ethnic identifiability: an experimental...
TRANSCRIPT
Ethnic Identifiability: An Experimental Approach*
James Habyarimana Georgetown University
Daniel N. Posner
University of California, Los Angeles
Macartan Humphreys Columbia University
Jeremy M. Weinstein Stanford University
Abstract
We report the results of an experimental project that investigates the determinants of ethnic identifiability – that is, how well individuals can correctly categorize the ethnic backgrounds of the people they encounter. Drawing on a subject pool of ninety-six university students from seven different ethnic groups, we find ethnic identifiability to be more difficult than is often assumed. We find that three factors determine the ability of subjects to identify the backgrounds of others: the characteristics of the person being identified (in particular, his or her ethnic group membership), the characteristics of the identifier (in particular, the extent of his or her exposure to other ethnic communities), and the level of information that the latter has about the former. We also investigate the ability of individuals to “pass” as members of other groups, and to identify “passers.” We find that “passers” are able to fool others roughly 45 percent of the time. Determinants of successful passing include the passer’s ethnic group membership, age, and SAT score. Our findings challenge micro-level theories of ethnic politics that assume that individuals can readily distinguish in-group members from out-group members. *The authors thank Chris Crabbe for his superb programming work; Dan Young, Donna Horowitz, and Kevin Thelen for their research assistance; the Russell Sage Foundation, the Harry Frank Guggenheim Foundation, the Harvard Academy for International and Area Studies, and the International Institute at UCLA for their financial support; and the staffs of the California Social Science Experimental Laboratory (CASSEL) at UCLA and the Center for International Studies and the Law School Library at USC. Extremely helpful comments were received from participants at the 9th meeting of the Laboratory in Comparative Ethnic Processes (LiCEP), University of Wisconsin, 7-8 May 2004. Protocols for the experiment are available on request from the authors.
Micro-level theories of ethnic politics nearly all depend on strong assumptions about the
ability of individuals to identify the ethnic backgrounds of the people with whom they interact.
Although a great deal of anecdotal evidence calls such assumptions into question, no systematic
research to date has attempted to assess how well individuals can distinguish in-group members
from out-group members or sort out-group members into their correct ethnic categories. This
paper reports the findings of an experimental project designed to fill this gap in knowledge.
Drawing on a sample of ninety-six undergraduate students from seven different ethnic groups,
we explore the determinants of what we term “ethnic identifiability.” Specifically, we test
whether subjects are able to classify the people whose images they are shown into the ethnic
categories with which the people themselves identify. We test how the characteristics of the
person viewing the images, the characteristics of the person whose images are viewed, and the
degree of information that the former has about the latter affect the probability of a correct ethnic
identification.1
We find that subjects are less able to distinguish in-group members from out-group
members and less able to sort non-co-ethnics into their correct ethnic categories than most
theories of ethnic politics assume. When shown pictures of other subjects, subjects miscoded in-
group members as out-group members 16 percent of the time and miscoded out-group members
as in-group members 7 percent of the time. Subjects were even less successful at identifying the
ethnic backgrounds of people from other ethnic groups. On average, subjects shown images of
1 By “correct identification,” we mean a match between the ethnic identity ascribed to the person by the
subject and the person’s own self-identification. For a more technical definition of “ethnic
identifiability,” see below.
1
people from other ethnic groups miscoded the other person’s ethnic background 33 percent of the
time.
Asking students to come to a computer laboratory, look at images of other students on a
computer screen, and guess those students’ ethnic backgrounds is, admittedly, quite far removed
from the real world situations that theories of ethnic politics endeavor to capture. Particularly in
situations where the costs of ethnic misidentification are high, actors will have incentives to
collect additional information about the person whose background they are trying to identify, and
such information will go well beyond the cues available to students viewing other students’
images on a computer screen in a laboratory setting. Moreover, precisely in situations where
actors will have incentives to figure out other people’s ethnic backgrounds, the people being
identified are likely to have equally strong incentives either to hide their true identities or to
make their identities more apparent.
To better capture such real world situations, and to improve the external validity of our
experiment, we also test the ability of subjects to simulate and dissimulate – that is, to convince
others of their true ethnic backgrounds and to pass as members of ethnic groups other than their
own. We find that the rate of correct ethnic identification rises from a baseline average of 71
percent (when subjects are shown pictures of other subjects) to 89 percent when they are shown
brief videos in which the other subjects try to convince them of his or her true ethnic identity.
The identification rate drops to 55 percent when subjects are shown videos of other subjects
trying to pass. The implication is that when individuals want either to convince others of their
ethnic group membership or (contra the assumptions of many models of ethnic politics) fool
them about their ethnic background, many of them are quite able to do so.
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THE ASSUMPTION OF UNPROBLEMATIC ETHNIC IDENTIFICATION AND EVIDENCE FOR ITS IMPLAUSIBILITY
The assumption that individuals can seamlessly identify the ethnic backgrounds of the
people they encounter – or, at the very least, unproblematically distinguish in-group members
from out-group members – is implicit in nearly all micro-level theories of ethnic interaction and
politics. From theories of in-group sanctioning (Greif 1989; Landa 1994; Fearon and Laitin
1996) to theories that emphasize the ability of ethnic groups to police their boundaries (Barth
1969; Laitin 1995; Fearon 1999; Caselli and Coleman 2002) to theories of ethnic or racial
discrimination (Akerlof 1970, 1976; Becker 1971) to experimental treatments of minimal groups
(Tajfel, Billig, and Bundy 1971), models of face-to-face ethnic interaction almost always depend
on the ability of actors to distinguish accurately between in-group members and outsiders. For
in-group sanctioning to work, the group memberships of transgressors must be clear.2 For ethnic
boundaries to be policed, the line between insiders and outsiders must be unambiguous.3 For
discrimination to be possible, peoples’ group backgrounds must be easily identified. In all of
these cases, identification failure undermines the predictions of the model.4
2 For example, Fearon and Laitin’s in-group policing model explicitly stipulates that actors know when
they are interacting with in-group members versus outsiders (1996: 721).
3 Caselli and Coleman (2002), whose model emphasizes the policing of boundaries, make an important
theoretical advance by permitting actors in their model to take costly actions to change their identities.
But they maintain the position that, once changed, an actor’s identity will be self-evident to the other
players in the game.
4 Quite apart from the role it plays in theories of ethnic competition and conflict, the easy identifiability of
ethnic groups is also one of the underlying assumptions in many accounts of ethnicity itself. Chandra
(2004), for example, argues that identifiability is one of the characteristics that sets ethnic groups apart
3
The strong implicit or explicit claim in these models that individuals can
unproblematically distinguish in-group members from outsiders flies in the face of evidence that
people’s ethnic backgrounds are sometimes extremely difficult to pin down. Despite widespread
assumptions to the contrary, people are often not very good – and often not as good as they
themselves believe – at classifying others in ethnic terms. The case study literature on ethnic
riots and communal conflict is filled with anecdotes illustrating this point. For example,
Horowitz relates the following story from Sri Lanka:
Sinhalese rioters suspected a man in a car of being a Tamil. Having stopped the
car, they inquired about his peculiar accent in Sinhala, which he explained by his
lengthy stay in England and his marriage to an English woman. Uncertain, but
able to prevent his escape, the rioters went off to kill other Tamils, returning later
to question the prospective victim further. Eventually, he was allowed to proceed
on his way, even though the mob knew it risked making a mistake, which in fact it
had: the man was a Tamil (2001: 130).
The eyewitness account of witness to a 1997 massacre by Hutu rebels in Buta, in southern
Burundi, offers a similar illustration:
There were 250 children, ages 11 to 19. On April 30, around 5:30, we heard
shots. In several minutes, the assailing rebels had become masters of the
seminary. The soldiers charged with protecting us had fled. A troop of rebels had
taken over the dormitories…The assailants gathered us in the middle of the room
from non-ethnic groups, and that this is one reason why ethnicity so frequently plays a role in situations
where information about other actors’ preferences, trustworthiness, and political orientation is limited
and/or costly to obtain.
4
and demanded that we separate into Hutus and Tutsi. The students refused. They
were united. Then the leader of the group, an enraged woman, ordered their
killing. There were 70 students. The assailants fired their grenades (National
Catholic Reporter, 22 February 2002).
In both of these examples, the attackers had difficulty coding the ethnic backgrounds of their
would-be victims. Similar uncertainty has marked the estimated two thousand backlash
incidents directed at Muslims and people of Arab descent in the aftermath of the September 11
terrorist attacks in the United States. More often than not, the victims of these hate crimes turned
out not to be Muslims or Arabs at all, but Sikhs, Indians, Pakistanis, Coptic Christians, and, in
one case, even an Iranian Jew (Human Rights Watch 2002).5 These examples starkly illustrate
that ethnic categorization is not nearly as straightforward as theories of ethnic conflict often
assume.6
Apart from challenging theories that assume that ascertaining a person’s ethnic
background is unproblematic, entertaining the possibility that people’s ethnic backgrounds might
not always be readily identifiable also opens the door to new hypotheses about the conditions
5 We cannot rule out the possibility that the perpetrators of these anti-Muslim acts simply did not know
that the Sikhs, Indians, and Pakistanis, and the others they attacked were not Arab Muslims, in which case
the miscodings would not be examples of ethnic misidentification as we treat it in this paper but simply of
not being aware that there were different categories into which the would-be victims might be coded.
6 The great lengths to which governments have historically gone to make members of particular ethnic
groups more readily identifiable – for example, the requirement that Jews wear the Star of David, that
Japanese-Americans wear markers indicating their Japanese descent, or that citizens carry national
identity cards with information about their ethnic or racial background (as is still the case in Israel,
Singapore, and Vietnam) – further underscores the difficulty that ethnic identification often presents.
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under which existing theories might and might not hold. Take, for example, the proposition that
ethnic identifiability varies across groups – a proposition for which our study provides strong
evidence. To the extent that this is the case, theorists of ethnic coalition building can use
identifiability as a determinant of coalition choice. Theorists of in-group policing can use it to
distinguish among communities with greater and lesser abilities to sanction their members, and
thus greater or lesser abilities to execute certain business transactions, organize collectively, or
prevent inter-group conflicts from degenerating into spirals of violence (Fearon and Laitin 1996).
Theorists of ethnic mobilization can use it to account for variation in the ease with which
political entrepreneurs may be able to organize – or organize against – particular communities.
Theorists of ethnic violence can use it to explain the form that conflict takes.
Regarding the latter, consider the wars in the north of Mali (1990-1995) and the south of
Senegal (1982-present). The two conflicts would seem to have much in common. Both involve
bids for separation by movements dominated by members of minority groups: the Tuaregs and
Maures in Mali and the Diola in the Casamance region of Senegal. However, differences in the
identifiability of the parties to each conflict have generated important differences in how group
members are mobilized and how violence is carried out. In Mali, the fact that the Tuaregs and
Maures – the “whites” – are readily identifiable has meant that ethnicity can be used to pressure
members of these groups, including intellectuals living in the capital, to join the rebel
movements. It has also allowed “black” sedentary groups and the Malian army to take reprisals
against arbitrary Tuareg and Maure civilians. The result has been a rapid polarization of camps
and the descent of the separatist struggle into communal violence. In Senegal, by contrast, such
ready association of individuals with ethnic groups has been more difficult. As a consequence,
the mobilization of partisans and the targeting of reprisals has been more difficult, and the
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intensity of violence has been much lower (Humphreys and ag Mohamed 2002). The contrasting
degree of ethnic identifiability has led to a sharp difference in the form of group mobilization and
the scope of violence in each case – a difference that would be hard to account for if we assumed
erroneously that all ethnic groups were equally identifiable.
As the foregoing discussion suggests, a finding that ethnic identifiability cannot be taken
for granted has important implications both for existing theories and for the development of new
ones. But how great is the distance between the assumption of unproblematic identifiability and
the reality? Anecdotes about the difficulty people sometimes have in pinning down the ethnic
backgrounds of others are suggestive, but what can be said systematically? How well, in fact,
can individuals sort the people they encounter into their correct ethnic categories? What factors
facilitate or impede their ability to do this? Are the members of some ethnic groups more easily
identified than others? What makes some people better identifiers than others? The experiments
we reported in this paper were designed to answer these questions.
RELATION TO PREVIOUS RESEARCH
Social psychologists have made important contributions to our understanding of how
individuals classify others into ethnic categories. The core of this body of research has been on
the relationship between prejudice and patterns of social categorization, a somewhat different
question from the one addressed in this paper. Nonetheless, these studies provide an important
methodological foundation for our experiment and are therefore worth reviewing.
In terms of experimental design, four major studies capture the evolution of approaches
to assessing how individuals categorize others into ethnic groups. Allport and Kramer (1946)
initiated this line of research with a study that asked a sample of university students to
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distinguish pictures of Jewish students from pictures of non-Jewish students. Participants were
given fifteen seconds to view each photograph before being asked to identify the person as
“Jewish,” “Non-Jewish,” or “Don’t Know.” Pettigrew, Allport, and Barnett (1958) improved on
this rudimentary design by introducing a stereoscope – a device that presents different images to
the left and right eye so that participants see a single merged object. The stereoscope enabled
Pettigrew and his colleagues to examine how individuals classify images that combine persons
from two different groups.
More recent research in this area has employed computer and video technologies to
explore the same questions. Blasovich, Wyer, Swart, and Kibler (1997) showed photos of white,
black, and ambiguous individuals to participants and measured the amount of time it took them
to identify the race of the person in the photo. Harris (2002) used a web-based survey of
university students in which participants were asked to categorize a set of photographs as white,
African-American, Latino, Asian-American, American Indian, Pacific Islander, or other. He
analyzed both the classifications the students made and their response times.
What determines identification success? The literature identifies a number of individual
and group-level factors that are associated with successful ethnic categorization. Early work
emphasized the role played by prejudice, as measured through questions about the subject’s
awareness of and opinion about members of other ethnic groups (e.g., Allport and Kramer 1946).
Secord (1959) introduced information into the experimental design, varying the directions given
to participants about their task. While some participants were told nothing about the race of the
people in the photographs, others were told that they all had “Negro” blood, no matter how white
they looked. Lent (1970) proposed that future studies introduce a new range of participant
characteristics including broader demographic variables, a subjective measure of the perceived
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situation of one’s racial group, a subjective measure of the relation of one to one’s group, and
objective measures of the relative position of each racial group in society. Most recently, Harris
(2002) focused on the impact of observer race, gender, and experience with other races on
identification choices. He found that whites and Asian-Americans more quickly classified
photographs and used many fewer racial groups to categorize the full set of photographs, while
other minority groups were more likely to see complexity in the photographs. Harris also
identified a strong relationship between a participant’s experience with other races and how he or
she classified the images.7
The experiment reported in this paper, while closest to Harris (2002), nonetheless goes
beyond this work in three important ways. First, the study involves a larger number of ethnic
groups than any other study to date. The early literature tended to focus narrowly on racial
categorization between blacks and whites or religious categorization between Jews and non-
Jews. Our study involves participants from seven different ethnic groups, encompassing a much
greater degree of phenotypical variation than in previous work.
Second, we go beyond Secord’s (1959) pioneering work in testing the effects of
information on ethnic identification. Participants in the experiment are exposed to three different
images of each subject, each providing a different level of information about the subject’s ethnic
background. First, they are shown a still photograph of the subject – a headshot – from which
7 The effects of ethnic group membership have also begun to be explored by experimental economists
(e.g., Fershtman and Gneezy 2001; Gil-White 2004; Ferraro and Cummings 2004). Yet in these studies
the emphasis is on the effects of ethnicity on behavior in experimental games rather than on the
identifiability of subjects. In fact, in all of these experiments, the identifiabilty of subjects as members of
particular ethnic groups is simply assumed.
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they can glean clues about the subject’s background from his or her appearance. Then they are
shown a brief video of the subject greeting them. This provides information about accent and
speech patterns, and thus further clues about the subject’s ethnic background. Finally they are
shown a brief video of the subject greeting them and saying his or her full name.8 By exposing
participants, in turn, to each of these levels of information, and asking them to guess the
subject’s ethnic background after each one, we are able to test the impact of information on
ethnic identification.
Finally, in addition to measuring the ability of individuals to identify the ethnic
backgrounds of others, we also explicitly test the ability of subjects to “pass.” In a world of
unproblematic ethnic identification, passing would not be an issue: it would be impossible. But
in a world where a person’s ethnic background cannot be determined with certainty without a
tremendous investment in information about the person’s family history, individuals will
sometimes have incentives to take advantage of the uncertainty of others to try to pass as
members of groups that will provide them with prestige, access, protection, or other benefits. As
noted earlier, this will particularly be the case in politically charged environments where the
costs of ethnic exposure are high. We test the ability of subjects to pass (and the ability of others
to catch them in their attempts) by recording video clips of subjects trying to convince people
that they are members of ethnic communities other than their own. We then show these videos
to other participants and ask them to guess the subject’s ethnic background.
8 A person’s name provides extremely important information about the subject’s ethnic group
membership (Isaacs 1975). Indeed, in Fershtman and Gneezy’s (2001) experimental study of
discrimination in Israel and Posner’s (forthcoming) analysis of ethnic voting in Zambia, names are
employed as the sole marker of ethnic group affiliation.
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DEFINING IDENTIFIABILITY
Before describing our experimental design, it will be useful to define formally what we
mean by ethnic identifiability. We define “successful” identification as a function of both the
characteristics of the identifier and the characteristics of the person being identified. Hence, for
individual A, individual B, and some information set I, we say that B’s identifiability for A, given
I, is given by the expected ability of A to place B in s, where s is one of a set of categories
{s1,s2,…sm}in an identity structure S, conditional upon criterion C, with the property that
criterion C places each element of the population into one and only one category.9 A group’s
identifiability, conditional upon an identity structure S and information set I, is measured by
taking the average across the individuals of that group of the average identifiability of each
individual within that group across the whole population. Thus, a group within structure S is
more identifiable than another if a typical member of that group is more likely to be placed into
their correct ethnic category by a typical member of the population.
EXPERIMENTAL DESIGN
The objective of the experiment was to determine whether participants could identify the
ethnic backgrounds of other participants when shown pictures and brief video clips of them. To
9 More formally, if gA(B |S,I) is A’s guess of the identity of B given structure S and information I, B’s
identifiability for A, (XBA | S,I), is defined as, (XBB | S,I)=Prob(gA(B |S,I)=s). We recognize that, in some
contexts, individuals may belong to multiple categories within any one identity structure, S. This is the
case for people who identify as mixed-race in the United States. For the purposes of simplicity, however,
we begin by assuming that individuals belong to only one category.
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distinguish between participants whose images were being shown and participants who were
viewing these images – each participant in the experiment played both roles – we refer to
participants playing the first role as “subjects” and those playing the second role as
“respondents.”
The participants in the experiment consisted of undergraduate students from the
University of California, Los Angeles (UCLA) and the University of Southern California (USC).
The participants were recruited from seven ethnic groups that have large presences on both
campuses: African Americans, Arabs, Asians, Caucasians, Indians, Persian/Iranians, and
Latino/as. Approximately 54 percent of the participants were recruited through ethnic student
associations on each campus. The other 46 percent were recruited from the regular subject
population of the California Social Science Experimental Lab (CASSEL) at UCLA. In neither
recruitment mechanism did we identify participants by evaluating their appearance. In the case
of those recruited through the student associations, we took membership in the association to
indicate membership in the ethnic group – an assumption we later confirmed with a question
about subjects’ ethnic backgrounds in an initial questionnaire. In the case of the students
recruited through the CASSEL subject pool, we determined group memberships through the
responses given in a screening questionnaire.10 We can thus rule out the possibility that our
10 We approached students who had signed up for other experiments at CASSEL and asked them to fill
out a short screening survey that contained a question about their ethnic background. We then contacted
students from our target groups and invited them to participate in the experiment.
12
recruiting methods might have biased our sample in favor of individuals who were particularly
“identifiable” as members of their respective groups.11
Before the experiment began, we collected three different images of each subject with a
digital camera. Each image was designed to provide more information about the participant’s
ethnic background than the previous one. First we recorded a headshot. Then we recorded a
brief video clip in which the participant greeted the camera and said “Hello, I am looking
forward to playing the game with you.”12 Then we recorded another brief video clip in which the
participant again greeted the camera, but this time also gave his or her full name (e.g., “Hello, I
am looking forward to playing the game with you. My name is John Doe.”). All three images
were filmed in front of an identical blue background. Participants also filled out a brief
questionnaire in which we collected information about their age, gender, ethnic background,
place of birth, parents’ educational background, exposure to various media, and SAT scores.
We also randomly drew a sub-sample of our participants and invited them to record three
additional videos in which they explicitly stated their ethnic backgrounds.13 For the first two
videos, we asked them to pretend that they were in a situation in which it was important that they
convince the person would view the video of their true ethnic background. By “true ethnic
11 We should note that we cannot rule out the possibility that members of campus ethnic associations
might be more “typically” Latino, Asian, Arab, and so forth in their appearances than members of the
broader student population. However, we think this is unlikely.
12 The subjects said that they were “looking forward to playing the game” because these images were also
used in a series of experimental games in which players saw pictures of their partners before playing each
round (see Habyarimana et al. 2004).
13 We invited thirty-six participants to record the additional images, of whom thirty-two accepted our
invitation and had their images recorded.
13
background” we explained that we meant the ethnic background that the participant used to
identify him or herself. We filmed two versions of this “simulation” video. In one, we asked the
participant to pretend that the person who would see the video was a co-ethnic. In the other, we
asked the participant to pretend that the person who would view the video was a non-co-ethnic.
Finally, we asked the participants to pretend that they were in a situation in which it was
important that they convince the person who would view the video that they belonged to an
ethnic group different from their own. That is, we asked them to try to “pass.” We asked them
to choose an ethnic group (other than their own) from a list of the seven groups included in the
experiment, and we filmed a “dissimulation” video in which the subject attempted to pass as a
member of that group. The instructions for this exercise are reproduced in Appendix A.
After we had collected the images of all subjects, we contacted all the participants by
email to invite them to visit our project web site to sign up for the experiment. Although 120
subjects had their images recorded, only 96 participated in the experiment. The experiment took
place at CASSEL at UCLA and at a computer classroom in the Law School library at USC.
When participants arrived at the lab/computer classroom they were given a card assigning them
to a computer and instructed to put on a pair of headphones that we provided.
Respondents were then shown a series of still images and videos of twenty-three subjects
and asked to guess each subject’s ethnic background.14 Respondents were told that they would
be paid 20 cents for each correct guess. They were also told that
14 The fact that all respondents had previously had their images recorded, and knew that their images
would later be shown to other participants, increases our confidence that respondents believed that the
subjects whose images they were being shown were real. Also, because respondents were not playing an
14
in some of the video clips you will see, the person will actually tell you what their
ethnic background is. Recognizing that it is sometimes advantageous for people
to try to “pass” as members of groups other than their own, it is possible that
some of the people may be lying about their ethnic backgrounds. You should
keep this in mind when you guess the backgrounds of the people whose images
you see. To earn the most money from this game, you will have to use your
judgment to figure out when people are telling the truth, and when they might be
giving you false information.
Respondents were first shown a headshot of each subject and asked if they knew the
person. Since roughly half of the respondent-subject pairings were between participants from the
same university, it was possible that respondents might know the subjects with whom they were
matched. If this was the case, then the respondent might have had information about the
subject’s ethnic background that went beyond the information provided within the context of the
experiment. The “do you know this person?” question was included to guard against this
possibility. If a respondent indicated that he or she knew the subject, then the subject’s
photograph was replaced with that of another subject.
Once the respondent indicated that he or she did not know the subject, the respondent was
asked to indicate his or her best guess of the subject’s ethnic background from a list of the seven
groups participating in the experiment. Respondents were also asked to indicate their certainty
about their guess on a four-point scale ranging from “a random guess” to “most certain.” We
also measured the respondent’s certainty in a second way, by recording the response time
interactive game with the subjects, it was less crucial than in most experiments that they believed their
partners were real.
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between the moment the headshot appeared (or the video ended) and the time they entered their
guess about the subject’s ethnic background.
Respondents were then shown the “greeting” video of the same subject and asked the
same two questions. They were told that they were free to change their answers if they saw
something in the video that caused them to reassess their earlier guess. Finally, respondents
were shown the “greeting with name” video and, again, asked to guess the subject’s ethnic
background and to indicate their certainty about their guess.
If the respondent happened to be paired with one of the thirty-two subjects for whom we
had recorded a simulation/dissimulation video, then the respondent was shown one additional
video. Approximately half of the time they were shown the dissimulation video and half of the
time they were shown the simulation video. If the respondent was shown the simulation video
and if the subject in the video was a co-ethnic, then the respondent was shown the “co-ethnic
simulation” video; if the subject was a non-co-ethnic, then the respondent was shown the “non-
co-ethnic simulation” video. After seeing the video, the respondent was again asked to guess the
subject’s ethnic background and to indicate his or her certainty about the guess. The instructions
read to subjects for the experiment are provided in Appendix B.
After viewing the images and guessing the ethnic backgrounds of the twenty-three
subjects, respondents completed a questionnaire that collected further information about the parts
of the world they had visited and/or lived in; whether they had a roommate and, if so, of what
ethnic background; and, for each of our seven ethnic groups, whether they would feel
comfortable having a member of that group as a close kin by marriage. At the conclusion of the
experiment, respondents were paid their winnings. In addition to their $5 show up fee, the
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maximum a respondent could have earned was approximately $15.60. On average, respondents
earned $11.13.
ARE THERE DIFFERENCES IN GROUP IDENTIFIABILITY?
Table 1 reports the percentage of successful identifications (by which we mean viewings
in which the respondent’s guess of the subject’s ethnic identity matches the way the subject self-
identifies), broken down by subject and respondent group type. Included in brackets under each
identification success rate is the total number of respondent-subject pairings of that type. We
further break down the results by the level of information that respondents had about subjects.
Table 1(a) presents identification success rates at our lowest level of information, when
respondents were shown headshots of the subjects. Table 1(b) presents success rates at
intermediate levels of information, when respondents were shown the recorded video greetings
prepared by the subjects. Table 1(c) presents success rates for viewings at our highest level of
information, when respondents were shown video greetings of the subjects in which the subjects
provided their names.
Reading down the columns, it is clear that some ethnic groups are much more identifiable
than others. Respondents had relatively little difficulty correctly identifying Asian, Caucasian,
and African American students at all three levels of information. Identification success rates
surpassed 95 percent for Asian subjects and 80 percent for subjects from the other two groups.
Respondents had much more difficulty correctly categorizing Arab, Indian, and Persian/Iranian
students. At low levels of information – roughly analogous to a situation in which the
respondent passed the subject on the street – identification success rates were below 45 percent.
For Arab students, identification success rates were only 26 percent. As these results suggest,
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and as we will show more systematically in a moment, the ethnic group membership of the
subject is by far the most important predictor of identification success.
Figure 1 provides a graphical representation of the distribution of identification success
rates across individual subjects within each ethnic group. Subjects from each ethnic community
are ranked from least to most identifiable. As the Figure makes clear, there is substantial
variation in the extent of ethnic identifiability across the members of each community in our
sample.15 Subjects from groups that are most easily identified in aggregate (Asian, Caucasian,
and African American) tend to cluster at high levels of identification success even at the
individual level. Although there are individuals within each of these groups that cannot be easily
identified, the graphs are heavily weighted toward the right, with high most subjects identified
correctly 100 percent of the time, or nearly so. For Arabs, Indians, and Persians/Iranians,
however, the situation looks quite different. The mean identification success rate is much lower
and the distribution of rates across individuals is more varied.
Might the differences in identification success rates at the group and individual levels be
the result of a priori beliefs on the part of respondents about the likelihood that subjects of
different types will appear in the sample? For example, might identification rates for Arabs be
low because it never occurred to most respondents that Arab students would be in the subject
pool? While we intentionally selected groups that were well-represented on the UCLA and USC
campuses, this is reasonable surmise. As a check, we compared the total distribution of ethnic
guesses with the actual distribution of ethnic groups in our sample of subjects. The close match
between the two increases our confidence that the low rates of identification success among
15 Unfortunately, our group samples are too small to (necessarily) capture the whole range of phenotypical
variation within each group.
18
some subject types reflect the difficulty of categorizing members of those groups, not a priori
beliefs about the likelihood that they would be encountered in the sample population. In
addition, prior to being asked to identify subjects’ backgrounds, the respondents had already
participated in two rounds of experimental games in which they had been exposed to 30
viewings of other subjects (see Habyarimana et al. 2004). So they almost certainly had a good
sense of the broad ethnic composition of the subject pool.
OTHER DETERMINANTS OF IDENTIFICATION SUCCESS
Our results so far suggest that identification success rates vary significantly across ethnic
groups. In particular, African American, Asian, and Caucasian subjects appear to be far easier to
identify than Arab, Indian, Latino/a or Persian/Iranian subjects. Next, we explore the other
factors that make successful identification more likely.
Are Some Groups Better at Categorizing than Others?
Reading across the rows in Table 1, it is apparent that the members of some ethnic groups
are more successful respondents than others. The variation in success rates across respondent
types is much narrower than the variation across subject groups (compare the degree of variation
in the row and column marginals), but there are nonetheless meaningful differences.
Persian/Iranian respondents exhibit the highest rates of identification success, surpassing the
average success rate in the sample (when headshots are viewed) by 10 percentage points.
Interestingly, the range of variation narrows as the amount of information that
respondents have about subjects increases. The gap between the most and least successful ethnic
groups decreases substantially (from 17.5 percent to 11.7 percent) as information levels increase
19
from headshots to video greetings with names. More information dilutes the advantage that
some respondent groups have in identifying the backgrounds of others.
Does it Help to be an In-Group Member?
The design of the experiment also enabled us to assess the impact of “co-ethnicity” – a
respondent being paired with a subject from the same ethnic group – on identification success
rates. Approximately 25 percent of the 2,203 total viewings in the experiment were co-ethnic
pairings.
Table 1 provides a rough sense of how identification success rates vary depending on the
make-up of the pairing. For each type of subject, we highlight in bold the respondent type with
the highest identification success rate. To the extent that individuals are better able to identify
members of their own ethnic community than outsiders, we would expect the bold cells to be
arrayed along the diagonal. A quick look at Tables 1(a-c) reveals a near diagonal, particularly at
high levels of information. African Americans are the only group that deviates substantially
from this expectation.16
Table 2 provides clearer evidence of the importance of co-ethnicity for identification
success. On average, across all viewings, identification success rates for co-ethnic pairings were
83.8 percent as compared to 66.8 percent for non-co-ethnic pairings. This finding is robust across
16 We suspect that this result is a product of the miscoding by African American respondents of one
subject – a very light-skinned person who self-identified as African American. This said, one would need
to explain why African American respondents were more prone to miscoding this subject than members
of other groups. Previous work by social psychologists offers one potential answer: non-minority groups
20
all three levels of information. Not surprisingly, the importance of co-ethnicity varies across
ethnic groups. For groups that are difficult to identify on average (e.g., Arabs, Indians, and
Persians/Iranians), identification success rates in co-ethnic pairings are substantially higher than
in non-co-ethnic pairings. The identification success rate for Persian/Iranian subjects, for
example, is more than twice as high in co-ethnic as in non-co-ethnic pairings. Co-ethnicity does
little to improve success rates where ethnic groups are already easy to identify (compare the
Persian/Iranian row with that for Asian, Caucasian, or even Latino subjects).
How Important is Information?
A key innovation in our experimental design is the introduction of different levels of
information. Across all viewings, the effect of information is small yet significant (see Table 2).
The identification success rate at high levels of information (when respondents are shown a
video greeting in which the subject revealed his or her name) is 75.6 percent as compared to 70.9
percent at low levels of information (when respondents were just shown a headshot of the
subject). This difference of 4.7 percentage points is much smaller than the co-ethnic effect
described above.
Increasing information also increases respondents’ certainty in their guesses. Average
certainty was 3.09 on scale from 1 (“a random guess”) to 4 (“most certain”) when respondents
were shown just the headshot and rose to an average of 3.33 when they were shown the video
image with the subject providing his or her name – a difference that is statistically significant.
tend to place individuals with unique features (including darker skin color) in what they call “extreme”
racial categories (Pettigrew, Allport, and Barnett 1958).
21
As with co-ethnicity, information has a more powerful impact on identification success
when the subject is from a group that is not easily identifiable. For example, Arab subjects are
identified successfully 36.1 percent of the time when they provide their names and only 26.1
percent when respondents are shown just a headshot. Providing names of Latino/a, Indian, and
Persian/Iranian subjects also has a powerful impact on identification success.
Do Individual Characteristics Matter?
We also explored the impact of respondents’ individual-level characteristics on
identification success rates. In particular, we assessed the impact of respondents’ exposure to
and attitudes toward members of other groups on their ability to correctly categorize subjects.
Respondents with significant exposure to particular ethnic groups exhibited higher
success rates in identifying subjects from these groups than respondents without such exposure.
We assessed the level of exposure by asking respondents to list the ethnic backgrounds of their
roommates. When respondents had roommates of the same ethnic group as the subject being
viewed, the identification success rate was 82.2 percent. Respondents without roommates from
the same ethnic group as the subject achieved success rates of only 67.8 percent.
A second measure of exposure is the respondent’s age. Because our sample was
composed entirely of college students, there was not a great deal of variation in age. It was,
however, possible to compare recent arrivals to the university campus with those who had
already completed a year or more. Given the ethnically diverse student populations at both
UCLA and USC, this extra time on campus almost certainly exposed many respondents to more
people from outside their own ethnic groups than they had been exposed to previously, and this
may have led to higher identification success rates. Consistent with this hypothesis, students
22
aged 19 and older recorded success rates of 4.6 percentage points higher than those 18 and
younger. The age effect provides an additional indication that exposure to a diversity of ethnic
groups may be an important predictor of identification success rates.
The ability of individuals to correctly identify the ethnic backgrounds of the people they
encounter may also be a function of attitudes toward members of different communities. Indeed,
this intuition is at the core of the literature on prejudice and ethnic categorization that we
reviewed earlier. To assess the impact of respondents’ attitudes on identification success, we
asked a battery of questions designed to measure the perceived social distance between
themselves and members of each of the six other groups in the experiment (Bogardus 1932). In
particular, we asked respondents whether they would feel comfortable having someone from
each ethnic group as a “close kin by marriage.” We then tested whether such attitudes had any
effect on the ability of respondents to categorize members of each group. As the results reported
in Table 2 reveal, that higher degrees of comfort with a subject’s ethnic group are, in fact,
associated with higher success rates in guessing the identity of that group’s members.
PUTTING IT ALL TOGETHER
Thus far, we have analyzed the factors that affect identification success rates
independently of one another. Table 3 presents the results of a series of probit regressions that
assess their effects together. For ease of interpretation, we report marginal coefficient estimates
(with values for the other explanatory variables set to their means). Column 1 reports a model
that includes only respondent-specific determinants of identification success with low
information (headshot only). As we might expect from the results reported in Table 2, measures
of respondents’ exposure to and attitudes toward members of the subject group enter
significantly. Respondents who have a roommate of the subject’s ethnic group and who feel
23
comfortable with a family member marrying someone of that ethnic group are more likely to
successfully identify the subject’s ethnic background. In addition, moving from a non-co-ethnic
pairing to a co-ethnic pairing increases the probability of identification success by 15 percentage
points. Age is substantively, but not statistically, significant in this specification.17
Columns 2 and 3 introduce subject group effects, where the omitted ethnic group
category is Caucasian. The results confirm the findings from Table 2: Arabs, Indians,
Persians/Iranians, and Latino/as are significantly more difficult to identify than Caucasians,
African Americans, and Asians. For example, compared to a Caucasian subject, an Arab subject
is 57.7 percent less likely to be correctly identified. Although the introduction of subject group
effects cuts the co-ethnic coefficient in half, the underlying finding remains: respondents are
much better able to identify members of their own groups than outsiders. Column 3, which
includes both subject group effects and respondent characteristics, makes it clear that the former
dominates the latter. Once subject characteristics are controlled for, social distance and
roommate effects disappear. However, a new variable – the number of world regions in which
the respondent has lived – enters negatively and significantly.
Column 4 adds respondent group effects to produce a complete model. The results
reinforce our earlier finding that, at low levels of information, some ethnic groups are
systematically better able to identify subjects than others. If a respondent is Persian/Iranian or
17 Increases in the level of education of the respondent’s father (a rough measure of the respondent’s
socio-economic status) are associated with a higher probability of identification success, while having
lived in the region in which the subject’s ethnic group originated tends to reduce the likelihood of success.
We do not read too much in to these findings, however, since both of these effects cease to be significant
when controls for subject characteristics are added to the model.
24
Asian, her probability of identification success increases 11.5 or 8.5 percentage points
respectively, relative to a Caucasian respondent.
Column 5 replicates the complete model (from column 4) on the sample of high
information viewings. While subject group effects are still strong, they are substantially reduced
in size. Subject’s names provide a powerful source of information for most respondents and this
reduces the difficulty of identifying members of the most difficult-to-classify subject groups.
The significance of being in a co-ethnic pairing also disappears, indicating that co-ethnicity
provides an advantage only at low levels of information – perhaps because respondents can draw
on visual cues that may not be obvious to others. The respondent group effect among
Persians/Iranians also disappears at high information levels.
Thus far, we have defined identification success in terms of whether the respondent
identifies the subject in terms of the ethnic group with which the subject self-identifies. An
alternative, less stringent, definition of success is whether the guess of the respondent accords
with the modal guess of all respondents who were shown pictures of that subject. We replicate
our main model using this broader understanding of identification success in column 6.
The results suggest that subject group effects become less important when we give
respondents the cushion of needing only to match the best guess of the population. Under these
circumstances, respondents tend to correctly guess the ethnic backgrounds of the most
identifiable subjects from each group. When they misidentify individuals, they tend to do so in a
similar way to the respondent population in general.
All of the models in Table 3 also report the association between identification success
and the certainty of the respondent about his or her guess. Certainty is measured in two different
ways: in terms of the time elapsed before the respondent enters his or her response and in terms
25
of the level of certainty he or she indicates. In every specification, longer response times are
associated with lower identification success and greater certainty is associated with higher
success. The latter suggests that respondents are able to make fairly accurate assessments of
whether or not they are likely to be correct in their guesses. A multinomial logit specification
using the full model in column 3 (not shown) reveals that respondents take longer to respond
when they are shown the image of a subject from an ethnic group that is, on average, difficult to
identify.
MAKING SENSE OF MISIDENTIFICATION
Table 4 explores ethnic misidentifications. We distinguish between two different kinds
of misidentification: false positives (where, for example, a respondent shown an image of a non-
Latino identifies him as Latino) and false negatives (where a respondent shown an image of a
Latino identifies him as something other than Latino) – Figure 2 illustrates. As Table 4 makes
clear, the frequency of false positives and false negatives varies significantly across groups.
Whereas respondents incorrectly identified non-Caucasians as Caucasian 11 percent of the time,
they incorrectly identified non-African Americans as African American less than 1 percent of the
time. Meanwhile, Arabs, Indians, and Persians/Iranians were extremely likely to be
misidentified as members of other groups. This happened nearly three-quarters of the time with
the Arab subjects in our sample and more than half the time with Indian and Perisan/Iranian
subjects.
In contradiction to theories that assume that ethnic groups are able to police their
boundaries, we find that subjects miscoded fellow group members as non-group members 16
percent of the time and miscoded non-group members as group members 7 percent of the time.
26
The gap between these two types of misidentification suggests that a bias runs against admitting
non-co-ethnics to one’s group. This is a rather remarkable finding given that the stakes for
making a Type I error were effectively zero in the context of the experiment.
Apart from its intrinsic interest, data on misidentification can also help measure the social
distance between ethnic groups. Groups whose members are commonly mistaken for one
another could be coded as “close” and those whose members are rarely or never mistaken for one
another could be coded as “distant.” Although Casselli and Coleman (2002) base a theoretical
model of conflict on a similar notion of social distance, we know of no attempts to measure
social distance in this way empirically. Table 5 provides two approaches to measuring the
distance between ethnic groups. Table 5(a) presents the distance between ethnic groups
calculated as the average of false positives and false negatives. Higher rates of misidentification
within a given pairing indicate greater proximity of the two groups. Reading down the columns,
it is clear that there is significant variation in the distance between ethnic groups. Arabs, for
example, are most often misidentified as Caucasian, Latino/a, and Persian/Iranian. They are
misidentified least often as African American and Asian. Interestingly, this measure of social
distance correlates with one based on affinities (as operationalized by willingness to accept a
person into one’s family by marriage). Caucasian, Latino/a, and Persian/Iranian respondents are
the most willing to accept Arabs as members of their families while Asians indicate an
unwillingness to accept Arabs into their families.
Table 5(b) presents a measure of social distance based purely on false positives –
potentially a cleaner measure of how often the identities of members of different groups are
confused by observers. The patterns are similar to those generated earlier. Again, Caucasian,
27
Latino/a, and Persian/Iranian subjects are most likely to be misidentified as Arabs, while African
American and Asian subjects are virtually never misidentified.
HOW DIFFICULT IS IT FOR SUBJECTS TO PASS?
One of the strengths of this study’s design lies in our ability to control and vary the level
of information that respondents have about subjects’ backgrounds. Even so, the regulated
information we provide does have an aspect of artificiality. In real world situations where the
stakes of misidentification are high, individuals will often collect additional information about
other peoples’ backgrounds before they make their decisions about how to assign these people to
ethnic categories. Although still one-sided (only the subject speaks, and no opportunity is
provided for the respondent to interrogate the subject), the simulation and dissimulation videos
described earlier provide a better approximation of such real world interactions. They also
permit us to investigate the ability of individuals to pass as members of groups different from
their own.
Table 6(a) reveals that rates of identification success reach their highest levels for
viewings in which the subject sees a simulation video.18 The identification success rate across all
viewings reaches 88.5 percent in the simulation sub-sample, exceeding the success rate at even
the highest previous levels of information (the video in which the subject provided his or her
name), where the success rate was 75.6 percent. Providing subjects with the opportunity to
18 This holds across all ethnic groups except for African Americans. Again, because one key subject in
the African American sample was light-skinned, many African American respondents, along with others
in the general population, assumed that the subject was trying to pass as an African American even when
the subject provided an explanation for her appearance in the simulation video.
28
convince respondents of their true ethnic background has a particularly powerful impact on
raising identification success rates for those groups that were most difficult to identify at lower
levels of information.
But providing subjects with the opportunity to mislead respondents about their ethnic
backgrounds substantially reduces identification success rates. As Table 6(b) indicates, the
overall success rate drops to 55.2 percent with dissimulation, from a success rate of 77.3 percent
for the dissimulation sub-sample when respondents viewed only a video greeting in which the
subject provided his or her name. As with identifiability, the ability to pass is something that
varies with the ethnic group of the subject. Arabs, Caucasians, Indians, and Latinos are
particularly good at passing, reducing the ability of respondents to correctly identify them by
between 27 and 39 percentage points.
Recall that the subjects that participated in the dissimulation exercise were told to pretend
that they were in a situation in which it was important that they convince the person who would
view the dissimulation video that they belonged to an ethnic group different from their own. We
provided subjects with a list of ethnic groups and asked them to select one of which they would
try to pass as a member. Given these instructions, subjects should have chosen a group in which
they thought they could reasonably expect to pass successfully. The choices that subjects made
should therefore provide some indication of the “revealed” distance between groups. Although
the sample of dissimulators is small (N = 32), it is clear that subjects tended to choose groups in
which they were more likely to pass successfully. Arabs, for example, were most likely to
dissimulate as Latino/as or Persians/Iranians, which supports our conjecture based on the
findings in Table 5 that these three groups are more proximate to one another. Arab subjects did
29
not tend to dissimulate as African American or Asian (and vice versa) – groups we measured as
particularly distant from Arabs (again, see Table 5).
In Table 7, we examine the determinants of successful passing. We employ three
different definitions of successful passing. In the first (columns 1-3), we ask whether the
respondent correctly identified the true ethnicity of the subject in spite of the fact that he or she
viewed a dissimulation video. A second definition (column 4) asks whether the respondent, after
seeing the dissimulation video, changed her answer from the correct to an incorrect ethnic
category.19 A final definition (column 5) asks whether the ethnic guess of the respondent
matched the ethnic group in which the subject was trying to pass. Thus, while the first definition
effectively asks “did the respondent see through the dissimulating subject’s attempt to pass?” this
third definition asks “did the subject fool the respondent into believing that he or she really
belonged to the group in which he or she professed to be a member?”
Column 3 shows that a number of individual and subject group effects are important for
successful passing. The older a subject is and the higher his or her SAT scores, the less likely
the respondent will be able to correctly identify him or her when he or she is trying to pass. Co-
ethnicity also gives a respondent a leg up, making it more difficult for the subject to deceive him
or her. Finally, group effects again loom large. Asians have more difficulty trying to pass
(relative to Caucasians), while Indians and Latinos are substantially more successful.
A number of these effects remain large and significant as we move to increasingly
stringent definitions of success. In particular, when we ask whether subjects were able to
convince respondents of their false identity (definition 3, reported in column 5), age and SAT
19 Recall that respondents had first seen, in secession, the head shot, video greeting, and video greeting
with name before they were then shown the dissimulation video of the same subject.
30
score emerge as powerful predictors. Asians, again, face the most difficulty in credibly
convincing others that they are not in fact Asian. And, interestingly, Latino/a respondents are
significantly less likely to believe the false story that subjects are telling (in columns 4 and 5).
One explanation for this finding is that many subjects tried to pass as Latino/a by speaking in
Spanish in their dissimulation video and “true” Latinos/as were able to see through this ruse.
POTENTIAL WEAKNESSES OF THE EXPERIMENTAL DESIGN
Our main goal in this paper has been to document that seamless ethnic identifiability
cannot be assumed and to show that it varies in systematic ways across groups. Our
experimental results make both of these points clear. But how far can we go in asserting their
generalizability?
Three major concerns regarding external validity merit consideration. The first is with
respect to the criterion we employ for determining successful ethnic identification – i.e., whether
there is a match between the respondent’s guess and the way the subject identifies him or herself.
How individuals choose to identify themselves may not always be related to how they “should”
be identified, as determined by genetics or family lineage. Indeed, previous work in social
psychology explicitly avoids the self-identification criterion, instead relying on panels of experts
to identify the ethnic group of a subject. Such an approach sounds odd in today’s world, where
ethnicity tends to be viewed more as a product of subjective self-definition than of genetics.
Nonetheless, the self-identification criterion is not beyond criticism. To the extent that the
failure of respondents to identify subjects correctly is a function of how the subjects themselves
choose to self-identify, this would lead us to overestimate subject group differences in
identifiability.
31
A second concern relates to the small sample size of some of our subject groups. While
our overall subject and respondent populations are similar to or larger than those in previous
experiments of this type, it is nevertheless fair to assert that our samples are unlikely to capture
the full range of phenotypical variation within each ethnic group in the real population.
Moreover, it is difficult for us to assess how our sample of university students differs
phenotypically or attitudinally from those who never made it to college (or at least, not to the two
selective universities from whose student populations our participants were drawn). To the
extent that students who “look ethnic” face discrimination that makes it more difficult for them
to gain entry to selective universities, it is possible that our sample substantially understates the
true range of variation in identifiability.
A final issue is the relative weakness of our results on the importance of exposure to and
attitudes toward other ethnic groups. Most of these individual-level effects are swamped by the
power of subject group dummy variables in the full model specification. Because we conducted
this experiment on a university campus where most individuals are exposed to a whole range of
ethnic groups on a regular basis, our sample may bias the results against finding strong
individual-level effects of attitudes and exposure. The strength and consistency of our finding on
the importance of age lends plausibility to this argument, suggesting that there is something
systematically different about students that are just arriving on campus as compared to those who
have been immersed in a diverse environment for some time.
CONCLUSION: IMPLICATIONS FOR THEORIES OF ETHNIC POLITICS
Our experimental evidence undermines the assumption, implicit or explicit in much of
the micro-level theoretical work on ethnic politics, that ethnic identifiability is a non-issue. In
32
contradiction to the assumption that individuals can seamlessly pigeonhole the people they
encounter into their correct ethnic categories, our respondents were unable to correctly identify
the ethnic backgrounds of the subjects whose images they are shown more than 30 percent of the
time. Our findings also show that identifiability varies across ethnic groups, across individuals
within groups, across levels of information, and across co-ethnic and non-co-ethnic pairings.
Our exploration of “passing” also generates results that are relevant to theories of ethnic politics.
In particular, the experiment suggests that members of some ethnic groups are much better able
to pass than others, and that these group-specific effects are far more important than individual-
level factors, including experience and intelligence.
Apart from the caution our findings suggest for models that depend on the ability of
actors to categorize their interacting partners, our results have at least four implications for
theories of ethnic competition and conflict more generally.
First, the results suggest that collective action may be easier for some ethnic groups than
for others. It has been suggested that one of the reasons that ethnicity so often emerges as an
axis of political mobilization is because ethnic groups possess institutions that facilitate the
punishment of defection by in-group members. But if identifiability is not perfect, then the
ability of groups to police their members will be undermined and the advantage they have for
collective action will disappear. To the extent that identifiability varies systematically across
ethnic groups, the ability of ethnic groups to achieve collective ends should vary as well.
Second, the results show that passing may be easier for some individuals and groups than
for others. Much contemporary work on ethnicity underscores the fact that individuals
sometimes have incentives to pass. Our findings confirm that people are indeed able to do this,
but they also show that individuals’ ability to pass varies with group and individual-level
33
characteristics. This has implications for the permeability of group boundaries and the ability of
groups to police them.
Third, the findings suggest that in bilateral transactions, individual choices may depend
on how much information a player has about the identity of his or her partner. Theorists cannot
assume that individuals play strategies conditional on the identity of their partner, or that the
identity of individuals is common knowledge within ethnic groups or the population at large.
The impact of a partner’s identity on a player’s strategy will be a function of the degree of
information that the player has about the partner, and thus the certainty that he or she has about
the partner’s background.
Finally, the experiment makes it clear that the costs involved in gathering information
about the ethnic identity of individuals may vary across groups. Theories that rely on within-
group punishment strategies as an enforcement mechanism cannot realistically ignore the costs
that may be incurred in establishing the ethnic identity of particular individuals. Our findings
suggest that these costs may be higher for some groups than others.
While the assumption that the ethnic backgrounds of individuals are readily apparent has
facilitated theoretical analysis, it has also prevented us from studying important aspects of ethnic
processes that should no longer be overlooked. Variation in identifiability may have real
consequences, as the stories of ethnic identification in Sri Lanka and Burundi suggest. Taking
these differences seriously – and incorporating them into models of ethnic politics – is a critical
next step in producing better theories that link ethnicity to cooperation and conflict.
34
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36
Table 1: Identification Success a. Headshots Only Ethnic Group of Subject (whose image is viewed)
African American
Arab
Asian
Caucasian
Indian
Latino/a
Persian/Iranian
Total
African American
72.41 (29)
17.65 (17)
93.48 (46)
85.19 (54)
18.18 (11)
65.85 (41)
25 (32)
65.22 (230)
Arab
89.47 (19)
43.75 (16)
96.88 (32)
86.05 (43)
33.33 (12)
44.44 (27)
41.67 (12)
70.19 (161)
Asian
80 (50)
23.68 (38)
97.18 (142)
82.20 (118)
55 (20)
64.10 (78)
35.14 (37)
74.12 (483)
Caucasian
81.16 (69)
25 (52)
94.81 (135)
87.34 (237)
48.39 (31)
47.58 (124)
35 (60)
70.48 (708)
Indian
84.62 (13)
27.78 (18)
86.96 (23)
62.69 (27)
100 (5)
57.14 (12)
37.50 (8)
63.48 (115)
Latino/a
81.25 (32)
22.22 (27)
91.38 (58)
82.93 (82)
10 (10)
63.64 (88)
40 (25)
68.32 (322)
Persian/ Iranian
80.56 (36)
33.33 (12)
100 (34)
87.50 (48)
80 (5)
70 (30)
78.95 (19)
80.98 (184)
Ethn
ic G
roup
of R
espo
nden
t (w
ho v
iew
s sub
ject
’s im
age)
Total
80.65 (248)
26.11 (180)
95.11 (470)
84.40 (609)
44.68 (94)
57.95 (409)
38.86 (193)
70.90 (2203)
b. Video Greeting Ethnic Group of Subject (whose image is viewed)
African American
Arab
Asian
Caucasian
Indian
Latino/a
Persian/Iranian
Total
African American
72.41 (29)
17.65 (17)
95.65 (46)
83.33 (54)
18.18 (11)
65.85 (41)
31.25 (32)
66.09 (230)
Arab
89.47 (19)
37.50 (16)
96.88 (32)
83.72 (43)
41.67 (12)
55.56 (27)
50 (12)
72.05 (161)
Asian
82.00 (50)
23.68 (38)
95.77 (142)
86.44 (118)
60 (20)
58.97 (78)
32.43 (37)
74.12 (483)
Caucasian
78.26 (69)
25 (52)
96.30 (135)
87.76 (237)
58.06 (31)
50 (124)
40 (60)
71.89 (708)
Indian
92.31 (13)
22.22 (21)
91.30 (23)
74.07 (27)
100 (5)
38.10 (21)
25 (8)
62.61 (115)
Latino/a
81.25 (32)
25.93 (27)
91.38 (58)
82.93 (82)
20 (10)
67.05 (88)
40 (25)
69.88 (322)
Persian/ Iranian
83.33 (36)
33.33 (12)
100 (34)
81.25 (48)
80 (5)
70 (30)
78.95 (19)
79.89 (184)
Ethn
ic G
roup
of R
espo
nden
t (w
ho v
iew
s sub
ject
’s im
age)
Total
81.05 (248)
46 (180)
95.53 (470)
85.06 (609)
51.06 (94)
58.19 (409)
40.93 (193)
71.67 (2203)
37
c. Video Greeting with Name Ethnic Group of Subject (whose image is viewed)
African American
Arab
Asian
Caucasian
Indian
Latino/a
Persian/ Iranian
Total
African American
86.21 (29)
23.53 (17)
84.78 (46)
85.19 (54)
9.09 (11)
82.93 (41)
34.38 (32)
69.57 (230)
Arab
78.95 (19)
56.25 (16)
93.75 (32)
81.40 (43)
66.67 (12)
66.67 (27)
41.67 (12)
74.53 (161)
Asian
82 (50)
36.84 (38)
95.07 (142)
82.20 (118)
75 (20)
73.08 (78)
29.73 (37)
76.60 (483)
Caucasian
82.61 (69)
40.38 (52)
95.56 (135)
86.92 (237)
61.29 (31)
71.77 (124)
55 (60)
78.25 (708)
Indian
100 (13)
27.78 (18)
82.61 (23)
81.48 (27)
80 (5)
61.90 (21)
37.50 (8)
68.70 (115)
Latino/a
87.50 (32)
29.63 (27)
89.66 (58)
84.15 (82)
30 (10)
71.59 (88)
48 (25)
72.98 (322)
Persian/ Iranian
72.22 (36)
33.33 (12)
94.12 (34)
79.17 (48)
80 (4)
86.67 (30)
94.74 (19)
80.43 (184)
Ethn
ic G
roup
of R
espo
nden
t (w
ho v
iew
s sub
ject
’s im
age)
Total
82.66 (248)
36.11 (180)
92.77 (470)
84.24 (609)
57.45 (94)
73.35 (409)
48.19 (193)
75.62 (2203)
38
Table 2: Determinants of Identification Success
Percent of Subjects Correctly Identified
Difference (p-value)
Subject is From
Respondent’s Ethnic Group
83.77 (536)
In-group/ Out-group
Pairing
Subject is Not From Respondent’s Ethnic Group
66.77 (1667)
17.00 (0.00)
Low (Headshot)
70.90 (2203)
Level of Information
High (Video Greeting with Name)
75.62 (2203)
4.72 (0.00)
Respondent Does Not Have a Roommate
from the Subject’s Ethnic Group
67.82 (1731)
Exposure to Members of the Subject’s Ethnic Group
Respondent Has a Roommate from the Subject’s Ethnic Group
82.20 (472)
14.38 (0.00)
18 Years or Younger
67.39 (506)
Respondent’s Age
19 Years or Older
71.95 (1697)
4.56 (0.05)
Respondent Would Not Feel Comfortable Having Someone of the Subject’s Ethnic
Group as Close Kin by Marriage
62.10 (694)
Social Distance between Respondent and Members of Subject’s Ethnic Group
Respondent Would Feel Comfortable
Having Someone of the Subject’s Ethnic Group as Close Kin by Marriage
74.95 (1509)
12.85 (0.00)
Table 2 Notes: All comparisons (except the second) are based on viewings in which the respondent sees a headshot of the subject. Column 4 reports the p-value of a test of the null hypothesis that the identification success rates are equal across the two categories.
39
Table 3: Determinants of Successful Ethnic Identification
Dependent Variable: Does the Ethnic Guess of the Respondent Match the
Self-Reported Ethnic Group of the Subject?
Dependent Variable: Does the Ethnic Guess of the Respondent Match the “Best Guess” of
the Population? (1) (2) (3) (4) (5) (6)
-0.093 -0.099 -0.100 -0.103 -0.080 -0.085 Seconds Elapsed Before Response (2.94)** (3.36)** (3.36)** (3.50)** (3.77)** (4.07)**
Certainty of Respondent 0.164 0.109 0.109 0.107 0.116 0.103 (9.53)** (5.64)** (5.63)** (5.53)** (7.23)** (7.06)**
Co-ethnic Pairing 0.150 0.070 0.053 0.062 0.027 0.045 (5.00)** (3.25)** (2.14)* (2.64)** (1.18) (2.12)*
Respondent Age > 18 0.047 0.051 0.047 0.036 0.046 (1.96) (2.15)* (1.97)* (1.94) (2.18)*
Respondent is Female 0.041 0.029 0.024 -0.012 0.048 (1.95) (1.38) (1.05) (0.56) (2.60)**
-0.015 -0.001 0.007 0.016 0.035 Respondent is UCLA Student (0.54) (0.02) (0.25) (0.60) (1.52) 0.016 0.016 0.014 0.006 0.015 Respondent’s Father’s Education
(2.03)* (1.95) (1.85) (0.81) (2.34)* -0.007 -0.016 -0.027 -0.023 -0.021 Number of World Regions
Respondent has Visited (0.95) (2.13)* (2.39)* (2.05)* (2.23)* -0.097 0.005 -0.017 0.037 -0.006 Respondent has Lived in Region of
Ethnic Group of Subject (2.59)** (0.17) (0.53) (1.12) (0.23) -0.041 0.022 0.025 0.012 0.008 Respondent has Close Friend from
Region of Subject’s Ethnic Group? (1.12) (0.75) (0.83) (0.43) (0.31) 0.066 0.019 0.042 0.032 0.016 Respondent is Comfortable with
Someone of Subject’s Group Marrying Relative
(3.24)** (1.14) (2.18)* (1.59) (0.98)
0.118 0.031 0.042 -0.023 0.048 Respondent has Roommate of Same Ethnic Group as Subject? (4.61)** (1.26) (1.76) (0.99) (2.03)* African American Subject -0.102 -0.106 -0.106 -0.074 -0.060
(0.77) (0.79) (0.79) (0.64) (0.73) Arab Subject -0.577 -0.576 -0.566 -0.475 -0.236
(4.09)** (3.88)** (3.78)** (3.67)** (2.98)** Asian Subject 0.124 0.113 0.115 0.045 0.061
(1.55) (1.34) (1.36) (0.64) (0.92) Indian Subject -0.363 -0.369 -0.356 -0.246 -0.307
(3.56)** (3.57)** (3.44)** (3.02)** (4.25)** Latino/a Subject -0.240 -0.261 -0.250 -0.138 -0.125
(2.69)** (2.83)** (2.72)** (1.61) (2.20)* Persian/Iranian Subject -0.424 -0.430 -0.413 -0.337 -0.316
(5.34)** (5.28)** (5.04)** (4.41)** (5.16)** -0.005 -0.030 0.022 African American Respondent (0.16) (0.92) (0.70)
Arab Respondent 0.085 -0.005 0.062 (1.88) (0.10) (1.75)
Asian Respondent 0.085 -0.010 0.057 (2.72)** (0.48) (2.00)*
Indian Respondent 0.048 -0.030 0.040 (1.03) (0.68) (0.97)
Latino/a Respondent 0.021 -0.042 0.005 (0.69) (1.41) (0.17)
Persian/Iranian Respondent 0.115 0.012 0.021 (2.80)** (0.33) (0.59)
Information Level Headshot Headshot Headshot Headshot Video w Name Headshot Observations 2203 2203 2203 2203 2203 2203
Table 3 Notes: Probit estimation, with marginal coefficient estimates (at mean values for the explanatory variables). Robust Z statistics are in parentheses. Significantly different than zero at 95% (*), 99% (**) confidence. Regression disturbance terms are clustered at the subject level. The omitted ethnic group category of subjects and respondents is Caucasian.
40
Table 4: Distribution of False Positives and False Negatives by Group
False Positives (Type I Errors)
False Negatives (Type II Errors)
African American
0.87 (1955)
19.35 (248)
Arab
4.45 (2023)
73.89 (180)
Asian
1.10 (1733)
4.89 (470)
Caucasian
11.36 (1594)
15.60 (609)
Indian
3.51 (2109)
55.32 (94)
Latino/a
7.80 (1794)
42.33 (411)
Ethn
ic G
roup
Persian/Iranian
6.12 (2010)
61.14 (193)
Table 5: Measures of Social Distance
a. Average of False Positives and False Negatives African
American
Arab
Asian
Caucasian
Indian
Latino/a Arab
1.24 (5)
Asian
0.22 (2)
0.53 (5)
Caucasian
2.02 (10)
17.34 (73)
0.22 (2)
Indian
1.01 (5)
10.23 (24)
0.96 (5)
3.97 (10)
Latino/a
6.1 (35)
11.73 (54)
2.42 (21)
12.13 (112)
6.54 (37)
Persian/ Iranian
1.62 (8)
16.61 (62)
0.88 (4)
11.15 (69)
17.94 (45)
10.45 (53)
b. False Positives Only African
American
Arab
Asian
Caucasian
Indian
Latino/a Arab
0.81 (2)
Asian
0 (0)
0 (0)
Caucasian
4.03 (10)
32.22 (58)
0.43 (2)
Indian
2.02 (5)
5.56 (10)
0.85 (4)
0.49 (3)
Latino/a
9.27 (23)
18.33 (33)
1.91 (9)
6.40 (39)
5.74 (7)
Persian/ Iranian
3.23 (8)
16.11 (29)
0.21 (1)
6.24 (38)
24.47 (23)
5.87 (24)
41
Table 6: Identification Success with Simulation and Dissimulation a. Simulation (truth-telling) Ethnic Group of Subject (whose image is viewed)
African American
Arab
Asian
Caucasian
Indian
Latino/a
Persian/ Iranian
Total
African American
50 (2)
25 (4)
100 (13)
100 (11)
100 (2)
83.33 (6)
100 (5)
88.37 (43)
Arab
66.67 (3)
100 (8)
100 (11)
100 (2)
100 (3)
100 (2)
96.30 (27)
Asian
80 (5)
25 (4)
97.37 (38)
78.57 (28)
100 (3)
92.86 (12)
77.78 (9)
86 (100)
Caucasian
81.82 (11)
66.67 (9)
100 (38)
96.67 (60)
100 (2)
86.39 (22)
100 (12)
93.55 (155)
Indian
40 (5)
100 (5)
100 (3)
100 (2)
100 (2)
100 (1)
83.33 (18)
Latino/a
66.67 (3)
33.33 (3)
90.91 (11)
72.73 (11)
100 (2)
81.25 (16)
100 (6)
80.77 (52)
Persian/ Iranian
60 (5)
100 (6)
75 (12)
100 (1)
75 (4)
100 (3)
80.65 (31)
Ethn
ic G
roup
of R
espo
nden
t (w
ho v
iew
s sub
ject
’s im
age)
Total
73.08 (26)
46.43 (28)
98.32 (119)
89.71 (136)
100 (12)
86.57 (67)
94.74 (38)
88.50 (426)
b. Dissimulation (trying to pass) Ethnic Group of Subject (who is trying to pass as a member of a different group)
African American
Arab
Asian
Caucasian
Indian
Latino/a
Persian/ Iranian
Total
African American
40 (5)
0 (3)
88.89 (9)
50 (14)
0 (6)
42.86 (7)
45.45 (44)
Arab
33.33 (3)
25 (4)
87.50 (8)
28.57 (7)
0 (3)
50 (2)
100 (1)
46.43 (28)
Asian
40 (5)
14.29 (7)
87.18 (39)
41.67 (24)
66.67 (3)
18.18 (11)
28.57 (7)
55.21 (96)
Caucasian
50 (6)
22.22 (9)
84.62 (39)
65 (60)
0 (5)
25 (24)
28.57 (14)
55.41 (157)
Indian
0 (0)
100 (6)
57.14 (7)
0 (0)
16.67 (6)
100 (2)
54.17 (24)
Latino/a
66.67 (3)
0 (5)
80 (20)
62.50 (24)
35.71 (14)
66.67 (3)
57.97 (69)
Persian/ Iranian
66.67 (3)
33.33 (3)
92.31 (13)
37.50 (8)
50 (2)
50 (4)
85.71 (7)
67.50 (40)
Ethn
ic G
roup
of R
espo
nden
t (w
ho v
iew
s sub
ject
’s im
age)
Total
48 (25)
15.15 (33)
86.57 (134)
55.56 (144)
21.43 (14)
25.37 (67)
48.78 (41)
55.24 (458)
42
Table 7: Determinants of Successful Passing
Dependent Variable: Does the Ethnic Guess of the Respondent Match the Self-Reported Ethnic Group of the
Subject?
Dependent Variable: Does the Respondent Change His/Her Guess from the Correct to the Incorrect
Category?
Dependent Variable: Does the Ethnic Guess of the Respondent Correspond with the Ethnic Group in
which the Subject Tried to Pass?
(1) (2) (3) (4) (5) Subject Age -0.098 -0.097 -0.098 -0.039 0.066
(2.98)** (2.38)* (2.46)* (1.92) (2.32)* Subject Female -0.045 0.139 0.125 -0.004 -0.140
(0.32) (1.03) (0.95) (0.05) (1.33) -0.004 0.039 0.039 -0.027 -0.053 Father’s Education (0.08) (0.96) (0.93) (1.24) (1.25)
SAT Score -0.088 -0.215 -0.221 0.014 0.168 (1.00) (2.28)* (2.32)* (0.30) (2.15)*
-0.333 -0.340 -0.197 0.138 African American Subject (1.21) (1.24) (2.64)** (0.84)
Arab Subject -0.231 -0.249 -0.013 0.084 (1.08) (1.20) (0.11) (0.88)
Asian Subject 0.470 0.469 -0.211 -0.392 (3.47)** (3.45)** (3.55)** (3.37)**
Indian Subject -0.241 -0.261 0.148 0.087 (1.99)* (2.25)* (1.50) (0.90)
Latino/a Subject -0.298 -0.303 0.196 0.285 (2.06)* (2.07)* (2.10)* (1.88)
Persian/Iranian -0.001 -0.001 -0.071 -0.039 Subject (0.01) (0.00) (1.04) (0.33)
0.136 0.149 -0.066 -0.018 Co-ethnic Pairing? (2.50)* (2.86)** (1.45) (0.33) -0.040 -0.013 0.098 African American
Respondent (0.49) (0.20) (1.13) Arab Respondent 0.096 -0.032 0.049
(0.90) (0.31) (0.34) Asian Respondent -0.014 0.029 0.015
(0.20) (0.55) (0.21) 0.123 -0.053 -0.009 Indian Respondent (1.10) (0.57) (0.09) 0.089 -0.138 -0.118 Latino/a Respondent (1.08) (2.65)** (1.80)
Persian/Iranian 0.224 -0.043 -0.129 Respondent (2.87)** (0.67) (1.67)
Observations 432 432 432 432 432 Table 7 Notes: Probit estimation, with marginal coefficient estimates (at mean values for the explanatory variables). Robust Z statistics are in parentheses. Significantly different than zero at 95% (*), 99% (**) confidence. Regression disturbance terms are clustered at the subject level. The omitted ethnic group category is Caucasian.
43
Figure 1: Percentage of Viewings with Correct Identification, by Group and Level of Information
African Americans: Headshot
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14
average = 80.7%
perc
ent c
orre
ct
African Americans: Greeting w Name
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14
average = 82.7%
perc
ent c
orre
ct
Arabs: Headshot
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9
average = 26.1%
perc
ent c
orre
ct
Arabs: Greeting with Name
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9
average = 36.1%
perc
ent c
orre
ct
Asians: Headshot
0
20
40
60
80
100
1 3 5 7 9 11 13 15 17 19 21 23
average = 95.1%
perc
ent c
orre
ct
Asians: Greeting with Name
0
20
40
60
80
100
1 3 5 7 9 11 13 15 17 19 21 23
average = 92.8%
perc
ent c
orre
ct
Caucasians: Headshot
0
20
40
60
80
100
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33
average = 84.4%
perc
ent c
orre
ct
Caucasians: Greeting with Name
0
20
40
60
80
100
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33
average = 84.2%
perc
ent c
orre
ct
44
Figure 1 (cont’d): Percentage of Viewings with Correct Identification, by Group and Level of Information
Indians: Headshot
0
20
40
60
80
100
1 2 3 4 5
average = 44.7%
perc
ent c
orre
ct
Indians: Greeting with Name
0
20
40
60
80
100
1 2 3 4 5
average = 57.5%
perc
ent c
orre
ct
Latinos: Greeting with Name
0
20
40
60
80
100
1 3 5 7 9 11 13 15 17 19 21 23
average = 73.3%
perc
ent c
orre
ct
Latinos: Headshot
0
20
40
60
80
100
1 3 5 7 9 11 13 15 17 19 21 23
average = 58.0%
perc
ent c
orre
ct
Persians/Iranians: Headshot
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11
average = 38.9%
perc
ent c
orre
ct
Persians/Iranians: Greeting with Name
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11
average = 48.2%
perc
ent c
orre
ct
45
Figure 2: Types of Misidentification
Subject is Latino/a Yes No
Yes
Correct Identification
False Positive
Respondent Identifies
Subject as Latino/a No
False Negative
Correct Identification
46
APPENDIX A: INSTRUCTIONS FOR COLLECTION OF SIMULATION/DISSIMULATION IMAGES
Thank you for attending this extra session. You were one of the participants we selected
randomly for some additional image recordings. We will pay you $10 for your participation today.
As part of the experiment, we are interested in knowing whether people are able to guess the
ethnic backgrounds of people whose images they see. To help us to explore this issue, we would like to
make some extra recordings in which you actually state your ethnic background and try to convince other
people of it.
What is your ethnic background? _______________
First, we want to redo the three images we collected earlier. [RECORD HEADSHOT, VIDEO
WITH GREETING, VIDEO WITH GREETING AND NAME]
Now, we would like to make two recordings in which you try to convince the person who will
view the video clip that you are _______. Some people do this by saying something about where their
family is from; others use words from a language they know. You can say anything you think might be
helpful to convince others of your ethnic background. We will limit your recording to 20 seconds.
For the first recording, imagine that you are speaking to someone else who also describes
themselves as _______. What would you say to them to convince them that you are also _______? You
have 20 seconds. Take a moment, if you like, to think about what you would like to say. [RECORD IN-
GROUP SIMULATION VIDEO]
For the second video clip, think about what you might say to someone who does NOT self-
identify as _______ if you wanted to convince them that you are _______. You have 20 seconds. Again,
take a moment, if you like, to think about what you would like to say. [RECORD OUT-GROUP
SIMULATION VIDEO]
Now we will record a final video clip in which we will ask you to try to convince somebody that
you are NOT, in fact, _______. Imagine instead, that you want to convince the person who will view the
video clip that you are from any one of the ethnic groups on this list.
47
African American
Arab
Asian
Caucasian
Indian
Persian/Iranian
Latino/a
If you wanted to convince the person that you were not _______ but from another ethnic group, which
group would you choose?
Now think about what you might say to convince the person that you are a member of this group.
You have 30 seconds. [RECORD DISSIMULATION VIDEO]
48
49
APPENDIX B: INSTRUCTIONS FOR ETHNIC IDENTIFICATION GAME20
Welcome back to the Human Interaction Project. For your participation today, you will receive a
show-up fee of $5 along with whatever you win in the games you will play today.
Today you will be participating in a single game. The purpose of the game is to investigate how
well people are able to identify the ethnic backgrounds of the people they encounter in everyday life.
Lots of theories in political science, sociology, and other disciplines assume that individuals can readily
identify the ethnic backgrounds of the people they interact with. This experiment is designed to test
whether this is really the case.
To do this, we are going to show you a series of photographs and brief video clips of different
people and ask you to guess the ethnic backgrounds of the people you see.
In some of the video clips you will see, the person will actually tell you what their ethnic
background is. Recognizing that it is sometimes advantageous for people to try to “pass” as members of
groups other than their own, it is possible that some of the people may be lying about their ethnic
backgrounds. You should keep this in mind when you guess the backgrounds of the people whose images
you see. To earn the most money from this game, you will have to use your judgment to figure out when
people are telling the truth, and when they might be giving you false information.
First, you will be asked whether you know the person. Here we mean, do you know this
individual personally, outside of the context of this experiment. It is quite possible that you may have
20 The ethnic identification experiment described in this paper was part of a larger experiment, the results
of which are reported in Habyarimana et al. (2004). Subjects had therefore already participated in two
rounds of experimental games, and seen images of other subjects in the context of playing these games.
This was the first time, however, that they were made aware that the project sought to assess the effects of
ethnic group membership on their decision-making, and this was the first time that they were explicitly
asked to identify the ethnic backgrounds of other players.
50
seen an image of this person before, as part of one of the earlier rounds of the experiment. Please only
indicate that you know the person if you have had a prior interaction with this individual in real life.
After viewing the picture of the person, you will be asked to guess the person’s ethnic identity
and to indicate your level of certainty about your guess.
You will see a series of three or four images of each person. Each image will provide you with
slightly more information about the person’s ethnic background. Each time, you will be asked to answer
the same questions. You should feel free to change your answers as you go along if the additional
information that you receive causes you to re-think your initial guess.
You will be paid 20 cents for each correct guess about the person’s ethnic identity. By “correct guess,”
we mean identifying the person in the same way that the person identified themselves to the
experimenters.