Judgment and Decision Making, Vol. 17, No. 2, March 2022, pp. 263-283
“When in Rome”: Identifying social norms using coordination games
Erin L. Krupka*
Roberto Weber#
Rachel T. A. Croson$
Hanna HooverX
|
Abstract:
Previous research in economics, social psychology, and sociology has
produced compelling evidence that social norms influence behavior. In this
paper we apply the Krupka and Weber (2013) norm elicitation procedure and
present U.S. and non-U.S. born subjects with two scenarios for which
tipping and punctuality norms are known to vary across countries. We
elicit shared beliefs by having subjects match appropriateness ratings of
different actions (such as arriving late or on time) to another randomly
selected participant from the same university or to a participant who is
born in the same country. We also elicit personal beliefs without the
matching task. We test whether the responses from the coordination task
can be interpreted as social norms by comparing responses from the
coordination game with actual social norms (as identified using
independent materials such as tipping guides for travelers). We compare
responses elicited with the matching tasks to those elicited without the
matching task to test whether the coordination device itself is essential
for identifying social norms. We find that appropriateness ratings for
different actions vary with the reference group in the matching task.
Further, the ratings obtained from the matching task vary in a manner
consistent with the actual social norms of that reference group. Thus, we
find that shared beliefs correspond more closely to externally validated
social norms compared to personal beliefs. Second, we highlight the
importance that reference groups (for the coordination task) can play.
Keywords: norms, coordination games, experiment
1 Introduction
Social norms, defined as collective perceptions of appropriate behavior,
are an important topic of study within psychology, sociology, and
economics. Within economics, social norms offer a possible explanation for
behavior that otherwise appears inconsistent with self-interest and also
help explain human coordination when multiple possible equilibria exist
(Sugden, 1995; Binmore & Samuelson, 2006; Gintis, 2014).
As social norms have gained prominence as a topic of study in their own
right or as an explanatory factor for observed behavior, recent work in
economics specifically targets the need for new tools that empirically
measure social norms. Krupka and Weber (2013) develop an incentive
compatible norm elicitation protocol that avoids the potential hypothetical
bias of unincentivized techniques and captures a key feature of social
norms: that they are jointly recognized (Harrison & Rutström, 2008; Roth
& Kagel, 2016).1 2 The protocol asks subjects to play an
incentivized coordination game that rewards subjects for coordinating on
the same rating of how appropriate an action is in a given choice context.
This method assumes that collectively recognized social norms create focal
points in the matching game — if there is a social norm that some actions
are more or less socially appropriate, respondents attempting to match
others’ appropriateness ratings are likely to rely on this shared
perception to help them do so. Thus, the incentives in the coordination
game elicit collective perceptions of appropriateness which they interpret
as an empirical measure of the social norm.
The primary research question we address is whether the responses from the
coordination task are appropriately interpreted as empirical measures of
social norms.3 We test
this in three ways. First, we test whether responses using the coordination
mechanism are systematically different from responses when no such
coordination mechanism is in place; thus, we can test whether the
coordination game yields different responses from a subject than the
unincentivized “what do you personally believe” survey approach. Second,
we test whether foreign-born nationals coordinating with foreign-born
nationals respond differently in the coordination task than U.S. born
nationals coordinating with U.S. born nationals; because foreign-born
nationals have different norms, the responses of the two groups should
differ in a fashion consistent with the difference in norms. Finally, we
test whether responses from the coordination task track real-world social
norms that we identify using independent source material (for, e.g.,
traveler’s guides on country tipping norms). We will refer to these as
ex-ante identified norms in what follows.
We find that appropriateness ratings for different actions vary with the
reference group in the matching task. Further, the ratings obtained from
the matching task vary in a manner consistent with the ex-ante identified
social norms of that reference group. Thus, we find that shared beliefs
correspond more closely to externally validated social norms than to
personal beliefs.
Our findings also contribute towards the measurement of social norms by
highlighting the central role of the reference group in determining the
salient social norm. We examine two situations where an individual may
belong to multiple social groups for which different social norms exist
for the same situation. The experiment exploits the natural variation of
social norms between two salient reference groups for participants – their
fellow students at the university and the subset of students born in the
same country. The coordination game we use to elicit norms is well suited
to this task as it has the advantage of allowing us to systematically vary
the reference group the respondent uses in their matching tasks.
Our findings join a recent study that elicits social norms for different
reference groups and distinguishes social norms from personal norms. Burks
and Krupka (2012) show that social norms, elicited using the coordination
game, are distinct from personal opinions and they demonstrate the
separate effect of personal opinions and social norms on behavior. Unlike
in Burks and Krupka (2012), this paper uses two well-articulated
real-world social norms — tipping and punctuality — to benchmark responses
in the coordination game against externally validated norms. We ask about
tipping and punctuality norms because it is possible to use independent
source material to obtain ex-ante descriptions of the social norm for
these domains and because the norms for these domains vary by country
(Lynn and Lynn (2004); Lynn (2006)). Further, in this paper we show an
additional way in which we can adapt the elicitation protocol to vary
reference groups to study social norms in groups where culture plays a
significant role in shaping those norms.
2 Definition of social
and personal norms
A long tradition in psychology distinguishes several different kinds of
norm constructs: social norms that describe what one ought to do,
descriptive norms that describe what is regularly done and personal norms
(Deutsch & Gerard (1955); Schwartz (1973); Cialdini et al. (1991)). We
follow this literature in distinguishing what one “ought” to do, or
injunctive norms, from customs or actions that people regularly take, or
descriptive norms (Bicchieri (2005); Deutsch and Gerard, (1955)). Both
kinds of norms influence behavior (Herrbach & Mignonac, 2007; Cialdini et
al., 1990; Krupka & Weber, 2009; Bicchieri & Xiao, 2009); Gneezy et al.,
2010). However, our primary focus in this paper is on injunctive norms,
i.e., those described by Elster (1989) as prescribing what one “should do”
or “should not do”, and on personal norms. We define injunctive norms as a
socially shared understanding regarding the in/appropriateness of a
particular behavior.
This definition emphasizes that social norms are characterized by a
“shared” understanding (See, for example, Elster, 1989; Bicchieri, 2005;
Miller and Prentice, 1996; Young, 2007).4 For example, Bettenhausen and
Murnighan (1991, p. 21) note that norms are “socially shared guidelines to
accepted and expected behavior.” Fehr and Gaechter (2000, p. 166) write that
norms are “based on a socially shared belief about how one ought to
behave.”
Distinguishing between collectively held norms and personal normative
beliefs (personal beliefs) is equally important, both in practice and in
testing theoretical predictions. Social norms are characterized by
shared knowledge while personal beliefs need not be known to any other
member of the group and are expectations one holds of oneself (Schwartz,
1973; Elster, 1989). Schwartz (1977) describes personal norms as
self-expectations for behavior constructed in specific situations on the
basis of generalized internalized values (however, personal norms are
similarly characterized in Elster, 1989, and Posner & Rasmusen
1999).5 It follows that an individual’s
personal normative beliefs need not track a group norm (Bicchieri, 2005;
Young, 2007). Thus, as an example, when a new student from another country
joins a U.S. university he may recognize that the social norm for tipping
in the U.S. is between 12% and 15% for lunch, but may personally believe
that tipping 5% after a restaurant lunch meal is appropriate. Therefore,
if one were to ask such an individual: “Do you think that tipping 5% is
something that you ought to do?” they might respond on the basis of what
they personally agree with (5%), even if they know that the relevant
social norm in the U.S. is higher (15%).
The norm elicitation method first proposed by Krupka and Weber (2013)
captures this joint recognition by using a specific behavioral economic
experiment — a coordination game over rating the appropriateness of
actions for a scenario that they describe to subjects. Camerer and Fehr
(2004, p. 34) note that coordination games can be paired with economic
incentives to reveal shared understanding and suggest that experimental
paradigms, such as simple coordination games, can prove useful for
measuring dimensions of shared perception.6 From a game-theoretic point of view, matching games such as the
one used in the norm elicitation protocol, have a number of equilibria and
nothing intrinsic to the game makes one equilibrium favored (or focal) over
the other. Schelling (1980) theorized and Mehta et al. (1994) and Sugden
(1995) demonstrated that prominence derived from common culture and shared
experiences can create focal points. Krupka and Weber (2013) showed that in
their games, norms created focal points that subjects used to successfully
coordinate. Further, it follows that personal norms, captured without an
incentive and without the coordination game, may significantly vary from
the views that are understood by group members to constitute the collective
norm.
3 Experimental design and hypotheses
Table 1 describes the experimental design. In this study, respondents
participate in six different stages or tasks.
| Table 1: Experimental design overview. |
Stage for each participant | Vignette | Target for matching ratings | Order Counter- balanced | Incentives to match responses? |
1. | Tipping | Coordinate ratings with other participants at the university | 21.5inRandomized the order of which target participants matched with | Yes |
2. | | Coordinate ratings with other participants born in the same
country | | Yes |
3. | | No target
(aka. personal beliefs) | Always after matching | No |
4. | Punctuality | Coordinate ratings with other participants at the university | 21.5inRandomized the order of which target participants matched with | Yes |
5. | | Coordinate ratings with other participants born in the same
country | | Yes |
6. | | No target (aka. personal beliefs) | Always after matching | No |
Each participant went through all six stages. Participants always read
about the tipping vignette first and were either presented with a
coordination game in which the target was to match with other participants
from the same university or with others who were born in the same country.
The order of the targets for matching was randomized. Once they completed
both coordination games, they were asked to rate each action according to
what they personally believed. The coordination tasks were incentivized
such that they received payment for correctly matching with the target and
personal beliefs were not. This pattern of tasks was repeated for the
punctuality vignette. The study also included a demographics survey.
The tipping vignette is always the first vignette subjects encounter as
they accomplish tasks in stage 1–3 in Table 1.7 In the “tipping vignette”,
subjects read a scenario where a third party must decide what percentage of
a $10 lunch bill to tip. Subjects read the following vignette:
“Individual A has just finished lunch at a restaurant. The bill for lunch
is $10.00. Individual A must decide how much, if any, tip to
leave. Individual A has seven possible choices which are indicated in the
table below. Individual A can choose only one of these options.” Subjects
are told to think about the following possible actions for Individual A:
give no tip (A pays $10 and a $0 tip), give a 5% tip (A pays $10 and a
$0.50 tip), give a 7% tip (A pays $10 and a $0.70 tip) give a 10% tip
(A pays $10 and a $1 tip), give 12% tip (A pays $10 and a $1.20 tip),
give 15% tip (A pays $10 and a $1.50) tip, give 20% tip (A pays $10
and a $2.00 tip).
The punctuality vignette is always the second vignette subjects encounter
as they accomplish tasks in stage 4–6 in Table 1. In the “punctuality
vignette” subjects read about a third party who must decide on whether to
arrive early, on time, or late to a meeting at the library. Subjects read
the following vignette: “Individuals A and B have agreed to meet in the
library to work on an assignment. They have agreed to meet at a
particular time. Individual A has to decide at what time to arrive for the
meeting. Individual A has seven possible choices which are indicated in
the table below. Individual A can choose only one of these options.”
Subjects were asked to think about the following possible actions for
Individual A: arrive 10 minutes early, 5 minutes early, exactly on time, 5
minutes late, 10 minutes late, 20 minutes late or 30 minutes late.
In each case, tipping or punctuality, if they are playing the coordination
game, then they must match ratings with another participant who is from
their university or born in the same country as they were to receive a
bonus payment (stages 1–2 and 4–5 in Table 1). The coordination task
asks them to rate the possible actions according to how socially
appropriate they are. Respondents judge the social appropriateness of
each action in the vignette on a four-point scale that ranges over
“very socially inappropriate”, “somewhat socially inappropriate”, “somewhat
socially appropriate” to “very socially appropriate.”8,9 They do the coordination ratings two times. If they are
matching with another randomly selected university student, then they are
paid if they successfully select the same appropriateness rating as that
participant. If they are matching ratings with another participant born in
the same country, then they are paid if they successfully select the same
appropriateness rating as that participant. In both coordination tasks,
respondents play a “pure matching” coordination game (Schelling, 1980;
Mehta et al., 1994) in which their goal is to anticipate the extent to
which others in the reference group will rate an action as socially
appropriate or inappropriate, and to respond accordingly.
Table 2 provides an example of a table subjects used to record their
responses when they were matching their responses with another randomly
selected university subject participating in the experiment. The ‘x’ marks
in the table are for exposition purposes and illustrate how a participant
could have responded.
| Table 2: Example of a response reporting form used in the coordination
game tor the tipping vignette. This table gives an example of the
response form and response format that was used to elicit participants’
beliefs about, in this example, tipping. For example, this hypothetical
respondent has indicated that they believe that most others in the target
group would say that giving “no tip” would be “very socially
inappropriate”, while most others in the target group would say that
giving “20% tip” would be “very socially appropriate”. |
Individual A’s choice
|
Very socially Inappropriate | Somewhat socially inappropriate | Somewhat socially appropriate |
Very socially appropriate |
Give no tip | X | | | |
Give a 5% tip | X | | | |
Give a 7% tip | | X | | |
Give a 10% tip | | | X | |
Give a 12% tip | | | X | |
Give a 15% tip | | | | X |
Give a 20% tip | | | | X |
After playing the two coordination tasks, the third task for each vignette
is not a coordination game but simply asks them to state their personal
beliefs about how appropriate the actions are. There is no reference group
in this stage (3 and 6, in Table 1) and it always comes after the two
coordination tasks have been completed for the vignette.
As can be seen in Table 1, the order of the vignettes was not
counterbalanced; tipping always came first. However, the order of the
coordination games was counterbalanced such that some were asked to first
match with another university student and then with another student born
in the same country, while for others this order was reversed. After the
two coordination games, subjects provided their own appropriateness
ratings without trying to match anyone else’s response (they had no target
and were paid no incentive). This three-stage sequence was completed twice
in the experimental session, once for the tipping vignette and once for
the punctuality vignette, such that each subject completed six segments in
total and a seventh segment asked them some basic demographic information.
Payment in the coordination game occurred a week later and consisted of a
$10 participation fee for all participants and the possibility of earning
additional money if their response was selected for payment in the
coordination tasks. To calculate payments for the coordination task, the
experimenter randomly selected one participant and matched their responses
to the responses provided by another subject from each of the two
reference groups. Specifically, the chosen subject’s ratings in the
coordination games would be matched with another randomly selected subject
from their university and then with a second randomly selected subject who
was born in the same country as the chosen subject.10 The selected participant
would receive an additional $1 for every rating that was the same rating
as the matched responses. As there are 7 response categories and 4
coordination games, the chosen subject had the opportunity to make an
additional $28 if all the responses coincided with the two matched
persons’ responses. In addition to the coordination incentive, the
selected subject would also receive $20 if every question was completed.
This payment scheme provides a salient incentive for participants to
accurately consider how other participants will judge the appropriateness
of any given action.
We interpret subject responses in the following manner. When subjects
are coordinating with a randomly selected university student participating
in the experiment, then this technique elicits the subject’s belief about
the normative evaluations of another student at the university, and in
aggregate is a proxy measure for the norm for the university. If the
subject’s reference group is from the set of participants who were born in
the same country, then this technique elicits the subject’s belief about
the normative evaluations of his nationals, and in aggregate is a proxy
measure for the norm for nationals from that country. If there is no
incentive structure and subjects are asked to state their personal
beliefs, then this technique elicits the subject’s personal belief and, in
aggregate, identifies a distribution of personal beliefs for a group.
3.1 Hypotheses
Our coordination game treatments consist of subjects matching their
responses with (1) a subject selected randomly from the sub-set of
subjects who were born in the same country and (2) a randomly selected
subject from all university students participating. Because our university
is located in the U.S., the university norm is likely to map very closely
to a U.S. norm (both because most students will be from the US and because
of the geographic location of the university). We tested the following
hypotheses:
H1: [Personal beliefs will differ from ratings in the coordination
treatments]
Personal beliefs for foreign-born respondents will differ
significantly from the ratings they provide in the coordination task where
the target is another university student.
H2: [Ratings(F,F) will differ from Ratings(US,US)]
For either tipping or
punctuality, the ratings of foreign-born nationals coordinating with
foreign-born nationals will differ from the ratings of U.S. born nationals
coordinating with U.S. born nationals.
H3: [Ex-ante identified norms will predict the direction of ratings
changes]
Respondent’s ratings when coordinating with foreign-born
(ratings(F,F)) will be different from ratings when U.S. born nationals
coordinate with other U.S. born nationals (ratings(US,US)) in a manner
that is consistent with ex-ante identified differences in the relevant
norms.
3.2 Procedure
Students from a northeastern business school in the U.S. were recruited to
take part in our experiment for partial fulfillment of their class
requirements and were paid a flat show-up fee of $10. A total of 165
participants were recruited. Sessions were conducted using groups of 17 to
35 participants and included another unrelated decision task that always
took place before this experiment. Instructions were read aloud and
participants were informed of the incentive structure for the task. Before
beginning the experiment, subjects read an example scenario which
demonstrated how to fill out the tables and how the matching would be
executed. One week after all the sessions had been conducted the
participant who was selected to receive additional payment was notified
publicly in class and paid.
4 Results
The sample size for our analysis was 155 subjects who were born in 19
different countries.11 Table 3 describes the distribution of
cultural-geographic clusters of our sample based on country of birth and
ex-ante social norms identified through reputable sources. Most of our
subject pool was born in the USA (70.3%). The largest non-U.S.
nationality represented was China (9.6%) followed by Singapore (3.2%)
and India (2.5%). Columns 5 and 6 give an overview of each country’s
tipping and punctuality norms.
| Table 3: Descriptive statistics for the sample. This table displays the
important descriptive characteristics of our data. In the final two
columns we also note the ex-ante identified norm for the country. The
ex-ante identified norms were collected from multiple independent sources. |
| | | | | Ex-ante identified norms |
Country born | N | Percent | Geographic cluster | Tip as % of Bill | Punctuality (vs. U.S.) |
USA | 109 | 70.32 | 21inNorth America (n=112) | 12% | 2 minutes late |
Canada | 3 | 1.94 | | 12% | + |
Brazil | 3 | 1.94 | 21inCentral/South America (n=7) | 9% | - |
Peru | 2 | 1.29 | | 7% | - |
Columbia | 1 | 0.65 | | 7% | - |
Ecuador | 1 | 0.65 | | 10% | - |
Russia | 3 | 1.94 | Europe (n= 6) | 9% | - |
Germany | 1 | 0.65 | | 2% | + |
Turkey | 1 | 0.65 | | 9% | - |
France | 1 | 0.65 | | 5% | + |
Singapore | 5 | 3.23 | Asia (n= 21) | 0% | + |
China | 15 | 9.68 | | 0% | + |
Taiwan | 1 | 0.65 | | 2% | + |
India | 4 | 2.58 | South Asia (n=4) | 7% | - |
Kenya | 1 | 0.65 | Africa (n=3) | 2% | - |
Uganda | 1 | 0.65 | | 4.5% | - |
South Africa | 1 | 0.65 | | 10% | - |
U.A.E | 1 | 0.65 | Middle East (n=1) | 0% | - |
Cayman Isl. | 1 | 0.65 | Caribbean (n=1) | 12% | - |
Total | 155 | 100 | | | |
Each country’s tipping norm was identified using at least three different
sources. We relied heavily on Fodor’s “How to Tip” (2002) travel guide and
Magellan’s World Wide Tipping Guide as well as Nancy Star’s book, The
International Guide To Tipping, on tipping in the service industry. Star
obtained her tipping guidelines by asking the service providers to fill
out a questionnaire in which, among other things, they were asked to state
their ‘average’ tip. Despite its print date (1988), the tipping ranges she
reports are consistent with those stated in our other sources.
Point estimates of punctuality norms were more difficult to obtain but it
was not difficult to learn whether a country generally viewed punctuality
favorably or unfavorably. Levine et al. (1980) asked 107 U.S. students
“at what time the average American would arrive” to “a lunch with a
friend.” Both U.S. males and females reported that the average American
would arrive 2–2.5 minutes late. We consider this (arriving 2.5 minutes
late) the punctuality norm for the U.S. While some studies report
punctuality norms (Levine et al. 1980; Levine, 2008; White et al., 2011;
Gelfand et al., 2011), we were unable to locate multiple references for
point estimates.12
For this reason we used a binary indicator that coded for whether a
country was less punctual or more punctual than the U.S. Other
country-specific punctuality norms were described as being later or earlier
than the U.S. (where earlier typically means arriving on time, and later
typically means arriving more than 5 minutes late). These norms were
obtained from multiple references and internet resources dedicated to doing
international business. There were no disagreements regarding how to
classify a country’s social norm relative to the U.S. from these
sources.13 Thus, we can
interpret Table 3 as a table that describes the country-specific ex-ante
identified social norms
To test H1, that personal beliefs differ from ratings in the coordination
treatment, we construct a measure of the respondent’s preferred action
(denoted as pTip and pPunctual). This is a measure of the average tip
percentage or minutes early/late that received the maximum appropriateness
rating in the personal belief rating task.14 Furthermore,
we let uTip and uPunctual be a participant’s guess about the most
appropriate action in the university-matching coordination task, and nTip
and nPunctual be the participant’s guess about the most appropriate action
in the nationality-matching coordination task. For the punctuality
scenario, arriving late is coded with a negative sign (i.e., arriving 10
minutes late is coded as –10) and arriving earlier is coded with a positive
sign (i.e., arriving 10 minutes early is coded as 10).
We start by focusing on non-U.S. born subjects because these participants
likely have personal beliefs that differ from those held at a university
located in the U.S. When these subjects coordinate with university
subjects, their guess about the most appropriate action should change to
identify the university social norm. To test this, we compute the
difference in ratings between pTip and uTip for each non-U.S. born subject
and then test whether the mean of the distribution of these differences is
zero.
Table 4 reports the means for pTip and uTip along with the mean of the
distribution of difference between pTip and uTip (reported in the column
labeled Avg(pTip–uTip)). These values are reported in columns 1 through
3.
Result #1a:
Non-U.S. born subjects personally prefer tipping a
lower amount than what they guess to be the most appropriate tipping
amount when matching with university students. This is consistent with
what we would predict from the norms identified in Table 1; U.S. tipping
standards are high relative to most other countries.
Support for result 1a comes from Table 4 column 3. The mean difference
(i.e., Avg(pTip–uTip)) is –0.991 and is significantly different from zero
(t(45)=-3.170, p=0.003).
| Table 4: Means Of Most Appropriate University Action And Personally
Preferred Action In The Tipping Vignette. Columns 1 and 2 report the
means of pTip and uTip and column 3 reports the mean of the distribution
of differences between pTip and uTip. Standard errors are reported
underneath the means in parentheses. |
| | pTip | uTip | Avg(pTip–uTip) |
| Non-U.S. Born (n=46) | 15.570 | 16.560 | –0.991 |
| | (0.391) | (0.321) | (0.312) |
| U.S. Born (n=109) | 16.940 | 16.660 | 0.275 |
| | (0.278) | (0.257) | (0.239) |
Subjects who were born within the U.S. have personal beliefs that align
with their guess for the appropriate action when matching with university
students. In this subsample, the mean of the distribution of differences
between pTip and uTip is 0.275 (column 3). It is higher than when they
coordinate with other university students but not significantly different
(t(108)=1.150, p=0.253). In other words, U.S.-born subjects’ personal
belief about the most appropriate action is not different from their guess
about the university norms for tipping.
Table 5 reports the same measures as Table 4, but for the punctuality
scenario. We conducted paired t-tests to assess whether respondents’
personally preferred punctuality differed from their best guess about the
university’s punctuality norm. However, because our punctuality norms are
more crude (such that we know if a country’s norms are to arrive earlier
than the U.S. norm or to arrive later), we partitioned subjects into two
groups: those who were born in countries where the ex-ante
norm is to arrive later than the U.S. norm and those who were born in
countries where the ex-ante norm is to arrive earlier than the U.S. norm
(shown in Table 3).
| Table 5: Means of most appropriate university action and personally
preferred action in the punctuality vignette. Columns 1 and 2 report
the means of pPunctual and uPunctual and column 3 reports the mean
difference between pPunctual and uPunctual. Standard errors are
reported in parentheses underneath the means. |
| | pPunctual | uPunctual | (pPunctual – uPunctual) |
| Arrive ’Early’ (n=26) | 4.230 | 3.850 | 0.385 |
| | (0.515) | (0.421) | (0.359) |
| Arrive ’Late’ (n=20) | 3.430 | 4.750 | –1.312 |
| | (1.021) | (0.541) | (0.991) |
Result #1b:
Although Non-US born subjects’ personally prefer
arriving at a different time than what they guess to be the most
appropriate arrival time when coordinating with university students, the
means of the distributions of these differences are not significantly
different from zero.
Support for result 1b comes from Table 5. For subjects born in ‘later’
countries, the mean difference between pPunctual and uPunctual is –1.313
and is not significantly different from zero (t(19)=–1.324,
p=0.201).15 Despite the lack of
statistical significance, the direction of the difference indicates that
these subjects prefer to arrive later in comparison to the guess of the
most appropriate time of arrival when matching with university students.
For subjects born in countries whose ex-ante norm is to arrive ‘earlier’ in
comparison to the U.S. norm, the mean difference between their pPunctual
and uPunctual responses is 0.385 and is also not significantly different
from zero (t(25)=1.072, p=0.294).16 Again,
despite the lack of statistical significance, the direction of the
difference indicates that these subjects prefer to arrive earlier in
comparison to the guess of the most appropriate time of arrival when
matching with university students.
To test H2, that the ratings of foreign-born nationals
coordinating with foreign-born nationals will differ from the ratings of
U.S. born nationals coordinating with U.S. born nationals, we begin with a
visualization. The responses used in this part of the analysis are from
subjects when they play the incentivized coordination game matching
ratings with a reference group of other students who were born in the same
country. We expect subjects to coordinate responses in such a way that
identifies ex-ante norms corresponding to their relevant reference-group.
For those born in the U.S., the ex-ante identified tipping norm is around
12%, whereas those not born in the U.S., Canada, or the Cayman Islands,
the ex-ante norm is less than 12%. To visualize how appropriateness
ratings differ amongst U.S.-born and non-U.S. born subjects, we plot the
average appropriateness ratings by action separately by place of birth in
Figure 1.
| Figure 1: Average Rating of Tip Percentage by Respondent Country of Birth.
This figure displays the average appropriateness rating by action and
origin of birth for the tipping vignette during the same country born
elicitation task. The y-axis reports the possible appropriateness ratings
(ranging from very inappropriate, –1, to very appropriate, 1). The
x-axis reports the tip percentages that were rated by
participants. Confidence intervals are calculated as
x ± tn−1,α/2 · s/√n where x is the
arithmetic mean, tn−1,α /2 is the α/2 lower quantile of
a tn−1 distribution. The sample size is n=46 for non-U.S. born and
109 for U.S.-born. The confidence level is α =5%. The sample
standard deviation is denoted with the symbol s in the confidence
interval calculation. |
For tipping percentages below 10%, foreign-born subjects report higher
appropriateness ratings for each action compared to U.S.-born subjects. In
other words, U.S.-matching-U.S. subjects believed it was more
inappropriate to leave a tip below 10%. For tips at or above 10%, the
appropriateness ratings were not statistically different
from one another.
For the punctuality scenario, we plot the average appropriateness ratings
by each action and by country of birth in which the norm is to arrive
‘on-time’ and countries in which the norm is to arrive ‘late’ in Figure
4.17
| Figure 2: Average Ratings by Arrival Time by Country-Born and Punctuality
Norm. This figure displays the average appropriateness rating by action in
the punctuality vignette during the same country born elicitation task.
The average ratings are broken out by whether the rater comes from the
U.S., a late or an on-time country. The y-axis reports the possible
appropriateness ratings (ranging from very inappropriate, –1, to very
appropriate, 1). The x-axis reports different arrival times relative to a
pre-agreed arrival time. Early arrival times include 30 minutes prior to
(-30) etc. and late arrival times are 10 minutes after (10 on the x-axis).
The plots are partitioned by responses from raters whose country of birth
has an ‘On-time’ or ‘late’ norm or if it is the U.S. norm. The ‘on-time’
countries include Canada, Germany, France, Singapore, China, and Taiwan.
‘Late’ countries include Brazil, Peru, Columbia, Ecuador, Russia, India,
Kenya, Uganda, South Africa, United Arab Emirates, and Cayman Islands.
Confidence intervals are calculated as in Figure 1. |
Figure 2 shows that most of the punctuality actions have indistinguishable
appropriateness ratings for U.S.-born and ‘on time’ foreign-born subjects.
When comparing U.S.-born subjects to ‘late’ countries, however,
foreign-born subjects believe it is more appropriate to arrive late
compared to U.S.-born subjects. The visual evidence suggests support for H2
(foreign-foreign pairs vs. U.S.-U.S.), and even some evidence for H3
(ex-ante norms predict direction of difference).
Result #2:
Foreign-born subjects matching with foreign-born
subjects reported different appropriateness ratings than U.S.-born subjects
matching with U.S.-born subjects during the nationality-matching task (H2).
The mean of nTip reported by non-U.S. born subjects is 2.928 less than that
reported by U.S. born subjects, which is statistically significant
(independent samples t(153)=-4.857, p=0.000). This supports the inference
that foreign-born subjects’ best guess about their country tip norms is
substantially lower than the guess of U.S. born subjects’ guessing U.S.
tip norms.
For the punctuality scenario, we broke the analysis into two sub-groups,
those whose actual norm is to arrive later than the U.S. norm and those
whose actual norm is to arrive earlier. Comparing ‘later’
non-U.S. subjects’ nPunctual responses to U.S. subjects’ nPunctual
responses, we find the difference in means is –1.784 and is almost
statistically significant at the 5% level (t(127)=–1.899, p=0.059). A
one-sided two sample t-test may be more appropriate, however, given the
directionality of the hypothesis (‘late’ countries view arriving late as
more appropriate), and the one-sided test is significant (p=0.029).
Comparing ‘earlier’ non-U.S. born subjects’ nPunctual responses to
U.S. born subjects’ nPunctual responses, the difference in means is 0.360
and is not statistically different from zero (t(133)=0.577, p=0.565). The
direction of the mean difference, however, is consistent with the
differences in the actual norms.
Returning to Figures 1 and 2, we also have visual evidence supportive of
H3 (that ex-ante identified norms will predict the direction of
ratings changes). A simple way to test H3 is to test whether
foreign-born subjects are responsive to their nation’s norms when told
that they will be matched with other nationals. Looking at responses of
those matching with others born in the same country, we identify the
tipping or punctuality norm for the nationality (using the information
provided in Table 1) and the participant’s best guess of their country’s
norms in the matching task with same nationality subjects (nTip or
nPunctual).
Result #3:
Respondent’s ratings when coordinating with
foreign-born (ratings(F,F)) differ from ratings when U.S.
born nationals coordinate with other U.S. born nationals
(ratings(US,US)) in a manner that is consistent with
ex-ante identified differences in the relevant norms.
Our first form of support for result 3 comes from testing whether the
correlation between a participant’s guess about their own country’s norm
(when coordinating with a fellow national) is correlated with the ex-ante
identified norm for their home country (i.e., with the actual norm of their
home country). For the tipping scenario, the correlation between the best
guess about their country’s norm, nTip, and the actual norm is 0.402
(p=0.000). For the punctuality scenario, the correlation between the
respondents’ best guess about their country’s norm, nPunctual, and the
actual norm is 0.149 (p=0.064). These correlations show that subjects’
coordination game responses, when coordinating with a fellow national, are
consistent with the ex-ante identified country norms.
We can also test whether foreign-born respondents adjust their guesses in a
predictable fashion when they have to coordinate with university subjects.
For example, foreign-born subjects from countries where lower tips are
acceptable would use their knowledge of U.S. norms and rate those same tip
amounts as less appropriate when coordinating with other university
students. As a result, ratings for foreign-born subjects coordinating with
university students should change in a predictable fashion.
Because some of the norms for countries outside of the U.S. are the same as
those in the U.S., we can also conduct our analysis on only the sub-sample
of foreign subjects whose ex-ante norm is below 12% (this excludes Canada
and the Cayman Islands born individuals). We expect these subjects will
guess higher levels of appropriateness for lower tip amounts when matched
with a same-country born subject than when matched with a
university-subject. A paired t-test on this subset of subjects, testing the
difference in means of foreign-born nationals’ best guess of the norms,
uTip and nTip, in the two coordination tasks, found that the mean of
uTip–nTip is 3.085 (t(41=4.401, p=0.000) and in the expected direction.
In parallel fashion, we did the same test for punctuality. For foreign-born
subjects who come from a country where the ex-ante norm is to arrive
’on-time’, the mean difference between their guess about university norms,
uPunctual and their guess about their country’s punctuality norms,
nPunctual, was 0.6730 and is almost statistically significant (t(25)=1.895,
p=0.069). For foreign-born subjects whose countries ex-ante norm is to
arrive ’late’, the mean difference between uPunctual and nPunctual was
–2.375 and is not statistically significant (t(19)=–1.538, p=0.140). The
difference in the preferred action by matching treatment reference-group is
consistent with the identified ex-ante norm, as those matching with
’on-time’ countries prefer to arrive earlier than when matching with
university-subjects. ‘Late’ counties, however, display no differences in
their preferred arrival time by university-matching or same-county matching
treatments.18 19
Overall, these results are consistent with our hypotheses. Personal ratings
sometimes differ from those elicited using the coordination tasks, ratings
using the coordination task are consistent with ex-ante identified norms
and differ depending on the target for the coordination game.
5 Conclusion
In this paper we test for whether the responses from a norm elicitation
experiment that uses incentivized coordination games can be appropriately
interpreted as an empirical measure of social norms. Furthermore, the responses elicited with the
coordination task track ex-ante identified real-world social norms and
respondents adjust their coordination game guesses such that they provide
different guesses when coordinating with subjects born in the same country
and when coordinating with subjects from the same university. Our results
provide evidence on the importance of eliciting shared beliefs as a key
component of identifying social norms and we offer novel evidence
supporting the use of coordination games for eliciting social norms from
different reference groups.
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