Available via license: CC BY 4.0
Content may be subject to copyright.
Meta-Psychology
, 2022, vol 6, MP.2019.2162,
https://doi.org/10.15626/MP.2019.2162
Article type: File Drawer Report
Published under the CC-BY4.0 license
Open data: Yes
Open materials: Yes
Open and reproducible analysis: Yes
Open reviews and editorial process: Yes
Preregistration: Yes
Edited by: Rickard Carlsson
Reviewed by: Rima-Maria Rahal, Ignazio Ziano, Adrien Fillon
Analysis reproduced by: Lucija Batinović
All supplementary files can be accessed at the OSF project
page: https://doi.org/10.17605/OSF.IO/Y2TF6
Four Failures to Demonstrate that Scarcity Magnifies
Preference for Familiarity
Stephen Antonoplis
University of California, Berkeley
Serena Chen
University of California, Berkeley
As economic inequality increases in the United States and around the world,
psychologists have begun to study how the psychological experience of scarcity impacts
people's decision making. Recent work in psychology suggests that scarcity—the
experience of having insufficient resources to accomplish a goal—makes people more
strongly prefer what they already like relative to what they already dislike or like less.
That is, scarcity may polarize preferences. One common preference is the preference for
familiarity: the systematic liking of more often experienced stimuli, compared to less
often experienced stimuli. Across four studies—three experiments and one cross-
sectional survey (all pre-registered; see https://osf.io/7zyfr/)—we investigated whether
scarcity polarizes the preference for familiarity. Despite consistently replicating people's
preference for the familiar, we consistently failed to show that scarcity increased the
degree to which people preferred the familiar to the unfamiliar. We discuss these results
in light of recent failures to replicate famous findings in the scarcity literature.
Keywords:
Scarcity, Familiarity, Open Science
With economic inequality rising markedly since
the 1980s, and especially since the 2007 global
recession (Piketty, 2014), scholars from various fields
have turned their attention to understanding the
effects of scarcity on human psychology. A growing
approach to this question investigates the impact of
the psychological experience of scarcity on
thoughts, feelings, and behaviors. In this file drawer
report, we sought to understand the impact of
scarcity on the familiarity bias, the systematic liking
of more familiar, compared to less familiar, stimuli
(Zajonc, 1968).
What is Scarcity?
Scarcity is defined as a lack of sufficient
resources for accomplishing a goal (Shah,
Mullainathan, & Shafir, 2012). Research has found
that it increases overborrowing and focus on the
present (Shah et al., 2012; Shah, Mullainathan, &
Shafir, 2018); increases the propensity to lie in order
to secure financial rewards (Gino & Pierce, 2009);
and increases the likelihood of taking risks and the
quickness to approach temptations if participants
grew up in a lower social class (Griskevicius et al.,
2013). Importantly, the resources for which a person
experiences scarcity can be of many different forms,
and the form of the resources (e.g., time or money)
is not thought to change the effects of scarcity on
human psychology (Mullainathan & Shafir, 2013). For
2
ANTONOPLIS & CHEN
instance, both time and material scarcity have been
found to increase overborrowing in the present
(Shah et al., 2012; Shah et al., 2018).
Most pertinent to the present research, recent
research suggests that scarcity polarizes
preferences (Zhu & Ratner, 2015). When offered a
choice between various products, participants more
strongly preferred their favorite (vs. non-favorite)
option when few of each option were available
(scarcity) versus when many were available
(abundance). This occurred because scarcity was
perceived as threatening, inducing higher arousal,
which has been previously shown to polarize
people’s preferences (e.g., Gorn et al., 2001; Mano,
1992, 1994). Below, we propose that this effect may
extend to the preference for familiarity.
The Familiarity Bias
Familiarity bias refers to the systematic
preference for more familiar stimuli over less
familiar stimuli, where familiarity is defined as
frequency of exposure. In other words, familiarity
bias describes the phenomenon that people tend to
like things they have been exposed to more often
simply because of the rate of exposure (Zajonc,
1968). Many classic studies in psychology suggest
that people normatively prefer more, to less, familiar
stimuli. For instance, research on the mere exposure
effect has shown that individuals rate stimuli more
positively if the stimuli occur more versus less
frequently in the participants’ natural environment,
as well as if the stimuli resemble more versus less
closely other stimuli in participants’ natural
environment (e.g., Johnson, Thomson, & Frincke,
1960; Zajonc, 1968, 2001). Familiarity bias has been
shown across many kinds of stimuli, including fruit
and vegetables (Zajonc, 1968), nonsense syllables
(Johnson et al., 1960), and people’s names
(Oppenheimer, 2004). Two meta-analyses have
examined the robustness of the phenomenon.
Across 208 experiments, Bornstein (1989) found the
effect to be quite reliable, although the impact of
publication bias on the results was difficult to assess
due to a lack of adequate tests for assessing these
effects at the time the meta-analysis was conducted.
Montoya et al. (2017) built on Bornstein’s (1989)
meta-analysis and found that the effect was reliable
across 118 studies and, using more appropriate tests,
that publication bias likely did not bias the
estimates. Thus, a large body of research indicates
that people, in general, prefer more to less familiar
objects. How might the psychological experience of
scarcity alter this preference? Below, we suggest
that scarcity may magnify people’s preference for
familiarity, making people more strongly prefer
familiarity under scarcity than not under scarcity.
Does Scarcity Increase the Familiarity Bias?
Recent research suggests that scarcity polarizes
preferences (Zhu & Ratner, 2015). When offered a
choice between various products, participants more
strongly preferred their favorite (vs. non-favorite)
option when few of each option were available
(scarcity) versus when many were available
(abundance). The researchers argued that this
occurred because scarcity was perceived as
threatening, inducing higher arousal, which has
been previously shown to polarize people’s
preferences (e.g., Gorn et al., 2001; Mano, 1992,
1994). When participants experienced scarcity (here,
of quantity), they felt threatened by it. This threat
increased their arousal, which restricted the
number of evaluative dimensions considered
relevant to the decision. One dimension, prior liking,
was deemed particularly relevant to the decision,
perhaps because threat constitutes a negative
affective experience and people experiencing
negative affect often choose simple decision
strategies (Mano, 1994). Finally, to determine their
preferences, participants more heavily relied on this
single dimension of prior liking, producing more
polarized preferences than would have resulted if
other, imperfectly correlated dimensions had been
incorporated into the decision.
Applied to the familiarity bias, such theorizing
suggests that, when experiencing scarcity, people
will feel more threatened and aroused, causing them
to use simpler decision strategies. Because the
familiarity bias is a common phenomenon
(Bornstein, 1989; Montoya et al., 2017; Zajonc, 1968,
2001) and familiarity is a simple judgment to make
(e.g., Glaze, 1928), people may rely on familiarity of
stimuli to guide their choices. As familiarity already
breeds liking (Zajonc, 1968), relying predominantly
on familiarity in a decision task should increase
stratification along familiarity. In other words,
people should come to more strongly prefer familiar
stimuli, relative to less familiar stimuli, under
scarcity. In addition, since scarcity is expected to
impact people similarly, regardless of its form
(Mullainathan & Shafir, 2013), the effect should
appear across any form of scarcity (e.g., material,
3
FOUR FAILURES TO DEMONSTRATE THAT SCARCITY MAGNIFIES PREFERENCE FOR FAMILIARITY
time, quantity). Hence, in this file drawer report, we
examined the effect of different forms of scarcity
(material, time, and quantity) on the familiarity bias.
The Present Research
Across four studies we tested whether scarcity,
in various forms, amplifies individuals’ preference
for familiar over unfamiliar stimuli. We aimed to
show that this pattern was consistent across various
kinds of more versus less familiar stimuli. Key to
note is that the studies by Zhu and Ratner (2015),
which we based ours on, took an idiographic
approach to measuring preferences, examining
changes in each participant’s favorite and non-
favorite options. All of the work they cited in support
of the hypothesized link between scarcity and
preferences took a nomothetic approach (e.g., more
vs. less risky options in terms of normative
probability; Gorn et al., 2001; Mano, 1992, 1994). In
line with this, the authors speculated that their
hypothesis would hold using a nomothetic approach
(p. 12, Zhu & Ratner, 2015). Hence, in the present
studies, we tested the effect of scarcity on
nomothetic preference for familiarity. This work,
then, should be understood as a generalizability test
of the work presented by Zhu and Ratner (2015),
rather than a replication (LeBel et al., 2019). In
addition, some of our studies, unlike those of Zhu
and Ratner (2015) used an incidental manipulation of
scarcity, in which the experience of scarcity was not
incorporated into the same situation or context as
the assessment of preference. As others have argued
(Bargh, 1992), incidental versus explicit manipulation
of social psychological phenomena is not crucial to
studying the phenomena. What is crucial is that
manipulations bring to mind whatever concept (in
this case, scarcity) is of interest, thereby allowing
this concept to shape how subsequent stimuli are
perceived. Thus, social psychological research on
scarcity has been able to use incidental
manipulations of scarcity without issue (e.g., Roux et
al., 2015).
For all studies, we pre-registered focal
hypotheses, data exclusion criteria, statistical
modeling, and dependent and independent variables
on the Open Science Framework (available at
https://osf.io/7zyfr/). This report is an exhaustive
report on all data available from research projects
relating to the topic, where at least one of the
authors was principal investigator, or have
otherwise the right to publish the results. This
includes not only null findings, or unexpected
findings, but also studies that are suspected to have
failed, with careful explanation of the circumstances
of the failure (e.g., experimental error, failed
manipulation check). The context surrounding how
these data were collected, and if they are somehow
connected to already published studies (e.g.,
dropped experiments) is carefully explained. We
report how we determined our sample sizes, all data
exclusions, and all measures in all studies. All
analyses were conducted in R (version 3.6.2; R Core
Team, 2019). Finally, to improve the paper’s
narrative, we report studies differently than the
chronological order in which they were conducted.
Study 1
For the initial test of our hypothesis, we sought
to combine methods from both the scarcity and
familiarity bias literatures in order to use non-
controversial, reliable methods. To manipulate
scarcity, we had people recall a time they
experienced scarcity (cf. Roux et al., 2015; Mani et al.,
2013). To measure preference for familiarity,
participants rated how much they liked both familiar
and unfamiliar given names and surnames (cf.
Oppenheimer, 2004), as well as nonsense syllables
(cf. Johnson et al., 1960). The key test of our
hypothesis was whether the scarcity manipulation
moderated participants’ preference for familiarity
such that this preference was heightened under
scarcity. The pre-registration form, study materials,
and data are available here: https://osf.io/7vtqr/.
Method
Following an informal lab policy of collecting 100
participants per between-subjects condition, 201
participants were recruited from Amazon’s
Mechanical Turk. Their demographic
characteristics matched typical samples on MTurk
4
ANTONOPLIS & CHEN
Table 1
Demographics Across All Studies (Proportions and Means)
Study
Study 1
Study 2
Study 3
Study 4
Gender
Man
.50
.61
.46
.50
Woman
.27
.39
.53
.50
Transgender
.00
.00
.004
.00
Decline to State
.23
.00
.004
.00
Race
White
.77
.75
.78
.67
Latinx/Hispanic
.06
.05
.08
.11
Black
.09
.08
.05
.10
Native American
.00
.02
.00
.00
Asian
.06
.11
.06
.00
Middle Eastern
.00
.00
.00
.00
Mixed
.01
.00
.04
.02
Other
.00
.00
.00
.04
Decline to State
.02
.00
.00
.00
Born in the U.S.
Yes
.83
.98
.94
–
No
.00
.02
.05
–
Decline to State
.17
.00
.01
–
Age (M, SD)
39.30
(10.97)
33.64
(9.58)
36.91
(11.66)
50.19
(16.72)
Income (M,SD)
$38,130
($25,871)
$36,093
($21,684)
$38,643
($29,447)
$72,053
($47,986)
Education
High School or Less
.13
.14
.12
.41
At Least
Some College
.86
.86
.88
.59
Decline to State
.02
.00
.00
.00
Note.
“–“ indicates that an item was not administered in the dataset. With the exceptions of age
and income, all numbers in cells are proportions.
(mostly White, mostly men, in their mid-30’s, had
some amount of college education, and earning a
relatively low income; Buhrmester et al., 2011) and
are reported in full in Table 1. Participants were
5
FOUR FAILURES TO DEMONSTRATE THAT SCARCITY MAGNIFIES PREFERENCE FOR FAMILIARITY
randomly assigned to a scarcity or control
condition. In the scarcity condition, participants
wrote about a time they felt their resources were
scarce (i.e., did not meet their needs; taken from
Roux et al., 2015). We expected writing about an
experience of scarcity to be affectively unpleasant
and threatening and, thus, different from most day-
to-day experiences. Hence, participants in the
control condition wrote about an experience they
had in the past week, whether an activity, an
interaction, or whatever came to mind.
After writing about a scarcity experience or a
recent experience, participants rated how much
they liked eight female given names (four familiar,
four unfamiliar), eight male given names (four
familiar, four unfamiliar), and eight surnames (four
familiar, four unfamiliar). Participants also rated
how good or bad they thought the meanings of
twenty-four nonsense syllables were in a foreign
language. All names were rated on a scale from 1
(=
dislike
) to 7 (=
like
). All nonsense syllables were
rated on a scale from 1 (=
bad
) to 7 (=
good
).
Participants were randomly assigned to either rate
all the names first and the syllables second, or the
syllables first and all the names second. This was
done to avoid order effects. Although the use of an
incidental manipulation indirectly related to the
dependent variable might seem problematic for
ecological validity, this practice is fairly common in
the scarcity (e.g., Mani et al., 2013) and familiarity
bias literatures (e.g., Muthukrishnan et al., 2009).
Female and male given names were taken from
the 2016 US Social Security Registry of Baby names
(available at https://namecensus.com/baby-
names/popular-girl-names-in-2016/ for female
names and https://namecensus.com/baby-
names/popular-boy-names-in-2016/ for male
names). For each gender, we selected four names
from the top twenty most common as the familiar
names (females: Isabella, Sophia, Emma, Olivia;
males: Jacob, Ethan, Michael, William) and four
names from the bottom twenty of the top 1000 (i.e.,
names 981–1000) as the unfamiliar names (females:
Lilith, Charleigh, Dania, Savannah; males: Truman,
Eliezer, Reuben, Bailey). We chose names from the
top and bottom of the top 1000 to make sure that
the frequencies of the names varied and that all
names were somewhat recognizable (i.e., to avoid
outlier names). We used this same process to select
surnames, though names were pulled from the most
recent (2010) US Census instead of Social Security
data (familiar: Smith, Johnson, Williams, Brown;
unfamiliar: Galloway, Bray, Nieves, Petty; data
available at
https://www.census.gov/topics/population/
genealogy/data/2010_surnames.html). We used
names as stimuli because prior work had obtained
familiarity effects using names (Oppenheimer,
2004).
The nonsense syllables were taken from Study 3
by Johnson et al. (1960). They found that syllables
obtaining low (0%), medium (47–53%), and high
(100%) rates of judged association with English
words (in Glaze, 1928) were thought to have better
(i.e., more “good”) meanings when participants were
told the syllables were words from foreign languages
and then judged how much the words referred to
“good” or “bad” things. Glaze (1928) obtained the
syllables’ associations with English words by asking
15 participants whether they could quickly form an
association to an English word for each syllable. The
association rates (i.e., low, medium, and high) are the
percentage of the 15 participants who reported
forming an association to a syllable. Johnson et al.
(1960) found that more familiar words (i.e., those
more frequently associated with known words) were
judged more positively than unfamiliar words.
After rating the names and nonsense syllables,
participants completed standard demographic
items (race, gender, income, education, subjective
SES) and an embedded attention check. All
participants were debriefed. Participants who did
not follow the manipulation instructions (e.g., copy
and paste text from a secondary document instead
of describing an experience; n=2), yielding a final
sample size of 199 participants (nControl=103,
nScarcity=96). Participants were paid $0.75 for
completing the study.
Results
Confirmatory Results
Data were submitted to multilevel models that
regressed liking ratings on stimulus familiarity,
experimental condition, and their interaction. In
6
ANTONOPLIS & CHEN
Table 2
B (SE) from hierarchical linear models for Study 1
Dependent Variable
Nonsense Syllables
Names
Regression Terms
Nonsense Syllables (0%
as reference)
Nonsense Syllables (0% & 47–
53% as reference)
Female Given
Names
Male Given
Names
Surnames
Scarcity
-0.11 (0.08)
-0.11 (0.08)
-0.26 (0.15)
-0.20 (0.15)
0.04 (0.18)
47–53%
0.66 (0.22) **
–
–
–
–
100%
0.91 (0.16) ***
1.11 (0.20) ***
–
–
–
Familiar
–
–
1.17 (0.33) *
1.30 (0.34) **
0.53 (0.35)
Scarcity X 47–53%
0.06 (0.12)
–
–
–
–
Scarcity X 100%
0.09 (0.12)
0.11 (0.14)
–
–
–
Scarcity X Familiar
–
–
-0.30 (0.21)
-0.02 (0.19)
-0.11 (0.19)
Note.
*
p
< .05, **
p
< .01, ***
p
< .001. “–” denotes that a regression term was not included in a model.
7
FOUR FAILURES TO DEMONSTRATE THAT SCARCITY MAGNIFIES PREFERENCE FOR FAMILIARITY
addition, all ratings were partitioned into random
intercepts within participants and random effects of
familiarity within participants. That is, we controlled
for the possibility that overall rating patterns might
vary across participants and that preferences for
familiarity might vary across participants. We also
included random intercepts and slopes for
experimental condition within stimuli in order to
prevent stimulus-specific effects from impact the
overall result. Table 2 shows the multilevel
regression results for all dependent variables.
Figure 1.
Scarcity condition depicted with filled circles and solid lines; control condition, with hollow circles
and dashed lines. All name ratings were in terms of liking (1=
dislike
, 7=
like
). Syllable ratings were in terms
of how
bad
(=1) or
good
(=7) participants thought it meant in a foreign language.
As expected, participants rated more familiar
given names as more preferable to less familiar given
names (females:
B
=1.17,
t
(6.62)=3.57,
p
=.010, pseudo-
R2=.66; males:
B
=1.30,
t
(6.51)=3.82,
p
=.008, pseudo-
R2=.69). In addition, relative to low-association
syllables, participants rated medium-association
syllables as better-sounding (
B
=0.66,
t
(22.58)=2.97,
p
=.007, pseudo-R2=.55) and high-association
syllables as better-sounding (
B
=0.91,
t
(25.43)=5.62,
p
<.001, pseudo-R2=.28). High-association syllables
were also rated as better-sounding than combined
medium- and low-association syllables (
B
=1.11,
t
(25.43)=5.62,
p
<.001, pseudo-R2=.45). Thus, all of
these given name and nonsense syllable stimuli
appeared to operate as expected in that they yielded
the normative preference for more to less familiar
objects. In contrast, preferences did not vary across
more and less familiar surnames (
B
=0.53,
t
(6.63)=1.50,
p
=.179, pseudo-R2=.25), suggesting that
the chosen surnames were inappropriate to test our
hypothesis. In addition, as expected, there were no
main effects of scarcity on ratings (
B
’s from -0.26–
0.03;
p
’s from .093–.771; pseudo-R2’s from .0004–
.04). Recalling an experience of scarcity did not
cause participants to rate all stimuli as more or less
preferable or good, relative to the control condition.
If this main effect had been observed, it might
suggest a different psychological effect of scarcity
8
ANTONOPLIS & CHEN
than hypothesized: that it makes people like or
dislike any stimuli more on top of any heightened
preference contrasts between subsets of stimuli.
Thus, our stimuli and scarcity manipulation mostly
conformed with our reasoning about how each
would function.
Our focal hypothesis—that scarcity would
magnify preferences for familiar over unfamiliar
objects—did not receive support (
B
’s from -0.30–
0.11;
p
’s from .155–.910; pseudo-R2’s from .00008–
.09). Figure 1 shows scatterplots and means across
conditions for all dependent variables. Table 3 lists
the means and standard deviations of ratings for
each group and dependent variable. In general,
means are quite consistent across experimental
groups. Any apparent moderation of ratings by
experimental group appears to come from
participants in the scarcity condition disliking
unfamiliar objects more, rather than liking familiar
options more. In fact, participants in the control
condition typically reported more liking of familiar
objects than those in the scarcity condition.
Exploratory Results
What proportion of participants preferred
familiarity?. Following a reviewer’s suggestion, we
checked the proportion of participants whose
personal preferences for familiarity matched the
normative preference. To do so, we examined the
distribution of random effects of familiarity,
calculating the percentage of participants with a
random effect greater than 0. After that, we re-ran
the models using only participants whose personal
preference matched the normative preference. We
Table 3
Mean (SD) for unfamiliar and familiar stimuli across two experimental conditions in Study 1
Dependent Variable
Experimental Condition
Nonsense
Syllables
Female Given
Names
Male Given
Names
Surnames
Control
0%
3.27 (1.43)
–
–
–
47–53%
3.89 (1.37)
–
–
–
100%
4.64 (1.48)
–
–
–
Unfamiliar
–
3.83 (1.82)
3.41 (1.79)
4.03 (1.65)
Familiar
–
5.15 (1.53)
4.72 (1.59)
4.61 (1.36)
Scarcity
0%
3.09 (1.44)
–
–
–
47–53%
3.78 (1.50)
–
–
–
100%
4.60 (1.60)
–
–
–
Unfamiliar
–
3.72 (1.87)
3.21 (1.86)
4.12 (1.64)
Familiar
–
4.74 (1.66)
4.51 (1.64)
4.60 (1.46)
Note.
“–” denotes that a regression term was not included in a model. The three levels of nonsense
syllables reflect association rates between syllables and English words made by participants (N = 15)
reported in Glaze (1928).
9
FOUR FAILURES TO DEMONSTRATE THAT SCARCITY MAGNIFIES PREFERENCE FOR FAMILIARITY
did this for all four outcomes. Across all outcomes, a
majority of participants’ personal preferences
matched the normative preference for familiar over
unfamiliar stimuli (from 77%–96%). Subgroup
analyses examining only participants who preferred
familiar over unfamiliar stimuli did not yield
substantively different results from the main
analyses. The critical interaction between familiarity
and scarcity remained non-significant (
p
’s from
.149–.879). These results suggest that focusing on
individual versus normative preference for
familiarity does not explain our null results.
Bootstrapped equivalence test. Though results
were inconsistent with our hypothesis, failure to
reject a null hypothesis is not equivalent to
demonstrating evidence in favor of the null. To
argue in favor of the null, one would need to show
that results are more consistent with some prior
belief about the distribution of data (i.e., Bayesian
analysis) or show that the observed effect falls
outside the range of effect sizes one considers
worth studying (i.e., smallest effect size of interest
in equivalence tests). We did not have a strong prior
about the effect size or a smallest effect size of
interest, so instead we bootstrapped effect sizes
(R2’s) for the key interaction test for our four
outcome variables. The bootstrapped estimate and
confidence intervals provide a sense of what the
true effect is likely to be, and other researchers may
decide whether effects in this range are worth
pursuing. We started the bootstrapping using the
full models from the confirmatory hypothesis tests.
If models consistently failed to converge, random
effects were dropped until the models consistently
converged. This process resulted in dropping
random effects of experimental condition and
stimulus familiarity for the surnames outcome and
for all pairwise comparisons between syllables
outcomes.
For surnames the average R2 was .002, 95% CI
[1.76e-6,.01]; for female given names, .11, 95% CI [2e-
4,.36]; for male given names, .04, 95% CI [3.75e-
5,.18]; high versus low syllables, .0005, 95% CI
[4.98e-7,.002]; medium versus low syllables, .0002,
95% CI [1.59e-7,7e-4]; and high versus medium and
low syllables, .001, 95% CI [1.24e-6,.005]. Clearly, the
inclusion or exclusion of random effects made a
large difference in the estimates and confidence
intervals. Outcomes with simpler models indicated
that the key interaction was unlikely to account for
very much variance at all. Outcomes with the full
model (i.e., female and male given names) indicated
that the key interaction could account for very little
variance or a considerable portion of the variance.
These results suggest that our samples were not
sufficiently powered to estimate the interaction’s
effect size using the full model.
Discussion
Recalling an experience of scarcity, versus a
recent experience, did not make participants more
strongly prefer familiar to unfamiliar objects. These
results do not appear to have happened because of
poor stimulus selection or alternative psychological
processes of scarcity. All but one set of stimuli
successfully recreated the normative preference for
familiarity, and overall rating patterns did not vary
across experimental conditions. In addition, manual
inspection of written responses to the manipulation
showed that most participants (99%) wrote a
relevant response. It is possible that our
manipulation did not work, but our lack of a
manipulation check prevents probing this
possibility. The manipulation has previously been
found to successfully manipulate felt scarcity in the
same population we used (Roux et al., 2015) and
follows the format of other successful manipulations
of broad social constructs and mindsets (e.g., social
power; Galinsky, Gruenfeld, & Magee, 2003; Kraus,
Chen, & Keltner, 2011). Hence, though a different
manipulation of scarcity might better test our
hypothesis, the present results seem more
consistent with the null hypothesis.
In the following studies, we tested whether
alternative manipulations of scarcity may produce
our predicted effect. We also tested the possibility
that the stimuli we used—names and nonsense
syllables—are inappropriate stimuli for testing our
hypothesis. Finally, we tested whether the predicted
effect is inappropriate to be tested with brief
experimental methods (e.g., the effect unfurls over
time) and instead better tested with an individual-
difference approach.
Study 2: A Different Manipulation of Scarcity
After finishing Study 1, we became aware of a
study by Litt, Reich, Maymin, and Shiv (2011) that
claimed to show our effect of interest. In two
studies, the authors found that, under increased
time pressure, participants were more likely to
select a strategy associated with a stimulus they had
previously been made more familiar with (i.e., an
incidentally familiar strategy), even though the more
familiar strategy was less helpful for their goal
10
ANTONOPLIS & CHEN
completion. Based on these results, the authors
concluded that scarcity (here, of time) increased
preference for familiarity. However, by varying the
utility of strategies for goal completion, the authors
added a factor to their design, a factor for which
they did not test all levels. The authors studied only
the condition where the familiar is less helpful than
the unfamiliar (familiar < unfamiliar), not where the
two are equal in helpfulness (familiar = unfamiliar) or
familiar is more helpful than unfamiliar (familiar >
unfamiliar). Thus, the studies do not indicate what
the baseline preference for familiarity is across
different utilities and whether the difference
observed in the reported studies results from time
pressure increasing preference for familiarity or
lack of time pressure opening people up to the
unfamiliar when it is more helpful. These studies
demonstrate only that under different amounts of
time pressure participants, on average, preferred a
more familiar object at different rates. They do not
provide information on whether participants’
behavior under time pressure represents a deviation
from standard rates of preference for familiarity.
To fully understand whether scarcity increases
preference for the familiar, we conducted a similar
study to Litt et al. (2011) that examined choices
across all possible utilities (i.e., familiar > unfamiliar,
familiar = unfamiliar, familiar < unfamiliar). In
particular, we adapted the general experimental
design of manipulating scarcity (here, as financial
pressure, instead of as time pressure), the familiarity
of possible strategies for completing the target goal
(here, as given names, instead of as primed
familiarity), and the degree of helpfulness of possible
strategies (here, where the more familiar strategy
could be more, equal, or less helpful, instead of only
less helpful). Thus, this study should not be
considered a replication of Litt et al. (2011) but
instead a conceptual replication and extension
(LeBel et al., 2019). The pre-registration form, study
materials, and data are available here:
https://osf.io/2tykn/.
Method
After designing the experiment, we, the authors,
disagreed about the likelihood it would show the
hypothesized effect and so devised a stopping rule
for participation based on how much money we
were willing to spend on the study. We decided to
first collect data from 66 participants (33
participants per between-subjects condition);
inspect the condition means; and if the means were
in the hypothesized order, proceed to collect data
until we reached our informal lab standard of 100
participants per between-subjects condition.
In total, 66 participants were recruited from
Amazon’s Mechanical Turk and paid $0.40 for
completing a 4-minute study. Their demographic
characteristics matched typical samples on MTurk
(mostly White, mostly men, in their mid-30’s, had
some amount of college education, and earning a
relatively low income; Buhrmester et al., 2011) and
are reported in full in Table 1. After consenting to
participate, participants were randomly assigned to
a scarcity or control manipulation. Participants then
read a passage instructing them to imagine a
hypothetical scenario for one minute. In the scarcity
condition, participants read the following passage,
similar to other passages used to manipulate
scarcity (e.g., Mani et al., 2013):
You’re short for rent this month. You need
about $1,000 to make it. Imagine you have the
opportunity to win this amount by playing some
small bets online. You are offered six bets, but
they are paired up into three pairs—and you have
only enough money to choose one of the bets in
each pair, for a total of three bets. Whichever
three you play, winning all three guarantees you
at least $1,000. The bets are presented on the
following pages. Which three do you choose?
Participants in the control condition read the
following passage, which we designed to trigger a
sense of abundance or non-scarcity:
Every few months you like to play some small
bets online and treat yourself to something nice
with whatever you win. Imagine that this month,
you’re offered six bets, but they are paired up into
three pairs—and you can only choose one of the
bets in each pair, for a total of three bets.
Whichever three you play, winning all three
guarantees you at least $1,000. The bets are
presented on the following pages. Which three
do you choose?
An embedded, invisible timer in the page
required participants to spend at least thirty
seconds reading and imagining the passage they
were shown. After thirty seconds had passed,
participants could advance to the next screen where
they were presented with three pairs of bets.
The bets asked participants to guess the rank of
a male or female given name’s frequency of
assignment within ±20 positions across all US
newborns in the next calendar year. The names used
were the same as in Study 1 (familiar: Isabella,
Sophia, Emma, Olivia, Jacob, Ethan, Michael,
11
FOUR FAILURES TO DEMONSTRATE THAT SCARCITY MAGNIFIES PREFERENCE FOR FAMILIARITY
William; unfamiliar: Lilith, Charleigh, Dania,
Savannah, Truman, Eliezer, Reuben, Bailey). For
each pair of bets, participants saw a new pair of
names, always chosen so that one was familiar and
the other, unfamiliar. In total, each subject saw six
of 16 possible names. Within each pair of bets, names
were matched on gender. Participants were
randomly assigned to see three female pairs, two
female pairs and one male pair, one female pair and
two male pairs, or three male pairs. Thus, for two
participants that saw two female pairs and one male
pair, one subject could see {Isabella vs. Lilith, Sophia
vs. Charleigh, Jacob vs, Truman} and the other,
{Sophia vs. Dania, Olivia vs. Savannah, Michael vs.
Bailey}. Each pair of names was randomly assigned
to one of the three pay rates (familiar > unfamiliar,
familiar = unfamiliar, familiar < unfamiliar), and the
order in which the three pay rates were presented
was randomized for each participant. This degree of
randomization was necessary to remove any
potential order or pairing confounds from the
results.
Each pair of bets was presented as follows:
Which do you play? (Both names are in the top
1000 most popular names.):
Earn [$400, $450, $350] if you guess the rank,
somewhere between 1 and 1000, of the name
[familiar name] relative to other names given to
US newborns next year (within 20 rank
positions).
Earn [$400, $350, $450] if you guess the rank,
somewhere between 1 and 1000, of the name
[unfamiliar name] relative to other names given
to US newborns next year (within 20 rank
positions).
All possible combinations of winnings totaled
more than $1000, as indicated in the initial
instructions participants read ($400 + $450 + $350 =
$1200; $400 + $450 + $450 = $1300; $400 + $350 +
$350 = $1100). Totals from winning all three bets
were equal if a subject chose all the familiar ($400 +
$450 + $350 = $1200) or all the unfamiliar bets ($400
+ $350 + $450 = $1200), so there was no monetary
incentive to prefer one over the other.
After selecting the three bets they would play,
participants completed the same demographic
items as in Study 1 (race, gender, education, income,
subjective SES) and an embedded check (i.e., “Please
select ‘Strongly Agree’ for this item.”). Following our
pre-registration, participants who failed the
attention check were removed from all analyses
(n=2), leaving a final sample size of 64 (nControl=32,
nScarcity=32).
Results
As pre-registered, after collecting data from
sixty-six participants, we inspected means across
conditions to determine whether participants’
choices were in the predicted directions. Overall,
participants favored to bet on the more familiar
option, even when it was worth less than the
unfamiliar option. Figure 2 plots means and standard
errors of choice across experimental condition and
bet payout. Contrary to our hypothesis that scarcity
would increase preference for familiarity,
participants in the scarcity condition appeared to
less strongly prefer the familiar option across all
bets (scarcity: MChooseFamiliar=2.06, SD=0.91; control:
MChooseFamiliar=2.31, SD=0.82) and to show a stronger
decline in preference for the familiar bet across
payouts (scarcity: from 84% choosing familiar in
familiar > unfamiliar to 53% choosing familiar in
familiar < unfamiliar; control: from 88% choosing
familiar in familiar > unfamiliar to 59% choosing
familiar in familiar < unfamiliar). Based on these
patterns and our pre-registration, we ceased data
collection.
Figure 2.
Bars are standard errors.
Exploratory Analyses
Planned analyses. Per a reviewer’s suggestion, we
conducted our planned statistical analyses on the
data from 64 participants. Following the qualitative
inspection, participants, on average, preferred the
familiar bet to the unfamiliar bet across all
12
ANTONOPLIS & CHEN
conditions (
B
=3.33 ,
z
=3.72,
p
<.001,
OR
=27.94, 95% CI
[4.83,161.49). This preference was not significantly
stronger in the control versus scarcity condition
(
B
=-0.73,
z
=-0.61,
p
=.541,
OR
=0.48, 95% CI
[0.05,5.02]), and it varied linearly across the bet
worth conditions such that the familiar option was
chosen most often when it was worth more than the
unfamiliar option and less often when the two were
equal in value or the unfamiliar option was worth
more (
B
=-1.00,
z
=-3.01,
p
=.003,
OR
=0.37, 95% CI
[0.19,0.71]). Finally, the interaction between scarcity
versus control condition and bet worth was not
significant (
B
=0.08,
z
=0.17,
p
=.866,
OR
=1.09, 95% CI
[0.41,2.85]), suggesting that the scarcity condition
did not make participants more strongly prefer
familiarity even when it was not in their interest to
prefer familiarity.
What proportion of participants preferred
familiarity?. Following Study 1, we checked the
proportion of participants whose personal
preferences for familiarity matched the normative
preference and re-ran the main analyses using only
these participants. One hundred percent of
participants showed the normative preference in
their personal preferences. Hence, restricting
analyses did not eliminate any deviant participants,
and results remained as reported above, suggesting
that scarcity did not make participants more
strongly prefer familiarity when it was not in their
interest to prefer familiarity.
Equivalence test. The odds-ratio effect size
(
OR
=1.09) and its 95% confidence interval
([0.41,2.85]) for the interaction between scarcity and
familiarity from the full model provide a simple
check of what effect sizes can be ruled out from the
present data. The confidence interval covers a very
large range of effect sizes, including both very large
negative effects (lower bound of 0.41), indicating
that, on average, participants in the scarcity
condition less heavily favored the familiar bet
relative to participants in the control condition as its
worth decreased), and very large positive effects
(upper bound of 2.85), indicating that, on average,
participants in the scarcity condition more heavily
favored the familiar bet relative to participants in
the control condition as its worth decreased).Thus,
these results are not very informative about the
range of effect sizes that can plausibly be ruled out.
This is to be expected from the small sample size.
Discussion
As in Study 1, we failed to reject the null
hypothesis per the conditions stipulated in our pre-
registration. Thus, we again failed to demonstrate
that scarcity magnifies the preference for
familiarity. Admittedly, our manipulation was
slightly non-intuitive and lacked high ecological
validity (who is short on rent but has enough money
to place somewhat large bets?), and that may have
impacted results. Despite this, it was internally valid
in that the names used showed the expected
familiarity bias. Moreover, being short on rent was a
common scarcity experience described in the
written responses to Study 1’s manipulation of
scarcity. Perhaps the problem emanated from our
control condition, winning extra money for a treat.
But given that participants in Study 1 considered
being short on money for critical things (e.g., rent,
textbooks, medical expenses) to be experiences of
scarcity, trying to win money for a non-essential
luxury experience (a treat) would seem to be a good
conceptual opposite of what MTurkers consider
experiences of scarcity. As we did not include a
manipulation check, it is not possible to test how
well the manipulation induced scarcity for
participants. Another possibility is that our stimuli
are inappropriate. Maybe our hypothesized effect
occurs for only a subset of all possible stimuli, and
given names lie outside that subset. Though we
weren’t sure what that subset would be, the stimuli
used in Zhu and Ratner (2015), who showed that
scarcity magnifies individual preferences, would
appear to be appropriate. Hence, we adapted an
experimental design from Zhu and Ratner (2015) for
a subsequent study.
Study 3: Alternative Stimuli
Having observed two non-significant results, we
thought it best to try a manipulation from the study
that reported the result inspiring our study.
Perhaps, the two manipulations of scarcity we had
used, though apparently valid in prior work, were
inappropriate for our research question. In addition,
the stimuli we used may have been inappropriate,
whereas stimuli from the original report should be
appropriate. Hence, we adapted the experimental
design of Study 1 from Zhu and Ratner (2015). In
particular, we used the same “buying groceries at
the market” paradigm (described further below) and
altered the kinds of groceries available in order to
13
FOUR FAILURES TO DEMONSTRATE THAT SCARCITY MAGNIFIES PREFERENCE FOR FAMILIARITY
match our theoretical question (described further
below). Thus, as stated in the Introduction, this
study should not be considered a replication of Zhu
and Ratner (2015), but instead a generalizability
study to normative preferences for familiarity (LeBel
et al., 2019). The pre-registration form, study
materials, and data are available here:
https://osf.io/4nxrb/.
Method
Zhu and Ratner (2015) used the following
procedure: First, participants were asked to report
their preferences for four flavors of yogurt (as a rank
order and rating scale from 0=
not at all
to 100=
very
much
). Participants were then asked to imagine they
were shopping for groceries and encountered a
“Pick Any 4 Yogurts for $1” sale on yogurt.
Participants were randomly assigned to one of two
conditions: resource scarcity, wherein only four of
each yogurt flavor was available, or resource
abundance, wherein forty of each yogurt flavor
remained. Finally, participants selected how much
of each flavor they wanted. Examining differences
between participants’ favorite (=rank 1) and non-
favorite yogurts (=all other ranks), Zhu and Ratner
(2015) found that participants in the scarcity, versus
abundance, condition reported a larger difference
between their favorite and non-favorite flavors for
both liking and share of chosen yogurts.
We altered this procedure as follows: First, we
used fruits as stimuli, rather than yogurt flavors, as
information on fruit familiarity, but not yogurt flavor
familiarity, was available. Second, we did not ask
participants for their preferences regarding the fruit
prior to the manipulation but instead selected fruit
to vary in their normatively defined familiarity. We
describe the experiment in greater detail below.
Whereas our lab normally collects 100
participants per between-subjects, we decided to
increase the number to 150 for the added statistical
power. Thus, we recruited three hundred
participants from Amazon’s Mechanical Turk
(paying $0.25 for a two-minute study). Their
demographic characteristics matched typical
samples on MTurk (mostly White, in their mid-30’s,
had some amount of college education, and earning
a relatively low income; Buhrmester et al., 2011) and
are reported in full in Table 1. As we did not have a
readily available dataset of yogurt flavor
consumption or production, we used fruit as stimuli
instead of yogurt flavors. We thought the use of fruit
was justified because they are a kind of food
typically consumed as a snack, like yogurt, and are
considered healthy, like yogurt. In addition, Zhu and
Ratner (2015) used a variety of stimuli, both food and
non-food, with no apparent heterogeneity in effect
presence. Because we wanted to examine whether
the effect extended to nomothetic preferences, we
did not ask participants for their fruit preferences
before choosing. Instead, we chose fruit that varied
in normative familiarity.
We selected fruit based on the amount consumed
in the US per year (USDA, 2016), how highly ranked
they were according to online ranking websites
(“Delicious” from Ranker.com, 2018; “Favorites” from
TheTopTens.com, 2018a; “Delicious” from
TheTopTens.com, 2018b), and the average calories
per serving of each fruit (USDA, 2016). In addition,
we also sought to select fruit that would be similarly
easy to eat (e.g., whether need to wash before
eating, whether need to peel skin off to eat). Based
on these criteria, we selected apples and bananas as
the more familiar fruit and oranges and peaches as
the less familiar fruit. Data for all the fruit we
considered are shown in Table 4. The fruit
considered for the study were constrained by the
kinds of fruit on which the USDA provided data. We
considered all fruit for which the USDA provided
data (except lemons, which are rarely eaten as a
snack in the US, where the study was conducted).
Following Zhu and Ratner (2015), participants
were told they were going to the grocery store to
buy some snacks for the day, and that there was a
sale on fruit at the grocery store. The grocery store
was allowing customers to buy four fruit consisting
of any combination of apples, bananas, peaches, and
oranges for $1. Customers could buy four apples for
$1; two bananas and two peaches for $1; one of each
fruit for $1; or any other combination of the four fruit
for $1. The conditions participants had been
randomly assigned to varied
14
ANTONOPLIS & CHEN
Table 4
Criteria for selecting fruit stimuli for Study 3
Consumption
Subjective Ratings
Additional Considerations
Fruit
Consumption
(lbs/year/capita)
Consumption
Rank
TopTen
Favorites
TopTen
Delicious
Ranker
Delicious
Average
Voted Rank
Calories
(1 serving)
Preparation
Apples
18.5
2
5
5
8
6
72
Wash
Bananas
27.5
1
4
6
5
5
105
Peel
Cherries
1.2
10
11
42
11
21.33
87
Wash
Grapefruits
1.9
9
25
46
28
33
82
Peel
Grapes
8.1
4
6
9
2
5.67
104
Wash
Orange
9.2
3
9
14
4
9
62
Peel
Peaches
2.9
7
10
12
6
9.33
59
Wash
Pears
2.8
8
15
47
13
25
95
Wash
Pineapples
7.3
6
7
4
9
6.67
82
Cut
Strawberries
8
5
1
2
1
1.33
46
Wash
Note.
Consumption data are from 2016 in the US. Subjective rating data were accessed in 2018. Spearman’s rho between
Consumption Rank and Average Voted Rank was .77 (
p
= .01), indicating that normative rated preference and actual
consumption were highly related. Bolded fruits were selected for use in the study.
15
FOUR FAILURES TO DEMONSTRATE THAT SCARCITY MAGNIFIES PREFERENCE FOR FAMILIARITY
by the amount of fruit available. In the scarcity
condition, there were only four of each fruit
available. In the control condition, there were 40 of
each fruit available. Participants indicated how
many of each fruit they wanted by writing a number
from one to four in empty boxes next to each fruit.
The experiment was programmed such that only
numbers could be entered into the boxes, and
participants could not progress in the experiment if
the sum of numbers entered did not equal four.
After indicating their choices, participants
completed an instructional manipulation check (“In
the scenario you just read, about how many of each
kind of fruit were available at the grocery store” with
≤ 5, 10–20, and > 35 as possible responses) and
reported demographic characteristics (income,
MacArthur Ladder, education, race, gender) and an
attention check embedded in the demographics
section (e.g., “Please selected ‘Strongly Agree’ for
this item.”). Per our pre-registration, participants
who failed the attention check (n=10) or answered
the instructional check incorrectly based on their
experimental condition (n=41) were excluded from
all analyses. These exclusions reduced the final
sample size to 249 individuals (nControl=108,
nScarcity=141).
Results
Confirmatory Results
Due to the clustered nature of our data, we used
a multilevel model for analysis. Participants’ fruit
choices were regressed on scarcity condition (=1;
dummy-coded), whether a fruit was familiar (=1;
dummy-coded), and their interaction. We included
random intercepts and random slopes of fruit
familiarity within participants, and random
intercepts and random slopes of experimental
condition within fruits (following the logic of Study
1). Note that the random effects within fruits depart
from the pre-registration’s specification of only
random effects within participants. We made an
error in the pre-registration and report the more
appropriate model here (though exclusion of
random effects within fruits does not change the
results). We further departed from the pre-
registration by using a Gaussian, instead of binomial,
distribution. The dependent variable is a count
variable, so a binomial distribution would have been
impossible to use. We did not use a Poisson
distribution because our data violate a key
assumption of it: that the variable is unbounded.
Participants could not select more than four of any
kind of fruit.
As expected, participants chose more familiar
(M=1.20, SD=1.02) than unfamiliar fruit (M=0.80,
SD=0.99),
B
=0.40,
t
(4.88)=4.62,
p
=.006, pseudo-
R2=.82. In addition, the scarcity condition did not
affect mean fruit choices across conditions
(scarcity: M=1.00, SD=1.08; control: M=1.00,
SD=0.95),
B
=0.00,
t
(184.7)=0.00,
p
=1.00, pseudo-
R2=.00. Contrary to our hypothesis, preference for
familiar fruit was not stronger in the scarcity than
control condition,
B
=-0.02,
t
(184.9)=-0.16,
p
=.875,
pseudo-R2=.0001. Consistent with Study 1,
participants showed nearly identical preferences for
familiar (scarcity: M=1.19, SD=1.05; control: M=1.20,
SD=0.98) over unfamiliar fruits (scarcity: M=0.81,
SD=1.07; control: M=0.80, SD=0.88) across our two
experimental conditions. Figure 3 displays these
results.
Figure 3.
Bars are standard errors.
Exploratory Results
What proportion of participants preferred
familiarity?. Following Study 1, we checked the
proportion of participants whose personal
preferences for familiarity matched the normative
preference and re-ran the main analyses using only
these participants. Eighty-two percent of
participants showed the normative preference in
their personal preferences. Restricting analyses to
participants who showed the normative preference
did not substantively change the results. The
16
ANTONOPLIS & CHEN
interaction between scarcity and familiarity
remained non-significant (
p
=.580).
Bootstrapped equivalence test. Following Study 1,
we used 5000 bootstrapped resamples to estimate
pseudo-R2 values for the key interaction. The mean
R2 was .001, 95% CI [1.62x10-6,.007]. This suggests
we can rule out pseudo-R2’s larger than .007 as
plausible effect sizes.
Discussion
In a third experiment, we failed to show that
situational scarcity magnifies the preference for
familiarity. Based on prior work, our experimental
manipulation would appear to be internally valid,
and our stimuli clearly are, as they replicated the
classic familiarity bias. In addition, our increased
sample size allowed us to find that the key
interaction between scarcity and familiarity is likely
very small, in fact much smaller than typical effect
sizes in social–personality psychology (Richards et
al., 2003). Whereas Studies 1 and 2’s results may be
explained in terms of methodological issues, the
results of Study 3 seem to more clearly suggest that
situationally induced scarcity does not magnify
preference for familiarity. Although we cannot rule
out that the manipulation did not induce scarcity for
participants, as we did not include a manipulation
check. Still, one possibility that remains untested is
that situationally induced scarcity does not alter
familiarity bias, but longer term scarcity does. We
tested this possibility in Study 4.
Study 4: Individual Differences
Whereas the apparent null effects in Studies 1–3
suggest that experimentally induced (i.e.,
situational) scarcity does not magnify situational
preference for familiarity, it remains possible that
longer term experiences of scarcity may be
correlated with higher preference for familiarity.
This would be the case if scarcity’s effect on
preference for familiarity builds up over time. Then,
repeated exposure to scarcity would be necessary in
order to observe differences in preference for
familiarity. Hence, in a final study, we examined
whether individual differences in both perceived
time and material scarcity correlated with individual
differences in preference for novelty. The pre-
registration form, study materials, and data are
available here: https://osf.io/spzv4/.
Method
Sample
Data came from the first phase of the Measuring
Morality dataset maintained by The Kenan Institute
for Ethics at Duke University (available at
https://kenan.ethics.duke.edu/attitudes/resourc
es/measuring-morality/). This was a nationally
representative sample of adults in the United States.
The full sample includes data from 1,519 individuals
(full details of demographics are reported in Table 1).
We selected items based on our own assessment of
whether they measured our constructs of interest.
We pre-registered that we planned to combine the
items into general indices but that items might be
dropped based on low correlations with other items
in the index.
Measures
To assess perceived material scarcity, we used an
index of the following items: “Agree that I just don’t
have enough money to live the life I would like to
live.” (code: ppl10018; from 1=
strongly agree
to
5=
strongly disagree
; reverse-scored), “Agree that
generally, I live from paycheck to paycheck.” (code:
ppfs0684; from 1=
strongly disagree
to 4=
strongly
agree
), “How would you rate your own personal
finances these days?” (code: ppfs0679; from
1=
excellent
to 4=
poor
), and “Are your personal
finances getting better these days, or worse?” (code:
ppfs0680; 1=
better
, 2=
worse
, 3=
same
; recoded so
that
worse
and
same
switched numerical values, and
then reverse-scored). All four items were
standardized prior to averaging and showed
acceptable internal consistency (α=.73).
To measure perceived time scarcity, we used an
index of the following items: “Agree that life is so
busy that I find I have less time to spend with family
and friends.” (code: ppl10008; from 1=
strongly agree
to 5=
strongly disagree
; reverse-scored), “Agree that
it is hard for me to find the time to be involved in
local/community matters.” (code: ppl10009; from
1=
strongly agree
to 5=
strongly disagree
; reverse-
scored), and “Agree that it is becoming increasingly
difficult to find the time to relax and unwind.” (code:
ppl10012; from 1=
strongly agree
to 5=
strongly
disagree
; reverse-scored). Although these items
differ from canonical manipulations of time scarcity
wherein participants have more or less time to
complete a task (Shah et al., 2012), we thought that
as individual-difference measures of time scarcity
they are sufficient. An experience of scarcity occurs
17
FOUR FAILURES TO DEMONSTRATE THAT SCARCITY MAGNIFIES PREFERENCE FOR FAMILIARITY
when a person lacks sufficient resources to meet a
goal or need. Our three items all referred to
normatively valued goals: personal relationships,
community involvement, and relaxing/leisure.
Thus, our items appear to satisfy the basic definition
of scarcity (lacking a resource—i.e., time—for valued
goals). Moreover, people might vary in the extent to
which they value these goals, but that is also true of
the small monetary awards typical of lab studies (cf.
Shah et al., 2012) and, thus, not unique to our items.
All three items were standardized prior to averaging
and showed good internal consistency (α=.81).
To measure preference for familiarity, we used
the following three items: “I think it is important to
do lots of different things in life. I always look for
new things to try.” (code: SV6; from 1=
very much like
me
to 6=
not like me at all
), “I often try new brands
because I like variety.” (code: PPADOPT2; from
1=
strongly agree
to 5=
strongly disagree
), and “Agree
that I am usually the first of my friends to try new
products and services.” (code: ppfs0687; from
1=
strongly disagree
to 4=
strongly agree
). Although
we had planned to combine these items into a single
index, they showed low internal consistency (α=.54),
so we used them as three separate items.
Note that sample sizes vary across hypothesis
tests because some questions were not asked to all
participants, some participants declined or refused
to answer questions, and some participants
reported being unsure of their response. We
recoded any responses of “Not asked”, “Refused”,
“Not applicable”, and “Not sure” as missing because
they were not substantive responses.
Results
Confirmatory Results
Checking preference for familiarity. To check
that we had selected appropriate items, in addition
to their face validity, we examined whether average
responses to our three items on preference for
familiarity were below the scale midpoint. That is,
did participants, on average, think that “look[ing] for
new things to try” was “not like me,” disagree that “I
often try new brands because I like variety,” and
disagree that they are the first of their friends to “try
new products and services”? Affirmations of these
questions would indicate average preferences to
avoid novelty, presumably in favor of seeking
familiarity. To test these hypotheses, we conducted
one-sample t-tests, comparing the sample mean to
the scale midpoint (3.5 for question 1, 3 for question
2, and 2.5 for question 3). We used one-tailed tests
because we had directional predictions, that sample
means would be lower than the scale midpoints.
The mean of question 1 (“I think it is important to
do lots of different things in life. I always look for
new things to try.”) was 3.96 (SD = 1.29), above the
scale midpoint of 3.5 (
p
=1.00). Questions 2 and 3,
however, showed the expected levels. The mean of
question 2 (“I often try new brands because I like
variety.”) was 2.75 (SD=1.14),
t
(1496)=-8.33,
p
<.001.
The mean of question 3 (“Agree that I am usually the
first of my friends to try new products and
services.”) was 1.94 (SD=0.86),
t
(1086)=-21.55,
p
<.001.
Thus, two of the familiarity preference items we
selected operated as expected, and one did not.
Scarcity and preference for familiarity. Table 5
displays bivariate correlations between time and
material scarcity and the three measures of
preference for familiarity. Evidence in favor of our
hypothesis would be a statistically significant
negative correlation between the scarcity and
familiarity measures. No such correlations were
obtained. Thus, we failed to reject the null
hypothesis across all six hypothesis tests.
18
ANTONOPLIS & CHEN
Table 5
Correlations between scarcity and familiarity preference (Study 4)
Scarcity
Time
Material
Items for Familiarity Preference
r
p
95% CI
r
p
95% CI
I think it is important to do lots of
different things in life. I always look for
new things to try.
.03
.209
[-.02,.08]
-.03
.212
[-.08,.02]
I often try new brands because I like
variety.
.08
.002
[.03,.13]
.02
.472
[-.03,.07]
Agree that I am usually the first of my
friends to try new products and services.
.05
.113
[-.01,.11]
.00
.921
[-.06,.06]
19
FOUR FAILURES TO DEMONSTRATE THAT SCARCITY MAGNIFIES PREFERENCE FOR FAMILIARITY
Exploratory Results
Alternative measures of scarcity. Prior research
on scarcity has used traditional measures of social
class (e.g., income, “mouths to feed”) as indicators of
scarcity (i.e., Mani et al., 2013; Shah et al., 2015). The
logic supporting this usage is that people with fewer
resources in general are also likely to have fewer
resources than they need. Hence, we estimated
post-hoc correlations between “mouths to feed”
(household income / sqrt(family size)) and our three
measures of preference for familiarity. Again,
scarcity, as “mouths to feed,” was uncorrelated with
preference for familiarity (
r
’s from -.01–.05,
p
’s from
.77–1.00).
What proportion of participants preferred
familiarity?. Following Study 1, we checked the
proportion of participants whose personal
preferences for familiarity matched the normative
preference and re-ran the main analyses using only
these participants. Thirty-five percent of
participants disagreed that it was important to try
new things in life. Forty-two percent disagreed that
they tried new brands because they like variety.
Seventy-five percent disagreed that they were the
first of their friends to try new products and
services.
Analyzing data for only participants who showed
the expected preferences for familiarity, material
scarcity was significantly negatively correlated with
thinking it is important to try new things in life (
r
=-
.09,
p
=.045, N=512) and with being the first of one’s
friends to try new products and services (
r
=-.08,
p
=.030, N=818), but not with trying new brands
because of taste for variety (
r
-.00,
p
=.966, N=619).
These results suggest that people experiencing
greater material scarcity had a relatively stronger
preference for familiarity. However, the p-values
were quite high, higher than would be expected if
these were true effects (Simonsohn et al., 2014), so
we are not entirely sure they are real. Time scarcity
was not significantly correlated with thinking it is
important to try new things in life (
r
=.02,
p
=.614,
N=497) or trying new products and services before
one’s friends (
r
=.01,
p
=.681, N=797), but was
significantly positively related to trying new brands
because of taste for variety (
r
=.09,
p
=.022, N=604).
That is, people who reported experiencing more
time scarcity exhibited a preference for novelty:
They were more likely to report trying new brands
trying new brands because of taste for variety. If
real, this is in the opposite of our predicted
direction.
Equivalence test. The correlation effect sizes (
r
’s
in Table 5) and their 95% confidence intervals (95%
CI in Table 5) provide a simple check of what effect
sizes can be ruled out from the present data. The
confidence intervals cover a relatively small range of
effect sizes, from
r
=-.06 to
r
=.13. This suggests we
can rule out
r
’s outside of the range of -.06–.13 as
plausible effect sizes. In addition, relative to other
effect sizes in social–personality psychology, these
fall near or below the 33rd percentile, suggesting
that the effect of scarcity on familiarity, assuming it
is not null, is smaller than most effects in social–
personality psychology.
Discussion
Taking an individual differences approach, we
found that participants preferred familiarity to
novelty and that scarcity, both as time and material
scarcity, did not magnify this preference. Consistent
with Study 3, an equivalence test suggested that the
effect of scarcity on familiarity bias, to the extent
that it exists, is very small. These results cohere with
our experimental results from Studies 1–3. In
aggregate, these results suggest that scarcity does
not increase preference for familiarity in states or in
longer term attitudes.
General Discussion
Across four pre-registered studies, we failed to
find evidence for the hypothesis that scarcity
polarizes preferences for familiarity. Three studies
tested this experimentally, using diverse stimuli and
manipulations. A fourth tested it using an individual
differences approach. Although perhaps surprising
given prior research (Zhu & Ratner, 2015), these null
results help identify a potential boundary condition
of when scarcity polarizes preferences. In
particular, scarcity may yield this effect only at the
idiographic level. When people experience scarcity,
versus abundance, they may exhibit stronger
preferences for things they themselves already like,
and not for things that are generally liked across
people.
Beyond the possibility that idiographic
preferences are key to the predicted effect of
scarcity increasing the familiarity bias, why else
might we have observed these null results? We do
not suspect it is an issue of study design. Poor
stimulus selection cannot explain these failures, as
we consistently found evidence for a normative
20
ANTONOPLIS & CHEN
preference for familiarity (seven of nine dependent
variables showed the effect). In addition, we do not
expect that we poorly operationalized scarcity. For
one, our operationalizations map onto the definition
of scarcity pretty well (e.g., not having enough time
or money or options to satisfy all of one’s desires).
Second, several of our operationalizations of
scarcity were used in prior research that found
effects of scarcity on psychological outcomes and
used manipulation checks to assess the key
assumption that they induced an experience or
perception of scarcity (with all
p
’s < .001; Litt et al.,
2011; Roux et al., 2015; Zhu & Ratner, 2015). Of
course, we cannot fully rule out the possibility that
our manipulations may have failed to induce a
psychological experience of scarcity, due to our not
including manipulation checks. Third, we used both
experimental and correlational designs, suggesting
the null result is not a feature of study type.
One possible explanation is low power. Studies 3
and 4, which had the largest sample sizes of all of our
studies, suggested that the key effect was quite
small, to the extent that it existed at all. In particular,
Study 3’s results suggested that the key effect was a
pseudo-R2 of .001, 95% CI [1.62x10-6,.007], and
Study 4’s results suggested that the key effect was
an
r
from -.06–.13 (R2 from .004–.017). None of our
studies was designed to detect an effect that small.
To the extent that methodological issues do not
explain the null results, a theoretical explanation is
possible, too. In particular, it may be the case that
scarcity does not have “secondary” effects in the
sense that it does not impact thoughts, feelings, or
behaviors that are not relevant to the immediate
context in which scarcity was experienced. Some
recent work on scarcity has begun to suggest that
scarcity may not have such “secondary” effects. For
instance, Camerer et al. (2018) failed to replicate the
finding that a brief experience of scarcity reduced
cognitive control on a subsequent, unrelated task
(i.e., ego depletion; originally reported as Study 1 in
Shah et al., 2012). In response, Shah et al. (2018)
replicated every study from their own 2012 paper
and found that none of scarcity’s secondary effects
replicated. These included the aforementioned
depletion effect, as well as neglect of future
demands and neglect of details helpful for future
tasks, but not the immediate task. Shah et al. (2018)
did, however, replicate all of the “primary” effects of
scarcity (i.e., greater present focus, more over-
borrowing). These failures to replicate suggest that
scarcity’s effects may be limited to the immediate
situation at hand (e.g., spending more time focusing
a shot when one has limited shots) and cease when
the situation changes (e.g., considering strategies
for future rounds, an unrelated cognitive control
task). Given that we studied a secondary effect (the
primary effects being typical mediators like stress,
arousal, etc.), our hypothesis may have been
doomed from the start. Still, at least one version of
the hypothesis has received empirical support (i.e.,
the idiographic approach; Zhu & Ratner, 2015), so
future research should determine the robustness of
these original results.
Conclusion
We failed to find support for the hypothesis that
scarcity magnifies the preference for familiarity.
These results may help place a boundary on prior
work showing similar results (Zhu & Ratner, 2015). At
the very least, they identify for other researchers a
hypothesis that is unlikely to be generative or,
alternatively, demonstrate several sub-optimal tests
of the hypothesis, which future researchers can
know to avoid.
Author Contact
Stephen Antonoplis, antonoplis@berkeley.edu,
Department of Psychology, University of California,
Berkeley, CA 94720, U.S.A.
Acknowledgements
The authors thank members of the Self, Identity,
and Relationships lab for their feedback on this
project.
Conflict of Interest and Funding
The authors hold no known conflicts of interest
relevant to this work. S. Antonoplis was funded by a
NSF Graduate Research Fellowship, Grant Number
DGE 1752814.
Author Contributions
S.A. and S.C. conceived the project and designed
the studies jointly. S.A. conducted data analyses and
drafted the manuscript, with critical feedback from
S.C.
Open Science Practices
21
FOUR FAILURES TO DEMONSTRATE THAT SCARCITY MAGNIFIES PREFERENCE FOR FAMILIARITY
This article earned the Preregistration+, Open
Data and the Open Materials badge for
preregistering the hypothesis and analysis before
data collection, and for making the data and
materials openly available. It has been verified that
the analysis reproduced the results presented in the
article. The entire editorial process, including the
open reviews, is published in the online supplement.
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