CONSEQUENCES OF REWARDS:
THE CREATION, PERPETUATION, AND EROSION OF SOCIAL INEQUALITY
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF SOCIOLOGY
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Sarah Katherine Harkness
May 2011
http://creativecommons.org/licenses/by-nc/3.0/us/
This dissertation is online at: http://purl.stanford.edu/zx554nf4443
© 2011 by Sarah Katherine Harkness. All Rights Reserved.
Re-distributed by Stanford University under license with the author.
This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.
ii
I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.
Karen Cook, Primary Adviser
I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.
Shelley Correll
I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.
Cecilia Ridgeway
Approved for the Stanford University Committee on Graduate Studies.
Patricia J. Gumport, Vice Provost Graduate Education
This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.
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iv
ABSTRACT
This dissertation focuses on how status and rewards jointly impact the creation,
perpetuation and erosion of social inequality. Rewards are objects or positions that
come to have differential levels of prestige when they are affiliated with groups of
varying status, such as certain types of educational degrees, technologies, awards, and
the like. Expectations about who we are and what we should be able to achieve are
formed based on a combination of both our characteristics and displayed status
markers. The first study experimentally tests whether rewards have the power to
create entirely new status characteristics and bases of inequality. The second study is
an examination of how assessments of competence and trustworthiness systematically
bias the distribution of rewards and, thereby, the perpetuation of inequality, by
examining how lenders perceive loan applicants and make funding decisions in
experimentally created lending markets. The third study explores whether rewards
have the power to neutralize status-based inequality when low status individuals are
rewarded with markers of a much higher honorific value than members of high status
groups.
v
ACKNOWLEDGEMENTS
Being at Stanford as a member of this remarkable intellectual community has
been one of the most rewarding times in my life. I am eternally grateful to both my
immediate and academic family for all of their encouragement and guidance. It is
only with their support that this dissertation could have been completed. Endless
thanks are due to my primary advisers, Karen Cook and Cecilia Ridgeway, whose
incisive critiques and insights I could not have done without. Every time we met
about this research project, I would come away with new ideas and renewed
motivation. This impressed upon me the importance of being a creative, conscientious
scholar and that there are always questions to be asked. The clarity and joy with
which they teach is inspirational. It is an absolute honor to be their student.
I am also indebted to Shelley Correll who always found aspects of this
dissertation that could be improved. Shelley’s intellectual rigor and dedication are
truly impressive, and I thank her for being such a motivating role model. Many thanks
are also due to Joe Berger and Buzz Zelditch. The foundational theoretical work of
this dissertation is theirs, and I am incredibly lucky to have received their guidance on
this project. I am also grateful to two of my undergraduate professors, Alison Bianchi
and Amy Kroska, who first introduced me to sociological social psychology. It is
their love for and dedication to this discipline that compelled me to become a
sociologist; without them I never would have applied to graduate school. I cannot
fathom what I would be doing instead.
I am also extremely fortunate to have been immersed in such an outstanding
department and to have met such creative and inspiring friends and colleagues. Many
vi
thanks are due to the members Social Psychology Workshop for their invaluable
feedback, support, and friendship. Being in this workshop taught me to be a more
complete scholar, and I will miss our weekly meetings tremendously. I would also
like to thank Sara Bloch, Susan Fisk, Alex Gerbasi, Jonathan Haynes, Sharon Jank,
Yan Li, Elizabeth McClintock, Stephen Nunez, Amanda Sharkey, Traci Tucker, and
Alec Watts for all of their help finishing this dissertation. In particular, I would also
like to thank Lynn Chin, not only for her wealth of insights and enthusiasm, but also
because without her peerless work for the Research Experience Program I never
would have been able to run the majority of the experiments for this dissertation. I
will always be in her debt.
A big thank you also goes to all of our administrators, especially Sarah
Giberman, Susan Martin, Randy Michaud, Chrissy Stimmel, and Susan Weersing.
With kindness, understanding, and laughter, you kept my world running.
I would also like to thank my parents, John and Suzanne, who have always
given me the freedom and support to explore life’s possibilities. I am grateful for all
of their trust, assurance, and encouragement. Thank you for instilling the sociological
imagination in me, and I am proud to carry on the family’s sociological tradition into
its third generation.
And I could not have completed this dissertation and degree without my
amazing husband, Michael—we make a fantastic team. Thank you for all the love,
laughter, patience, understanding, flexibility, strategizing, planning, and the thousands
of things you do every day that make us that much happier. I dedicate this dissertation
to you.
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TABLE OF CONTENTS
ABSTRACT…………………………………………………………………………...iv
ACKNOWLEDGEMENTS…………………………………………………………....v
TABLE OF CONTENTS……………………………………………………………..vii
LIST OF TABLES…………………………………………………………………...viii
LIST OF FIGURES…………………………………………………………………....x
1. INTRODUCTION: THE CONSEQUENCES OF REWARDS…………………….1
2. SPREAD OF STATUS VALUE: THE CREATION OF STATUS
CHARACTERISTICS………………………………………………………………6
References…………………………………………………………………….45
3. THE PERPETUATION OF INEQUALITY: STATUS DYNAMICS IN
LENDING MARKETS…………………………………………………………….49
References…………………………………………………………………….99
4. REWARD INTERVENTIONS: THE EROSION OF SOCIAL INEQUALITY?..106
References…………………………………………………………………...142
viii
LIST OF TABLES
2.1 Means or Proportions of Demographic and Bargaining Variables…………...27
2.2 Mean Bargaining Profit Differences by Reward Level...……………………..28
2.3 Mean Ratings of the Value of the Rewards Relative to In-Group Status…….30
2.4 Mean Ratings of “Most People’s” Evaluations of the Rewards’ Possessors
by End Reward Level…………………………………………………………31
2.5 Mean Ratings of “Most People’s” Evaluations of Personal Response Style
Relative to In-Group Status…………………………………………………...33
2.6 Mean Ratings of “Most People’s” Evaluations of Personal Response Style
by End Reward Level………………………….……………………………...34
2.7 Mean Ratings of Personal Evaluations of Personal Response Style
Relative to In-Group Status…………………………………………………...36
2.8 Mean Ratings of Personal Evaluations of Personal Response Style
by End Reward Level…………………………………………………………37
2.9 Mean Difference in the Proportion of Stay Responses……………………….39
2.10 Proportion of Stay Responses ANOVAs……………………………………..40
3.1 Means or Proportions of Demographic Characteristics and Funding
Decision Variables by Study and Participant Group…………………………66
3.2 Study 1: Estimated Mixed-Effects Linear Regression Coefficients for the
Effects of Gender, Race, and Writing Ability on the Competence and
Trustworthiness Scale………………………………………………………...69
3.3 Study 1: Estimated Mixed-Effects Linear Regression Coefficients for the
Effects of Gender, Race, Writing Ability, and Assessments of Competence
and Trustworthiness on Funding Assessments……………………………….77
3.4 Study 1: Estimated Mixed-Effects Linear Regression Coefficients for the
Effects of Gender, Race, Writing Ability, and Assessments of Competence
ix
and Trustworthiness on Loan Amount Given………………………………...78
3.5 Study 2: Estimated Mixed-Effects Linear Regression Coefficients for the
Effects of Gender, Race, and Writing Ability on Funding Assessments……..86
3.6 Study 2: Estimated Mixed-Effects Linear Regression Coefficients for the
Effects of Gender, Race, and Writing Ability on Loan Amount Given………87
4.1 Means or Proportions of Demographic Characteristics by Study Medium
and Condition………………………………………………………………..125
4.2 Mean Ratings of Reward Traits by Condition………………………………127
4.3 Mean Ratings of Reward Levels’ Status and Competence by Condition…...129
4.4 Estimated Mixed-Effects Linear Regression Coefficients for the Effects of
Reward Level on the Status Value Scales…………………………………..130
4.5 Estimated Mixed-Effects Logitstic Regression Coefficients for the Effects
of Condition on the Number of Trials Participants Changed Their Initial
Answer………………………………………………………………………133
4.6 Mean Ratings of Partners’ Status and Competence by Condition…………..135
4.7 Estimated Mixed-Effects Linear Regression Coefficients for the Effects of
Partners’ Reward Level, and the Education Status Characteristic on the
Status Value Scales………………………………………………………….136
4.8 Mean Ratings of the Education Status Characteristic’s Status and
Competence by Condition…………………………………………………...138
x
LIST OF FIGURES
3.1 Study 1: Predicted Assessment of Competence and Trustworthiness by
Applicants' Gender and Racial Background……………………………….....70
3.2 Study 1: Predicted Assessment of Competence and Trustworthiness by
Applicants' Gender, Racial Background, and Writing Ability………………..74
3.3 Study 2: Predicted Likelihood of Repaying the Loan by Applicants'
Gender and Racial Background………………………………………………89
3.4 Study 2: Predicted Likelihood of Funding Loan by Applicants' Gender,
Racial Background, and Writing Ability……………………………………..90
3.5 Study 2: Predicted Loan Amount Given by Applicants' Gender, Racial
Background, and Writing Ability……………………………………………..93
4.1 Proportion of Trials Participants Changed Their Initial Answer to Agree
with Either Partner by Condition……………………………………………132
1
1. INTRODUCTION: THE CONSEQUENCES OF REWARDS
This dissertation is an examination of the interrelations between status, social
rewards, and resources and how these processes create and shape inequality. In
everyday interaction, we are defined not only by our master statuses, such as our
gender and ethnic backgrounds, but also by our awards and positions, which may have
differential social value. Many of these distinctions combine to create an
understanding of who we are, what kinds of behaviors can be expected of us, how
valuable our contributions should be, and the like. These expectations undergird our
shared and accepted social reality, with many using status and reward distinctions as
shorthand for assessments of competence and worth.
Questions therefore arise as to how individuals are affected by the
configuration of valuable rewards in their environment, namely how rewards are
normatively distributed between and within social groups. Can rewards create new
status groups? If we use status information when deciding who should receive more
resources, as prior theory and research suggests, what social groups are most
disadvantaged by this association in contemporary markets? Can the perpetuation of
inequalities based on status distinctions be impeded through the use of reward-based
interventions? This dissertation endeavors to begin to answer these questions.
Paper 1
In the first study, I seek to demonstrate how power, exchange, and status
mutually reinforce each other to create new bases for social inequality. Veblen argues
2
that the accumulation of social esteem and honor is the driving force behind all
behavior, and in order to achieve higher levels of status one must consume visible,
honorific goods, such as unpaid leisure. To this day honorific goods have social
significance and are highly esteemed, sought after objects. But what does the
ownership of these rewards really mean to others? It may be possible for these status
symbols to confer or spread their status onto those who possess them, such that people
gain or lose status by virtue of the rewards they have. If we do process the status
information that rewards connote in this fashion, is this spread enough to produce
entirely new status groups by virtue of their association with esteemed or devalued
status objects?
Once the rewards’ value is formed through the association of these markers
with extant status characteristics, this project experimentally tests whether the status
value conveyed by these rewards spreads onto a nominally distinct characteristic of
those who come to possess this reward. The results generally indicate that the states
of the nominal characteristic gain or lose status and influence through their association
with differentially valued rewards. For example, if a new immigrant group,
organization, or college department becomes affiliated with high status objects, the
overall perception of these groups might become more prestigious by virtue of this
association alone. We therefore process the valuations conveyed by status markers in
the same manner as status characteristics, and differential rewards can create new
status distinctions with resulting behavioral expectations, such as divergent
assessments of competence, worth, and influence.
3
Paper 2
The second research project explores how rewards affect the perpetuation of
status and wealth inequality by examining status discrimination in lending markets.
One of the key ways individuals accumulate new wealth is first through the receipt of
credit, yet research has documented pervasive group-level disparities in funding
outcomes, such as in obtaining mortgages and venture capital funding. New forms of
credit market have arisen wherein borrowers are financed by their peers and not by
traditional banking institutions with the partial intent of mitigating these inequities.
Even in these peer-to-peer markets, however, borrowers tend to display their personal
information, which conveys many indicators on which bias and discrimination can be
based.
Evidence suggests that these disparities are at least partially due to the actions of
lenders, yet we lack explanations for the mechanisms behind these disparities. In the
second paper, I put forth one possible mechanism. When lenders assess each borrower,
they are at least in part, and perhaps implicitly, assessing the relative competence and
trustworthiness of the borrower against the field of possible borrowers. These
appraisals systematically bias funding decisions, even when the borrowers’ have
commensurate financial histories. Lenders may feel as though they can entrust their
resources to those of higher status in that they assume that these borrowers have the
competence necessary to use the funds faithfully and responsibly and are able to repay
the loan.
The results indicate that these indicators strongly influence a wide array of
funding decisions such that status becomes a means by which lenders compare
4
applicants to determine whether and how to fund them, even when the applicants have
similar financial histories. These assessments also systematically vary by gender and
racial categories, such that the funding that African American females receive is
similar to that of white males. Not only are African American females rewarded at the
same level, they are also rated as having slightly higher levels of competence and
trustworthiness than white males. Possible explanations for the combined effect of
gender and race are explored and what this suggests for status characteristics theory
are discussed at the conclusion of this paper.
Paper 3
The continuation of status-based inequality is one of our most important and
pervasive social problems because this source of inequality is founded on essentially
illegitimate assumptions of competence and worth, which can cloud ability
assessments and alter opportunity structures and resource distributions. While rewards
might have the power to create and maintain social inequality when they are relevant
to preexisting status distinctions, rewards may have the capability to strip away the
power of status characteristics as well. This project offers a preliminary step toward
assessing the extent to which a redistribution of socially valued rewards alters status-
based inequality.
In the first two papers, the reward distributions are congruent with the status
groups, such that high status actors are associated with highly desired rewards and low
status actors with devalued rewards. When the situation is reversed, whereby low
status individuals are rewarded with markers of a much higher honorific value than
5
members of high status groups, the relative status advantages of these groups may be
neutralized. The process of using rewards as an intervention can be fraught with
complexity, however. While some types of rewards convey a definite sense of
prestige, value, and ability regardless of their possessors, such as Nobel Prizes, the
meaning of numerous others may not be as immune to the status of those with whom
they are affiliated. Thus, if the reward becomes associated with a group of conflicting
status value, particularly when the reward is relatively novel, the estimation of the
reward and what it connotes may gain or lose value according to the status of its new
possessors.
The third study of this dissertation offers a preliminary experimental test that
assesses the extent to which a redistribution of socially valued rewards alters status-
based inequality. The results tentatively suggest that the meaning of the rewards
becomes contaminated when they are inconsistently affiliated to status groups. Thus,
at least under the particular conditions of this study’s experiment, rewards do not seem
to intervene in status processes. In this paper’s conclusion, future research designs are
discussed to further explore the potential of the reward intervention mechanism.
6
2. SPREAD OF STATUS VALUE: THE CREATION OF STATUS CHARACTERISTICS
Status and power have been prominent features of theory and research since
the turn of the last century. Weber ([1916] 1946) made one of the earliest distinctions
between power and status, specifying that they are distinct features of the social world
that can have a reciprocal, mutually reinforcing relationship. Veblen ([1899] 2005)
argues that the accumulation of social esteem and honor is the driving force behind all
behavior, and in order to accumulate this status one must consume visible, honorific
goods, such as unpaid leisure. To this day, honorific goods have social significance
and are highly esteemed and coveted. These objects or positions become markers of
prestige, honor, and competence when affiliated with groups who have high status,
and they can make others feel as though they are “in the know” and have distinction
and taste when they come to possess these items.
Obtaining these valued possessions can be an end in and of itself, but these
social rewards may also produce something more. What does the ownership of these
rewards really indicate to others? Evidence tends to suggest that status symbols confer
or spread their status onto those who possess them, such that people gain or lose status
by virtue of the rewards they have (Bierhoff, Buck and Klein 1986; Cook 1975;
Harrod 1980; Lerner 1965; Stewart and Moore 1992). For instance, levels of
attractiveness, including variation due to grooming and clothing, can negatively
impact assessments of credibility and competence (Lennon 1986; Lerner 1965; Patzer
1985; Quereshi and Kay 1986; Rhode 2010). Indeed, almost half of American
7
workers believe that it is reasonable to not hire someone on the basis of their clothing
(Rhode 2010).
As a separate example, there appears to be a form of a generalized Matthew
effect (Hunt and Blair 1987; Merton 1968, 1988) related to faculty hiring decisions.
Graduating from prestigious departments tends to have a direct impact on assessments
of job candidates’ overall competence and value while even altering hiring committees
appraisals of the candidates’ academic records (Bedeian and Feild 1980; Bedeian,
Cavazos, Hunt, and Jauch 2010; Carson and Navarro 1988; Crane 1965; Long,
Allison, and McGinnis 1979). In this case, the prestige of the degree-granting
institution, a type of status marker, confers status onto its students such that evaluators
expect these students to be more proficient and thrive in the future, holding constant
actual ability and accomplishments.
If we do process the status information that rewards connote in this fashion, is
this spread enough to produce entirely new status groups by virtue of their association
to esteemed or devalued status objects? For example, if new organizations come to
use and be affiliated with valued status markers, such as certain technologies,
esteemed occupations, or prestigious awards, the perception of this group overall may
become more revered through this association alone. As an illustrative example of
this process, consider the case of the creation of a new research center. In academic
communities, the use of certain varieties of methodological techniques and statistical
technologies generally come to be imbued with status as members of the community
tend to believe that those who use them are highly proficient, even if the various
methods accomplish essentially the same task. When a new center is founded, one of
8
the possible ways through which it can gain status in the academic community may be
through which technologies its predominant members use, thereby helping to create a
new status group. Over time, this new status distinction may diffuse throughout the
community and this center’s members may enjoy status advantages even if they begin
to use a much wider array of technologies.
This research seeks to test this possible mechanism by which new status
groups are created through their association with rewards of various levels. As
Bourdieu argues, symbolic elements of our culture are not only a structural product
but should also have the power to construct new social structures (Bourdieu 1990,
1991). This research offers a test of this contention as well as an extension of status
construction theories by using the status value of possessions as an additional
mechanism in the production of status. More broadly, this is a study of the impact
status symbols can have on social hierarchies.
THEORETICAL BACKGROUND
This research is firmly entrenched within the expectation states research
program of structural social psychology. This body of theory and research seeks to
explicate status processes and their effect on interpersonal relations and inequality.
Within this program, status characteristics theory (hereafter SCT) elucidates how
existing status differences pattern behaviors related to having differential levels of
power and influence in small groups (Berger, Cohen and Zelditch 1966, 1972). This
theory applies to groups who are working together to reach a mutually valued goal
9
(i.e., who are collectively and task-oriented), such as juries or students working on a
group project for their class.
There are two main kinds of personal characteristics that can distinguish group
members: diffuse and specific status characteristics. Diffuse status characteristics are
culturally defined, socially significant characteristics (e.g., gender or race/ethnicity)
that have varying states (e.g., male-female, White-African American). These various
attributes have differential esteem, honor, and prestige valuations as defined by the
dominant culture that correspond to the level of performance ability a person with a
particular state is assumed to have (Berger and Fisek 2006). Specific status
characteristics are associated with the ability to perform particular tasks, such as
computer skills or business aptitude.
Once a problem-solving group is differentiated by at least one diffuse status
characteristic or by a characteristic that is relevant to the task, individuals will assign
expectations about the performance and potential contributions of group members
based on the valuation of the states of their status characteristics. The characteristic
will be relevant to expectations regarding the individual’s performance on the group’s
task unless it is directly challenged (the burden of proof principle). Behavioral
inequalities favoring the actors who have highly-valued status characteristics will then
emerge with respect to opportunities granted to speak, actual level of participation,
evaluations of others' performance, and the influence members have to change the
mind of others while solving the problem. The power and prestige hierarchy and
expectation hierarchy are mutually reinforcing, and they will remain highly stable over
time (Berger and Conner 1974).
10
While SCT is mainly concerned with the status of people, another branch of
EST, the status value theory of distributive justice, is concerned with the status of
possessions (Berger, Zelditch, Anderson and Cohen 1972). The theory is based on a
reformulation of Veblen's ([1899] 2005) notion of honorific value that desired objects,
tangible or not, can symbolize status and social standing. Components of the social
world, such as people or objects, come to have status value when they are uniquely
related to elements that do have this type of valuation. If these elements are
consistently appreciated, the related non-status valued object will acquire the same
level of status value. Thus, people expect those with positively valued states of status
characteristics to possess highly status-valued objects.
Status Construction Theories
Traditionally in the expectation states research program, the characteristics that
carry performance expectations, thereby affecting power and prestige behaviors, are
well-known, culturally defined, preexisting distinctions (e.g., age, education, and
gender); however, the question of how nominally distinct characteristics obtain status
value, emerge as status characteristics, and diffuse throughout a society is also within
the purview of this program (Ridgeway 1991, 1997; Berger and Fisek 2006).
Ridgeway (1991, 1997) was the first to posit how initially non-valued
characteristics come to have status value and performance expectations. Nominally
distinct characteristics, such as artistic preference, distinguish only a "mere difference"
between group members, and social identity theorists maintain that this minimal
difference is sufficient to create in-group bias (Tajfel 1978). When a state of this
11
characteristic becomes more positively valued and more influential in a localized
setting, individuals in the valued group are more likely to believe this judgment
because it reinforces their established in-group bias. A nominally distinct
characteristic becomes a status characteristic when even the disadvantaged group
members believe that most people perceive their group to be less worthy or competent
than the opposing group. It is this assumption of consensus that makes a newly valued
characteristic a social fact (Berger and Luckmann 1966; Ridgeway 1997).
Ridgeway (1991) contends that one environment in which status characteristics
are initially constructed is through goal-oriented, small-group interactions. In these
settings, one way that a non-valued characteristic can obtain status value is though an
association with a structural advantage. Structural advantage here refers to different
levels of material resources, such as wage disparities. When various states of a
nominal characteristic are associated with different levels of resources, the conditions
are set for the status construction process. These situations are "doubly dissimilar"
interactions because individuals are distinguished by their characteristics and by the
amount of resources they acquire. Research demonstrates that when people possess
differential resources, they create performance expectations about themselves and
other group members in direct relation their unequal resources (Bierhoff, Buck and
Klein 1986; Cook 1975; Harrod 1980; Lerner 1965; Stewart and Moore 1992), such
that competence, assertiveness, certainty, and confidence are positively related to
control over resources.
These behaviors form the interactional hierarchy of influence and esteem in a
group working together on a socially important task. Bolstered by these differential
12
resources, a consensual, valid interactional hierarchy will form in the group. People
then generate performance expectations based on this interactional hierarchy virtually
instantaneously. Resources, therefore, create structural advantages for the rich, and,
consequently, they will gain high levels of influence, esteem, and elevated
expectations for their performance.
After the interactional hierarchy and power and prestige order is formed,
members are likely to attribute the influence and esteem garnered from their relative
structural advantages with their corresponding states of the novel characteristic. This
association creates a valued status characteristic in this immediate situation. Over
successive social encounters, the behavioral expectations come to be associated with
the characteristic itself and not with the original structural advantage as others behave
as though the expectations surrounding the states of the new characteristic are social
facts. Interactions that confirm this new conception help to diffuse this belief
throughout the society, thereby creating a stable status characteristic (Ridgeway, 2006;
Ridgeway, Backor, Li, Tinkler, and Erickson 2009; Ridgeway and Balkwell 1997).
Recently, Berger and Fisek's (2006) have proposed a new mechanism
motivating the construction of diffuse status characteristics. They argue that, aside
from control over resources, nominal characteristics' association with characteristics
that have preexisting status value and related performance expectations can also be a
first step in the creation of a diffuse status characteristic. The status value of the
originating salient characteristic(s) is predicted to spread to the corresponding states of
the nominal characteristic as long as the initial status valued characteristics are
consistently appraised.
13
This spread of status value process is virtually the same as that which is found
in the status value theory of distributive justice (Berger et al. 1972). The degree to
which the non-valued states of the nominal characteristic acquire positive or negative
status value varies as a function of the number, task relevance, consistency, and
valence of the valued status characteristics a person embodies. This can also vary by
the number of people in the task situation, in addition to other possible relational
factors, such as sentiments among group members. Once this spread has occurred,
performance expectations are associated with the divergent states of the characteristic,
and successive validating social encounters will establish the trait as being a stable,
widely agreed-upon status characteristic.
THEORETICAL EXTENSION
This research explores the process by which the status value of resources is
created through their association with preexisting status characteristics, and how these
rewards possibly create new status characteristics via their ownership. The presumed
mechanism by which initially non-valued states of objects and characteristics obtain
their differential levels of prestige and worth is through the spread of status value
process; therefore, this work is a theoretical extension of Berger and Fisek’s (2006)
status construction theory.
When the actors who possess status valued goods or positions enter into task
situations with others, if the only thing that actors know about each other is the status
value of their possessions and that they differ on a nominal state of a characteristic, the
status value of their rewards should spread to the corresponding states of the new
14
characteristic for the actors in the situation. The initially non-valued states of a
characteristic acquire status value as a function of the number, task relevance,
consistency, and valence of the original characteristics used to form the status value of
the object (Berger and Fisek 2006; Thye and Witkowski 2003). The resulting value of
the newly created characteristic is predicted to be commensurate with the valence of
this initial reward valuation. Actors should then attach differential performance
expectations in accordance with the newly acquired status value of the states of the
novel characteristic as a function of this esteem.
Once states of an initially non-valued characteristic are assigned a consistent
status evaluation and the associated performance expectations about the actors are
activated, this characteristic will become a status characteristic in the local setting.
The proposed effect of status-valued rewards should work independently of any
structural advantage conferred by monetary inequities. Again following Berger and
Fisek's (2006) reasoning, this new status characteristic will only become stable
through subsequent, validating encounters in the absence of the initial reward
disparities. These new beliefs achieve legitimacy through multiple encounters in
which a social consensus is formed about the valuations and expectations attached to
the characteristic.
Hypotheses
For this proposed research project, I hypothesize that in the spread of status
value from valued status characteristics to resources:
Hypothesis 1: The rewards controlled by high-status actors will have greater status
15
value than those held by low-status actors.
When the status value of rewards and an initially non-valued characteristic of
two states are the only qualities that differentiate a task-group, I hypothesize:
Hypothesis 2: As the status value of actors' rewards increases, the corresponding status value of their nominal characteristic will also increase.
Hypothesis 3: As the status value of actors’ nominal characteristic increases, the
greater the behavioral expectation advantage attributed to these actors will be.
The first hypothesis pertains to the spread of status value from valued
characteristics to resources, thereby creating rewards of differential esteem. The
second set of hypotheses concerns whether the status value of rewards spreads to an
initially non-valued characteristic. When this nominal characteristic is ascribed status
value by members of the task group, they are predicted to assign greater performance
expectations to those with characteristics of greater status value. This process must
occur for members with both valued and devalued rewards and characteristics as a
status characteristic does not indicate in-group bias but widely held beliefs about the
capabilities and worth for those with certain qualities (Ridgeway 1991, 1997).
The ensuing experiment therefore tests whether status value spreads from
status characteristics to initially non-valued objects, and then onto a nominally distinct
characteristic of the new possessors of these now status-valued objects. With the
absence of any distinguishing information about a interaction partners, except that
they control an objects of differential status value and have a different states of an
initially non-valued characteristic, the objects’ status value should spread to the
16
nominal characteristic to create a new status characteristic with related performance
expectations.
EXPERIMENTAL DESIGN
The experiment has two phases, which correspond to the two sets of
hypotheses above. In the first phase, the participant works with a partner with known
status characteristics on a bargaining task through a computer. In the second phase the
participant works with a different partner, and they are only differentiated by status
valued rewards and a novel, initially non-status valued characteristic. In each phase,
participants negotiate and interact with a partner, and the objects that they use to
exchange, bargaining chips of two different colors, should become status valued
rewards during the first phase of the experiment. These dyads are “doubly dissimilar”
in that partners always differ with respect to their characteristics (status valued in the
first phase and initially nominal in the second) and the level of their rewards’ status
value. The partners are actually simulated actors to control the task cues given by the
“partner,” the relative status of the “partner,” and the way the interaction unfolds.
This study employs a 2x2 factorial design in which conditions are divided by the
relative status of the participants’ first partners (higher status or lower status) and the
status value of the items controlled by the participants’ second partners (high status
value or low status value)1.
At the start of the first phase of this study, participants have either a status
advantage or disadvantage over their partners according to the states of their salient
1 The partners were always matched by gender. 2 The experimenter does not know the color of the second chip allocation either, as this portion of the
17
status characteristics. Over the course of the first phase of the study, the initially
nominally distinct chips should take on status value that is commensurate with the
valuation of the states of their characteristics. Newly status-valued chips should then
retain their meaning throughout the second phase. There are four resulting conditions
in this study. Participants may have high status-valued rewards in the first phase (i.e.,
they have a status advantage over their first partner resulting in controlling a particular
type of chip) and then control the chips of high status value in the second phase (i.e.,
the color of chips they control does not change throughout the study) (“High High”).
Second, participants can begin with high status-valued rewards and then control low
status-valued rewards in the second phase (i.e., the color of chips they control changes
when they negotiate with their second partner) (“High Low”). Third, participants
begin with devalued rewards (i.e., they have a status disadvantage relative to their first
partner) and are then allotted high status-valued rewards in the second phase (i.e., the
color of the chips they control changes when working with the second partner) (“Low
High”). Finally, participants can control devalued rewards throughout the study (i.e.,
the chip color does not change from the first to the second phase) (“Low Low”).
These conditions are then collapsed to create the two master conditions of this study
by the reward level with which the participants began the study and the reward level
they had in the second phase of the study.
Phase 1: Creation of Status Valued Rewards
The procedures for the first phase closely follow those used by status value
researchers (Thye 2000; Thye and Witkowski 2003). Upon arrival to the lab, the
18
participants go into their individual study rooms. They complete a brief survey in
which they indicate their gender, age, race/ethnicity, how many quarters they have
taken courses at Stanford and their grade point average. Participants then take a
“meaning insight” ability test. Expectation states researchers frequently use this
meaning insight task as a valid way to create an ability that only has meaning in the
experiment’s immediate setting (Berger and Conner 1974; Berger, Fisek, Norman and
Zelditch 1977). In each round, participants and their partners must match an English
word to a supposed root word from an ancient language. In reality, there is never a
correct answer to the task. Upon completion of this test, the experimenter personally
informs the each participant of the first partner’s demographic information and
meaning insight score.
In the “high beginning status” condition, the partner is a 24-year-old
engineering graduate student with a 4.0 grade point average and has very high
meaning insight ability relative to the participants. In the “low beginning status”
condition, the participants work with a sixteen-year-old vocational-track high school
student with a 2.3 grade point average and who has lower meaning insight ability than
their own. These traits are then made relevant to the two different chip colors
immediately prior to the first phase of negotiations to create the differential rewards.
Phase 1: Negotiated Exchange
Participants and their partners are then allocated thirty color-coded chips,
either purple or orange, according to their relative status. For instance, it could be that
when the participants have a higher status relative to their partner, they control purple
19
chips and their partners use orange chips. People do not tend to show a personal
preference for the color purple or orange when they exchange for resources, so these
objects should have no initial valuations (Thye 2000). Nevertheless, the computer
randomly assigns the color of the chips associated with the relative status of the
participants in each session to ensure that color preference is not conflated with the
status value of the chips.
To ensure that the participants are aware of the relevance between the chip
color assignment and their relative status, the experimenter informs them that they
have earned the right to use purple or orange chips according to their performance on
the meaning insight task, their age, and educational background. If the participants
question why this is the case, the experimenter responds that the color assignment
helps to keep track of the differences between the participants and their partners.
Thus, the experimenter authorizes a link between the chips and the status
differences among the participants and their partners, which grants the spread of status
value process. It is at this point that the value of the states of the players’ status
characteristics should spread to the two different chip colors. When this occurs, a
status-valued reward is created with two differential levels commensurate to the
estimation of the initial states of the characteristics.
Although the color of the chips varied, their instrumental value is kept constant
due to the payment structure of the subsequent negotiations, as payment is directly tied
to the number of chips that the participants accrue, regardless of the color of the chips
they have accumulated. This also ensures that the participants are motivated to
accumulate as many chips as possible. Additionally, because the chips are not
20
assigned an exact monetary value, participants should treat the chips as purely
resources that are utilized to obtain higher pay at the end of the study but do not have a
defined value of their own. In actuality, all participants are paid the same amount for
their participation.
The participants then engage in twenty rounds of negotiations, with each round
lasting no more than two minutes. At the start of each exchange round, the participant
and partner have the opportunity to send an initial offer. The exact person who begins
the round is determined on a first-come, first-serve basis. If, after a random amount of
time, the participant has not given an initial offer, the partner sends an offer. The
partner’s behavior in the negotiations is manipulated through pre-programmed
exchange rules meant to simulate reasonable and feasible bargaining behavior.
Once an initial offer is sent, the recipient can accept it, forfeit the round, or
send a counteroffer. Thus, the person who begins the round is relatively
undetermined, but the round proceeds with both players alternating decisions until the
round is over.
When giving offers (counter or initial), players can increase, decrease, or retain
the last exchange offer. The only constraint is that they must request between 0 and
30 chips (i.e., they can increase/decrease their asking price by 1 chip, 2 chips, 3 chips,
etc.). The round finishes and the next begins when either player agrees to the
immediate offer, forfeits the round, or time runs out.
In the negotiations, the participants can only send requests to the partner
according to how many of the partner’s chips they want (i.e., “I want [x] number of
my partner’s purple chips”). The participants do not know how many of their own
21
chips their partner receives, but they do know that generally the more chips they
request, the fewer chips the partner obtains. Power-dependence exchange theorists
have used this negotiation procedure to reduce equity-seeking behavior among their
players (Cook and Emerson 1978; Lawler and Yoon 1996; Molm, Peterson and
Takahashi 2001). If participants were to know that they were only dividing a fixed set
of chips between themselves and their partner, they tend to quickly and continually
divide the resource pool equally due to concerns of fairness.
The importance of this first phase of negotiations is that over the course of
these exchanges, the participants should associate the color of the chips with the
relative status of their partner. Thus, the status of their partner spreads to the color of
their chips, which confers a certain level of status value to the chips, thereby making
them rewards of relative value.
Phase 2: Creation of a Nominal Characteristic
At the start of the second phase, the participants change partners, and these
new partners are also simulated. The final time the experimenter interacts with the
participants before the debriefing process is to inform them that they are going to work
with a new partner in the last portion of the study. Thus, the remainder of the study is
double-blind. To create the nominal status characteristic that differentiates the
participants from their second partners, participants complete a “Personal Response
Style” test wherein they indicate their preference for a series of Klee and Kandinsky
paintings (Ridgeway, Boyle, Kuipers and Robinson 1998; Ridgeway and Erickson
2000). The computer script informs the participants that researchers have recently
22
discovered a new individual trait, personal response style, and people around the
world tend to personify one of the two states of this characteristic, S2 and Q2. The
program then notifies the participants that their test results indicate that they embody
one type of response style, while the results of their partners’ test show that they typify
the opposite style.
Participants could not have had any prior experience with the qualities of
people with the S2 or Q2 personal response style as this characteristic does not exist
outside of the laboratory. Therefore, participants should not have a prior valuation or
expectations for S2s and Q2s. Nevertheless, the computer randomly assigns the
participants the S2 or Q2 response style in each session to ensure that artistic
preference is not conflated with the status value of the chips.
The program then allocates chips to the participant and the new partner for a
second phase of bargaining and the computer script creates a relevance bond between
the states of the novel characteristic and the assigned chip colors, again either purple
or orange. This link is made using the same language that was used in the first phase
of the experiment. The program informs the participants that because they embody
one response type, they will be using a particular chip color, while their partner
typifies the opposing response style and will be using the other color of chips. For
instance, the program may state that because the participant exemplifies the S2
response style, they will be using purple chips, whereas the partner has the Q2
response style and will be using orange chips. The computer again randomly selects
23
the colors associated with this second chip allocation, although the participant is not
made aware of this2.
Participants may retain the same type of status-valued objects throughout the
study or the status value may change in this second phase. In this second exchange
task, the chip’s status value is divorced from its original direct association with high or
low status exchange partners. They then exchange chips with their new partners until
they complete a total of fifteen exchanges. Participants should have enough time
during this task to form an association between the second chip color assignment and
the nominal characteristic distinction between themselves and their new partner.
In this second set of negotiations, the computer’s bargaining rules are the same
as those of the first exchange exercise. All participants, regardless of whether they
control the same or different rewards in the second phase, experience the same actions
and reactions by their partners. Therefore, the partners do not enact an influence
hierarchy based on relative status.
Status Value of New Characteristic
Prior to assessing the relative influence between the participants and their
partners, the participants complete a brief questionnaire in which they evaluate various
qualities of S2’s and Q2’s. This is done at this particular point in the experiment to
give the participants an opportunity to reflect upon and draw connections between the
reward differences and the new response style characteristic. It also gives them an
2 The experimenter does not know the color of the second chip allocation either, as this portion of the study is double-blind. Because of this, the experimenter cannot give task cues about the second partner, and, therefore the experimenter does not prompt an interactional hierarchy between the participants and their second partners.
24
opportunity to be made aware of and articulate any beliefs they have about the
characteristic prior to interacting with their partner again. In this questionnaire, the
participants are given a chance to associate the behavior of their new partners with the
differences in their nominal characteristic. This survey contains semantic differential
questions pertaining to participants’ perceptions about S2s’ and Q2s’ status,
competence, and considerateness according to their own opinions and to what they
think most people believe (Ridgeway et al. 1998). This questionnaire assesses
whether personal response style has become a status characteristic, as the participants
must indicate the breadth of their status evaluations.
The following adjective pairs anchor the status and power questions:
respected/not respected, low status/high status, leader/follower, and
powerful/powerless. The competence questions pertain to the pairs of:
competent/incompetent, capable/incapable, and knowledgeable/not knowledgeable.
The questions related to the states’ considerateness include: considerate/inconsiderate,
pleasant/unpleasant, likable/unlikable, cooperative/uncooperative3. The participants
answer these questions with a virtual slide-rule that measures their responses along a
continuous 100-point scale. The status, competence, and considerateness questions
were averaged to create scales related to these three components of status value.
3 Considerateness is an important dimension of status value to explore as many have argued (Conway, Pizzamiglio, and Mount 1996; Fiske, Cuddy, Glick, and Xu 2002) that status has two sides: the agentic/competent/high status face and the deferential/reactive/low status face.
25
Influence
To measure the degree of influence the partners have over the participants,
they then complete a contrast sensitivity task with their partners. Researchers in the
expectation states tradition frequently use this test as a valid behavioral measure of
influence. In each round, participants and their partners view a picture that contains
black and white tiles. Their task is to decide whether the picture contains a greater
area of black or white. These pictures were created so that they truly contain an equal
proportion of black and white space; therefore there is never a correct answer. The
participants indicate their initial choice before being informed of their partners’
preliminary decision. The simulated partners disagree with the participants on all but
five rounds so as to give the impression of an actual social interaction while still
providing a large number of critical disagreement trials. The participants must decide
whether to stay with their original selection or defer to the selection of their partners.
If the participants change their selection to that of their partners, their partners and,
presumably, their partners’ relative status advantage are influencing them. The
proportion of the participants’ stay responses (p(s)) indicates the extent to which they
are influenced by their partners, with a smaller proportion indicating that the
participants are heavily influenced.
Status Value of the Rewards
Lastly, the participants complete a series of questions related to their
estimation of the monetary and status valuation of the two colors of chips. The
participants first indicate the overall value of the chips and whether they thought the
26
chips had any monetary value4. They then rate how they and subsequently how “most
people” would rate a person who is signified in this study with purple and with orange.
The aforementioned status, competence, and considerateness questions are used for
this series of questions and are again averaged to create three scales of the rewards’
status value.
RESULTS
Participants and Descriptive Statistics
A total of 88 participants completed this study. Of these, four revealed
comprehension issues and nine were deemed too suspicious during the debriefing
process, and they were dropped from the analyses. The final sample size for the
subsequent analyses is 75. 51 females and 24 males participated in the study, and the
average age of participants is 19, with a range of 18 to 23 years of age (please see
Table 1). The average profit in the first phase of bargaining is 22.93 chips, and
participants tended to increase the amount of the partners’ chips they requested as they
proceeded through this first phase. In this first bargaining period, participants or the
computer forfeited almost a third of the time, and about twenty percent of the rounds
timed-out. The average profit in the second phase of bargaining is 23.52, and there
were about the same number of forfeits (27%) and timeouts (15%). The average
proportion of stay responses (p(s)), the behavioral measure of interpersonal influence,
is .66. Participants did not change their final decisions on the contrast sensitivity test
about two-thirds of the time. 4 Questions related to the value of the first partner’s chips are asked prior to the start of the second phase of the study.
27
Table 2.1. Means or Proportions of Demographic Characteristics and Bargaining Variables
Variables Mean (Std. Dev.) Minimum Maximum
Female .64 Age 19.12 (1.26) 18 23
Bargaining Phase 1 Profit (All Rounds) 22.93 (4.87) 8 28 Profit (Last 10 Rounds) 23.80 (5.12) 8 29 Forfeits .29 Timeouts .21
Bargaining Phase 2 Profit (All Rounds) 23.52 (5.03) 7 28 Profit (Last 10 Rounds) 24.09 (4.91) 8 28
Forfeits .27 Timeouts .15
P(s) .66 .35 1.00 Note: Sample size for this analysis = 75.
Bargaining Differences
Overall the average profit increases over the course of each bargaining phase
according to t-test analyses of the average differences. When comparing the first few
rounds to the last ten rounds, there is a significant increase in profit in both the first
phase of negotiations (t = -4.99, p < .000, two-tailed test) and the second (t = -5.51, p
< .000, two-tailed test) (results not shown). There are no significant differences in the
number of forfeits or timeouts by condition (results not shown). Table 2 displays the
differences in the two phases of bargaining between the two master conditions. Profit
does not differ by status difference in either the first or second rounds of negotiations;
therefore, participants did not behave differently toward their partners by condition.
28
Regardless of the qualities of participants in either phase of the study, they
appear to “test the waters” with their partners to see how many chips they can garner
from them. This behavior produces the only significant result related to profit: the
profit in the final rounds of negotiation is significantly higher than in the beginning. It
is important to note that since profit did not differ by status level, participants did not
develop any consistent structural advantages in any of the conditions. Additionally,
because the participants’ negotiating behavior did not vary by condition, any
differences in influence and status value in the second phase could not have been the
result of a behavioral influence hierarchy present during the exchanges.
Table 2.2. Mean Bargaining Profit Differences by Reward Level
Rewards: Start with High Rewards End with High Rewards Variables Mean T-Statistic Mean T-Statistic Bargaining Phase 1 Profit (All Rounds)
Low Rewards 22.69 -.55
High Rewards 23.30
Bargaining Phase 1 Profit (Last 10 Rounds)
Low Rewards 23.69 -.32
High Rewards 24.06
Bargaining Phase 2 Profit (All Rounds)
Low Rewards 23.32 -.36
High Rewards 23.74
Bargaining Phase 2 Profit (Last 10 Rounds)
Low Rewards 23.86 -.50
High Rewards 24.42
Note: Sample size for this analysis = 75; +p < .10; *p < .05; **p < .01; ***p < .001 (two-tailed tests).
29
Behaviorally, participants attempted to garner as many chips as the simulated
partner would allow. The status of the chips did not alter this pattern. This might
indicate that the participants only thought the chips had pecuniary value. Their
assessments of the monetary value of the chips suggest that this is not the case. This
appraisal hovers around the midpoint of the scale, which indicates that the participants
were unsure of whether the chips had monetary value at all.
In the first bargaining phase, a relevance bond is created between the status
characteristics of the participants and partners and the chip color, whereas in the
second this link is formed between the chip color and the nominal characteristic,
personal response style. It is important to therefore assess whether the participants’
valuation of the chips persists throughout these two phases. As the t-test analyses in
Table 3 demonstrate, this valuation does tend to endure. When participants begin the
study with a status advantage, they view their chips as having significantly higher
value than those controlled by their partner (t = 2.59, p < .05, two-tailed test); whereas
when the initial partners is of higher status than the participants, the participants rate
their own chips as having lesser value than those of their partners (t = -2.44, p < .05,
two-tailed test). This demonstrates that the chips obtain reward valuations consistent
with the status differences apparent between the participants and their first partners.
In the second phase, the valuation of the resources is not quite as distinct. The
participants who end the study with the devalued reward also view their chips as
having less value than those of their second partners (t = -1.86, p < .10, two-tailed
test), but there is no significant difference in the chip valuation of those who end the
study with the chip that had high valuation in the first phase. These results also
30
support the first hypothesis, although those who have a reward advantage at the end of
the study do not tend to acknowledge this advantage.
Participants also gave their impressions of how they expect most people would
perceive those that possessed the two different chips, thereby assessing what the
rewards indicate about their possessors. These questions were coded according to
whether the two types of chips were associated with either high or low status in the
first phase of the study. As displayed in Table 4, chips that are purported to have a
high reward level convey a greater degree of status and competence (t = 3.50, p <
.000 and t = 3.03, p < .01 respectively, two-tailed tests) about those who control them.
These results also support the first hypothesis.
Table 2.3. Mean Ratings of the Value of the Rewards Relative to In-Group Status (with Standard Deviations)
Beginning Reward Group Level End Reward Group Level
Variables High Low High Low
Overall Value
Own Group 63.12 (24.95) 51.68 (26.54) 54.30 (24.41) 48.06 (24.86)
Other Group 47.71 (28.28) 62.29 (24.38) 55.25 (26.54) 56.89 (22.76)
Difference 15.41 (34.73)* -10.61 (27.81)* -.95 (21.30) -3.88 (28.15)+
Note: Sample size for this analysis = 75; t-test analysis; +p < .10; *p < .05; **p < .01; ***p < .001 (two-tailed tests).
31
Table 2.4. Mean Ratings of “Most People’s” Evaluations of the Rewards’ Possessors by End Reward Level
Variables Mean T-Statistic
Status Scale
High Reward Level 62.69 3.59***
Low Reward Level 55.75
Competence Scale
High Reward Level 64.08 3.03**
Low Reward Level 58.62
Considerateness Scale
High Reward Level 56.76 -.93
Low Reward Level 58.43
Note: Sample size for this analysis = 75; +p < .10; *p < .05; **p < .01; ***p < .001 (two-tailed tests).
Overall, this analysis generally shows support for the first hypothesis that once
the chips become relevant to the various status characteristics apparent in the first
phase of the study, the chips come to have differential levels of status value. This also
replicates the general findings of previous status value investigations (Thye 2000;
Thye and Witkowski 2003). Importantly, these valuations persist over the course of
the study and across conditions. Only those who are benefitted by their rewards in the
second phase do not fully acknowledging their advantage. Generally, however, these
results indicate the strength of these beliefs regarding the chips’ valuation.
Status Value of Personal Response Style
In addition to perceiving that the chips have differential levels of status value,
participants also tend to differentially value the previously unknown characteristic,
32
personal response style, according to predicted reward dissimilarities. To assess the
status value of the personal response style groups, status, competence, and
considerateness scales were created from the exit questionnaire5. The expectation is
that when participants have high rewards during the second phase of the study, they
will evaluate the third-order beliefs about how most people would evaluate their own
personal response style as being higher in status and competence but lower in
considerateness than the other response style; conversely, those with devalued rewards
are expected to report that their own group has lower status and competence but higher
considerateness according to what most people believe. Analyses of the average
differences between the study’s conditions generally support this conclusion (see
Table 5).
5 The alpha-levels of these scales range from .77 to .89.
33
As predicted, those with high rewards see their own state of the characteristic
as having higher status (t = 2.69, p < .01, two-tailed test) and competence (t = 2.04, p
< .05, two-tailed test) but lower considerateness (t = -2.93, p < .01, two-tailed test).
Those with devalued rewards do not report there to be a significant difference between
the status and competence of the response styles, though the relationship is in the
predicted direction. Additionally, they view their group as being slightly more
considerate (marginally significant, two-tailed test). These results generally indicate
that participants believe most people would say that states of the novel characteristic
Table 2.5. Mean Ratings of “Most People’s” Evaluations of Personal Response Style Relative to In-Group Status (with Standard Deviations)
Final Reward Level
Variables Ends with High
Rewards Ends with Low
Rewards
Status Scale
Own Group 61.69 (14.28) 59.74 (9.23)
Other Group 55.29 (13.48) 58.25 (9.47)
Difference 6.39 (15.01)** -1.49 (9.62)
Competence Scale
Own Group 62.16 (16.05) 61.88 (11.47)
Other Group 58.83 (15.05) 62.04 (10.82)
Difference 3.33 (10.35)* -0.16 (8.92)
Considerateness Scale
Own Group 56.27 (13.82) 59.83 (11.03)
Other Group 61.98 (13.76) 55.75 (12.71)
Difference -5.71 (12.33)** 4.08 (14.15)+
Note: Sample size for this analysis = 75; +p < .10; *p < .05; **p < .01; ***p < .001 (two-tailed tests).
34
have differential status value by reward level, suggesting that personal response style
has become, or at least is becoming, a status characteristic.
When the estimations of the response styles are assessed more inclusively by
the reward-level each state was associated with at the end of the study, the resulting
averages are also in the predicted direction (see Table 6). Participants report that most
people believe the response style associated with valued rewards has more status and
competence (both marginally significant, two-tailed tests) but lower considerateness (t
= -3.25, p < .01, two-tailed test) than the reward-disadvantaged response style. These
results tend to support the second hypothesis.
Table 2.6. Mean Ratings of “Most People’s” Evaluations of Personal Response Style by End Reward Level
Variables Mean T-Statistic
Status Scale
High Reward Level 60.08 1.77+
Low Reward Level 57.37
Competence Scale
High Reward Level 62.10 1.64+
Low Reward Level 60.25
Considerateness Scale
High Reward Level 56.03 -3.25**
Low Reward Level 60.97
Note: Sample size for this analysis = 75; +p < .10; *p < .05; **p < .01; ***p < .001 (two-tailed tests).
35
Turning to personal assessments of the relative status, competence, and
considerateness of the two personal response styles, the expectation is again that those
who are highly rewarded will see their style as being higher in status and competence
but lower in considerateness, whereas the opposite is expected for those with a reward
disadvantage6. Table 7 presents the t-test analyses of this prediction. Similarly to the
results for the third-order beliefs, highly rewarded participants’ personal assessments
of the novel characteristic are in the expected direction. These participants believe
that their response style is higher in status (t = 3.14, p < .01, two-tailed test) and
competence (t = 2.58, p < .05, two-tailed test) than the opposing style. They also
view their style as having lower considerateness, but this result is not statistically
significant. Conversely, those with devalued rewards view their own response style as
having slightly higher status (marginally significant, two-tailed test). This result runs
contrary to predictions and is, interestingly, also inconsistent with the general direction
of their third-order beliefs. This suggests that they acknowledge that there is a
common, accepted belief that their style is of lower status, but they do not personally
subscribe to this belief. The difference between assessments of considerateness is
marginally significant (two-tailed test), and is, however, in the predicted direction.
6 The alpha-levels for these scales range from .76 to .93.
36
When the personal evaluations are evaluated by reward level alone, we see that
personal evaluations are not as differentiated by condition as the assessments of third-
order beliefs (see Table 8). As expected, the status and competence of the advantaged
response style is higher than that of the disadvantaged state, but these results are only
marginally significant in a one-tailed test (p < .07 and p < .06, respectively). Personal
assessments are open to social-desirability bias as it is not always appropriate to state
that one has an advantage, as is possibly also the case when those with high rewards in
the second phase stated that their chips did not have significantly higher status value.
It also may be easier for those who are reward-disadvantaged to state that they believe
Table 2.7. Mean Ratings of Personal Evaluations of Personal Response Style Relative to In-Group Status (with Standard Deviations)
Final Reward Level
Variables Ends with High
Rewards Ends with Low
Rewards
Status Scale
Own Group 64.43 (14.52) 61.94 (11.54)
Other Group 57.38 (14.24) 58.84 (10.81)
Difference 7.05 (14.19)** 3.10 (10.69)+
Competence Scale
Own Group 65.98 (15.75) 65.19 (12.49)
Other Group 60.68 (16.63) 63.65 (12.68)
Difference 5.29 (12.97)* 1.54 (9.40)
Considerateness Scale
Own Group 59.88 (15.67) 61.94 (12.88)
Other Group 60.12 (13.19) 58.32 (13.81)
Difference -0.24 (12.33) 3.62 (13.55)+
Note: Sample size for this analysis = 75; +p < .10; *p < .05; **p < .01; ***p < .001 (two-tailed tests).
37
their characteristic to not also be devalued. It is important to note, however, that even
though these personal assessments are not very distinct, the participants report that
“most people” do see these differences, thereby acknowledging the existence of a
broader social reality attached to the states of the novel characteristic.
Table 2.8. Mean Ratings of Personal Evaluations of Personal Response Style by End Reward Level
Variables Mean T-Statistic
Status Scale
High Reward Level 61.82 1.47+
Low Reward Level 59.50
Competence Scale
High Reward Level 64.89 1.53+
Low Reward Level 62.79
Considerateness Scale
High Reward Level 59.15 -1.22
Low Reward Level 60.97
Note: Sample size for this analysis = 75; +p < .10; *p < .05; **p < .01; ***p < .001 (one-tailed tests).
Overall, these results tend to indicate that the novel characteristic obtained
status value and partially support Hypothesis 2. In addition to social desirability bias,
another possible explanation for the lack of significant results could be that these
assessments may have been affected by the bargaining behavior of the simulated
partners. In all conditions and phases, the partners evidenced poor bargaining skills,
as the participants were able to extract the same amount of chips from both of their
partners regardless of purported status or reward differences. Thus, before rating the
38
novel characteristic, the participants had just come from a situation in which their
second partners’ competence at bargaining was demonstratively deficient. The
partners’ modest bargaining ability could have generalized to the assessments of the
novel characteristic’s competence and status. Nevertheless, even if this was the case,
there is still evidence to suggest that those who ended the study with devalued rewards
stated that both markers convey a differential degree of status to those who possess
them.
Influence
Do the relative reward levels lead to expectation advantages and the enactment
of a new status hierarchy based off of the states of the novel characteristic? The
results indicate that this is the case as those with a reward advantage deferred less
frequently to their partner than those with a reward disadvantage (t = -2.00, p < .05,
two-tailed test) (see Table 9). Importantly, the measure of influence does not
significantly vary by gender, personal response style, or the reward level with which
the participants began the study. ANOVA analyses also show that the final reward
level is the only significant predictor of the variation in the proportion of stay
responses (F = 5.01, p < .05) (see Table 10). Thus, although the valuation of the
response styles was not always sharply defined, participants did behave differently by
the final reward-level.
39
Table 2.9. Mean Difference in the Proportion of Stay Responses
P(s)
Variables Mean T-Statistic
Gender
Male .69 1.26
Female .64
Personal Response Style
S2 .65 -.09
Q2 .66
Beginning Rewards
Low Rewards .65 -.51
High Rewards .66
End Rewards
Low Rewards .62 -2.00*
High Rewards .69 Note: Sample size for this analysis = 75; +p < .10; *p < .05; **p < .01; ***p < .001 (two-tailed tests).
40
Table 2.10. Proportion of Stay Responses ANOVAs
Model 1 Model 2 Model 3 Model 4 Variables SS MS F SS MS F SS MS F SS MS F
Main Effects Female .04 .04 1.56 .03 .03 1.33 .03 .03 1.17 .00 .00 .15 Personal Response Style (PRS) .00 .00 .14 .00 .00 .00 .00 .00 .11 .01 .01 .43 Start with High Rewards .00 .00 .11 .00 .00 .06
End with High Rewards .08 .08
3.34+ .12 .12
5.01* Interactions Start High x Female .01 .01 .22 Start High x PRS .00 .00 .05 End High x Female .01 .01 .50 End High x PRS .02 .02 .69
Female x PRS .02 .02 .92 .08 .08
3.28+
Residual 1.74 .02 1.70 .03 1.66 .02 1.58 .02
R2 .03 .04 .07 .11 Note: Sample size for this analysis = 75; +p < .10; *p < .05; **p < .01; ***p < .001.
41
There is an important caveat, however, as to whether this fully supports the
third hypothesis. Because personal response style is always associated with reward
differences in the second phase of this study, the influence disparity may have been
due simply to these reward inequalities. There is no ultimate way of showing that the
new characteristic obtains enough valuation to independently cause these behavioral
differences. This is especially a concern given the moderate support for hypothesis 2
in that participants generally only formed third-order beliefs about the novel
characteristic. Not being able to know for certain whether participants would act
according to only their beliefs about the new characteristics in the absence of reward
disparities is a flaw in this study's design, and one that should be rectified in future
research.
DISCUSSION
The results from this study show preliminary support for the argument that
status markers have the power to create altogether new status groups and structures of
behavioral inequality. The proposed spread of status value mechanism has multiple
stages in that the status value of preexisting status characteristics first create stable
reward disparities before the rewards’ status value can spread to novel characteristics.
Cleanly manipulating and teasing apart each step in this process is a difficult
experimental task, and one at which this study has only partially succeeded.
This design’s main strength is that it divorced the rewards created by the initial
status characteristic differences from the novel characteristic introduced in the second
phase. This was accomplished by having two unique partners for the participant to
42
interact with and by reversing the reward levels in half of the conditions between the
first and second phase. Additionally, the two simulated partners were programmed to
use the same bargaining rules in the first and second phase of the study, which means
that the partners did not convey divergent behaviors that could have caused the
resulting influence differences. Thus, there could not have been a transfer of
behavioral inequalities between the first and second phase across all of the conditions
(Markovsky, Smith, and Berger 1984; Pugh and Wahrman 1983).
The negotiations may have introduced a different type of behavioral dynamic,
however. Across conditions and phases, not only did participants negotiate similarly
with their partners, they were also quite successful at obtaining large amounts of chips
from them. Participants’ competence assessments may have been confounded due the
association between the partners’ feeble bargaining skills, their associated reward
level, and personal response style.
One of the biggest faults of this study’s design is that final reward levels were
never separated from the nominal characteristic. While the novel characteristic did
obtain a degree of third-order status evaluations, it is unclear whether these valuations
are enough to produce behavioral inequalities without the aid of associated reward
disparities. It is therefore uncertain whether the clear influence differences revealed
with this design could be found if the partners only differed by the newly valued
characteristic.
A future study could rectify these two main failings by eliminating the two
phases of negotiated exchange and by including a final phase wherein the participants
interact with a new partner who only differs from the participants by the state of the
43
novel characteristic. To create the reward, participants could view a document that
lists past participants and their extant status characteristics. This information would be
color-coded such that those who have high status are highlighted with one color and
those who have lower status are marked with an opposing color. The study would also
have two phases wherein the participant works on an influence task, such as a joint
meaning insight task, with a first partner who differs from the participant by their
reward level and state of a novel characteristic. In the second phase, the participants
would obtain a new partner, presumably from a separate participant pool, who only
differs from them by the state of the novel characteristic. They would then take a
different influence task with the second partner, such as the contrast sensitivity task.
If influence inequalities are found in this second phase, we would be more certain that
these differences are due to the status value of the new characteristic alone.
CONCLUSION
In everyday interaction, we are defined not only by our master statuses, such as
our gender and ethnic backgrounds, but also by our awards and positions, which may
have differential social value. Many of these distinctions combine to create an
understanding of who we are, what kinds of behaviors can be expected of us, how
valuable our contributions should be, and the like. These expectations undergird our
shared and accepted social reality, with many using status and reward distinctions as
shorthand for assessments of competence and worth.
Veblen ([1899] 2005) was one of the first to argue that honorific goods have
social significance and are highly esteemed, sought after possessions. Since then,
44
researchers have continued to note the relative importance these status markers have
on social interaction (Bourdieu 1990, 1991), namely how their differential allocation
impacts assessments of status, competence, importance, and influence (Cook 1975;
Ridgeway 1991, 1997; Stewart and Moore 1992). This project expands this line of
research by examining how the underlying meanings conveyed by differential rewards
impact the creation of new status groups.
This paper puts forth a new extension of status construction theories, more
specifically Berger and Fisek (2006), by using the status value of objects as an
additional feature in the process of status creation. This study tests whether status
value spreads from preexisting status characteristics to initially non-valued resources
(see Thye 2000) and then onto a nominally distinct characteristic of the new
possessors of this object. In the absence of any distinguishing information about an
interaction partner, except that she controls status valued rewards and has a different,
initially non-valued characteristic, the results generally indicate that the states of the
nominal characteristic will gain status and influence through their association with the
reward. Notably, even after this new association is made between the reward and the
new status characteristic, the results also suggest that we will continue to evaluate
additional, unspecified possessors of this reward in the same fashion.
We therefore process the valuations conveyed by status markers in the same
manner as status characteristics, and under certain conditions differential rewards can
create new status distinctions with resulting behavioral expectations. Rewards can
play an important part in the status construction process in that groups do appear to
obtain differential levels of status and influence from the status markers they display.
45
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49
3. THE PERPETUATION OF INEQUALITY: STATUS DYNAMICS IN
LENDING MARKETS
The accumulation of wealth continues to be one of the predominant economic
disparities groups experience. One of the key ways that we begin to accumulate
wealth is first through the receipt of credit as people generally do not have the funds
needed to purchase houses, businesses, tuition, and the like outright. However, the
distribution of credit, such as in the mortgage and business loan market, can be fraught
with bias (Carter, Brush, Greene, Gatewood, and Hart 2003; Munnell, Tootell, and
McEneaney 1996; Oliver and Shapiro 1995; Ross and Yinger 1999, 2002). What
remains unclear is whether there are possible interpersonal mechanisms that are a
source of such bias. This study is an examination of one such possible mechanism:
how assessments of status and trustworthiness systematically bias funding allocations
in lending markets, and, thereby, the future accumulation of new wealth.
Past research documents the presence of disparities in a wide array of lending
markets, including the receipt of mortgage and business loans (Munnell et. al 1996;
Ross and Yinger 1999, 2002; Williams, Nesiba, and McConnell 2005). Using a wide
array of statistical and methodological techniques, researchers have demonstrated the
persistence of a systematic minority-white gap in the receipt of housing loans
(Blackburn and Vermilyea 2006; Munnell et al. 1996; Ross and Yinger 1999, 2002).
According to data from the 1991 Home Mortgage Disclosure Act, African Americans
and Hispanics are about 82% less likely to receive housing loans than comparable
whites (Munnell et al. 1996). Ross and Yinger (2002) conclude that legitimate
50
business practices cannot fully account for this disparity.
Additionally, audit studies demonstrate that black testers are less likely to
receive a quote for a loan, are often quoted higher interest rates, are given less
coaching from loan officers than white testers, and loan officers are less likely to look
for factors to justify accepting their loans (Lawton 1996; Siskin and Cupingood 1996;
Smith and Cloud 1996; Smith and DeLair 1999; Williams et al. 2005). Existing
evidence therefore suggests that lenders’ underwriting standards systematically differ
by the racial and ethnic background of the applicants (Ross and Yinger 2002).
Racial and ethnic minorities are also more likely to receive subprime loans,
which are mortgages associated with higher rates, fees, and closing costs, and,
consequently, greater risks to the borrower (Apgar & Calder 2005; Calem, Gillen, and
Wachter 2004; Immergluck & Wiles 1999; Williams et al. 2005). This appears to be
at least partially due to the conduct of the lenders themselves. For instance, audit
studies by Williams et al. (2005) demonstrate that white applicants are more likely to
be led to lenders’ prime mortgage division than African American applicants.
While minorities generally receive subprime loans overall, these loans also tend
to be concentrated in minority housing areas, thereby leading to reverse redlining.
This creates a “dual mortgage market” (Immergluck and Wiles 1999) where favorable
mortgages are concentrated in white areas and subprime loans in minority
neighborhoods. Lest we think that these findings are really the result of an underlying
income effect, research suggests that even at the highest income level, African
Americans are about three times more likely to receive subprime mortgages than
comparable whites (Calem et al. 2004; Williams et al. 2004).
51
Beyond mortgage markets, racial and ethnic minorities are also
disproportionately more likely to receive additional dealer markups on car loans and
generally pay a greater price for the vehicles (Ayres 1995; Ayres & Siegelman 1995;
Charles, Hurst, and Stephens 2008; Cohen 2007). For example, even after controlling
for applicants’ background information, bargaining strategy, and credit background,
Ayres and Siegelman (1995) have shown that dealers less flexible in their negotiations
with African American consumers, which leads to higher car prices. There is also
evidence to suggest that although women begin over half of the startup companies in
the United States, only about 7% of their businesses receive venture capital funding
(Brush, Carter, Gatewood, Greene, and Hart 2004; Carter et al. 2003; Greene, Brush,
Hart, and Saparito 2001).
In recent years, new lending markets have been created that allow individuals
to request loans from others without using traditional banking institutions as an
intermediary. Known as peer-to-peer lending markets, these arenas provide an online
forum in which individuals publicly display their lending requests and credit
information in the hopes of finding lenders who will finance portions of the loan.
Although peer-to-peer lending markets are a relatively nascent, they already account
for nearly half a billion dollars in loans, and this figure continues to grow. Peer-to-
peer markets, such as prosper.com and lendingclub.com, tout this funding method as
one free from the restrictions and uncertainties of traditional banking institutions,
wherein the lack of overhead costs leads to lower service fees, interest rates are
competitively set, and the only barrier to obtaining a loan are the judgments of fellow
peers. In these markets, those who desire loans are able to tell their own story
52
regarding who they are and the reasons for their request, in addition to providing more
conventional information related to their credit history.
While the ability to give such information may make the applicants feel as
though they are not devoid of personal identity, thereby creating a greater sense of
control over their self-presentation and connection to potential lenders, these stories
also convey many indicators on which bias and discrimination can be based. Much as
in traditional credit markets, lenders are not only examining the credit histories and
basic loan information, which in theory should be the only things they use in making
their decisions, but lenders can also see pictures and personally written descriptions
detailing the purposes of the loan and who the applicants’ are. Recent research into these peer-to-peer markets indicates that there are several
processes that impact the likelihood of receiving funding, in addition to those that are
purely financial. Ravina (2008), for instance, finds that there is a large beauty
premium in that those who are rated as more beautiful in their application profiles are
more likely to receive loans. In an important work examining racial discrimination
apparent in prosper.com’s market, Pope and Sydnor (2011) conclude that, controlling
for credit history, African Americans are 25-35 percent less likely to receive loans,
and, when they do, their interest rates are significantly higher than those of
comparable whites.
This brief summary of lending market disparity brings us to the question of
why do these disparities persist. If the credit histories and financial worthiness of
applicants are held constant why do we continue to see these inequalities in the receipt
of funding across a wide array of arenas? And is this simply the effect of one factor,
53
such as gender or race, or do the various demographic characteristics that borrowers
exhibit act in concert to influence lenders’ decisions?
The evidence suggests that these disparities are at least partially due to lender
discrimination. Some of the possible causes of these disparities may be general
prejudice and stereotypes of the lenders’ (see Pager and Shepherd 2008 for a review).
The organizational conditions of the lending environment may also amplify the
opportunities to discriminate wherein the very rules of the organization may be
interpreted or written in such a way that they favor those who are already advantaged.
While many researchers have proposed many potential explanations for why lenders
appear to discriminate, we do not yet know whether and how any of these beliefs
translate into action (Pager and Shepherd 2008). Uncovering the presence of
discrimination does not allow us to then infer back to account for what led someone to
behave in that fashion, and this leaves us with the important question of what the
causal mechanisms may be.
I propose that lenders use status as an indicator of creditworthiness, such that
even among those with comparable credit histories, borrowers’ status characteristics
further differentiate applicants. These characteristics insinuate differential levels of
competence and trustworthiness, which affect applicants’ funding. This study,
therefore, addresses one potential path through which preexisting status divisions and
social inequalities perpetuate themselves by predicting that those who are advantaged
by only their characteristics, including their ascribed traits, will have their economic
and social circumstances enhanced by virtue of their status distinctions. By examining
the decision-making processes behind lending, this project also helps illuminate how
54
implicit bias and assessments of competence that vary by status groups, such as by
diffuse racial and gender categories, affect funding decisions in traditional credit
markets more broadly. Thus, this research seeks to expand and give theoretical
context to this line of research by examining how indicators of status bias the
likelihood of receiving loans and at what amount.
STATUS AND ASSESSMENTS OF CREDITWORTHINESS
The central argument of this paper is that status distinctions systematically bias
assessments of creditworthiness and are one of the mechanisms driving lending
discrimination. Credit applicants’ status characteristics help to form a prevailing
impression of their competence and trustworthiness to lenders. These perceptions
guide the decision whether to fund the applicant and to what extent even for those of
comparable credit histories. To further explicate the role of status characteristics and
reward distributions, this discussion will now turn to a discussion of the relevant
theory and research on status processes.
Status characteristics theory (hereafter SCT) elucidates how existing status
differences pattern power and prestige behavior in small groups (Berger 1958; Berger,
Cohen and Zelditch 1966, 1972). There are two main types of personal characteristics
that can distinguish group members: diffuse and specific status characteristics.
Diffuse status characteristics are the ubiquitous, socially significant characteristics of a
given culture (e.g., gender or race/ethnicity) that have varying states (e.g., male-
female, white-African American). Specific status characteristics are associated with
the ability to perform particular tasks, such as having differential computer skills or
55
business aptitude. The various states of either type of characteristic have differential
esteem, prestige, and competence valuations as defined by the dominant culture that
correspond to the level of performance ability a person with a particular state is
assumed to have (Berger, Fisek, Norman, and Zelditch 1977).
Once a characteristic differentiates people in a setting or is relevant to the task,
group members will develop differential expectations related to the performance of the
each person according to whether the states of their status characteristics are esteemed
or devalued. Differential performance expectations pertain to which person the group
expects to be more competent at the task and make more valuable contributions.
These expectations then implicitly direct behavior and determine outcomes and
evaluations in a self-fulfilling manner.
In addition to affecting assessments of worth and competence and behavioral
expectations, the distribution of rewards and resources is also influenced by status
distinctions. Reward expectations theory (hereafter RET), (Berger, Fisek, Norman,
and Wagner 1983, 1998), a theoretical branch of SCT, predicts that individuals,
regardless of their relative status, will allocate larger amounts of valued resources to
high-status actors, while low-status actors will be allocated fewer rewards or more
devalued objects. The more divergent the status differences between actors are, the
greater the reward inequalities will be. The reverse process also operates so that those
who are allocated more rewards in a task situation (e.g., those who are paid more)
come to have more influence in the group (Bierhoff, Buck and Klein 1986; Cook
1975; Harrod 1980; Lerner 1965; Stewart and Moore 1992).
56
Culturally embedded referential structures govern this allocation process.
Referential structures are sets of widely held beliefs regarding how reward levels are
normatively associated with status-valued characteristics. RET recognizes three
different types of referential structures: categorical, ability, and outcome structures.
Categorical structures pertain to the relationship between diffuse status characteristics
and rewards. Ability structures relate specific task abilities and the determination of
reward levels. Outcome structures associate past achievement with rewards. These
three types of referential structures together form a basis for allocation norms, and
situations differ with regards to which structures are more relevant in the regulation of
reward distributions. For example, survey research indicates that there is a high-
degree of consensus in American society that earnings across a wide variety of
occupations should be based on at least outcome and ability structures (Alves and
Rossi 1978; Jasso and Rossi 1977).
Referential structures affect reward allocations when differential states of at
least one status characteristic are salient in an interaction and these states are the basis
of a normative referential structure. If at least one status characteristic is salient in an
interaction and if this characteristic is included in a referential structure, this reward
structure will also become salient. Expectations about “who should get what” are then
relevant in the situation when reward allocations are made. For instance, consider a
group whose members must make funding decisions. Further assume that educational
differences are apparent in the target group, and, consequently, status differences
based on educational background are salient in the situation. If a referential structure
also exists within the culture that associates higher educational attainment with higher
57
reward levels, this referential structure will become relevant to the situation. Group
members will then form reward expectations in line with the referential structure and
should fund accordingly.
As related to lending markets, people must evaluate a series of loan applications
and decide whom among them to fund. Within any profile, multiple status indicators
may be present and salient, such as gender, race/ethnicity, education level, and writing
aptitude, among others. Lenders should form reward expectations in line with the
referential structure and, therefore, will be more likely to fund applicants of higher
overall status. Because these applicants are assumed to be highly competent and more
able to repay the loan, high status applicants have an advantage over low status
applicants with the same financial histories in obtaining funding. For example, to the
extent that there is the cultural conception that African Americans have lower status,
and thereby lower competence, than whites, lenders will be less likely to give funding
to African Americans than to comparable whites.
Trust and Trustworthiness
I further contend that assessments of trust and trustworthiness are related to
status and aid in the lenders’ decision-making process. Many have argued that trust
has at least two forms: cognitive trust, which is based on assessments of competence
and expectations of reliably reaching a goal, and affective trust stemming from
discerning whether someone is considerate and mindful of others’ welfare (Cook and
Gerbasi 2009; Jeffries and Reed 2000; Ouchi 1981; Peters, Covello, and McCallum
1997; Rempel, Holmes, and Zanna 1985). While both high and low status individuals
58
can garner affective trust under certain conditions, cognitive trust should vary by
status because it is rooted in perceptions of competence. The expectation of higher
status people is that they can be entrusted with the group’s welfare: to make beneficial
decisions and accomplish the task with a positive outcome. Individuals can trust those
of higher status to succeed at a given task (e.g., starting a new business venture or
earning a degree) because these individuals are assumed and expected to have either
the specific or generalized ability necessary to do so.
Thus, high status individuals are believed to be more likely to display and
engender this type of trustworthiness, especially in highly uncertain environments like
lending markets wherein the opportunity for malfeasance is rather high. Potential
lenders can trust a high status person to be responsible with the loan, to use it
faithfully, and pay it back on time, thereby resulting in the greater likelihood of the
request being funded. Assessments of trustworthiness should therefore operate in
concert with evaluations of competence, based on status characteristic differences, in
affecting funding decisions.
Argument Summary
Lending markets have a high level of uncertainty tied to them. There is no way
of really telling whether a particular borrower will be willing or able to pay back the
loan, and many of the traditional indicators of creditworthiness, such as credit scores,
are merely guides and are not foolproof. There is no way for lenders to predict the
future. In this uncertain situation, I propose that when lenders assess each borrower,
they are at least in part, and perhaps implicitly, assessing the relative status and
59
trustworthiness of the borrower against the field of possible borrowers. These
appraisals systematically bias funding decisions, even when the borrowers’ have
commensurate financial histories. Lenders may feel as though they can entrust their
resources to those of higher status in that they assume that these borrowers have the
competence necessary to use the funds faithfully and responsibly and are able to repay
the loan. Additionally, in these markets multiple status indicators can be salient as
lenders generally do not only examine one applicant in isolation and each application
itself can quite naturally contain multiple status indicators. This study therefore also
assesses how various status signals, especially those related to gender, race, and
education, combine to affect funding decisions.
EMPIRICAL TEST
In order to analyze the role status plays in determining which loan applicants
receive funding, I recreated key aspects of lending markets in two separate studies.
The first study tests the proposed theoretical mechanism for how status, competence,
and trustworthiness affect the lending process. The second study employs a relatively
new experimental method in the social sciences, conjoint analysis (Caruso, Rahnev,
and Banaji 2009; Green, Krieger, and Wind 2001; Orme 2009), to examine whether
and to what degree extant status characteristics combine to bias funding decisions.
To better reflect the diverse backgrounds of lenders in various types of credit
markets, I obtained participants from two sources. The first are alumni from a private
university on the West Coast, and the second are from a community college also on
the West Coast. Two different samples from each population were taken for each
60
study. Because these participants are not necessarily trained lenders, both studies
mimicked the peer-to-peer lending model, including its environment, conditions, and
borrower profiles. Most importantly, all participants took the study online using their
own computers without being fettered by time constraints. It should be mentioned,
however, that this environment is not wholly unlike that of more traditional lending
scenarios wherein lenders are still evaluating multiple borrowers and risking certain
assets, though the effect of the organizational context in this particular study is
virtually eliminated.
STUDY 1
The first study addresses how assessments of status, competence and
trustworthiness affect the lending process, thereby examining this project’s proposed
theoretical mechanism. The profiles used in both this and the subsequent study were
created for the purposes of this research project but actual listings posted in
prosper.com’s market were used to guide the content of each application. Peer-to-peer
loan applications generally contain a description of the purpose of the loan and a
summary of the applicants’ financial viability, which are both provided by the
borrower. Borrowers also select a picture for their listing, with about half choosing a
their own photograph and two-thirds of this group providing a picture of at least one
adult (Pope and Sydnor 2011)1. The organization also provides the applicants’ credit
1 Prosper recently altered this practice so that borrowers are now not allowed to include their own pictures to display along with their loan descriptions. Borrowers can and do, however, provide their own pictures in their personal profile pages, which are linked to their loan requests. Borrowers can also personalize their profile names. Thus, there are still multiple ways in which important social clues can be supplied in these profiles and listings.
61
report and a letter grade that designates the average net loss rate of a listing. To
control for financial history, no credit report was provided to the participants. Instead,
all applicants had the second highest ranking of “B” to control for financial variability
in risk assessments related to these borrowers. To maintain the sense that these are
actual credit applications, the loan amount requested by each borrower and the loan’s
interest rate randomly varied within a very limited range. Additionally, the borrowers
loan requests were always greater than the amount that the participants were allowed
to lend to reflect the fact that lenders in these particular markets tend to only finance a
fraction of the total loan request.
To vary the gender (male or female) and race/ethnicity (white or African
American) of applicants, I developed a series of avatar pictures that were included in
each listing. In this way, I could control the presence of various status cues, including
the applicants’ age, level of attractiveness and happiness2, so that the information in
the pictures clearly differed by just gender and ethnic background. Participants were
informed that the avatars were based off of the pictures provided by the real
borrowers, with the avatars being used instead to protect the identity of the applicants.
Additionally, the use of this type of manipulation is not unique to this study as fMRI
researchers who investigate stereotype activation regularly employ cartoon stimuli in
their research (Mitchell, Ames, Jenkins, and Banajii 2009).
Finally, the borrowers’ writing ability, a proxy for their educational aptitude
and social capital, was varied in the descriptions of the loans’ purpose and the
applicants’ financial viability. In more traditional, face-to-face lending situations, this
2 The pictures were all evaluated equally along these dimensions by a group of independent raters.
62
would be akin to speaking very colloquially, using slang language, and not writing
well in correspondences with the lender. These descriptions were either all
grammatically correct or contained more informal grammatical errors, such as using
abbreviations, not using proper capitalization or punctuation, and making basic
spelling errors. The content of these descriptions and the overall presence of these
errors reflect the content of actual peer-to-peer listings. In fact many of the
descriptions were taken directly from borrowers’ public listings. Nevertheless, these
descriptions were kept relatively brief (generally a sentence or two) due to time
constraints, the desire to maintain comparability across profiles, and to help ensure
that no other characteristics became salient.
The purpose of the loan also varied with respect to whether the person was
applying for a debt consolidation or business loan, but all loans had a three-year term.
Thus, the profiles presented differed by the gender, race/ethnicity, writing ability, of
the loan applicants, as well as by the type of loan. This produced a total of 16
different profiles.
Procedure
In this first study, participants assessed the qualities of two loan applicants and
then made a series of funding decisions. One applicant was always a white male with
strong writing ability (i.e., the borrower that had the most possible status advantage)
who was applying for a debt consolidation loan, which is the most common type of
loan in peer-to-peer markets. Whether this applicant was evaluated first or last was
randomly designated to help prevent ordering effects. Using the same applicant in all
63
of the conditions helped constrain the number of conditions to a total of 163 in
addition to providing a consistent applicant profile against which participants would
compare the other borrowers.
After viewing the each profile, the participants were asked to make various
funding decisions. They first indicated the extent to which they expected the borrower
to likely repay the loan and how likely they would be to fund the applicant on 100-
point scales. They were also asked to put themselves in the position of being a
potential lender with a total of $1000 to either lend out, partially or fully, for potential
future gain if the loan is repaid or to keep for other purposes of their own choosing.
Under this hypothetical situation, they indicated whether they would like to fund this
particular applicant, and, if they did, how much of this pool of money they would like
to lend. Because this question asks participants to put themselves in a hypothetical
situation, the risk of providing funding is much lower than when lenders experience an
immediate cost to supplying a loan that may not be repaid. This is a strong test of
whether status indicators bias funding decisions because the participants are not
risking their own money and have no financial reason to not lend the full amount since
the applicants all have the same credit histories.
After making these preliminary lending decisions, the participants were then
asked to evaluate various qualities of the borrowers. To measure the applicants’
perceived competence – a traditional measure of status – participants rated the
3 This number also includes two sub-control conditions wherein both applicants were white males with strong writing ability applying for a debt consolidation loan. In these control conditions, the order or which borrower appeared varied to ascertain whether being evaluated first or last advantaged borrowers. Analysis (not shown) revealed that the order with which the applicants are evaluated did not influence assessments of competence, trustworthiness, or the various funding decisions.
64
borrowers according to whether they are capable, organized, skilled, competent,
confident, responsible, independent, and intelligent on 100-point scales (Correll,
Benard, and Paik 2007; Cuddy, Fiske, and Glick 2004). They were also asked to
report how trustworthy they viewed each applicant to be on the same scale.
Participants then listed the applicant’s pros and cons, which compelled the participants
to more fully process the information provided in the profiles. Finally, participants
reported their demographic information, including their gender, race/ethnicity, and
age.
Hypotheses
The intention of this study is to assess how aspects of gender, ethnic
background and perceived educational aptitude combine to influence assessments of
competence and trustworthiness and whether these assessments influence funding
decisions independent of the borrowers’ financial background. Therefore, this study
evaluates the proposed theoretical mechanism that status characteristics bias reward
distributions through differential assessments of competence and trustworthiness4. To
this end, I hypothesize:
4 SCT’s traditional scope pertains to groups who are working together to solve a valued task, such as juries and certain types of work groups. The circumstances under which these funding decisions are made, however, do not necessarily meet these original scope conditions. Researchers have recently begun to broaden the scope of SCT by arguing that status processes also operate in situations where one is asked to evaluate the work and contributions of others who differ on at least one salient, valued characteristic (e.g., Correll et. al 2007; Foschi, Lai, and Sigerson 1994; Foschi, Sigerson, and Lembesis 1995; Foschi and Valenzuela 2007; Webster and Driskell 1983). Given the evaluative setting of determining the creditworthiness of loan applicants, this study falls under these more encompassing scope conditions. Lenders must take into account all of the information provided by borrowers, including their credit histories as reported by external and legitimate agencies, in order to successfully perform their roles. Borrowers have an incentive to present themselves in the best possible fashion to attract lenders, and savvy lenders should carefully process this information to make profitable decisions. Additionally, lenders do not simply evaluate one borrower over the course of their careers but multiple
65
Hypothesis 1: The greater the status advantage of the applicant, as defined by
prevailing status characteristics, the more competence and trustworthiness will
be ascribed to the applicant.
Hypothesis 2: The more competent and trustworthy applicants are perceived to
be, participants will be more likely to expect that they will repay their loan.
Hypothesis 3: The more competent and trustworthy applicants are perceived to
be, participants will be more likely to fund their loan requests.
Hypothesis 4: As perceptions of the applicants’ competence and
trustworthiness increase, greater sums of money will be lent to the applicants.
STUDY 1 RESULTS
Sample
A total of 463 individuals participated in this first study: 154 undergraduates
and 309 alumni (please see Table 1). There are some differences between the
undergraduate and alumni samples. Expectedly, the average age of alumni is higher
than that of the undergraduates (t = 59.52, p < .000, two-tailed test), but the alumni
sample also has proportionally fewer females (t = 5.14, p < .000, two-tailed test) and
Asians (t = 22.76, p < .000, two-tailed test) but more whites (t = 25.91, p < .000, two-
tailed test). The alumni’s funding decisions are also more conservative: they believe different applicants. This should increase the salience of the various status characteristic differences between the applicants in the lenders’ portfolio.
66
applicants are less likely to repay their loans (t = 9.70, p < .000, two-tailed test), less
likely to fund the loan requests (t = 19.46, p < .000, two-tailed test), and lend less
money to the borrowers (t = 22.42, p < .000, two-tailed test). Finally, the
undergraduates are more likely to rate the borrowers as competent and trustworthy (t =
7.51, p < .000, two-tailed test).
Table 3.1. Means or Proportions of Demographic Characteristics and Funding Decision Variables by Study and Participant Group Study 1 Study 2 Variables Undergraduates Alumni Undergraduates Alumni
Female .73 .63** .67 .59***
Age 22.64 (5.69)
50.73*** (14.12)
25.35 (8.75)
49.51*** (15.11)
Ethnic background White .43 .81*** .37 .80***
African American .04 .03 .04 .05
Latino .09 .08 .09 .06
Asian .36 .09*** .40 .05***
Other .09 .00 .10 .04
Likelihood of applicant repaying loan
69.45 (21.65)
57.65*** (21.34)
63.48 (26.12)
55.32*** (23.63)
Likelihood of funding each loan request
49.09 (29.36)
24.55*** (24.42)
50.89 (30.37)
38.57*** (28.51)
Loan amount 464.84 (265.10)
254.61*** (245.20)
477.80 (294.01)
250.44*** (244.62)
Proportion selected to receive funding
.83 .50***
Competence/trust rating
5.93 (1.40)
5.16*** (1.18)
N = 154 309 170 103
Note: SDs in parentheses; 463 individuals participated in Study 1 and 273 individuals participated in Study 2; +p<.10; *p<.05; **p<.01; *** p<.001 (two-tailed tests).
67
Multivariate Analysis
To test this study’s hypotheses, I employed mixed-effects linear regression as
this study used a repeated-measures design in which the participants answered the
same series of questions for both of the applicants (Bates 2010; Jiang 2007). This form
of regression allows for proper specification of the variance and correlation of non-
independent observations while more effectively handling missing data as compared
to repeated measures ANOVA modeling (Gueorguieva and Krystal 2004; Krueger and
Tian 2004).
In all of these models, the characteristics of the borrowers (i.e., gender, race,
and writing ability), the type of loan, and the participants’ demographic information
(i.e., their graduate standing, age, and minority status) were included as fixed-effects
parameters, with a random effect designated by each participants’ ID. The applicants’
gender, race, writing ability, loan type were all coded as dummy variables, with males,
whites, those with poor writing ability and those applying for debt consolidation loans
coded as 1. The participants’ graduation and minority status were also coded as
dummy variables, with alumni and minority participants coded as 1. The competence
and trustworthiness variables are highly correlated and were averaged to create an
overall scale of competence and trustworthiness (mean = 5.45, SD = 1.32, alpha =
.95)5. This provides support for this paper’s contention that status and trustworthiness
processes work together, at least in this particular context.
5 Exploratory factor analysis also reports one underlying factor for these measures, with trustworthiness having a moderately high factor loading.
68
Competence and Trustworthiness
The first hypothesis predicts that higher status applicants, as defined by the
contemporary status distinctions, will be associated with greater levels of competence
and trustworthiness. Table 2 displays the effects of the applicants’ characteristics on
the competence and trustworthiness scale; the three models add interaction effects of
the applicants’ characteristics onto prior models, with the third model containing the
three-way interaction between the applicants’ gender, racial background, and
presumed writing ability. As illustrated in the third model of Table 2, applying for a
debt consolidation loan is negatively associated with assessments of competence and
trustworthiness (b = -0.41, p < .01). For this group, applicants have already
demonstrated a lack of care and financial acumen regarding their spending practices,
thereby showing a general lack of competence in this arena. As one participant
described, “I think I’m biased because I think the fact that he’s asking for a loan to
consolidate credit card debt makes him irresponsible.” The extent to which
participants viewed the application for a debt consolidation loan as indicative of a lack
of financial ability and responsibility demonstrates that loan type can be an indicator
of status in these markets.
69
Table 3.2. Study 1: Estimated Mixed-Effects Linear Regression Coefficients for the Effects of Gender, Race, and Writing Ability on the Competence and Trustworthiness Scale
Competence and Trustworthiness Scale
Variables Model 1 Model 2 Model 3
Male applicant -0.14 (0.13)
0.00 (0.25)
-0.34 (0.29)
White applicant -0.24+ (0.13)
-0.32 (0.25)
-0.68* (0.30)
Poor writing ability -1.03*** (0.13)
-0.47+ (0.26)
-0.85** (0.31)
Debt consolidation loana -0.25+ (0.13)
-0.37** (0.14)
-0.41** (0.14)
Applicant Interactions: White x male
0.21 (0.27)
0.75* (0.37)
White x poor writer -0.23 (0.27)
0.48 (0.42)
Male x poor writer -0.65* (0.27)
0.03 (0.41)
White x male x poor writer -1.21* (0.54)
Participant Controls: Alumi participant
-0.43* (0.18)
-0.45* (0.18)
-0.45* (0.19)
Age of participant
-0.01* (0.01)
-0.01* (0.01)
-0.01* (0.01)
Minority participant 0.13 (0.13)
0.15 (0.13)
0.15 (0.13)
Intercept 6.81*** (0.23)
6.65*** (0.27)
6.87*** (0.29)
Random Effect Variance Termb: Intercept
0.69 (0.07)
0.70 (0.07)
0.71 (0.07)
Note: Models fit using restricted maximum likelihood (REML); SEs in parentheses; N = 463 participants; +p<.10; *p<.05; **p<.01; *** p<.001. a Comparison group are those requesting a business loan. b Expressed as a standard deviation.
70
Loan type, therefore, may be a more legitimate indicator of competence and
trustworthiness than the applicants’ ascribed characteristics, whose combined effects
on status are illustrated in the third model of Table 2. The interaction of gender and
race is significant and positive (b = 0.75, p < .05), which shows that being male
increases assessments of competence and trustworthiness for whites but not for
African Americans. To get a better sense of this interaction, Figure 1 displays the
levels of competence and trustworthiness by the applicants’ gender and racial
background as predicted from model 3. The statistically significant average group
differences are also reported in this figure.
a Letters signify statistically significant t-test differences; Capital letters represent that the difference is significant in a two-tailed test (p < .05); lowercase letters represent that the difference is marginally significant in a two-tailed test (p < .10); subscript letters represent that the difference is marginally significant in a one-tail test (p < .10).
5.41
5.15
5.23
5.48
5.0
5.1
5.2
5.3
5.4
5.5
5.6
Male Female
Com
pete
nce
and
Trus
twor
thin
ess S
cale
Gender
Figure 3.1. Study 1: Predicted Assessment of Competence and Trustworthiness by Applicants' Gender and Racial Backgrounda
White
African American
c, d
A
B, d
A, B, c
71
The main effect of gender is negative, which would suggest that African
American males are rated as having lower competence and trustworthiness than
African American females, but this effect is not significant (b = -0.34, p = .244). A t-
test analysis of the average difference between African American males and females
reveals, however, that there is significant difference between these two groups, with
African American females having higher assessments of competence and
trustworthiness (please see Figure 1; t = 2.09, p < .05, two-tailed test). Turning back
to the third model of Table 2, we see that the main effect of racial background is
significant and negative, indicating that white females are evaluated as having lower
status than African American females. T-test analysis also bears this out (t = 2.42, p <
.05, two-tailed test). As seen in Figure 1, African American females are also rated as
having marginally higher competence and trustworthiness than white males (t = 1.74,
p = .083, two-tailed test). No significant differences were found between the status
assessments of white females and African American males.
These are somewhat surprising findings and ones that contradict the theory’s
prediction that the states of the status characteristics would basically aggregate (Berger
et al. 1977). SCT would predict that if male and white are the advantaged states of
gender and racial background, then white males should be rated as having the highest
levels of competence and trustworthiness because they are doubly advantaged and
African Americans females would experience what is essentially double jeopardy.
Instead, participants viewed African American females as having marginally higher
competence and trustworthiness than white males, while white males and African
American females have higher relative status and trustworthiness than African
72
American males and white females. As will be shown, this general trend persists
throughout the analysis of study 1.
Turning now to the effect of writing ability as displayed in model 3 in Table 2,
there is generally a negative effect across the gender and racial categories for
evidencing poor writing ability on assessments of competence and trustworthiness (b
= -0.41, p < .01). Providing a summary of your loan needs that includes grammatical
errors may be a signal of a lack of education or social capital as the borrower is not
savvy enough to understand that one is trying to foster what is essentially a
professional business relationship with the lenders. As one participant stated of an
applicant with poor writing, “…the applicant doesn't seem very intelligent. My first
impression of him is of a person who wrote this as a text to his friend. He doesn't look
very professional.”
While there is an overall negative effect of displaying poor writing on
assessments of competence and trustworthiness, the magnitude of the effect does vary
by gender and racial category as shown in the three-way interaction of model 3 (b = -
1.21, p < .05). To help grasp the details of this interaction, Figure 2 displays the
predicted assessment of competence and trustworthiness by the applicants’ gender,
racial back, and exhibited writing ability. Among those who showed strong writing
ability, African American females were again rated as having higher levels of
competence and trustworthiness than white males (t = 3.04, p < .01, two-tailed test)6.
Overall, white females are the only group for whom writing ability does not
6 While African American females’ have the highest average rating of competence and trustworthiness within this group of superior writers, their differences with white females and African American males do not quite reach marginal levels of significance (white females: t = 1.19, p = 0.117, one-tailed test; African American males: t = 1.28, p = 0.102, one-tailed test).
73
significantly alter status assessments (t = 0.61, p = .54, two-tailed test). White males,
however, are the most disadvantaged by including grammatical mistakes in their loan
applications, and participants view this group as having significantly lower
competence and trustworthiness than white females (t = 2.00, p < .05, two-tailed test)
and African American females (t = 2.49, p < .05, two-tailed test).
74
a Letters signify statistically significant t-test differences; Capital letters represent that the difference is significant in a two-tailed test (p < .05); lowercase letters represent that the difference is marginally significant in a two-tailed test (p < .10); subscript letters represent that the difference is marginally significant in a one-tail test (p < .10).
5.51 5.29
5.62
5.92
4.20
5.00 4.83
5.14
4.0
4.5
5.0
5.5
6.0
6.5
White Male White Female African American Male African American Female C
ompe
tenc
e an
d Tr
ustw
orth
ines
s Sca
le
Gender
Figure 3.2. Study 1: Predicted Assessment of Competence and Trustworthiness by Applicants' Gender, Racial Background, and Writing Abilitya
Superior Writing Ability
Poor Writing Ability
A, E, F
E
B, g
A, D, i
B, h, i
C, D
C, F, g
h
75
White males have the double advantage of their gender and racial background,
and when they evidence poor writing, they are violating a generalized normative
expectation that they should be proficient in other domains. For this group, perhaps
this inconsistent trait has a deeper impact due to these expectations (Berger et al.
1977). It is important to note, however, that the other applicant in this condition was
always a white male who presented strong writing skills; thus the saliency of writing
ability in this condition was the strongest it could be because no other characteristics
were salient. So this effect among white males may at least in part be due to the
extreme salience of this writing characteristic.
Funding Assessments
Are those rated as more competent and trustworthy more likely to be
advantaged when lenders make their funding assessments? The results suggest that
this is indeed the case (please see Table 3 and 4, which contain the funding assessment
analyses). The first models contained in Table 3 and 4 report the basic model with
only the main effects of the applicants’ characteristics for each of the funding
assessment dependent variables: perceived likelihood of the applicant repaying the
loan, perceived likelihood of funding the applicant, and loan amount given. The
second models include the addition of the gender, racial background, and writing
ability interactions. Finally, the third models present the full model with the
assessments of competence and trustworthiness1.
1 The third models reported in Table 3 do not contain the applicant interactions because they were not significant in the second models and were also not significant when added into the full model (analyses
76
As reported in Table 3, participants reported that men and African Americans
are expected to be the less likely to repay their loans than women and whites (b = -
4.20, p < .05 and b = 3.61, p < .05 respectively), but these expectations do not persist
in the analysis of the other funding decision variables. The interaction of gender,
racial background, and conveyed writing ability is again negative and significant in the
second model for the amount of money lent to the borrowers (b = -210.86, p < .05);
however, this interaction is no longer significant when assessments of the applicants’
competence and trustworthiness are added to the model (b = -86.63, p = .384).
The lenders’ assumptions about the applicants’ competence and trustworthiness have a
consistently positive and significant impact on their funding judgments. As
participants’ perceptions about applicants’ competence and trustworthiness increase,
they are more likely to expect that the borrowers will repay their loan (b = 8.55, p <
.000), they are more likely to lend money to the applicant (b = 9.43, p < .000), and the
more money they will lend (b = 102.62, p < .000, model 4). Additionally,
assessments of competence and trustworthiness are the only significant predictor of
whether a lender is likely to fund an applicant, and at what amount. This provides
strong support for hypotheses two through four.
not reported). The fourth model in Table 4 eliminates these applicant interactions because they were not significant when assessments of competence and trustworthiness were added in model three.
77
Table 3.3. Study 1: Estimated Mixed-Effects Linear Regression Coefficients for the Effects of Gender, Race, Writing Ability, and Assessments of Competence and Trustworthiness on Funding Assessments
Variables Likelihood of Applicant Repaying Loan
Likelihood of Funding Each Loan Request
Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Male applicant -5.14** (1.71)
-3.31 (3.92)
-4.20* (1.85)
1.11 (2.07)
-2.66 (4.70)
3.28 (2.33)
White applicant 0.42 (1.71)
0.54 (3.91)
3.61* (1.82)
-1.10 (2.07)
-1.60 (4.69)
0.02 (2.29)
Poor writing ability -10.83*** (1.76)
-9.63* (3.96)
-1.71 (1.93)
-6.68** (2.15)
-1.41 (4.75)
2.10 (2.44)
Debt consolidation loana 0.22 (1.69)
0.16 (1.82)
3.02+ (1.79)
-2.68 (2.04)
-5.06* (2.18)
0.29 (2.26)
Competence and trustworthiness scale
8.55*** (0.61)
9.43*** (0.78)
Applicant Interactions: White x male
-1.15 (4.80)
7.07 (5.75)
White x poor writer
0.35 (5.45)
-4.53 (6.53)
Male x poor writer .
-3.14 (5.42)
2.19 (6.49)
White x male x poor writer
1.06 (7.29)
-11.23 (8.74)
Participant Controls: Alumni participant
-9.48** (2.86)
-9.51** (2.87)
-2.96 (2.81)
-28.89*** (3.62)
-29.27*** (3.64)
-24.67*** (3.78)
Age of participant -0.15+ (0.08)
-0.15+ (0.08)
-0.09 (0.08)
0.19* (0.10)
0.20 (0.10)
0.27 (0.11)
Minority participant -2.83 (2.11)
-2.83 (2.11)
-2.59 (2.00)
3.54 (2.67)
3.64 (2.68)
3.24 (2.69)
Intercept 80.87*** (3.41)
80.09*** (4.06)
21.39*** (5.38)
46.48*** (4.25)
46.42*** (5.00)
-15.73* (6.95)
Random Effect Variance Termb: Intercept
14.27 (0.85)
14.26 (0.85)
11.71 (0.97)
18.57 (1.02)
18.84 (1.02)
16.69 (1.11)
Note: Models fit using restricted maximum likelihood (REML); SEs in parentheses; N = 273 participants; +p<.10; *p<.05; **p<.01; *** p<.001. a Comparison groups are those requesting a business or student loan. b Expressed as a standard deviation.
78
Table 3.4. Study 1: Estimated Mixed-Effects Linear Regression Coefficients for the Effects of Gender, Race, Writing Ability, and Assessments of Competence and Trustworthiness on Loan Amount Given
Variables Loan Amount Given
Model 1 Model 2 Model 3 Model 4
Male applicant 8.30 (22.33)
2.57 (50.08)
48.14 (51.56)
-3.24 (23.98)
White applicant -11.76 (22.94)
-9.62 (49.31)
71.28 (54.17)
-2.92 (24.02)
Poor writing ability -53.89* (23.74)
-2.12 (50.38)
148.69** (56.09)
41.99 (25.49)
Debt consolidation loana -30.70 (22.43)
-54.14* (23.30)
-15.19 (24.46)
2.27 (23.71)
Competence and trustworthiness scale
100.06*** (8.15)
102.62*** (8.17)
Applicant Interactions: White x male
47.05 (60.77)
-44.36 (64.68)
White x poor writer
8.60 (70.24)
-92.16 (76.55)
Male x poor writer .
17.34 (69.80)
-50.77 (72.93)
White x male x poor writer
-210.86* (94.95)
-86.63 (99.46)
Participant Controls: Alumni participant
-231.09*** (27.75)
-234.77*** (38.10)
-217.68*** (38.00)
-213.34*** (37.64)
Age of participant 0.53 (1.00)
0.70 (1.01)
1.75 (1.08)
1.64 (1.07)
Minority participant 7.49 (27.98)
8.96 (28.22)
-4.36 (26.95)
-8.11 (26.72)
Intercept 487.91*** (43.54)
468.42*** (51.46)
-212.53*** (76.75)
-164.33* (70.23)
Random Effect Variance Termb: Intercept
176.74 (11.02)
181.42 (10.90)
155.95 (11.40)
151.11 (11.60)
Note: Models fit using restricted maximum likelihood (REML); SEs in parentheses; N = 273 participants; +p<.10; *p<.05; **p<.01; *** p<.001. a Comparison groups are those requesting a business or student loan. b Expressed as a standard deviation.
79
SUMMARY OF STUDY 1
The results from Study 1 suggest that lenders’ assessments of applicants’
general competence and trustworthiness do significantly alter their funding decisions.
This provides strong support for the proposed status mechanism that lenders appear to
rely in part on assumptions about who these borrowers are even when applicants’
evidence similar levels of creditworthiness. These results also suggest that many of
the qualities of the borrowers affect funding decisions by first impacting the lenders’
assumptions of their relative competence and trustworthiness. Expectations derived
from the applicants’ characteristics then impact the lenders’ funding decisions. Thus,
status and trustworthiness are likely a mechanism behind lending discrimination in
that they mediate the relationship between socially significant categories and lending
outcomes.
Some of the borrowers’ characteristics may be at least partially within their
control, such as the financial reasons behind why they applied for a debt consolidation
loan or the quality and formality of their writing and self-presentation; however, the
lenders also appear to rely on assumptions based on the applicants’ ascribed
characteristics, specifically their gender and racial background, which are illegitimate
bases on which to form funding assessments.
The way in which these traits combined to influence assessments of status was
not, however, always in a manner that would be predicted by SCT. Specifically,
African American females, whom the theory would predict as being the most status-
disadvantaged, were rated as having essentially the highest levels of competence and
trustworthiness. Perhaps in this situation in which people are actively seeking
80
assistance to improve their lives, stereotypes associated with African American
females as strong, responsible women who are able to get things accomplished no
matter the situation or cost become salient and impact lenders’ funding decisions and
status assessments of the applicants (Browne and Kennelly 1999; Collins 1990;
Harris-Lacewell 2001; Kennelly 1999).
An opposing explanation could be that these findings are at least partially due
to social desirability bias suggesting that participants are not always able or willing to
respond authentically about socially sensitive topics, especially in social research.
Consequently, participants respond in what they believe to be a more socially
acceptable or normative fashion (Greenwald and Banaji 1995; King and Brunner
2000). In Study 1, the procedure made differences in the applicants’ characteristics
extremely salient, as they were always being compared to a white male who evidenced
having a strong writing ability. Due to this salience, social desirability bias is more
likely to pose a problem and influence this study’s results.
STUDY 2
To help verify the combined effects of gender, race, and education, a second
study was run that employed a relatively new technique for the social sciences,
conjoint analysis (Caruso et al. 2009; Green et al. 2001; Orme 2009). This method is
designed to more effectively uncover implicit attitudes while reducing social
desirability bias.
81
Conjoint Analysis
Conjoint analysis, popularized in marketing research, allows researchers to
uncover implicit attitudes by presenting individuals with sets of profiles that vary in
pre-determined, theoretically important ways. Respondents then select which profile
they most prefer or how greatly they prefer each profile in a given context. Because
people evaluate the various features of the profiles in concert, they tend to be better
able to ascertain what their relative preferences are than when they are asked to report
their preferences without points of comparison (Caruso et al. 2009). Additionally,
since multiple features are manipulated simultaneously, people are not asked to
directly compare socially sensitive characteristics, such as gender or ethnic
background, in isolation. Instead, these potentially sensitive attributes can be
embedded within broader, more complex profiles. Thus, the method generally reduces
social desirability bias while uncovering implicit beliefs.
For instance, Caruso et al. (2009) employed this method to gauge relative
preferences for trivia partners. The potential partners varied according to their
education level, IQ, trivia experience, and body weight. While participants reported
that body weight had an inconsequential effect on their choice, in actuality participants
selected partners with lower IQs, by about 11 points, to have a thin partner. As related
to lending, while respondents may state that they consider, say, a business loan more
fundable than a debt consolidation loan, their behavior may indicate an underlying
preference for relatively high status borrowers as defined by their ascribed
characteristics.
82
Procedure
There are many different types of conjoint analysis (see Orme 2009 for an
overview), but this study employs the single concepts variation. In single concepts
conjoint analysis, participants examine and rate a random series of individual profiles2.
The general set-up for this study mimics that of the first: participants were asked to
evaluate loan applications that all fell under the same credit ranking, the terms of the
loans were identical, and the amount requested and the loans’ interest rate randomly
varied within a limited range.
The content of these profiles slightly differed from that of the first study. The
applicants again differed by their gender (male or female), race/ethnicity (white or
African American), and writing skill (with and without grammatical errors in the
application materials); however, in the second study, borrowers could also apply for a
school loan in addition to a debt consolidation or business loan3. The combination of
these characteristics created a total of 24 profiles for the participants to evaluate. Each
participant rated half of these 24 profiles4 but all rated the four baseline profiles:
borrowers seeking a debt consolidation loan who used correct grammar but who
varied by gender and race/ethnicity5. Participants therefore evaluated a set of 14
randomly generated profiles.
2 Research indicates that single concepts conjoint analysis produces essentially the same results as those obtained through pairwise comparisons conjoint analysis, wherein participants evaluate a series of paired profiles and must determine the extent of their preferences for one profile relative to the other (Caruso et al. 2009). 3 Subsequent analysis revealed that funding decisions did not significantly vary by between whether the loan was for a school or business purpose. These two loan types are combined in the reported analyses. 4 Participants only viewed half of the profiles due to the time constraints associated with using the alumni sample. 5 Debt consolidation loans are the most prevalent type of loan in peer-to-peer markets; therefore this type of loan was selected as being the most basic.
83
For each session, participants evaluated one application at a time. They were
asked to carefully read through the application before answering a series of questions
related to each profile. The participants were again asked to indicate how likely they
believed each candidate would be to repay the loan and how likely they would be to
fund each applicant on a 100-point scale. If the participants stated that they would
likely fund the applicant, they were then asked that if they had $1000 to either give to
the applicant or retain for another purpose, how much would they lend to the
applicant. Once they finished evaluating an application, they proceeded to assess next
profile until they sequentially evaluated all 14 applications. Participants also reported
their demographic information, including their gender, race/ethnicity, and age at the
end of the study.
STUDY 2 RESULTS
Sample
170 undergraduates and 103 alumni participated in study 2 for a total of 273
participants (please refer back to Table 1). As in Study 1, the alumni are older, on
average, than the undergraduates (t = 32.11, p < .000, two-tailed test). The alumni
sample also contains proportionately fewer female (t = 2.94, p < .01, two-tailed test)
and Asian participants (t = 10.02, p < .000, two-tailed test) but more white participants
(t = 11.76, p < .000, two-tailed test). Again, the undergraduates generally make more
generous funding decisions: they are more likely to think the applicants will repay
their loans (t = 7.86, p < .000, two-tailed test), are more likely to want to fund the
listing (t = 13.42, p < .000, two-tailed test), lend more money (t = 11.13, p < .000,
84
two-tailed test), and are more likely to ultimately fund either applicant (t = 10.16, p <
.000, two-tailed test).
Multivariate Analysis
Mixed-effects linear regression was used again for this study’s analyses as
participants repeatedly made the same three kinds of funding assessments across 14
credit applicants (Bates 2010; Jiang 2007). As in Study 1, the characteristics of the
borrowers (i.e., gender, race, and writing ability), the type of loan, and the
participants’ demographic information (i.e., their graduate standing, age, and minority
status) were included as fixed-effects parameters, with a random effect designated by
each participants’ ID. The applicants’ gender, race, writing ability, loan type were all
coded as dummy variables, with males, whites, those with poor writing ability, and
those applying for debt consolidation loans coded as 1. The participants’ graduation
and minority status were also coded as dummy variables, with alumni and minority
participants coded as 1.
Funding Decisions
The purpose of this study is to ascertain whether and how the status indicators
present in Study 1 persist in shaping funding decisions (i.e., perceived likelihood of
the applicant to repay the loan, how likely the participant is to give a loan to the
applicant, and the loan amount given). As such, the funding assessment models
reported in Table 5 and 6 mirror those from Study 1. The only theoretically important,
85
consistent, and significant predictor in this study was the assessment of the borrower’s
competence and trustworthiness.
These models suggest that there is a persistent negative effect of requesting a
debt consolidation loan and displaying poor and improper writing across all of the
funding assessment variables. Thus, participants find business and school loans more
creditable and are more apt to fund those who have strong writing statements. These
results are generally consistent with those from Study 1, except that this more
sensitive methodological technique reveals a direct effect of these applicant
characteristics on lenders’ funding choices.
86
Table 3.5. Study 2: Estimated Mixed-Effects Linear Regression Coefficients for the Effects of Gender, Race, and Writing Ability on Funding Assessments
Variables Likelihood of Applicant Repaying Loan
Likelihood of Funding Each Loan Request
Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Male applicant -0.60 (0.69)
-3.96** (1.17)
-2.87* (1.33)
-2.15** (0.73)
-6.56*** (1.25)
-4.03** (1.41)
White applicant 0.10 (0.69)
-2.52* (1.15)
-1.45 (1.30)
0.88 (0.73)
-2.42* (1.22)
0.06 (1.37)
Poor writing ability -11.40*** (0.70)
-11.54*** (1.22)
-10.30*** (1.42)
-13.18*** (0.75)
-13.78*** (1.30)
-10.88*** (1.51)
Debt consolidation loana -3.67*** (0.70)
-3.70*** (0.70)
-3.69*** (0.70)
-4.52*** (0.75)
-4.55*** (0.75)
-4.53*** (0.75)
Applicant Interactions: White x male
5.88*** (1.39)
3.78* (1.85)
7.22*** (1.48)
2.33 (1.96)
White x poor writer
-0.76 (1.39)
-3.14 (1.96)
-0.74 (1.48)
-6.29** (2.09)
Male x poor writer .
0.99 (1.44)
-1.49 (2.04)
.
1.87 (1.54)
-3.91+ (2.17)
White x male x poor writer
4.80+ (2.79)
11.16*** (2.97)
Participant Controls: Alumni participant
-13.39*** (3.03)
-13.37*** (3.03)
-13.37*** (3.03)
-20.22*** (4.07)
-20.19*** (4.07)
-20.20*** (4.07)
Age of participant 0.16+ (0.09)
0.16+ (0.09)
0.16+ (0.09)
0.08 (0.12)
0.08 (0.12)
0.08 (0.12)
Minority participant -1.11 (2.22)
-1.14 (2.22)
-1.14 (2.22)
3.81 (2.98)
3.76 (2.98)
3.79 (2.98)
Intercept 67.11*** (3.20)
67.91*** (3.25)
68.14*** (3.27)
54.87*** (4.25)
57.03*** (4.30)
55.69*** (4.31)
Random Effect Variance Termb: Intercept
14.27 (0.75)
14.27 (0.75)
14.27 (0.75)
19.60 (0.98)
19.61 (0.98)
19.61 (0.98)
Note: Models fit using restricted maximum likelihood (REML); SEs in parentheses; N = 273 participants; +p<.10; *p<.05; **p<.01; *** p<.001. a Comparison groups are those requesting a business or student loan. b Expressed as a standard deviation.
87
Table 3.6. Study 2: Estimated Mixed-Effects Linear Regression Coefficients for the Effects of Gender, Race, and Writing Ability on Loan Amount Given
Variables Loan Amount Given
Model 1 Model 2 Model 3
Male applicant -13.17+ (7.14)
-40.25** (12.06)
-21.09 (13.62)
White applicant 5.94 (7.14)
-25.96* (11.79)
-7.31 (13.30)
Poor writing ability -114.08*** (7.38)
-109.83*** (12.77)
-87.26*** (14.80)
Debt consolidation loana -59.05*** (7.35)
-59.55*** (7.33)
-59.56*** (7.33)
Applicant Interactions: White x male
63.09*** (14.37)
26.38 (18.84)
White x poor writer
1.62 (14.48)
-41.63* (20.39)
Male x poor writer .
-11.47 (15.03)
-56.91** (21.29)
White x male x poor writer
87.50** (29.09)
Participant Controls: Alumni participant
-267.40*** (39.72)
-267.27*** (39.71)
-267.53*** (39.71)
Age of participant 2.13+ (1.15)
2.14+ (1.15)
2.15+ (1.15)
Minority participant 49.65+ (28.91)
49.48+ (28.90)
49.81+ (28.90)
Intercept 459.93*** (41.28)
473.73*** (41.70)
463.51*** (41.83)
Random Effect Variance Termb: Intercept
189.26 (9.62)
189.27 (9.62)
189.30 (9.61)
Note: Models fit using restricted maximum likelihood (REML); SEs in parentheses; N = 273 participants; +p<.10; *p<.05; **p<.01; *** p<.001. a Comparison groups are those requesting a business or student loan. b Expressed as a standard deviation.
88
Turning now to the impact of the applicants’ gender and racial background, we
see again that the magnitude of the effects that gender, racial background, and writing
aptitude have are conditional on each other. Notably, the effects of each combination
on funding decisions are very similar to the influence they had in predicting divergent
assessments of competence and trustworthiness in Study 1. Beginning with projected
repayment, the interaction between gender and racial background is significant (b =
3.78, p < .05), whereas the three-way interaction with writing ability is only
marginally significant (b = 4.80, p < .10). As displayed in Figure 3, white males and
African American females are viewed as equally likely to repay a loan (t = 0.28, p =
.779, two-tailed test), as are white females and African American males (t = 0.46, p =
.645, two-tailed test). White males and African American females are also perceived
as being more likely to repay a loan than white females and African American males6.
6 A figure for the three-way interaction with writing aptitude was not included, as it is only a marginally significant predictor of anticipated repayment.
89
a Letters signify statistically significant t-test differences; Capital letters represent that the difference is significant in a two-tailed test (p < .05); lowercase letters represent that the difference is marginally significant in a two-tailed test (p < .10); subscript letters represent that the difference is marginally significant in a one-tail test (p < .10).
The three-way interaction of the applicants’ gender, racial background, and
writing precision is a significant predictor of whether the lender foresees giving a loan
to the particular borrower (b = 11.16, p < .000). To better appreciate the details of this
interaction, Figure 4 reports the predicted likelihood of funding the request by each
combination of this interaction of borrower characteristics. Without taking apparent
writing ability into consideration, participants are about equally likely to fund white
males and African American females (t = 0.89, p = .372, two-tailed test), and African
American males are the least likely to receive a loan (difference with white females: t
= 1.87, p < .10, two-tailed test).
62.11
59.59 59.16
62.33
57.0
58.0
59.0
60.0
61.0
62.0
63.0
Male Female
Lik
elih
ood
of R
epay
ing
the
Loa
n
Gender
Figure 3.3. Study 2: Predicted Likelihood of Repaying the Loan by Applicants' Gender and Racial Backgrounda
White
African American
A, c
A, D
B, c
B, D
90
a Letters signify statistically significant t-test differences; Capital letters represent that the difference is significant in a two-tailed test (p < .05); lowercase letters represent that the difference is marginally significant in a two-tailed test (p < .10); subscript letters represent that the difference is marginally significant in a one-tail test (p < .10).
49.51
51.21
47.01
51.25
40.52
34.61 33.26
40.84
32.0
34.0
36.0
38.0
40.0
42.0
44.0
46.0
48.0
50.0
52.0
White Male White Female African American Male African American Female
Lik
elih
ood
of F
undi
ng L
oan
Gender
Figure 3.4. Study 2: Predicted Likelihood of Funding Loan by Applicants' Gender, Racial Background, and Writing Abilitya
Superior Writing Ability
Poor Writing Ability E, K, L
F, K, M
G, L, N
E
G, I, J
H, I
H, M, N
F, J
A
A, C, d
b, d
b, C
91
Overall, writing ability has a significantly negative impact on whether the
participants fund a request across all of the gender and racial groups. For those whose
applications did not include writing mistakes, African American males are less likely
to receive loans than white and African American females (t = 2.27, p < .05 and t =
1.98, p < .05, respectively in two-tailed tests). Assessing those with poor writing
ability, we see the continuing trend that white females and African American males
are less likely to be funded than white males and African American females.
Finally, the loan amount given is also similarly affected by the three-way
interaction of the applicants’ characteristics (please see Model 3 of Table 6). Figure 5
displays the predicted loan amount given by the applicants’ gender, racial background,
and perceived writing ability. African American males and white females are again
the most disadvantaged regardless of how well their applications are written. White
females are given marginally less money than African American females (t = 1.68, p <
.10, one-tailed test), while African American males are provided with fewer funds than
both white males (t = 2.33, p < .05, two-tailed test) and African American females (t =
2.89, p < .01, two-tailed test).
Across all of the gender and racial groups, writing ability again has a
consistent and negative impact on how much each applicant is credited. Additionally,
whether the applications contained grammatical errors influenced each gender and
racial group in essentially the same manner as in the prior analysis of funding
likelihood. Once more we see that for those with strong writing statements, African
American male applicants are provided with less money than African American
females (t = 1.75, p < .10, two-tailed test) and white females (t = 1.36, p < .10, one-
92
tailed test). Among the poor writers, African American females are lent about the
same amount of money as white males (t = 0.41, p = .682, two-tailed test), and these
two groups are both given more funds than white females and African American
males.
93
a Letters signify statistically significant t-test differences; Capital letters represent that the difference is significant in a two-tailed test (p < .05); lowercase letters represent that the difference is marginally significant in a two-tailed test (p < .10); subscript letters represent that the difference is marginally significant in a one-tail test (p < .10).
438.59 433.30
418.19
442.02
352.63
312.02
287.69
360.96
275.00
300.00
325.00
350.00
375.00
400.00
425.00
450.00
White Male White Female African American Male African American Female
Loa
n A
mou
nt G
iven
Gender
Figure 3.5. Study 2: Predicted Loan Amount Given by Applicants' Gender, Racial Background, and Writing Abilitya
Superior Writing Ability
Poor Writing Ability
D, J, K
E, J, L
F, K, M
D
F, h, i
G, h
G, L, M
E, i
A
A, C
b
b, C
94
SUMMARY OF STUDY 2
The use of the conjoint method uncovers a consistent and significant effect of
the applicants’ characteristics on lenders’ funding assessments. The general finding
across these models is that white males and African American females are similarly
advantaged across all funding decisions, and their applications are evaluated more
favorably than both those of white females and African American males. This result
mirrors that which was reported in the analysis of competence and trustworthiness
from Study 1, excepting that African American females were evaluated as having the
highest levels of competence and trustworthiness. Importantly, the conjoint analysis
technique used in Study 2 was specifically designed to reduce the effects of social
desirability bias to uncover implicit attitudes and preferences; yet, these results
generally replicate those from Study 1 in that the status beliefs associated with gender
and race do not have a simple, additive impact on lending decisions.
DISCUSSION
In both studies we are able to see the combined effect of gender and racial
categories on a wide array of funding decisions and in assessments of competence and
trustworthiness. The intersections of race and gender indicate that African American
males continue to be highly disadvantaged in a wide array of lending decisions, while
the funding that African American females receive is similar to that of white males.
Not only are African American females rewarded at the same level, they are also rated
as having slightly higher levels of competence and trustworthiness than white males.
95
This is a somewhat surprising result in that SCT would predict that the status
valuations associated with the states of gender and racial background would
essentially aggregate, which is what we see with white males relative to African
American males and white females. In broad terms, the stereotypes associated with
these groups generally conform to SCT’s predictions. White females are generally
prescribed to be communal, such as being more emotional, passive, and ineffectual
than white males (Diekman and Eagly, 2000; Prentice and Carranza 2002; Rudman
and Glick 2001). Consequently, lenders may perhaps implicitly assume that white
females are not able to use the loans appropriately and are therefore less likely to have
their requests fulfilled. African American males tend to be viewed as lazy,
threatening, or overly aggressive, and thus they may not be responsible with a loan
(Devine and Elliot 1995; Eberhardt, Goff, Purdie, and Davies 2004; Lane, Banaji,
Nosek, and Greenwald 2007; Pager 2007; Quillian 2006, 2008).
Indeed, many participants held a harsher standard against African American
males and white females in their descriptions of the applicants’ pros and cons in Study
1 (Foschi 1989, 2000). They are more critical that these two groups did not have the
highest credit rating, even though participants were informed that all of the applicants
have the same credit history. A few participants were openly against funding a white
female. For instance, one participant reported, “Applicant 1 [the white male] sounds
better1.” Another said, “I trust men more than I trust women,” while a third stated that
the only negative about a particular applicant was that she “is a woman.” Participants
were also generally more suspecting of African American males’ employment.
1 Both applicants exhibited strong writing ability in this case.
96
Various participants made statements along the lines of “his employment is probably
temporary,” that “his job may not be secure,” and that he is generally “too evasive.”
Simultaneously, participants in these conditions made overly positive statements about
the white male applicants. For instance, one participant listed the benefits of funding
the African American male applicant as “none,” but said that the white male “seems
intelligent and hardworking.”
Stereotypes of African American females, however, suggest that people tend to
view this group as being strong black women trying to do it all against the odds;
therefore, they are perhaps more worthy of assistance (Browne and Kennelly 1999;
Collins 1990; Harris-Lacewell 2001; Kennelly 1999). Harris-Lacewell (2001) defines
this concept thusly:
In her contemporary form, the strong black woman is a motivated,
hardworking breadwinner. She is always prepared ‘to do what needs to
be done’ for her family and her people. She is sacrificial and smart.
She suppresses her own emotional needs while anticipating those of
others. She has a seemingly irrepressible spirit unbroken by a legacy of
oppression, poverty, and rejection. (P. 3)
The statements made by the participants from Study 1 demonstrate the salience
of this conception. Many participants reported that the African American female
applicant was “intelligent, smart, seems to be reliable since she has a good credit
history,” and that she seems “well organized and trustworthy” and “dependable.”
Another participant stated, “She has enough self-confidence to go online and request a
97
loan.” This sentiment was echoed by another, saying, “I think this candidate would
make her best effort to repay the loan.”
These results suggest the persistence of this strong black woman stereotype in
this lending environment. This conception of African American females is certainly
not always beneficial, however. For instance, it can generate great stress and social
pressures, and observers may see their commitment and responsibility to their job as
stemming from a “need to do it” rather than a “wanting to do it,” which is considered
to be a more desirable trait in the labor force (Kennelly 1999). Still, in this particular
environment this stereotype does appear to advantage African American females.
As related to expectation states theories, it is already accepted that status
evaluations are contingent on the group’s task and that different situations can
generate various levels of inequality (Balkwell 1991). This work demonstrates that
under certain conditions the expectations about certain groups may even reverse. It
may then be beneficial for status theorists to allow for a more intersectional approach
to understanding the combined effects of gender and race (Browne and Misra 2003).
Even though the over-arching status mechanism operates as expected, the attitudes and
beliefs about each combination of traits may be relatively unique, as opposed to
simply being characterized as what is essentially multiple jeopardy. Those who work
within this research program have been relatively good about allowing for the
presence of contextual effects, but how these contexts affect the way status
characteristics combine has not been well investigated.
98
CONCLUSIONS
By examining the decision-making processes of potential lenders with an
experimental framework, this project helps illuminate how assessments of competence
and trustworthiness that vary by status groups may be a driving mechanism behind the
funding variations found in traditional credit markets more broadly. Status becomes a
means by which lenders compare applicants to determine whether and how to fund
them, even when the applicants have similar financial histories. This research offers a
potential cause behind the discrepancies in underwriting standards captured in
previous research, and, thereby, provides one potential solution to the puzzle of
continued lending discrimination and the accumulation of new wealth.
This work also has implications for online markets in that this study
demonstrates how social cues can bias judgments in online settings in which we do not
necessarily have the benefit of face-to-face interaction that perhaps dampens the
saliency of certain status cues. Indeed, relatively small actions that are common in
online settings, such as not proof-reading messages or using texting language, can give
big signals to evaluators about one’s competence and trustworthiness. As online
transactions become an increasingly popular venue for business, schooling, dating, and
the like, this type of bias may become ever more important.
99
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4. REWARD INTERVENTIONS: THE EROSION OF SOCIAL INEQUALITY?
The perpetuation of social inequality is one of the grand social issues of the
modern era. Many social programs aim to reduce such inequality by rewarding
disadvantaged groups through increasing their access to valued occupations, positions
of authority, monetary resources, and esteemed awards. Some of these programs
include merit-based awards for minority scholars, foundations that provide
microfinance services to women in developing countries (such as the MicroLoan
Foundation and Friends of Women’s World Banking), comparable worth policies, and
affirmative action more broadly. At the core of many of these initiatives is a two-
pronged attack against discrimination: first, due to discrimination we should have
policies in place to better assure fairness and equality, and, second, the hope that the
redistribution of financial, educational, and occupational advancements to
disadvantaged, low status groups will help to increase their social standing and
opportunities over time.
Many have argued that one of the key reasons why inequality remains
pervasive is that those who control access to resources and rewards are primarily
advantaged groups who, intentionally or not, continually reallocate these rewards to
similar, high status others (Jacobs 1989; Kanter 1977; Padavic and Reskin 2002;
Reskin 1988; Williams 1992). By having limited access to these rewards, such as
obtaining college degrees from prestigious schools, high status occupations, and
public acknowledgments of achievements, low status groups continue to be at a
disadvantage. In the ubiquitous case, reward distributions are congruent with status
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groups, such that high status actors are associated with highly desired rewards and low
status actors with unattractive or devalued rewards. When the situation is reversed,
however, as is the case in some of the aforementioned social programs, the status
disadvantaged may enjoy an increase in their assumed competence and ability by
virtue of these rewards. Perhaps, even the overall estimation of those who have the
same characteristics may be positively affected over time.
The process of using rewards as an intervention can be fraught with
complexity, however. While some types of rewards convey a definite sense of
prestige, value, and ability regardless of their possessors, such as Nobel Prizes,
McArthur Genius Grants, and some high-ranking political offices, the meaning of
numerous others may not be as concrete and immune to the status of those with whom
they are affiliated. Additionally, once valued objects are more freely bestowed or
obtained and begin to filter throughout a population, a tipping point may be reached
wherein the value of the status symbols begins to be contaminated. With newer
rewards that do not have a strong valuation, there may be an even quicker tendency for
the objects to lose their meaning once they are noticeably coupled with disadvantaged
groups.
Indeed, there are numerous instances of groups trying to protect the value of
their awards by further excluding those who are eligible to receive them. For
example, the Theatre Development Fund has bestowed the Wendy Wasserstein Prize
to young female playwrights every year since 2007; however, in 2010, they initially
concluded that that year’s eligible playwrights were not “worthy” enough for the
reward (Healy 2010). Amid much criticism, the Fund later decided to alter how it
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evaluates each candidate so that its members felt as though they could legitimately
give this prize to one of the female candidates, thereby further protecting the meaning
of the Wasserstein Prize in their estimation. As one member of the committee, Ms.
Ettinger, stated, “As a funder, we must be able to insure the integrity of the prize”
(Healy 2010).
Understanding the relationship between rewards and those who control them is
an important step towards fully understanding how rewards might work to reduce
social inequality. This research seeks to ascertain whether the reward alone is enough
to diminish the effect of status differences on processes of inequality, or whether, once
a reward is affiliated with disadvantaged groups, the reward itself looses value and
prestige through this association, thereby diminishing any power it may have had to
affect social change.
STATUS-BASED INEQUALITY
While there are many different types of inequality, this research focuses on
status-based inequality. Inequality of this sort arises when people or groups are
distinguished by certain characteristics that convey divergent levels of status, esteem,
competence, and prestige. Expectation states theory is a research program within
structural social psychology that seeks to explicate these status processes and their
effect on interpersonal relations and inequality. Within this program, status
characteristics theory (hereafter SCT) elucidates how existing status differences
pattern behaviors related to having differential levels of power and influence in small
groups (Berger, Cohen and Zelditch 1966, 1972). This theory applies to groups who
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are working together to reach a goal (i.e., who are collectively and task-oriented), such
as juries or students working on a group project for a class. These status processes
also operate when a person evaluates the work and contributions of others who have
differing states of at least one, salient status characteristic (Correll et. al 2007; Foschi,
Lai, and Sigerson 1994; Foschi, Sigerson, and Lembesis 1995; Foschi and Valenzuela
2007; Webster and Driskell 1983).
There are two main kinds of personal characteristics that can distinguish group
members: diffuse and specific status characteristics. Diffuse status characteristics are
culturally defined, socially significant characteristics (e.g., gender or race/ethnicity)
that have varying states (e.g., male-female, white-African American). These various
attributes have differential esteem, competence, and prestige valuations as defined by
the dominant culture that correspond to the level of performance ability a person with
a particular state is assumed to have (Berger and Fisek 2006). Specific status
characteristics are associated with the ability to perform particular tasks, such as
computer skills or business aptitude.
The basic form of SCT is as follows. Once a group is differentiated by at least
one diffuse status characteristic or by a characteristics that is relevant to the task,
individuals will assign expectations about the performance and potential contributions
of group members based on the valuation of the states of their status characteristics.
The characteristic will be relevant to expectations of individual’s performance on the
group’s task unless it is directly challenged (the burden of proof principle). Behavioral
inequalities favoring the actors who have highly-valued status characteristics will then
emerge with respect to opportunities granted to speak, actual level of participation,
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evaluations of others' performance, and the influence members have to change the
mind of others while solving the problem. The power and prestige hierarchy and
expectation hierarchy are mutually reinforcing, and they will remain highly stable over
time (Berger and Conner 1974).
Status Interventions
In addition to examining the formation and continuation of the power and
prestige hierarchy, researchers have also investigated the impact of certain
interventions intended to reduce these inequalities. These interventions have entailed
the activation of inconsistent status elements to neutralize or overcome the
consequences of negatively valued states of diffuse status characteristics. Pugh and
Wahrman (1983) demonstrate that when experimenters inform a mixed-gender group
that the female members are highly competent at an ability that is relevant to the
current task, while the male partners are not, the males’ influence is greatly reduced.
This alteration in level of influence also continued through a second identical task with
new partners. Markovsky, Smith, and Berger (1984) also show that a similar type of
ability intervention reduced, and in some cases reversed, the effect of education level
on deference behaviors. This effect also continued when the subjects worked with a
second partner on a different task, although the strength of the effect was not as strong
in the second phase.
According to SCT, when status characteristics differentiate people who are
participating on a task together that has clear goals, people process all relevant status
information practically instantaneously in order to form an overall expectation of
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whose performance will contribute the most and be the most valued in terms of
solving the problem at hand. When the characteristics are consistent, the process is
relatively straightforward; however, when the states are inconsistent, people must
process both sets of information to form their performance expectations (Berger,
Fisek, Norman, and Zelditch 1977; Berger, Norman, Balkwell, and Smith 1992). This
process occurs through the combining of organized subsets, wherein the
characteristics’ states of positive and negative valence are sorted into unique sets of
information. An attenuation principle governs the processing of these subsets
wherein each additional piece of information within a subset has marginally less
impact on changing the aggregated performance expectation (Berger et al. 1977;
Berger et al. 1992). If a group member has many positive characteristics but markedly
fewer negative attributes, these negative traits will still have great importance because
the numerous positive characteristics are progressively less consequential in forming a
final expectation of who will be more or less active and influential in the group.
Thus, inconsistent status information is not disregarded regardless of how
much of this information exists, at least in situations in which SCT is applicable.
Status interventions have an impact precisely because they introduce inconsistent
information regarding each of the group members. When these discrepancies are
directly relevant to an imminent task through specific status attributes, these ability
structures can even override the effect of some diffuse status characteristics
(Markovsky et al. 1984). This research has shown the fruitfulness of status
interventions, but the interventions studies thus far are largely based on providing
ability distinctions. In application, providing aptitude information may be one way of
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lessening status-based inequality, but there are also many programs that aim for the
same goal by providing rewards and valued acknowledgements to disadvantaged
groups. Theoretically, the impact of rewards should be as consequential to achieving
this end. I will now turn to a discussion of rewards and how they may be relevant to
the diminution of status hierarchies.
Rewards
While SCT is mainly concerned with the status of people, another branch of
expectation states theory, the status value theory of distributive justice (hereafter
SVDJ), is concerned with the status of objects and positions (Berger, Zelditch,
Anderson, and Cohen 1972). The theory is based on a reformulation of Veblen's
([1899] 2005) notion of honorific value in that desired objects, tangible or not, can
symbolize status and social standing. Elements in the social world, such as people,
objects, or positions, come to have status value when they are uniquely related to
elements of the social world that do. If these elements are consistently valued, then
the related non-status valued object will acquire the status value of these elements
through a spread of status value process wherein the preexisting valuation is ascribed
onto the new object. Rewards thereby come to have status value, and are reified
markers that connote prestige and honor (Veblen ([1899] 2005).
Central to the current proposed research project is reward expectations theory
(hereafter RET), which is an interrelation of SCT and SVDJ (Berger, Fisek, Norman,
and Wagner 1983). RET explicates how rewards are allocated according to status
differences. The general prediction derived from RET is that people, regardless of
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their relative status, will allocate larger amounts of valued rewards to high-status, as
opposed to low-status actors, while low-status actors will either be allocated fewer
valued rewards or devalued objects. The more divergent the status differences
between actors are, the greater the reward inequalities will be; while the resulting
reward distribution will be more equivalent when the status elements are more
inconsistent. Additionally, the reverse of this process also operates in that those who
are allocated more rewards in a task situation (e.g., those who have higher salaries)
will come to have more influence in the group (Bierhoff, Buck and Klein 1986; Cook
1975; Harrod 1980; Lerner 1965; Stewart and Moore 1992).
Thus, people expect those with positively valued status characteristics to
possess highly status-valued objects and positions due to the ascription of their
characteristics’ assessment to their resources. The effect of rewards is also potentially
quite powerful in that when those who are allocated highly esteemed rewards are also
differentiated by an initially non-valued characteristic, the status value of the reward
spreads to the associated state of the nominal characteristic, thereby beginning the
creation of a new status characteristic (please see Chapter 1). The spread of status
value is an especially important process that may even affect the valuation of
preexisting status characteristics as well.
Reward Allocation and Status Characteristics
Rewards provide status information about their possessors, and those who
acquire highly valued rewards come to have more influence in groups than those who
do not (Bierhoff et al. 1986; Cook 1975; Harrod 1980; Lerner 1965; Stewart and
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Moore 1992). If rewards are reified status objects that convey relative status
advantages to their possessors, this implies that when status disadvantaged people
control highly valued rewards, they should receive a status advantage by virtue of this
ownership. When the status value of the reward and salient status characteristics is
consistent, then the possessor of the reward should experience an attenuated increase
in her relative influence in a group.
If the reward and salient status characteristics are inconsistent, however, the
situation becomes unbalanced, which produces tension and prompts the actors to bring
these social elements back to equilibrium (Berger et al. 1972). In this situation, the
status value of the reward should be processed along with that of the characteristics, as
the principle of organized subset combining suggests. As Berger, Fisek, Norman, and
Wagner (1983) state: “allocating a positive state of a goal object to an actor increases
the actor’s overall task expectation, while allocating the negative decreases the actor’s
overall task expectation.” Accordingly, reward allocations that are inconsistent with
the states of active status characteristics may produce at least a change in the particular
actor’s status and influence in the direction of the valence of the reward’s valuation,
relative to similar others without such rewards. Therefore, I hypothesize:
Hypothesis 1: As the status value of the actors’ rewards increases, the actors will be evaluated as having greater overall status assessments.
Hypothesis 2: As the status of the actors increases, the greater the behavioral
expectation advantage attributed to these actors will be. Once the reward is relevant to the state of a status characteristic, the reward
level’s status value may not only affect the actor’s relative status in the immediate
encounter, but the valuation may spread to the actors’ associated characteristics
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themselves. If this spread does occur, the evaluation of the characteristic overall
should change congruently to the valuation of the rewards. Pugh and Wahrman’s
(1983) and Markovsky et al.’s (1984) intervention work suggests that this may be the
case in that interventions aimed at altering the ability-level of disadvantaged groups
continued to have a positive effect on the relative influence of those from
disadvantaged status groups in subsequent encounters with new partners. The
experience of inconsistent ability information may therefore have altered the overall
valuation of those from disadvantaged status groups. This leads to the following
hypothesis as related to reward interventions:
Hypothesis 3: As the status value of actors' rewards increases, the corresponding status value of the states of their preexisting status characteristics will also increase.
Interventions that provide esteemed rewards to disadvantaged status groups
may help to increase the relative power and prestige of these groups precisely because
these possessions convey additional status information about those who acquire them
and, consequently, others who are similar to them. Support for these hypotheses
would offer a social and cognitive mechanism that could reduce this form of
inequality.
Reward Contamination
When rewards are inconsistently allocated to status groups, the status and
influence of the disadvantaged actors and their pertinent characteristics may increase,
while the advantages previously experienced by high status actors may decrease as
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SVDJ and RET posit. Nevertheless, the spread of status value is a complex process,
and the very act of associating a valued object with a low status person may
contaminate the value of the object instead of increasing the prestige of the actor. For
instance, when occupations and disciplines feminize, the wages, esteem, and resources
that were previously enjoyed by these workers, such as having advancement
opportunities and control over working conditions, tends to decrease (Catanzarite
2003; Cohn 1985; England, Allison, and Wu 2007; Strober and Arnold 1987), and this
may be at least partially due to the devaluation of “women’s work.”
Objects become imbued with status when they are uniquely associated with
status groups (Berger et al. 1972). Once this association is made, the reward becomes
a reified marker of the status of that particular group. However, if the reward becomes
affiliated with a group of conflicting status value, particularly when the reward is
relatively novel and its meaning may still be in flux, the estimation of the reward and
what it connotes may shift to reflect the estimation of this new affiliation. In the case
of interventions, if the reward’s significance is not immutable, its allocation to those of
disadvantaged backgrounds may be of little to no assistance, as the audience would
come to alter only the value of the reward to reflect that of its new possessors. This
possibility leads to the following hypothesis:
Hypothesis 4: When highly valued rewards are made relevant to devalued states of a status characteristic, the status of the reward will decrease.
If support is found for hypothesis 4, we would not expect there to be a
significant alteration in the relative influence associated with the actors in this
situation as compared to those of the same states of the characteristic but without
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rewards. Additionally, we would not expect there to be an alteration in the status
value of the associated characteristics. Thus, if inconsistent reward allocations alter
the impressions of the meaning of the reward instead of the person possessing it, the
use of rewards for status interventions may not have a simple mechanism of
combining status information as RET contends1.
EXPERIMENTAL DESIGN
The purpose of this experiment is to create a reward by making a color-coding
scheme relevant to preexisting status characteristics, with the color scheme becoming
the marker of either high or low status by virtue of this association. Depending on the
condition, this reward marker is then consistently or inconsistently associated with
another status characteristic, education level, to assess how the valuation of the marker
affects the influence and qualities of the characteristic. The consistency of the reward
allocation denotes this study’s two conditions.
Cover Story
In this experiment, participants are told that the purpose of the study is to
understand how groups who work together only via a computer and not necessarily
simultaneously, work together to make decisions and come to consensus. The
participants are informed that they are the third members of a three-person team.
Their role in the team is to evaluate the performance of their two teammates who
1 Due to the nature of this study’s experiment and the sensitivity of the measurement tools, hypotheses 1 through 3 are set in opposition to hypothesis 4. A more thorough examination of the reward process may find that the spread of status value affects the impressions of the rewards, their possessors, or both, thereby working together dynamically overtime.
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worked together on a language task as well as supplying their own answer to the task.
To help ensure that the participants have a sense of group cohesiveness and a shared
fate with their other teammates, the participants are told that their answers will be
combined with those of their partners to generate a final team score. All participants
were compensated with course credit, but participants are informed that teams that
perform extremely well on the task will receive a bonus credit. The participants’
partners are actually simulated actors to control for the task cues given by the partners,
the relative status of the partners, and the way the interaction unfolds.
Creation of Status Valued Rewards
In the first phase of the study, the rewards are created by virtue of assigning a
color-coding scheme to extant status characteristics. The procedure follows that
which has been used in status construction research (Ridgeway, Backor, Li, Tinkler,
and Erickson 2009; Ridgeway, Boyle, Kuipers, and Robinson 1998; Ridgeway and
Erickson 2000), as they are a parsimonious and effective way of creating such an
association. At the beginning of the session, the participants complete a brief survey
in which they indicate their gender, age, and their grade point average. To introduce a
novel ability trait to further differentiate the associated reward levels, participants then
complete an individual contrast sensitivity test wherein they must select between two
pictures which contains the greater amount of white area for a total of ten rounds. In
actuality, all of the pictures contain the same proportion of white space, but
participants are led to believe that the test is indicative of visual ability.
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The participants then examine an information sheet that details the
aforementioned demographic information of previous participants in addition to their
scores on the contrast sensitivity task. This list is color-coded, with each person’s
information highlighted in either green or yellow. The participants are prompted to
carefully examine the information sheet, including the color-coding scheme,
presumably while the computer loads the partners’ responses on the language task.
On the information sheet, the status backgrounds (i.e., age, gender, education
level, and contrast sensitivity test scores) of paired former participants are provided.
Within each pair, one partner always has higher states on the status characteristics than
the other, though not all of the status characteristics are salient within the pair to add
believability; some paired participants may both be high school students or are the
same gender, for instance. The entries for the status-advantaged partners are always
highlighted in green, while the relatively lower status partners are highlighted in
yellow. It is during this point that the association between the valuation of these
characteristics’ states and each color should be made, thereby allowing the colors to
become differentially valued rewards.
This should occur through the spread of status value process wherein the
previously non-valued color scheme takes on a relatively higher or lower status value
as determined by the associated status advantages of the various characteristics. Once
this transpires, the colors should be seen as either esteemed or devalued status
markers. This procedure is similar to that used by Thye (2000) to create status valued
rewards wherein color-coded bargaining chips were made relevant to participants’
differing status attributes prior to negotiating with each other using these chips. In
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Thye’s (2000) study, as in this one, the different colors are purportedly used to help
identify the players’ actions.
The participants are then informed about their own two teammates whose
group performance they are to evaluate. In all conditions, one partner is a local
graduate student from a private university and the other is a regional, South Bay high
school student. The color-coding scheme is then assigned to each of these partners by
highlighting this background information and their choices and responses on the
ensuing language task in each of the two status marker colors. In the reward-
consistent condition, each partner’s information and decisions are marked with the
color that connotes the same level of status as their educational level (i.e., the graduate
students are indicated in green and the high school students’ responses are highlighted
in yellow). In the reward-inconsistent condition, the partners are marked with the
color of the opposing valuation (i.e., green for the high school student and yellow for
the graduate student).
Influence Task
The participants then evaluate their partners’ responses on a meaning insight
language task (hereafter MIT). The MIT is a frequently used task by expectation
states researchers to measure influence. On this task, participants are presented with
an English word and two possible ancient language root woods. The overall goal of
the task is to select which root word was used to create the current English word. In
reality, there are no correct answers to this task.
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Due to the nature of this three-person group, the traditional MIT’s structure has
been altered. In the classic version of MIT, the participant makes an initial choice,
and subsequently learns what her partner has independently selected before making a
final choice between the two root words. In this study’s revision to this task, the
participants still give an initial answer to each question. The participants then see a
color-coded reproduction of their partners’ own initial responses, as well as a short
justification for why they made their selections. The participants then provide their
own final answer and provide any comments they have about their partners’ reasoning.
The choices and reasoning given by the partners are actually scripted so that
the two partners’ initial selections differ on twenty out of the twenty-five rounds of the
MIT, as in the classic MIT, and to control for the justifications provided by each
partner2. Preferences for the reasoning of one partner over the other cannot be
affected by the veracity of the argument as there is no correct answer, but may provide
further means for the participants to justify their agreement with one partner over the
other. Nevertheless, the root words and justifications were pretested without
information about the partners’ status to ensure that participants equally selected each
word in their initial and final decisions.
In the classic version of the MIT, the proportion of rounds in which the
participants stay with their initial selection in the twenty disagreement rounds
constitutes the measure of influence, p(s). As p(s) decreases, the participant is more
heavily influenced by the initial selection of her partner. For this three-partner
version, there are now two partners who disagree with each other, and the participant
2 “Pum has the same number of letters as ‘eye’” is an example of one such rationalization.
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who must decide which partner she believes is “correct.” In this revised manipulation,
the measure of influence instead pertains to the proportion of times the participant
changed her initial choice to agree with particular partner (i.e., p(c)a or p(c)b for
partners a and b). As p(c)x increases, the participants are altering their initial opinion
to agreeing more with one partner over the other and are thereby more heavily
influenced by this partner.
Measuring Status of the Partners, the Color Scheme, and the States of the Status
Characteristic
Finally, subjects fill out an exit questionnaire that contains questions pertaining
to the qualities of their partners, their rewards, and the salient education level status
characteristic. To assess the personal status of the partners, the participants provide
their own assessments of each partner’s status and competence. They also answer the
same questions related to how they think most people would evaluate high school and
graduate students. These questionnaire items are also adapted from those used in
status construction research (Ridgeway et al. 1998). The following adjective pairs
anchor the status questions: respected/not respected, low status/high status, and
leader/follower. The competence questions pertain to the pairs of:
competent/incompetent, capable/incapable, and knowledgeable/not knowledgeable.
The participants answer these questions with a virtual slide-rule that measures their
responses along a continuous 100-point scale. The status, and competence questions
are averaged to create scales to analyze these components of status value.
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Lastly, the participants complete a series of questions related to their
estimation of the value of the rewards, i.e., the color-coding scheme. They first
indicate whether they think it is more difficult to have their responses highlighted with
either green or yellow, whether they think a person would get more respect if her
answers were identified with green or yellow, whether a person would have more
influence if their responses were in one of the two colors, and whether most people
would prefer to have their responses highlighted in green or yellow. Finally,
participants answer how they think “most people” would rate a person who is signified
in this study with green and with yellow. The aforementioned status and competence
questions are again used to anchor these scales and are also averaged to create the two
scales related to the rewards’ status value.
RESULTS
Sample
A total of 41 people participated in this study: 22 participants in the reward-
consistent condition and 19 in the reward-inconsistent condition. Due to time
constraints on data collection, this study was run both in-person and online. The main
difference between the two mediums is that while the in-person participants
continually had the information sheet in front of them while taking the MIT and
answering the questionnaire, the online participants only viewed an electronic copy of
the information sheet before learning about their particular partners. Both groups
viewed this electronic version of the information sheet for at least one minute, and all
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participants are reminded of their partners’ level of education and associated reward
levels prior to answering the questionnaire.
There are a total of 17 in-person and 24 online participants (please see Table
1). While there are some demographic differences between the two samples, their
responses and behaviors did not differ by the key theoretical variables. The online
sample contains more females (z = 2.9, p < .01, two-tailed test), Latinos, and those of
multiple ethnic backgrounds (z = 2.6, p < .05; z = 11.7, p < .000; two-tailed tests,
respectively); but there are fewer whites and African Americans (z = 2.7, p < .01; z =
4.2, p < .000; two-tailed tests, respectively), and this sample is generally younger than
the in-person sample (t = 2.2, p < .05, two-tailed test).
The demographic background of those in either condition also slightly differs.
When rewards are allocated inconsistently according to the partners’ education level,
there are fewer females (z = 3.3, p < .01, two-tailed test) and Asians (z = 2.1, p < .05,
two-tailed test) but more Latinos (z = 3.9, p < .000, two-tailed test) who participated as
compared to those in the reward consistent condition. Lastly, across conditions and
study medium, participants rated the status and competence of both partners combined
as slightly above the midpoint of the scale and changed their initial MIT answers on
14-17% of the rounds.
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Table 4.1. Means or Proportions of Demographic Characteristics by Study Medium and Condition Study Medium Condition
Variables In-Person Online Reward
Consistent Reward
Inconsistent
Female .5 .7** .7 .5**
Age 23.2 (6.3)
21.6* (5.1)
22.3 (5.7)
22.2 (5.7)
Ethnic background White .4 .3** .3 .3
African American .1 .0*** .1 .1 Latino .1 .2* .1 .2*** Asian .4 .5 .5 .4*
Other .0 .1*** .1 .1
Status Scale 64.0 (20.0)
64.3 (17.3)
64.1 (19.3)
64.3 (17.7)
Competence Scale 65.2 (18.5)
64.3 (17.3)
65.8 (19.5)
64.3 (17.7)
Change initial answer .2 .2 .1 .2
N = 17 24 22 19 Note: SDs in parentheses; +p<.10; *p<.05; **p<.01; *** p<.001 (two-tailed tests).
Reward Value
In the first portion of this study, the color-coding scheme is associated with
extant status characteristics in order to create the reward. This scheme must become
imbued with status value before it can possibly affect the status hierarchy between the
participants’ partners. It is therefore important to first assess whether the association
between the two types of highlighting and the status characteristics salient in the
information sheet created the color reward. The results indicate that this is the case.
Across conditions, participants are more likely to state that the purported high reward
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color is more difficult to obtain than the color that was associated with devalued states
of the status characteristics (z = 11.1, p < .000, two-tailed test), that the high reward
color conveys greater respect and influence (z = 6.3, p < .000; z = 5.2, p < .0000; two-
tailed tests, respectively), and that they believe that most people would prefer to have
the high reward color (z = 9.8, p < .000, two-tailed test) (results not reported).
Participants are more likely to believe in the color scheme’s differential
appraisals when they are in the reward consistent as opposed to the inconsistent
condition, however (please see Table 2). While those in both conditions believe that it
is more difficult to obtain the valued color, those in the inconsistent condition are less
likely to state that the intended high reward conveys a greater level of respect and
influence. They are also less likely to state that most people would prefer to have the
high reward than those in the consistent condition. This preliminarily indicates that
the scheme’s reward value may begin to be redefined by the partners’ education level
such that the valued colors loses status when it is associated with a lower status
partner.
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The participants’ responses on the status and competence scales related to the
value of each reward level also support this conclusion3. As displayed in Table 3, the
intended high reward conveys a greater degree of status and competence onto those
who possess it (t = 6.0, p < .000; t = 5.3, p < .000; two-tailed tests, respectively) only
when the reward is consistently assigned according to the partners’ relative education 3 The alpha-levels of these scales are both .9, and exploratory factor analysis also reports one underlying factor for each of these measures.
Table 4.2. Mean Ratings of Reward Traits by Condition
Variables Mean Z-Score
More difficult to obtain high versus low reward
Rewards consistent with partners’ characteristics 0.8
0.7
Rewards inconsistent with partners’ characteristics 0.8
More respect conveyed with high versus low reward
Rewards consistent with partners’ characteristics 0.8
4.4***
Rewards inconsistent with partners’ characteristics 0.5
More influence conveyed with high versus low reward
Rewards consistent with partners’ characteristics 0.7
1.9+
Rewards inconsistent with partners’ characteristics 0.6
Most people prefer to have high versus low reward
Rewards consistent with partners’ characteristics 0.9
4.5***
Rewards inconsistent with partners’ characteristics 0.6
Note: Sample size for this analysis = 41; SDs in parentheses; +p < .10; *p < .05; **p < .01; ***p < .001 (two-tailed tests).
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status. In the inconsistent condition, wherein the high school partner receives the
valued reward and the graduate student is given the opposing marker, participants do
not clearly differentiate the status value of the associated rewards.
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Mixed-effect linear regression also demonstrates that the qualities conveyed by
the color-coding scheme significantly vary by the consistency of the reward allocation,
even when controlling for the participants’ characteristics (please see Table 4).
Mixed-effect linear regression is used to account for the non-independence of these
status value questions that are repeated across multiple stimuli (Bates 2010; Jiang
2007). These models contain fixed effects related to reward level, condition, and
participant controls. A random effect, as defined by the participants’ identification
code, is also included in these models. The significant main effect of reward level
demonstrates that those in the reward-consistent condition evaluated the reward
associated with the high status partner as conveying greater levels of status (b = 30.8,
p < .000) and competence (b = 28.6, p < .000) as expected. The significant main effect
of condition in each model (b = 21.8, p < .01; b = 13.9, p < .05, respectively) shows
that participants evaluated the status and competence conveyed by the intended
Table 4.3. Mean Ratings of Reward Levels’ Status and Competence by Condition
Condition
Variables Rewards Consistent with
Partners’ Status Characteristics
Rewards Inconsistent with Partners’ Status Characteristics
Mean T-Statistic Mean T-Statistic
Status Scale
High reward 76.7
(11.7) 6.0***
61.9
(19.7) -0.5
Low reward 45.8
(18.6) 65.5
(18.9)
Competence Scale
High reward 77.4
(14.0) 5.3***
65.5
(14.6) 0.7
Low reward 48.7
(19.5) 61.7
(15.5) Note: Sample size for this analysis = 41; SDs in parentheses; +p < .10; *p < .05; **p < .01; ***p < .001 (two-tailed tests).
130
devalued reward as actually being higher in the inconsistent condition wherein this
particular reward is associated with the high status partner. The significant interaction
term between anticipated reward level and condition in these models (status: b = -34.9,
p < .000; competence: b = -24.8, p < .000) indicates that those in the reward-
inconsistent condition rated the status value of the intended esteemed reward, which is
associated with the low status partner, as being significantly lower than those in the
reward-consistent condition, wherein this marker is linked with the high status partner.
Table 4.4. Estimated Mixed-Effects Linear Regression Coefficients for the Effects of Reward Level on the Status Value Scales
Status Value Scales
Variables Status Scale Competence Scale
Rewards inconsistent with partners’ status characteristics
21.8** (6.5)
13.9* (5.6)
High reward 30.8*** (5.8)
28.6*** (5.3)
Inconsistent x high reward -34.9*** (8.7)
-24.8** (7.9)
Participant Controls: Female participant
4.5 (5.0)
3.2 (4.5)
Age of participant
0.4 (0.4)
0.3 (0.4)
Minority participant 5.3 (5.0)
1.6 (4.7)
Online study 0.1 (4.7)
-0.4 (4.2)
Intercept 30.3* (12.2)
38.3** (11.1)
Random Effect Variance Terma: Intercept
0.0 (0.0)
0.0 (0.0)
Note: Models fit using restricted maximum likelihood (REML); SEs in parentheses; N = 41 participants; +p<.10; *p<.05; **p<.01; *** p<.001. a Expressed as a standard deviation.
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These results suggest that the color-coding scheme obtains distinct levels of
status value only when the rewards are allocated consistently according to additional
extant characteristics. Even though participants in both conditions viewed the same
information sheet that led to the creation of a stable status marker in the reward-
consistent condition, those in the inconsistent condition did not report a clear
distinction between reward levels. In this inconsistent condition, the reward’s
valuation becomes more vague when it is inconsistently affiliated with the partners’
education background differences.
Behavioral Influence
Even though the status value of the color-coding scheme significantly differs
by condition, does reward-level alter the partners’ influence over the participants?
The data show that when participants changed their initial responses on the meaning
insight task, they are more likely to be influenced by the partners’ educational
background and not their reward level. Figure 1 displays the proportion of trials in
which participants changed their initial responses by condition and the partners’
reward level. When the participants changed their initial responses, they are
significantly more likely to agree with the graduate students’ opinion in both
conditions regardless of reward level. In the consistent condition, participants agree
with the highly rewarded graduate student in 79% of the changed rounds, while in the
inconsistent condition, the reward-disadvantaged graduate student influences the
participants’ in 73% of these particular trials. Mixed-effect logistic regression also
indicates that participants are about equally likely to alter their final choices and, thus,
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be influenced in either condition, as demonstrated by the lack of significance of
condition in the first model of Table 5 (b = 0.2, p = .33). When the partners' relative
status differences are taken into account in the second model, participants are
significantly less likely to agree with the highly rewarded high school student partner
than the highly rewarded graduate student (b = -2.6, p < .000). These results indicate
that allocating previously esteemed rewards to those with a disadvantaged status
background does not elevate their relative influence.
a Letters signify statistically significant z-score differences; Capital letters represent that the difference is significant in a two-tailed test (p < .000).
0.8
0.3 0.2
0.7
0.0
0.2
0.4
0.6
0.8
1.0
Consistent with Status Inconsistent with Status Prop
ortio
n of
Cha
nged
Ans
wer
s
Condition
Figure 4.1. Proportion of Trials Participants Changed Their Initial Answer to Agree with Either Partner by Conditiona
High Rewards
Low Rewards
A, C
A, D
B, C
B, D
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Table 4.5. Estimated Mixed-Effects Logitstic Regression Coefficients for the Effects of Condition on the Number of Trials Participants Changed Their Initial Answer
Changed Initial Answer
Variables Agreement with Either Partner
Agreement with High Reward Partner
Rewards inconsistent with partners’ status characteristics
0.2 (0.2)
-2.6*** (0.5)
Participant Controls: Female participant
0.1 (0.3)
-0.3 (0.5)
Age of participant
-0.1*** (0.0)
-0.1 (0.1)
Minority participant 0.0 (0.1)
0.1 (0.3)
Online study -0.2 (0.2)
-0.4 (0.5)
Intercept 0.9 (0.7)
3.1* (1.4)
Random Effect Variance Terma: Intercept
0.3 (0.2)
0.0 (1.3)
Note: SEs in parentheses; N = 41 participants; +p<.10; *p<.05; **p<.01; *** p<.001. a Expressed as a standard deviation.
Status Assessments of Partners and the Education Status Characteristic
Participants’ evaluations of their partners’ status and competence also indicate
that the rewards did not detectably alter their opinion of their partners4. Instead, the
partners’ relative status and competence is aligned with their educational background.
As reported in Table 6, the highly rewarded graduate student is rated as having
significantly higher status and competence than the high school student with the
devalued reward (t = 3.0, p < .01; t = 3.8, p < .000, respectively). In the reward
4 The alpha-levels for the status and competence scales related to the assessments of each partner and the education status characteristic are .7, .7, .8, and .7, respectively. Exploratory factor analysis also reveals one underlying factor for each of the status and competence scales.
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inconsistent condition, however, the reward-disadvantaged graduate student is rated as
having about the same average status as their counterpart in the opposing condition
(consistent graduate student average status score = 71.5; inconsistent graduate student
average status score = 70.9) and significantly higher status than the highly rewarded
high school student (t = 3.0, p < .05). The competence ratings of the two partners in
the inconsistent condition also favors the reward-disadvantaged graduate student,
though this difference is only marginally significant (t = -1.8, p = .08).
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Mixed-effect linear regression also supports this conclusion (please see Table
7). The significant, negative interaction terms between condition and the highly
rewarded partner in the first two models demonstrate that the reward advantaged high
school partner is evaluated as having lower status and competence (b = -26.0, p <
.000; b = -24.5, p < .000, respectively), while the main effect for condition in these
models shows that the reward-disadvantaged graduate student has significantly greater
status and marginally higher competence (b = 12.9, p < .05; b = 7.7, p < .10,
respectively). The main effect of the partners’ reward level indicates that the highly
rewarded graduate student in the reward-consistent condition has significantly greater
evaluations of status and competence (b = 14.6, p < .01; b = 17.0, p < .000,
respectively).
Table 4.6. Mean Ratings of Partners’ Status and Competence by Condition
Condition
Variables Rewards Consistent with
Partners’ Status Characteristics
Rewards Inconsistent with Partners’ Status Characteristics
Mean T-Statistic Mean T-Statistic
Status Scale
High reward partner 71.5
(10.7) 3.0**
59.3
(13.2) -2.2*
Low reward partner 57.5
(14.8) 70.1
(15.2)
Competence Scale
High reward partner 76.3
(10.3) 3.8***
60.8
(9.0) -1.8+
Low reward partner 58.9
(15.4) 68.2
(14.0) Note: Sample size for this analysis = 41; SDs in parentheses; +p < .10; *p < .05; **p < .01; ***p < .001 (two-tailed tests).
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Table 4.7. Estimated Mixed-Effects Linear Regression Coefficients for the Effects of Partners’ Reward Level, and the Education Status Characteristic on the Status Value Scales
Variables Reward Level of Partner Education Status
Characteristic
Status Scale
Competence Scale
Status Scale
Competence Scale
Rewards inconsistent with partners’ status characteristics
12.9* (5.6)
7.7+ (4.6)
1.3 (4.7)
0.6 (4.4)
High reward partner 14.6** (4.3)
17.0*** (3.3)
Inconsistent x high reward partner
-26.0*** (6.0)
-24.5*** (4.6)
Graduate studentsa
32.6*** (3.5)
28.4*** (3.4)
Inconsistent x graduate students -1.6
(5.1) -0.5 (3.7)
Participant Controls: Female participant
-3.8 (4.7)
-3.4 (4.2)
1.5 (3.9)
-0.5 (3.7)
Age of participant
0.1 (0.4)
0.0 (0.4)
0.4 (0.3)
0.5 (0.3)
Minority participant 0.7 (4.9)
-6.1 (4.4)
8.3* (3.9)
-2.9 (3.9)
Online study 2.2 (4.4)
2.5 (4.0)
-2.2 (3.5)
0.8 (3.5)
Intercept 55.4*** (11.1)
64.5*** (10.1)
31.9*** (9.1)
43.5*** (9.0)
Random Effect Variance Termb: Intercept
8.3 (2.5)
8.6 (2.0)
5.9 (2.3)
6.2 (2.2)
Note: Models fit using restricted maximum likelihood (REML); SEs in parentheses; N = 41 participants; +p<.10; *p<.05; **p<.01; *** p<.001. a Comparison group is high school students. b Expressed as a standard deviation.
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In general, these data suggest that the reward may have an attenuated effect on
increasing the graduate students’ and decreasing the high school students’ status and
competence assessments when rewards are consistently assigned to the partners. The
rewards may also have had a negligible impact at reducing the difference between
these evaluations in the reward inconsistent condition, especially related to
competence assessments, but this effect is not strong enough to reverse the status and
competence evaluations.
The average assessment of the education status characteristic also follows this
pattern, though the differences in the reward-inconsistent condition are more distinct
(please see Table 8). In both conditions, the overall evaluation of graduate students’
status and competence are significantly higher than the evaluations of the high school
students’ (status: t = 7.8, p < .000; t = 7.6, p < .000; competence: t = 6.5, p < .000; t =
7.3, p < .000). Mixed-effect linear regression also indicates that the assessments of
this status characteristic are not altered by the consistency of the reward allocation
(please refer back to Table 7). The only significant predictor of these status appraisals
is the state of this education status characteristic (b = 32.6, p < .000; b = 28.4, p <
.000, respectively). Although rewards may have had a slight impact on altering the
participants’ evaluations of their partners, their inconsistent allocation did not have an
effect on altering their assessments of these two states of the educational status
characteristic.
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SUMMARY AND CONCLUSIONS
The continuation of status-based inequality is one of our most important and
pervasive social problems because this source of inequality is founded on essentially
illegitimate assumptions of competence and worth, which can cloud ability
assessments and alter opportunity structures and resource distributions. Indeed, one of
the reasons why this type of inequality is so pervasive is that those who control the
distribution of resources and rewards continually reallocate these advantages to
additional high status groups (Jacobs 1989; Kanter 1977; Padavic and Reskin 2002;
Reskin 1988; Williams 1992). When certain traits and characteristics become imbued
with status value, these states become bases on which esteem, prestige, honor,
resources, and influence are given, even implicitly.
Table 4.8. Mean Ratings of the Education Status Characteristic’s Status and Competence by Condition
Condition
Variables Rewards Consistent with
Partners’ Status Characteristics
Rewards Inconsistent with Partners’ Status Characteristics
Mean T-Statistic Mean T-Statistic
Status Scale
Graduate student 79.3
(11.0) 7.8***
79.8
(9.4) 7.6***
High school student 46.4
(13.7) 47.5
(12.5)
Competence Scale
Graduate student 80.8
(9.3) 6.5***
78.1
(8.5) 7.3***
High school student 51.9
(16.8) 52.3
(11.1) Note: Sample size for this analysis = 41; SDs in parentheses; +p < .10; *p < .05; **p < .01; ***p < .001 (two-tailed tests).
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Many have sought ways of intervening in this process, either through social
programs that aim to reduce such inequality by distributing resources and honor to
disadvantaged groups, or by introducing differential ability information in immediate
social interactions (Markovsky et al. 1984; Pugh and Wahrman 1983). This project
offers a preliminary step toward assessing the extent to which a redistribution of
socially valued rewards alters status-based inequality. Specifically, when those with
disadvantaged status characteristics hold valued symbols of status, such as certain
awards, occupations, and positions, does their status and influence increase by virtue
of their possession of these valued rewards?
The results of this study suggest that this is not the case, at least under the
particular conditions of this experiment’s one-shot context. When valued rewards are
affiliated with status-disadvantaged individuals, the rewards begin to lose their status
value and, thereby, do not alter the status hierarchy present in the group and the
general status assessments of the group members’ status characteristics more broadly,
as predicted by hypothesis 4. There is some evidence to suggest that the rewards have
an attenuated effect toward further differentiating the particular group members’
overall status and competence when the rewards are consistently distributed, but they
may only have at most a negligible impact on reducing the divergence in these
evaluations when they are inconsistently allocated. Nevertheless, the partners do not
appreciably gain or lose influence by virtue of their reward level, and, importantly, the
status and competence assessments of the group members’ salient status characteristic
do not vary by the type of reward distribution. In sum, differing reward levels are
generally unsuccessful at reducing the inequalities present in this study’s task groups.
140
These are tentative conclusions, however, since there is reason to believe that
certain aspects of this study’s design may not have allowed the reward to have a strong
enough impact. It may be that this study’s reward does not have a robust enough
valuation to guard it against status contamination when it is inconsistently allocated. In
situations wherein the valuation of the status markers is uncertain or relatively new,
the results suggest that the these rewards are far more susceptible to having their
meaning redefined by those who possess them and may not help reduce inequality.
In this study, multiple status characteristics including age, gender, education
level, and grade point average, are used to create a reward through the spread of status
value process. This has the advantage of creating a reward that only takes its meaning
from how it is defined in the experiment as participants could not have experienced
the study’s color coding reward outside of the laboratory. Even though the reward is
created through a consistent link between the levels of these multiple characteristics, it
may be that this particular reward is too novel to not be susceptible to the particular
status background of the participants’ teammates. Additionally, affiliating the reward
with educational background and then distinguishing the partners by a similar
educational difference may have provided the conditions for a swift status
contamination in the reward-inconsistent condition. Future work could vary the type
of reward used to ascertain whether it is possible for rewards to a greater impact on
reducing status inequality. For example, a more exclusive reward, such as a
university-wide or national award, could be used in place of the color-coding scheme.
Another prominent concern with this study’s design is that the rewards are
associated with the partners’ clearly hierarchically divergent education levels. This
141
may have overwhelmed the new rewards’ appraisal and any effect it may have had.
Education is also a keenly valued status characteristic that strongly suggests
differences in competence, ability, and development. Moreover, it is more legitimate
to assume ability differences derived from differing educational backgrounds than
based on ascribed characteristics like gender and race, and the status beliefs
surrounding ascribed characteristics tend to be more uncertain (Balkwell 1991).
Notably, these demographic groups are also generally the targets of social programs
that reallocate rewards. Future research could affiliate the color-coding reward with
the partners’ gender or racial background to ascertain whether rewards can reduce
inequalities resulting from their demographic differences.
Understanding how socially valued rewards can alter the relative status of
those who possess them is an important step towards finding additional mechanisms
that intervene in the processes of status-based inequality. Using reward allocations in
this manner is very complex, and the results of this study suggest that when rewards
are relatively nascent, they may not have an appreciable impact on status assessments
and behavioral inequalities. While the results of this study do not show support for this
particular reward intervention mechanism, further research may demonstrate that
rewards can indeed reduce status-based inequality when they are affiliated with
characteristics that are less powerfully linked with expectations of competence or
when stronger forms of rewards are used.
142
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