identifiable victims
JEL Classification: J-17
“There is a distinction between an individual life and a statistical life. Let a 6-year-old
girl with brown hair need thousands of dollars for an operation that will prolong her
life until Christmas, and the post office will be swamped with nickels and dimes to save
her. But let it be reported that without a sales tax the hospital facilities of Massachu-
setts will deteriorate and cause a barely perceptible increase in preventable deaths—not
many will drop a tear or reach for their checkbooks. (Schelling, 1968)
“The death of a single Russion soldier is a tragedy. A million deaths is a statistic.
Joseph Stalin (quoted in Nisbett and Ross, 1980:43)
In late 1987, eighteen-month old Jessica McClure spent 58 hours trapped in a well, and
Americans responded with sympathy, a tremendous rescue effort, and money. The Mc-
Clures received over $700,000 in donations for “baby Jessica” in the months after her
rescue, and eventually a popular television movie, “Everybody’s Baby: The Rescue of
Jessica McClure, was made about the incident (People Weekly, November 2, 1987; April
16, 1990; Variety, May 31, 1989). At the time, there was no question but that everything
possible should and would be done to rescue the child; cost was no object. If similar
resources were spent on preventative health care for children, hundreds of lives could be
saved. Yet it is difficult to raise money for efforts directed at saving such “statistical”
victims.
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The story of “baby Jessica” is simply one example of the “identifiable victim effect:”
society is willing to spend far more money to save the lives of identifiable victims than to
save statistical victims. This has been remarked upon in treatises on public policy (Gore,
1992), in scholarly works (Schelling, 1968; Calabresi and Bobbitt, 1978; Viscusi, 1992;
Whipple, 1992), the medical literature (Redelmeier and Tversky, 1990) and the popular
press (Toufexis, 1993).
The identifiable victim effect plays a role in many important policy issues. Recently, it
has received special prominence in the national debate over funding priorities for health
care, where expensive measures are often taken for identified individuals, but funding for
preventative care seems to be lacking. For example, a recent effort to separate conjoined
twins, whose probability of surviving the operation was estimated to be less than 1%, was
used in some media to highlight the discrepancies between extravagant health care fund-
ing for “last-ditch efforts to save the few” and modest funding for basic and preven-
tative care that would benefit the many (Toufexis, 1993). In debates over the North
American Free Trade Agreement, opponents could identify specific individuals who
would lose their jobs if the agreement was approved, whereas proponents could refer only
to the additional “statistical” jobs that would presumably result (Goodman, 1993). Iden-
tifiable victims need not be human: in 1988 a multi-national effort spent millions to rescue
three grey whales trapped under the Arctic ice cap, while at the same time the Japanese
whaling industry was spending millions to locate and harvest whales (Linden, 1988).
Identifiable victims seem to produce a greater empathic response, accompanied by
greater willingness to make personal sacrifices to provide aid. One might think, therefore,
that the large literature on empathy, altruism, and helping behavior would provide clues
about why identifiable victims are treated differently from statistical victims. However, the
literature on helping behavior focuses almost exclusively on the factors that cause people
to aid identified victims (see, e.g., Latané and Darley, 1970, or Piliavin et al., 1981), and
much of this literature looks at factors, such as the number of potential aiders and the
costs of providing aid, that are not obviously relevant to the problem of why identifiable
and statistical victims are treated differently. Likewise, the literature on empathy and
altruism has been concerned primarily with the question of whether “true”—that is self-
less—empathy actually exists (see, e.g., Cialdini et al., 1987, and Batson et al., 1991),
which again seems to have little relevance to the question of why identifiable and statis-
tical victims produce such a different response. We have not seen any explicit treatment of
the identifiable victim effect in either literature.
In those literatures where it has been discussed, the distinction between identifiable and
statistical victims is typically treated as a simple dichotomy, and the frequency with which
it is mentioned reinforces this view. However, the simplicity of the distinction is deceptive:
in practice, there are several differences between identifiable and statistical victims, any
one of which could account for their differential treatment.
Our goal in this paper is to gain a better understanding of the psychological underpin-
nings of the identifiable victim effect. We do not attempt to explain the effect at a deeper
level—e.g., to explain at an evolutionary level how or why humans have come to respond
more strongly to identifiable than to statistical victims. Based on discussions with col-
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leagues and a combing of the literature, we came up with four factors that differ between
statistical and identifiable victims that could potentially account for their differential
treatment. Although we do find that one of these factors appears to be an important cause
of the effect, it is possible that other factors we have not identified also play a role.
In what follows, we first discuss these four differences between identifiable and statis-
tical victims which may be responsible for the effect. We then discuss the normative status
of the effect in relation to each of the four possible causes. Finally, we present findings
from two studies designed to test the four possible causes and to determine whether, if
supported, they are consciously endorsed.
1. Potential causes of the identifiable victim effect
1.1. Vividness
When an identifiable person is at risk of death, the media tell us a lot about them, and we
may come to feel that we know them
1
. Research on “vividness” has shown that specific,
concrete examples have far greater influence on what people think and how they behave
than more comprehensive but pallid statistical information (Nisbett and Ross, 1980).
Situations with identifiable victims are often characterized by all the major factors that
convey vividness: the stories are very emotional (victims featured in the media are often
particularly sympathetic, helpless, or blameless), we see visual images of the victim in
newspapers and on television, and we see the events unfold in real-time—without the
emotional distance provided by a historical perspective. For example, we see the picture
of the small girl who is trapped in the well, interviews with her tearful parents on
television, and live coverage of the desperate attempt to rescue her. These vivid details
may result in a perceived familiarity with the victim, making it seem more important to
undertake extraordinary measures to save that person. As Schelling (1968) expresses it,
“the more we know, the more we care.
Indeed, many public relations and marketing tactics seem to be premised on the view
that the vividness of an identifiable victim will enhance the public’s desire to “do some-
thing” about the problem. For example, the “poster child” for MS fund raising, and the
pictures and life stories that accompany requests for money to prevent malnutrition point
to a widespread belief that concrete details increase the public’s concern. Likewise,
arguments for and against the proposed “three strikes and you’re out” federal sentencing
policy rely on vividness to create sympathy for their position: arguments for implementing
such a system discuss specific victims of violent crimes who would not have been vic-
timized had the “three strikes” policy been in place (Skelton, 1993), whereas arguments
against the policy focus on relatively harmless three-time offenders who would face
lifetime incarceration (Egan, 1994).
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1.2. Certainty and uncertainty
A second distinction between identifiable and statistical victims is that identifiable deaths
are usually certain to occur if action is not taken, whereas statistical deaths, by definition,
are probabilistic. Since they are certain to occur if action is not taken, the subjective
importance of identifiable deaths may be enhanced by the “certainty effect”—the ten-
dency to place disproportionate weight on outcomes that are certain relative to those that
are uncertain but likely (Kahneman and Tversky, 1979). In addition, there is compelling
evidence that people are typically risk-seeking for losses (Kahneman and Tversky, 1979;
Tversky and Kahneman, 1981, 1986; Cohen, Jaffrey, and Said, 1987). Risk-seeking for
losses implies that a certain loss is seen as worse than an uncertain loss with the same
expected value. For example, most people prefer a 50;50 chance of losing $100 or losing
nothing to a certain loss of $50. Risk-seeking for losses has been demonstrated for
non-monetary losses as well: in one well-known study (Tversky and Kahneman, 1981),
subjects were given two identical scenarios in which lives were at risk and were asked to
choose between two treatment options. In one case, the scenarios were worded in terms of
lives saved (gains), and in the other they were worded in terms of lives lost (losses).
Consistent with the prediction that people are risk-seeking for losses, most subjects in the
lives lost condition chose the riskier treatment option. Risk-seeking for losses implies that
the number of certain (identifiable) fatalities that is deemed “equivalent” to uncertain
(statistical) fatalities is less than the expected number of statistical deaths. Both the
certainty effect and risk seeking for losses, therefore, may contribute to the tendency to
treat identifiable (and thus certain) victims as more worthy of attention than statistical
victims.
1.3. Proportion of the reference group that can be saved
Public perceptions of risk are responsive to the distribution of risk among the population
as well as to the absolute level of risk (Slovic, Fischhoff, and Lichtenstein, 1980). In
general people are more concerned about risks that are concentrated within a geographic
region or population than about those that are dispersed (National Research Council,
1989). This concern with the concentration of risk may help to explain the identifiable
victim effect: identifiable victims represent highly concentrated distributions of risk
within a specific reference group. In effect, identifiable victims become their own refer-
ence group, creating a situation where n out of n people will die if action is not taken. For
example, if 120 people are likely to die in a plane crash this year, these are only 120
people out of the millions who fly. Once a plane carrying 120 passengers crashes with all
aboard lost, however, these are 120 fatalities out of the 120 on board the plane.
There is considerable evidence that, holding the number of victims constant, people’s
concern increases as the applicable reference group shifts. For example, people are less
tolerant of the risks of vaccination when there is a smaller “risk group” for vaccine side
effects, even when members of that risk group could not be identified a priori (Ritov and
Baron, 1990). Similarly, economic studies of the “value of life” have found that the value
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of avoiding death or injury increases as the baseline probability of death or injury in-
creases (Weinstein, Shepard, and Pliskin, 1980; Viscusi and Evans, 1990; Horowitz and
Carson, 1993). Willingness to pay for small reductions in risk can be extrapolated to
calculate a value of life. For example, the value of life can be calculated as 10 times the
willingness to pay to avoid a 10% chance of death, or as 100 times the willingness to pay
to avoid a 1% chance of death. The willingness to pay to avoid a 10% chance of death is
greater than ten times the willingness to pay to avoid a 1% chance of death, resulting in
a higher overall value of life when the baseline risk is high. Since a high proportion of the
reference group at risk implies a high probability of fatality, or high baseline risk, for each
member of the risk group (e.g., if 25 out of 100 will die the baseline risk for each member
of the reference group is 0.25, but if 25 out of 50,000 will die, the baseline risk for each
member of the reference group is 0.0005), these findings are consistent with an increase
in concern about fatalities when the reference group is small relative to the number at risk.
According to the proportion of the reference group at risk explanation, there is not a
strict dichotomy between identifiable and statistical lives. Instead, identifiable victims lie
at one end of a continuum running from low probability risks spread over the entire
population (statistical deaths) to certain death for every member of the population (iden-
tifiable deaths).
1.4. Ex post versus ex ante evaluation
Identifiable victims are actual people who are very likely to die or be injured, whereas
statistical victims are, as the term implies, simply statistics. In other words, with identi-
fiable victims, both they and we know, at the time we have to decide what to do, that they
are likely to die as the result of a preventable or addressable cause. The decision about
rescuing an identifiable victim, or the evaluation of the value of rescuing the victim, is
usually made ex post, or after, the occurrence of some risk-producing event. In contrast,
the evaluation of the value of addressing risks to statistical victims is usually made ex
ante, or before the risk-producing event has occurred. (Weinstein, Shepard, and Pliskin,
1980). The ex post/ex ante distinction appears to be the identifiable victim effect itself,
after other factors that covary closely with the identifiable/statistical discrepancy—i.e.,
vividness, certainty, and the proportion of the reference group at risk—have been elimi-
nated.
Ex post evaluation makes it more difficult to apply cost-benefit principles in deciding
what to do, and instead makes issues of responsibility and blame salient. Once a victim
has been identified ex post, people can no longer “withdraw to a naked statistical analysis
of the cost-effectiveness of the effort, whereas people have few reservations about doing
so ex ante (Gillette and Hopkins, 1988). In addition, peoples’ perceptions and judgments
of “risk” depend in part on the saliency of blame (Douglas, 1992). The possibility for the
recognition of responsibility, and thus the attribution of blame, is clear in the ex post case
and almost absent in the ex ante case.
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2. Normative status of the identifiable victim effect
Scholars are divided about the identifiable victim effect’s normative status. Emphasis on
saving identifiable victims has been deplored as irrational (MacLean, 1986; Whipple,
1992), and praised as humanizing (Glover, 1977; Calabresi and Bobbitt, 1978; Gibbard,
1986; Gillette and Hopkins, 1988). For example, MacLean (1986) argues that activities
undertaken to rescue identifiable victims, when compared to the efforts spent to reduce
statistical risks, “defy economic or even risk-minimizing sense, whereas Gibbard (1986)
asserts that it is immoral not to act when identifiable lives can be saved. Although the
normative status of the effect may not be important for individual decision making, it is
extremely important when identifiable victims are used to justify or defend specific policy
decisions. How should the sympathy we feel for identifiable victims affect public policy?
The normative defensibility of the effect depends, in part, on its cause. For example,
since people generally obtain more information about, and see more vivid descriptions of,
identifiable victims than statistical victims, identified victims may be seen as more famil-
iar. Although people might reasonably respond in a more emotional fashion to familiar or
vivid victims, it is less reasonable to endorse a policy that gives higher priority to more
familiar victims, all else held equal, than to less familiar victims. This view would amount
to allowing media coverage to determine aid allocation.
The normative status of the effect of certainty lies in a grey area. Although many people
display an analogous pattern of behavior when deciding between certain and uncertain
monetary losses, it is not clear whether risk seeking for losses is a principle that people
do, or should, consciously endorse or, if so, whether that the same principle should apply
to decisions involving lives. Indeed, it is always possible to reframe a decision involving
tradeoffs between numbers of deaths as one involving saving lives.
There may be normative arguments for being concerned with the distribution as well as
the absolute magnitude of potential harm. MacLean (1986) believes that we should “dis-
tribute risks of death equally or in proportion to the distribution of expected benefits.
Along similar lines, several moral philosophers argue that “fairness” or equity is a critical
factor both in defining justice and in evaluating risks (Rawls, 1971, 1993; Shrader-
Frechette, 1991; Raynor, 1992), a view which is especially cogent in light of the envi-
ronmental justice movement, and the claim that the risks of hazardous waste disposal and
pollution are borne disproportionately by minorities and by the poor (Bullard, 1993;
Cushman, 1994).
However, in other situations taking the risk distribution into account seems less defen-
sible. Given that reference group size is often a matter of framing—a reference group of
arbitrary size can be specified for virtually any hazard—a blanket endorsement of a policy
that treats fatalities differently based on what proportion of the reference group they
compose is normatively dubious. For example, it probably makes no sense to treat a
disease that kills 100% of the 10% of the population susceptible to it differently from one
that kills 10% of the 100% of the population susceptible to it. However, some reference
groups may be more normatively defensible than others. Thus, even after careful consid-
eration, one might be more upset about a disease that kills an entire family or people in
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a small geographic area than one that kills a similar number of victims from around the
country.
The normative status of the distinction between ex post and ex ante evaluation of risks
is also ambiguous. Although ex post evaluation of identifiable victims may bring into play
powerful emotions that do not apply to statistical victims, it is not clear what role those
reactions should play in making policy decisions.
These four possible causes of the identifiable victim effect are, it seems, differentially
defensible. Thus shedding light on which, if any, of these causes are responsible for the
effect will also help to determine its normative status. In addition, it is not clear whether
the identifiable victim effect is the result of a reasoned response, or if it is a gut-level,
instinctive response. Whatever the cause of the identifiable victim effect, it would be
interesting to ascertain whether people embrace it as a principle of decision making. That
is, do we value identifiable lives more than statistical lives unconsciously and possibly
unintentionally, or do we continue to value identifiable lives more after reflection? The
answer to this question may also reflect on the normative status of the effect.
3. The studies
Our first goal was to determine which of these four potential causes, if any, contribute to
the identifiable victim effect. In both studies, subjects read risk and accident scenarios in
which each cause was either present or absent, and then rated the importance of saving the
victim(s)
2
. In the first study we had a second goal: to determine if people continue to
distinguish between identifiable and statistical victims when faced with an explicit choice
between saving identifiable versus statistical lives. To address this question, we included
two judgment conditions: rating and direct comparisons.
3.1. Study 1
Method. To test the various explanations for the identifiable victim effect, and to deter-
mine whether, if supported, they are consciously endorsed, we developed sets of scenarios
in which the four causal factors were manipulated. We asked subjects to judge the im-
portance of reducing risks in each, by having them either rate or compare the scenarios.
To investigate whether the identifiable victim effect is unconscious, or whether it per-
sists in the face of obvious comparisons of identifiable and statistical victims, we tested
subjects in two conditions. In the rating condition, subjects saw the scenarios in random
order, with scenarios related to the same explanation separated by other questions. They
rated the importance of eliminating risks in each scenario (compared to other risks for
which the government has responsibility) on a one-to-five scale, where 1 was “not de-
serving of attention, and 5 was “one of the most important. In the direct comparison
condition, subjects read the scenarios designed to test a single explanation together, and
chose the situation in which it was more important to eliminate the risk. “Equally impor-
tant” was given as an option. The direct comparison version makes it clear to subjects that
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the same number of lives could be saved in each case, and makes the manipulation highly
salient. Given this salience, we can assume that any choice other than indifference is the
result of a conscious judgment on the part of subjects about the importance of the
distinction. In the rating condition, on the other hand, subjects were unaware of what was
special about the scenarios they viewed. For example, those who were exposed to a victim
described in detail to increase vividness were not aware that there was another condition
in which details were not provided. Thus, they would have a much harder time avoiding
a gut-level response, even if they believed that normatively it should not affect their
judgments.
If a particular effect operates unconsciously and unintentionally, differences caused by
the manipulation should be evident in the rating condition but not in the direct comparison
condition. Alternatively, if the asymmetry is consciously endorsed, we would expect it to
be equally strong in both the rating and the comparison groups, or possibly even stronger
in the latter.
To test whether vividness increases the apparent importance of undertaking a rescue
attempt, we presented two scenarios involving a traffic accident in which a victim was
injured and required immediate, possibly expensive, medical help. The two scenarios are
presented in the first panel of Table 1. The two situations are identical except that in one
case no information about the victim was provided, and in the other case we provided a
brief description of the victim. If vividness is a critical factor in making decisions about
life-saving actions, subjects should rate it as more important to save the vividly described
victim than the anonymous victim.
Testing the effect of uncertainty is complicated by the fact that (at least) two types of
uncertainty might be relevant—uncertainty about whether a specific individual will be-
come a victim, and uncertainty about whether that individual, once a victim, will die. In
this first study, the effect of certainty versus uncertainty was examined with two scenarios
in which the expected number of fatalities from a contaminated food source was held
constant (at ten), but in one case the deaths are certain to occur if action is not taken, and
in one case they are probabilistic. In the probabilistic case, the scenario explicitly stated
that fewer or more than ten may die. These scenarios are shown in Table 2. If people are
risk seeking when it comes to deaths, we would expect most subjects to find it more
important to prevent the ten certain deaths than to prevent the ten probabilistic deaths.
To test whether the proportion of the risk group that can be saved affects subjects’
preferences for risk-reducing projects, we developed two scenarios involving traffic fa-
talities and different at-risk populations. Table 3 presents these two scenarios. In both,
“exactly” 25 lives could be saved by a new safety program, but the number of people
specified as being at risk was 50,000 in one case and 25 in the other. According to the
proportion-of-the-reference-group hypothesis, the problem should be seen as more severe
as the percentage of the reference group at risk increases, so that saving 25 out of 25 at
risk will be considered more important than saving 25 out of 50,000.
Finally, to test whether people believe that ex post decisions to save lives are more
important than ex ante decisions to protect them, we developed two parallel scenarios
where one individual was at risk from a pesticide being field-tested (see Table 4). The
scenarios differed only in whether action could be taken before or after the pesticide had
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Table 1. Scenarios testing the effect of vividness.
Study 1
[Anonymous] There has been a traffic accident on a remote section of the highway, and a person has been
seriously injured. This person requires a helicopter rescue and immediate medical treatment to save his life.
[Vivid] There has been a traffic accident on a remote section of the highway, and a young secretary has
been seriously injured. The secretary was traveling by herself, on her way to spend the weekend with her
parents. She requires a helicopter rescue and immediate medical treatment to save her life.
Study 2
The 1994 earthquake in Southern California caused approximately 34 deaths, thousands of injuries, over
a billion dollars of structural damage to buildings, and seriously damaged six of the major freeways in the
region. Two days after the earthquake, rescue workers discovered a victim trapped in a collapsed parking garage
located very near Highway 17. The news story reproduced below describes the rescue efforts that were under-
taken at the time.
Example of news story with a “described” victim
Rescue Workers Find Quake Survivor
Efforts to remove survivor underway
Los Angeles, Calif. Jan. 15.
Rescue workers were losing hope.
It’s not possible, not in that
wreckage. There couldn’t be any-
one alive in there.
A few still hoped. Perhaps,
they thought, as they probed the
wreckage of the municipal park-
ing garage at the 23rd Ave. exit of
Highway 17 with high-tech survi-
vor, and are working as quickly as
possible to sort through the rubble
to reach him.
Don Grisom, the attendant at
the parking garage, remembers
waving to Bob Wright as he went
to his car shortly before 3 pm.
Then the earthquake struck and
the garage collapsed. Mr. Grisom
was able to run to safety, but saw
Mr. Wright just getting into his
car as the building began to come
down. Mr. Wright, 42, is a local
high school teacher and basketball
coach. His wife, Mary, has not
left the equipment, perhaps some-
one could have survived the col-
lapse.
Finally, just after 6 am, a
worker using a special fiber optic
camera to search parts of the ga-
rage that are currently inaccessible
spotted a hand moving in the win-
dow of a car. Someone was alive
in that mountain of rubble—im-
prisoned in a tomb of concrete,
but alive nonetheless. Scene since
she heard her husband might be
trapped inside. She is ecstatic that
he has been found, but is very
concerned. “It’s frightening, she
says, “But Bob is strong and has
an incredible zest for life. I know
he will hold on for me and for
Jimmy [the couple’s 3 year old
son]. I am hoping and praying for
him, and I’m sure he is going to
be OK.
While rescue workers are
cautiously optimistic, the survivor
appears to be pinned in his car,
underneath tons of concrete and
Search of the partially col-
lapsed parking structure began
yesterday afternoon, after a park-
ing attendant, who miraculously
escaped the collapsing garage dur-
ing the earthquake, remembered
seeing a man walk into the garage
to pick up his car at about 3 pm,
minutes before the quake hit.
Workers and paramedics have
made voice contact with the steel
rubble, and in a particularly un-
stable section of the collapsed
structure. Before full-scale extrac-
tion measures can begin, parts of
the building must be stabilized.
City engineers are on the site di-
recting the stabilizing work, but
are unsure how long it will be
before they will be able to remove
the survivor. However, rescuers
are also working to create an ac-
cess route so paramedics can
reach the survivor quickly and
attend to any critical injuries.
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been applied. In the ex post case the individual had been exposed and had to be located;
in the ex ante case the individual was about to be exposed, unless she could be located
first. The ex post/ex ante distinction suggests that the former scenario will be judged more
important.
Table 1. (Continued)
What isn’t in this news story is a controversy over the effect of nearby freeway traffic on the parking
structure. Highway 17 was not damaged by the earthquake, and was a primary alternative route for all traffic
traveling from the south into downtown Los Angeles. Consequently, the highway was carrying much more
traffic than usual. The highway passes directly adjacent to the parking garage, and many engineers were
concerned that vibrations from the traffic on the highway could cause more damage to the parking garage,
further endangering the trapped victim. These engineers suggested shutting down the highway until the victim
could be extracted from the collapsed garage. However, the highway was estimated to be carrying about 230,000
cars per day, all of which would be diverted onto surface streets if it were shut down. This would add an average
of an hour an a half to commuting time into downtown. Keeping the highway open would not endanger the
rescuers, but could produce further structural shifting, with risks to the trapped victim. If you had been in the
position of having to make a decision about the freeway, how strongly would you have supported closing the
freeway?
Table 2. Scenarios testing the effect of certainty and uncertainty.
Study 1
[Uncertain] A major food distributor has just discovered that its newly introduced yogurt can cause death
to individuals who are allergic to its new ingredient. Approximately 1% of the general population is allergic to
this particular ingredient, but the existence of the allergy is not well known. The yogurt has been distributed to
a large number of retailers, and sold to about 1000 different consumers. Each consumer therefore has a 1%
chance of becoming ill and dying from the yogurt, and the best estimate is that 10 people will die, but more or
fewer may die depending on the prevalence of the allergy. These people can be saved if all the consumers are
located and treated.
[Certain] A major food distributor has just discovered that a small number (10 containers) of the yogurts
it distributed yesterday were contaminated, and that the contamination will result in the death of anyone who has
eaten the yogurt, unless an antidote drug is administered. The yogurt was distributed to a large number of
retailers, and has since been sold to approximately 1000 consumers. 10 deaths will result unless the consumers
are located and the antidote drug administered. If all the consumers are located and the antidote is administered,
no one will be harmed.
Study 2
On average, about 100 children under the age of 4 die each year from suffocation associated with thin-layer
plastic (bags and wrappings) in the United States. Over the past 10 years, annual deaths have ranged from [92
to 110] [34 to 168]. Most of these deaths are associated with plastic bags used by dry cleaners and with plastic
wrapping from toy packages. Some scientists have suggested that replacing plastic with paper wrappings for
these purposes would virtually eliminate the problem of children suffocating on plastic wrap. Legislation has
been introduced that will require all dry cleaners and toy manufacturers in the U.S. to begin packaging with
paper rather than plastic in 1996, with complete phase-out of plastic by the year 2000. Think about other
legislation concerned with safety that is being, or could be, considered, from legislation you don’t care at all
about to legislation you care very much about. Relative to other legislation, what priority would you place on
having this legislation passed and implemented?
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These scenarios may seem, in isolation, to be artificial and unrealistic. This is in part
because real-world identifiable victims are often characterized by several of these distinc-
tions, so it is extremely difficult to manipulate them independently in a complete factorial
design.
The existence of any one of the four factors implies the existance of at least one other
factor. For example, if a victim is certain, that victim also composes a large proportion of
the reference group (in this case, certain victims automatically become their own refer-
ence group, and in fact, compose 100% of the reference group). However, when a large
portion of the reference group is at risk, those victims do not have to be certain victims.
For example, everyone in a population could be susceptible to a specific disease, but the
probability that the disease will kill a susceptible individual might be quite low.
Figure 1 illustrates the relationships between the four factors, where arrows represent
logical implication. For example, the arrow from certain to high proportion of reference
group indicates that a certain victim also represents a high proportion of the reference
group. However, there is no arrow from high proportion to certain, so victims who
comprise a high proportion of the reference group are not necessarily certain victims.
The complex relationships between these four possible causes make it impossible to
develop scenarios in which all four causes are varied independently. For example, if factor
A is necessary for factor B, the combination of B, not A, is logically impossible. However,
no one factor is both necessary and sufficient for any other factor, and thus no two of these
factors are perfectly confounded. This makes it possible to examine the effect of each
cause independently, with the other three held constant, as we did in these two studies.
Table 3. Scenarios testing the effect of the proportion of the reference group that can be saved.
Study 1
[Large reference group] Approximately 50,000 people die every year in traffic accidents in the United
States. A new program has been proposed that will save exactly 25 of these 50,000 lives every year.
[Small reference group] 25 people die every year in traffic accidents on a specific highway interchange. A
new program has been proposed that will eliminate the risks at this interchange. If the program is undertaken,
it is expected that there will be no further fatalities at this interchange.
Study 2
At an intersection in downtown Pittsburgh, there are several automobile-pedestrian accidents every year,
and in each of the past three years 2 pedestrians have been killed at that intersection. These pedestrian accidents
account for [2 of 4 people who died in accidents at that intersection] [2 of 112 people who died in auto-related
accidents in southwestern Pennsylvania] [2 of 1700 people who died in auto-related accidents in Pennsylvania]
last year. To reduce the risks to pedestrians, city engineers have proposed installing automobile barriers between
the sidewalks and the streets surrounding this intersection, and building a pedestrian overpass. Although
expensive, they believe these measures will eliminate the possibility of future pedestrian accidents at the
intersection. At the same time, there are many other useful traffic safety and improvement projects proposed
both for the city and for the state, and there is not enough funding to implement them all. Relative to other
automobile and traffic safety projects you know about or can imagine, what priority do you think should be
given to funding this project?
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Subjects. The questionnaire was administered to 70 undergraduates at Carnegie Mellon
University, 30 visitors to a mall in south suburban Boston, and 27 visitors (mainly stu-
dents) to the University of Pittsburgh and Carnegie Mellon University student centers.
Forty-one respondents received the direct comparison questionnaire, and 86 received the
rating version. We tested approximately twice as many subjects in the rating condition
because the statistical power of the planned between-subjects comparisons is lower than
Table 4. Scenarios testing the effect of ex post and ex ante evaluation.
Study 1
[Ex post] The EPA has approved a field test of a new pesticide and it has been applied to a wheat field. It
has just been discovered that someone from a nearby community was exposed to toxic levels of the pesticide
during the application and that if they are not treated quickly, they will die. Locating this individual will require
immediate door-to-door search of the local community.
[Ex ante] The EPA has approved a field test of a new pesticide and is about to apply it to a wheat field. It
has just been discovered that someone from a nearby community is camping near the field and will soon be
exposed to toxic levels of the pesticide, such that they will die from the exposure. To prevent this exposure will
require conducting a thorough search of the nearby area.
Study 2
Park rangers for the California Department of Parks have just closed a small area in the remote Sierra
Nevada to camping and hiking, due to the discovery of strong toxic chemicals in a fresh-water spring located
near an abandoned mine. Campers in that region are required to sign in and sign out with park officials, so all
campers are now being warned to steer clear of this abandoned mine. However, in checking the filed hiking
plans of visitors currently in the general area, they discover that a hiker already on the trail has plans to camp
in the now-closed area near the abandoned mine. They suspect he intends to use water from the springs for
cooking and drinking. According to the hiker’s plans, he [will enter the contaminated area tomorrow] [entered
the contaminated area yesterday]. Rangers have the option of asking the local search and rescue team to go after
the hiker to [prevent him from drinking the contaminated water and send him to camp elsewhere] [see if he used
the contaminated water and if so, bring him out to seek medical attention]. However, if they send the search and
rescue team after the hiker, they will be unavailable for other emergencies which may arise. Consider the
seriousness of other problems you know about or can imagine that the search and rescue team may be called on
to handle. Relative to these other emergencies, what priority would you place on sending the search and rescue
team after the hiker?
Figure 1. Logical relationships between the four possible causes. Arrows represent implication.
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the within-subject comparisons of the direct comparison condition. All subjects received
all versions of each scenario, with scenarios designed to test the same cause separated by
other questions.
Results. Despite the heterogeneity of the three sample populations, there were no signifi-
cant differences in their responses, and no order effects, so the data were aggregated. The
results for both the rating and the direct comparison conditions are summarized in Table
5. This table lists the number of respondents rating or choosing each scenario as more
important. For each of the four possible causes, the first column of data shows the number
(fraction in parentheses) of subjects whose responses are consistent with the explanation
being tested. For example, if the vividness of the victim is a cause of the identifiable
victim effect, subjects should say it is more important to take action when the victim is
vividly described. The first column of data shows the number of subjects who ranked the
scenarios in an order consistent with this explanation. The second column of data shows
the number answering in the direction inconsistent with the explanation, and the third
column shows the number who rated the two programs as equally important, or who chose
“equally important” in the direct comparison version. The final column indicates whether
there is a significant difference between the number of subjects responding in the pre-
Table 5. Number of subjects rating each scenario as more important: Study 1.
Vivid Anonymous
Equally
important Significance
Vividness
Ratings 8 (.06) 13 (.15) 64 (.79) NS
Direct Comparison 4 (.10) 1 (.03) 35 (.88) NS
Certain Uncertain
Equally
important Significance
Certainty and uncertainty
Ratings 26 (.30) 6 (.07) 54 (.63) ,0.005
Direct Comparison 11 (.29) 8 (.21) 19 (.50) NS
Smaller
reference
group
Larger
reference
group
Equally
important Significance
Proportion of the reference group
Ratings 44 (.51) 13 (.15) 29 (.34) ,0.001
Direct Comparison 21 (.51) 3 (.07) 17 (.41) ,0.001
Ex post Ex ante
Equally
important Significance
Ex post/ex ante
Ratings 12 (.14) 24 (.28) 50 (.58) (1)
Direct Comparison 7 (.17) 9 (.22) 25 (.61) NS
Numbers in parentheses show the fraction of respondents answering in each order.
(1) p , 0.05, significant in opposite direction from prediction.
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dicted direction and the number responding in the opposite direction. To determine sig-
nificance, the number of subjects responding in the predicted direction was compared with
the number responding in the opposite direction using a sign test.
The modal response to most of the scenarios was to rate the two situations as equiva-
lent. That is, for questions testing each possible explanation, subjects felt it was equally
important to save lives in the two scenarios. However, the responses of those subjects
expressing a preference are quite interesting.
Subjects did not rate the familiar victim as more important than the anonymous victim,
either in rating or direct comparison, contrary to the result that might be expected if
vividness is a primary determinant of the effect. There is no significant difference between
the responses in the rating and direct comparison conditions (x
2
(2) 5 4.49, p ' 0.11).
The rating task showed people to be significantly more concerned about certain than
about uncertain deaths, but the direct comparison task did not. In this case, the difference
between respondents in the direct comparison and the rating versions is marginally sig-
nificant (x
2
(2) 5 5.47, p , 0.1).
Subjects are significantly more concerned with saving lives when they represent a large
portion of the reference group. In fact, for the proportion-of-the-reference-group ques-
tions, the modal response was to rate saving 25 out of 25 casualties as more important
than saving 25 out of 50,000. This was the case in both the rating and the direct com-
parison versions, and further, there is no significant difference in the responses from the
two conditions (x
2
(2) 5 1.8, p ' 0.4).
Finally, subjects in the rating condition felt it was more important to take action ex ante
(preventative measures) than to take action ex post (remedial measures). This is the
opposite of the result that is expected if the ex post/ex ante distinction is a cause of the
identifiable victim effect. However, the effect of this distinction was not significant in the
direct comparison condition. Note, however, that there is no significant difference between
the responses in the rating and direct comparison conditions (x
2
(2) 5 0.60, p ' 0.7).
Discussion. The results of this experiment provide support for two possible causes of the
identifiable victim effect: the certainty effect and the proportion-of-the-reference group
hypothesis. Furthermore, the differences between responses in the rating and direct com-
parison conditions indicate that the effect of certainty appears unintended, whereas the
percentage reduction in risk is something that subjects consciously take into account.
We found no support for the hypothesis that the vividness with which a victim is
described increases subjects’ desire to save that victim. Apparently simply knowing that
“a person” is definitely at risk and can be saved by taking action is enough to engender our
concern.
Finally, the hypothesis that the distinction between ex post and ex ante action is critical
was not supported by our results. To the degree that subjects care about this distinction,
they appear to believe that preventative actions are more important than remedial actions.
The lack of response to the vividness manipulation is not easily explained, unless there
was something specific about the “vivid” victim that made her unsympathetic. However,
in retrospect, there may be alternative explanations for some of our other results. The
questions testing the certainty hypothesis are acknowledged to be highly unlikely, al-
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though the two scenarios are similarly implausibile, so this alone would not be enough to
produce the effect found. However, a food distributor can be blamed for a contamination
problem much more easily than for allergic reactions. The saliency of blame may have
contributed to the perceived importance of saving the more certain victims. The scenarios
testing the proportion of the reference group hypothesis describe two programs, each of
which is estimated to save 25 lives. The program saving 25 out of 25 might naturally be
seen as more effective than that saving 25 out of 50,000, or more believable, or more
cost-effective. These reactions would result in subjects rating saving 25 out of 25 lives as
more important than saving 25 out of 50,000 lives. Finally, in the scenarios testing the
difference between ex post and ex ante action, the ex ante decision could be simply not to
apply the pesticide to the field. The existence of an easy and costless solution to prevent
the risk ex ante may account for the direction of the effect.
3.2. Study 2
Given the somewhat surprising results obtained from the first study—the failure to find a
vividness effect and the strong effect of reference group size—we designed a second study
to test further the same four possible causes of the identifiable victim effect. Again we
used a variety of different scenarios, manipulating the vividness of the victim description,
the degree of uncertainty about the risks, the proportion of the reference group at risk, and
whether the proposed action takes place before or after the risks are realized.
Method. Given the weak response to the various manipulations in Study 1, our first goal
was to validate the identifiable victim effect itself by comparing subjects’ reactions to an
obvious example of an identifiable versus statistical fatality, where the identifiable case
demonstrates most or all of the four characteristics and the statistical case demonstrates
none. We developed two scenarios involving lead poisoning in children, presented in Table
6. In both cases action could be taken that had a 0.1% chance of saving a life. In the
identifiable case the victim was a child who was hospitalized with acute lead poisoning,
Table 6. Scenarios testing the effect of identifiable and statistical victims.
Study 2
[Identifiable] Suppose that you are a hospital administrator running a large metropolitan hospital under
tight budget constraints. A young child has been brought in with acute lead poisoning, and is unlikely to live.
His physician has suggested trying new, untested treatment that might help. However, the treatment is experi-
mental, very expensive, and is estimated to have only a 0.1% chance of saving the child’s life. Assume that you
decide not to approve the treatment, and that the child dies. How personally responsible would you feel?
[Statistical] Suppose that you are a hospital administrator running a large metropolitan hospital under tight
budget constraints. A local community group has requested that the hospital provide free lead level screening
tests to all children in the community. Comprehensive lead screening of all children would be very expensive,
the experience with lead levels in the community suggest there is only a 0.1% chance any child will be exposed
to fatal levels of lead under current circumstances. Assume that you decide not to institute the lead-level
screening program, and later in the year a child dies from acute lead poisoning. How personally responsible
would you feel?
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and certain to die unless a new, experimental treatment is used (and likely to die even with
the treatment). So the victim is described (vivid), comprises 100% of the reference group,
is certain die without treatment, and has already experienced the risk. In the statistical
case the proposed action is preventative (community-wide lead screening tests), and the
victim is an anonymous child, comprising a small proportion of the reference group (all
children in the community), who is not certain to be exposed or die from the risk, and who
has not yet experienced the risk. In both scenarios subjects were asked to assume that they
did not take action (did not approve the treatment or did not fund a testing program) and
a child died. They were asked to rate how responsible they would feel, on a one to seven
scale. Since the victim in the identifiable scenario possesses all of the characteristics we
hypothsize may cause the identifiable victim effect, we predicted that subjects will feel
more responsible for that death than for the death of one anonymous child in the com-
munity.
To test the vividness hypothesis more thoroughly we developed a set of scenarios which
include a variety of specific victims, and different levels of description. In this scenario a
victim is trapped in a collapsed structure after an earthquake and rescue efforts are
underway. Subjects read a realistic news story describing the situation, where the victim
is presented either (1) generically, as “a man” or “a woman, (2) with a description
including the sex, age, occupation, marital status, and quotes about the victim from
friends or relatives, or (3) with the same description and a picture. After reading about the
victim, subjects were asked how strongly they would support (on a one to seven scale) a
very expensive action (closing a nearby freeway) to reduce risks marginally to the trapped
victim. Two generic and eight specific victims were created, half male and half female.
The ages and occupations of the victims were randomly selected using the base rates of
the ages and occupations of U.S. citizens. Using a variety of victims should reduce the
possibility that subjects are simply responding to a very sympathetic (or unsympathetic)
victim. The second panel of Table 1 presents the scenario and an example of one of the
victim descriptions. If the vividness of the description matters, subjects should be more
willing to close the freeway for the victims who are described, or described and pictured,
than for generic victims. Subjects were also asked to indicate how much they cared about
what happened to the trapped victim.
Uncertainty about expected fatalities can come from two sources: uncertainty about
who will be a victim, and uncertainty about how likely any given victim is to die. In Study
1, we attempted to manipulate only the second type of uncertainty. In Study 2, we
combined these two types of uncertainty and presented a range of possible fatalities. The
uncertainty scenarios involved young children suffocating on thin-layer plastic wrap and
bags. The scenarios are shown in Table 2. Subjects were told that the average number of
deaths from suffocation for children under the age of 4 is 100 per year, with a range of 92
to 110 (low variance case) or 34 to 168 (high variance case). They were asked to indicate
how strongly they would support legislation requiring the phasing out of thin-layer plastic
in packaging and drycleaning. While in neither of these scenarios are fatalities certain, if
people are more concerned about “certain” fatalities, they should be more concerned
about the fatalities in the low variance case, which are “less uncertain, than in the high
variance case. To test whether the possibility of zero fatalities carries any special signifi-
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cance, we ran a second uncertainty study where the expected number of deaths was 20,
and the uncertainty ranged from 15 to 25, or from 0 to 40.
To test the effect of the proportion of the reference group that can be saved, we again
used a scenario involving traffic fatalities. These scenarios are presented in Table 3. To
reduce any ambiguity about the locations of the fatalities, and the type and efficacy of the
risk-reducing actions, we described a specific type and location of accident (pedestrian
fatalities at a single intersection in downtown Pittsburgh), as well as the proposed actions
to eliminate those risks (installing auto barriers and a pedestrian overpass). Only the size
of the reference group was varied (2 of 4, 112, or 1700), and subjects were asked to
indicate what priority they would assign to the proposed project on a one–to–ten scale.
Finally, the ex post/ex ante distinction was tested with a more believable scenario than
in the earlier study (see Table 4). A camper in the remote mountains either has been
exposed to dangerously contaminated water and is in need of immediate help (ex post), or
is about to be exposed and in need of warning (ex ante)
3
. Subjects were asked to rate the
importance of rescuing this victim.
Subjects. The questionnaire was administered to 121 adult visitors to the Pittsburgh
International Airport. All subjects answered only one question from each set of scenarios:
that is, one question designed to test each of the hypothesized causes. Questions related
to each hypothesis were randomized over 24 different versions of the survey. The second
question involving uncertainty was answered by 100 adult visitors to the airport.
Results. To reduce variance caused by inter-subject heterogeneity in average concern for
victims and in use of the scales, we normalized each subject’s responses by subtracting
from each concern rating the weighted average of all that subject’s concern ratings. Table
7 shows the mean normalized rating for each question. The last column indicates whether
the difference in mean ratings reaches statistical significance, by either a t-test or an F-test,
as appropriate. As shown in the table, most of the questions did not yield significantly
different mean ratings.
Subjects feel significantly more responsible for the specific, identifiable victim in the
medical (lead-level) questions who dies because s/he doesn’t get treatment than for the
anonymous victim who dies because the county-wide lead screening program is not
approved. This is simply the identifiable victim effect itself, demonstrated in an experi-
mental setting.
We again observed an unexpected effect of vividness, even though we stengthened the
vividness manipulation and took pains to ensure that victim described was statistically
representative. Although in Study 1 subjects were more concerned about the anonymous
victim, in this study subjects stated that they cared the most when a verbal description of
the victim was provided and slightly less when the victim was described simply as “a
man” or “a woman, although the difference did not approach statistical significance.
However, surprisingly, subjects cared least when the a picture of the victim was included
along with the description. No consistent effect was observed of vividness on willingness
to support the risk-reduction project.
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In a slight departure from our earlier results, Study 2 failed to find significant differ-
ences in the importance ratings for the questions testing the effect of different levels of
uncertainty.
Consistent with the results of Study 1, however, subjects place significantly higher
priority on a project that is estimated to save two lives if those lives represent a high
proportion of the reference group (2 out of 4), than on that identical project if those same
two lives represent only a small proportion of the reference group (2 out of 1700).
Although this effect is significant only at the .06 level (two-tailed test), the ANOVA is
conservative because it does not take account of the ordering of means, which is as-
predicted.
Finally, we did not find a significant effect for the ex post/ex ante distinction.
Discussion. The results of this experiment provide additional support for one possible
cause of the identifiable victim effect: the proportion of the reference group at risk appears
to be an important factor affecting subjects’ support for risk-reducing actions.
As in Study 1, these results do not support the hypotheses that more vivid descriptions
increase subjects’ concern for, or desire to save, that victim, that people care more about
more certain than less certain fatalities, or that ex post victims are more important than ex
Table 7. Mean rating for each scenario in Study 2.
n Mean rating Significance
Identifiable/Statistical
Identifiable 54 21.61 p , .005
Statistical 60 22.40 [t(112)]
Vividness–support project
no description 38 1.12 NS
description 41 .61 [F(2,111)]
description with picture 35 1.03
Vividness–care about victim
no description 38 .44 NS
description 41 .64 [F(2,111)]
description with picture 35 .17
Certainty and uncertainty
low variance (92–110) 58 .70 NS
high variance (34–168) 55 1.12 [t(112)]
low variance (15–25) 49 5.90 NS
high variance (0–40) 51 5.23 [t(98)]
Proportion of the reference group
large (2 of 4) 39 .66 p , .06
medium (2 of 112) 35 .19 [F(2,111)]
small (2 of 1700) 40 2.25
Ex ante/ex post
ex ante 59 .74 NS
ex post 55 1.09 [t(112)]
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ante victims. The uncertainty finding is a slight departure from the results of Study 1,
where certain risks were judged more important than uncertain risks, although only in the
rating condition.
4. General discussion
Although there have been numerous references to the identifiable victim effect, this study
is, to the best of our knowledge, the first to examine the effect systematically. Despite the
superficial simplicity of the distinction between identifiable and statistical lives, we noted
that there are actually several differences between them that could account for their
differential treatment.
One major surprise to emerge from the studies is that vividness does not appear to have
an effect on subjects’ willingness to support risk–reducing actions. When we have spoken
to friends and colleagues about this project, many propose vividness as the explanation for
the identifiable victim effect. We should note, however, that our research is not the first to
obtain weak vividness effects (Taylor and Thompson, 1982).
Based on our research, of course, we cannot conclude that all vividly described victims
will be seen as no more important than anonymous victims in terms of decision making.
Certainly it would be possible to create scenarios with a particularly compelling or sym-
pathetic victim, or a vivid scenario, in which subjects would express a preference for
saving the “familiar” victim. Our experiment attempted to see if more detailed informa-
tion about the victim would, by itself, cause the identifiable victim effect. The questions
of what information about a victim increases our sympathy, and of what, if any, informa-
tion will make us care more about one identified victim than about another identified
victim are interesting issues discussed in the literature on helping behavior (see, e.g.,
Piliavin, Rodin and Piliavin, 1969), but not addressed by our study.
When victims are identified it is clear exactly how many people will die, but when
victims are statistical it is always possible that more or fewer will die. In our first study,
subjects felt avoiding certain fatalities was more important than avoiding uncertain fa-
talities when they were not able to compare scenarios directly. However, subjects judged
certain and probabilistic deaths as equally important when they compared the two situa-
tions directly. Thus, the judgment that it is more important to address certain risks than to
address probabilistic risks appears to be an unconscious one. Our second study found no
significant difference between the reaction to high or low variance distributions when the
expected number of deaths is held constant.
Our findings suggest that the major cause of the identifiable victim effect is the relative
size of the reference group compared to the number of people at risk. Identified victims
constitute their own reference group, 100% of whom will die if steps are not taken to save
them. Further, the response to the relative size of the reference group is consistent even
under conditions where subjects explicitly contrasted scenarios which clearly differed only
in the fraction of those at risk who can be saved. Thus consideration of the proportion of
those at risk who can be saved appears to be a factor subjects would endorse for making
decisions about risk-reducing activities.
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The ex post/ex ante results are somewhat less surprising on reflection. It appears that
once we know an individual is definitely at risk, there is no difference in the importance
of taking action after the risk is realized or before the risk has occurred: in fact, there is
a slight preference for preventative action. Conventional wisdom suggests this result, as
we often hear “an ounce of prevention is worth a pound of cure. Perhaps the ex post/ex
ante distinction is simply a short-hand way for referring to the multitude of emotional and
ethical issues that come into play once a victim has been identified; isolated from those
issues, it does not appear to produce the identifiable victim effect.
In combination, these results point to the somewhat surprising conclusion that the
identifiable victim effect, per se, may be wholly attributable to the effect of the relative
size of the reference group. We wonder whether the identifiable victim effect could more
accurately (but less elegantly) be labeled the “percentage of reference group saved effect.
If the identifiable victim effect is, in fact, largely due to the relationship between
identifiability and the size of the reference group, this raises significant questions about
the normative status of the effect and the role it should play in policy decisions, because
the normative arguments for a reference group effect are tenuous. The reference group is
often largely a matter of framing, and it is difficult to defend a distinction between a
situation where there is a group of 10 randomly distributed “vaccine sensitive” people
who are at risk of death from a flu vaccine, but who cannot be identified beforehand, and
a situation in which 10 random people will be killed by the same vaccine.
Most policy decisions about risk involve statistical fatalities, while most private deci-
sions involve identifiable fatalities. The normative status of the effect is not necessarily
relevant to private decisions—no one would declare it irrational for parents to go to all
extremes to save the life of their child. However, it is relevant to public policy deci-
sions—we can legitimately ask whether it makes sense for society to go to extremes to
save one identified life when those resources could be spent more productively to save a
larger number of statistical lives. As Keeney (1995) notes, there is no right or wrong
answer. He suggests that we may want to assign different economic values to identified
and statistical lives. But which of these values should form the basis for policy decisions?
Allowing risk policies to vary depending on whether the victims are identified or statis-
tical may create incentives for advocates of one policy to play up the identifiable victims
that could be saved under that policy, while pointing out that we don’t know who will be
saved under another. However, Viscusi (1992) points out that if we assign a higher value
to saving any identified victim than to saving a statistical victim, then perhaps we need to
rethink how we value statistical victims, since at some point all victims are identified.
Questions about whether and how identifiable and statistical victims should be consid-
ered differently in policy decisions are not easily answered. However, given the arbitrari-
ness of the reference group that applies to a specific risk, it seems inadvisable to recom-
mend reference group size as an input into public policy except, perhaps, when the group
is defined geographically or by a sensitive demographic characteristic.
Why did the plight of young Jessica McClure engender such sympathies and such a
strong response? Certainly she represented a very sympathetic victim, and the media
coverage of the event ensured that we knew a great deal about her. It would seem heartless
to suggest that she not be saved, or that a cost-benefit analysis be conducted before rescue
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efforts could commence. However, our study points to two other factors that may have
been the most important in producing the powerful response: she was certain to die if not
removed from the well, and she comprised 100% of the risk group.
Acknowledgements
We thank Jonathan Baron, Graham Loomes, Keith Murnighan, Daniel Read, and Peter
Ubel for helpful comments. This work was supported under a National Science Founda-
tion Graduate Research Fellowship, and by the Center for Integrated Study of the Human
Dimensions of Global Change (NSF Grant #SBR 95-21914). Any opinions, findings,
conclusions, or recommendations expressed in this paper are those of the authors and do
not necessarily reflect the views of the National Science Foundation.
Notes
1. Although the identifiable victim effect may apply to many less severe, impacts, our focus in this paper is on
fatal victims.
2. Scenario studies of this type have several limitations. First, the manipulated factors will almost inevitably
interact with the specific content of the scenarios, raising questions about external validity (see Shotland,
1983). Second, subjects rate their own level of concern, raising the issue of self-presentation and the
accuracy of introspection.
3. We thank an anonymous reviewer for suggesting this scenario.
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