Understanding the Conditions of Bias:
Essays on Gender Differences in Evaluation Outcomes across
A
Three Empirical Contexts
MASACHUSETTS INSTITUTE
OF TECHNOLOLGY
by
JUN 02 2015
Mabel Lana Botelho Abraham
B.A., Mathematics, Providence College, 2003
S.M., Management Research, Massachusetts Institute of Technology, 2013
LIBRARIES
Submitted to the Sloan School of Management
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
at the
Massachusetts Institute of Technology
June 2015
Massachusetts Institute of Technology 2014. All Rights Reserved.
Signature of
redacted
Author:
Mabel Abraham
Sloan School of Management
Signature redacted
May 7, 2015
Certified by:
-to erto Fernandez
illiam F. Pounds Professor of Management
Professonr of O~r anizatnion St1-udies
L11V
g
Thesis Supervisor
I>
Signature redacted
Accepted by:
Ezra Zuckerman Sivan
Nanyang Technological University Professor
Professor of Technological Innovation, Entrepreneurship, and Strategic Management
Chair MIT Sloan PhD Program
1
Understanding the Conditions of Bias:
Essays on Gender Differences in Evaluation Outcomes across
Three Empirical Contexts
by
Mabel Lana Botelho Abraham
Submitted to the Sloan School of Management on May 6, 2015 in partial fulfillment of the
requirements for the degree of Doctor of Philosophy in Management
Abstract
This dissertation contributes to our understanding of when and how gender is incorporated
into the evaluation of individuals, leading to unequal outcomes for similar men and women. Prior
research has shown that because ascriptive characteristics, such as gender, are associated with
widely-held performance expectations, evaluators often rely on gender as an indicator of quality,
particularly when quality is uncertain or indeterminate. Whereas existing research has importantly
documented that gender differences in evaluation outcomes exist, this dissertation shifts the focus to
uncovering the conditions under which this is the case as well as the underlying mechanisms driving
these observed gender differences. Specifically, the three papers in this dissertation contribute to our
understanding of the evaluative mechanisms perpetuating gender inequality by answering the
following overarching research question: Under what conditions and how do evaluation processes lead
to different outcomes for comparable men and women, particularly when more relevant indicators of
quality are available to evaluators? I draw on data from three distinct empirical contexts to examine
when and how evaluations of similar men and women vary within social networks, a financial market
setting, and an organization. I pay particular attention to the often levied criticism of gender
inequality research, namely failure to adequately account for underlying quality or performance
differences. I show that the gender of the evaluatee, or the individual being evaluated, plays a role
beyond serving as a proxy for missing quality information and that male and female evaluators
incorporate gender differently under certain conditions.
Thesis Committee
Roberto Fernandez
William F. Pounds Professor of Management
Emilio Castilla
Associate Professor of Management
Ray Reagans
Alfred P. Sloan Professor of Management
Susan Silbey
Leon and Anne Goldberg Professor of Sociology and Anthropology
2
TABLE OF CONTENTS
ACKNOW LEDGEM ENTS..............................................................................................................................................5
I
INTRODUCTION.................................................................................................................................................9
1.1
2
OUTLINE
..........................................................................................................................................
10
EXPLAINING UNEQUAL RETURNS TO SOCIAL CAPITAL AMONG ENTREPRENEURS.......................................12
2.1
INTRODUCTION......................................................................................................................................................
12
2.2
THEORY AND HYPOTHESES .......................................................................................................................................
15
2.2.1
2.2.2
2.2.3
2.2.4
2.3
3
DISSERTATION
Generating network advantages through activation..............................................................................
The role of resource-holders' gender beliefs in evaluations .....................................................................
The role of third-parties in evaluations....................................................................................................
Gendered occupations and bias...................................................................................................................
DATA AND M ETHODS .............................................................................................................
,...............................
16
17
19
21
23
2.3.1
Research Setting and Data...........................................................................................................................
2.3.2
Em pirical Strategy.........................................................................................................................................
2.3.3
Variables and Empirical Model....................................................................................................................
2.4
RESULTS: W OMEN GET LOWER RETURNS TO SOCIAL CAPITAL .......................................................................................
2.4.1
Replacem ent events .....................................................................................................................................
2.4.2
Robustness checks and ruling out alternatives........................................................................................
23
26
30
32
33
39
2.5
DISCUSSION ..........................................................................................................................................................
42
2.6
FIGURES AND TABLES..............................................................................................................................................47
NAMING YOUR DAUGHTER JACK: THE EFFECT OF GENDER IN ATTENTION AND EVALUATION ....................
3.1
INTRODUCTION ......................................................................................................................................................
3.2
THEORY AND HYPOTHESES.......................................................................................................................................
3.2.1
3.2.2
3.2.3
3.2.4
3.3
3.3.1
3.3.2
3.3.3
3.3.4
3.3.5
3.3.6
3.4
3.4.1
3.4.2
3.4.3
3.4.4
3.4.5
The Role of Status-based Mechanisms of Discrimination for Gender Inequality ....................................
Double Standards Theory and Gendered Outcom es .................................................................................
Stages of Evaluation.....................................................................................................................................
Search Costs..................................................................................................................................................
DATA AND M ETHODS .............................................................................................................................................
Em pirical Context...........................................................................................................................................
Sam ple ..........................................................................................................................................................
Dependent Variables: The Evaluation Process...........................................................................................
Independent Variable: Gender.....................................................................................................................
Control Variables..........................................................................................................................................
Empirical M odel............................................................................................................................................
RESULTS ...............................................................................................................................................................
Is there a Gender effect?. ..............................................................................................................................
Ruling out the alternative that wom en are doing things differently? ....................................................
Status and Increased Search Costs...........................................................................................................
Unobserved gender differences ...................................................................................................................
Robustness Checks........................................................................................................................................
58
58
62
62
62
66
68
69
69
75
75
76
76
79
79
79
82
86
87
88
3.5
DISCUSSION ..........................................................................................................................................................
88
3.6
FIGURES AND TABLES..............................................................................................................................................
92
4
FORMALIZATION REVISITED: CONSIDERING THE EFFECTS OF MANAGER GENDER AND DISCRETION ON
CLOSING THE W AGE GAP.......................................................................................................................................100
4.1
INTRODUCTION....................................................................................................................................................
4.2
THEORY AND HYPOTHESES.....................................................................................................................................
103
Form alization of Pay and Gender Inequality .............................................................................................
103
4.2.1
3
100
4.2.2
Considering Manager Gender and Differences in Gender Biases using Social Identity Theory ............... 106
4.2.3
Explaining Variation in Manager Preferences Using Status Characteristics Theory ................................ 108
4.3
DATA AND M ETHODS ...........................................................................................................................................
110
111
Research Setting .........................................................................................................................................
4.3.1
4.3.2
Participants.................................................................................................................................................
112
4.3.3
Com pensation Structure and Form alization ..............................................................................................
113
4.3.4 M easures ....................................................................................................................................................
115
4.3.5 Analytical Strategy .....................................................................................................................................
119
4.4
RESULTS .............................................................................................................................................................
120
4.4.1
Descriptive Statistics...................................................................................................................................120
4.4.2
Overall Effect of Form alization on Gender Pay Inequality ........................................................................
122
4.4.3
Form alization and M ale versus Fem ale M anager Impact on Pay ............................................................
123
4.5
DIScUSSION ........................................................................................................................................................
127
4.5.1
Importance of Considering Characteristics of Manager and Formalization............................................. 128
4.5.2
Im portance of Accounting for Context-specific Status Ordering............................................................... 129
4.5.3
Lim itations and Future Research................................................................................................................
130
4.5.4
Im plications for Organizations ...................................................................................................................
132
4.6
5
6
FIGURES AND TABLES............................................................................................................................................
CO NCLUSION
134
.................................................................................................................................................
143
5.1
CONTRIBUTIONS...................................................................................................................................................
143
5.2
FUTURE RESEARCH...............................................................................................................................................
145
REFERENCES ..................................................................................................................................................
4
147
ACKNOWLEDGEMENTS
The adage that it takes a village has never rung truer for me than it does today. The intellectual
transformation from student to scholar is simply not possible in solitude. I am indebted to so many
for their immeasurable contributions to both my intellectual development and personal growth
leading up to this point. It is a privilege to have this opportunity to express my gratitude to those
who have supported me in reaching this milestone.
I owe tremendous thanks to each of my committee members. I would like to begin by thanking the
chair of my dissertation committee,
Roberto Fernandez,
for his selfless dedication to
my
development. Roberto has been, and I am sure will continue to be, an extraordinary advisor to me. I
credit him for guiding me through the transition from a consumer to a producer of knowledge. I
fondly recall spending countless hours in his office working on every stage of our first project
together, from idea generation through drafting a working paper. It was through this apprenticeship
that I developed a taste for good research and learned to be meticulous in my own work.
Importantly, Roberto also recognized when to let go. He slowly stepped back, while remaining
accessible, and forced me to become an independent thinker. At the time this shift felt challenging,
but as I reflect on my experience it is clear that this balanced support enabled me to launch my own
research program and prepared me to enter the next phase of my career. In addition to teaching me
to conduct strong research, Roberto also provided love and support along the way, often reminding
me that he was a "full-service advisor". Every time I have thanked Roberto, he simply reminds me
to pay-it-forward. I will certainly do my best and can only hope to be as generous an advisor to my
future students as he has been to me. Roberto, I am indebted to you for being so thoughtful and
patient with me. For this and so much more, I thank you.
Susan Silbey has been an endless source of knowledge and inspiration. Not only has she taught me
how to be a thoughtful researcher, but she has also helped me to define my identity as a scholar and
to consider how this identity fit into the whole. After the birth of my daughter, it was Susan who
helped me navigate those early months of balancing motherhood and my work, a much more
challenging transition than I had anticipated. As I prepared for the job market, Susan told me a
personal story where she, as the mother of two young daughters, was able to accomplish what
seemed like an insurmountable task by remaining calm and focused. I carried her words with me
through the following months as I slowly, but steadily, refined my research and put together the
intricacies of my application packets. Her words were profound, but what was even more impactful
was the subtle way that she reminded me of these same principles every time we interacted. She
always made herself available when I needed her most and simultaneously gave me much needed
positive reinforcement while pushing me along every dimension that needed improving. Thank you,
Susan. While I have far from mastered the multiple facets of this craft, I am, without doubt, a
better scholar because of you.
I have also benefited greatly from working with Ray Reagans. He has often given me a renewed
sense of direction about the appropriate theoretical framework or methodology, but even more
5
importantly he has consistently provided positive reinforcement, saying things like "your parents
must be so proud". I am not sure he realizes that these positive words were often what I needed
most to keep pushing forward when things became challenging. I am especially grateful to Ray for
helping me work through many phases of the job market process. He really listened closely (several
times, even when I may not have been making complete sense) and helped me to remain calm and
confident during an otherwise stressful time. Ray, I thank you for showing me by example that it is
possible to be a successful academic, while remaining true to myself.
Emilio Castilla has always provided careful attention to detail, particularly during my early years in
the program when my uncertainties were heightened. On numerous occasions, he willingly spent
hours talking through my analytical questions and helping me to identify the appropriate methods.
Emilio also taught me an important lesson about being a constructive critic of other's research - to
always highlight what is done well, in addition to offering criticism. Emilio, in a world where we
quickly learn to be critics, thank you for reminding me how to be most constructive in the process.
I have also benefited immensely from the guidance and mentorship of many other MIT faculty.
While this list is certainly not exhaustive, I owe special thanks to Ezra Zuckerman, Kate Kellogg,
and Lotte Bailyn, who generously provided feedback on much of my work. Ezra tirelessly contributes
to the development of the members of the MIT PhD community, always willing to engage deeply
and being generous with his feedback. My first interactions with Ezra were in the classroom, where
he helped me understand what Economic Sociology meant and sparked my interest in engaging with
scholars in this area through my own research. He is a masterful teacher, with the uncanny ability to
help students learn to identify theoretical gaps and develop clear theoretical deductions. I am
humbled by his willingness to always make time to attend my presentations or provide feedback on
my drafts, despite the many demands on his time. Thank you, Ezra, for helping to build our
academic community and for demonstrating what it really means to be a contributing member.
Kate and Lotte were my first points of contact at MIT and I am so grateful for their constant
investment in me. Despite not having any formal obligations to provide support and feedback, I have
always been able to rely on both of them. As a rising junior faculty member, I hope to emulate
Kate, who has always made time for me despite facing inevitable time constraints associated with
the tenure clock. I thank both of you for your endless support over the years. Not only is my
research better as a result of your thoughtful feedback, but you have taught me how to remain
balanced in a profession that can sometimes feel all-consuming.
I owe great thanks to both The Kauffman Foundation and The American Association of University
Women (AAUW) for the generous funding they provided me. Relatedly, I also thank my dear friend,
Rosa Taormina, for providing incomparable research support. I cannot thank you enough for
internalizing my need for accuracy and doing everything possible to help me meet these goals. You
went above and beyond to help and I truly value our friendship.
I have had the privilege of sharing my time at MIT with a tremendous group of fellow students,
many of whom have become close friends. To Julia and Aruna, having both of you to laugh and cry
6
with over the years has been so important to me. I truly value our friendship and look forward to
continuing to support each other as we enter the next chapter. I also thank Ben Rissing, Phech
Colatat, Oliver Hahl, Eunhee Sohn, Roman Galperin, Josh Krieger, Jason Greenberg, JP Ferguson,
Melissa Mazmanian, Joelle Evans, and Michael Bikard for providing advice and support through the
years.
I am also grateful for the broader MIT community. I owe a special thank you to Matthew Bidwell
and Susan Perkins for always providing thoughtful, practical advice about navigating this career.
Susan, I am especially thankful for all of the support you offered during the job market and for
always being available to chat. To Hillary Ross and Sarah Massey, thank you for helping me stay
(close to) on time for deadlines over the years and for always offering a warm smile.
Having my brother, Tristan Botelho, as a colleague over the past four years has greatly contributed
to my success and, even more importantly, my happiness in the PhD program. It is a rare
opportunity to get to share in professional triumphs and failures with someone you also consider a
best friend. I feel deeply blessed to be in that minority. Tristan, my most treasured memories as a
PhD student involve you and I certainly cannot imagine this process without you in it. You have
always been my best critic, while also being my biggest supporter, continuously pushing me just
hard enough to help me inch towards the best possible version of myself. Most importantly, you have
always been there to celebrate my successes and work through my failures as if they were your own.
I often worry that I take far more than I give, but I hope I am half the support system for you that
you have always been for me. It is without question that our collaboration has been the most fun
project that I have worked on and I hope it is the first of many. Thank you for being the most
amazing brother I could ask for and for giving me a wonderful sister and goddaughter. Kate, I
cherish your love and support, and Rowan, Dinha loves you so much.
I am most indebted to my wonderful parents, Manuel and Gloria Botelho, for teaching me the
importance of education, supporting me every step of the way, and never second-guessing my ability
to make it. You have always encouraged me to pursue my dreams and to seek happiness above all
else, while never pushing me towards some preconceived notion of success. While you both
continuously tell me how proud you are of me, I want you to know that I owe each of my successes
to you. Above all else, I simply thank you for giving selflessly of yourselves and for loving me
unconditionally.
Mom, you are the most loving, patient, and supportive person and I am endlessly grateful for being
on the receiving end of our relationship. Among so many things, you have taught me to value all
people and recognize that each person has a contribution to make. You are the definition of
authenticity, always acting in a manner that is consistent with your words and encouraging me to be
myself through honesty and openness. I am especially thankful to you for being the same amazing
source of love and support for Emery that you are for me. Thank you for spending several nights per
week in our small townhouse and for doing so with so much love, expecting nothing in return. I am
grateful for the opportunity to spend so much quality time with you during this period. I cannot
7
thank you enough. As I begin the next phase of my career, it brings me tremendous joy to know
that my role as teacher also indirectly fulfills your own dreams.
Dad, you have always worked tirelessly to be sure I was able to follow my dreams. Watching you
work with determination and persistence, always committing to everything that you do, taught me
the path to success. Though our work is quite different, our approach is so similar. Thank you for
teaching me never to be afraid of hard work and that putting in some sweat and tears is often the
only way. These lessons have been my source of motivation during times that I was most unsure of
myself.
To my in-laws, the Abrahams, I am blessed to have a family by marriage that is as incredible as
anyone could ask for. To my mother-in-law, Linda, I am especially grateful to you for making the
weekly trek to Arlington to care for Emery. Not only did you provide me with the support that I
needed to reach this milestone, but you also gave Emery the gift of your time and love. You are an
amazing Nana and I am forever grateful for your relationship with her.
Last, but far from least, my deepest love and appreciation goes to my husband, Joe, and my
daughter, Emery. Joe, I cannot imagine any part of this journey without you by my side, and
honestly, I simply do not want to. Thank you for (almost) always knowing exactly what I needed,
even when I did not know myself, and for always being a source of calmness and stability. Most of
all, I thank you for being an equal partner in everything we do. Leaving Emery repeatedly for travel
was hard on me, but I was able to do it and remain focused because I knew you were working twice
as hard to keep things together at home. You never wavered in your faith in me, even when I
questioned my own ability to do this. You gave me the confidence that I needed to persevere, even
when insecurity set in. I owe my ability to "wrap it up", and the fact that I remained sane through
the process, in no small part to you. I am forever grateful that I get to share my life with you.
Emery, you bring me so much happiness and inspire me every day through your endless curiosity
and unconditional love. You remind me to remain true to what is really important and to take time
to enjoy every moment. In ways I hope you someday understand, you reignited my passion for my
work and gave me the strength to cross the finish line. I strive to do right by you and, if nothing
else, I hope to lead by example in showing you that you can accomplish anything.
Joe and Emery, I dedicate this dissertation to you. As I look forward to our next adventure, I am
filled with excitement and anticipation about what our future holds. I love you both more than you
will ever know.
Mabel Abraham
May 4, 2015
Arlington, MA
8
1
INTRODUCTION
Gender inequality is a persistent feature of the U.S. economy, with women commonly earning
lower wages and being underrepresented in elite occupations and organizational positions (cf. Elvira
& Graham, 2002; Petersen & Morgan, 1995). This dissertation focuses on one potential source of
unequal outcomes for men and women: the evaluation process. Across a wide range of organizational
and market settings, an individual's occupational and financial success is at least partly contingent
on the assessment of an evaluator or audience (Fernandez & Fernandez-Mateo, 2006; Zuckerman,
Kim, Ukanwa, & von Rittman, 2003). In making these assessments, evaluators often lack objective
information about quality or performance, forcing them to rely on status signals as indicators of
expected performance (Kovacs & Sharkey, 2014; Podolny, 1994, 2005; Simcoe & Waguespack, 2011).
Prior research suggests that because ascriptive characteristics, such as gender, are associated with
widely-held. performance expectations, evaluators often rely on gender as an indicator of quality,
&
particularly when quality is uncertain or indeterminate (Becker, 1957; Berger et al., 1977; Correll
Ridgeway, 2003). This reliance on gender often leads to a female disadvantage since, as the lowerstatus gender, women are commonly expected to be worse performers (Correll & Ridgeway 2003).
There is even some evidence that gender is incorporated into evaluations despite more relevant
indicators of quality being available (e.g. Foschi, 1989).
To fully understand the effect of gender in evaluative processes, it is necessary to move beyond
simply documenting that men and women receive different evaluations, and to instead disentangle
the conditions under which this is the case and the underlying mechanisms. By more carefully
examining how the evaluations of similar men and women differ across distinct evaluative processes,
this dissertation first identifies scope conditions for when assessments of similar men and women
vary, and second identifies how evaluative processes contribute to these observed gender differences.
Specifically, across the three essays of this dissertation I show that gender plays a role beyond
serving as a proxy for missing quality information and that male and female evaluators incorporate
gender differently under certain conditions.
9
This dissertation fills this gap by answering the following central research question: Under what
conditions and how do evaluation processes lead to different outcomes for comparable men and
women, particularly when more relevant indicators of quality are available to evaluators? To address
this question, I draw on data from three unique empirical contexts to examine when and how gender
affects: resource allocation within social networks, attention and ratings within a financial market
context, and wages among employees within an organization. I pay particular attention to the often
levied criticism of gender inequality research, namely failure to adequately account for underlying
quality or performance differences by gender. Across the three essays of this dissertation, using fieldbased data and employing both econometric and qualitative research methods, I remedy this by
comparing similar men and women.
1.1
DISSERTATION OUTLINE
The next three chapters of this dissertation each present independent research papers related to
the central question outlined in this introductory chapter. In Chapter 2, I examine whether resource
exchange within social networks leads to unequal benefits for male and female entrepreneurs. For
networks to yield benefits, resource-holders, as evaluators, decide with whom to share resources, or
activate social ties. I study business referral networks where non-competing male and female
entrepreneurs exchange business referrals, or connections to potential new clients. I find that despite
having access to the same social ties, women still receive fewer referrals than similar men in the same
social structure. In addition to identifying this gender imbalance, this paper suggests a new network
mechanism for gender inequality: anticipatory third-party bias. I show that evaluators do not favor
men when deciding whether to hire a fellow male versus female network group member for their own
personal use. However, they do exhibit a male preference when connecting their outside contactsclients, family members, and friends-to their fellow network group members.
In Chapter 3, together with my coauthor Tristan Botelho, I examine whether there are
differences in the attention and subsequent ratings men and women receive in a financial market
setting comprised of investment professionals. This research assesses whether gender remains salient
10
in the evaluation process within a competitive market setting, despite the presence of more prevalent
and freely available performance information. We study an online platform where investment
professionals openly share and view investment recommendations. Because there is a visible marketbased performance metric assigned to each recommendation and investment professionals strive to
identify the most lucrative recommendations, we would not expect gender to play a role in the
decision making process about which investment recommendations to view. However, we find that
recommendations posted by women receive less attention than do recommendations posted by men,
though there is no evidence of a gender-related performance difference. We find evidence that a
driving force of this bias stems from attempts to reduce search costs. This gender difference in
attention is most prevalent when evaluators are selecting recommendations from a large set of
possible options where search costs are higher. Furthermore, we do not find a gender difference in
subsequent
stages
of the
evaluative
process
where
the
evaluator
is focused
on a
single
recommendation.
Chapter 4 shifts the focus to the evaluator. Specifically, I examine whether male and female
evaluators, in this setting managers, differ in how they use the discretion afforded by less formalized
pay systems to affect gender pay inequality among employees. Existing research in management,
economics,
and sociology has largely argued that allowing managerial discretion through less
formalized
pay systems perpetuates
inequality.
However,
implicit
in this argument is that
organizational decision makers uniformly adopt these policies, and specifically adopt these in a way
that leads to a male advantage. I draw on social identity theory and status characteristics theory to
argue that this is unlikely to be the case because it is important to consider how male and female
managers may use discretion differently. Among 857 employees in 120 retail branches of a financial
services firm, I find evidence of less gender pay inequality in terms of less formalized components of
pay for employees in the lowest organizational ranks reporting to a female, relative to a male,
manager.
Chapter 5 concludes by summarizing the core contributions of this dissertation and offering a
few suggestions for future research.
11
2 EXPLAINING UNEQUAL RETURNS TO SOCIAL
CAPITAL AMONG ENTREPRENEURS
2.1
INTRODUCTION
Despite fifty years of political mobilization and dedicated public policy, American working
women maintain a consistently lower economic position relative to similarly qualified men (e.g., Bird
& Sapp, 2004; Elvira & Graham, 2002; Petersen & Morgan, 1995). Women are particularly
disadvantaged in terms of entrepreneurial outcomes. While there have been recent increases in the
rate at which women establish new businesses (U.S. Small Business Administration, 2001), women
own less than 30 percent of all U.S. firms (U.S. Department of Commerce, Economics and Statistics
Administration, 2010, p. 1) and women-owned businesses continue to be smaller and less lucrative
than firms owned by men (U.S. Department of Commerce, Economics and Statistics Administration,
2010; see also Bird & Sapp, 2004; Hundley, 2001; Loscocco, Robinson, Hall, & Allen, 1991; Merrett
& Gruidl, 2000; Robb & Coleman, 2009). Since individuals commonly choose entrepreneurship as a
means for career attainment and economic growth (cf. Sorensen & Sharkey, 2014), uncovering the
mechanisms
contributing to gender
differences
in entrepreneurial
outcomes
is of particular
importance.
These gender differences, coupled with the central role of social networks for entrepreneurs (e.g.,
Hallen, 2008), have resulted in a burgeoning literature focused on the role of social ties for the
pervasiveness of gender inequality in entrepreneurship. As Stewart (1990: 149) stated, "For many
entrepreneurs, their most significant resource is a ramifying personal network." But like the gender
differences in the social networks of men and women more broadly, it has been argued that female
entrepreneurs have less valuable networks (e.g., Aldrich, 1989; Aldrich, Elam, & Reese, 1997;
Aldrich, Reese, & Dubini, 1989; Ruef, Aldrich, & Carter, 2003). The sorting of women into lower
status occupations and organizational positions embeds them in networks with fewer high status and
influential contacts than those of men (Brass, 1985; Campbell, 1988; Ibarra, 1997; Marsden, 1987;
McGuire, 2000, 2002; Moore, 1990; Smith, 2000) and, as a consequence, women generate lower
12
returns from social capital than do men (Braddock & McPartland, 1987). Because high status actors
occupy central positions that facilitate access to network resources (Lincoln & Miller, 1979), people
with higher status contacts receive greater advantages when using their social ties. Thus, women's
differential network structures and composition is posited as a key factor in the pervasive under&
representation and lower success rates of women among entrepreneurs (Aldrich, 1989; Cromie
Birley, 1992; Katz & Williams, 1997; Renzulli, Aldrich, & Moody, 2000).
Although lacking access to others' resources constrains women's opportunities, it is unclear
whether men and women with the same access to resources through social ties would receive equal
benefits. The mere presence of social ties does not necessarily guarantee that resources will be
exchanged. For networks to yield benefits, actors possessing resources must decide to share those
resources with network contacts, in effect, behaviorally activating their social ties (Lin, 2001; cf.
Smith, 2005). In other words, resource-holders serve as evaluators deciding with whom to share
resources, or activate a social tie. Despite the central role of resource exchange within networks for
generating benefits from social ties, we do not have a clear understanding of how gender affects
resource sharing, and whether this resource allocation process has implications for gender inequality.
Scholars have argued that in many situations diffuse status characteristics such as gender influence
&
evaluations of actors in a way that disadvantages women (e.g., E. J. Castilla, 2008; Castilla
Benard, 2010; Correll & Benard, 2006; Correll, Benard, & Paik, 2007; Ridgeway, 2011; Turco, 2010),
suggesting that gender may also play a role in how resources are exchanged among network contacts.
These gender-based expectations have been found to be particularly salient in male-dominated areas,
such as entrepreneurship (cf. Gupta, Turban, Wasti, & Sikdar, 2009). Female entrepreneurs are
commonly perceived to be less competent (Buttner & Rosen, 1988; Thebaud, 2010) and to lack
credibility (Carter and Cannon, 1992; Moore & Buttner, 1997). Thus, to develop a more complete
understanding of how networks contribute to gender inequality among entrepreneurs, it is critical to
examine how the process of resource allocation within networks impacts gender inequality. This
research moves beyond structural network accounts of gender inequality to determine whether men
and women with the same access to social ties receive equal benefits, or returns to social capital.
13
In this paper, I show that women receive fewer benefits through social ties than do similar men
in the same social structure. I study business referral network groups to examine how referrals, or
connections to potential new clients, are exchanged among entrepreneur-members.' A comparison of
the referrals accruing to men and women with the same access to social ties reveals that female
entrepreneurs receive fewer business referrals. Consequently, this study identifies that access to
valuable social ties alone will not necessarily eliminate gender differences in outcomes produced
through social ties. Resource allocation plays a role in perpetuating gender inequality as women
receive fewer benefits through social ties relative to similar men with access to the same social ties.
Further, I posit a new network-based mechanism to explain this gender inequality, which I term
anticipatory third-party bias. I show that while members do not favor men when directly connecting
to other members in their network group, they do exhibit a male preference when connecting these
fellow network group members to their outside contacts-clients, friends, and family members.
Where entrepreneur-members expect that a client, friend, or family member has a preference for
men over women, they disproportionately give referrals to male rather than female network group
members. In other words, men and women are equally likely to receive business directly from their
network contacts, but women receive far fewer connections to the clients, family, and friends of their
network contacts, or third-party referrals. Further, I find that this gender difference in receiving
third-party referrals is most pronounced among women in male-dominated occupations. Thus,
despite having equal access to network resources through social ties, women receive lower returns to
their social capital because resource-holders respond to what they believe are the gender preferences
of others, or third-parties.
To demonstrate
how resource
allocation,
rather
than
differential
network
composition,
contributes to gender inequality, this study compares the resources acquired through social ties by
similar men and women embedded in the same network groups. It is only possible to identify
whether and how resource exchange within networks affects gender inequality by holding network
I use entrepreneur-members, members, network group members, and network contacts interchangeably to refer to
members of these business referral groups. To differentiate between the person deciding whether to give a referral to a
fellow member and the potential recipient of these referrals I use resource-holder and resource-seeker respectively.
14
structure constant. In this respect, the tendency for networks to be gender-segregated poses an
empirical challenge. To overcome this challenge, I collected six years of referral data from a popular
business networking club organized to help entrepreneurs generate new business through social ties.
This setting offers a unique opportunity to observe how resources are exchanged among male and
female entrepreneurs in the same networks. Entrepreneurs in these clubs represent a wide range of
occupations and meet weekly to exchange business referrals that connect recipients with potential
clients. I unobtrusively collected all referral data, observed three groups for two years, and
interviewed 18 entrepreneur-members. In my analysis, I focus on 416 cases where a member exits a
group and is replaced by a new member in the same occupation. Leveraging these replacements,
particularly those involving a change in gender, allows for a comparison of men and women in the
same occupations, with access to the same social ties, thus providing better identification of any
gender difference in returns to social capital.
I proceed as follows. First, I develop hypotheses about whether and why women receive fewer
resources than do men embedded in the same social networks. I explain how gender preferences, or
performance expectations, may prevent women from generating the same returns to social capital,
despite having the same access to social ties, as men. Second, I introduce the research setting and
analytical method used to test whether women receive lower returns to social capital and to identify
the role of expectations about third-party preferences in perpetuating gender inequality. Third, I
present results which demonstrate that actors' anticipation about the gender preference of their
contacts prevents sharing referrals with women under certain conditions. This finding suggests a new
mechanism by which networks perpetuate gender inequality: anticipatory third-party bias in social
capital activation. Therefore, access to valuable social ties alone will not necessarily mitigate gender
differences in outcomes from using social ties. I conclude with a discussion of the theoretical and
organizational implications of the research findings for our understanding of how social networks
contribute to gender inequality among entrepreneurs and more broadly.
2.2
THEORY AND HYPOTHESES
15
2.2.1
Generating network advantages through activation
Social networks are often conduits of valuable resources, such as information and influence,
which can be leveraged to achieve desirable outcomes (Burt, 1995; Granovetter, 1995; Lin, 2001;
Podolny & Baron, 1997). As such, social capital refers to the potential and actual set of resources
made available through an actor's direct and indirect social ties to others (Coleman, 1988; Lin,
2001). The advantages associated with social ties, however, are not automatic. An actor's network
structure and composition provides the potential set of resources available through social ties, but
&
within this structure actors exercise agency, and preferences (Emirbayer & Goodwin, 1994; Gulati
Srivastava, n.d.; Zeng & Xie, 2008). The mere presence of social ties alone does not necessarily mean
that resources will be exchanged. In his theory of social capital, Lin (2001: 29) makes a clear
distinction between resources that are available through social ties and resources that are "accessed
and/or mobilized in purposive action". Therefore, for networks to yield benefits, social capital must
be both available and activated such that a resource-holder shares resources with a network
contact(Lin, 2001; cf. Marin, 2012; Smith, 2005). In this way, network structure and composition
constrain choice (Zeng & Xie, 2008) by providing the potential social capital, or pool of resources, to
which actors have access and the opportunity to exploit.
While we still lack a comprehensive understanding of how people use social ties to generate
benefits (e.g., Ibarra, Kilduff, & Tsai, 2005), recent research has shown that individuals often
activate only a subset of the social relations to which they have access. For example, limitations in
the cognitive recall of relationships (Smith, Menon, & Thompson, 2012) and reputation concerns
(Marin, 2012; Smith, 2005) may prohibit actors from activating social ties. In considering factors
that may prevent resource-holders
2
from giving resources to particular network contacts, labor
market studies examining when people provide their social ties with job information are particularly
illustrative. For example, in a study of the black urban poor, decisions about whether to share job2 Choice can be exercised by either the resource holder in deciding whether or not to share resources (Marin, 2012;
Smith, 2005) or the resource seeker in deciding whether or not to express needs (Hurlbert, Haines, & Beggs, 2000). In
my setting, all actors are similarly seeking and expressing needs for resources, which I discuss further in the results
section. Thus, I focus on the role of the resource holder in deciding whether to share resources with others and how
this process impacts gender differences in the benefits men and women receive through social ties.
16
related information with potential job-seekers are largely influenced by expectations of how doing so
will impact the information holder's reputation (Smith, 2005). Similarly, among professional workers,
information holders protected their reputation with their social ties by refraining from sharing
information about job opportunities unless their contacts had explicitly asked or the job was a clear
match (Marin, 2012). The importance of activation for understanding how social ties generate
benefits has been clearly established. Some scholars have begun to examine how activation
contributes to inequality in benefits (e.g. Smith, 2005), however, extant research has not focused on
gender inequality.
2.2.2
The role of resource-holders' gender beliefs in evaluations
In social networks, resource-holders act as evaluators assessing the quality of network contacts
before deciding whether to share sought-after resources (cf. Smith, 2005). Ascriptive characteristics,
such as gender, influence the evaluation of actors in ways that generally disadvantage women (E.
Castilla, 2008; Castilla & Benard, 2010; Correll & Benard, 2006; Correll et al., 2007; Ridgeway, 2011;
Turco, 2010). Commonly, evaluators are unable to observe the actual quality of the individuals being
assessed, forcing them to rely on more accessible indicators of expected quality (Podolny, 1993,
2005), often historically marked by gender, race, religion and social class.
Both status-based and statistical theories of discrimination posit that, because gender is
conventionally understood to convey information about an actor's quality, or expected performance,
evaluators incorporate gender into their assessments to remedy this problem of uncertainty, that is,
lacking knowledge of actual quality. Status-based preferences stem from beliefs that gender is a
status characteristic, where men are more highly valued, and generally perceived as more competent,
than are women (Berger, Fisek, Norman, & Zelditch, 1977; Correll & Ridgeway, 2003). These
performance expectations are often incorporated into evaluations of male and female actors (Correll
and Benard, 2006) such that men's performances are evaluated more positively than those of similar
women (e.g., Ridgeway & Correll, 2004; Ridgeway & Smith-Lovin, 1999; Wagner & Berger, 1993)
leading to a male social advantage (Webster & Hysom, 1998). Similarly, economic theories of
statistical discrimination posit that if quality or performance is difficult to observe directly and has
17
different distributions for men and women, it may be rational for an evaluator to prefer men (for
reviews, see Correll and Benard 2006; England 1994, pp. 60-63). Therefore, if an evaluator has had
more positive experiences with men than with women, it would be logical to use this information
when comparing a male and female candidate to conclude that the man is more likely to be of
higher quality.
However, reliance on gender as signal of quality may persist even when actual, more relevant
performance information is freely available (Berger et al., 1977; Correll & Ridgeway, 2003). Double
standards theory, a particular status-based account of discrimination, suggests that gender serves as
a distinguishing characteristic in assessments of competence even when men and women with equal
performance are compared (e.g., Foschi, 1989). According to this perspective, discrimination arises
because stereotypes about the relative expected performance of men and women influence
evaluations beyond freely available evidence of equal past performance, particularly when the
evaluator is sorting through a large number of candidates (e.g. Botelho and Abraham, 2014).
While the role of gender for network activation remains unclear, these theories of discrimination
suggest that resource-holders may favor men when assessing whether or not to share resources with
network contacts. In job search, for example, using networks tends to channel women into low-wage,
female-dominated jobs because women commonly receive job information from female contacts
(Drentea, 1998; Hanson & Pratt, 1991; Mencken & Winfield, 2000). Based on this observed
association, it is often assumed that women lack access to social contacts possessing more valuable
job information. It is plausible, however, that despite having access to high status network contacts
with information about better jobs, women are not receiving information from these contacts. In
other words, if individuals with valuable job information are choosing to share this information with
their male, but not female, contacts, women may be relegated to relying on a subset of their network
for job information.
Thus, resource-holders may take gender into account when assessing whether or not to share
resources with other entrepreneurs in their network such that:
18
Hypothesis 1: Female entrepreneurs will receive fewer resources from network contacts
than will similar male entrepreneurs in the same network.
2.2.3
The role of third-parties in evaluations
The arguments presented so far highlight how resource-holders' gender beliefs or stereotypes may
drive them to disproportionately favor male network contacts over comparable women. This
challenges the assumption that men and women with access to the same social ties will receive the
same benefits.
Alternatively,
expectations
about
the gender
preferences
of relevant
others,
irrespective of an evaluator's personal beliefs or preferences, may also lead an evaluator to favor
men. Networks are social structures where actors may be influenced not only by their personal
beliefs, but also by how they believe their actions will be perceived by a third party. At the most
basic level, the exchange of resources between network contacts, or social capital activation, involves
at least two social actors. In many cases, however, the sharing of resources within networks is a
triadic process (cf. Rubineau & Fernandez, 2014). In addition to a resource-holder and a resourceseeker, there are often third-parties such as other actors or organizations (e.g. employers), involved
in, or observing, resource exchanges between network contacts. For example, the exchange of job
information involves an information holder who knows of a vacancy, a job seeker receiving the
information, and the employing organization looking to fill that vacancy. In this example, the
employer represents a third-party involved in the direct sharing of job information between two
network contacts.
Generally, third-party observers, or audiences, play an important role in shaping actor behavior.
The presence of an audience serves to impose expectations of what is considered appropriate
behavior in that particular context (Zuckerman, Kim, Ukanwa, & James von Rittmann, 2003). This
is especially pronounced when the consequences, success or failure, of an actor's decision is
not her personal quality criteria, but rather the expectations of her audience (Clark, Clark,
&
contingent on the reaction of others. Under these conditions, a decision maker is challenged to satisfy
Polborn, 2006; Emerson, 1983; Jensen, 2006; Ridgeway & Correll, 2006). For example, employment
agencies, acting as hiring intermediaries, are strongly motivated to anticipate their client's---the
19
hiring firm's---ideal candidate in order to select the most appropriate applicants from the overall
pool (Fernandez-Mateo & King, 2011).
Specifically, if decision makers know the conclusion that their audience wants them to reach,
they tend to bias their choices in an effort to satisfy the audience (Lerner & Tetlock, 1999). When
there is uncertainty about a third-party's preferences, third-order inferences about status, or
assessments of who or what most other people believe to be of higher status, suggest that it is likely
for this actor to prefer men (Correll, Ridgeway, Zuckerman, Jank, & Jordan-Block, 2014). Thus,
insofar as a decision-maker is dependent on approval from a third-party and anticipates that the
third-party is aware of the status structure (i.e. men higher status), it may be rational for a decisionmaker to favor high-status options, even when the audience's true preferences are unknown and the
decision-maker personally does not endorse these beliefs.
There is evidence suggesting that anticipating the preferences of an audience, or third party, may
serve to perpetuate gender inequality. Economists have long posited that inequality in labor market
outcomes is at least partly a function of customer discrimination (Neumark, Bank, & Nort, 1996). In
employees leads hiring agents to disproportionately select male candidates (Fernandez-Mateo
&
terms of gender inequality in hiring, expectations of customer or client preferences for male
King, 2011). Similarly, the likelihood that a female attorney is promoted to partner is significantly
greater when the law firm has women-led clients (Beckman & Phillips, 2005).
Therefore, in social networks, expectations about the gender preferences of relevant third-parties
is proposed to play a role in resource-holders decisions of whether to share resources, such that:
Hypothesis 2: Female entrepreneurs will receive fewer resources from network contacts
than will similar male entrepreneurs in the same network when the exchange of resources
involves a third-party.
Importantly, the proposed role of third-parties and the accountability they evoke can exist both
under conditions where the resource-holder has personal status beliefs or biases and under conditions
where the resource-holder has more egalitarian preferences (c.f. Correll et al. 2014). For anticipatory
20
third-party biases to play a role, the success of the resource-holder's social capital activation decision
must simply be contingent on approval from a third-party.
2.2.4
Gendered occupations and bias
The degree to which a third-party has a preference for men is likely to be context dependent.
More generally, the potential for bias is magnified when an actor occupies a role that is incongruent
with his or her gender (Foschi, 1989). The notion of the ideal worker (Acker, 1990; Williams, 1999)
offers an explanation for the heightened use of gender in evaluations of actors in gender incongruent
roles. The ideal worker image defines the characteristics of the individuals who are expected to be
strong performers (Gorman, 2005). As such, individuals are commonly evaluated against this
norm(Heilman, 1983). To the extent that the ideal worker image is gendered, it is increasingly likely
that evaluations favor members of one gender group over another as gender is particularly salient in
these contexts (cf. Turco, 2010).
Consistent with this view, female disadvantage has been posited to be most pronounced when
women are in roles or perform tasks typed as male (Foschi, 1989) or where they are the numerical
minority (Kanter, 1977). The numerical representation of men and women in a particular occupation
is a primary driver of perceptions that the role is typed as male or female. For example, masculine
personality and physical attributes are perceived as essential for success in occupations historically
dominated by men (Cejka & Eagly, 1999). Some research has shown that evaluators tend to give
lower assessments to women occupying an occupational role that is gender incongruent, or maletyped (Eagly, 2004). As compared to their male counterparts, women tend to be evaluated as less
competent in roles traditionally held by men (Davison & Burke, 2000; Eagly & Karau, 2002), with
their successes commonly attributed to luck rather than skill (Swim & Sanna, 1996). Therefore, as
related to hypothesis 2, the degree to which a female network contact occupies a role that is gender
incongruent is likely to have implications for resource-holder's decisions of whether to share resources
when the information sharing involves a third-party such that:
21
Hypothesis 3a: When the exchange of resources involves a third-party, there will be a
greater female penalty in receiving resources from network contacts among women in maledominated occupations, such that female entrepreneurs will receive even fewer resources
relative to similar male entrepreneurs in the same network when in male-dominated
occupations.
It is less clear whether men occupying gender incongruent roles face similar penalties. Male
minorities have been posited to reap benefits from their position as tokens ascending to high status
positions within these occupations more quickly than do women (Kmec, 2008; Williams, 1992).
Despite these advantages, men are posited to encounter discrimination from people outside of their
profession (Williams, 1992). Therefore, it may be reasonable for a resource-holder to anticipate that
third-party contacts have a preference for women in female-typed occupations. For example, a
resource-holder may expect that his third-party contact has reservations about a male child-care
provider, an occupation traditionally held by women, and as a result, refrain from making referrals
in this case. Further, recent audit studies have suggested that in occupations where women are the
clear majority, such as secretarial positions, applications from women are favored over applications
from men (Booth & Leigh, 2010; Riach & Rich, 2006). Therefore, the degree to which a male
network contact occupies a role that is gender incongruent is likely to have implications for resourceholder's decisions of whether to share resources when the information sharing involves a third-party
such that:
Hypothesis 3b: When the exchange of resources involves a third-party, there will be a male
penalty in receiving resources from network contacts among men in female-dominated
occupations, such that male entrepreneurs will receive fewer resources relative to similar
female entrepreneursin the same network when in female-dominated occupations.
One possible explanation for these mixed findings regarding the outcomes for men in femaledominated roles stems from the overarching status ordering of gender. In general, gender is a status
characteristic, where men are higher status and, as a result, generally more highly valued and
perceived as more competent, than are women (Berger et al., 1977; Correll & Ridgeway, 2003). Since
gender is a diffuse status characteristic where men are typically seen as the higher status gender,
even when men are in female-typed roles, there may be a higher tolerance for this inconsistency.
Thus, when a resource-holder is assessing the gender preferences of a third-party, the threshold for
22
assuming a female preference is likely higher. For example, a resource-holder may be more apt to
take a chance and refer his client to a male child-care provider than to a female contractor. As a
result, the degree to which a network contact occupies a role that is gender incongruent is likely to
have greater implications for resource-holder's decisions of whether to share resources for women
than for men such that:
Hypothesis 4: There will be a greater penalty in receiving resources from network contacts
for women in male-dominated occupations than for men in female-dominated occupations
when the exchange of resources involves a third-party.
DATA AND METHODS
2.3
2.3.1
Research Setting and Data
Data for this study were collected from "RefClubs", a popular organization providing a forum
for entrepreneurs to exchange information and business referrals. Entrepreneurs pay annual dues to
become members of RefClubs and gain access to a networking group of other entrepreneurs in the
same geographic region. The primary purpose of the organization is to bring together individuals
seeking to grow their businesses and provide them with a forum for sharing information about
potential new clients. These are instrumental networking groups where individuals join with the
explicit goal of establishing and leveraging social ties with other members of the group to generate
new business.
I have studied the exchange of referrals among entrepreneur-members within 37 individual
RefClubs network groups using archival records including each and all individual exchanges. I
collected, coded, and analyzed records of referrals from these network groups for the years 20072013. To deepen my understanding of this empirical context, between October 2011 and October
2013 I spent 40 hours observing individual weekly group meetings across 10 unique groups and
conducted 18 semi-structured interviews with individual entrepreneur-members.
The quantitative data I analyze include complete information on the referrals generated and
received by each of the 2,310 members in these 37 unique network groups. These data offer two key
23
advantages for testing the hypotheses I have outlined. First, through analysis of network records, I
unobtrusively observed the actual exchange of resources among entrepreneurs allowing me to move
subjective perceptions of survey respondents
(Bernard, Kilworth, & Sailer, 1981; Quintane
&
beyond common approaches relying on self-reports. Self-reports have been found to be tainted by the
Kleinbaum, 2011) calling into question the reliability of network surveys (Bernard et al., 1981),
although recently, studies have made progress in avoiding respondent biases by using email exchange
records (e.g., Kleinbaum, 2012; Kleinbaum, Stuart, & Tushman, 2013).
Second, in these referral networks, men and women are co-located within the same network
groups and thus have the same structural opportunity to access resources. This allows me to hold
network structure constant and identify how resource exchange within networks affects gender
disparities. Without knowledge of the opportunity structure for accessing resources, or the set of
social ties that could have been activated, it is not possible to distinguish between structure- and
activation-based explanations. Self-reports, for example, do not capture the entire network, or the
opportunity structure, from which resources are generated. Without holding constant network
structure, observations of women's reduced benefits could be the result of women's differential access
to valuable contacts or of women's inability to mobilize social ties (see McDonald 2011 for a similar
critique questioning whether feminized networks are less valuable).
[INSERT FIGURE 1]
Figure 1 depicts the structure and composition of these network groups. Within each group there
are an average of 40 entrepreneur-members. The gender composition of these network groups mirrors
that of women's presence in entrepreneurship more generally (e.g., U.S. Department of Commerce,
Economics and Statistics Administration, 2010, p. 1), with approximately 35 percent of members
being female. Entrepreneur-members work in a wide range of occupations, as illustrated in Figure
2A. Within each network group, there is only one member from each occupational specialty, thus
reducing the role of competition among members in referral exchange. The specification of
occupational specialty, as opposed to broader occupation or profession categories depicted in Figure
24
2A, is deliberate as members may overlap in the latter. For example, a group may have two
members from the legal occupation, both of whom are attorneys, where one specializes in estate
planning and the other in family law. While these members are in the same broad occupation, they
are not in competition as they are not vying for the same customer base. The non-competing nature
of these groups reduces the likelihood that observed gender differences in referral patterns result
from men and women being differentially impacted by competition.
Weekly group meetings have a routinized format for entrepreneur-members to learn about each
other's businesses and to exchange business referrals. Meetings run from 90 to 120 minutes and are
scheduled outside of normal business hours, typically held either in the early mornings or evenings.
This time is largely structured with an opportunity for informal interaction among members before
the start of the meeting. During meetings each member is allotted a time slot to speak publicly to
the group about specific business and referral needs. This short presentation provides each member
the opportunity to educate other members about their business in order to facilitate referral
exchange. Since all members partake in this request for referrals, coupled with the instrumental
nature of these groups, this setting minimizes concerns that observed gender differences are due to
women's tendency not to ask for help (e.g., Babcock & Laschever, 2003).
It is also during these weekly meetings that business referrals, or information about potential
new clients, is exchanged among members. Referrals generated in these groups provide a non-trivial
source of revenues for members. On average, each member receives approximately 23 referrals per
year contributing to approximately $10,000 in new business per member per year. Each member has
the opportunity to activate a tie with, or pass information about a potential new client to, another
member during the meetings. Making a referral to another member involves completing a form
where a copy of this information goes directly to the member receiving the referral and a second
copy is entered into the groups' records by an administrator. In addition, all business referrals
exchanged between members and successful business transactions that resulted from earlier referrals
are announced verbally to the group. Through this process of publicizing resource exchange,
25
members may become aware of the relative value that each other member brings to the group in
terms of generating referrals and business revenues.
There are two distinct types of business referrals exchanged among entrepreneur-members, which
I refer to as direct use and third-party referrals. In direct use referrals, a member expresses interest
in directly hiring another member in the group to provide a service. For example, the accountant in
Group A (Figure 1) indicates to the real estate agent, also in Group A, that she would like to hire
him to help her find new office space. Since direct use referrals reflect the personal consumption of
members, these referrals represent the referring member's, or resource-holder's, personal gender
preferences or beliefs. Alternatively,
third-party referrals involve a member providing another
member with information about a third-party contact---client, family member, or friend---who is not
a member of the group but may be interested in hiring the member. In third-party referrals, the
accountant activates the tie to the real estate agent by connecting the real estate agent to one of her
clients, whom the real estate agent would not have access to otherwise. Therefore, third-party
referrals are subject to expectations of the gender preferences of a referring member's third-party
contact.
[INSERT FIGURE 2A]
[INSERT FIGURE 2B]
2.3.2
Empirical Strategy
To identify whether women receive fewer referrals than men with access to the same social ties,
the natural approach would be to count the number of referrals male and female entrepreneurs
receive and estimate a negative binomial regression with gender as the main predictor variable of
interest. By design, the empirical setting I use in this study provides an opportunity to compare men
and women co-located within the same networks. In this setting all members within an individual
networking group have the same opportunity to obtain business referrals. As a result, I am able to
hold network structure constant by including network group fixed effects allowing for a comparison
of the number of referrals men and women within the same network group receive. In addition, to
26
account for potential gender differences in contributions to other members and participation in the
group, this model would include relevant controls including number of referrals given to others,
attendance, and tenure for each entrepreneur-member.
However, this empirical approach would fail to consider the possibility of occupational sorting. It
&
is well-established that men and women tend to be sorted into different occupations (Fernandez
Sosa, 2005), particularly among entrepreneurs where men and women historically own businesses in
different
industries
(Rosenfeld,
2002).
As
Figure 2B
reveals,
the
representation
of female
entrepreneur-members varies across occupations with women being underrepresented in some and
overrepresented in others. Since approximately 35 percent of members are female, female members
are underrepresented in occupations at the far bottom-left of Figure 2B and overrepresented in
occupations at the top-right. Thus it is plausible that, despite having access to the same social
capital, observations that women receive fewer referrals than do men in the same group are a
function of occupational sorting. For example, entrepreneur-members may refer to female members
in their group less frequently not because they are women but rather because there is less demand
for the particular businesses that women tend to represent, such as education and training, than for
businesses dominated by men, such as construction and trades. As aforementioned, within any single
group in my setting there can only be one member from each detailed occupational category.
Therefore, simply comparing the number of referrals men and women receive does not directly allow
for a comparison of men and women in the same occupations who also have access to the same
social ties. One way to begin to take occupational sorting into account is to compare men and
women in the same occupations across network groups using occupation fixed effects. While this
approach accounts for occupational heterogeneity, and reveals a similar female penalty in referrals
received (see Table 4, Model 3), it does not compare men and women with the same access to social
ties.
To more carefully compare similar men and women, I use a unique identification strategy that
simultaneously accounts for occupation and network group. I analyze cases where a member exits
from a group and is replaced by a member in the same detailed occupation ("replacers"). This offers
27
a natural laboratory for examining my research question. Leveraging these replacers allows for a
comparison of men and women in the same occupations, with access to approximately the same
social ties, providing controlled conditions for better identification of the gender differences in
returns to social capital where they exist. While at any given time each group has only one person in
each detailed occupation (e.g. one real estate attorney), once a member decides to exit the group
(i.e. leaver) it is possible for another person in that same occupation to join (i.e. replacer). As Figure
1 reveals, without focusing on these replacers, achieving either of these objectives would come at the
expense of the other. Therefore, comparing the returns to social capital for members involved in a
replacement event provides an opportunity for cleaner identification of the gender differences in
returns to social capital.
During my study window, I observed 416 of these replacement events where a member exits a
group and is replaced by another member in the same occupational specialty. To ensure that the
replacers and the leavers had access to approximately the same social capital and were employed in
the same detailed occupation, I used strict boundaries for defining replacement events. First, I define
replacements using the most narrow definition of occupational specialty available. For example, a
replacement event is defined as a real estate attorney leaving and being replaced by another real
estate attorney, as opposed to an attorney being replaced by an attorney. Since men and women are
sorted into different detailed occupations, using this more fine-grained occupational definition
accounts for gender differences even within broad occupation categories. Second, I include only
replacement events where the replacers enter a group within 12 months of the leaver exiting the
same group, with a mean length of 133 days between the exit and replacement, to maximize the
overlap in social ties available to each of the involved members. In any 12 month period, greater
than 70 percent of members remain, 3 resulting in the leaver and the replacer having approximately
identical access to resources through social ties in this context.
3 One way to improve the overlap in the social ties available to leavers and replacers is to shorten the period between
the leaver exiting and the replacer entering the group. For example, within a six month window approximately 87
percent of the group remains intact. But it takes groups several months (approximately four months on average) to
replace an exiting member. Therefore, shortening this window results in a drastic reduction in the number of cases of
28
[INSERT TABLE 1]
Table 1 illustrates the types of replacement events that I observed. Of the 416 replacement
events meeting the criteria outlined above, approximately 60 percent involved a same-gender
replacement: While these cases alone do not provide a means for comparing the relative number of
referrals women receive relative to comparable men, these provide a baseline for comparing the
replacement cases involving a gender switch enabling me to rule out a key alternative explanation.
Without these same-gender replacements, it would not be possible to disentangle whether an
observation that women replacing men received fewer referrals was due to the replacer's gender or to
a newcomer effect. Comparing differences between the referrals received by replacers and leavers
across the different categories provides a means for isolating gender effects from the effects of being a
new member to the group.4
[INSERT TABLE 2]
Importantly, the subsample of entrepreneur-members
involved in a replacement event are
representative of the study population in a number of key ways. Replacements occurred in each of
the 37 chapters in my study population and represent most occupation specialties present.
Unsurprisingly, the occupation specialties not represented among replacement events are those that
are least common in this setting, each representing less than five percent of all members. The gender
composition of the members involved in the replacements is identical to the study population, and to
women's presence among entrepreneurs more generally, with 35 percent being female. Furthermore,
a leaver being replaced by a new member in the same detailed occupation. The results are substantively robust to the
following alternative specifications: shortening the window to a six month period while broadening the definition of
same occupation and shortening the window to a nine month period while using the same fine-grained occupational
definitions without the detailed occupation fixed effects. Because the goal of this empirical strategy is to more
carefully compare men and women within the same occupations, the analyses focuses on replacements within the
same detailed occupation and occurring within 12 months of a leaver's exit.
4 This does not rule out the possibility that there may be differences in the tenure and years in a member's life cycle
with the group between the leavers and the replacers. For these differences to impact my results, the differences
between leavers and replacers would need to vary across replacement types in a way that led to underestimating the
referrals that replacers received in the male-to-female group but not in the other groups. These differences do not
appear to be sizable nor to differ across groups. To minimize this concern, I also estimate models limited to
replacement events where the absolute difference in tenure for the replacer and leaver was less than six months. The
results are robust to this alternate specification as presented in the appendix, Table Al.
29
the distribution of men and women to occupations among this subsample mirrors the broader
pattern, with most occupations being within two percentage points. As shown in Table 2, the
pattern of referrals received overall and by referral type (i.e. direct versus third-party referrals) is
largely identical among the replacements and the study population. Replacers, on average, give
slightly fewer referrals to, and generate less money for, other entrepreneur-members in their group
per year.' All models control for the referral giving behavior of the replacer and the leaver to be sure
that these differences are not affecting the results.
2.3.3
Variables and Empirical Model
The analysis in this study examines whether there are gender differences in the number of
referrals each member received per year among male and female entrepreneurs in the same network
groups. This analysis uses three dependent variables - direct use referrals received, or the total
number of times a member receives requests for services directly from other members in the network
group for their own consumption (i.e. personal or business use) per year, third-party referrals
received, or the total number of times a member receives connections to the third-party contacts (e.g.
clients) of other members in the network group per year, and total referrals received, or the total
number of direct and third-party referrals a member receives per year. Differentiating between direct
referrals received and third-party referrals received allows for testing the mechanism that resourceholder's expectations of the gender preferences of others drive observed gender differences, as stated
in hypothesis 2. Whereas third-party referrals received involve a third-party, the decision to make a
direct referral is based only on the preferences of the resource-holder. Thus, comparing the referrals
women receive relative to men across these two types of referrals provides a way to differentiate
whether anticipated third-party gender preferences or personal preferences are driving observed
gender differences.
s The observed difference in referrals given and dollars generated by the replacer group relative to the broader study
population is the result of slight tenure differences between these groups. Replacers have nearly one year shorter
tenure on average. Limiting the study population to those with the same tenure as the replacer group eradicates this
difference.
30
All models include controls for the following individual characteristics---the number of total
referrals given by a member per year; the total number of additional meetings a member partook in
with other members in addition to weekly meetings per year; tenure with the group; the total
number of weekly meetings a member missed, or absences, per year; and the total dollar amount a
member generated for other members in thousands, or dollars generated, per year. To examine
gender differences within network group and occupation, I also include fixed-effects for network
group and occupation, using the Bureau of Labor Statistics Standard Occupational Classification
(SOC) codes, in some models as noted.'
The first set of models leverage the full study population to establish whether there is evidence
of an overall female disadvantage. I estimate negative binomial regression models to predict total
referrals received, which is a skewed count variable.
The remaining analyses focus on the replacement events to more carefully isolate how resource
exchange among social ties differentially benefits men and women. In these models, I compare men
and women in the same detailed occupations, with access to approximately the same social ties, to
determine whether there are gender differences in the number of referrals men and women receive.
Limiting the analysis to these replacement events helps to rule out the alternative that observed
gender differences are due to occupational sorting.
This analysis of the entrepreneur-members involved in replacement events focuses on the
difference in the average number of referrals received by a replacer and the leaver that was replaced
in terms of each of the three measures of referrals. For each measure - the average number of total
referrals received by a member, the average total number of direct use referrals received by a
member, and the average total number of third-party referrals received by a member - I calculated
the difference in the number of referrals received between the replacer and the leaver that they
replaced as follows, Y, 1= Y, - Y 1,
6
where Y1 represents each of the three types of referrals, r indexes
In my main analyses I use the SOC major groups codes. My main results are robust to using the more
fine-grained
SOC minor group codes. Because the intercoder reliability for coding occupations into the major codes was higher, 97
percent versus 88 percent, I report results based on models using the SOC major codes.
31
the replacer and 1 indexes the leaver. As such, a negative value in these dependent variables indicates
that the replacer received fewer referrals on average than did the leaver whom he replaced.
The key independent variables are the four types of replacement events - male-to-male, femaleto-female, female-to-male, and male-to-female. I created four dummy variables to capture each of
these categories with male-to-male as the reference group. The effect of each of the three remaining
categories represents how the difference between the referrals received by a replacer and leaver in the
given category compares to the difference between male replacers and the male leaver they replace in
the reference category. All models in the analysis of replacements include the aforementioned
controls for individual characteristics at both the level of the leaver and the replacer.
While replacers are compared directly to the leaver that they replace, which by definition is
within network group and detailed occupation, comparisons across categories of replacement are not
necessarily within group or occupation. Therefore, in a final model for each measure of referrals
received I include fixed-effects for network group and occupation, using Bureau of Labor Statistics
SOC codes, to account for the effect of potential variation across chapters and occupations in these
comparisons.
I estimate two sets of ordinary least squares (OLS) regressions predicting the difference between
-
what the replacer received relative to the leaver he replaced: first overall - total referrals received
and then by referral type - direct referrals received and third-party referrals received.
2.4
RESULTS: WOMEN GET LOWER RETURNS TO SOCIAL CAPITAL
Given that the focus of this study is to uncover the network mechanisms that contribute to
gender differences in the resources male and female entrepreneurs receive, Table 3 compares male
and female members in the study population in terms of each of the relevant variables. The
motivation for this comparison is to determine whether male and female entrepreneurs studied differ
in substantive ways that could contribute to differences in the number of referrals they receive. Table
3 reveals that, on average, male and female members do not differ in terms of the number of
32
referrals they generate for other members, the amount of money they generate for other members
through referrals, or their tenure with the group. Women engage in more additional meetings and
have fewer absences from the weekly meetings relative to men. Despite evidence that women
contribute at least comparably to men, they receive fewer referrals. This gender differences in
referrals seems to be largely driven by third-party referrals, with women receiving over 26 percent
fewer third-party referrals than men. While this comparison does not include any controls, it
indicates that female entrepreneurs receive fewer resources through their network ties than do male
entrepreneurs on average, despite being more involved.
[INSERT TABLE 3]
[INSERT TABLE 4
Table 4 presents results from negative binomial regression models estimating the effect of
member gender on the total number of referrals received to determine whether women receive fewer
total referrals than do men. Model 1 contains only control variables, revealing that on average,
members receive more referrals when they contribute more to the group. Specifically, the number of
additional meetings a member engages in, the number of referrals a member gives to others, and the
amount of money a member generates for others, are each strong predictors of the benefits that they
receive. In line with the comparison of means, Model 2 reveals that women received approximately
25 percent fewer referrals per year than men in the same network group, conditional on the
covariates. Model 3 introduces occupation fixed effects, and removes network group fixed effects, to
compare men and women across network groups who are in the same broad occupations. Accounting
for potential gender differences in occupations, women still received nearly 15 percent fewer referrals
than men. The reduction in the magnitude of the gender difference suggests that occupational
sorting is a contributing factor to observed differences. Thus, despite having access to the same
social ties, women receive fewer referrals than men who are contributing similarly to the group.
2.4.1
Replacement events
33
Total referrals. To provide a precise test of hypothesis 1, this next set of analyses focuses on
the subsample of members involved in a replacement event to compare men and women in the same
detailed occupations within the same networking group. In Table 5, the constant term provides the
estimated difference in the number of referrals that the replacer received, on average, relative to
what the leaver received for the omitted category, male-to-male replacements, conditional on the
covariates. In other words, the constant tells us how many more or fewer referrals a male replacing a
male receives relative to the man he replaced. The coefficients on male-to-female, female-to-male,
and female-to-female dummy variables provide estimates of whether the difference in what the
replacer received relative to the leaver he or she replaced in each respective replacement category is
greater or less than observed differences in the male-to-male replacement category. As Model 2 in
Table 5 shows, the gap between what a replacer receives relative to the leaver being replaced is far
wider in the male-to-female category than in the male-to-male category. Whereas a male in the
male-to-male category receives approximately the same number of total referrals as the leaver he
replaced, as evidenced by the non-significant constant term, a female replacer in a male-to-female
replacement event receives five, or 23 percent, fewer referrals than the man whom she replaced. The
introduction of occupation fixed effects in Models 3 and 4 does not lessen this observed gender
difference. The penalty for female replacers in the male-to-female category is even greater when more
fine-grained occupation categories are included (Model 4), with women generating nearly seven fewer
referrals than the men they replace.
[INSERT TABLE 5]
-
The coefficients of the other two replacement categories - female-to-female and female-to-male
are not significant indicating that replacers in these categories, like the male replacers in the maleto-male category, receive a similar number of total referrals as the person whom they replaced. I also
conducted a post-estimation F test to determine whether the effect for the male-to-female category
differs significantly from the female-to-female and female-to-male categories. The results of this test
reveal that these differences are also statistically significant. In other words, with the exception of
female replacers in the male-to-female category, there is no penalty or advantage for replacers, on
34
average, for entering a network group relative to the person they replaced. Taken together, these
results provide support for hypothesis 1 revealing that resource exchange directly contributes to
gender inequality in outcomes.
Type of referral. The previous section showed that female entrepreneurs receive fewer total
referrals than their male counterparts with access to the same social ties and in the same detailed
occupation. This section presents additional analyses aimed at uncovering the mechanism driving
this gender difference and testing hypothesis 2. I find that expectations that a client, family
member, or friend, prefers to work with male entrepreneurs, or what I call anticipatory third-party
bias, lead members in these network groups to disproportionately give referrals to male group
members under certain conditions.
To disentangle the anticipatory third-party bias mechanism proposed in this study, Table 6
presents estimates from models predicting the difference in number of referrals replacers receive
relative to the referrals received by the leaver they replaced by referral type---direct use referrals
(Model 1) and third-party referrals (Model 2). These models reveal that the gender difference
observed is solely driven by women receiving fewer third-party referrals than their male counterparts.
Women receive as many direct use referrals as the men whom they replace, as indicated by the nonsignificant coefficient of male-to-female in model 1, but over five fewer third-party referrals as shown
by the coefficient of male-to-female in model 2. Since male leavers replaced by women receive
approximately 17 third-party referrals (presented below in Table 8), this difference translates to
women receiving 30 percent fewer third-party referrals than the men they replace.
[INSERT TABLE 6]
Whereas third-party referrals involve one member giving the information of his outside contact
to another member, direct use referrals only involve the two members in the group. Thus, unlike
direct use referrals, the success of third-party referrals requires that the outside, or third-party,
contact be satisfied with the referrer's decision to make a referral. Members commonly mentioned
taking the preferences of their clients, family members, and friends into account. When discussing
35
the criteria used to decide whether to make third-party referrals to fellow entrepreneur-members in
interviews, members commonly stressed the importance of a good match between their fellow
contacts in their network group and their third-party contacts outside of the group. For example,
one entrepreneur-member shared: "You can be a pretty good judge of who is going to hit it off with
who and certain things like that. It really comes down to a fit."
Together, these results provide support for hypothesis 2 suggesting that expectations about the
gender preferences of third-party contacts not in the group serve to restrict the number of referrals
women receive relative to men.
Gender composition of occupation. To provide additional support that anticipatory thirdparty biases are leading to fewer referrals for women, this section tests hypotheses 3a, 3b, and 4, by
exploring variation in observed gender differences in third-party referrals based on the gender
composition of the occupation an entrepreneur-member occupies.
To the extent that members are anticipating the gender preferences of a third-party when
deciding whether or not to make referrals, it is reasonable for members to expect that their contacts
are particularly likely to have a preference for men in roles that are traditionally performed by men,
as posited in hypothesis 3a. Similarly, to the extent that third-party contacts have a preference for
women, this is most likely in female-dominated occupations, as posited in hypothesis 3b. Figures 3A
and 3B graph the difference in the predicted number of third-party referrals received by replacers
relative to the leaver they replaced when the replacer's gender is congruent versus incongruent with
the gender-type of the occupation. As in the models presented in Table 6, the underlying OLS
models used to generate Figures 3A and 3B predict the number of third-party referrals received
based on the type of replacement and introduce dummy variables for the sex composition of the
occupation - male occupation for Figure 3A, to test hypothesis 3a, and female occupation for Figure
3B, to test hypothesis 3b. These models include both the main effect of male or female occupation
and interactions between replacement type and these dummy variables. These interaction terms
estimate how differences in the referrals received by replacers versus leavers varies based on the
36
degree to which the occupation that the men and women being compared occupy is gendered. The
estimates from these models were used to calculate the predicted difference in the number of
referrals that a replacer received relative to the person they replaced based on the gender of both
actors and the gender composition of the occupation.
Figure 3A introduces male occupation, which takes the value of one for occupations where 70
percent or more of occupants are male, and compares the relative referrals received by replacers
versus leavers in the male-to-female category to the difference for the relevant baseline group, maleto-male. Consistent with hypothesis 3a, Figure 3A reveals a clear pattern: the penalty in third-party
referrals received by a female replacer relative to male leaver she replaced is most pronounced in
occupations that are predominantly male. Whereas women in occupations that are more than 70
percent male receive 4.6, or 25 percent, fewer referrals than the men they replaced, there is no
gender difference in referrals received by men and women in occupations that are less than 70
percent male.
Figure 3B introduces female occupation, which takes the value of one for occupations where 70
percent or more of occupants are female, and compares the relative referrals received by replacers
versus leavers in the female-to-male category to the difference for the relevant baseline group,
female-to-female. This figure depicts that there is no significant difference between these two
categories of replacements suggesting that men are not penalized for entering female-typed
occupations in this setting. As a result, I do not find support for hypothesis 3b. Rather, when the
man replaces a female leaver he is not penalized in terms of third-party referrals, even when
occupying a role traditionally held by women.
[INSERT FIGURE 3A]
[INSERT FIGURE 3B]
Together these results provide support for hypothesis 4, that the degree to which a network
contact occupies a role that is gender incongruent has greater implications for resource-holder's
decisions of whether to share resources with women than with men. Even stronger, these results
37
suggest that this penalty only impacts women. Among members involved in a replacement, the only
notable gender difference is when a woman replaces a man in a male-typed occupation, where thirdparty contacts are likely to have the strongest gender preferences.
Moving from replacement events to the overall study population, Figure 4 graphs the effect of
the gender composition of the occupation a member occupies on the predicted number of third-party
referrals received by men versus women. The underlying model for Figure 4 predicts the number of
third-party referrals received based on gender using a negative binomial regression. As in the models
presented in Table 4, the underlying model includes the same set of controls and introduces percent
male occupation as an independent variable, which is an ordinal variable ranging from 1-7 and
measuring the degree to which actors within each occupation tend to be men. This model includes
both the main effect of percent male occupation and interactions between female and percent male
occupation. This interaction term estimates how gender differences in third-party referrals received
varies based on the degree to which the occupation that the men and women being compared
occupy. To generate Figure 4, the estimates from this model were used to calculate the predicted
number of referrals that a man and woman will receive based on the gender composition of their
occupation.
[INSERT FIGURE 4]
[INSERT FIGURE 5]
Consistent with hypothesis 4, Figure 4 reveals that women receive fewer third-party referrals
than their male counterparts in male-dominated occupations. Whereas women in occupations that
are more than 70 percent male receive 63.6 percent fewer referrals than their male counterparts,
there is no gender difference in referrals received by men and women in occupations that are less
than 50 percent male. To confirm that this pattern is due to anticipated third-party biases, Figure 5
depicts the effect of the gender composition of the occupation a member occupies on the predicted
number of direct-use referrals received by men and women. This figure reveals that there are no
significant differences in the number of direct use referrals men and women receive. Thus, women
38
only receive fewer third-party referrals than do men and this gender difference is limited to
comparisons of men and women in male-typed occupations. This finding provides additional support
for my proposed mechanism, namely anticipatory third-party bias. Gender differences in the referrals
men and women receive result from expectations about the gender preferences of others.
Taken together, the results presented in this paper reveal that despite having access to the same
social ties as male entrepreneur-members, women receive fewer referrals. Further, this study provides
support that this gender differences is driven by anticipated third-party biases. It is only when
entrepreneur-members anticipate that their clients, friends and family members are likely to prefer
to work with men over women that they disproportionately give referrals to male members within
their network group.
2.4.2
Robustness checks and ruling out alternatives
Screening effect. One possible alternative mechanism for the finding that women receive fewer
third-party, but not direct use, referrals relates to potential unobserved gender differences in on-thejob performance or competency. The argument would be that members hire female members (i.e.
direct use referrals) and discover that these women are poor performers. As a result, they do not
connect their third-party contacts to the female members in their group. While this is a plausible
alternative explanation, neither my quantitative nor qualitative data provide evidence that supports
this alternative explanation.
To the extent that actors in these groups are not referring to women because they hire them and
realize that they are poor performers, we would expect that as a woman receives more direct use
referrals she would receive fewer third-party referrals. The logic here is that, if women are lower
quality performers, the more direct use referrals they receive the more likely members in the group
will learn of their lower quality through informal mechanisms. Based on this logic, as other members
learned about a woman's lower quality, either by hiring the female member themselves of through
word-of-mouth from other members who had hired the female member, they would be less likely to
connect their outside contacts to the female member in the group.
39
[INSERT TABLE 7]
Table 7 presents the results from a model estimating the number of third-party referrals a
replacer received as a function of the number of direct use referrals he or she received. If this
alternative screening mechanism is driving observed gender differences, we would expect that the
number of direct use referrals a woman received would, at least partly, account for observed gender
differences in third-party referrals received. In other words, by introducing the number of direct use
referrals a woman receives as a predictor in this model, we would expect to see a reduction in the
main effect of the male-to-female coefficient. This is not the case, however, as the results are
identical to those presented in Table 6. On average, women receive approximately six, or 35 percent,
fewer third-party referrals than the men that they replace, even after controlling for the number of
direct use referrals they received in that year.
In line with this quantitative evidence, my interviews do not suggest that entrepreneur-members
are using direct use referrals to screen women. Generally, competency does not seem to be a major
concern among members. When asked about experiences with, or knowledge of, poorly performing
others, members typically stated that those who were not strong in their area of specialty typically
did not last long in the group. Furthermore, they had a difficult time recalling cases where a member
was a poor performer. It was often the case that multiple members within a networking group
offered the same story of a poor performer, suggesting that this was in fact an uncommon
occurrence. As a result, members did not center their decisions of whether to refer on competence.
As one entrepreneur-member stated: "Competency is important, but most of the people that I've seen
are competent in their field. I haven't seen anyone in our [group] that I don't feel can do the job."
Even when asked about the criteria used for assessing others in the group, members often did
not mention assessing quality or on-the-job performance. Rather, members discussed the central
importance of learning about other members in the group as people. As one member shared: "Unless
you get to know the people in the group, it's very hard to give that referral. You have to learn who
they are". Another member stresses the importance of the characteristics of the person over their
40
performance stating: "I'm only going to make referrals if I know the person I'm referring to. I know
the quality of who they are as people because my prejudice is that if they are a good quality of person
the quality of their work will be fine."
Taken together, both my quantitative and qualitative evidence cast doubt on the alternative
that direct use referrals serve as a screening mechanism excluding women from the consideration set
for third-party referrals. Thus, it is unlikely that members are directly hiring female members,
realizing that they are poor performers, and consequently not referring their outside contacts to the
women in the group.
Differences
in the quality of leavers. A second possible alternative mechanism for the
finding that women receive fewer third-party referrals than the men they replace relates to potential
heterogeneity in the leavers by replacement category. Specifically, if women are replacing top
performing men, but men are replacing lower performing men, it would be possible that women
receive fewer referrals than the men they replace because they are replacing the stars in the group.
Similarly, if women are replacing very poor performers they may receive less not because they are
women, but rather because other members in the group do not value the particular occupation they
represent. In either case, my results could be biased, such that a female penalty would be
exaggerated.
While this is unlikely, it is also falsifiable. To examine whether this could be the case, Table 8
depicts the mean number of third-party referrals received by leavers in each of the four replacement
categories. As this table shows, male leavers receive on average approximately 16 to 17 third-party
referrals and female leavers receive approximately 13. Importantly, the mean number of referrals
received by a leaver does not differ significantly based on whether the male leaver is replaced by a
same or opposite gender member. This captures potential differences due to the quality of the leaver
as well as potential differences in the opportunity for generating referrals for a given occupation.
Thus, it is unlikely that differences in the benefits received by the leavers account for the finding
that women receive less than the men that they replace.
41
[INSERT TABLE 8]
Women do not ask. Another plausible explanation for the observed difference in the number
of referrals received by women versus men is that women are not asking or expressing a need for
referrals. Research has often argued that women are less likely to make requests than their male
counterparts (Babcock & Laschever, 2003). To the extent that women do not make their needs
known, they could receive fewer referrals not because other members in the group are favoring men,
but rather because men ask, it is easier to identify opportunities to refer to them.
In this empirical context, however, this is an unlikely explanation for two key reasons. First, by
simply joining these instrumental business networking groups, members are all seeking to grow their
businesses by gaining connections to new potential clients through business referrals. Second, by the
design and structure of the weekly meetings, all members in these groups publicly state their needs
during the meeting. As I observed, all members leveraged the opportunity to discuss their business
and express the types of referrals they were seeking at that time. Furthermore, members can sign up
for a longer presentation to pitch their work and educate the other members on how to make
referrals to them during the weekly meeting. A comparison of the propensity for men and women to
sign up for these slots reveals that both genders are equally likely to do so. Therefore, in this setting
I do not find evidence of gender differences in requesting referrals from fellow network contacts.
2.5
DIsCUSSION
Uncovering the sources of gender differences in the benefits men and women receive through
social ties has been the focus of an extensive body of research. Lower returns to social capital for
women are commonly attributed to gender differences in the composition and structure of social
networks (McGuire, 2000). Undoubtedly, lack of access to valuable social ties contributes to women's
inability to generate benefits from social ties. Existing research is less clear, however, about whether
men and women with access to the same social ties receive equal returns. This study addresses this
gap in our understanding of how resource exchange, or the activation of social ties, contributes to
42
gender inequality by comparing the relative benefits received by male and female entrepreneurs with
the same access to social ties.
Using a unique empirical setting where male and female entrepreneurs are members of the same
business networking groups, I find that women receive fewer referrals through social ties than do
similar men in the same network group, but this female penalty is not ubiquitous. The results
presented in this study indicate that this female disadvantage is limited to cases where a resourceholder deciding whether to share resources with a network contact expects -that a relevant thirdparty---such as a client, friend, or family member---has a preference for men over women, which I
call anticipatory third-party bias. Whereas men and women are equally likely to receive business
directly from their network contacts, women receive far fewer connections to the clients, family, and
friends of their network contacts, or third-party referrals. Further, I show that this gender difference
in referrals only exists among women in occupations traditionally occupied by men. This finding
provides further support for the central role third-parties play in the relative benefits men and
women receive through their social ties.
This paper makes two main contributions to the study of the network sources of gender
inequality. First, by showing that women receive fewer benefits through social ties than men despite
having the same access, this study provides evidence that access to social ties is insufficient for
eradicating gender differences in the benefits of networks. Resource allocation also plays a role in
perpetuating gender inequality. Network scholars have long argued that specifying individual-level
differences in how actors use social ties provides an opportunity for making theoretical progress (e.g.,
Ibarra et al., 2005; Renzulli & Aldrich 2005). This study reinforces the importance of focusing on
how resources are exchanged among network contacts. To gain a deeper understanding of how
networks affect gender inequality in particular, future research must carefully account for both the
structure and composition of networks and the process of resource exchange, or activation, within
networks.
43
Second, this paper identifies a new network-based mechanism explaining gender inequality, which
I call anticipatory third-party bias. The finding that women only receive lower returns to their social
capital when resource-holders anticipate the gender preferences of others, or third-parties, provides
support for this mechanism. Expectations about the gender preferences of others, or anticipatory
third-party bias, influence resource-holders' decisions of whether to share resources with network
contacts in a way that limits the benefits women receive through social ties. The fact that resourceholders only exhibit a male preference in occupations that are traditionally held by men provides
further support for this mechanism. To the extent that a resource-holder is anticipating the
preferences of a third-party, it is reasonable to expect that the third-party prefers men in
occupations that are male-typed. In other words, the assumption that a third-party contact prefers a
male electrician is more likely to be accurate than the assumption that a third-party contact prefers
a male nurse. Future research could further uncover the conditions that make third-party biases
more or less prevalent in evaluations. Resource-holders may differ in their propensity to incorporate
gender into their decisions of whether to share resources based on their own gender or on the gender
of their outside contact, or third-party. Understanding whether anticipated third-party biases
operate differently for certain actors or under certain conditions would provide a more nuanced
understanding of how and when resource exchange within networks disadvantages women relative to
men.
This study also makes a contribution to research on how ascriptive characteristics affect
evaluations more generally by providing empirical evidence for the role of third-parties in
perpetuating gender inequality. A challenge in identifying how anticipatory third-party beliefs
contribute to gender inequality is disentangling this mechanism from other competing explanations.
Specifically, existing evidence consistent with the notion that anticipating the preferences of others
leads actors to disproportionately favor men over women has been unable to account for personal
gender beliefs, or preferences (e.g. Fernandez-Mateo & King, 2011). To develop comprehensive
theories about sources of gender inequality, and to redress these inequities, it is necessary to more
carefully identify not only the potential mechanisms, but also the extent to which these mechanisms
44
are responsible for observed gender differences in outcomes. This study offers a first attempt to
disentangle these competing mechanisms by comparing the relative returns men and women receive
from social ties in cases where decisions to share resources are contingent on a third-party versus
cases where decisions are based on personal preferences. Future work could further disentangle these
processes by comparing decisions to share resources under conditions with and without a third-party
made by the same evaluator. This will allow for further identification of how anticipating the
preferences of a third-party impacts gender inequality in the benefits received through social ties by
accounting for each individual's personal gender preferences.
In terms of identifying when third-parties are more apt to perpetuate versus reduce inequality,
this study provides evidence consistent with the importance of visibility, or transparency. Acting in
accordance with the anticipated preferences of others is particularly likely when the presence of bias
in decisions is not observable, or visible, to others. Of course, third party audience expectations can
serve to mitigate reliance on ascriptive characteristics in evaluations (Salancik & Pfeffer, 1978; P.
Tetlock, 1992; Valian, 1999). By imposing expectations, audiences serve to create accountability, or
impose sanctions for behavior that is in violation of established norms (Adut, 2005; Jensen, 2006).
Therefore, concerns that the third-party will sanction biased behavior may discourage reliance on
gender in decisions of whether or not to share resources. But an audience is only able to impose its
expectations, tacitly or otherwise, onto a decision maker if this bias is visible to the evaluating
audience (see Castilla (2008) for a similar discussion about the importance of transparency). This
study provides support for the presence of bias under conditions where a third-party is unable to
perceive whether a decision-maker is exercising bias. Since the third-party contacts of entrepreneurmembers are unlikely to know that a member refrained from connecting them to another member of
the group, bias is not visible to third-parties in this setting.
In conclusion, my findings help to explain how networks contribute to the underrepresentation
and lower success of women among entrepreneurs. Even if female entrepreneurs have access to the
same networks, and engage similarly within these networks, as their male counterparts, I find that
they may still receive fewer benefits. Thus, to the extent that entrepreneurs strategically shape their
45
networks (Burt, 2007; Stuart & Sorenson, 2007), simply gaining access to valuable social ties may
not lead to the same benefits for men and women. Further, even if network contacts are not
exercising overt bias, or male preference, in deciding with whom to share resources, expectations
about collective gender beliefs of others may serve to constrain actors in a way that perpetuates
gender inequality. The gender preferences of third-parties are likely to be particularly salient in
assessments of entrepreneurs because entrepreneurship is a male-dominated area (Gupta et al.,
2009). Therefore, redressing gender inequality in the benefits that male and female entrepreneurs
receive through social ties requires more than providing women with access to better networks or
eliminating personal gender stereotypes or beliefs. Equipping entrepreneurs with knowledge about
the role of third-parties for gender inequality provides a first step toward diminishing gender
differences in the benefits available through social ties. Since actors are not bound to anticipating
the gender preferences of third-parties, more equitable network-based outcomes are possible if actors
avoid basing decision on these expected gender preferences, or at least learn the true preferences or
expectations of others where possible.
46
2.6
FIGURES AND TABLES
Figure 1. Structure of groups
Group B
Group A
IT Consultant
Real Estate Agent
Real Estate Agent
T
stw
-
IT Consultant
Figure 2A. Distribution of Entrepreneur-members to Occupation Categories based on Bureau of
Labor Statistics Standard Occupational Classification (SOC) Codes
Business
and Finaidal Specialists
Construction sind Trades
Office and Administrative Support
Retail and Services Sales
Arts, Design. Entertainnent, and Mediai
Legal
Computer
Healthbare Pramctitioiners
Cmornunity and Soial Services
Building and Grounids MITaintenan ce
-IFPrint and Production
Personal Care and Service
Installation
and Repair
Architecture and Engineering
Food Preperation and Service
Advertising, Marketing, and Promotions
Healthcare Support
Transportationi and Material Moving
Protectivo Services
Education and Training
0
so
100
150
47
200
250
300
350
Figure 2B. Gender Composition of Occupation Categories based on Bureau of Labor Statistics
Standard Occupational Classification (SOC) Codes
0.9
0.
0.6
0.2
0.1
0
N
x
,
-P>
&,
NC4
t
VZN
<
44kO'
&-
Figure 3A. Predicted Difference in Third-party Referrals Received by
Replacer Relative to Leaver in Male to Female Replacements versus Male
to Male replacements by Gender Composition of Occupation
7E
a)
-0-
E
z
-
=19
Ct
at
80;
0
1
Male-dominated Occupation =1
-
Male-to-Male
- -f
- -
Male-to-Female
Error bars represent 95% confidence intervals
48
Figure 3B. Predicted Difference in Third-party Referrals Received by
Replacer Relative to Leaver in Female to Male Replacements versus
Female to Female replacements by Gender Composition of Occupation
w
z
0
Female-dominated
0
-
,D
Occupation
Female-to-Female
Error bars represent 95% confidence
-
=I
+--Female-to-Male
intervals
.S-
CL
4. Predicted Overall Gender Difference in Third-party Referrals
Figure
CQ
by Gender Composition of Occupation
Received
E
<30%
30 -40%
50 - 60%
40 -50%
Percent Male in Occupation
-t-Fem
-
-
60 -70%
- F
Error bars represent 95% confidence intervals
49
> 70%
Figure 5. Predicted Overall Gender Difference in Direct Use Referrals
)
Received by Gender Composition of Occupation
0)
"0
E)
.zD
< 3 %
3
'0
0-5%
0-6%
PretMln
a)
6
'0
Ocpto
MN40
Errr
brs9% epeset
cnfdene
iteral
50
0
TABLE 1. Replacement Events by Type
% of Total
Number of Unique
Number of
Replacement
Persons
Replacements
Events
Male to Male
376
188
45.19%
Male to Female
176
88
21.25%
Female to Female
132
66
15.87%
Female to Male
148
74
17.79%
Total
832
416
100%
TABLE 2. Comparing Participation and Referral Behavior of Replacement Sample to Study Population, Yearly
Averages
Study Population
Replacement Sample
Mean
s.d.
Min
Max
Mean
s.d.
23.58
19.68
0.00
196.00
23.35
20.29
0.00
Min
Max
Referrals Received
Total Referrals Received
196.00
Direct Use Referrals Received
7.69
9.73
0.00
73.00
7.37
11.20
0.00
73.00
Third-party Referrals Receive
15.90
15.06
0.00
188.00
15.99
15.20
0.00
151.00
24.18
20.30
0.00
264.00
22.42
18.97
0.00
264.00
8.20
15.98
13,045.00
8.87
16.44
30,410.57
0.00
0.00
0.00
85.00
248.00
789,598.40
7.07
15.35
9,935.09
8.23
15.89
31,500.74
0.00
0.00
0.00
73.00
248.00
661,666.00
14.49
3.03
4.04
15.62
2.89
4.89
0.00
0.15
0.00
75.00
16.83
52.00
12.75
2.17
4.42
13.36
2.12
5.31
0.00
0.15
0.00
75.00
14.41
52.00
Referrals Given
Total Referrals Given
Direct Use Referrals Given
Third-party Referrals Given
Dollars Generated
Participationin Group
Additional Meetings
Tenure
Absences
N
2,310
832
51
TABLE 3. Basic Descriptive Statistics for Key Variables by Gender, First Year in Dataa
Female Members
Male Members
Mean
s.d.
Min
Max
Mean
s.d.
Total Referrals Received
20.22
17.99
0.00
137.00
24.74
22.84
0.00
Direct Use Referrals ReceivE
7.94
10.74
0.00
68.00
8.23
10.52
0.00
73.00
Third-party Referrals ReceiN
12.28
11.80
0.00
98.00
16.51
17.85
0.00
188.00
23.28
18.77
0.00
160.00
22.40
20.83
0.00
205.00
7,528.50
18,235.82
0.00
257,744.00
8,072.73
19,517.00
0.00
389,108.00
Additional Meetings
15.25
15.87
0.00
75.00
10.95
12.91
0.00
74.00
Tenure
1.41
1.75
0.15
12.35
1.56
2.04
0.15
12.83
Absences
3.84
4.84
0.00
40.00
4.41
5.55
0.00
46.00
First Year Observed
2.36
1.40
1.00
5.00
2.47
1.46
1.00
5.00
Dollars Generated
t-testb
Max
196.00
*
Total Referrals Given
Min
1488
822
persons
'Comparisons robust for each nt'year in the data and by calendar year. Use first year because captures all members.
b indicates whether differences in values for male and female entrepreneurs are statistically significant based on two-sided t-tests
*p
0.05, ** p
0.01, *** p
0.001
52
Table 4. Negative Binomial Regressions Predicting Total Referrals Received
per Year
Additional Meetings
0.009 *
(0.001)
0.003
**
(0.001)
Tenure
Absences
Dollars Generated (in 000's)
0.011
-0.149
(0.032)
0.010 ***
(0.023)
(0.001)
(0.001)
0.004 ***
(0.001)
0.013 ***
0.003
(0.001)
0.010
0.030
(0.006)
-0.006
(0.006)
-0.006
(0.004)
(0.003)
(0.003)
(0.002)
0.000
*
*
(0.000)
Constant
-0.251 *
2.656 ***
0.000
(0.000)
2.735 ***
*
Total Referrals Given
Model 3
*
Female
Model 2
*
Model 1
-0.007
**
0.001
0.000
2.991 ***
(0.110)
(0.118)
(0.051)
Year
Yes
Yes
Yes
Group
Yes
Yes
No
No
No
Yes
485.320
548.460
1479.670
Fixed Effects
Occupation
Wald Chi-squared
DF
45
46
30
Persons
2,310
2,310
2,310
Person-years
5,588
5,588
5,588
Notes: Standard errors, in parentheses, are clustered by individual member. Models
including occupation fixed effects use BLS SOC major codes. Results are robust to more finegrained occupational fiexed effects.
* p
0.05, ** p
0.01, *** p
0.001
53
Table 5. OLS Regressions Predicting Difference in Total Referrals Received by Replacer
Relative to Exiter
1
Model
Model 2
Model 3
Model 4
Replacement Type
Female-to-Female
Female-to-Male
-5.257
*
*
-5.305
(2.462)
-6.832
*
Male-to-Female
(2.631)
(2.719)
-3.639
-1.511
-1.062
(2.661)
(3.157)
(3.329)
0.437
1.377
1.633
(2.478)
(2.724)
(2.813)
Controls for Replacer
Tenure
0.302
(0.056)
0.213
(0.058)
(0.090)
(0.092)
0.170
(0.085)
3.150
(1.520)
Absences
Dollars Generated (in 000's)
*
0.287
0.219
*
0.161
(0.085)
***
*
*
*
0.307
(0.055)
*
Additional Meetings
0.310
(0.055)
*
Total Referrals Given
2.962
2.041
1.972
(1.516)
(1.585)
(1.620)
-0.104
-0.117
-0.117
-0.126
(0.206)
(0.206)
(0.213)
(0.217)
-0.023
-0.019
-0.028
-0.017
(0.000)
(0.050)
(0.051)
(0.052)
Controls for Exiter
Additional Meetings
-0.177
-0.202
(0.124)
Tenure
-0.870
-0.944
Dollars Generated (in 000's)
-0.224 **
(0.083)
(0.125)
(0.520)
(0.521)
-0.204
(0.085)
-0.199
-0.191
(0.130)
(0.135)
-0.864
-0.967
(0.538)
0.395
0.387
0.297
(0.548)
0.299
(0.187)
(0.188)
(0.193)
(0.195)
*
Absences
-0.251 **
(0.080)
*
-0.264 **
(0.080)
0.145
(0.000)
**
*
Total Referrals Given
0.150
(0.053)
**
0.141
(0.054)
**
0.150
(0.054)
-6.186
-0.545
4.507
21.596
(10.132)
(10.419)
(11.455)
(14.280)
Yes
Yes
Yes
Yes
Occupation - SOC Major
No
No
Yes
No
Occupation - SOC Minor
No
No
No
Yes
0.235
0.242
0.244
416
416
416
Constant
Fixed Effects
Group
Adj R-squared
Observations
Notes: Standard errors in parentheses.
* p! 0.05, ** p
0.01, *** p
0.001
54
0.250
416
**
Table 6. OLS Regressions Predicting Difference in Direct Use versus Thirdparty Referrals Received by Replacer Relative to Leaver
Model 1: Difference in
Model 2: Difference
Direct Use Referrals
in Third-party
Replacement Type
-0.005
-5.231
(1.251)
(2.221)
0.445
-1.610
(1.295)
(2.665)
Female-to-Female
Female-to-Male
*
Male-to-Female
0.107
0.934
(1.501)
(2.299)
0.066
0.237
Total Referrals Given
*
Controls for Replacer
(0.027)
Additional Meetings
Tenure
Dollars Generated (in 000's)
(0.048)
0.092
0.122
(0.043)
(0.076)
0.080
1.946
(0.754)
(1.338)
0.064
-0.181
(0.101)
(0.185)
-0.035
0.007
(0.024)
(0.043)
Absences
***
Controls for Exiter
Additional Meetings
-0.201
(0.070)
-0.062
-0.137
(0.062)
(0.109)
(0.554)
-0.309
(0.256)
(0.454)
*
Tenure
-0.024
(0.039)
Absences
*
Total Referrals Given
0.120
0.178
(0.092)
(0.163)
0.022
0.119
(0.026)
(0.045)
Dollars Generated (in 000's)
3.590
0.865
*
Constant
**
(9.668)
5.447
Fixed Effects
Group
Yes
Yes
Occupation
Yes
Yes
0.1739
0.193
416
416
Adj R-squared
Observations
Notes: Standard errors in parentheses. Occupation fixed effects for SOC major codes
used. Results robust to more fine-grained occupation fixed effects.
*p
-
0.05,
** p
0.01, *** p
0.001
55
Table 7. OLS Regressions Predicting Difference in Third-party Referrals Received
based on Difference in Direct Use Referrals Received by Replacer Relative to Leaver
Replacement Type
Male-to-Female
*
-5.772
(2.531)
Female-to-Female
-0.918
(3.108)
Female-to-Male
0.573
(2.932)
Screening Effect
Direct Referrals Received
0.106
(0.141)
MFxDirect
0.124
(0.258)
FFxDirect
-0.154
(0.287)
FMxDirect
-0.008
(0.274)
Controls for Replacer
0.234
Total Referrals Given
***
(0.049)
Additional Meetings
0.129
(0.077)
Tenure
1.742
(1.362)
Absences
-0.168
(0.181)
Dollars Generated (in 000's)
0.009
(0.044)
Controls for Exiter
Total Referrals Given
-0.202
**
(0.071)
Additional Meetings
-0.129
(0.110)
Tenure
-0.335
(0.461)
Absences
0.193
(0.165)
Dollars Generated (in 000's)
0.121
**
(0.046)
Constant
-0.438
(9.791)
Fixed Effects
Group
Yes
Occupation
Yes
Adj R-squared
Observations
Notes: Standard errors in parentheses.
0.1875
Uccupation
tixed effects
robust to more fine-grained occupation fixed effects. '
* p
for
416
SOU major codes used. Results
0.05, ** p
56
0.01, *** p
0.001
Table 8. Comparing Third-party Referrals Received by Leavers
Involved in a Replacement Event, by Replacement Type
Third-party Referrals
Mean
s.d.
Min
Max
Male-to-Male
15.93
12.96
0.00
69.00
Male-to-Female
17.19
14.63
0.00
83.00
Female-to-Male
13.74
11.04
0.00
53.00
Female-to-Female
13.41
12.81
0.00
97.00
57
3 NAMING YOUR DAUGHTER JACK: THE EFFECT OF
GENDER IN ATTENTION AND EVALUATION
3.1
INTRODUCTION
There are a wide range of organizational and market settings where an evaluator is tasked
with assessing a set of candidates with the explicit goal of selecting the most qualified from this set.
For example, this exercise is common in labor markets, where job applicants are evaluated by a
hiring manager (Fernandez & Fernandez-Mateo, 2006); financial markets, where securities and firms
are assessed by investors and analysts (Zuckerman, 1999, 2004); and cultural markets, where books,
films, and music are appraised by consumers and critics (Kovacs & Sharkey, 2014; Salganik, Dodds,
& Watts, 2006). A persistent challenge facing evaluators across these contexts is that the actual
quality of the options being compared is not easily observable, forcing them to rely on accessible
indicators of expected quality. To minimize the effect of signals, and to achieve unbiased results, in
taste tests, for example, the evaluator is often blinded, or made unaware of the identity of the
producer of the product that she is evaluating. However, the exclusion of signals is difficult, and in
some cases their absence may result in suboptimal outcomes
(Spence,
1973), therefore,
as
uncertainty increases signals are commonly relied upon (Podolny, 1994, 2005; Simcoe & Waguespack,
2011).
This central role of signals turns the evaluation process into more of an art than a science,
with recent causal evidence showing that ratings may stem from factors unrelated to underlying
quality (Muchnik, Aral, & Taylor, 2013). The art of the evaluation process, along with the fact that
the outcomes of these evaluations have significant economic impact, has spurred a long tradition of
research questioning whether evaluation processes contribute to systematic disadvantage for some
groups (Bertrand & Mullainathan, 2004; Foschi, 1996, 2009: 200; Foschi, Sigerson, & Lembesis, 1995;
Foschi & Valenzuela, 2012; Pager, Bonikowski, & Western, 2009; Reskin, 1988). How ascriptive
characteristics affect the multiple stages of the evaluation process, especially in competitive markets,
where more objective quality or performance information is available, however, remains unclear. In
58
this paper, we aim to understand how gender affects the evaluation process by focusing on two
stages: first, the selection stage, and second, the rating and feedback stage. We unpack this question
using a competitive market setting where performance is important, several relevant and objective
screening criteria are available to the audience of evaluators, and gender is easily inferred, but not
needed.
In trying to understand the persistence and effect of ascriptive characteristics on the
evaluation process, early economic theories of discrimination would suggest that evaluations based
on attributes not directly related to productivity are costly and thus should be competed away in an
efficient market (Altonji & Blank, 1999; Arrow, 1971; Gary Stanley Becker, 1957; Charles & Guryan,
2008). Based on this perspective, because of competitive pressures, we would not expect evaluators
seeking
to
maximize
performance
to base
their
assessments
of candidates
on
ascriptive
characteristics. However, research has documented that the reliance on ascriptive characteristics,
such as gender, in evaluation processes serves to perpetuate inequality (Bielby & Baron, 1986; Perry,
Alison Davis-Blake, & Kulik, 1994; Reskin & Roos, 1990), as a result of either unconscious evaluator
stereotypes (Heilman, 1980), or more deliberate efforts to maintain majority group privilege (Reskin,
1988). Status-based theories of discrimination posit that a preference for members of a particular
group emerges because of higher performance expectations for members of higher status groups
(Ridgeway & Correll, 2004; Ridgeway & Smith-Lovin, 1999). While it is well established that these
micro-level selection and evaluative processes contribute to disadvantage for certain ascriptive
groups, identifying whether and the conditions under which ascriptive characteristics lead to an
advantage for higher status groups is less clear (Correll & Benard, 2006). Additionally, we lack an
understanding of how these characteristics affect outcomes for the candidates that move beyond the
initial selection stage of evaluation.
Undoubtedly, a primary explanation for our limited understanding of the extent to which
status based mechanisms contribute to persistent inequality is the challenge of collecting appropriate
data. Isolating the role of status-based discrimination for gender inequality is particularly difficult as
it is necessary to examine gender inequality in a context where statistical discrimination is not likely
59
at play. Statistical discrimination posits that differences in the treatment of groups is a logical
response to the problem of limited information (Arrow, 1971; Phelps, 1972). According to this
perspective, if a valued characteristic is difficult or costly to observe, and has different distributions
for men and women, treating these groups differently would be rational.
Using unique data from an online platform where investment professionals openly share
relevant investment recommendations,
this study addresses this challenge by providing direct
evidence that status-based mechanisms of discrimination play an important role for perpetuating
gender inequality. Because there is a universal, unbiased, market-based performance metric assigned
to each investment recommendation, which is visible to all members of the platform, evaluators in
this setting do not need to rely on gender as a proxy for performance. We find that evaluators are
more likely to view investment recommendations linked to male investment professionals than those
linked to female investment professionals. Specifically, this study provides support for a particular
form of status-based discrimination, namely double standards theory, which posits that even when
performance information is available, biased evaluators will favor men over equally qualified women
because generally men are perceived as the higher status actor (Berger, 1977; Correll & Ridgeway,
2003).
This study also demonstrates the conditions under which status-based discrimination is most
prevalent. While we find that that evaluators prefer male candidates over women in the initial
selection stage, once men and women are selected into the consideration set we do not find evidence
of a female penalty in the subsequent rating and feedback stage. Furthermore, we find that
evaluators are particularly likely to invoke double-standards favoring men in the initial selection
stage when they are faced with high search costs. By isolating the role of status-based mechanisms
of discrimination for perpetuating gender inequality, this study identifies not only how, but also
when evaluators use gender as a criterion despite having more relevant indicators of performance
with which to assess candidates. We posit that together these findings provide evidence that double
standards are applied, such that women are held to a higher standard, as a means for sifting through
a high volume of information, but not when search costs decrease. Furthermore, this strategically
60
selected context allows us to rule out a strong alternative explanation that these observed gender
differences are associated with female non-conformity. In the financial markets, an actor's propensity
to take risk, or risk-appetite, is a salient characteristic. Importantly for our research question, women
are commonly perceived as being more risk-averse (Alden, 2013). To the extent that women in this
setting are more risk-averse, it would be plausible that statistical discrimination plays a prominent
role with evaluators using gender as a proxy for this trait; therefore, it is important to rule out this
alternative.
This study does not, nor does it claim to, falsify or rule out the possibility that statistical
discrimination also plays a significant role in perpetuating inequality in some cases. Since statistical
explanations are based on evaluators relying on gender as a proxy for desirable characteristics that
they are not able to directly observe, statistical discrimination would be least likely in our setting.
Therefore,
using the selected context, this study adjudicates
between these two competing
explanations by providing confirming evidence that status-based mechanisms of discrimination,
particularly double standards, contribute to gender inequality in outcomes under certain conditions.
Additionally, the ability to follow a subject through the evaluation process is rare, therefore, we are
also able to identify how the use of gender changes from the selection stage to the rating and
feedback stage of the evaluation process.
We proceed as follows. First, we develop hypotheses about whether and when we should
expect evaluators to exhibit a preference for male candidates by drawing on theories of status-based
discrimination and double standards. We explain how the impact of evaluators may differ at
different stages of evaluation and based on the number of options available for consideration. Second,
we introduce the research setting and analytical method used to test whether status-based
mechanisms of discrimination account for gender inequality in the attention or ratings candidates
receive from evaluators. Third, we present results which demonstrate whether and when double
standards lead to a female penalty in this setting. We conclude with a discussion of the theoretical
and managerial implications of the research findings for our understanding of gender inequality
resulting from evaluative processes.
61
3.2
3.2.1
THEORY AND HYPOTHESES
The Role of Status-based Mechanisms of Discrimination for Gender Inequality
Status-based theories of discrimination posit that a preference for the members of a
particular group emerges because of higher performance expectations for members of higher status
groups. A status characteristic emerges when an observable social distinction is attributed to a
widely held cultural belief that actors with one category of the characteristic have greater
competence and social value than actors with an alternate category (Berger,
1977; Berger,
Rosenholtz, & Zelditch, 1980; Wagner & Berger, 1993). Because status characteristics carry these
performance expectations, it is posited that evaluators, either consciously or otherwise, use these
characteristics when assessing candidates (Correll & Benard, 2006). As a result, actors with the
positive state of the characteristic, or higher-status, benefit from social advantages relative to actors
possessing lower-status states of the characteristic (Webster & Hysom, 1998).
In the case of gender, status beliefs about expected quality or performance lead evaluators to
favor men, the higher status actors, because they are expected to outperform women. For example,
as the higher-status gender, men are expected to be more competent, have more influence over
others in a group, and have their performances evaluated more positively than their female
counterparts (Ridgeway & Correll, 2004; Ridgeway & Smith-Lovin, 1999). To the extent that
ascriptive characteristics become imbued with status (Ridgeway & Correll, 2000), it is not surprising
that evaluators form these expectations that subsequently inform evaluations about candidates.
Furthermore, this is in line with status theories more broadly predicting that status is used as an
indicator of quality, especially in times of uncertainty.
3.2.2
Double Standards Theory and Gendered Outcomes
Despite this consensus that status plays a central role by signaling quality, the importance of
status varies. Podolny (1994) argued that status is most important when uncertainty makes it
difficult to evaluate the quality of a product prior to its exchange. For example, younger firms
receive greater benefit from relationships with high status partners than do older firms because the
62
quality of younger firms is more uncertain (Stuart, Hoang, & Hybels, 1999). Similarly, more
experienced evaluators may rely on status less because they are more capable of discerning quality
(Jensen, 2006). These studies suggest that the significance of status for evaluations declines as
evaluators are able to assess candidate quality more precisely and directly.
Alternatively, double standards theory, a particular status-based account of discrimination,
suggests that gender7 serves as a distinguishing characteristic in assessments of competence even
when men and women with equal performance are compared (e.g., Foschi, 1989). According to this
perspective, discrimination arises because stereotypes about the relative expected performance of
men and women influence evaluations. Standards are defined as, the "norms defining requirements
for the inference of an attribute, such as the level of performance considered necessary to conclude
that a person is competent" (Foschi, 2000). Importantly for this study, double standards theory
centers on situations where these standards are not established a priori by a third party, but rather
are formulated and applied by the same evaluator (e.g., Foschi, 1989). Double standards exist when,
instead of the same set of norms, or requirements, being applied to all performers, there is more than
a single performance-requirement for the inference of levels of competence (Foschi, 2000).
A useful distinction for understanding how differences in standards contribute to unequal
outcomes for men and women is to classify these in terms of strictness, where a strict standard for
ability requires additional evidence of competence than does a lenient standard (Foschi & Foddy,
1988). At the core of this theory is the notion that if members of lower status groups (e.g., women)
are systematically evaluated using stricter standards, these lower status actors will be perceived as
being less competent than similarly performing members of the higher status group (e.g., men) who
are evaluated using less strict standards (Foschi, 1989). Because strong performances are unexpected
from the lower status group, when a man and woman exhibit an equal, good performance, the
female performance is assessed using a stricter standard.
While double standards theory has provided insight on the role of status characteristics more broadly, we
will center our discussion on gender as this is the focus of this paper.
63
These assessments of competence are proposed to contribute to gender differences in
outcomes for men and women, thus serving as a potential source of inequality. For example, a double
standard would exist if evaluations on an exam deemed a score of 70 as passing for some, but a score
of 80 was necessary to pass for others. In relation to gender, these standards would perpetuate
inequality if men were held to the former, while women the latter. Alternatively, women may be held
to a universalistic standard, whereas men are advantaged by particularistic standards where
exceptions to this standard occasionally make requirements more lenient for men (Lorber, 1984).
This is not to say, however, that universal standards applied similarly to men and women may not
disadvantage women. For example, one explanation for why few women advance to equity partner
level in law firms is that both men and women understand this role as requiring them to privilege
work considerations over family (Pinnington & Sandberg, 2013). To the extent that both men and
women who place priority on family obligations are disadvantaged, this would be considered a
universal standard, even if women are disproportionately affected due to their tendency to place
primary attention on family. Double standards theory, and this study, is centered on the formercases where different standards are used to evaluate men and women.
Extant experimental research has been foundational in establishing underlying theory and
providing empirical evidence for the existence of double standards, yet the extent to which double
standards shape gender inequality in organizational and market settings remains unclear. Numerous
laboratory studies, testing whether, and under which conditions, double standards are activated,
have found support for the theory-even when male and female subjects perform similarly, women
are evaluated using stricter standards and thus perceived as less competent (Foschi, 1996; Foschi,
Lai, & Sigerson, 1994; Foschi et al., 1995). These studies have found that, in many cases, minority
groups face more stringent evaluation criteria (Foschi et al., 1994). For example, when paired with a
partner in laboratory studies, subjects set higher standards for female higher performing partners
than they did for male partners (Foschi, 1996). Further, audit studies comparing the likelihood that
racial minorities, as compared to whites, progress in the hiring process show that evaluators prefer
higher-status candidates (e.g., white applicants) over lower-status applicants (e.g., black applicants),
64
even when comparing resumes for equally qualified applicants from each status category (Bertrand
& Mullainathan, 2004; Neumark et al., 1996; Pager et al., 2009).
However, more recent examinations offer conflicting predictions about the role of gender in
&
eliciting lower competence assessments for women altogether (Foschi & Lapointe, 2002; Foschi
Valenzuela, 2008, 2012; Jasso & Jr., 1999; Okamoto & Smith-Lovin, 2001). For example, Foschi and
Lapointe (2002) found that subjects were similarly influenced to update their response (i.e., reject
influence) upon learning that their partner disagreed with their response, irrespective of whether the
partner was male or female, suggesting that male and female partners were equally influential. In
line with this perspective, some recent studies attempting to identify whether double standards exist
in hiring processes have found that subjects evaluating similarly qualified male and female resumes
did not show a preference for the male applicant (Foschi & Valenzuela, 2008, 2012). In some cases,
research has found that while men were neutral, women exhibited reverse double standards, thus
positing that male participants show no bias and female participants may be overcompensating
(Foschi & Valenzuela, 2008).
Furthermore, by design, these lab experiments are not subject to the competitive pressures
that commonly influence behavior in organizational and market settings. To the extent that
evaluations based on attributes not directly related to productivity are competed away in efficient
markets (Altonji & Blank, 1999; Arrow, 1971; Gary Stanley Becker, 1957; Charles & Guryan, 2008),
we would not expect evaluators seeking to maximize performance to base their assessments of
candidates on ascriptive characteristics. Therefore, the role of ascriptive characteristics, such as
gender, are unclear when a competitive market is analyzed. Further, we lack an understanding of
how these ascriptive characteristics affect multiple stages of the evaluation process.
Thus, the prevalence of double standards for perpetuating gender inequality remains unclear.
Conflicting evidence stemming from experimental studies coupled with the absence of field studies
examining the role of double standards in the presences of competitive forces, leaves a gap in our
understanding of whether, and under what conditions, double standards are actually activated in
65
practice. Understanding whether male and female actors are evaluated using different standards is
critical because we know that such standards are norms specifying the level and type of outcome
required to infer ability (Foschi, 1996). To the extent that women are systematically evaluated using
more stringent standards, women will be viewed as having lower ability levels than similarly
performing men. Therefore, the standards used to judge ability may play a key role in perpetuating
social and economic inequalities.
In order to develop a complete theory about the factors
contributing to gender inequality, and specifically the role of double standards in perpetuating this
inequality, it is necessary to address this gap. It is only through this form of scientific inquiry that
we will be able to develop effective solutions to redress gender inequality in groups, organizations,
and the economic sector.
Given that the finance industry, particularly investment management (e.g., hedge fund,
mutual fund), is male dominated (Alden, 2013), it presents a well-suited setting for testing whether,
and under what conditions, double standards contribute to gender inequality. A key scope condition
of the theory is that double standards disadvantaging women are expected for tasks that are either
male, or ambiguous, in terms of gender association (Foschi, 1989). The ideal worker (Acker, 1990;
Williams, 1999) defines the characteristics of the individuals who are expected to be strong
performers (Gorman, 2005). This image of the ideal worker shapes evaluative processes as candidates
are commonly evaluated against this norm (Heilman, 1983). The investment industry's ideal worker
image has been found to be gender-typed male "because [the ideal worker image] conflicts directly
with macro-cultural stereotypes about women and defines commitment to the occupation as
incompatible with motherhood" making gender particularly salient in this context (Turco, 2010).
Hypothesis
1: Evaluators will use stricter standards when
evaluating investment
recommendations made by female investment professionals such that recommendations
submitted by women will be less likely to be viewed than recommendations submitted by men.
3.2.3
Stages of Evaluation
The process of evaluation is commonly a two-stage process: the selection stage, where
candidates are first selected to identify the subset that will be given further consideration, and
66
subsequent assessment, where members of this reduced set are evaluated more closely and given
feedback (Gensch, 1987; Hiubl & Trifts, 2000; Kovacs & Sharkey, 2014). In examining the role of
status in evaluative outcomes, extant research has commonly focused on one of these two stages,
thus failing to uncover the degree to which discrimination persists across stages (Kovacs & Sharkey,
2014). Status has been found to play an important role in both stages of the evaluation process. In
line with hypothesis 1, in the selection stage, status provides a signal of quality helping to clarify any
underlying uncertainty (Goode, 1979; Podolny, 1994, 2005; Simcoe & Waguespack, 2011). As the
evaluation process moves from selection stage to the rating and feedback stage we would expect
uncertainty and the reliance on and effect of status to attenuate, however, there is recent evidence to
the contrary (Kovacs & Sharkey, 2014).
Extant research focused on the effect of stratification on important economic outcomes has
found discrimination at both stages of the evaluation process. Hiring audit studies have found
evidence that applicant's names, and other signals suggesting the gender or race of a candidate on a
resume, lead to biased outcomes in the resume screening process (Bertrand & Mullainathan, 2004;
Goldin & Rouse, 2000; Neumark et al., 1996; Pager et al., 2009). For example, resumes suggesting
that the applicant was a white male were considerably more likely to be called for an interview than
those with names likely to be for female or minority applicants. Other research, focusing on the
evaluation and feedback stage of the evaluation process, has also found bias (E. J. Castilla, 2008).
Despite being hired into the firm and receiving equal performance ratings, for example, Castilla
(2008) shows that pay continues to lag for women and racial minorities.
While double standards theory does not differentiate between these two stages, we propose
that the female penalty will be moderated in the second stage. To the extent that status-based
discrimination is contributing to a preference for male candidates in the first stage, those with the
strongest biases against women, or preferences for men, would likely exclude women from the
consideration set in the first stage. As a result, the evaluators who are most apt to be assessing
female candidates in the second stage are those with either weaker, or non-existent, gender biases.
Further, uncertainty decreases in the second stage as more information becomes available and
67
evaluators have increased interactions with candidates. As a result, we would expect that evaluations
are more likely to be based on the available objective criteria. For these reasons, evaluators will be
less likely to employ double standards, or to base assessments on gender, when evaluating candidates
in the second stage.
Hypothesis 2: Evaluators will use more objective criteria in the second stage of evaluation,
such that investment recommendations submitted by women will receive less of a penalty in
the ratings or feedback received.
3.2.4
Search Costs
Whereas statistical discrimination is invoked to address a problem of limited information,
excessive information poses a different problem impacting the conditions under which status-based
discrimination will be most prevalent. The set of actors or objects being considered during the
selection stage of the evaluation process varies in size. As it increases, it is reasonable to expect
greater uncertainty in the selection stage process and a greater reliance on the use of signals to
understand quality (Podolny, 1994, 2005; Spence, 1973). For example, in the labor market, the
applicants' level of education, graduating institution, and grade point average may be used to
narrow the set to a more reasonable number. At the firm level, Stuart and colleagues (1999) find
that, in the biotechnology industry, affiliations between new firms and established partners helps
signal quality and are most important in times of uncertainty. Further, other work has found that
entrepreneurial firms will even incur a cost in order to obtain these high-status affiliation signals
(Hsu, 2004), and that under conditions of decreased search and uncertainty these status signals do
not impact important outcomes (Simcoe & Waguespack, 2011).
A similar logic likely applies with respect to the conditions under which double standards
will be activated. To the extent that gender is viewed as a status characteristic and used as a means
for sifting through large amounts of information, we would expect the prevalence of double
standards to be a function of the number of candidates being evaluated. Specifically, if evaluators are
selecting from a large number of candidates with similar performance, it is more likely that they will
use gender as a means of simplifying the evaluative process, thus resulting in a male advantage. If
68
instead the observed bias is the result of systematic distaste for women, then we would not expect
search costs to affect the selection stage of the evaluation process.
Hypothesis 3: There will be a greater female penalty in viewership during periods where
there is a high-volume of investment recommendations, such that recommendations posted by
women will be less likely to be viewed in higher-volume than in lower-volume periods.
3.3
3.3.1
DATA AND METHODS
Empirical Context
We study the Real Investor's Club (RIC, a pseudonym), a private online platform that
brings together buyside (e.g., hedge fund, mutual fund) investment professionals with the goal of
sharing relevant investment recommendations with other investment professionals. The main goal of
the platform is for investment professionals to share profitable buy or sell (i.e., short selling)
recommendations for stocks connected to firms. Once an investment recommendation is submitted,
it is added to the repository of all existing recommendations on RIC and is then among the set that
may be selected for viewing and subsequently for rating and feedback. In this context, a given
investment
professional
may
simultaneously
act as
an
author
by
writing
an
investment
recommendation to be viewed by others, and as an evaluator by selecting and assessing the
recommendations posted by others. Importantly, interviews with members of RIC reveal that most
investment professionals using the platform do not know the other investment professionals on the
platform prior to joining. Thus, individual investment professionals do not have prior knowledge
about others on this platform that would potentially influence their evaluations of recommendations.
In
submitting
an
investment
recommendation,
the
author
posits
a buy
or
sell
recommendation for a security connected to a firm (e.g., common stock) along with an in-depth
justification for this position. The analysis provides the price target, or the price the author proposes
that the security will reach in the future, and the investment horizon, or the length of time the
author expects it will take for this price target to be reached (e.g., eight months). The detailed
justifications
average
over 1,000 words.
And,
the
content
of these write-ups vary
across
recommendations, but broadly they include a justification for the price target, a summary of
69
supporting information gleaned from company reports (e.g., quarterly reports), a discussion of
macroeconomic trends, and a discussion of the valuation technique used (e.g., discounted cash flow).
This setting is particularly well-suited for testing our hypotheses for several reasons. First, as
detailed below, investment recommendations are evaluated by other investment professionals in two
stages: the first stage of selection and the second stage of rating and feedback. These multiple stages
of evaluation provide an opportunity to unpack whether and how biases in evaluation persist. Second
and third, also discussed in further detail below, performance information is visible to evaluators and
provides an unbiased signal of the author's quality. To effectively and accurately test how double
standards contribute to gender inequality in evaluations it is necessary to isolate the role of gender
above and beyond the presence of other, more relevant, indicators of quality. Finally, this setting
allows us to isolate the action of individual investment professionals.
Here, a market-based
performance measure is directly attributed to each investment recommendation and each investment
recommendation is directly linked to individual investment professionals. Given that funds are
commonly managed by a team and sometimes multiple managers, the majority of extant research
has been unable to attribute performance of investment professionals directly to a single manager or
analyst. As a result, studies have either focused on fund-level outcomes to make claims about
individual performance or relied on individual-level data for non-investment professionals. Together
these features of our setting enable us to develop a more nuanced understanding of the effects of
gender in evaluations by isolating the role of gender in evaluations from other possibly confounding
factors and processes.
Process
of
evaluation
and
the
evaluating
audience.
When
the
investment
professionals are not working on submitting a new recommendation, or acting as authors, they are
consumers of the recommendations posted by others thus serving as evaluators in this setting.
Investment recommendations are evaluated across two stages. The first, or selection, stage is where
the evaluator opts whether or not to 'click' and view a given investment recommendation (i.e., give
it attention). The second, or the rating and feedback, stage provides the evaluator an opportunity to
give the investment recommendation a numerical rating and leave comments or questions. This
70
rating and feedback stage is conditional on the first stage, such that for an evaluator to assess an
investment recommendation in this stage she must have first decided to view the idea in the
selection stage.
Investment recommendations are listed in reverse chronological order with the most recently
posted recommendation listed first. Evaluators are presented with a minimal amount of information
in the first stage of evaluation. Prior to selecting a recommendation for view (i.e., the first stage of
evaluation),
the evaluator
is able to see
the following
information
about
the investment
recommendation: the firm name for the stock being recommended, the recommendation (e.g., long
versus short), and the return since the recommendation
was submitted; and the following
information about the investment professional who submitted the recommendation: their name and
their employer's name. In order to access additional information about a given recommendation the
evaluator must click the recommendation,
thus including the recommendation in the subset
surviving the selection stage of evaluation. To facilitate the search for recommendations, evaluators
have the ability to screen recommendations along several objective criterion, such as industry, firm
size, or expected return.
When interviewed, most investment professionals claimed that their goal was to find high
quality investment recommendations that could help them improve performance. While a few of the
investment professionals interviewed stated that they keep their search to certain industries or firms,
the majority claimed to not restrict their search to a specific screening protocol. However, these
investment professionals also stated that they were less likely to take action (buy or sell) a security
based on an investment recommendation outside of their expertise. While the salience of gender in
the finance industry is unmistakable, interviews with investment professionals in this context make it
unclear
whether
evaluators
incorporate
the
author's
gender
in assessments
of
investment
recommendations.
Most investment professionals stated that they inferred a recommendation author's gender
from the name listed on the recommendation. As one investment professional stated, "When I see a
71
name like Mary it sticks out because there are so few [women] in this industry." Most investment
professionals
argued
that
gender
did not enter
into their
consideration
when
choosing
a
recommendation to view. The decision to select, or view, a particular recommendation was posited
to be based solely on the potential of providing information that if followed would lead to the
generation of profits. As one industry insider stated, "I am in the business of making money, I don't
care who you are, as long as you can help me in that goal." However, another insider had a different
viewpoint about the industry, "Whoever says there is no bias against females in this industry is
bullshitting you."
In the second stage, the evaluator is presented with the all of the facts and information
included in the investment recommendation. In this rating and feedback stage of the evaluation, the
evaluator can choose to rate the idea along two dimensions-the quality of the analysis and the
return-as well as provide comments on the recommendation. RIC's intended purpose of the quality
of analysis rating is to measure whether the investment professional was able to get their point
across in a rigorous manner. In other words, this rating is intended to assess whether the position
was substantiated by relevant and well thought out analysis. Importantly for our study, the quality
of analysis rating is was referred to by one interviewee as the "good analyst rating" demonstrating
the intimate link between a recommendation and its author. In interviews, investment professionals,
both on and off RIC, frequently discussed the importance of a well presented analysis in both
supporting an investment recommendation and indicating the investment professional's skill. The
return rating is even more
straightforward,
as it measures the evaluator's
belief that the
recommendation (e.g., buy versus sell) and the magnitude (e.g., price target) is correct and likely to
be achieved. Comments are free-form entries that allow evaluators to leave feedback or reactions
related to the recommendation.
Performance. To effectively and accurately test the role of double standards in these
evaluations, it is necessary for performance to be visible, standard, and accorded via an unbiased
mechanism. First, since the reliance on ascriptive characteristics is not completely surprising in the
absence of other easily observable indicators of quality, to test whether double standards are being
72
applied in evaluations it is necessary that the evaluator is able to clearly and easily observe relevant
performance information. To the extent that performance is as easy to view directly as gender,
basing evaluations on gender suggests an evaluator bias that cannot be attributed to a lack of
information (e.g., statistical or status-based accounts). Second, this visible performance measure
must be standardized, thus conveying information about quality that is interpreted similarly by
evaluators. In other words, for the performance metric to indicate quality what constitutes a good
versus a bad performance must be agreed upon by the vast majority of the actors in the setting.
Third, to assess gender differences in evaluative outcomes the performance metric must itself not be
biased. For performance to be accorded via an unbiased mechanism any two actors taking the same
action must receive the same performance outcome. An underlying bias in the attribution of
performance would confound the results, such that what appears to be a double standard may
actually be the result of bias in properly assessing performance.
While previous research has leveraged lab-based experimental designs that largely meet these
conditions (Foschi, 1996; Foschi & Valenzuela, 2008, 2012), the construction of performance in these
studies is neither natural nor definitively unbiased. Hence, as aforementioned, variation in the degree
to and direction in which these performance metrics have been unbiased in past studies may account
for the heterogeneity of results. Even though subjects are commonly told that performance is
assessed by an unbiased third party, this may not be enough to convince an evaluator. Further, the
lack of a competitive market in these studies dilutes the meaning and importance of performance
(Altonji & Blank, 1999; Arrow, 1971; Gary Stanley Becker, 1957; Charles & Guryan, 2008).
The investment industry, and RIC in particular, provides a well-suited setting for this
analysis given the visible, standard, and unbiased nature of performance within the industry.
Performance
being
visible
means
that investment
professionals
can
observe
how a given
recommendation is performing before deciding whether to view the recommendation. Performance in
the investment industry is measured as the rate of return on a given investment (realized return,
Rt). It is calculated simply as, R
= (pt+-pt)+dt
, where Pt+i is the price of the security at the time
of sale, dt accounts for any distributions during the investment period (e.g., dividends), and Pt is the
73
price of the security at the time of its purchase. Lastly, few processes are as unbiased as a security's
performance which is accorded by the stock market, making this a great strength of our setting. If
any actor, regardless of ascriptive characteristics,
takes a certain position in the market her
performance would be identical to any other actor that took the same position at that same time.
During the time period under study, the difference of the average investment recommendation's
return, above the S&P 500, between men and women is only about 0.30 percentage points, with
women outperforming men.8 However, this difference is not statistically significant (p = 0.97).
Low accountability
and motivation for a lack of bias in selection.
recommendation is posted, it enters a repository of all recommendations
Once a
allowing investment
professionals to sort through recommendations using broad classifications before choosing to 'click'
on the recommendation to view its entirety. After an investment professional chooses to view a
recommendation they have the option to anonymously rate the recommendation and/or comment
and ask questions regarding details of the recommendation. The author of a recommendation cannot
track how many other investment professionals viewed their recommendation. Because neither
viewers nor raters are visible to the investment professional being evaluated, evaluators in this
setting are not accountable for their evaluations to the author or to a broader audience of other
evaluators. Generally, accountability serves to reduce the propensity for evaluators to base their
decisions on bias (Salancik & Pfeffer, 1978; P. E. Tetlock, 1992), but the low-level of accountability
in this setting makes it probable that evaluators will act in accordance with their preferences.
Furthermore, investment professionals are motivated to make accurate assessments of others,
especially in selecting which investment recommendations to view. Given that evaluators in this
setting are time constrained and are hoping to gain knowledge about potential investment
opportunities that will be lucrative, we will assume that it is rational for these evaluators to select
the best investment recommendations.
This return calculation assumes that the stock was held for approximately the time stated in the
investment recommendation. Financial market data were only available until the end of 2013, therefore, the
return was calculated until the end of the investment horizon or until the end of 2013.
8
74
3.3.2
Sample
This study will analyze unique and proprietary data collected from RIC on the investment
professionals using this platform, the investment recommendations posted, and the evaluations
conducted by these investment professionals. The data span the period from 2009 to 2013 and are
supplemented by external financial databases. Only recommendations pertaining to firms listed on a
U.S. exchange (e.g., NASDAQ and NYSE) and covering common stock-as opposed to debt or
options-will be analyzed in this study. Using these boundaries, this study focuses on the 3,003
recommendations
submitted by 1,345 investment professionals.
On average,
each investment
professional posted 2.23 recommendations during this 5 year window. For analysis, detailed data
were collected on the characteristics of both the investment professionals and the recommendations.
These measures will be used to isolate the effect of gender at both the selection and rating and
feedback stages of evaluation. Further, to rule out a possible alternative explanation-that females
may be acting in a manner systematically different from their male counterparts, as frequently
highlighted in studies of gender (Baldiga, 2013; Bromiley & Curley, 1992; Ding, Murray, & Stuart,
2006; Eckel & Grossman, 2008; Fernandez-Mateo, 2009; Halaby, 2003; Miller & Hoffmann, 1995)measures of risk aversion were also collected and analyzed.
3.3.3
Dependent Variables: The Evaluation Process
Selection stage and rating and feedback stage. For the first stage of the evaluation
process,
our analysis will focus on the level of attention received by a given investment
recommendation in the selection stage of evaluation. This will be measured by the outcome variable
views (logged), which is operationalized as the number of times a recommendation is 'clicked on', or
selected. For the second stage of the evaluation process, our analysis will utilize three measures (all
logged), write-up rating, return rating, and total comments. Each rating is measured on a 1-10 scale,
with 10 being the highest rating. The write-up rating assesses the quality of the write-up, whereas
the return rating assess the likelihood that the recommendation will reach its price target. Total
comments represents the total number of comments a given investment recommendation receives
from the time the recommendation was posted through the end of the study window. It is important
75
to note that in order for an evaluator to provide a rating or a comment on a recommendation, it is
necessary for the evaluator to have selected to view the recommendation. It is this fact about our
empirical context that allows for identifying the role of gender at each stage of evaluation.
3.3.4
Independent Variable: Gender
At both stages of evaluation, the gender of the investment recommendation's author is not
directly noted. Evaluators are only able to infer gender from the author's name', which is visible in
both the selection and rating stage. Inferring individual-level ascriptive information from a name is
not unique to this setting (Bertrand & Mullainathan, 2004; Neumark et al., 1996; Pager et al.,
2009). We used the IBM InfoSphere Global Name Management Tool to score each investment
professional's name in terms of likelihood to be female. This tool takes as its input an individual's
first name and then compares that name to its database of 750 million names from around the
world. Names are then scored in terms of how likely the name is female on a scale from 0 to 99, with
a score of 0 signifying almost zero probability that the name is traditionally female. Figure 1
provides a histogram of the names on RIC. As expected, the great majority of names are associated
with the male gender (approximately, 86.45% percent have a score less than six), and only about
4.43 percent of the names are more likely to be female than male (i.e., the name received a score of
50 or higher). The effect of this variable will demonstrate the degree to which evaluators favor
investment recommendations from male investment professionals in the selection and ratings stages
of evaluation in this setting.
[INSERT FIGURE 1]
3.3.5
Control Variables
9 Members of RIC are not required to provide their gender, and only approximately 48 percent do, with no
difference between men and women. Others can find this information, however, as discussed in the results
section of this paper. Our results indicate that evaluators are unlikely to be looking within RIC for this
information as we find that our results hold even when limiting the analysis to self-identified male
investment professionals.
76
Both investment professional- and recommendation-level characteristics will be included as
controls. Importantly, in our models, we attempt to control for the variables that are visible to an
evaluator when assessing an investment recommendations at a given stage of the evaluation process
as well as other variables that could affect the evaluation process. At the investment professionallevel, these include measures for education, the rank of both undergraduate and graduate institution;
location; and the number of investment recommendations posted before the focal recommendation.
At the recommendation-level,
we control for recommendation type (i.e., long or short), firm
information, and include both year and industry fixed effects.
For undergraduate education, the 2013 US News College Ranking (US News and Report,
2013) was used to match investment professional undergraduate institutions to its respective
ranking. For graduate education, the 2013 US News MBA Ranking (US News and Report, 2013)
was used to match investment professional graduate institutions to its ranking and 2013 Financial
Times Global MBA Ranking (Financial Times, 2013) was used for non-US business schools. To deal
with missing rankings, investment professionals were grouped into four categories: top ranked
(under)graduate institution (for a ranking of 1-10), mid ranked (under)graduate institution (for a
ranking of 11-50-reference category), bottom ranked (under)graduate institution (for a ranking of
51-100),
and unranked (under)graduate institution.
Additionally, we created the variable no
graduate school to account for those who did not attend graduate school and coded it one for these
individuals. 10 Since all investment professionals in this setting have at a minimum an undergraduate
degree, using these rankings together with the indicator of no graduate school serves as a proxy for
education level. To control for location we use two dichotomous variables: major city and non-US
location. Major city represents all cities where two percent or more of investment professionals are
located, and is coded one for all investment professionals from these cities. For example, some of
these cities include: Boston, Chicago, New York City, and San Francisco. Additionally, the variable
Results are robust to changing the ranking cutoff. Additionally, there may be a concern of investment
professionals attending a school previously when it was ranked differently, however, an analysis of the
ranking showed that while there are a lot of changes, very few schools moved among these defined buckets.
10
77
non-US location is coded one for all investment professionals located in a city outside of the United
States.
At the recommendation-level, short is the dichotomous variable that takes the value of one if
the investment recommendation recommends short selling, and zero if instead it is recommending
taking a long position (i.e., buy and hold). For investment horizon we use a dichotomous variable,
short investment horizon, that takes the value of one if the investment professional is recommending
holding onto the stock for less than a year and zero otherwise. We also control for the investment
style of the recommendation: growth, value (reference category), event-based, or other; firm size (i.e.,
price per share * shares outstanding), expected return (price target-priceat reco m endation , logged);
and week-one performance. Here, week-one performance is measured as the return of the focal
investment recommendation (Rt,i) in the first week after being posted, less how the market (S&P
500) performed during this same period (Rtm). In other words, the degree to which the investment
recommendation outperformed the market benchmark during this period. Our decision to use one
week performance is based on the fact that recommendations receive more than 50% of views within
the first five calendar days of being posted, with a long right tail in the distribution.
Table 1 provides summary statistics for each of the variables used for the analyses. This
table splits the data into two panels: Panel A includes summary statistics for the investment
recommendations where the investment professional's female name score is less than or equal to 10,
and Panel B includes summary statistics for the investment recommendations where the investment
professional's female name score is greater than 10. We find that the most consistently unbalanced
variables between these two groups is related to education. Evidence suggests that those more likely
to be classified as female attended higher ranked institutions. Given the few number of females in
the investment industry, it is not surprising that there would be a higher bar for entry into the
profession. Therefore, to the extent that women differ from men in terms of key independent
variables in this setting, we find evidence that women possess better indicators of quality than do
men in terms of education.
78
[INSERT TABLE 1]
3.3.6
Empirical Model
We begin our empirical analysis by evaluating the effect of an investment professional's
gender, inferred from their name, on a given recommendation's viewership. Specifically, we estimate
the following ordinary least squares (OLS) regression:
Y = fl 1 Female Name Score + yXi + At + 6j + Ej,
where Y is the views (logged) and i indexes the recommendation; Xi is a vector of recommendationand investment professional-level controls; Ai is a vector of year dummies that control for time
trends; and 6i represents 24 Global Industry Classification Standard (GICS) industry group
subcodes, and an indicator for a missing GICS group subcode. Robust standard errors are clustered
at the investment professional-level given the possibility that viewership may be correlated within
investment professional. For the second stage of the evaluation process, Y represents total comments,
write-up rating, or return rating (all logged). Ideally, names would be randomly assigned to
recommendations and then evaluated by the audience, however, this is not the case. Our ability to
closely mimic the data generation process of inferring gender from a name and the ability to control
for most of the information that investment professionals claim to use, and have available to them
through the platform, increases our confidence in measuring the effect of gender on our outcomes
variables.
3.4
3.4.1
RESULTS
Is there a Gender effect?
To understand whether there are double standards in the first stage of evaluation, we
examine whether investment recommendations posted by authors with a name more likely to be
associated with a female receive fewer views. We use the number of views (logged) that a given
investment professional's recommendation receives in these analyses. Regardless of the model
specification used (Table 2), we find evidence that there is a negative effect of approximately -0.0014
79
for every point increase in female name score (p < 0.01). For example, in approximately the middle
of the scale, an investment recommendation posted by an individual named Bowen, which is scored a
60, receives approximately
8.40 percent
fewer views than a recommendation
posted by the
unquestionably male name of Matthew, scored a 0. On the far end, the penalty increases: a
recommendation posted by an individual named Mary (scored a 99) receives approximately 14.17
percent fewer views than a recommendation submitted by Matthew. Therefore, we find support for
hypothesis 1. There is evidence that in the selection stage of the evaluation process evaluators use a
stricter standard and investment recommendations submitted by investment professionals likely to
be female are significantly less likely to be selected, or to receive attention.
From Table 2, it is evident that this is a competitive market. Evaluators are attracted to
investment recommendations that have a positive return in the short-term indicated by the positive
coefficient of week-one performance (p < 0.05). Additionally, and in line with conversations with
investment professionals, risk is rewarded. Riskiness can be measured a few different ways, such as
recommending a short selling position, an aggressive price target, and shorter investment horizons.
The number of views is higher for recommendations that more aggressive price targets (expected
return, logged) and for short selling recommendations. Investment recommendations that for a short
selling positions receive almost 25 percent more views than a recommendation for a buy position (p
< 0.001). Similarly, investment recommendations with more aggressive price targets are significantly
more likely to be viewed in the selection stage.
While these models control for this risk taking behavior, one may still worry that men and
women are acting systematically different in terms of risk taking behavior. Specifically, the audience
may be using gender as a proxy for an individual's risk appetite, or the audience may be sorting on
risk-loving characteristics, thereby excluding females. In other words, to the extent that male and
female investment professionals systematically differ in their propensity to be risk taking, observed
gender differences in attention may be the result of the audiences' preference for higher risk, and not
for men per se, thus overstating the female penalty.
80
[INSERT TABLE 2]
Next, we focus on second stage of the evaluation process, the rating and feedback stage.
Here, evaluators give rating scores and comments to those recommendations that they selected to
view. Overall, we find support for hypothesis 2. Conditional on the recommendation being viewed,
women do not statistically differ from men in terms of number of comments received (Table 3).
Therefore, once an investment professional chooses to view a recommendation, the gender of the
investment professional does not seem to play any role in likelihood that the recommendation
receives a comment. We then estimate the role of gender for the evaluator's rating of the
recommendation's write-up quality (logged; Table 4, Models 3A and 3B) and return rating (logged;
Table 4, Models 4A and 4B) to determine if women receive lower ratings than do men, conditional
on receiving a rating. In terms of ratings, there is no evidence to suggest that female name score
effects the write-up quality rating or the return rating that a given investment recommendation
receives. Since there is a small number of observations with a female name score greater than zero
we may still be interested in the coefficient's magnitude. However, the economic significance of these
outcome variables is small, and even positive in one case (the number of comments).
[INSERT TABLE 3]
[INSERT TABLE 4]
In summation, we find support for both hypothesis 1 and hypothesis 2. There is strong
evidence that women receive less attention in this setting: women receive fewer views than men
when controlling for various investment professional- and recommendation-level
characteristics.
However, we do not find evidence that this bias persists in subsequent evaluation. Therefore, given
that an investment professional's female name score does not affect the ratings or comments a
recommendation receives, we only find evidence of a double standard in the first stage of the
evaluation process. Of course, we cannot rule out the fact that all of the biased evaluators are
selected out of the second stage of the evaluation process. However, given the fact that few of the
variables included in the analysis helped predict variation in the number of comments or the ratings
81
received, it is more likely that the second stage of the evaluation process depends on the actual
investment recommendation and not ascriptive characteristics. These results bring an important
question to the forefront: Is this bias in the selection stage due to a mechanism or simply bias
against women?
3.4.2
Ruling out the alternative that women are doing things differently?
While not explicitly tested, one common interpretation of recent findings contradicting
double standards theory is that the status value of gender is declining (Foschi & Valenzuela, 2008,
2012). And indeed, a number of recent studies support this perspective suggesting that the status
value of gender is decreasing and gender is becoming a less salient characteristic (Jasso & Jr., 1999;
Okamoto & Smith-Lovin, 2001). There is, however, some evidence suggesting an alternative
mechanism that may account for these conflicting results-that the presence of other desirable
characteristics, more commonly demonstrated by men, drive what appear to be double standards.
Specifically, evaluators may show preference not for men, but for certain characteristics that are
perceived to be indicators of future performance (Foschi & Valenzuela, 2008). To the extent that
men are more likely to possess these desirable characteristics, preferences for these characteristics
may be interpreted as double standards disadvantaging women. But, instead there may be a
universal standard and both men and women possessing the characteristic will be evaluated as more
competent than those who do not possess the characteristic.
Since in the financial market, the ideal worker is male (Acker, 1990; Williams, 1999)
characteristics typically associated with men are likely to be most valued. Over time, members of a
given occupation come to share an image of the ideal worker that includes the characteristics and
behaviors of people who performed the work successfully in the past or are expected to perform it
successfully in the future (Gorman, 2005). This image serves as an unofficial norm, becoming the
standard against which candidates are evaluated and that specifies hiring and promotion criteria
(Heilman, 1983). In order to attribute observed differences to the activation of double standards it is
necessary to rule out the possibility that these results are not driven by preferences for other
characteristics, especially those commonly associated with gender.
82
Specifically, research in economics, management, and sociology commonly discuss the link
between gender and risk-aversion with many studies finding evidence that women display greater
levels of risk aversion in various contexts (Baldiga, 2013; Bromiley & Curley, 1992; Ding et al., 2006;
Eckel & Grossman, 2008; Fernandez-Mateo, 2009; Halaby, 2003; Miller & Hoffmann, 1995). Halaby
(2003) finds that men prefer high-risk high-return career paths. Similarly, Fernandez-Mateo (2009)
finds that in contract employment women are less likely to change clients. In a recent experimental
study, Baldiga (2013) administered exam questions to male and female students. When asked to
answer these questions, participants had similar completion rates. However, when participants were
notified that there would be a penalty for wrong answers, women were four to six percent more
likely to skip a question.
In the finance industry, the link between women and risk-aversion is particularly salient
(Barber & Odean, 2001; Jianakoplos & Bernasek, 1998; Niessen & Ruenzi, 2007; Sunden & Surette,
1998) (Bajtelsmit & VanDerhei, 1997; Beckmann & Menkhoff, 2008). In studies of lay investors,
women have been found to allocate their investments more conservatively (Sunden & Surette, 1998)
and trade less often than men (Barber & Odean, 2001). A recent survey of research and press
reports discussing women in fund management, completed by the National Council for Research on
Women, concluded that women investment professionals take a measured and long-term approach to
risk [for Research on Women, 2009]. Similarly, Beckmann and Menkhoff (2008), as well as Niessen
and Ruenzi (2007), find that women fund managers are more risk averse in their investing.
Accounting for risk-aversion is critical for assessments of double standards theory focusing on gender
because if the action being evaluated has underlying risk, especially since there is evidence that risk
is rewarded in this industry, failing to control for measures of risk will lead to confounded results.
Further, even if the actor exhibits superior performance as a result of being risk-averse, she may still
receive a penalty when being evaluated by an audience that does not act in a similar fashion, nor
values this approach.
Given that risk aversion has been found to be correlated with gender in general settings
(Baldiga, 2013; Bromiley & Curley, 1992; Ding et al., 2006; Eckel & Grossman, 2008; Fernandez-
83
Mateo, 2009; Halaby, 2003; Miller & Hoffmann, 1995) as well as financial markets (Barber & Odean,
2001; Beckmann & Menkhoff, 2008; Jianakoplos & Bernasek, 1998; Niessen & Ruenzi, 2007; Sunden
& Surette, 1998), this is a plausible alternative explanation for the observed female penalty in views,
or the first stage of evaluation. If on average female investment professionals are being more riskaverse in their recommendations than the majority of their audience, a preference for riskier
recommendations and not gender may explain the female penalty in views. This is especially true in
this setting since the audience can observe indicators of risk aversion before deciding to view an
investment recommendation. Additionally, given the evidence that risk is preferred in this setting, a
penalty associated with risk-aversion is particularly plausible in this context.
Historically it has been difficult to unpack the role of gender and risk aversion given that
these two characteristics
are commonly highly correlated.
Inherently,
measuring risk at the
individual-level is empirically difficult: "Reliable demographic data on individual risk attitudes is
virtually nonexistent" (Friedman, 1994). This has led most studies to rely on self-reported measures
of risk-aversion. An issue, however, is that they could be positively biased, as women have been
shown to have greater concerns about violating gender stereotypical behaviors (Deaux & Major,
1987; Heilman, 1980). Therefore, it is plausible that women may self-report their risk-attitudes and
preferences in a way that aligns with what is expected instead of how they actually behave.
This issue is also salient in the investment management industry. An additional empirical
roadblock in this literature stems from the use of fund-level measures of risk. Specifically, even
though most funds have one portfolio manager the choice to add or remove securities to the fund
often resembles more of a democracy than a dictatorship. This is important because a fund-level
measure may confound the risk-attitudes of the team with those of the individual female manager.
While these results may speak to gender differences in management style, they may not be
informative about the individual's actual risk preferences. Given that women have been found to be
less confident, especially in male dominated fields (Beyer, 1990; Beyer & Bowden, 1997), it is also
plausible that female investment professionals are more affected by the team consensus. Therefore, a
true measure of risk would focus on the decision solely of the individual.
84
Using our unique setting, we are able to address this challenge to better isolate the role of
gender in evaluation above and beyond differences in risk-taking. We will address these empirical
difficulties in two ways, first, by using investment professional-recommendation-level measures of risk
aversion: recommendation of short selling, short investment horizon, and expected return (logged).
The use of multiple measures of risk aversion addresses the concern that any given proxy of riskaversion may capture other effects. Though no single measure is enough to categorize an actor as
risk-averse, evaluating all of these measures together attenuates this concern. Short selling is the
practice of selling borrowed shares of a security with the promise of purchasing and returning these
shares to the lender-along with any distributions, such as a dividend-at a later time. This
strategy, which is predicated on the opinion that the security in question is over-valued, is in direct
contrast to taking a long position, which is based on an expectation that the security is undervalued. Investment professionals will be compared in their likelihood to recommend a short-selling
position.
A second measure of risk-aversion is based on the use of short investment horizon.
Historically, shorter investment horizons are seen as riskier, as this usually indicates that the
recommendation is exploiting an anticipated market change or condition as opposed to a long-term
view (Barber & Odean, 2001). Therefore, we will examine whether there are gender differences in
investment professionals likelihood of recommendation being for an investment horizon of less than
one year. Our third measure of risk-aversion is based on the expected return of a given investment.
In other words, how aggressive was the price target chosen by the investment professional. The goal
of any investment recommendation is to hit the stated price target.
Therefore, investment
professionals are more apt to meet this goal, thus assuming less risk, when minimizing the gap
between the security's price on the day of recommendation and the price target. For example, if an
investor believes a stock currently priced at $15.00 per share will increase, they will be more likely to
be correct if they state the price target at $17.50 as opposed to a more aggressive target of $21.50.
While this also can be viewed as a confidence measure, we are using it to measure the investment
85
professional's risk appetite. A risk-averse investment professional would be more likely to recommend
a smaller gap, in other words, have smaller rates of expected return.
Using these measures of risk aversion we only find an association between the likelihood of a
shorter time frame and female name score (p < 0.05). Interestingly, this association is opposite of
what we would expect if women were more risk averse: more female names tend to recommend
investment horizons that are shorter (i.e., more risky). Thus, if anything, there is weak evidence that
women may be slightly more risk-loving in this setting. All other measures provide no evidence that
there is a link between risk aversion and female name scores (Table 5). While there is still a valid
concern that no single measure is enough to categorize a group as risk-averse, the lack of consistent
evidence across all three of these measures suggests that, in this setting, men and women do not
systematically differ in terms of risk-aversion. Therefore, while risk is favored in this context, this
alternative explanation does not seem to be a driver of our observation that evaluators prefer
recommendations by male authors. This result allows us to rule out endogeneity concerns related to
gender and actions closely related to gender.
[INSERT TABLE 5]
3.4.3
Status and Increased Search Costs
To test if gender was treated as a status characteristic, hypothesis 3 posited that any
differences should be more pronounced when searching becomes more difficult (Podolny, 1994, 2005;
Simcoe
& Waguespack,
2011).
Using a year-long subset
of microdata
of each investment
professional's viewing habits, we find that a recommendation gets more than 50 percent of its
viewership within the first five days of being posted--with the distribution having a long right-tail.
We constructed a variable, traffic, which summed the total number of recommendations in the fiveday window, before and after the recommendation was posted. The distribution of this variable is
similarly concentrated. Given this distribution we constructed two dichotomous variables, low-traffic
and high-traffic, which represent the bottom and top deciles of traffic, respectively. As we would
expect, a given recommendation receives less total views (about 11.82 percent less) when posted
86
during a high traffic window and more total views when posted during a low-traffic window relative
to recommendations posted during an average traffic window (Table 6).
To test hypothesis 3, or the effect of female name score in times of increased search costs, we
interacted female name score with both the low-traffic and high-traffic variables. First, we intuitively
find that there is a negative relationship between increased traffic and viewership, and a positive
relationship between decreased traffic and viewership. Then, using the interaction, we find that as
female name score increases viewership receives a larger penalty during a high-traffic period, and no
penalty in times of low search costs. This provides evidence that gender is used as a sorting
mechanism during times of increased search costs, which is consistent with the idea that status
indicators are most salient under these conditions and supports hypothesis 3.
[INSERT TABLE 6
3.4.4
Unobserved gender differences
There may still be concerns that women are acting in a different manner that cannot be easily
measured. In an ideal examination we would combine features of laboratory studies with those of a
competitive market setting by randomly assigning names to these investment recommendations. To
address this concern, we limited our sample to those investment professionals who self-report as
male. The rationale being that an analysis on this subset allows us to control for unobserved
heterogeneity related to gender. Additionally, if our results hold it suggests that evaluators are not
conducting their own due diligence using industry records and the Internet.
When the sample is limited to this subset of self-reported males, our results are robust. We
find the same relationship between the author's female name score and the number of views a
recommendation receives (Table 7). In other words, investment recommendations posted by men
with more ambiguous or female names (e.g., Bowen) received fewer views than did those posted by
men with more male names (e.g., Matthew)
87
This check helps rule out the concern that women on this platform may be acting differently
in a hard to measure way. Descriptively (Table 2) men and women are largely matched in terms of
observed characteristics, but this is insufficient for ruling out that an unobserved gender difference,
though unlikely given the robustness of our models, is driving our female penalty in views. If our
effect were due to unobserved gender differences, we would not expect to see this female penalty
present in this subsample.
3.4.5
Robustness Checks
While a strength of the IBM InfoSphere Global Name Management Tool is the fact that it
uses a vast and diverse database for its naming algorithm, there may be concern that the evaluators
are not familiar with the likely gender of more unfamiliar names. To alleviate this concern, we used
the frequently occurring first name data from the 1990 public use U.S. Census [U.S. Census Bureau,
1995]. Approximately 80.45 percent of the investment professionals first names were matched to
these census data. We then restricted the analysis to investment recommendations that were
submitted by an investment professional with a first name that was on this list. Upon rerunning the
main analyses, we find that our results are robust (Table Al).
3.5
DISCUSSION
Evaluation processes are a cornerstone of most market and firm settings, and their outcomes
have significant economic implications. It is often the case, due to constraints, that only a small
subset of a given population can be selected for further evaluation. The inherent uncertainty in this
process has led to an influential literature that stresses the importance of signals and categorization
in this process (Podolny, 2005; Spence, 1973; Zuckerman, 1999), and highlights how these processes
are unscientific at times. Given the unequivocal role and implications of these evaluative processes, a
burgeoning literature has focused on uncovering whether these processes contribute to systematic
disadvantage for some groups (Bertrand & Mullainathan, 2004; Foschi, 1989, 1996, 2009; Foschi et
al., 1995; Foschi & Valenzuela, 2012; Pager et al., 2009; Reskin, 1988). This research has shown
evidence of bias in multiple stages of the evaluation process, but is unable to test the persistence of
88
bias for those that make it to the second stage of evaluation, or hold the evaluator constant.
Additionally, it has been difficult to identify the underlying mechanisms that drive reliance on
ascriptive characteristics in evaluations.
Our study identifies the role of gender for evaluations in a setting where evaluators are
motivated to identify the most qualified candidate and where more objective signals of quality are
available. In the first stage of the evaluation process, we find evidence of an economically substantial
attention penalty, where women are as much as 14 percent less likely to be selected. However, in the
second stage of evaluation we do not find evidence of a gender-based penalty. Therefore, it seems
that bias occurs only in the first stage of the evaluation process and does not play a role in
subsequent, more detailed assessments.
It is then important to try to understand the mechanisms that contribute to this first stage
bias. Consistent with viewing gender as a status characteristic (Berger, 1977; Correll & Ridgeway,
2003), we find that gender is most salient as a sorting mechanism in times of high search costs.
Importantly, we are able to rule out the popular alternative explanation that women are more risk
averse than males. Overall, this study provides more direct evidence that status-based mechanisms
of discrimination, and not simply taste-based preferences, play an important role in perpetuating
gender inequality.
Our ability to examine the role of gendered names among a subset of confirmed males in the
study population is particularly revealing. The fact that men with a gender ambiguous or more
female name receive fewer views than do men with more clearly masculine names provides
compelling evidence of a female penalty in the first stage of evaluation. This approach provides clean
identification of the effect of gender as a persistent signal even when more relevant indicators of
quality are present and cheaply observable.
Despite the pervasiveness of gender in the first stage of evaluation, our results highlight the
importance of taking performance or actual quality into account when estimating the impact of
gender. A common criticism of gender inequality research is a failure to adequately account for an
89
unbiased measure of quality or performance. As our results indicate, performance plays a key role in
evaluator's decisions of whether to view a given recommendation. Failing to capture performance,
therefore, could lead to a gross overestimation of gender effect. Future research aiming to identify
the extent to which women are disadvantaged in evaluations must carefully account for relevant
indicators of quality or performance. It is only through a meticulous comparison of men and women
that we can uncover the mechanisms serving to perpetuate gender inequality and subsequently take
steps to redress these inequities.
Our findings also suggest that there is not a systematic bias against women in our setting.
The central role of search costs coupled with a lack of evidence of a second stage penalty suggest
that gender is being used as a means for sorting through information. In other words, evaluators are
not simply exercising a universal preference for men, but rather using gender as a status signal to
facilitate in the identification of quality. Thus we argue that status-based discrimination, or double
standards, are evoked to resolve uncertainty related to a problem of excessive information. While
statistical discrimination is employed under conditions of limited information, our results indicate
that ascriptive characteristics may also be used under conditions where evaluators need to resolve
uncertainty resulting from too much information. In this way, relying on gender may not indicate a
bias against women, but rather a mechanism for simplify large amounts of information.
The role of gender in this setting may be particularly salient given the low concentration of
women in the investment industry (Beckman & Phillips, 2005). Based on Kanter's Men and Women
of the Corporation, among investment professionals women are tokens-defined as a member of
minority representation (less than 15 percent) in a setting that also has a dominant group (1977).
Balancing the gender composition within the industry is difficult. From the perspective of the supply
side, the women in this setting seem to be among those with highest quality education credentials,
therefore, there seems to be a dearth of women from lower ranked schools possibly suggesting a
higher bar for entry. Further, pinpointing demand side mechanisms is difficult. For example, another
possibility, as found in law firms by Beckman and Philips (2005), is that the gender composition of
exchange partners may be a major driver of the industry's gender composition.
90
Regardless of the factor, it is important to face this issue. Recently, there has been an
attempt by certain state governments and institutions to increase the number of women in the
investment industry. For example, a report by the Rothstein Kass Institute highlighted state-level
initiatives to increase
the involvement of women
in the investment management
industry.
Connecticut and Maryland have allocated over $60 million combined to support funds that are led
by women [Institute, 2012: 8].
Further, knowledge sharing platforms, such as the setting highlighted in this research, are able to
give equal access to investment professionals and may be crucial in helping attenuate this bias. This
increased exposure to women should help lessen the reliance on ascriptive characteristics by making
gender less salient. Women investment professionals that were interviewed credited platforms, such
as RIC, with allowing women to "take down the traditional barriers" and have a voice among their
peers. Future research could benefit from identifying whether the evaluative processes contributing
to gender inequality persist in non-male typed settings. To achieve this goal it is necessary to
examine whether gender factors into evaluations in settings like the one we study, where there is
objective performance information and multiple stages of evaluation.
91
3.6
FIGURES AND TABLES
Figure 1. Distribution of Female Name Score
20
40
80
Female Name Score
80
92
10o
Table 1: Summary Statistics of Key Variables
Panel A: Female Name Score of less than or equal to 10
Obs.
Mean
Std. Dev.
Min.
Max.
Dependent Variables (all logged)
Views
Write-up Rating
Return Rating
Total Comments Count
2,647
2,541
988
2,647
5.122
1.995
2.027
0.879
0.611
0.245
0.315
0.876
2.944
0.693
1.099
0.000
7.466
2.398
2.398
4.19
Control XVariables-idea-Level
Recommendation: Short
Week-One Performance
Expected Return (logged)
Idea Type: Growth
Idea Type: Event-based
Idea Type: Other
Short Investnment Horizon
Market Cap.
2,647
2,647
2,647
2,647
2,647
2,647
2,647
2,647
0.159
0.006
0.569
0.198
0.156
0.100
0.393
13.824
0.366
0.072
0.415
0.399
0.363
0.301
0.488
1.918
0.000
-0,333
0.010
0.000
0.000
0.000
0.000
8.917
1.000
0.837
4.894
1.000
1.000
1.000
1.000
20.204
2,647
2,647
2,647
2.357
.736
0.050
3.496
0.441
0.218
0.000
0.000
0.000
31.000
L000
2,647
2,647
2,647
2,647
2,647
2.647
2,647
0,238
0.116
0.322
0.272
0.029
0.037
0.564
0.426
0.320
0.467
0.445
0.169
0.190
0.496
0.000
0.000
0
0.000
0.000
0.000
0.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
Control Variables User-Level
Number of Past Ideas
Location: Major City
Location: Non-US
Education (Ref: Mid-Rank)
Undergraduate Rank: Top
Undergraduate Rank. Bottom
Undergraduate Rank: Unranked
Graduate Rank: Top
Graduate Rank: Bottom
Graduate Rank: Unranked
Graduate Rank: No Grad.
1.000
Panel B: Female Name Score of greater than 10
Obs.
Meatn
Std. Dev.
Mirt.
Max.
Dependent Variables (all logged)
Views
Write-up Rating
Return Rating
Total Comments Count
356
333
142
356
5.034
1.991
1.967
0.910
0.650
.268
0.383
0.875
0.000
0.693
1.099
0.000
7.343
2.398
2.398
4.585
Control \variables-Idea-Level
Recommendation: Short
Week-One Performance
Expected Return (logged)
Idea Type: Growth
Idea Type: Event-based
Idea Type: Other
Short Investment Horizon
Market Cap.
356
356
356
356
356
356
356
356
0.177
0.004
0.537
0.250
0.169
0.104
0.497
14.068
.382
0.066
0.316
0.434
0.375
0.306
0.501
1.856
0.000
-0.450
0.010
0.000
0.000
0.000
0.000
9,275
1.000
0.267
2.152
1.000
1.000
1.000
1.000
19.433
356
356
356
2.618
0.817
0.062
4.116
0.387
0.241
0.000
0.000
0.000
22.000
1.000
1.000
356
356
356
356
356
356
356
0.275
0.065
0.261
0.351
0.008
0.051
0.503
0.447
0.246
0.440
0.478
0.092
0.219
0.501
0.000
0.000
0.000
0.000
0.000
0.000
0.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
Control Variables~-User-Level
Number of Past Ideas
Location: Major City
Location: Non-US
Education (Ref: Mid-Rank)
Undergraduate Rank: Top
Undergraduate Rank: Bottom
Undergraduate Rank: Unranked
Graduate Rank: Top
Graduate Rank: Bottom
Graduate Rank: Unranked
Graduate Rank: No Grad.
93
Table 2: OLS Regression of Views (logged) on Female Name Score.
Unit of analysis is user-idea.
Model 1 k
0.0014
*
Recommendation: Short
Expected Return (logged)
Idea Type: Growth
Idea Type: Event-based
Market
-0.1082
(00283)
0.1660
(0.0350)
***
Horizon
C(ap.
*
***
(0.0493)
0.01.92
(0.0239)
-0.0265
***
(0.0069)
Week-One Performance
Number of Past
0.1832
(0.0322)
-0.1117
(0.0280)
0.0496
Idea Type: Other
Short Investment
0.2511
(0.0424)
0 1935
(0.0330)
Ideas
Location: Major City
Location: Non-US
1.ndergraduate Rank: Top
0.1596
(0.0346)
0.049 1
(0.0490)
0.0 125
(0.0239)
-0.0225
(0.0066)
0.3132
(0.1394)
0.0112
(0.0052)
0.1111
(0.0344)
0.0376
(0.0549)
0.1103
(0.0366)
0.1133
Undergradiate Hank: Bottom
(0.0501)
0.0934
indergraduate Rank: Unranked
(0.0339)
-,0.0001
(0.0555)
-0.1005
(0.0906)
Graduate Rank: Top
Graduiate [lank: Bottom
Graduiate [Rank: Unranked
0.0962
Graduate Rank: No Grad.
(0.0686)
0.0262
(0.0529)
Constant
M-Square Adj.
Observations
5.2362
**
(0.0005)
0.2438
(0.0412)
***
5.0073
(0.1148)
(0. 126 9)
0.251
0.266
3,003
3,003
Note: Models contain year and industry fixed eflects.
Robust Standard errors clustered at the user-level are in parentheses.
Significance Levels: + p ! 0.10, * p 5 0.05, ** p S 0.01, *** p S 0.001.
94
*
0.0013
(0.0005)
**
*
Female Name Score
Model 113
**
Table 3: OLS Regression of Comments Count (logged) on Female Name Score.
Unit of analysis is user-idea.
Model 2
Idea Type: Growth
Idea Type: Event-based
Idea Type: Other
Short Investment Horizon
Market Cap.
Week-One Performance
Number
of
+
-0.0171
(0.0267)
-0.0188
(0.0071)
-0.1847
(0.1863)
Past Ideas
-0.0029
(0.0053)
-0.0844
(0.0324)
0.0458
(0.0570)
-0,0308
(0.0338)
0.0602
(0.0464)
0.0471
(0.0355)
0.0046
(0.0500)
0.1605
(0.0745)
0.0483
(0.0746)
-0.0108
(0.0467)
** -2.9828
(0.2008)
0.408
3,003
Location: Major City
Location: Non-US
Undergraduate Rank: Top
Undergraduate Rank: Bott nm
ludergraduate Raik: LUranked
Graduate Rank: Top
Graduate Rank: Bottom
Graduate Rank: Unranked
Graduate Rank: No Grad.
Constant
0.0007
(0.0007)
0.8545
(0.0322)
0.0376
(0.0443)
0.0569
(0.0362)
-0.0030
(0.0322)
0.0645
(0.0374)
0.0684
(0.0535)
---0.0137
(0.0265)
-0.0210
(0.0071)
*..
+
Recommendation: Short
Expected Return (logged)
0.0005
(0,0007)
0.8432
(0.0315)
0.0332
(0.0447)
0.0673
(0.0357)
-0.0080
(0.0323)
0.0604
(0.0379)
0.0717
(0.0542)
-2.9.988
*
(0.1998)
-Square Adj.
0.403
Observations
3,003
Note: Models contain year and industry fixed eflects.
Robust Standard errors clustered at the user-level are in p4 rentheses.
* p 5 0.001.
Significance Levels: + p
0.10, 1 p < 0.05, ** P < 0.01.
95
*
Views (logged)
Model 2B
*
Fenale Name Score
Table 4: OLS Regression of Write-up Quality (logged) and Return Ratings (logged) on Female Name Score.
Unit of analysis is user-idea.
Write-up
Model 3A
Female Name Score
0.0001
(0.0003)
0.0268
(0.0172)
0.0321
Recommendation: Short
Expected Return (logged)
(0.0118)
Idea Type: Growth
-0.0670
(0.0127)
0.0354
Idea Type: Event-based
(0.0140)
Idea Type: Other
Short
Investment
Market
0.0482
(0.0199)
-0.0062
(0.0101)
-00165
(0.0026)
Horizon
Cap.
Week-One Performance
Number of Pasti deas
Location: Major City
Location: Non-US
Undergraduate Rank: 'op
Undergraduate Rank: Bottorn
Return
Model 3B
-0.0002
(0.0003)
0.0232
(0.0169)
0.0283
(0.0110)
-0.0685
(0.0127)
0.0334
(0.0138)
0.0504
(0.0199)
-0.0074
(0.0101)
-0.0152
(0.0025)
0.2132
(0.0598)
0.0041
(0.0016)
0.0186
(0.0148)
0.0067
(0-0265)
0.0288
(0.0144)
0.0241
-*
Model 4A
-0.0007
(0.0006)
-0.0282
(0.0363)
-0.0102
(0.0318)
-0.0999
(0.0273)
-0.0007
(0.0006)
-0.0310
(0.0353)
-0.0222
(0.0320)
-0.1021
(0.0277)
0.0204
(0.0261)
0.0325
(0.0416)
-0.0280
(0.0205)
-0.0184
(0.0057)
0.3946
(0.1387)
0.0224
0.0267
(0.0258)
(0.0416)
-0.0212
(0.0206)
-0.0193
(0.0056)
0.0028
(0.0024)
0,0245
(0.0236)
-0.0336
(0.0473)
0.0347
(0.0266)
0.0385
(0.0338)
0,0375
(0.0).')
Undergraduate Rank: Unranked
Graduate Rank: Top
GradIate Rank: Bottom
Graduate flank: Unranked
Graduate Rank: No Grad.
Constant
2.1838
(0.0419)
It-Square Ad
0. 4
Observations
2,874
Note: Models contain year and industry hxed effects.
Robust Standard errors clustered at the user-level are in parentheses.
Significan ce Levels: -+ p 5 0.10, * p 5 0.05, ** p < 0.01, ** p . 0.001.
Table 5:
0.0099
(0.01,42)
0.0097
(0.0219)
-0.0515
(0.0378)
-0.0424
(0.0296)
0.0068
(0.0203)
2.1307
(0.0453)
0.095
2,874
(0.0255)
0.0109
(0.0363)
-0.1001
(0.0733)
-0.0545
(0.0563)
-0.0183
(0.0344)
2.4053
(0.1743)
0.060
1,130
*12.3549
OLS Regression Evaluating Risk-Aversion. Unit of analysis is user-idea.
Short Ren.
Short Horizon
Expected Return (log)
Fenale Namei Score
0.0003
(0.68)
0.0015
(2.12)
*
-. 0004135
(-1.17)
Note: Models contain user- and idea-level controls, excluding other risk aversion measures, as well as
year and industry fixed effects.
Robust Standard errors clustered at the user-level. T-statistic in parentheses.
Significance Levels: + p K 0.10, * p K 0.05, ** p < 0.01, *** p K 0.001.
96
Model 4B
(0.1770)
0.og
1,130
Table 6: OLS Regression of Vi ews (logged) on Female Name Score
in times of increased traffic. U nit of analysis is user-idea.
Model 5A
Low Traffic
(0.0005)
-0.1182
(0.0392)
0.0847
(0.0289)
*
**
Female Name Score X High Traffic
-0.0033
(0.0013)
-0.0006
Female Name Score X Low Traffic
+
-0.0010
**
*
High Traffic
-0.0015
(0,0005)
-0.1385
(0.0376)
0.0812
(0.0276)
**
*
Female Name Score
Model 5B
Short Investment Horizon
Market Cap.
Location: Majoy City
Location: Non-US
Undergraduate Rank: Top
Undergraduate Rank: Bottom
Undergraduate Rank: Unranked
Graduate Rank: Top
Graduate Rank: Bottom
Graduate Rank: Unranked
*
*
*
0.1026
**
(0.0371)
0.1232
(0.0540)
0.0874
(0.0341)
-0.0143
(0.0597)
-0.1141
(0.0904)
-0.1194
*
*
(0.0730)
*
*
-0.1134
(0.0281)
0.1590
(0.0346)
0.0385
(0.0482)
0.0089
(0.0237)
-0.0236
(0.0067)
0.1192
(0.0351)
0.0400
(0.0590)
*
Idea Type: Other
(0.0321)
*
*
Idea Type: Event-based
-0.1143
(0.0281)
0.1595
(0.0346)
0.0410
(0.0483)
0.0081
(0.0237)
-.0.0235
(0.0067)
0.1212
(0.0351)
0.0374
(0.0592)
0.1046
(0.0371)
0.1227
(0.0540)
0.0869
(0.0341)
-0.0141
(0.0598)
-0.1133
(0.0902)
-0.1177
**
*
(0.0321)
Idea Type: Growth
*
*
*
0.2509
(0.0409)
0.3287
(0.1402)
0.1882
*
Expected Return (logged)
*
(0.0731)
Graduate Rank: No Grad.
0.0140
(0.0568)
5.0443
*
Constant
(0.1318)
0.269
R-Square Adj.
Observations
3,003
Note: Models contain year and industry fixed effects.
Robust Standard errors are in parentheses.
Significance Levels: + p < 0.10, * p < 0.05, ** p < 0.01, *
97
0.0146
(0.0568)
5.0404
(0.1319)
0.269
3,003
p
< 0.001.
*
Week-One Performance
0.2486
(0.0410)
0.3306
(0.1398)
0.1885
*
(0.0015)
Recommendation: Short
Table 7: OLS Regression of Views (logged) on Female Name Score
Sample only those users who are confirmed males.
Unit of analysis is user-idea.
Model 6A
Recommendation: Short
*
-.0.0021
(0.0009)
0.2173
*
Female Name Score
0.1313
(0.0409)
-0.1586
(0.0388)
0.1424
(0.0457)
0.0035
(0.0774)
Idea Type: Event-based
idea Type: Other
Short Investment Horizon
0.0333
(0.0372)
Number of Past Ideas
0.0116
(0.0049)
-0.0260
(0.0094)
Market Cap.
Location: Major City
Location: Non-US
Undergraduate Rank: Top
Undergraduate Rank: Bottom
Undergraduate Rank: Unranked
Graduate Rank: Top
Graduate Rank: Bottom
**
**
0.1424
**
(0.0489)
0.1120
(0.0723)
0.0812
(0.0581)
0.0667
(0.0666)
0.0611
(0.0477)
-0.0549
(0.0814)
-0.2142
+
Idea Type: Growth
*
Expected Return (logged)
*
0.4889
(0.1941)
*
Week-One Performance
*
(0.0603)
-0.1556
(0.0947)
Graduate Rank: No Grad.
-0.0595
(0.0803)
Constant
5.3032
(0.1790)
R-Square Adj.
0.239
Observations
1,405
Note: Models contain year and industry fixed effects.
*
(0.1241)
Graduate Rank: Unranked
Robust Standard errors are in parentheses.
Significance Levels: + p < 0.10, * p K 0.05, ** p K 0.01, *
98
p K 0.001.
Table Al: OLS Regression of Evaluation Process (all logged) on Female Name Score-Census
Unit of analysis is user-idea.
Week-One Performance
Expected Return (logged)
Views (logged)
Model A2
-0.0012
(0.0005)
0.2627
(0.0495)
0.2131
(0.1579)
0.2045
(0.0412)
*
*
0.0744
(0.0383)
0.8505
(0.0359)
-0.0013
(0.0060)
0.0043
(0.0358)
0.0823
(0.0424)
0.1240
(0.0632)
Number of Past Ideas
Idea Type: Growth
Id
Type: Event-based
Idea Type: Other
Short Investment Horizon
Market Cap.
Location: Major City
Location: Non-US
Undergraduate Rank: Top
Undergraduate Rank: Bottom
Undergraduate Rank: Unranked
Graduate Rank: Top
Graduate Rank: Bottom
Graduate
Rank: Unranked
Graduate Rank: No Grad.
Constant
R-Scquare Adj.
Observations
-0.1220
(0.0308)
0.1682
(0.0383)
0.0430
(0.0564)
0.0322
(0.0270)
-0.0247
(0.0074)
0.1122
(0.0377)
0.0685
(0.0757)
0.1079
(0.0406)
0.1157
(0.0570)
0.1015
(0.0378)
*
*
**
*
*
-0.0001
(0.0004)
0.0222
(0.0190)
0.2087
(0.0687)
0.0382
(0.0116)
Return Rating
Model A4
-0.0008
(0.0007)
-0.0600
(0.0411)
0.3449
(0.1530)
-0.0214
(0.0444)
+
+
+
(0.0139)
0.0295
(0.0153)
0.0617
(0.0212)
-0.0097
(0.0114)
-0.0120
(0.0028)
0.0159
(0.0156)
0.0089
(0.0331)
0.0329
(0.0155)
0.0216
(0.0208)
-0.0003
(0.0159)
0.0137
(0.0245)
0.0304
*
(0.0429)
-0.0488
(0.0341)
0.0140
(0.0223)
2.0746
(0.0500)
0.090
2,311
0.0031
(0.0031)
0.0963
-0.0684
(0.0296)
-0.0269*
(0.0079)
-0.0905
*
(0.0357)
0.0724
(0.0739)
-0.0141
(0.0365)
(0.0503)
0.0632
(0.0392)
-0.0126
(0.0550)
0.0983
(0.0848)
0.1101
(0.0906)
-0.0084
(0.0517)
-2.8876
(0.2248)
0.415
2,416
Rating
Model A3
0.0036
(0.0018)
0.0650
Note: Models contain year and mdustry hxed effects.
Robust Standard errors clustered at the user-level are in parentheses.
Significance Levels: + p < 0.10, * p < 0.05, ** p < 0.01, *** p K 0.001.
99
+
-0.0108
-0.0305
(0.0624)
0.1627
(0.1017)
-0.1585
(0.0835)
-0.0104
(0.0598)
5.0562
(0.1453)
0.274
2,416
0.0006
(0.0007)
0.0298
(0.0526)
0.3079
(0.2162)
WVrite-up
+
(0.0311)
0.0224
(0.0310)
0.0608
(0.0510)
-0.0367
(0.0234)
-0.0171
(0.0066)
0.0147
(0.0270)
-0.0850
(0.0671)
0.0571
(0.0306)
0.0295
(0.0377)
0.0348
(0.0303)
-0.0007
(0.0407)
0.0382
(0.0952)
-0.0231
(0.0796)
-0.0171
(0.0379)
2.3460
(0.1943)
0.058
885
*
Recommendation: Short
Comments
*
Feinale Naine Score
Views
Model Al
matched first names.
4 FORMALIZATION REVISITED: CONSIDERING THE
EFFECTS OF MANAGER GENDER AND DISCRETION
ON CLOSING THE WAGE GAP
4.1
INTRODUCTION
Despite fifty years of political mobilization and dedicated public policy, the gender wage gap
remains one of the most persistent forms of workplace inequality (Cha & Weeden, 2014; Cotter,
DeFiore, Hermsen, Kowalewski, & Vanneman, 1997; England, 1992; Huffman & Velasco, 1997;
Reskin, 2000). Comparisons of earnings for male and female workers in the United States
consistently reveal that women earn substantially less than similarly qualified men working in the
same organizational positions. In an effort to uncover the sources of this form of inequality, a
growing body of research has focused on the role of organizational practices and pay systems
(Baron, Davis-Blake, & Bielby, 1986; Briscoe & Kellogg, 2011; E. Castilla, 2008; Castilla, 2011;
Castilla & Benard, 2010; Fernandez & Fernandez-Mateo,
2006; Kalev, Kelly, & Dobbin, 2006;
Reskin, 2003). In particular, the degree to which organizational systems are formalized," or
&
governed by clear written rules and procedures (Anderson & Tomaskovic-Devey, 1995; Huffman
Velasco, 1997; Weber, 1958), has been shown to have direct implications for the level of pay
inequality among employees.
Most studies on formalization and pay inequality demonstrate that compensation systems
that are highly formalized, with unambiguous rules, reduce sex-based bias by limiting decision
&
makers' use of discretion and subjectivity when evaluating employees (Baron et al., 1986; Elvira
Graham, 2002; Pfeffer & Cohen, 1984; Reskin, 2000; Sutton, Dobbin, Meyer, & Scott, 1994). These
studies argue that when managers have discretion they are subject to decision-making biases, which
lead them to favor male employees when allocating resources (Reskin, 2000; Ridgeway, 1997). Thus,
formalized organizational pay practices that constrain managerial discretion are posited to minimize
1 See Elvira and Graham (2002) for a detailed discussion of pay formalization.
100
gender disparities because they limit the opportunities for managerial biases to factor into employee
compensation decisions (Elvira & Graham, 2002; Reskin, 2000).
and pay equity into question (E. Castilla, 2008; Dencker, 2008; Kalev et al., 2006; Konrad
&
Conversely, a handful of recent studies call this positive relationship between formalization
Linnehan, 1995). Some of this research has found that, when compared to less formalized practices
that allow managerial discretion, formalized personnel practices are associated with higher levels of
gender inequality (Dencker, 2008). For example, Dencker (2008) posits that less formalized personnel
practices may empower organizational decision makers to redress inequality, particularly when
organizations face pressures for gender equity, in a way that highly formalized pay systems do not.
Thus, recent evidence suggests that the discretion offered by less formalized work practices may lead
to gender equitable
outcomes under some conditions.
longstanding research finding that organizational
Furthermore,
this is consistent
with
bureaucracies and formalized routines, more
broadly, exacerbate gender and racial inequality in the workplace (Acker, 1990; Kanter, 1977).
In this study, I advance our understanding of the relationship between formalization of pay
and gender pay inequality by shifting the debate to focus on uncovering the conditions under which
less formalized personnel practices reduce pay inequality. In particular, I consider the gender of the
decision maker to determine whether male and female managers differ in how they use the discretion
afforded by less formalized pay systems. While there may be a number of conditions under which
less formalized practices serve to redress inequality, manager gender is of central importance as it is
not clear that both male and female decision makers share pro-male biases. For example, ingroup
biases have been posited to lead male managers to allocate resources, such as wages, in ways that
benefit other men and disadvantage women (Gary S Becker, 1957; Bielby, 2000; Bridges & Nelson,
1989; Cotter et al., 1997; Ely, 1995; Nelson & Bridges, 1999; Reskin, 2000). Thus, as a result of a
preference for the ingroup, female managers may favor members of their gender ingroup, namely
female employees. In support of this view, while not directly considering pay formalization, research
in the organizational demography tradition suggests that a greater proportion of women in
management is associated with less gender wage inequality among non-managerial
101
employees
Hultin & Szulkin, 1999; Joshi, Hui Liao, & Jackson, 2006; Kulis, 1997; Pfeffer, Davis-Blake,
&
(Baron, Mittman, & Newman, 1991; Cohen & Huffman, 2007; Huffman, Cohen, & Pearlman, 2010;
Daniel J. Julius, 1995).
I demonstrate that the effect of pay formalization on gender pay inequality varies by
manager gender. Consistent with most extant research, I do not find inequality in terms of highly
formalized components of pay. Both male and female managers display similar equitable practices.
On the other hand, I demonstrate variation in pay equity under less formalized components of pay.
In particular, I find less inequality in base salary, which is far less formalized in this setting, for
employees reporting to female, as compared to male, managers. However, female managers do not
ubiquitously use discretion to close the gender pay gap. I find that they only reduce pay inequality
among employees in female-typed positions in the lowest organizational ranks. As such, my findings
provide conditional support for the perspective that less formalized personnel practices may result in
gender pay equity for employees reporting to female, but not male, managers. Thus, failing to
account for manager gender may lead to erroneous, or oversimplified, claims that less formalized
organizational policies perpetuate workplace inequality in all cases. Since allowing managers
discretion also provides firms with the opportunity to enhance performance (Gomez-Mejia & Balkin,
1992), understanding when less formalized practices lead to equitable outcomes for men and women
is critical for designing pay systems that simultaneously benefit the firm and its employees. My
findings suggest that less formalized pay systems may be effective for achieving gender pay equity
only in settings where women are present in management and oversee employees in female-typed,
lower-status positions.
Undoubtedly, a primary explanation for our limited understanding of when less formalized
pay practices reduce gender pay inequality is the challenge of collecting appropriate data. To identify
whether male and female managers impact employee pay differently when given discretion via less
formalized pay systems is particularly difficult. It requires comparing relative wages for male and
female employees reporting directly to male versus female managers in a setting where there are
components of pay that vary in the degree of formalization. Using longitudinal data from a large
102
retail financial services firm in which I was able to link employees to their managers, this study
addresses this challenge. Specifically, I analyze whether the relationship between formalization of pay
systems and within-job gender pay inequality varies by manager gender. By simultaneously
considering formalization of pay policies and manager gender, this study identifies both the
conditions under which, and how, less formalized reward systems reduce gender pay inequality
within organizations.
I proceed as follows. I begin with a discussion of current theory on formalization and gender
inequality. Drawing on social identity theory and status characteristics theory, I then develop
hypotheses about whether, and when, male and female managers may use the discretion afforded by
less formalized organizational pay policies differently. Next, I introduce the research setting and
analytical method used to test whether manager gender explains the conditions under which less
formalized practices reduce gender pay inequality among employees. Then, I present results which
demonstrate that under certain conditions there is evidence of less gender inequality in terms of less
formalized components of pay for employees reporting to female, relative to male, managers. I
conclude with a discussion of the theoretical and managerial implications of my findings for our
understanding of gender pay inequality, and workplace inequality more broadly, as well as the
importance of considering characteristics of the manager in inquiries regarding workplace inequality.
4.2
4.2.1
THEORY AND HYPOTHESES
Formalization of Pay and Gender Inequality
Organizational resources, including wages, are allocated on the basis of the organization's
reward structure
(E. Castilla, 2008; Gibbons,
1998). While generally managers possess some
discretion over employee pay within this structure, organizational practices and processes govern the
degree of discretion available to managers (Elliott & Smith, 2004; Hultin & Szulkin, 1999, 2003).
Specifically, organizations can elect pay systems that vary in degree of formalization (Dobbin,
Sutton, Meyer, & Scott, 1993; Osterman, 1987), or the extent to which written rules and procedures
govern pay decisions (Anderson & Tomaskovic-Devey, 1995; Huffman & Velasco, 1997; Weber, 1958).
103
Even within a firm the degree of pay formalization may vary, with some components of pay being
more formalized than others (Elvira & Graham, 2002; Gomez-Mejia & Balkin, 1992).
By establishing explicit practices guiding managerial
decisions related to pay, highly
formalized pay systems constrain the discretion managers have over subordinate pay decisions
relative to less formalized systems (Bielby, 2000; Reskin & McBrier, 2000). To illustrate how
manager discretion
is constrained,
consider commission pay for sales
employees where the
commission an employee receives is a fixed percentage of that individual's sales. As a result of the
unambiguous rule governing commission pay decisions, managers do not have any discretion over
final commission pay in this highly formalized pay system.
Because they allow for minimal managerial discretion, highly formalized pay systems are
commonly thought to be effective in reducing workplace gender inequality (Bielby, 2000; Reskin,
2003). Generally, organizational decision makers are posited to perpetuate inequality by, either
consciously or unconsciously, favoring male employees when allocating resources (Reskin, 2000;
Ridgeway, 1997). Whereas managers are unlikely to make personnel decisions based solely on merit
when left to their own discretion (Castilla, 2011; Perry et al., 1994; Reskin, 2003), formalized
personnel practices are posited to lead to more equitable outcomes by removing this managerial
discretion from personnel decisions (Baron & Bielby, 1980; Pfeffer, 1977; Reskin, 2000). In fact,
opportunity legislation and of the human resources profession since the late 1960s (Anderson
&
formalized personnel practices have been a central tenet in the development of equal employment
Tomaskovic-Devey, 1995; Dobbin & Kelly, 2007; Huffman & Velasco, 1997; Kalev et al., 2006; Reskin
& McBrier, 2000).
Furthermore, a considerable body of empirical research supports this positive relationship
&
between formalization of organizational reward systems and gender equitable outcomes (Anderson
&
Tomaskovic-Devey, 1995; Baron, Hannan, Hsu, & Kogak, 2007; Elvira & Graham, 2002; Pfeffer
Cohen, 1984; Reskin & McBrier, 2000; Sutton et al., 1994). For example, Elvira and Graham (2002)
compare three types of pay, varying in the degree of formalization, for male and female employees
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within a financial services firm. While not taking manager gender into consideration, they find that
with the most formalized type of pay there is less gender pay inequality on average. In an
examination of hiring outcomes, Baron and colleagues (2007) find that high-tech firms with a full
time HR employee, which they argue signals that more objective employment practices are in place,
hired more female scientists. Similarly, using firm-level data to examine the effects of organizations'
employment practices on sex-based ascription in access to managerial jobs, Reskin and McBrier
(2000) find that firms with more formalized personnel practices had a greater proportion of women
in management positions.
While the preponderance of empirical evidence supports the argument that more formalized
pay systems serve to reduce gender pay inequality, some studies call this result into question (Acker,
1990; E. Castilla, 2008; Castilla, 2011; Castilla & Benard, 2010; Dencker, 2008; Huffman & Velasco,
1997; Kanter, 1977; Konrad & Linnehan, 1995). For example, Dencker (2008) finds a higher level of
gender inequality in promotion under highly formalized personnel practices, arguing that formalized
practices prevent organizational decision makers from responding to gender equity pressures.
Relatedly, other research suggests that less formalized practices, which offer more managerial
discretion, may actually serve to reduce inequality even in the absence of explicit goals for equity. In
an experimental design, Castilla and Benard (2010) found that presenting subjects with language
emphasizing managerial discretion was associated with a bias in favor of women and conjectured
that the presence of discretion "may create the perception of the existence of bias and may therefore
motivate individual attempts to correct it" (Castilla & Benard, 2010: 567).
These mixed results raise an important question:
Under what conditions might less
formalized pay systems serve to reduce, rather than perpetuate, gender pay inequality? It is only by
advancing this line of inquiry that we can uncover the effect of different organizational reward
systems on gender pay inequality. By focusing on whether less formalized organizational reward
systems perpetuate inequality, an implicit assumption of existing research on formalization and
gender inequality has been that organizational decision makers will use the discretion afforded by
these policies uniformly. Theories of ingroup preference and status, however, shed doubt on this
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assumption. Therefore, it is imperative to extend this debate by including the characteristics of the
organizational decision makers, such as their gender, in our inquiries. While I did not directly
measure managerial
intentions or motivations, I draw on social identity theory and status
characteristics theory to argue that when given discretion through less formalized pay systems male
and female managers may differ in their propensity to disadvantage women, or to favor men.
4.2.2
Considering Manager Gender and Differences in Gender Biases using Social
Identity Theory
Social identity theory provides a basis for understanding why male and female managers' pay
decisions are likely to impact gender pay inequality among non-managerial employees differently.
According to this theoretical perspective, individuals use visible demographic attributes, such as
gender, to classify themselves and others (Ashforth & Mael, 1989; Tajfel & Turner, 1979). Using
these categorizations, demographically similar others are classified as members of the ingroup,
whereas those who are demographically dissimilar are seen as members of the outgroup. This
categorization process is posited to be largely automatic, occurring "independently of decision
makers' group interests or their conscious desire to favor or harm others" (Reskin, 2000: 321).
Nonetheless, it leads individuals to favor ingroup members, and in some cases disfavor outgroup
members, in evaluations and reward allocations (Bielby, 2000; Hewstone, Rubin, & Willis, 2002;
Reskin, 2000).
There are at least two reasons to expect that male and female managers will engage, either
consciously or not, in a gender-based in- versus out-group categorization process, leading to a
preference for their gender ingroup, with implications for employee pay. First, gender is a salient
characteristic in the workplace as gender differences among individuals are clearly visible (Ridgeway,
1991). As a result, employees are more likely to engage in gender-based categorization and tend to
associate with, and to privilege, other same-gender employees (Elliott & Smith, 2004; Gorman, 2005;
McPherson, Smith-Lovin, & Cook, 2001). Second, while the propensity to make distinctions between
the in- and outgroup is universal, it is most pronounced for actors occupying positions of power
(Goodwin, Operario, & Fiske, 1998) as these individuals often aim to maintain the status quo (Jost
106
& Banaji, 1994). Given their situational power within the organization, male and female managers
may be more predisposed to engage in this categorization process.
Thus, just as the discretion afforded by less formalized organizational reward systems may
lead male managers to advantage their male subordinates, female managers may act in favor of their
female subordinates. Consistent with this notion that managers are apt to favor members of their
gender ingroup, gender pay inequality has been attributed to the historical predominance of men in
managerial positions because men allocate resources, such as wages, in ways that benefit other men
and disadvantage women (Gary S Becker, 1957; Bielby, 2000; Bridges & Nelson, 1989; Cotter et al.,
1997; Ely, 1995; Nelson & Bridges, 1999; Reskin, 2000). As the representation of women in
management in the United States has been rising, 2 a body of research in the organizational
demography tradition argues that a greater proportion of women in management is associated with
greater gender pay equity among non-managerial employees (Baron et al., 1991; Cohen & Huffman,
2007; Huffman et al., 2010; Hultin & Szulkin, 1999; Joshi et al., 2006; Kulis, 1997; Pfeffer et al.,
1995). Since these studies do not account for the degree of formalization characterizing pay systems
in the settings under study, it is difficult to infer that these observed associations are the result of
female manager impact. To the extent that these pay systems were highly formalized, for example,
female managers may not even have had the opportunity, even if they wanted, to redress gender pay
inequality among employees. By simultaneously considering formalization of pay and manager
gender, this study more accurately isolates whether female managers serve to reduce gender pay
inequality when given discretion through less formalized pay systems.
Given the extensive evidence showing the behavioral effects of ingroup and outgroup
membership categorization, I predict that the relationship between formalization of pay systems and
employee pay will vary by manager gender such that:
Hypothesis la: For less formalized components of pay, female employees will earn
more when reporting to a female, as compared to a male, manager.
12 Data supporting this trend obtained from a U.S. Bureau of Labor Statistics report entitled "Women in the Labor
Force: A Databook." The report was issued in May 2014 and accessed in March 2015 via www.bls.gov.
107
Hypothesis 1b: For less formalized components of pay, male employees will earn
more when reporting to a male, as compared to a female, manager.
4.2.3
Explaining Variation in Manager Preferences Using Status Characteristics
Theory
Despite the general tendency for individuals to prefer people like themselves, there is
evidence that individuals may act ambivalently toward the ingroup, or even advantage
members of the outgroup, in some cases. Thus, this difference in employee pay for those
employees reporting to female versus male managers may not be ubiquitous. Specifically, I
argue it varies based on the status and gender-typing of the job of the target, or employee,
being assessed.
Status characteristics theory is useful for understanding why this is the case.
According to this perspective, status characteristics emerge when greater social esteem and
competence are accorded to members possessing one category of the characteristic (e.g. male,
white) than another (i.e. female, black) (Berger et al., 1977). Because these status beliefs are
widely shared, it is posited that both members of the high- and low-status groups accept
that the high-status group is perceived as generally more competent than the low-status
group. For members of high status groups, these consensual status beliefs intensify the
ingroup preference, "[b]ut these same status beliefs blunt the ingroup bias of lower status
group members who are torn between sticking with their own or favoring those from highstatus groups" (Ridgeway, 2014: 79). As such, these status beliefs "transform competing ingroup favoritisms into a consensual belief that, whether individuals like it or not, people in
one group are accorded more social respect than are those in another" (Ridgeway & Correll,
2006: 433). Consequently, members of lower-status demographic groups are generally less apt
to favor ingroup members than are members of high-status groups (Bettencourt, Charlton,
Dorr, & Hume, 2001; Ely, 1994, 1995; Ibarra, 1992; Ridgeway & Correll, 2006).
Generally in the U.S., gender is one such status distinction where men are more
highly valued, and generally perceived as more competent, than are women (Berger et al.,
108
1977; Correll & Ridgeway, 2003).
Since this gender status belief is widely-held
and
institutionalized, there may be social costs associated with challenging this status ordering,
leading people to largely comply even if they do not personally endorse that men are higher
status (Ridgeway & Correll, 2004, 2006). As a result of this gender-based status difference,
ingroup preferences and preferences for the higher status gender align for men. On the other
hand, for women these preferences are at odds and potentially pose a tension. Thus, while
male and female managers may generally favor their same gender employees, as members of
the lower-status gender, female managers may not always exercise a preference for their
female employees.
Extant research suggests that gender status beliefs are strongest, and result in the
greatest male advantage, when actors performing tasks that are stereotypically masculine, or
higher-status, are being evaluated (Ridgeway,
Ridgeway & Smith-Lovin,
1999).
1993, 1997; Ridgeway & Correll, 2004;
Jobs become labeled as masculine when there is a
predominance of men employed in the job, such that over time, even in sex-segregated
settings, both managers and occupants of the job reinforce this gender stereotypic labeling
(Ridgeway, 1997). Unsurprisingly, jobs typically higher in the organizational hierarchy are
typed male as the traits seen as necessary for success in these higher-status jobs are more
&
commonly associated with men (Eagly, 1987; Heilman, Block, & Martell, 1995; Rudman
Glick, 1999). On the other hand, jobs lower in the organizational hierarchy are typically
considered female and perceived to be lower-status (Baron & Newman, 1990; Ibarra, 1993;
Kilbourne, England, & Beron, 1994). For example, Glick (1991) found a negative relationship
between prestige and the belief that occupations are female-typed, or require feminine
personality traits (see also England, Herbert, Kilbourne, Reid, & Megdal, 1994).
Research examining the prevalence of pro-male biases suggests that female managers,
like their male counterparts, are most apt to privilege members of the higher status gender
(i.e. male employees) when evaluating employees in higher-status, male-typed positions. For
example, in lab-based studies, whereas both men and women exhibit a preference for men in
male-typed tasks, only male subjects have been found to favor men in gender neutral tasks
109
(Foschi et al., 1994). Similarly, Moss-Racusin and colleagues (2012) found that both male
and female faculty members favored male student applicants over identical female applicants
in the traditionally male-typed fields of biology, chemistry and physics. Furthermore, this
tendency for women to hold pro-male biases when evaluating others in higher-status, maletyped, as compared to lower-status, female-typed, tasks may be most pronounced among
women in higher levels of the organizational hierarchy. Some studies have found that women
in managerial
positions are more
likely
to distance themselves
from other women
(Chattopadhyay, Tluchowska, & George, 2004; Derks, Laar, Ellemers, & Groot, 2011;
Duguid, Loyd, & Tolbert, 2012) and initiatives targeted at advantaging the position of
women (Ng & Chiu, 2001).
Following this logic, female managers are most likely to share the same preferences as
male managers and, as a result, not favor their ingroup (i.e. female employees) when
assessing employees in male-typed, high-status positions. A direct implication of the research
outlined here is that, to the extent female managers do favor their ingroup, they are most
likely to do so when assessing employees in lower-status,
female-typed organizational
positions. While generally it is less clear whether female employees will be favored in femaletyped jobs, with research suggesting at most a modest female advantage (Ridgeway, 1993;
Ridgeway & Correll, 2004; Swim & Sanna, 1996), these studies have not considered manager
gender. Since female managers are less apt to hold pro-male biases when assessing others in
female-typed, lower-status organizational positions, I propose that female managers will favor
their gender ingroup in these positions, such that:
Hypothesis 2a: For less formalized components of pay, female employees in the
lower-level, female-typed organizationalpositions will earn more when reporting to a
female, as compared to a male, manager.
Hypothesis 2b: For less formalized components of pay, male employees in the
lower-level, female-typed organizationalpositions will earn less when reporting to a
female, as compared to a male, manager.
4.3
DATA AND METHODS
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4.3.1
Research Setting
I examine whether the relationship between formalization of pay systems and gender pay
inequality varies by manager gender using data from multiple retail branches of a globally diversified
financial services firm (FinServ). I leveraged the richness of data that results from focusing on a
single firm in order to accurately test the hypotheses outlined herein (Fernandez, Castilla, & Moore,
2000; Sorensen, 2000). FinServ provided the annual personnel databases for all employees in its U.S.
operations for the 41-month period from January 1996 through May 1999 (the study period). These
databases contained demographic characteristics,
including gender, race, and age, as well as
employment specific characteristics, including date of hire, pay, and performance.
These data are particularly well-suited for identifying whether less-formalized pay systems
lead to different levels of pay inequality based on whether employees report to a female versus a
male manager for at least four reasons. First, employees within FinServ's branch system were eligible
to receive two distinct forms of compensation, base and bonus pay, with the latter being far more
formalized." Examining whether male and female managers pay decisions differ in terms of more
formalized pay, where managers do not have discretion, provides a way to rule out an alternative
explanation. Specifically, observing a difference in more formalized pay by manager gender would
suggest that an unobserved factor, and not the manager, was driving the observed difference.
Second, the same manager makes the pay decisions for each of their employees in terms of
both types of pay, allowing for a within-manager analysis of pay for male and female employees
reporting to male versus female managers. By comparing pay decisions made by the same group of
evaluators, this setting allows me to reduce concerns that observed differences, or a lack of a
difference, in employee pay by manager gender is driven by unobserved differences in the types of
managers making each pay decision.
A detailed discussion of these two components of pay, particularly focusing on how these differ in degree of
formalization, is presented in the section below titled Compensation Structure and Formalization.
13
111
Third, personnel databases included complete workgroups with organizational codes linking
individual managers to each non-managerial employee reporting to them. To the extent that
managers have the discretion to affect employee outcomes, they are most apt to have an impact on
those employees reporting to them directly (c.f. Hultin & Szulkin, 2003). Therefore, these data
present a rare opportunity for a proximate examination of the relationship between relative pay for
male and female employees, for both more and less formalized types of pay, and the gender of their
direct manager.
Fourth, by examining differences in pay outcomes for employees reporting to female versus
male managers within FinServ's retail branches, this study compares structurally equivalent subunits
(i.e. branches), allowing for a commensurate comparison of the effects of male versus female
managers on employee pay. Each manager in this setting oversees a single comparable branch that
employs individuals in each of the five non-managerial positions. These managers operate under the
same organizational context and pay guidelines, which is particularly important given the focus of
this study. By comparing branches within the same organization, this analysis exploits variation in
manager
gender
across these
subunits
while holding potentially
confounding
organizational
attributes constant. My setting allows me to disentangle whether the gender of an employee's
manager
has implications
for her pay from other
organization-
or branch-level
alternative
explanations, by isolating manager gender while holding constant organization-level policies and
controlling for attributes that vary across branches.
4.3.2
Participants
FinServ's retail branches are client facing offices that offer a wide range of financial products
and services to retail financial services customers. Among others, these services and products include
mutual fund accounts, mortgages and margin lending accounts. These retail branches employ
individuals in six unique organizational positions: five non-managerial and one managerial, as
depicted in Figure 1. Each branch employs an average of approximately nine non-managerial workers
all reporting to a single branch manager.
112
Using human resources records, I constructed a longitudinal database for all full-time nonmanagerial employees and managers employed in FinServ's 120 retail branches across eight U.S.
states during the four-year study period. Since employees enter and exit the branches at different
points, this database of full-time employees is unbalanced, with each employee being present in the
retail branches data between one and four years. During the study period, employees may be present
for all years of the study, be hired into the branch system after the beginning of the study, leave the
branch system before the end of the study window, or both enter and exit during the study window.
For example, an employee present for the entire duration of my study will have a total of four
person-year observations. On average, non-managerial branch employees are present in these data for
two and a half years. The analyses included a total of 897 unique full-time non-managerial
employees, for a total of 2,139 person-years for non-managerial employees, and 156 full-time branch
managers, for a total of 359 person-years for branch managers.
4.3.3
Compensation Structure and Formalization
As aforementioned, FinServ's compensation structure included base salary and bonus pay for
all branch employees, which together constituted total compensation for employees in the branch
system. In order to understand the compensation structure and the relative degree of formalization
for each of the two types of pay, I leveraged various organizational documents, primarily FinServ's
employee handbook and materials from a presentation on the compensation structure targeted to
branch managers. These materials provided explicit evidence of the degree of formalization
characterizing these two components of pay (cf. Elvira and Graham 2001).
Base salary comprises the greatest proportion of total compensation, as described in Table 1,
for employees across all branch positions. Generally, highly formalized pay systems are characterized
by unambiguous rules and procedures (Anderson & Tomaskovic-Devey, 1995; Huffman & Velasco,
1997; Weber, 1958), which was not the case for base salary at this firm. Organizational documents
prescribed the approved base salary range for every level of the job hierarchy and provided explicit
guidelines that employees should be compensated within the bounds of their pay grade. Within
these wide pay ranges branch managers had discretion over setting employee base salary. The upper
113
bound of pay ranges were deliberately written as "up to" the prescribed dollar amount in an effort
to "leave room for flexibility", according to the employee handbook. Furthermore, for any given
position there were up to four possible pay grades with a spread between the 50th percentile of the
lowest and highest pay grade within a position of up to 20,000 dollars. For example, for lowest-level
organizational position of teller, the pay ranged from just under $18,000 to nearly $31,000. Managers
were not given explicit criteria for assigning employees to a particular pay grade within the job
category or for establishing employee pay within a given grade. Furthermore, in a presentation on
the compensation structure, FinServ communicated to branch managers the organizational goal to
"empower managers to make compensation decisions" and to "institute flexible versus mechanical
compensation". Consistent with this perspective, the employee handbook indicates that the "branch
manager owns compensation decisions." Thus, base salary was not highly formalized as characterized
by the loosely defined guidelines around these pay decisions.
In contrast, bonus pay at FinServ was more formalized allowing managers less discretion, as
there were clear organizational guidelines dictating how managers would determine bonus pay for
individual employees. Bonus pay was allocated based on "one consistent, cohesive plan," according
to the employee handbook. Generally, bonus pay tends to be variable and not guaranteed, often
being tied to financial performance of the organization or unit (Gerhart & Milkovich, 1990). In line
with this argument, bonus pay was based on a combination of employee performance
and
performance of the branch. One might expect that a high level of managerial discretion would enter
at the point of performance evaluations, but because performance ratings were limited to three
categories - below par, at par, and above par - and based on objective criteria there was minimal
discretion at this stage." While managers were not mandated to allocate a certain percentage of
FinServ, branch managers evaluate all employees along six predetermined performance dimensions, which are the
same for all employees in the same branch position. Managers assess whether the target employee is below par, at
par, or above par for each dimension and based on these individual scores assigns a single overall performance rating
of below par, at par, or above par. Each branch position comes with same performance goals, used to create a
scorecard for each branch position with which managers assess employees. While the manager has the ability to
reallocate the position goals, changes affect the criteria used to assess all employees within a position. Thus, these
criteria used for evaluation are not negotiated with individual employees. The branch goal is the sum of all the
position goals.
1At
114
employees to each of the three performance categories, over 60 percent of employees received a rating
of at par. Consistent with the notion that formalization limits variability in decisions across
managers (Finkelstein & Hambrick, 1990) , there was no difference in the distribution of male and
female employees to the three performance categories by manager gender. Furthermore, bonus pay
decisions were monitored by both the senior managers overseeing branch managers and the nonmanagerial employees receiving the bonus pay. Specifically, all bonuses were approved centrally by
the business unit manager and the formula used to calculate bonus pay was available to employees.
Another key measure for the degree of formalization of pay is the amount of documentation devoted
to defining a component of pay (Elvira & Graham, 2002; Gomez-Mejia & Balkin, 1992). For
FinServ, the organizational documents devoted far more attention to bonus pay relative to base
salary. This illustrates that at FinServ bonus pay was far more formalized than was base salary, such
that managers had more discretion over base salary pay decisions.
4.3.4
Measures
Annual Pay. The main dependent variables in this study are the two components of annual
non-managerial employee pay, which, as described above, vary in degree of formalization. Formalized
pay was measured using annual bonus pay, which was highly formalized allowing minimal managerial
discretion. Less formalized pay was measured as annual base salary, which allowed managers greater
discretion over compensation decisions. As is commonly the case for employee compensation, both
formalized pay and less formalized pay were log-normally distributed, and therefore, I transformed
each of the measures of pay to achieve a distribution that more closely resembled the normal
distribution.
Organizational positions. Figure 1 depicts the hierarchy of positions, as represented in
FinServ documents, as well as the key responsibilities of each role. FinServ refers to the position of
branch manager as a "manage others" position, given that a key set of responsibilities is related to
personnel management and managing the branch team. The five non-managerial branch positions
follow a clear hierarchy and there is a structure of cumulative skills and knowledge (see Figure 1).
115
Incumbents of each position were expected to be expert in particular skills associated with their
position as well as the skills of each lower level position. For example, representatives are focused on
unsecured products and sales and service, but they are also responsible for the tasks of tellers,
namely basic products and transactions.
[INSERT FIGURE 1]
At FinServ, organizational positions vary in their gender-typing and status, making these
positions particularly well-suited for testing hypotheses 2a and 2b. First, as depicted in Figure 1, the
organization categorizes each of the five non-managerial jobs as either a clerical or a sales position,
with all positions except for the lowest-level position of teller being labeled as sales. As is evident in
the job descriptions (Figure 1), the position of teller is the only branch position without a sales
component as this position deals exclusively with customer service tasks. The degree to which
positions contribute to sales increases along the branch hierarchy. Generally, sales jobs are gendertyped as masculine, whereas clerical occupations, including bank teller, are largely perceived to be
female-typed (e.g. Glick, 1991). Consistent with this gender-typing of jobs argument, women at
FinServ tend to be concentrated in the lower-level organizational positions involving minimal sales
related tasks (Figure 2). Thus, lower-level positions, particularly the position of teller, are femaletyped.
[INSERT TABLE 1]
Second, as with most organizations, lower-level positions at FinServ were not only femaletyped, but also lower-status. Consistent with the branch hierarchy depicted in Figure 1, Table 1
reveals that both the mean annual base salary and bonus pay increase steadily along the
organizational hierarchy, with the lowest paid position being teller and the highest non-managerial
position being relationship manager. As depicted in the last column of Table 1, the proportion of
total pay that is bonus pay also generally increases along the hierarchy, with bonus pay ranging from
approximately 1.5 to 7.5 percent of total annual pay. Taken together, these distinctions between
116
occupational positions, particularly with jobs lower in the organizational hierarchy being female"
typed and lower-status, further validate the use of the branch jobs to test hypotheses 2a and 2b.
Organizational positions was measured using dummy variables for each of the 5 nonmanagerial branch positions - teller (0
=
"not teller," 1 = "teller"), representative (0 = "not
representative," 1 = "representative"), officer (0 = "not officer," 1 = "officer"), executive (0
=
"not
executive," 1 = "executive"), and relationship manager (0 = "not relationship manager," 1 =
"relationship manager").
To facilitate interpretation, the lowest position in the organizational
hierarchy, teller, was omitted from all models and serves as the reference category.
Gender variables. The main explanatory variables are employee female and manager
female to capture the gender of the focal employee and the employee's manager, respectively (0 =
"male," 1 = "female"). The main focus of this study is to determine whether the relative wages for
male versus female employees differs by the manager's gender. Therefore, the interaction between
manager female and employee female is used to capture this difference. Furthermore, to test
hypotheses 2a and 2b, some models also include the three-way interactions between employee female,
manager female, and each organizational position to assess whether any observed difference in
employee pay by manager gender varies by organizational position.
Control variables. To reduce potential confounding
effects, I controlled for several
variables at the individual-, manager-, and branch-level that are commonly thought to correlate with
employee pay. At the non-managerial employee-level, personnel databases were used to determine
length of organizational tenure, age, marital status, ethnicity, and performance
for all non-
managerial employees. I control for tenure and age to account for differences in human capital.
Employee age and employee tenure are measured in years for each year from 1996 to 1999 based on
The choice to focus on jobs, as opposed to the broader categorization of clerical versus sales, is
deliberate as it
allows for within-job wage comparisons where men and women are performing the same tasks. It is well established
that a substantial portion of gender wage inequality is due to the differential sorting of men and women into different
jobs (e.g. Peterson & Morgan, 1995). For example, in this case, because women are disproportionately employed in
the lower-level positions, lumping all sales jobs together would lead to an overestimation of the wage gap (i.e. wages
for women would be downward weighted towards the lower-level position of representative, whereas wages for men
would be weighted toward the higher-level positions).
15
117
the employee's date of birth and date of hire, respectively. To account for potential differences in pay
linked to ethnicity, I include non-white to represent the employee's ethnicity (0
"white," 1 = "non-
white"). Married identifies whether an employee is married in a given year (0
=
"married")
married
and is
included
because
extant research
has
found that
"not married," 1
=
women are
disadvantaged relative to their male counterparts (e.g. Correll et al., 2007; Killewald & Gough,
2013).
In addition to employees' demographic characteristics,
16
the models also include measures of
promotion and performance, both of which are likely associated with employee pay. Promoted
represents whether an employee received a promotion during the study period (0 = "not promoted,"
1 = "promoted"), which could indicate a new position or an increase in pay grade. Performance was
assigned by an employee's manager and is based on a one to three scale as explained above. The
performance scores capture how well an employee meets expectations and represent the following:
one indicates "does not meet expectations", two indicates "meets expectations", and three indicates
"exceeds expectations". Since performance was missing at random for approximately 155, or 17
percent of, non-managerial employees, models including performance are based on the remaining 742
non-managerial employees.
At the manager-level, personnel databases were used to determine branch manager's length
of organizational tenure and age. These controls were included to capture potential differences in
manager compensation decisions for more versus less experienced managers. For example, less
experienced employees may seek legitimacy and, as a result, be more reluctant to exercise discretion.
Manager age and manager tenure, each measured in years for each year from 1996 to 1999, were
calculated based on the manager's date of birth and date of hire, respectively.
A third set of controls is related to branch-level attributes. Larger, more lucrative branches
may be of higher status, indicating that managers at larger branches may differ in some substantive
Educational information (i.e. highest degree) is only populated for 39 percent of employees included in the study
population. This information is provided by employees on a voluntarily basis. Since this information is not missing at
random, education is not included in the main analyses. However, for robustness, including education does not
significantly change the main results. Results of this analysis for tellers are included in the appendix, Table Al.
118
way from, or have more resources than, managers of smaller, less profitable branches. Therefore, all
models control for branch size, coded as the number of non-managerial employees for each year from
1996 to 1999. As a proxy for branch performance and market share, data on branch revenues were
collected from the Federal Deposit Insurance Corporation (FDIC).1
Branch revenues were log-
normally distributed so this variable was transformed to achieve a distribution that more closely
resembles the normal distribution. The gender composition of the work group may also have
implications for gender pay inequality (c.f. Joshi et al., 2006). Therefore, using the count of male and
female employees in a branch in a given year, percent female was calculated for each branch. Lastly,
all models include state fixed effects for the state where the branch is located to account for any
regional differences in wages.
4.3.5
Analytical Strategy
I estimated various cross-sectional time-series linear models using the method of generalized
estimating equations (GEE) 8 to examine when and how gender pay inequality differs based on
manager gender. I used multivariate analyses with the logarithm of pre-tax less formalized pay (i.e.
base salary) and formalized pay (i.e. bonus pay) as the dependent variables to examine whether
relative pay for male and female employees differs by manager gender. Because employees in this
setting were nested within branches, individuals within branches may have more in common than
individuals across branches. This indicates that employees within branches may not be independent,
violating a key assumption of ordinary least squares (OLS), therefore OLS may result in biased
estimates of standard errors. Furthermore, the data I analyzed were pooled, cross-sectional time
series (yearly) data. A common approach for analyzing data that are structured in this way is to use
fixed-effects models, which would capture within-individual and within-branch, over time variation.
Given my research question, I am inherently interested in the gender of non-managerial employees
These data were collected based on a useful suggestion from an anonymous reviewer.
18 Results are largely consistent when estimating models using less structure on the variance-covariance matrix (i.e.
1
OLS).
119
and the gender of branch manager, two attributes that are time-invariant.1 9 Therefore, a fixed-effects
model is not suitable for this study as both employee gender and branch manager gender would be
dropped from such models.
As aforementioned, the data that I analyzed are longitudinal and include repeat observations
for individuals over time. Therefore, I ran diagnostics to test two key assumptions of these models,
namely that errors are homoscedastic and not auto-correlated across time periods. As expected,
errors in these data are heteroscedastistic, as the variance of the errors varies across individual nonmanagerial employees. Additionally, and as expected, the Woolridge test for autocorrelation in panel
data reveals that there exists first-order auto-correlation, where the errors at time t are correlated
with the errors at time t-1. Thus, longitudinal GEE models accounting for both heteroscedasticity
and first-order autocorrelation were used to estimate the effect of manager gender on gender-based
wage inequality.
4.4
4.4.1
RESULTS
Descriptive Statistics
[INSERT TABLE 2]
Table 2 provides overall means, standard deviations, and correlations for all variables used in
this study. In terms of distribution of employees across branch positions, Table 3 shows that over 75
percent of all employees occupy one of the three lower-level positions of teller, representative or
officer. Consistent with the notion that women are underrepresented in higher-level organizational
positions (Albrecht, Bj6rklund, & Vroman, 2003; Morrison, White, Velsor, & Leadership, 1987;
Powell & Butterfield, 1994), Figure 2 reveals that the proportion of women in the lower-level
19 Theoretically, branch manager gender may vary in cases where a branch is managed by a female manager in one
year and a male manager in a subsequent year (or vice versa), for example. During this time period, however, these
switches in manager gender were very rare (n = 50). Only 3 branches, for a total of 50 individual non-managerial
employees, experienced such an event. Furthermore, the direction of this switch in branch manager gender was not
consistent: 1 branch went from having a female to a male manager and the other 2 branches went from a male to a
female manager. Therefore, branch fixed effects would not allow for estimating parameters for the effect of manager
gender for most cases in this study.
120
positions of teller and representative is considerably higher than in higher-level positions. However,
this observed pattern is independent of manager gender. Therefore, while I am not able to draw any
conclusions regarding the impact of female relative to male managers on this distribution of women
across levels of the organizational hierarchy, 0 the gender composition of jobs is the same for both
male and female managers.
[INSERT FIGURE 2]
[INSERT TABLE 3]
The branch managers comprise approximately 13 percent of all full-time employees in the
retail branches. Each branch is headed by a single manager with sole personnel responsibilities to
oversee all branch employees and operations. As presented in Table 1, the average annual base salary
for managers was approximately $65,000 and exactly half of all managers were female. While Finserv
was committed to generally fostering diversity and providing equal employment opportunities, this
equal representation of women in management is not the result of explicit gender equity initiatives.
Given that the focus of this study is to determine whether the degree of gender inequality among
employees differs based on the gender of their manager, it is important to identify if male and female
managers in this setting differ in any substantive ways other than gender. I found that male and
female managers differed in pay with female managers earning slightly less than male managers on
average (p < 0.05), despite being approximately two years older (p < 0.05) and having nearly three
more years of experience (p < 0.01) on average.
Given the aim of this study, it is necessary to not only compare male and female managers,
but also whether male and female managers oversee comparable branches and individuals. While
female managers run branches that are similar in terms of number of employees and gender
20
While this pattern is evident, this is simply a descriptive pattern as these are post-hire data
which do not provide
insight into the mechanisms leading to this apparent glass ceiling (for a similar discussion of the limitations of posthire data see Fernandez & Weinberg, 1997). For example, not knowing the proportion of women that are in the
consideration set for higher-level positions (i.e. relationship manager) makes it equally plausible that 1) women are
simply not applying to higher level jobs (as has been shown by Barbulescu & Bidwell, 2013, for example) or 2)
managers are showing a preference in selection for male applicants for higher level jobs. In order to isolate the
mechanisms at play in creating these observed patterns it is necessary to examine pre-hire data.
121
composition, female managers head branches with significantly lower branch deposits on average (p
< 0.01). This is consistent with the perspective that even within the same organizational positions,
women are the lower status group (Ridgeway, 1991, 1997) managing less powerful, non-growing
organizational units (Stover, 1994) than their male counterparts. To be sure that these differences
are not accounting for any observed relationship between employee pay and manager gender, all
models control for manager age, manager tenure, and branch deposits.
To compare characteristics of the employees that male and female managers oversee, Table 4
presents key descriptive statistics for all variables of interest for employees by both employee and
branch manager gender. To attribute differences in the degree of gender inequality in less formalized
components of pay to the gender of the manager, it is necessary that male and female managers be
overseeing employees that are similar on key dimensions. Therefore, the goal of this comparison is to
determine whether the employees reporting to male and female managers differ in any substantive
way so that these attributes can be included as controls in the regression models.
On average, female non-managerial employees reporting to female managers do not differ
significantly along any dimension from those reporting to male managers. On the other hand, male
non-managerial employees reporting to female managers earned lower annual base salary and bonus
pay (p < 0.05), are slightly older (approx. 2.5 years, p < 0.05), and are more apt to be married (p <
0.05), on average than male employees reporting to male managers. Given the overall similarities
between employees reporting to male and female managers, any observed difference in employee pay
based on manager gender is unlikely to be driven by characteristics of the employees. But to be sure
this is the case, all models control for each available employee characteristic.
[INSERT TABLE 4]
4.4.2
Overall Effect of Formalization on Gender Pay Inequality
To highlight the shortcomings of previous studies failing to simultaneously account for
formalization of pay and manager gender, the first two sets of models in Table 5 present the average
gender difference in pay for male and female non-managerial employees across organizational
122
positions by type of pay. These models account for formalization of pay by separately predicting less
formalized pay (models la and 2a) and formalized pay (models lb and 2b), but do not factor in
manager gender. Consistent with most existing research on formalization and pay inequality, I find
an overall gender wage gap for less formalized pay, but not formalized pay. While model la
predicting less formalized pay reveals that on average across organizational positions women earn a
base salary that is 21 percent (p < 0.001) less than that of similar male counterparts, model lb
indicates that there is no gender difference among employees in formalized pay, or bonus pay. As
model 2a and 2b reveal, this finding is robust to the inclusion of performance, with evidence of an
even larger gender disparity in formalized pay (model 2a) between male and female employees with
equal performance ratings.
Based on these results, one would conclude that less formalized pay systems ubiquitously
lead to unequal pay for men and women. While not entirely inaccurate, I will show that claims
about the relationship between formalization and gender pay inequality from this oversimplified
analysis lead to incomplete conclusions. In the ensuing discussion, I demonstrate the importance of
simultaneously considering formalization of pay, manager gender, and organizational positions to
better understand the conditions under which less formalized pay systems may instead lead to
equitable pay outcomes.
[INSERT TABLE 5]
4.4.3
Formalization and Male versus Female Manager Impact on Pay
To test the hypotheses set forth in this study, the remaining analyses examine whether the
relationship between formalization and employee pay varies by manager gender or the organizational
position of the non-managerial employee. Models 3a and 3b in Table 5 take manager gender into
account. By including manager gender in the models predicting less formalized and formalized pay, I
am able to determine whether male and female managers use the discretion afforded by less
formalized pay systems differently, particularly in a way that impacts gender pay inequality (Hia,
1b). Then, the models in Table 6 examine this relationship by the organizational position of the non-
123
managerial employees. An examination by organizational position establishes whether the impact of
female managers on employee pay varies across the organizational hierarchy, or the gender-typing of
the position (H2a, 2b).
Main Effects of Manager Gender on Employee Pay. Since both male and female
managers are apt to favor their same-gender employees, hypothesis la and lb proposed that in terms
of less formalized pay, where managers have greater discretion over pay outcomes, employees would
earn more when reporting to a same-, rather than opposite-, gender manager. Consistent with
hypothesis 1b, model 3a in Table 5 shows that, on average, male employees reporting to male
managers earn approximately five percent (p < 0.01) higher base salaries than do male employees
reporting to female managers. On the other hand, the non-significant coefficient of the interaction
between manager female and female in Model 3a reveals that there is no difference in female
employees' base salaries based on their manager's gender. Thus, model 3a in Table 5 provides
support for hypothesis 1b, but not hypothesis la.
To highlight that female managers only impact less formalized components of pay, Model 3b
in Table 5 compares employees' formalized pay based on whether they report to a male versus female
manager. As expected, given that formalization restricts managerial discretion, there is no difference
in formalized pay (i.e. bonus pay) for men or women irrespective of whether they report to a sameversus opposite-gender manager. This is evidenced by the non-significant coefficients of each of the
manager gender variables (main effect and two-way interaction).
Female
Manager Impact on Pay by Organizational Position. Before assessing
whether female managers' impact on employee pay varies by organizational position as predicted by
hypotheses 2a and 2b, the first two sets of models in Table 6 examine the degree of gender pay
inequality within each non-managerial branch position. In terms of less formalized pay, Model la
shows that female non-managerial employees earned lower base salaries across all occupational
positions. As shown by the coefficient of female and the interactions between female and each of the
organizational positions, women earned between four and 13 percent less than did similar men in the
124
same branch position, conditional on all covariates. Similarly, Model 2a reveals that while
performance is significantly related to base salary, controlling for performance does not account for
gender differences in pay. In fact, for the positions of teller, officer, and relationship manager, the
magnitude of the gender pay differences are magnified after accounting for performance. In contrast
to the observed gender differences in less formalized pay, models lb and 2b show that male and
female employees earn comparable bonus pay in each organizational position. In sum, these results
provide evidence that there is overall within-job gender pay inequality in terms of less formalized pay
across all organizational positions, but not in terms of formalized pay.
To test hypotheses 2a and 2b, the last two models in Table 6 (models 3a and 3b) are fully
interacted models estimating the effect of manager gender on less formalized pay and formalized pay,
respectively. To determine whether the observed impact of female managers on gender pay inequality
varies by organizational level, it is necessary to compare the relative pay, in terms of both formalized
and less formalized components, received by male and female employees in the same jobs, reporting
to female versus male managers. These effects are represented by the coefficients of manager gender
(main effect, two-way interactions, and three way interactions).
[INSERT TABLE 6]
Both hypothesis 2a and 2b proposed that the effect of female managers on less formalized
components of employee
pay would be limited to employees in lower-status, female-typed
organizational positions. Because the position of teller is the reference category, the negative and
significant coefficient of the main effect of manager female and the positive and significant coefficient
of the interaction between manager female and female in model 3a provides support for hypothesis
2a and 2b. Specifically, among tellers reporting to female managers women earn base salaries
approximately six percent (p < 0.05) higher, and men earn base salaries over four percent (p <
0.05) lower, than do female and male tellers reporting to male managers. An examination of the
other coefficients including manager female (two-way and three-way interactions) reveals that there
125
is no significant difference in employee pay for those reporting to a female, relative to a male,
manager in any of the other organizational positions.
Model 3b reveals that female managers only compensate employees differently than male
managers in terms of less formalized pay systems, as evidenced by the non-significant coefficients of
all variables including manager gender (main effect, two-way, and three-way interactions). Because
managerial discretion is constrained, there is no difference in formalized pay (i.e. bonus pay) for men
or women in any branch position based on whether they report to a same- versus opposite-gender
manager.
As a result of the impact of female managers on less formalized pay, male and female tellers
reporting to female managers receive nearly equal base salaries whereas there is a seven and one half
percent (p < 0.01) wage differential among tellers reporting to male managers. Therefore, in support
of hypothesis 2a and 2b, these results show that when female managers are afforded discretion via
less formalized pay systems, they close the gender pay gap, but only among employees in lowerstatus, female-typed organizational positions.
The fact that the impact of female managers is limited to tellers helps to explain why
hypothesis la was not supported. Based on models aggregating across organizational positions
(Table 5, model 3a), there was no evidence of female managers paying female employees higher base
salaries than do male managers. Since female managers do not pay female employees more than do
male managers in the other four organizational positions, it is not surprising that the average effect
of manager female was not significant in this pooled model. To fully elucidate the relationship
between manager gender and less formalized components of employee pay, it was necessary also to
account for the organizational positions employees occupy.
In summary, these analyses provide conditional support for the notion that male and female
managers impact employee pay differently when given discretion over pay decision via less formalized
pay systems. Whereas models not accounting for manager gender and organizational position
suggested that less formalized pay systems universally lead to inequitable pay outcomes, the more
126
nuanced approach taken in this study suggests that there are boundary conditions to this claim.
Female managers used the discretion afforded by less formalized pay systems to pay employees in the
lower-level organizational ranks more equally than did male managers. Together these results
highlight the importance of considering both manager gender and organizational positions in
examinations of the relationship between pay formalization and gender inequality.
4.5
DIsCUSSION
Gender differences in earnings are a persistent feature of the U.S. labor market, despite the
increased organizational and policy initiatives aimed at eradicating this form of inequality. Scholars
have largely posited that formalized pay systems that reduce managerial discretion through
unambiguous written rules serve to reduce gender pay inequality (e.g. Elvira & Graham, 2002;
Reskin, 2000), though some recent studies call this into question (e.g. E. Castilla, 2008; Dencker,
2008). Drawing on social identity theory and status characteristics theory, this study developed and
tested hypotheses that the impact of pay formalization for gender pay inequality varies based on the
gender of the organizational decision maker and the organizational position the manager oversees.
Using unique personnel data from 120 branches of a large retail financial services firm, I
demonstrate that manager gender and the organizational position of the employees being assessed do
in fact matter. Specifically, failing to account for these may lead to erroneous, or oversimplified,
claims about the relationship between pay formalization and gender pay inequality. By analyzing the
degree of gender pay inequality across two components of pay, namely base salary, which is less
formalized, and bonus pay, which is highly formalized, I am able to first show that male and female
managers impact gender pay inequality differently. Consistent with existing research, generally
formalization was associated with more equitable pay in this setting, as evidenced by the equal
bonuses awarded to male and female employees irrespective of manager gender. However, contrary to
what most of the literature would expect, I find evidence of greater gender equity in terms of less
formalized pay systems for employees reporting to female managers. Specifically, I find that female
managers use the discretion afforded to them by less formalized pay systems to pay male and female
127
employees more equitably than do male managers. This effect is limited to employees in the lowest
organizational ranks providing conditional support for the perspective that less formalized personnel
practices may result in gender pay equity for employees reporting to female, but not male, managers.
4.5.1
Importance of Considering Characteristics of Manager and Formalization
These findings have several implications for research on the formalization of rewards systems
and gender inequality. First, this study demonstrates the importance of simultaneously considering
characteristics of the decision maker and formalization of organizational reward systems. By taking
manager gender into account, this study helps us move beyond our understanding of whether
formalized pay systems are an effective way to redress workplace inequality, to identifying when this
is most likely to be the case. In terms of gender inequality, most existing research has led to
conclusions that less formalized pay systems, which allow managers more discretion, lead managers
to privilege male employees and devalue female employees (e.g. Elvira & Graham, 2002; Reskin,
2000; Ridgeway, 1997). Implicit in this theory is that all organizational decision makers will use the
discretion afforded by less formalized pay systems uniformly. However, my study finds that, unlike
male managers, when female managers have discretion they advantage their female employees, thus
reducing gender pay inequality. Therefore, this study suggests that the common association between
formalization of pay systems and gender inequality is at least partly due to the historical
overrepresentation of men in decision-making positions. As such, this study offers a corrective to
existing research on formalization of pay and gender inequality by showing that it is necessary to
consider manager gender.
Relatedly, my study directly builds on recent research closely examining the relationship
between formalization and inequality by highlighting an additional condition under which less
formalized practices redress inequality. For example, Dencker (2008) offers a provocative finding that
when organizations are facing pressures for gender equity, less formalized practices empower
managers to react to these pressures and reduce inequality in promotion of higher-level employees.
By introducing manager gender, my study offers a possible extension to Dencker (2008). In his
study, women comprised a nontrivial proportion of managers, ranging from nearly 36 to over 57
128
percent across functions and departments. Thus, it is plausible that female managers, in particular,
are contributing to the female advantage observed during the period where practices were less
formalized, further highlighting the importance of accounting for manager gender.
My study also contributes to the literature on the impact of women in management for
gender inequality by highlighting the importance of accounting for both manager gender and
formalization of reward systems. Specifically, I demonstrate that failing to account for formalization,
which dictates the level of discretion managers have over pay decisions, may lead to incorrect
estimates and conclusions about whether having women in management leads to more equitable pay.
For example, while not accounting for formalization, Penner and colleagues (2012) use withinorganization employment records for a grocery retailer operating under a collective bargaining
agreement. " They find that there is no difference in gender wage inequality among supermarket
workers based on the gender of the manager to whom they report, arguing that like their male
counterparts
female managers devalue work performed by female employees.
However,
since
collective bargaining agreements are commonly associated with more formalized pay systems and
less managerial discretion (c.f. Hirsch, 2012), it is plausible that their finding is at least partly due to
female managers not having sufficient discretion over employee pay to have an impact in this setting.
4.5.2
Importance of Accounting for Context-specific Status Ordering
Second, this study demonstrates that the impact of female managers on gender pay
inequality varies by organizational position, or level of the hierarchy. As members of the lower status
group, female managers face a tension between favoring members of their gender ingroup and
favoring the higher-status outgroup members. Therefore, to understand when female managers are
Penner et al. (2012) makes the claim that managers have control in their research setting; however the retail grocer
they study has several distinct features that cast doubt on the appropriateness of this setting for addressing whether
female managers impact wage inequality. First, as the original paper analyzing these data states, "all non-managerial
employees were covered by collective bargaining agreements" in this setting (Ransom & Oaxaca, 2005: 222).
Therefore, employee wages are likely based on union negotiations severely limiting managerial discretion.
Furthermore, the within job wage ranges for both non-managerial and managerial employees were narrow, with
standard deviations for hourly wages ranging from $0.00 to $1.13. Therefore, while the authors do not find that
female managers reduce gender inequality, it is plausible that this finding may be attributed to particular features of
the setting. For additional details on this setting, see Ransom and Oaxaca (2005).
129
most apt to favor their female employees it is necessary to consider conditions under which we
expect the preference for the ingroup to dominate the preference for the high-status other. In line
with status characteristics theory, which highlights the importance of taking the local status ordering
into account (e.g. Ridgeway & Correll, 2006), this study considers how female managers may be
more
apt
to
reduce
gender
inequities
for lower-status
employees,
or
those in lower-level
organizational positions.
Relatedly, this study offers a refinement to the conclusions in organizational demography
research examining whether the presence of women in management leads to lower levels of gender
inequality on average by highlighting the importance of accounting for the jobs of employees being
assessed. Extant studies have largely found that a greater proportion of women in management leads
to more pay equity among non-managerial employees (e.g. Baron et al., 1991; Cohen & Huffman,
2007; Huffman et al., 2010; Hultin & Szulkin, 1999; Joshi et al., 2006; Kulis, 1997), concluding that
female managers redress inequality. However, these studies are conducted at the organizational level
analyses and aggregate employees across jobs and levels of the organizational hierarchy. As Table 5
shows, not accounting for organizational level at FinServ would similarly lead to an overly simplified
conclusion. While not entirely inaccurate, concluding that female managers redress inequality is
incomplete because once organizational level is taken into account it becomes evident that female
managers only reduce inequality for employees in the lowest-level organizational position of teller.
4.5.3
Limitations and Future Research
Because this study is based on data from a single organization, it is necessary to consider
limitations to the generalizability of results. While it is not possible to definitively rule out, there is
no reason to assume that the managerial practices of this firm are atypical of other large firms (c.f.
Kalleberg, Knoke, Marsden, & Spaeth, 1996). This is especially unlikely given that the analysis is
centered on the population of branches of a Fortune 500 retail bank. Furthermore, the validity of
this critique would require that not only the organization, but also the compensation decisions of
individual male and female managers within the organization, be atypical. Given that the branches
studied are distributed across eight U.S. states and are managed by 156 different branch managers,
130
these branches are parallel organizations with variation in the key independent variable, namely the
gender of the branch manager. Again, as with any case study, it is not possible to discount this
explanation in absolute terms; yet, it is unlikely that the female managers in this setting impact
gender wage inequality differently than would female managers in other settings. To bolster the
generalizability of these findings, future research could replicate the empirical strategy used in this
study across firms. A second limitation stems from constraints resultant from the records maintained
by FinServ. Specifically, I was unable to control for employee education2 2 or job tenure. I address
this by including age, as a proxy for experience, and organization tenure, as a proxy for job tenure.
23
There is also an opportunity for future research to expand on this study by considering other
manager characteristics that may serve as bases for ingroup categorization in examinations of
formalization and inequality. While it is plausible that in- versus outgroup categorizations along
other status distinctions, such as race, lead to similar results as those observed in this study,
assuming these processes operate the same way across different status characteristics may lead to
incorrect conclusions (Joshi et al., 2006; Ridgeway, 2014). Relatedly, my finding that the impact of
female managers on pay varied by organizational position suggests that future research should
simultaneously consider characteristics of the manager and the context-specific status ordering.
These approaches will yield a more complete understanding of the conditions under which providing
managers with discretion via less formalized rewards systems may lead to more equitable outcomes.
Another profitable direction for future research is to further unpack the mechanisms driving
female managers to use the discretion afforded by less formalized pay systems to both pay female
employees more and pay male employees less than do male managers. Existing theories of ingroup
favoritism suggest that actors will advantage their ingroup. The most direct way that female
Education was a self-reported variable not required of FinServ Employees. As a result this field was missing
for
nearly 70% of the employees in my study. Even more importantly for this study, men underreported educational
information such that it is not missing at random. Despite this large amount of missing data, Table Al in the
appendix predicting base salary for tellers (the only position for which there is a manager gender effect) includes
education and reveals results identical to those in the main analyses.
23 During my study window, only 77 out 857 individuals, or 1 in approximately 11 people, were promoted. Therefore,
in the absence of direct job tenure information, organization tenure provides a strong proxy for job tenure at FinServ.
22
131
managers could favor their ingroup is by paying of their female employees more than do male
managers. A second way that female manager could elevate the position of women relative to men is
by paying male employees less than do male managers. An observable difference in employee pay
would result for employees reporting to female versus male managers if female managers engage in
either, or some combination of both, of these compensation strategies. While I find that female
managers do both, and importantly in a manner that leads to more equal pay for employees, these
data do not allow me to isolate the mechanism driving this finding. Thus, to more fully unpack how
female managers compensate employees differently from male managers, a profitable direction for
future research may be to further isolate these mechanisms.
4.5.4
Implications for Organizations
In an ideal scenario, organizational practices and rewards systems would be adopted
uniformly by all managers and in a manner that resulted in equitable outcomes for all employees.
While striving for this ideal should be the underlying objective of organizations seeking gender
equity, in the absence of that, my study suggests we should pay attention to the organizational
practices that are at a manager's disposal and how they may be used differently across managers.
From a practical standpoint, my research warns against blindly assuming that either increasing
formalization, or increasing the presence of women in management, will provide an effective means
for reducing gender pay inequality. Increasing pay formalization may be more effective for reducing
gender pay inequality in settings where women are absent from management or managers oversee
employees in higher-status,
male-typed jobs. Similarly, increasing the presence of women in
management is most apt to lead to gender equitable outcomes in a setting where formalization of
rewards systems is low, thus allowing female managers discretion, and female managers oversee
employees in lower-level, female-typed positions. In order to develop appropriate strategies for
reducing gender pay inequality, organizations must concurrently consider the potential role of both
female managers and level of the employee they oversee.
Similarly, since allowing managers discretion has been posited to provide firms with the
opportunity to enhance performance (Gomez-Mejia & Balkin, 1992), my research points to when
132
allowing discretion may simultaneously yield benefits for the firm and its employees. While
discretion is commonly perceived to exacerbate gender inequality, my findings suggest that this is
not always the case. Rather than favoring women, the female managers in my setting used discretion
to close the gender earnings gap among lower-level employees by paying these male and female
employees more equally. Thus, these findings suggest that less formalized systems allowing
managerial discretion are most apt to advantage the firm without exacerbating gender inequities
when women are in management and overseeing lower-level employees.
133
4.6
FIGURES AND TABLES
FIGURE 1
Branch Positions: Hierarchy and Description of Responsibilities
Non-managerial Positions
Pst
Magra
Rtlaionaship
anager
Ex ccniv.
4
bhi li
( flticer
*
itttpitsn
.
\Ivane. -'I
busiw
s
I
.
os
s
m
ani
m-4ow
iiii
i
I
ot
simet
rv
ItwfIaIitt14
t.*
TelIed rts
i
Iiusn wo
.
stm11
-----
k
.11c+
.1 3 Ii k
Sales
Clerital
Posit ions
Position
FIGURE 2
Percent Female by Job for Non-Managerial Employees, by Manager
Gender, Yearly Average Over 4 Years
. . ..T
*1~
I
*1
T
a
FiIal( Manger
a Male
20%
10%
1 %4
Telltr
Ihepro;SI11ent
ivI
Officvr
xive
Relatgioship
.Nmaager
Branch Position
134
'Managcr
TABLE
1
Mean Annual Base Salary and Bonus Pay by Position for All Retail Branch Employees,
Yearly Average Over 4 Years
Annual Pay (in dollars)
Base Salary
Bonus Pay
Branch Position
Mean
s.d.
Mean
s.d.
As Percentage of
Total Annual
Pay
Teller
24,301.31
3,179.76
378.27
592.71
1.53%
Representative
28,400.35
4,023.26
762.45
904.60
2.61%
Officer
38,635.47
7,550.21
1,746.06
2,716.79
4.32%
Account Executive
48,598.29
6,585.50
3,932.69
6,616.21
7.49%
Relationship Manager
60,159.13
11,678.58
3,062.93
4,104.40
4.84%
Branch Manager
65,342.91
14,002.73
4,446.74
5,099.57
6.37%
135
Variable
1. Annual base salary
2. Annual bonus pay
3. Bonus pay (as % of base pay)
4. Female (%)
5. Age
6. Organizational tenure
7. Non-white
(%)
8. Married (%)
9. Performance
10. Teller (%)
11. Representative
(%)
12. Officer (%)
13.
14.
15.
16.
17.
18.
19.
Executive (%)
Relationship Manager
Manager female (%)
Manager age
Manager tenure
Branch size
Percent female (%)
20. Branch deposits (in
0
(%)
000s)
TABLE 2
Descriptive Statistics and Correlations among Study Variables in Person-Period Data Seta
Mean
s.d.
1
2
3
4
5
6
7
8
9
10
11
12
35,821.81
12,256.55
1,493.75
2,913.68
0.27
0.03
0.05
0.94
0.15
0.67
0.47
-0.26 -0.11 -0.06
36.59
10.05
0.32 -0.03 -0.07
0.14
6.42
6.85
0.22 -0.03 -0.06
0.18
0.55
0.56
0.50
-0.18 -0.06 -0.04
0.06 -0.12 -0.08
0.44
0.50
0.14 -0.02 -0.03
0.02
0.14 -0.06
0.01
2.33
0.54
0.12
0.28
0.30
0.01 -0.08 -0.10
0.06
0.06
0.25
0.43
-0.56 -0.22 -0.22
0.11 -0.15 -0.13
0.14
0.02 -0.11
0.14
0.35
-0.25 -0.10 -0.06
0.12 -0.07
0.00
0.06 -0.06 -0.04 -0.24
0.46
0.50
0.21
0.08
0.13
0.00
0.13
0.13 -0.08 -0.07
0.08 -0.54 -0.38
0.06
0.23
0.26
0.21
0.16 -0.15
0.00 -0.07 -0.09
0.02
0.08 -0.14 -0.10 -0.23
0.08
0.28
0.60
0.16
0.07 -0.19
0.10
0.02 -0.08
0.17
0.00 -0.17 -0.12 -0.28
0.46
0.50
-0.06 -0.04 -0.02
0.04
0.06
0.03 -0.01
0.01 -0.02 -0.01
0.03
0.04
39.99
8.74
-0.01 -0.02 -0.03
0.08
0.06
0.06
0.01
0.03 -0.01 -0.04
0.06
0.02
2.32
6.28
0.00 -0.04 -0.05
0.05
0.07
0.18 -0.01
0.07 -0.01
0.01
0.02 -0.01
9.05
4.48
0.00
0.05
0.07 -0.07 -0.10 -0.10
0.06
0.06
0.05 -0.01
0.00 -0.02
0.67
0.21
-0.19 -0.08 -0.06
0.44
0.08
0.02
0.02
0.03 -0.08
0.06
0.08
0.01
87,230.02 68,923.26
0.03
0.01
0.00 -0.10
0.00
0.11
0.12
0.07
0.07
0.07 -0.03 -0.06
*n = 2,319, except for performance where n = 1,821. Coefficients with an absolute value equal to or larger than .04 are significant at p < .05.
13
14
15
16
17
18
19
-0.07
-0.03
0.01
0.01
-0.16
0.07
0.16
0.03
-0.08
0.10
-0.19
0.20
-0.13
0.19
-0.07
-0.04
0.11
0.19
-0.15
0.42
-0.23
-0.07
-0.03
-0.03
-0.04
0.06
-0.06
-0.04
TABLE 3
Distribution of Employees across Branch Positions,
Yearly Average Over 4 Yearsab
Organizational Position
Percentage of Total
Employees
Teller
23.85
Representative
13.83
Officer
38.29
(276)
(160)
Account Executive
(443)
4.24
Relationship Manager
(49)
6.31
Branch Manager
(73)
13.48
(156)
a
Parentheses indicate number of employees
bThere
are a total of 104 employees (including 77 non-managerial
employees) who change postion within the branch during the years
under study. Therefore, the combined total of employees (n=1,157,
including managers) is greater than the number of unique
employees (n = 1,053).
137
TABLE 4
Basic Descriptive Statistics for Non-managerial Employee-level Variables,
by Employee Gender and Manager Gender, Yearly Average Over 4 Years
Female Managers
Male Managers
Mean
s.d.
Mean
s.d.
Annual base salary
33,422.14
9,185.85
Annual bonus pay
1,281.57
2,290.27
1,270.22
2,367.02
10.53
Variables
t-testa
Female Employees
Age
33,752.16 10,429.10
37.80
10.09
37.32
Tenure
7.45
6.50
7.11
6.96
Performance
2.33
52.64
2.33
53.89
Married (%)
42.86
49.52
46.09
49.88
Non-white (%)
Promotion (%)
56.25
49.64
58.68
49.27
10.27
30.38
10.60
30.80
persons
29 9b
335 b
person-years
672
755
Annual base salary
38,705.04
14,787.99
41,449.06 15,173.16
Annual bonus pay
1,534.42
2,362.82
2,224.90
4,546.41
Age
36.13
10.00
33.57
8.37
Tenure
4.87
6.97
4.57
6.57
Performance
2.28
0.54
2.35
54.40
Married (%)
48.18
50.05
38.88
48.81
Non-white (%)
52.15
50.00
51.10
50.05
Promotion (%)
10.89
10.02
30.07
persons
140
179b
person-years
303
409
a
*
Male Employees
indicates whether differences in values for male and female managers are statistically
significant based on two-sided t-tests
b There are a total of 56 employees who worked for both
a male and a female manager
during the study period (i.e. switchers). Therefore, the combined total of employees
reporting to male and female managers is greater than the number of unique employees
by 56 as these people are counted for years reporting to male managers and years
reporting to female managers (n = 953).
** p
0.05
0.01
p
0.001
*
p
138
TABLE 5
Generalized Estimation Equation Regression Models Predicting Log Annual Base Salary and Log Annual Bonus Pay of Non-Managerial
Model la
Variables
(Base Salary)
Female
Model lb
Less Formalized Pay Formalized Pay
Model 2a
Model 2b'
Less Formalized Pay Formalized Pay
(Bonus Pay)
(Base Salary)
-0.208***
0.173
(0.020)
(0.221)
Model 3ac
Model 3bc
Less Formalized Pay Formalized Pay
(Bonus Pay)
(Base Salary)
-0.223***
0.114
-0.227***
0.068
(0.022)
(0.220)
(0.024)
-0.050**
(0.281)
0.144
(0.017)
0.013
(0.324)
0.112
(0.021)
(0.384)
Manager Female
Manager Female x Female
(Bonus Pay)
Individual -level controls
Performance
0.018***
1.343***
0.017***
1.340***
Weekly hours
0.007
0.097
(0.005)
0.008
(0.161)
0.013
(0.005)
0.008
(0.161)
0.014
(0.005)
Employee age
0.010***
(0.099)
-0.033**
(0.006)
0.009***
(0.096)
-0.039***
(0.006)
0.009***
(0.096)
-0.040***
Employee tenure
0.015***
(0.012)
0.060***
(0.001)
0.015***
(0.011)
0.028
(0.001)
0.014***
(0.011)
0.027
Married
(0.002)
0.074***
(0.018)
0.495*
(0.002)
0.063**
(0.017)
0.405*
(0.002)
0.063**
(0.017)
0.406*
Non-White
(0.019)
-0.074***
(0.196)
-0.206
(0.021)
-0.071***
(0.194)
-0.176
(0.021)
-0.072***
(0.194)
-0.172
(0.019)
0.027***
(0.196)
Promoted
1.353***
(0.020)
0.026***
(0.193)
1.176***
(0.020)
0.025***
(0.193)
1.175***
(0.007)
(0.348)
(0.008)
(0.351)
(0.008)
(0.350)
(0.001)
Manager -level controls
Manager age
Manager tenure
-0.000
-0.010
-0.001
-0.013
0.000
-0.014
(0.000)
0.001
(0.011)
-0.016
(0.000)
0.001
(0.011)
-0.021
(0.001)
0.000
(0.011)
-0.021
(0.001)
(0.015)
(0.001)
(0.015)
(0.001)
(0.015)
Branch -level controls
Branch size
Percent female (%)
0.001
0.066**
0.001
0.100***
0.001
0.102***
(0.001)
(0.024)
-0.041
-0.969
(0.001)
-0.049
(0.024)
-1.145*
(0.001)
-0.055*
(0.025)
-1.171*
(0.023)
(0.514)
(0.027)
(0.519)
(0.027)
(0.520)
Log of Branch deposits
(in
000s)
Constant
Observations
Number of GEID
Time Periods
Wald Chi2
DF
-0.023
0.021
-0.016
-0.238
-0.019
-0.216
(0.017)
10.728***
(0.192)
9.348*
(0.018)
10.699***
(0.193)
6.581
(0.018)
10.733***
(0.194)
6.387
(0.296)
(4.655)
(0.335)
(4.563)
(0.334)
(4.578)
2,139
2,139
897
897
1,821
742
1,821
742
1,821
742
1,821
742
4
4
4
4
4
4
674.33***
88.21***
557.05***
183.18***
577.45***
184.60***
19
19
20
20
22
22
SAll models include controls for scheduled weekly hours, employee age, employee tenure, married, non-white, promoted, manager age, manager tenure, number of
non-managerial employees in branch, percent female non-managerial employees in branch, branch deposits, and fixed effects for the state where the branch is
located. The omitted category for non-white is "white'; for married is 'single'; for job title is 'teller"; for female is 'male"; and for manager female is "manager
male".
b Standard errors in parentheses
c These models include a control for performance. Because performance is missing for 115 employees, these models are based on 742 employees.
* p
0.05
** p
** p
5
0.01
0.001
139
TABLE 6
Generalized Estimation Equation Regression Models Predicting Log Annual Base Salary and Log Annual Bonus Pay of Non-Managerial
Employees by Organizational Positiona,
Model la
Variables
Less Formalized Pay Formalized Pay
(Base Salary)
Female
Model lb
Model 2ac
b
Model 2b'
Less Formalized Pay Formalized Pay
(Bonus Pay)
(Base Salary)
-0.038*
0.588
(0.017)
(0.413)
(Base Salary)
-0.040*
0.173
-0.075**
-0.191
(0.020)
(0.442)
(0.024)
(0.613)
Manager Female x Female
Officer
Executive
Relationship Manager
Rep x Female
Officer x Female
Exec x Female
Relation x Female
Model 3bc
(Bonus Pay)
Manager Female
Representative
Model 3a'
Less Formalized Pay Formalized Pay
(Bonus Pay)
-0.045*
-0.254
(0.023)
(0.723)
0.063*
0.792
(0.027)
(0.828)
0.142***
0.796
0.130***
0.499
0.127***
-0.220
(0.017)
(0.575)
(0.018)
(0.607)
(0.024)
(0.852)
0.341***
2.017***
0.338***
1.395**
0.322***
1.154
(0.016)
(0.413)
(0.017)
(0.441)
(0.022)
(0.614)
0.549***
1.245*
0.549***
0.231
0.527***
0.185
(0.025)
(0.598)
(0.027)
(0.591)
(0.032)
(0.780)
0.731***
1.974***
0.725***
1.558**
0.724***
1.368
(0.026)
(0.546)
(0.028)
(0.559)
(0.035)
(0.744)
-0.048*
0.277
-0.043*
0.457
-0.013
1.363
(0.019)
(0.648)
(0.021)
(0.678)
(0.028)
(0.950)
-0.043*
-0.383
-0.054**
-0.106
-0.023
0.279
(0.018)
(0.480)
(0.020)
(0.506)
(0.026)
(0.701)
-0.050
1.517
-0.058
2.133*
-0.058
2.143
(0.035)
(0.860)
(0.037)
(0.836)
(0.049)
(1.108)
-0.094**
-0.725
-0.127**
-0.003
-0.082
0.588
(0.035)
(0.767)
(0.040)
(0.812)
(0.049)
(1.033)
Rep x Mgr Female
Officer x Mgr Female
Exec x Mgr Female
Relation x Mgr Female
Rep x Female x Mgr Female
Officer x Female x Mgr Female
Exec x Female x Mgr Female
Relation x Female x Mgr Female
-0.004
1.474
(0.033)
(1.193)
0.019
0.523
(0.026)
(0.846)
0.023
0.007
(0.045)
(1.159)
-0.020
0.300
(0.044)
(1.065)
-0.049
-1.903
(0.038)
(1.340)
-0.054
-0.875
(0.033)
(0.985)
0.003
0.116
(0.068)
(1.662)
-0.098
-1.463
(0.064)
(1.684)
Individual -level controls
Performance
Weekly hours
Employee age
Employee tenure
Married
Non-White
Promoted
0.014**
1.244***
0.013**
1.244***
(0.005)
(0.160)
(0.005)
(0.160)
0.014***
0.090
0.013**
0.026
0.013**
0.025
(0.004)
(0.096)
(0.004)
(0.094)
(0.004)
(0.094)
0.006***
-0.045***
0.006***
-0.048***
(0.011)
0.006***
(0.001)
-0.047***
(0.001)
(0.011)
(0.001)
(0.011)
0.007***
0.046**
0.007***
0.018
0.007***
0.019
(0.001)
(0.017)
(0.001)
(0.017)
(0.001)
(0.017)
0.027*
0.475*
0.024*
0.431*
0.025*
0.436*
(0.010)
(0.193)
(0.011)
(0.194)
(0.011)
(0.194)
-0.029**
-0.024
-0.024*
-0.082
-0.025*
-0.089
(0.010)
(0.191)
(0.011)
(0.191)
(0.011)
(0.191)
0.026***
1.373***
0.026***
1.197***
0.026***
1.192***
(0.007)
(0.350)
(0.007)
(0.352)
(0.007)
(0.352)
140
TABLE 6 (continued)
Generalized Estimation Equation Regression Models Predicting Log Annual Base Salary and Log Annual Bonus Pay of Non-Managerial
Employees by Organizational Positiona,
Model
Variables
la
Model
lb
Less Formalized Pay Formalized Pay
(Base Salary)
(Bonus Pay)
Model 2a
b
Model 2bc
Less Formalized Pay Formalized Pay
(Base Salary)
(Bonus Pay)
Model 3a
Model 3bc
Less Formalized Pay Formalized Pay
(Base Salary)
(Bonus Pay)
Manager -level controls
Manager age
Manager tenure
0.000
-0.012
0.000
-0.016
0.001
-0.018
(0.000)
(0.010)
(0.000)
(0.011)
(0.000)
(0.011)
0.000
-0.017
0.000
-0.021
0.000
-0.021
(0.001)
(0.015)
(0.001)
(0.014)
(0.001)
(0.015)
Branch -level controls
Branch size
Percent female (%)
Log of Branch deposits (in
Constant
Observations
Number of GEID
Time Periods
Wald Chi2
DF
000s)
0.000
0.057*
-0.000
0.091***
-0.000
0.093***
(0.001)
(0.023)
(0.001)
(0.024)
(0.001)
(0.024)
-0.045*
-0.835-
-0.046*
-0.937
-0.045*
-0.947
(0.019)
(0.501)
(0.021)
(0.511)
(0.021)
(0.513)
-0.007
0.121
-0.005
-0.157
-0.006
-0.125
(0.009)
(0.186)
(0.010)
(0.189)
(0.010)
(0.190)
10.613***
6.904
10.552***
5.713
10.582***
5.613
(0.198)
(4.520)
(0.220)
(4.486)
(0.220)
(4.510)
2,139
2,139
1,821
1,821
1,821
1,821
897
897
742
742
742
742
4
4
4
4
4
4
3899.55***
167.48***
3121.15***
240.39***
3142.89***
245.87***
27
27
28
28
38
38
a All
models include controls for scheduled weekly hours, employee age, employee tenure, married, non-white, promoted, manager age, manager tenure, number of nonb Standard errors in parentheses
c These models include a control for performance. Because performance is missing for 115 employees, these models are based on 742 employees.
*p!
0.05
0.01
*
p
0.001
141
Table Al
Generalized Estimation Equation Regression Model Predicting Log Annual Base Salary for Tellers
Model
Variables
a, b
la
Less Formalized Pay (Base Salary)
Female
-0.104*
Manager Female
-0.131**
Manager Female x Female
0.236***
(0.05)
(0.04)
(0.06)
Individual -level controls
0.004
Performance
(0.01)
0.063
Bachelors Degree
(0.04)
Weekly hours
-0.031*
Employee age
-0.001
(0.01)
(0.00)
Employee tenure
0.031***
Married
0.107**
(0.01)
(0.04)
0.03
Non-White
(0.05)
Manager -level controls
Manager age
-0.001
Manager tenure
-0.006
(0.00)
(0.00)
Branch -level controls
Branch size
-0.003
Percent female (%)
-0.024
(0.00)
(0.08)
0.026
Log of Branch deposits (in 000s)
(0.03)
Constant
8.575***
(0.72)
Observations
69
Number of GEID
33
4
Time Periods
Wald Chi2
104.88***
DF
21
models include controls for scheduled performance, education, weekly hours, employee age, employee tenure,
married, non-white, manager age,
manager tenure, number of non-managerial employees in branch, percent female non-managerial employees in branch, branch deposits, and fixed
a All
effects for the state where the branch is located. The omitted category for non-white is "white"; for married is "single"; for job title is "teller"; for
female is "male"; and for manager female is "manager male".
b Standard errors in parentheses
*
* p
0.05
** p
0.01
p
0.001
142
5
5.1
CONCLUSION
CONTRIBUTIONS
This dissertation enhances our understanding of the effect of gender in evaluative processes
by carefully elucidating when and how assessments of similar men and women vary systematically.
Whereas prior research has largely focused on documenting that gender is often taken into account
in evaluations, this dissertation shifts our focus to identifying the conditions under which this is the
case and explicating the evaluative mechanisms perpetuating these observed gender differences. In
addition to the specific contributions of the independent research papers, as detailed in each of the
preceding chapters, this dissertation also makes four broader contributions to research on gender
inequality and evaluative processes.
First, by more carefully detailing when and how gender affects evaluative processes, this
dissertation extends existing theory stemming from lab-based studies in the status characteristics
tradition. These studies have shown that even when evaluators have performance information they
sometimes base assessments on gender (e.g., Foschi, 1989). Despite documenting the persistence of
gender in evaluations, these studies have neither identified scope conditions for when we might
expect this to be the case nor uncovered the mechanisms leading to the inclusion of gender in the
presence of performance information. Thus, this dissertation helps to fill this gap.
Second, this dissertation demonstrates evaluators may use gender not only to deal with a
problem of missing performance information, but also as a means for solving other types of
information problems. Specifically, as detailed in Chapter 2, I show that evaluators only exhibit a
male preference when the success of their evaluations is based on approval from a third-party
contact. Similarly, in Chapter 3, we find that evaluators are most apt to use gender as a sorting
heuristic when faced with increased search costs. These findings are consistent with the general
argument that the inclusion of gender in evaluations is a means for remedying information
inefficiencies, though existing research has focused on the use of gender to address missing
performance or quality information. These results provide evidence that evaluators also incorporate
143
gender as a means for solving other types of information problems, namely to sort through too much
information and to satisfy the unknown preferences or expectations of others. Though this behavior
can still be classified as biased, since I do not find evidence that men are objectively outperforming
women in my settings, my results suggest that it is not only overt bias that leads to unequal
evaluations of men and women, but more subtle processes are also often at play.
Third, this dissertation
helps advance a burgeoning body of literature showing how
evaluators' assessments are influenced by their relevant audience.
I find that the evaluator's
propensity to incorporate gender is also a function of whether they are the end-user who is
evaluating for their own consumption, or acting as a mediator who is accountable to an audience. As
detailed in Chapter 2, when an evaluator moves from a position as the end user to a position as a
mediator they are more apt to base their assessments on the gender of their network contacts. I
argue that this shift in how gender affects evaluations is because in the latter case the evaluator's
assessment incorporates what they anticipate to be the preferences or expectations of their own
evaluating audience, in this case their third-party contacts. This notion that evaluators' decisions are
influenced by a relevant audience, or third-parties, is not entirely novel. For example, economists
attribute
labor
market
inequality
partly
to
customer
discrimination,
where
managers
disproportionately hire employees who they believe their customers prefer (e.g. Neumark, Bank, and
Van Nort, 1996). In terms of gender specifically, expectations of customer or client preferences for
men over women has been posited to contribute to a male advantage in hiring (Fernandez-Mateo
and King, 2011) and in promotion decisions (Beckman and Phillips, 2005). However, these studies
have been unable to account for personal gender beliefs, or preferences, making it unclear to what
extent these findings are actually attributable to the presence of third-party actors. By comparing
how the same evaluators incorporate gender in their assessments both when they rely on approval
from a third-party and when they do not, this dissertation more precisely identifies that the effect of
gender in evaluative processes is related to the presence of third-parties.
Fourth, this dissertation highlights the importance of accounting for characteristics of the
evaluator more generally, and gender in particular. Specifically, Chapter 4 provides some initial
144
evidence that male and female evaluators may differ in their propensity to factor a candidate's
gender into their assessments and enact organizational processes differently. I find evidence of less
gender pay inequality among lower-level employees reporting to a female, as opposed to a male,
manager. Thus, this dissertation suggests that who the evaluator is matters, such that it may be
incorrect to assume that gender affects evaluations uniformly across
different
categories of
evaluators.
5.2
FUTURE RESEARCH
There are several fruitful paths for future research in further addressing the unifying research
question from these projects. While the preceding chapters include recommendations for specific
research directions stemming from each study, here I focus on two broader research directions
following from this dissertation and my plans to advance these.
First, this dissertation points to the importance of taking the perspective of the evaluator to
identify whether the propensity to exhibit gender bias in evaluations differs based on characteristics
of evaluator. With the exception of Chapter 4, this dissertation centers on identifying the extent to
which there are differences in the evaluations received by similar men and women. While inherently
these gender differences emerge because evaluators incorporate gender in their assessments, to
sharpen our understanding of the precise mechanisms driving these differences, future research
should more directly examine how these processes differ across evaluators, or categories of
evaluators. For example, the core findings in Chapter 2 - that anticipatory third-party biases result
in female entrepreneurs receiving fewer third-party referrals when they are in male-dominated
occupations - raises the question of whether this anticipatory bias is reinforced by both male and
female referrers (e.g. the evaluators). Using data on the characteristics of the evaluator, I plan to
examine whether the propensity to base decisions about resource sharing on gender varies by the
resource-holder's own gender, the gender of their third-party, or the interaction of these.
Second, though this dissertation suggests that receiving lower assessments leads to a female
disadvantage, these studies are not able to causally link observed gender differences in evaluations to
145
substantive outcomes. As a result, the ultimate implications of gender differences in evaluations for
gender inequality remain unclear. For example, the findings in Chapter 2 on referrals among
entrepreneurs raise the following question: How much lower are revenues for female entrepreneurs as
a result of receiving fewer third-party referrals? Similarly, the results in Chapter 3, that female
investment professionals receive less attention, raises the following question: Does receiving less
attention impact the propensity of women sharing recommendations in the future? The logical next
step in uncovering the effect of gender in evaluative processes is to identify how observed gender
differences in assessments translate to substantive differences in ultimate outcomes for men and
women. To begin to address this gap, I plan to use data on revenue generated from referrals to
assess the economic implications of receiving fewer third-party referrals for women. Linking gender
differences in evaluations to inequality in tangible outcomes for men and women will help clarify the
extent to which evaluative processes actually contribute to the persistence of gender inequality.
Despite gains in the relative position of women, inequality persists. In order to devise
solutions to remediate these inequities, it is necessary to understand the precise sources of these
gender differences. By examining when and how evaluation processes, in particular, contribute to
unequal outcomes for similar men and women, this dissertation begins to make headway in this
direction and elucidates paths for deepening our understanding of this timely and important issue.
146
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