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The Relationship Between Mental Representations of Welfare Recipients and Attitudes Toward Welfare

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Scholars have argued that opposition to welfare is, in part, driven by stereotypes of African Americans. This argument assumes that when individuals think about welfare, they spontaneously think about Black recipients. We investigated people’s mental representations of welfare recipients. In Studies 1 and 2, we used a perceptual task to visually estimate participants’ mental representations of welfare recipients. Compared with the average non-welfare-recipient image, the average welfare-recipient image was perceived (by a separate sample) as more African American and more representative of stereotypes associated with welfare recipients and African Americans. In Study 3, participants were asked to determine whether they supported giving welfare benefits to the people pictured in the average welfare-recipient and non-welfare-recipient images generated in Study 2. Participants were less supportive of giving welfare benefits to the person shown in the welfare-recipient image than to the person shown in the non-welfare-recipient image. The results suggest that mental images of welfare recipients may bias attitudes toward welfare policies.
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Psychological Science
2017, Vol. 28(1) 92 –103
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DOI: 10.1177/0956797616674999
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Research Article
Economic inequality in the United States is at historically
high levels (Saez & Zucman, 2014). Income inequality is
associated with a variety of problems, including reduced
interpersonal trust, increased violent crime, and short-
ened life expectancy (Wilkinson & Pickett, 2009). The
majority of Americans say they would prefer a more equi-
table distribution of wealth (Norton & Ariely, 2011), yet
many vote against public assistance programs aimed at
reducing inequality (Bartels, 2005; Gilens, 1999). Identify-
ing the factors that influence the distribution of resources
in societies is an important problem across the social sci-
ences. Understanding the psychological mechanisms that
link such societal-level factors to individual preferences
about redistribution is a critical problem for psychologi-
cal science.
An influential macroeconomic model suggests that as
inequality increases, a greater share of the population has
income below the mean income level, and, as a result,
demand for redistribution should rise (e.g., Meltzer &
Richard, 1981). However, demand for redistribution in
the United States has remained relatively stable despite
rising inequality (e.g., Ashok, Kuziemko, & Washington,
2015; Kuziemko, Norton, Saez, & Stantcheva, 2015). One
often-suggested reason for this finding is that people may
oppose redistribution if they believe it will benefit racial
minorities (Harell, Soroka, & Iyengar, in press; Lee &
Roemer, 2006).
674999PSSXXX10.1177/0956797616674999Brown-Iannuzzi et al.Mental Representations of Welfare Recipients
research-article2016
Corresponding Authors:
Jazmin L. Brown-Iannuzzi, University of Kentucky–Psychology, 207 K
Kastle Hall, 107 Funkhouser Dr., Lexington, KY 40506
E-mail: jazmin.bi@uky.edu
B. Keith Payne, University of North Carolina at Chapel Hill–Psychology,
312 Davie Hall, 235 E. Cameron Ave., Chapel Hill, NC 27599
E-mail: payne@unc.edu
The Relationship Between Mental
Representations of Welfare Recipients
and Attitudes Toward Welfare
Jazmin L. Brown-Iannuzzi1, Ron Dotsch2, Erin Cooley3,
and B. Keith Payne4
1Department of Psychology, University of Kentucky; 2Department of Psychology,
Utrecht University; 3Department of Psychology, Colgate University; and 4Department
of Psychology and Neuroscience, University of North Carolina at Chapel Hill
Abstract
Scholars have argued that opposition to welfare is, in part, driven by stereotypes of African Americans. This argument
assumes that when individuals think about welfare, they spontaneously think about Black recipients. We investigated
people’s mental representations of welfare recipients. In Studies 1 and 2, we used a perceptual task to visually
estimate participants’ mental representations of welfare recipients. Compared with the average non-welfare-recipient
image, the average welfare-recipient image was perceived (by a separate sample) as more African American and more
representative of stereotypes associated with welfare recipients and African Americans. In Study 3, participants were
asked to determine whether they supported giving welfare benefits to the people pictured in the average welfare-
recipient and non-welfare-recipient images generated in Study 2. Participants were less supportive of giving welfare
benefits to the person shown in the welfare-recipient image than to the person shown in the non-welfare-recipient
image. The results suggest that mental images of welfare recipients may bias attitudes toward welfare policies.
Keywords
stereotyped attitudes, socioeconomic status, social cognition, open data, open materials
Received 4/4/16; Revision accepted 9/28/16
Mental Representations of Welfare Recipients 93
Citizens may oppose welfare programs for many rea-
sons, but researchers have long suspected that racial
biases may inform attitudes toward them (e.g., Edsall &
Edsall, 1991; Fox, 2004; Gilens, 1996, 1999; Lee & Roemer,
2006; Peffley, Hurwitz, & Sniderman, 1997). Racial atti-
tudes are consistently associated with attitudes toward
welfare (Gilens, 1995; Peffley et al., 1997; Sears & Cirin,
1985; Wetts & Willer, 2015). The notion that racial atti-
tudes guide welfare preferences is based on the assump-
tion that when individuals think about welfare, they
spontaneously think about “undeserving” (e.g., lazy,
incompetent) African American recipients. This psycho-
logical leap has been assumed, but never directly tested.
In one of the few experimental studies to investigate
the link between race and welfare attitudes, participants
were randomly assigned to learn about a Black or White
mother receiving public assistance (Gilens, 1996). The
correlation between negative impressions of the welfare
recipient and opposition to welfare was almost twice as
strong when the recipient was Black as when she was
White. This experiment demonstrates that racial attitudes
become more relevant for welfare attitudes when the
recipient is explicitly identified as Black rather than
White. It remains unclear, however, what kind of repre-
sentations people spontaneously access when they think
about welfare recipients.
African Americans make up 32% of recipients of Tem-
porary Assistance to Needy Families (TANF), but only
13% of the general population (U.S. Department of
Health and Human Services, 2012). Despite this overrep-
resentation, more than two thirds of TANF recipients are
not African American. White Americans make up another
32% of TANF recipients, and Hispanic Americans another
30%. A statistically accurate representation of the “typi-
cal” beneficiary would presumably look like a multiracial
composite with approximately equal representation of all
three groups. However, stereotypes are influenced not
only by statistical base rates, but also by a variety of cog-
nitive processes, emotions, and ideological motivations
(Hamilton, 2015; Hilton & von Hippel, 1996). In the
research reported here, we asked whether participants’
representations of welfare recipients depicted Black
recipients, and whether those representations contrib-
uted to attitudes opposing redistributive policies.
Using a procedure to visually estimate participants’
mental representations, we tested three specific hypoth-
eses. First, we hypothesized that mental representations
of the typical welfare recipient would depict a Black
recipient. Second, we hypothesized that when the aggre-
gated images of a typical welfare recipient and a typical
non–welfare recipient were shown to a second group of
participants, they would be less supportive of awarding
welfare benefits to the typical welfare recipient. Third,
we hypothesized that to the extent that the person
depicted in the welfare-recipient image was perceived as
Black, that person would also be stereotyped as unde-
serving of assistance, and that deservingness would in
turn predict the level of support for awarding welfare
benefits to him or her.
Study 1 and Study 2
We report Studies 1 and 2 together because Study 2 was
a close replication of Study 1. These studies were both
conducted in two phases: the image-generation phase
and the image-rating phase. In the image-generation
phase, participants completed a reverse-correlation task,
which allowed us to generate visualizations of their men-
tal images of welfare recipients and non–welfare recipi-
ents (Dotsch & Todorov, 2012; Dotsch, Wigboldus,
Langner, & van Knippenberg, 2008; Imhoff & Dotsch,
2013; Imhoff, Woelki, Hanke, & Dotsch, 2013; Krosch &
Amodio, 2014; Mangini & Biederman, 2004). We began
with a single face, which was a morphed composite of a
White woman, a Black woman, a White man, and a Black
man (see Fig. 1). Then, we added random visual noise to
this base face to create many variants. Participants were
presented with pairs of the faces and selected from each
pair the face that looked more like a welfare recipient.
We did not mention race in any way, so that any effects
of race could emerge spontaneously from participants’
mental images.
In Study 1, participants chose the image in each pair
that looked more like “a welfare recipient.” By superim-
posing the selected images, we constructed an average
representation of welfare recipients. The unselected
images were superimposed to create a non-welfare-
recipient composite image for comparison. In Study 2,
participants in one group selected the image that looked
more like a welfare recipient, whereas participants in
another group selected the image that looked more like
someone who did not receive welfare. The images
Base Image
Stimuli
Fig. 1. The base image used in the reverse-correlation task and three
examples of the stimuli presented to participants.
94 Brown-Iannuzzi et al.
selected by the former group were superimposed to cre-
ate an average representation of welfare recipients, and
the images selected by the latter group were superim-
posed to create an average representation of non–welfare
recipients. This procedure allowed us to approximate
participants’ mental representations because images
selected as looking like welfare recipients shared com-
mon features with participants’ imagined welfare recipi-
ent, and images selected as looking like non–welfare
recipients shared common features with participants’
imagined non–welfare recipient. Aggregating images
amplified the features they shared with participants’ men-
tal representations and reduced the random variation in
unshared features.
Although other implicit measures assess associations
between semantic categories (e.g., the Implicit Associa-
tion Test; Greenwald, McGhee, & Schwartz, 1998) or
spontaneous affective and semantic responses to stimuli
(e.g., the affect misattribution procedure; Payne, Cheng,
Govorun, & Stewart, 2005), those methods do not cap-
ture participants’ mental imagery. The advantage of using
a reverse-correlation procedure was that this data-driven
method allowed us to infer perceivers’ internal visual
representation of welfare recipients. Participants could
generate images that varied orthogonally on multiple
dimensions, so that, for example, gender could vary
independently of race, emotional expression, and other
physiognomy. To the extent that features occurred
together, this covariation reflected features of the partici-
pants’ representations rather than constraints of the
paradigm.
In the image-rating phase, a new sample of partici-
pants rated the images constructed from the responses of
the original samples. The new participants did not know
how the images were generated, and welfare was not
mentioned.
Method
Image-generation phase
Study 1: participants and procedure. We recruited 118
American participants (92 women, 26 men) from the par-
ticipant pool in an introductory psychology course. They
participated in exchange for course credit. The racial-
ethnic composition of the sample was as follows: 61.3%
White, 17.6% Asian, 16.0% Black, and 4.2% Hispanic. The
average age was 19.04 years (SD = 2.68). The median
income was between $75,001 and $100,000 annually.
The stimuli used in the reverse-correlation task were all
generated from the same base face. This base face was cre-
ated by morphing together four images: an image of an
African American male, an image of an African American
female, an image of a White American male, and an image
of a White American female. Then, noise was superimposed
on the base face. The noise consisted of superimposed trun-
cated 2-cycle sinusoid patches in all combinations of six
orientations (0°, 30°, 60°, 90°, 120°, and 150°), five spatial
scales (2, 4, 8, 16, and 32 patches per image), and two
phases (0, π/2), with random contrasts.
Participants completed 400 trials of the reverse-correlation
task. On each trial, two stimuli were presented side by
side, and participants were instructed to select the stimulus
that most resembled a welfare recipient. One stimulus in
each pair had a particular noise pattern superimposed on
the base face, and the other stimulus had the exact oppo-
site (the negative) noise pattern superimposed on the base
face. As did Dotsch and Todorov (2012), we used opposite
noise patterns to maximize the differences between the
two images in each pair and to simplify data analysis. The
pairs of stimuli were presented in random order.
After participants completed the reverse-correlation task,
they were asked about their attitudes toward welfare (ques-
tions taken from Gilens, 1996). Finally, participants reported
their age, gender, income, level of education, political-party
affiliation, political ideology, and race-ethnicity. Because
we created average images—averaged across responses
and across participants—these demographic measures are
not relevant for our main hypothesis, but were collected in
order to characterize the sample.
Study 2: participants and procedure. We recruited
238 participants (125 women, 113 men) from Amazon
Mechanical Turk. The racial-ethnic composition of the
sample was as follows: 76.5% White, 10.1% Asian, 8.0%
Black, and 5.4% other races or ethnicities. The average
age was 36.51 years (SD = 11.94). The median income
was between $25,001 and $50,000.
Participants were randomly assigned to either the wel-
fare-recipient or the non-welfare-recipient condition. The
welfare-recipient condition was an exact replication of
Study 1. In the non-welfare-recipient condition, partici-
pants were asked to “decide which photo looks most like
someone who does NOT receive welfare (that is, some-
one who supports him/herself without receiving wel-
fare).” Participants in both conditions completed 400
trials of the reverse-correlation task. The stimuli were the
same stimuli used in Study 1. Again, participants were
then asked about their attitudes toward welfare (Gilens,
1996) and responded to some demographic questions
that were collected in order to characterize the sample.
Image processing. Using the R package rcicr 0.3.0
(Dotsch, 2015), we computed an average welfare-recipient
image and an average non-welfare-recipient image. For
Study 1, the average welfare-recipient image was created
by superimposing on the base face the average of the
noise patterns of all selected images across all participants.
The average non-welfare-recipient image was created by
Mental Representations of Welfare Recipients 95
superimposing on the base face the average of the noise
patterns of all nonselected images across all participants.
The resulting average images are displayed in the top row
of Figure 2. For Study 2, the average welfare-recipient
image was created by superimposing on the base face the
average of the noise patterns of all selected images across
all participants in the welfare-recipient condition. The
average non-welfare-recipient image was created by
superimposing on the base face the average of the noise
patterns of all selected images across all participants in the
non-welfare-recipient condition. The resulting average
images of the welfare recipient and nonrecipient are dis-
played in the bottom row of Figure 2.
Image-rating phase. To quantify the properties of
these images, we asked separate samples, blind to the
way the images were created, to rate the images. Raters
in each study were randomly assigned to rate either fea-
tures related to the face’s appearance (i.e., race, gender,
likeability, attractiveness, and happiness) or traits related
to deservingness (e.g., laziness, competence, humanness,
agency). We chose to measure these sets of features sep-
arately so that participants did not perceive a connection
between the appearance features (especially race) and
deservingness-related traits.
Given this mixed design, we needed at least 90 partici-
pants in each condition (180 total for each study) to have
adequate power (.80) to detect a small effect ( f = .15;
G*Power software; Faul, Erdfelder, Buchner, & Lang,
2009). An attention check was included as the first ques-
tion in both studies because past research suggests that
such attention checks can improve data quality (Oppen-
heimer, Meyvis, & Davidenko, 2009). Participants rated
the images generated by the original samples without
knowing anything about how those images were gener-
ated. Nothing about welfare was mentioned to
participants.
Participants were told they would rate a few images
on a series of dimensions. Four of the images were filler
items (stimuli used in the image-generation phase) so
that the comparison between the two images of interest
would not be salient to participants. The order in which
the critical images were presented was counterbalanced
to avoid order effects. In the appearance-rating condi-
tion, participants rated each image on race (1 = definitely
African American, 6 = definitely White American), gen-
der (1 = definitely male, 6 = definitely female; reverse-
coded), likeability (1 = extremely unlikeable, 6 = extremely
likeable), attractiveness (1 = extremely unattractive, 6 =
extremely attractive), and happiness (1 = extremely
unhappy, 6 = extremely happy).
In the deservingness-rating condition, participants
rated each image on humanness (1 = extremely inhu-
man, 6 = extremely human), laziness (1 = extremely
hardworking, 6 = extremely lazy; reverse-coded), and
hostility (1 = extremely gentle, 6 = extremely hostile;
reverse-coded). We also asked participants to rate each
image on agency, experience, and competence. We used
items from Gray, Gray, and Wegner (2007) to assess
agency and experience. For agency, we asked partici-
pants to rate how the person depicted compared with
the average person in ability to plan, exert self-control,
act morally, and remember things (1 = much less capable,
6 = much more capable). Responses to these four items
were averaged together to create an index of agency. For
experience, we asked participants to rate how the person
depicted compared with the average person in ability to
feel pain, pleasure, fear, and joy (1 = much less capable,
6 = much more capable). Responses to these four items
were averaged together to create an index of experience.
Two more items measured the degree to which the per-
son seemed competent (1 = extremely incompetent, 6 =
extremely competent) and intelligent (1 = extremely unin-
telligent, 6 = extremely intelligent). Responses to these
two items were averaged together to form an index of
competence. Finally, participants answered demographic
Study 1 Average Images
Study 2 Average Images
Average
Welfare Recipient
Average
Non–Welfare Recipient
Average
Welfare Recipient
Average
Non–Welfare Recipient
Fig. 2. The average classification images generated in Study 1 (top)
and Study 2 (bottom).
96 Brown-Iannuzzi et al.
questions, including questions about their gender, age,
and race-ethnicity.
Study 1 participants. Participants (N = 230) were
recruited from Amazon Mechanical Turk. We excluded 24
participants from analyses because they failed the atten-
tion check.1 The final sample included 206 participants
(83 women, 122 men, 1 person who did not report gen-
der). The racial-ethnic composition of the sample was
as follows: 79.6% White, 6.8% Black, 5.3% Hispanic, and
8.3% “other” or multiracial. The average age was 33.28
years (SD = 11.56), and the median income was between
$35,000 and $39,999.
Study 2 participants. Participants (N = 237) were
recruited from Amazon Mechanical Turk. We excluded
28 participants from analyses because they failed the
attention check. The final sample included 87 men, 112
women, and 10 individuals who did not report their gen-
der. The racial-ethnic composition of the sample was as
follows: 78.9% White, 9.1% Hispanic, 5.3% Black, and
6.7% “other.” The average age was 38.46 years (SD =
13.91), and the median income was between $35,000 and
$39,999.
Results
Study 1: image-rating results. First, we investigated
whether ratings of the welfare-recipient image differed
from ratings of the non-welfare-recipient image. Figure 3
presents the means and 95% confidence intervals (CIs)
for the 11 ratings of these two images (see Table S1 in the
Supplemental Material available online for the specific
values, as well as effect sizes). As predicted by our pri-
mary hypothesis for this study, participants rated the
welfare-recipient image as appearing significantly more
Female
Unlikeable
Unhappy
Unattractive
Low Experience
Low Agency
Inhuman
Hostile
Incompetent
Lazy
African American
123456
Welfare Recipient Non–Welfare Recipient
White American
Hardworking
Competent
Gentle
Human
High Agency
High Experience
Attractive
Happy
Likeable
Male
Fig. 3. Mean ratings of the critical images in Study 1. Error bars represent 95% confidence
intervals.
Mental Representations of Welfare Recipients 97
African American (less White) than the non-welfare-
recipient image.
We also investigated the stereotypes associated with
the images. Given stereotypes depicting African Ameri-
cans as undeserving of public assistance and research
suggesting that African Americans are sometimes dehu-
manized (e.g., Goff, Eberhardt, Williams, & Jackson,
2008), we hypothesized that the person depicted in the
welfare-recipient image would be rated as lazier, more
incompetent, more hostile, and less human than the per-
son depicted in the non-welfare-recipient image. Results
were consistent with these predictions. Further, partici-
pants rated the person depicted in the welfare-recipient
image as less agentic and as having less experience than
the person depicted in the non-welfare-recipient image.
We explored whether there was a general valence effect
and found that, indeed, participants rated the welfare-
recipient image as less attractive, less happy, and less
likeable than the non-welfare-recipient image. We also
explored whether the welfare-recipient image would be
considered more or less feminine than the non-welfare-
recipient image, but we found that participants rated the
welfare-recipient image as more feminine. However, both
images were rated as more male than female overall.
These results suggest that, although the participants rat-
ing the images knew nothing about how they were cre-
ated or what they had to do with welfare, attributes
stereotypically associated with welfare recipients were
apparent in the images.
Study 2: image-rating results. Again, we investigated
whether ratings of the welfare-recipient image differed
from ratings of the non-welfare-recipient image. Figure 4
presents the means and 95% CIs for the 11 ratings of the
two images (see Table S2 in the Supplemental Material for
specific values, as well as effect sizes). As hypothesized,
Female
Unlikeable
Unhappy
Unattractive
Low Experience
Low Agency
Inhuman
Hostile
Incompetent
Lazy
African American
123456
White American
Hardworking
Competent
Gentle
Human
High Agency
High Experience
Attractive
Happy
Likeable
Male
Welfare Recipient Non–Welfare Recipient
Fig. 4. Mean ratings of the critical images in Study 2. Error bars represent 95% confidence
intervals.
98 Brown-Iannuzzi et al.
the welfare-recipient image was rated as more representa-
tive of African Americans than was the non-welfare-
recipient image. Also as predicted, the person depicted in
the welfare-recipient image was rated as lazier, more
incompetent, and more hostile than the person depicted
in the non-welfare-recipient image. Additionally, the per-
son depicted in the welfare-recipient image was perceived
to be less human, to be less agentic, and to have less
mental experience than the person depicted in the
non-welfare-recipient image. Finally, we found that the
welfare-recipient image was rated as less attractive, happy,
likeable, and masculine than the non-welfare-recipient
image. Overall, these findings consistently replicated the
effects in Study 1: When people imagine welfare recipi-
ents, they tend to imagine an African American who
appears lazy and incompetent.
Relationship between the images generated in
Study 1 and Study 2. Visual inspection of the images
generated in the two studies shows that they were very
similar. To objectively measure their similarity, we exam-
ined the correlations between the lightness values for
each pixel across the four images (see Table 1). The
images were first masked with an oval shape so that the
correlations would primarily reflect the face area of the
images. As expected, the welfare-recipient images were
positively correlated across Studies 1 and 2. The non-
welfare-recipient images were also positively correlated
across the studies. The positive correlation between the
non-welfare-recipient images suggests that the anti-image
created in Study 1 was similar to the average image cre-
ated in the non-welfare-recipient condition in Study 2,
even though the images were created using different
decision tasks. Finally, the welfare-recipient image from
Study 2 was negatively correlated with the non-welfare-
recipient images from Study 2 and Study 1. (Note that
the welfare-recipient and non-welfare-recipient images
within Study 1 were correlated −1.0 because they were
anti-images of each other; this redundancy does not
apply in Study 2 because the recipient and nonrecipient
images were generated from separate samples). These
correlations confirm that the images generated were
highly similar across the two studies.
Discussion
Face images were generated by samples of participants
who heard no mention of race. Those images were then
rated by new samples who heard no mention of welfare.
And yet, the images revealed significant relationships
between representations of welfare recipients and racial
categories. Overall, these findings suggest that when
individuals think about welfare recipients, they tend to
imagine an African American who appears, to naive
observers, to be relatively lazy and incompetent. Com-
pared with the people depicted in the non-welfare-
recipient images, those depicted in the welfare-recipient
images appeared to be less human, to be less agentic,
and to have less mental experience. The welfare-recipient
images also appeared relatively unhappy, unattractive,
and unlikeable. These data suggest that race and negative
stereotypes are integrally linked to mental representa-
tions of welfare recipients. However, we did not test
whether mental images of a typical welfare recipient
affect attitudes toward welfare. We investigated this ques-
tion in Study 3.
Study 3
In our third study, we used a within-subjects experimen-
tal design to investigate whether the average welfare-
recipient and non-welfare-recipient images generated in
Study 2 would influence participants’ support for award-
ing or withholding welfare benefits. Participants were
asked to view the two images and to rate their support
for giving the pictured persons welfare benefits.2 We
hypothesized that participants would be more supportive
of giving welfare benefits to the person depicted in the
average non-welfare-recipient image than to the person
depicted in the average welfare-recipient image. Given
Table 1. Correlation Among the Pixels of the Images Generated in Studies 1 and 2
Study and image
Study 1 Study 2
Welfare
recipient
Non–welfare
recipient
Welfare
recipient
Non–welfare
recipient
Study 1
Welfare recipient
Non–welfare recipient −1.00
Study 2
Welfare recipient 0.55 −0.55
Non–welfare recipient −0.57 0.57 −0.71 —
Note: None of the 95% confidence intervals for these correlations included zero.
Mental Representations of Welfare Recipients 99
this within-subjects design, we needed at least 90 partici-
pants to have adequate power (.80) to detect a small
effect ( f = .15; G*Power software; Faul et al., 2009).
Method
Participants (N = 229; 91 men, 124 women, 1 participant
who reported “other,” 13 participants who did not report
gender) were recruited from Amazon Mechanical Turk.
The racial-ethnic composition of the sample was as fol-
lows: 79.9% White, 5.7% Hispanic, 5.7% Asian, 3.1%
Black, and 5.6% “other.” The average age was 39.66 years
(SD = 13.14), and the median income was between
$50,000 and $59,999.
Participants were told that they would see a series of
“fuzzy” images of real people. The blurry nature of the
images was explained by telling participants that the
images looked distorted because they were composites of
photos of people who had applied for government wel-
fare programs. Participants were told that some of the
applicants turned out to be responsible recipients of wel-
fare benefits, whereas others were irresponsible recipients
of the benefits. Then, participants were asked to make a
series of judgments about each image. They were given no
indication whether each image was supposedly a compos-
ite of responsible or irresponsible welfare recipients. The
images presented were the average welfare-recipient and
non-welfare-recipient images generated in Study 2 and
four filler images (the same filler images used in Study 1
and 2), which were included so that the comparison
between the two images of interest would not be salient.
Participants rated each image on perceived race (1 =
definitely African American, 6 = definitely White Ameri-
can). We measured perceived deservingness by asking
participants to rate each image on the degree to which
the pictured person seemed competent (1 = extremely
unintelligent, 6 = extremely intelligent) and hardworking
(1 = extremely lazy, 6 = extremely hardworking), the
extent to which the pictured person seemed responsible
(1 = extremely irresponsible, 6 = extremely responsible),
and the extent to which they believed the pictured per-
son would use food stamps responsibly (1 = definitely
would not use responsibly, 6 = definitely would use
responsibly) and would use cash assistance responsibly
(1 = definitely would not use responsibly, 6 = definitely
would use responsibly). Finally, participants were asked
how much they would support giving the pictured per-
son food stamps (1 = completely unsupportive, 6 = com-
pletely supportive) and cash assistance (1 = completely
unsupportive, 6 = completely supportive). Participants
also answered demographic questions, including ques-
tions about their gender, age, and race-ethnicity.
For exploratory purposes, participants completed
three measures we expected might be associated with
the predicted effects. One measure assessed participants’
attitudes toward welfare (Gilens, 1996). The second
measure assessed participants’ perceptions of increasing
diversity in America (Craig & Richeson, 2014; Wetts &
Willer, 2015). The third measure assessed the degree to
which participants thought welfare was for racial minori-
ties. The findings for these measures are presented in the
Supplemental Material.
Results
Figure 5 presents the means and 95% CIs for the ratings
of the two critical images (see Table S3 in the Supple-
mental Material for specific values, as well as effect sizes).
First, we investigated whether the results for perceived
race, competence, and work ethic replicated the findings
of the previous studies and found that they did. The wel-
fare-recipient image was rated as significantly more Afri-
can American than the non-welfare-recipient image.
Additionally, the person depicted in the welfare-recipient
image was rated as less competent and hardworking than
the person depicted in the non-welfare-recipient image.
Next, we tested our two primary hypotheses for Study
3. As predicted, the person depicted in the welfare-
recipient image was rated as less responsible (generally),
and less responsible with food stamps and cash assistance,
than the person depicted in the non-welfare-recipient
image. Additionally, as hypothesized, participants were
less supportive of giving food stamps and cash assistance
to the person depicted in the welfare-recipient image than
to the person depicted in the non-welfare-recipient image.
Overall, the results from this experiment suggest that peo-
ple’s mental images of welfare recipients can have a causal
influence on their attitudes toward welfare.
Finally, we investigated the associations among differ-
ences in perceived race of the images, perceived deserv-
ingness of the people depicted, and support for giving
those people welfare benefits (see Table 2; all differences
were calculated by subtracting the rating for the welfare-
recipient image from the rating for the non-welfare-
recipient image). The difference in the perceived race of
the images was significantly associated with the difference
in perceived deservingness and support. If race stereotypes
tie mental images of welfare recipients to perceptions of
deservingness, then we would expect perceived race to
mediate the effect of the images on perceived deserving-
ness. Moreover, if deservingness links race to support for
providing welfare benefits, then we would expect the
association between perceived race and support for pro-
viding welfare to be mediated by deservingness.
To test this predicted pattern, we used a two-condition
within-participants statistical mediation analysis with
10,000 bootstraps (MEMORE macro; Montoya & Hayes,
2016). The stereotype variables (perceived competence,
100 Brown-Iannuzzi et al.
responsibility, and work ethic) were averaged together to
create one index of deservingness. Then, the variables
were entered into the model in their raw-scale form
(Montoya & Hayes, 2016). Results of the mediation analy-
sis are displayed in Figure 6. The indirect effect of the
two-mediator sequential pattern was significant, as indi-
cated by the fact the 95% CI did not include zero, b = 0.96,
95% CI = [0.41, 1.60]. This finding is consistent with the
hypothesis that perceived race of the image informed per-
ception of deservingness, which in turn was the more
proximal predictor of support for giving welfare benefits.
The mediation model simultaneously tested two alter-
native single-mediator pathways. First, it tested whether
the effect of the image on support for welfare was medi-
ated by perceived race alone. The indirect effect was not
significant, b = −0.04, 95% CI = [−0.46, 0.36]. Second, the
model tested whether the effect of the image on support
for welfare was mediated by perceived deservingness
alone. Because stereotypes of Black Americans and ste-
reotypes of welfare recipients overlap, it was possible that
perceptions of deservingness could explain the observed
effects independently of race. However, this indirect
effect was also not significant, b = −0.01, 95% CI = [−0.57,
0.48].
General Discussion
We have reported evidence that people’s mental images
of welfare recipients tend to look African American and
to be associated with traits suggesting that they are unde-
serving of government assistance. First, participants
chose images that they believed looked like welfare
Unsupportive of Giving Food Stamps
Unsupportive of Giving Cash Assistance
Irresponsible With Food Stamps
Irresponsible With Cash Assistance
Irresponsible
Lazy
Incompetent
African American
123456
White American
Competent
Hardworking
Responsible
Responsible With Cash Assistance
Responsible With Food Stamps
Supportive of Giving Cash Assistance
Supportive of Giving Food Stamps
Welfare Recipient Non–Welfare Recipient
Fig. 5. Mean ratings of the critical images in Study 3. Error bars represent 95% confidence intervals.
Table 2. Results From Study 3: Correlational Analysis of Rating
Differences Between the Welfare-Recipient and Non-Welfare-
Recipient Images
Rating
Perceived
race
Perceived
deservingness
Perceived deservingness .25
Support for giving welfare benefits .20 .81
Note: None of the 95% confidence intervals for these correlations included
zero.
Mental Representations of Welfare Recipients 101
recipients. Then, separate samples of participants per-
ceived images aggregated from the selected images to
depict African Americans who were lazy, incompetent,
and inhuman (relative to aggregated images of non–
welfare recipients). Finally, when new participants con-
sidered giving welfare benefits to the people depicted in
the images, the qualities of those mental images influ-
enced their support for awarding benefits.
These findings are consistent with previous research
suggesting that opposition to welfare is associated
with negative attitudes toward African Americans
(Gilens, 1995, 1996, 1999) and that these attitudes are
more negative in areas with higher proportions of Afri-
can Americans in the local population (Luttmer, 2001).
Our results extend those findings by suggesting that
mental representations of welfare recipients may be a
subtle psychological mechanism linking racial bias
with support for giving welfare benefits to individuals
in need.
The samples whose responses were the basis for the
constructed images of welfare recipients were blind to
our hypothesis that the images of the recipients would
look more African American than the images of the non-
recipients, and the samples rating the images were blind
to our hypothesis that people would be less supportive
of awarding welfare benefits to the images of the typical
welfare recipient than to the images of the typical non–
welfare recipient. This is important because the images of
welfare recipients generated from participants’ responses
could have appeared to be images of competent and
hardworking African Americans. Or they could have
appeared to be images of undeserving Whites. Yet the
average image of a welfare recipient was rated as depict-
ing an undeserving African American. The merging of
race and these stereotypes of deservingness emerged
spontaneously from the participants’ representations.
Limitations and future directions
This research used convenience samples, which leaves
open questions about how broadly the effects generalize
to other populations. Although online samples recruited
through Mechanical Turk tend to be more diverse than
American college samples, they still do not reflect the
diversity of the United States (Buhrmester, Kwang, &
Gosling, 2011). Future research should investigate mental
representations of welfare recipients in representative
samples. Future research should also investigate how
mental images of specific subtypes of welfare recipients
differ. Many people distinguish, for example, between
the deserving poor and the undeserving poor. Peffley et
al. (1997) found that when African American welfare
recipients were described as hardworking (as opposed to
lazy), participants had more positive attitudes toward
welfare. Do such subtypes reduce the influence of race,
or simply serve as a proxy for different race stereotypes?
That is, if people are asked to imagine hardworking wel-
fare recipients, will their mental images look more White
than African American?
Conclusion
The distribution of resources presents a fundamental
question facing citizens in democracies. Even though the
level of economic inequality is reaching historically high
levels in the United States, citizens tend to oppose redis-
tributive policies (e.g., Gilens, 1995; Harell et al., in
press). Citizens’ mental representations of the people
who benefit from redistribution may help explain why.
These representations may contribute to growing eco-
nomic inequality because they trigger group-based dis-
tinctions in the mind of some citizens when they think
about the optimal distributions of resources.
Image
Perceived Race
Support for
Giving Benefits
b = 0.59*
b = –0.33, p = .12
Perceived
Deservingness
b = 0.27*
b = –0.01, p = .86
b = –0.01, p = .97
b = 3.88*
b = 0.91*
Fig. 6. Two-mediator sequential model depicting the relationship between support for giving welfare
benefits and image type (1 = non–welfare recipient, 0 = welfare recipient), as mediated by perceived
race (Mediator 1: higher numbers = more White) and deservingness (Mediator 2: higher numbers = more
deserving). Asterisks indicate significant coefficients (*p .001).
102 Brown-Iannuzzi et al.
Action Editor
Bill von Hippel served as action editor for this article.
Author Contributions
J. L. Brown-Iannuzzi and B. K. Payne developed the study con-
cept. All the authors contributed to the study design. Testing
and data collection were performed by J. L. Brown-Iannuzzi
and B. K. Payne. J. L. Brown-Iannuzzi performed the data anal-
ysis and interpretation under the supervision of R. Dotsch, E.
Cooley, and B. K. Payne. J. L. Brown-Iannuzzi drafted the man-
uscript, and R. Dotsch, E. Cooley, and B. K. Payne provided
critical revisions. All the authors approved the final version of
the manuscript for submission.
Declaration of Conflicting Interests
The authors declared that they had no conflicts of interest with
respect to their authorship or the publication of this article.
Supplemental Material
Additional supporting information can be found at http://pss
.sagepub.com/content/by/supplemental-data
Open Practices
All data and materials have been made publicly available at
figshare and can be accessed at https://dx.doi.org/10.6084/
m9.figshare.c.3468495.v2. The complete Open Practices Disclo-
sure for this article can be found at http://pss.sagepub.com/
content/by/supplemental-data. This article has received badges
for Open Data and Open Materials. More information about the
Open Practices badges can be found at https://osf.io/tvyxz/
wiki/1.%20View%20the%20Badges/ and http://pss.sagepub.com/
content/25/1/3.full.
Notes
1. Results of Study 1 and Study 2 were not substantively dif-
ferent when all participants, including the ones who failed the
attention check, were included in the analyses.
2. This study was replicated with the average images created
in Study 1. However, in the replication study, we did not ask
participants to rate the perceived race of the average welfare-
recipient and non-welfare-recipient images. Overall, the results
of this replication study were similar to the results of Study 3.
For a detailed description of the method and results of the rep-
lication study, see the Supplemental Material.
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Policy feedback scholars argue the relationship between policy and politics is dynamic and reciprocal. For instance, policies “make citizens,” teaching the public who deserves positive government treatment and who does not. Furthermore, individual experiences with policy shape participation and beliefs about government, which shapes future policy. But few scholars have examined how experiences with a law shape attitudes toward those targeted by policy. We use a survey of 3000 respondents on MTurk (including an over‐sample of people of color) to show how direct and indirect experience with policy shapes social constructions of politically relevant groups. Specifically, we examine how direct (personal) and indirect (via someone they know well) experience with two policy areas (criminal justice and social welfare) shape perceptions of the targets of criminal justice and welfare policy. We find the effect of policy contact is racialized; policy contact has a greater effect on white respondents compared to Black respondents. But despite this contact, whites' attitudes about groups' deservingness remain lower than those of their Black counterparts. Los estudiosos de la retroalimentación de políticas argumentan que la relación entre política y política es dinámica y recíproca. Por ejemplo, las políticas “hacen ciudadanos”, enseñando al público quién merece un tratamiento gubernamental positivo y quién no (Schneider e Ingram 1993, Soss 2000, Mettler y Soss 2004). Además, las experiencias individuales con la política dan forma a la participación y las creencias sobre el gobierno (Bruch, Ferree y Soss 2010, Mettler 2005, Weaver y Lerman 2010), que luego dan forma a la política futura. Pero pocos académicos han examinado cómo las experiencias con una ley dan forma a las actitudes hacia aquellos a los que se dirige la política. A partir de Kreitzer y Smith (2018), utilizamos una encuesta de 3000 encuestados en MTurk (incluida una muestra adicional de personas de color) para mostrar cómo la experiencia directa e indirecta con la política da forma a las construcciones sociales de grupos políticamente relevantes. Específicamente, examinamos la experiencia directa (personal) e indirecta (a través de alguien que conocen bien) con dos áreas de política (justicia penal y bienestar social) que dan forma a las percepciones de los objetivos de la justicia penal y la política de bienestar. Encontramos que el efecto del contacto político está racializado; el contacto de política tiene un mayor efecto en los encuestados blancos en comparación con los encuestados negros. Pero a pesar de este contacto, las actitudes de los blancos sobre el merecimiento de los grupos son inferiores a las de sus contrapartes negras. 研究政策反馈的学者认为,政策与政治之间的关系是动态的、互惠的。例如,政策“造就公民”,其教育公众谁值得或不值得积极的政府对待(Schneider and Ingram 1993, Soss 2000, Mettler and Soss 2004)。此外,个人的政策经验影响了政策参与和对政府的信念(Bruch, Ferree, and Soss 2010, Mettler 2005, Weaver and Lerman 2010),进而影响未来政策。不过,很少有学者分析法律方面的经验如何影响对政策目标的态度。基于Kreitzer和Smith(2018)的研究,我们对MTurk上的3,000名受访者进行调查(包括一项有色人种的过采样),以展示在政策方面的直接和间接经验如何影响政治相关群体的社会建构。具体而言,我们分析了两个政策领域(刑事司法和社会福利)方面的直接(个人)经验和间接(通过熟人)经验如何影响对刑事司法和福利政策目标的看法。我们发现,政策接触的效果是种族化的;与黑人受访者相比,政策接触对白人受访者的影响更大。不过,尽管存在这种接触,白人对群体应得性的态度仍低于黑人。
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Este artículo examina la interacción entre el estatus socioeconómico y las creencias que justifican la desigualdad en lo relativo al apoyo a la redistribución. Utilizando datos de una encuesta de São Paulo (Brasil) (N=928), encontramos que el apoyo a la redistribución estuvo negativamente asociado con el estatus socioeconómico subjetivo; pero estuvo positivamente relacionada con el estatus socioeconómico objetivo. Además, las creencias que justifican la desigualdad moderaron esta relación. Por un lado, el efecto negativo del estatus subjetivo sobre el apoyo a la redistribución fue más fuerte para las personas que apoyan las creencias que justifican la desigualdad. Por otro lado, el efecto positivo entre el estatus objetivo y el apoyo a la redistribución solo ocurrió para las personas que rechazaron estas creencias. Los resultados sugieren que el efecto del estatus socioeconómico sobre el apoyo a la redistribución depende del grado de apoyo a las creencias ideológicas que justifican la desigualdad.
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People see their own group as more typical of a larger, superordinate category than they see other, included subgroups (ingroup-projection). This basic effect is not restricted to verbally encoded characteristics but also expands to the domain of what people think superordinate group members typically look like. Despite the robustness of the ingroup-projection phenomenon, it could be argued that it is a side effect of an even more basic process of seeing groups and individuals as similar primarily to the self (selfprojection). In the present research, the authors sought to address and rule out this potential alternative explanation of visual ingroup-projection as an artifact of self-projection to the subgroup and the superordinate group. Thirty-one participants completed three two-image, forced-choice reverse correlation image classification tasks to create subjective, prototypical images, called classification images, of (a) themselves; (b) their national ingroup (German); and (c) the larger, superordinate group (European). With the use of partial pixel correlations, the objective, unique physical similarity between pairs of classification images was calculated. Both the selfimage and the ingroup image independently predicted the superordinate group image, indicating that both self-projection and ingroup-projection contribute to visual mental representations of superordinate group faces.
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Theory: Social psychological theories of social stereotyping are used to generate a series of predictions about how and when whites' stereotypes of African-Americans are likely to bias their evaluations of blacks in the areas of welfare and crime. Hypotheses: The degree to which whites endorse negative stereotypes of blacks not only tends to bias their judgments of black (versus white) welfare recipients and criminal suspects, but also affects the way they respond to counter-stereotypical information about the target. Methods: Regression analysis and analysis of variance of data from a series of survey experiments with 1,841 whites in which the race and other attributes of welfare mothers, welfare recipients, and drug suspects were manipulated. Results: Whites holding negative stereotypes are substantially more likely to judge blacks more harshly than similarly described whites in the areas of welfare and crime policy. We also find that even whites with strongly negative perceptions of blacks respond quite favorably to them when confronted with individuating information that clearly contradicts their stereotype. By way of contrast, respondents who reject negative stereotypes of African-Americans display a remarkable consistency in their responses across both the race and the individuating information of the target.
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Past work suggests that support for welfare in the US is heavily influenced by citizens' racial attitudes. Indeed, the idea that many Americans think of welfare recipients as poor Blacks (and especially as poor Black women) has been a common explanation for Americans' lukewarm support for redistribution. Here, we draw on a new online survey experiment conducted with national samples in the US, UK and Canada, designed to extend research on how racialized portrayals of policy beneficiaries affect attitudes toward redistribution. We designed a series of innovative survey vignettes that experimentally manipulate the ethno-racial background of beneficiaries for various redistributive programs. The findings provide, for the first time, cross- national, cross- domain, and cross-ethno-racial extensions of the American literature on the impact of racial cues on support for redistributive policy. Our results also demonstrate that race clearly matters for policy support, although its impact varies by context and by the racial group under consideration.
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Researchers interested in testing mediation often use designs where participants are measured on a dependent variable Y and a mediator M in both of 2 different circumstances. The dominant approach to assessing mediation in such a design, proposed by Judd, Kenny, and McClelland (2001), relies on a series of hypothesis tests about components of the mediation model and is not based on an estimate of or formal inference about the indirect effect. In this article we recast Judd et al.'s approach in the path-analytic framework that is now commonly used in between-participant mediation analysis. By so doing, it is apparent how to estimate the indirect effect of a within-participant manipulation on some outcome through a mediator as the product of paths of influence. This path-analytic approach eliminates the need for discrete hypothesis tests about components of the model to support a claim of mediation, as Judd et al.'s method requires, because it relies only on an inference about the product of paths-the indirect effect. We generalize methods of inference for the indirect effect widely used in between-participant designs to this within-participant version of mediation analysis, including bootstrap confidence intervals and Monte Carlo confidence intervals. Using this path-analytic approach, we extend the method to models with multiple mediators operating in parallel and serially and discuss the comparison of indirect effects in these more complex models. We offer macros and code for SPSS, SAS, and Mplus that conduct these analyses. (PsycINFO Database Record
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Traces the rise of the American Republican party to its current, almost unassailable position in presidential elections. The authors argue that the main voting issues in the USA since the mid-1960s have been underpinned by racial anxiety and resentment of welfare liberalities.