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RACE AND SEX STRATIFICATION BEHIND THE ORGANIZATIONAL CHART; AN INVESTIGATION OF EMPLOYEES' INFORMAL NETWORKS IN A WORK ORGANIZATION
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of
Philosophy in the Graduate School of The Ohio State University
By
Gail M. McGuire, M.A.
*****
The Ohio State University 1997
Dissertation Committee: Approved by Professor Barbara F. Reskin, Adviser
Professor Lowell L. Hargens Adviser Professor Patricia Yancey Martin Department of Sociology DMI Number: 9801745
UMI Microform 9801745 Copyright 1997, by UMI Company. All rights reserved.
This microform edition is protected against unauthorized copying under Title 17, United States Code.
UMI 300 North Zeeb Road Ann Arbor, MI 48103 Copyright by Gail M. McGuire 1997 ABSTRACT
Scholars have documented that informal networks influence workers' entry into work organizations. For example, studies have found that most workers acquire their jobs through informal contacts rather than through formal search methods. However, we have little knowledge about employees' informal networks inside work organizations. As a result, we lack an understanding of the informal mechanisms through which resources, power, and influence are distributed at work.
I address this void by examining two aspects of networks at work-employees' likelihood of having high-status network members and the amount of work-related help employees receive from their network members. Using ordinary least squares regression. I examine the structural and personal determinants of these two aspects of employees' networks. This study is based upon survey data from 1150 individuals employed at a large financial services corporation.
I uncovered a system of informal stratification in which power and resources were unequally distributed that was based, in part, upon employees' race and sex.
Women and people of color were less likely than men and whites to have high-status network members and to receive work-related help from their network members. These race and sex differences in networks were mainly due to the fact that white
women and people of color occupied organizational positions that decreased their
opportunity to interact with and to attract other employees. For example, women and
people of color were less likely than men and whites to occupy high-ranking positions,
to exercise job power, and to hold jobs that encouraged inter-group communication.
Thus, the formal system of stratification influenced with whom employees had the
opportunity to form network relationships.
I extended our understanding of tie strength by examining how the strength of employees' network relationships influenced the work-related help that employees received from their network members. I found that employees gained more work-
related help from their strong ties than their weak ties. These results differ from job- search research, which has highlighted the importance of weak ties, because providing help at work requires more effort and motivation than does providing information on job openings. These results suggest that the "bridging" benefit of weak ties is relevant for obtaining resources that do not require much effort or risk on the part of the provider, whereas strong ties are critical for employees' ability to perform their jobs and thus success at work.
Ill Dedicated to my family for all of their love and laughter
IV ACKNOWLEDGMENTS
I must first thank the person who has provided support, guidance, and
inspiration throughout my graduate career, Barbara Reskin, my advisor. Barbara
edited probably thousands of pages of my work. She spent countless number of hours
helping me to tell a good story without sacrificing empirical rigor. I thank Barbara for
her patience with my frantic phone calls, such as when the computer ate Chapter 4, as
well as supporting me through the difficult events that occurred in my life outside of
graduate school. Barbara's integrity, insight, work ethic, and commitment to social justice all served to inspire me throughout my graduate career. I also thank Barbara
for taking the difficult role of pushing me to always do better.
This dissertation benefited in numerous ways by the critical and creative eye of
Patricia Yancey Martin. Pat helped me to form my research question, to develop my
survey instrument, and to see the "big picture. " Her comments were essential in breathing life into the numbers. Pat provided constant support and encouragement throughout the dissertation process. I thank Pat for always believing in me and in this project. I thank Lowell Hargens for his patience in teaching me statistics and for always having his door open. I also thank Lowell for his critical and candid feedback (e.g.,
"Bad!") at every stage of my dissertation research.
I thank my cherished friend, Cynthia Pelak, for her sharp editing skills, her insightful questions, and her willingness to read a chapter at a moment's notice. She was also an outstanding envelope stuffer, along with her teammate, Jordan! Cynthia was an incredible source of support and nurmrance throughout my dissertation process.
She helped me keep some semblance of emotional and mental health through our phone calls and coffee-house outings. Cynthia’s words to me, "Just tell me what you need," were such a comfort. Thank-you, Cynthia!!!
I thank Michael McCrary for his insightful and thorough comments on several chapters, his feedback on my early work, and his continual offers of assistance.
Michael was another lucky friend who got roped into stuffing over 1700 surveys into envelopes. Mike was always there to help me find a cite or answer a statistics question. Thank-you, Mike, for your generosity and friendship.
This dissertation also benefited from the sole member of my dissertation support group—Jo Reger. My dissertation meetings with Jo helped me to set goals, to acknowledge my accomplishments, and to be patient! Jo also read early proposal drafts, survey drafts, and helped to stuff envelopes. Most importantly, I thank Jo for her friendship, her healing words, and for making me laugh throughout this dissertation process.
VI I thank my buddy, Ann Marie Flores, for her years of support of and confidence in me. I don't know what I would have done without our weekly runs and venting sessions! I also thank Ann for editing chapters and helping me with the survey. Thank-you, Ann, for calling to check-up on me and for your attempts to keep me social!
I thank Wayne Santoro for the many hours of labor that he provided at the beginning of this dissertation. Wayne read numerous proposal drafts, survey drafts, and early papers. He stuffed envelopes, pasted labels, and ran numerous errands.
Wayne also helped me to clarify my research questions and theoretical firamework. I thank Wayne for encouraging me, believing in me, and in some instances carrying me.
I also thank him for helping to make graduate school fun and for helping me to become a better scholar through our co-authorship.
I offer my deep appreciation to Mark W. from USF. Mark hired me for my position at USF, helped me gain corporate approval for my study, helped organize my pilot survey, and reviewed the survey instrument. I also thank Mark for his enthusiasm for this project and his encouragement of me.
Many other people contributed their skills and time to this project as well.
Patricia Drentea and Shana Pribesh edited chapters and Kim Davies reviewed early papers. I thank Herminia Ibarra for sharing her survey instrument and Karen Campbell for reviewing my survey instrument. Dick Haller and Joan Amfield both provided essential computer assistance. Thanks to Andrew Newman for helping me with initial conceptualizations. I thank Bob Kaufman for always being available to answer a
vii statistical question. Special thanks to Katherine Brown for her comradeship and for providing me with a model of feminist scholarship and activism.
I thank my family—Dad, Mom, Colleen, Sharon, John, Gary, Missy, Rachael,
Zachary, Jacob—for all of the love and support they gave me through graduate school.
In particular, I thank my two sisters, Colleen and Sharon, for nurmring me and for keeping me grounded. I thank Dad for filling our household with political discussions about social problems and for cultivating my passion for understanding and changing social inequality. Finally, I thank my mother for encouraging me to be curious about the world, to ask why, and to take joy in the process of discovery.
VllI VITA
January 9, 1966...... Bom - Norwich, Connecticut
1991...... M.A. Sociology, University of Illinois—
Champaign-Urbana
1989 - 1991...... Graduate Teaching and Research Associate,
University of Illinois-Champaign-Urbana
1991 - present...... Graduate Teaching and Research Associate,
The Ohio State University
PUBLICATIONS
Gail M. McGuire and Barbara F. Reskin. 1993. "Authority Hierarchies at
Work: The Impacts of Race and Sex." Gender and Society 7(4):487-506.
FIELDS OF STUDY
Major Field: Sociology
Minor Fields: Work, Stratification, Gender, and Race
ix TABLE OF CONTENTS
Pagç
Abstract...... ii
Dedication ...... iv
Acknowledgments ...... v
Vita...... ix
List of Tables ...... xii
Chapters:
1. Introduction ...... 1
The Likelihood of Having High-Status Network Members ...... 3 Work-Related Help From Network Members ...... 4 Contributions of Study ...... 4 Conceptualizing Networks ...... 8 Theoretical Perspectives for Understanding Employees' Networks 9 Network Theory...... 9 Social Exchange Theory...... II Ranter's Structural-Numerical Perspective ...... 12
2. Data and Methods ...... 17
An Overview of U.S. Finance ...... 17 The Physical Structure of U.S. Finance...... 18 The Social Structure of U.S. Finance ...... 19 Getting In ...... 24 Data Collection Procedures ...... 26 Unit of Analysis ...... 27 Sampling and Survey Strategy ...... 27 Measurement of Dependent Variables ...... 31 Measurement of Independent Variables ...... 35 Dyads Within Employees' Networks...... 43 Method of Analysis ...... •...... 44
3. A Profile of USF Workers and Their Network Members: Differences by Race and Sex ...... 50 High-Status Network Members and Work-Related Help from Network Members ...... 51 Potential Determinants of Having High-Status Network Members and of Receiving Help from Network Members ...... 53 Discussion of Results ...... 66
4. Multivariate Results for Employees' Likelihood of Having High-Status Network Members ...... 71 Multivariate Results ...... 89 Discussion of Results ...... 100
5. Multivariate Results for Work-Related Help From Network Members ...... I ll Multivariate Results ...... 124 Discussion of Results ...... 136
6. Conclusion ...... 143 Contributions of Research ...... 143 Limitations of Study ...... 151 Future Research...... 157
References...... 161
Appendix: Cover Letters and Survey Instrument ...... 172
XI LIST OF TABLES
Table Page
2.1 Percentage Distributions for White Men, White Women, Men of Color, and Women of Color in USF's Home Office, in the Sample Pool, and among USF Respondents (total number of employees in parentheses)...... 47
2.2 Frequency Distribution for Respondents' Number of Network Members...... 48
2.3 Sampling Weights for White Men, White Women, Men of Color, and Women of Color ...... 49
3.1 ANOVA Results by Employees' Race and Sex ...... 69
4.1 Correlations Between Independent and Dependent Variables ...... 107
4.2 OLS Regression of Likelihood that Employees have High-Status Network Members (N=986) ...... 109
5.1 OLS Regression of Amount of Work-Related Help from Network Members (N=986) ...... 141
XII CHAPTER 1
INTRODUCTION
"Somewhere behind the formal organizational chart at Indsco was another, shadow structure in which dramas of power were played out" (Kanter 1977:164).
The study of informal networks can be traced back to early anthropological studies that sought to understand the "web" and "interweaving" of social relationships
(for reviews see Wellman 1983:158; Scott 1991:5). However, the concept of an informal network was mainly used as a metaphor in these early anthropological studies (Berkowitz 1982; Scott 1991). The development of the sociogram by Moreno was one of the first attempts to "systematize this metaphor into an analytic diagram"
(Scott 1991:10).
Work scholars have also had a long-standing interest in informal networks.
For instance, early labor studies highlighted how workers’ informal interactions affected the work process and the distribution of power in the workplace (Lipset,
Trow, and Coleman 1956; Roethlisberger and Dickson 1964; Burawoy 1979). The famous Hawthorne studies, for example, used sociograms to diagram the structure of workers’ informal relationships (see Scott 1991:18). Contemporary scholarship has focused on how informal networks help individuals to find jobs. For instance, most individuals find their jobs through informal contacts and individuals who use informal
job contacts acquire higher-paying and higher-status jobs than individuals who use
formal search methods, such as job postings (Lin, Ensel, and Vaughn 1981; Lin and
Dumin 1986; DeGraaf and Flap 1988; Marsden and Campbell 1990; Boxman,
DeGraaf, and Flap 1991; Granovetter 1995; Drentea 1997)/
While scholars have documented how informal networks influence workers’
entry into work organizations, we have little knowledge about employees’ informal
networks inside work organizations. As a result, we do not understand the informal
processes through which resources, power, and influence are distributed at work.
This study addresses this void by examining two related aspects of employees’
networks. First, I investigate the factors that affect employees’ likelihood of having
high-status network members. Second, I examine the factors that influence the
amount of work-related help that employees receive from their network members.
Using survey data from 1150 individuals employed at a large financial services
corporation, I examine how employees’ personal characteristics as well as aspects of
employees’ jobs and work settings influence these network outcomes.
‘ Contemporary network research has been dominated by studies of community and neighborhood networks, however (for example, see Laumann 1973; Fischer 1982; Wellman and Wortley 1990; Campbell and Lee 1992). The Likelihood of Having High-Status Network Members
Examining the factors that affect employees’ likelihood of having high-status network members is important for understanding the distribution of power and influence behind the formal organizational chart/ According to Campbell, Marsden, and Hurlbert (1986:98), "Access to high status others Is an important part of the information and influence mechanisms of networks as resources." This is because high-status employees control people, decisions, information, budgets, and corporate culture. High-status network members also interact with a wider range of workers than low-status network members and thus have more diverse resources at their disposal (Lin 1982:133; Lin and Dum 1986:376).
Much research highlights the benefits of being connected to high-status employees. For example, individuals with high-status contacts find more prestigious jobs than those with low-status contacts (Lin, Ensel, and Vaughn 1981:398; Lin and
Dumin 1986:366; DeGraaf and Flap 1988:463). Case smdies indicate that managers’ advancement depends upon the power of their network alliances (Kanter 1977a: 181;
Jackall 1988:45). High-status network members can advocate for employees in controversial situations and help employees bypass the corporate hierarchy, allowing them to complete their work quickly. Having high-status contacts also provides employees with "reflected power," which is power that is obtained by being associated with influential individuals. As Kanter (1977a: 182) explained, "Much of
^ I use the term "status" to refer to network members’ prestige, power, and control over resources (Campbell, Marsden, and Hurlbert 1986:98-99; Ibarra 1995:685). I elaborate on this definition in Chapter 2. the power of relatively junior people comes not from their own resources but from the
’credit’ extended to them because there appears to be a more powerful set of resources in the distance," that is, their sponsors. In sum, having high-status network members seems to be pivotal in employees’ acquisition of power and help on the job and thus for their work performance and advancement.
Work-Related Help From Network Members
Informal networks channel important resources, such as information, influence, and support, according to research on community networks (Fischer 1982;
Wellman and Wortley 1990).^ However, there are few empirical accounts of what workers’ network members provide to them (Cook and Whitmeyer 1992; Nohria
1992). As a result, we lack an understanding of the informal mechanisms through which resources are distributed at work. I examine one type of resource that workers receive from their network members—work-related help. This analysis is one step towards understanding the extent to which structural opportunities and personal characteristics influence the work-related benefits that workers receive from their network members.
Contributions of Study
This research addresses two critical issues in the sociology of work and organizations. First, this study provides a rare account of race and sex stratification within an organization’s informal social structure. Stratification scholars have
^ For example, Wellman and Wortley (1990:562-563) found that community network members provided each other with emotional aid, small services, large services, financial aid, and companionship. investigated how race and sex affect employees’ formal positions in organizations
(Bielby and Baron 1984; Mueller and Parcel 1986; McGuire and Reskin 1993), yet few studies have explored how employees’ race and sex affect the characteristics of their informal networks (for a theoretical overview, see Ibarra 1993). According to
Granovetter (1995:177), "... understanding the complex network processes by which inequities are produced and reproduced, and how such reproduction can be discouraged, has not had . . . priority on the research agendas of labor scholars . . .
In my view, this is the single research gap most in need of being filled."
I investigate several related questions regarding networks and stratification. I examine whether white women and people of color are less likely than white men to have high-status network members and to receive work-related help fi-om their network members. In addition, I investigate the factors that explain race and sex differences in these two network outcomes. In particular, I explore if the mechanisms for acquiring high-status network members and work-related help from network members depend upon employees’ race and sex. For example, I examine if high- ranking positions are as helpful women’s networks as they are to men’s networks.
Exploring how employees’ race and sex influence their informal networks could help us to understand stratification mechanisms at the meso, or middle, level of work organizations. Second, this study examines how tie strength between network members affects the work-related resources that employees obtain from their network members/ Research has highlighted the advantages of using weak ties to find jobs
(Granovetter 1974; Lin and Dumin 1986). Weak ties are more likely than strong ties to help individuals find jobs because weak ties connect individuals to people outside of their immediate social circle who possess novel information (Granovetter
1982:112). However, little research has examined the effects of tie strength between workers on the amount of resources that workers obtain from their network members.
In addition to these two main contributions, this research provides an exploratory examination of how "social capital" is distributed in the workplace and what determines employees’ acquisition of social capital. Social capital refers to the resources accrued through social relations that enable individuals to achieve some instrumental end (Coleman 1988:98).^ Informal networks can provide workers with social capital in that they help individuals to acquire resources, such as information and influence, that would be difficult to obtain on their own (Campbell, Marsden, and
Hurlbert 1986; Coleman 1988; Burt 1995).® Social scientists developed the concept
Tie strength reflects the level of intimacy in a relationship. Individuals who are weakly tied to each other are not emotionally close and interact infrequently (e.g., acquaintances). People with strong ties, in contrast, share trusting and close relationships (e.g., family members).
® For an earlier usage of social capital see Loury (1977:176).
® Scholars recognize that social capital is a public, as well as a private, good. For example, a community that is composed of close-knit ties (e.g., those that interact frequently) may have less crime than a community composed of detached individuals (see Coleman 1988:116). of social capital in reaction to the individualistic focus of human capital, which refers
to individual characteristics that influence productivity (Coleman 1988:98,100; Burt
1995:2)/ In contrast to human capital, social capital assumes that workers’
achievements depend upon whom they know as well as upon what they know. The
concept of social capital is useful in helping us to think about the types of networks
that are most beneficial to workers. However, our understanding of the production
and use of social capital in the workplace is far from complete. Conceptualizations of
social capital are vague and do not clearly identify the criteria for social capital.
Scholars refer to so many types of social phenomena as social capital that the concept
lacks analytic precision. For example, Coleman (1988:105-109) claims that the safety
produced by strong community ties as well as the personal attention that parents
provide to their children are types of social capital. While scholars seem to agree that
social capital is a product of social relationships, there is little clarity on the types of
relationships that produce or limit social capital.
This study is a preliminary step towards better understanding the acquisition of
social capital at work. I assume that the network outcomes I examine are two types
of social capital that workers can acquire. High-status network members are potential
sources of social capital because high-status employees influence corporate decisions and have access to many valuable resources. High-status network members may provide employees with resources that they would have difficulty obtaining on their
’ Some scholars argue that social capital can provide an employee with human capital, can enhance the power of his/her existing human capital, and can eliminate the need for human capital (Coleman 1988:110; Burt 1995:2). own. Network members who provide employees with work-related help also constitute a type of social capital in that the resources and services they provide may help employees to obtain other work rewards, such as promotions and important assignments. Admittedly, a comprehensive analysis of social capital in the workplace would need longitudinal data to determine how different characteristics of informal networks (e.g., size, range, status) influence various work rewards. While this study lacks such data, it suggests factors that influence employees’ social capital at work.
Conceptualizing Networks
My conceptualization of networks draws from social exchange theory, which defines a relation as an actual exchange of services or resources between people
(Cook 1982:178; for other approaches see McCallister and Fischer 1983:79). Social interactions constitute a network when they involve a pattern of exchanges.
According to Cook (1982:178), "Various network theorists . . . have commented on the natural affinity between exchange theory and the analysis of social networks" because of exchange theorists’ concrete concepmalization of networks. This approach focuses on patterns in people’s behavior, rather than on social psychological constmcts, such as beliefs (Wellman 1983:162; Marsden 1990:436).
One of the challenges of defining networks is distinguishing informal from formal networks. Formal networks are mandated by a person or an organization and the individuals in them must interact to complete some goal. Task forces and committees are examples of formal networks (Ibarra 1993:58). Informal networks, in contrast, are not officially established by organizations and "involve more
8 discretionary patterns of interaction, where the content of relations may be work related, social, or a combination of both" (Ibarra 1993:58). Informal networks often have multiple purposes and their boundaries are less clear than formal networks. In fact, they often overlap with formal networks. For example, two employees may be assigned to work together on a project by their manager, but they may develop an informal relationship in addition to their formal one. They may talk about issues unrelated to their project and socialize with each other outside of work. Thus, informal ties often develop out of formal ties. In sum, I conceptualize informal networks as patterned social interactions between employees that are not mandated by an organization, but can and often do include formal ties.
Theoretical Perspectives for Understanding Employees’ Networks
Network Theory
Network theory is a general perspective to understand social structure, rather than a predictive theory with specific propositions (Wellman 1983 :158; Emirbayer and
Goodwin 1994:1414).* Nevertheless, network theory provides a conceptual framework for understanding workers’ informal interactions. Network scholars adopt a stmcturalist perspective rather than a normative perspective to understand informal interactions.^ A structuralist perspective focuses on how an individual’s position in a social system provides him/her with various opportunities for action, but also places
* The theory underlying network research is not well developed, although network scholars have created sophisticated techniques to analyze data.
’ Normative explanations focus on the norms and attitudes adopted by an individual through his/her socialization to explain his/her behavior (Wellman 1982:W; 1983:162). constraints on his/her behavior. Network scholars are particularly interested in explaining the structural arrangements that enhance and diminish individuals’ access to resources (Wellman 1983:163). Network theorists build upon the Simmelian notion that the form or structure of relationships affects the resources and services that network members provide to employees (Wellman 1983:159).
There are at least five factors that influence with whom employees form network relationships and the resources that employees receive from their network members, according to network theorists (Wellman 1983; Campbell, Marsden, and
Hurlbert 1986; Lin and Dumin 1986; Wellman and Wortley 1990; Campbell and Lee
1992). First, employees’ organizational location influences employees’ opportunity to interact with other employees and thus affects the characteristics of their network members. For example, it is easier to develop network ties in job settings that encourage employees to interact with each other than in settings where employees’ interactions are restricted. Second, employees need sufficient time to interact with other employees in order to develop informal relationships with them. The more time employees spend with each other, the more likely they are to develop trusting relationships. Trust is important to establish in informal relationships because they lack formal contracts to guide their transactions (Blau 1964:97). Third, employees need to possess resources and credibility to attract potential network members. Social exchange theory, which I describe below, suggests that workers seek to maximize the resources they obtain from their network members and thus form ties to individuals who have something of value to exchange with them. Fourth, the strength of a
10 network relationship (i.e., how close it is) should influence the amount and type of resources that employees obtain from their network members. Scholars have found that weak ties provide network members with novel information, while strong ties provide individuals with social support (Wellman and Wortley 1990; Granovetter
1995). Fifth, thephysical proximity of a network member should affect how accessible he/she is to an employee and thus the frequency with which he/she provides help to an employee. Neighbors, for example, often exchange services because of their physical convenience (Wellman and Wortley 1990:570).
Social Exchange Theory
Social exchange theory assumes that people seek to maximize their rewards and minimize their costs (Blau 1964; Cook 1982). Social exchange theorists predict that employees form ties to individuals who will benefit them, now or in the future.
For example, the principles of social exchange state that if I help you solve a work- related problem, I expect you to return the favor when I am in need of help (Blau
1964:4). Furthermore, if a worker receives a valuable good from someone, then he/she is obliged to return a good that is equivalent in value (Homans 1958).'°
Social exchange differs from economic exchange in that social obligations and trust guide the social exchange process rather than legal contracts (Blau 1964:97)."
‘° Social exchanges can involve unequal exchanges of resources and services because individuals possess different amounts of power and resources. A person with few alternatives to an exchange relationship is less likely to maximize their rewards from a given interaction than a person with many alternatives (Blau 1964:99).
" Network scholars argue that trust is more easily achieved in homophilious relationships (e.g., same-race and same-sex) than in non-homophilious relationships
LI Social exchange theory provides a framework for interpreting some of the
determinants of employees’ likelihood of having high-status network members and of
receiving work-related help from their network members. This theory indicates that
employees’ bargaining power should be critical in determining their likelihood of
having high-status network members and of receiving help from their network
members. For example, social exchange theorists would predict that employees who
have access to corporate resources have network members who are higher in status than employees with no access to corporate resources because the former have something of value to exchange with high-status em ployees.T hus, high-status employees have an incentive to interact with employees who have access to corporate resources.
Ranter’s Structural-Numerical Perspective
I draw upon Kanter’s (1977a; 1977b) structural-numerical perspective to understand how employees’ sex and race may affect their likelihood of having high- stams network members and the amount of work-related help they receive from their network members. Kanter was one of the first scholars to theorize the role of gender in organizations. Like network theorists, Kanter adopted a structural approach to understand workers’ informal behaviors. However, in contrast to network theorists.
(Ibarra 1993). Homophilious ties tend to be stronger than non-homophilious ties, and therefore more trusting.
For example, a high-status employee in Ibarra’s (1997:97) study stated, "I develop the network contacts I need to get the job done, period. If I don’t need them, I don’t talk to them." This quote suggests that high-status employees form ties to workers who can potentially benefit them.
12 Kanter offers a framework for understanding how and why the networks of white men, white women, men of color, and women of color may differ. While many studies have challenged Kanter’s findings (for reviews see Martin [1985] and Zimmer
[1988]), little research has examined her hypotheses in regards to race and sex differences in employees’ informal networks.
Kanter offers two possible explanations for race and sex differences in informal networks. The first explanation suggests that the relationship between employees’ race, sex, and access to beneficial networks is due to employees’ organizational characteristics, rather than their race or sex. Race and sex differences in organizational location affect with whom employees’ interact and thus influence with whom they form network relationships. For instance, Kanter (1977a:53) found that men’s jobs provided them with more opportunities to communicate with workers across the company than women’s jobs. As a result, men’s jobs provided them with more opportunities to meet other workers and to display their skills to potential network members. In sum, Kanter suggests that employees’ race and sex influence their job characteristics and organizational location which, in turn, determine the types of network relationships they form.
Kanter’s second explanation, in contrast, suggests that there are conditions under which employees’ race and sex directly affect the characteristics of their informal networks. Kanter argued that workers’ informal interactions are shaped by their numerical representation in a work setting. When a group comprises a very small proportion of the employees in an organization (approximately 15 percent).
13 members of that group are treated as tokens, according to Kanter (1977a; 1977b).
While tokens meet the formal requirements for organizational entry, they do not have the auxiliary traits, such as being white or male, of organizational insiders (Zimmer
1988:65). Tokens’ small numbers put them at a disadvantage in a number of ways.
First, tokens’ behavior is often interpreted through the use of stereotypes. For instance, Ely (1995:613-615) found that women in male-dominated law firms were more likely than women in sex-integrated law firms to attribute differences between men and women to gender-role stereotypes. Second, tokens are more carefully scrutinized than other workers and their accomplishments tend to be dismissed. For instance, women in male-dominated law firms rated women’s attributes less favorably than women in sex-integrated firms (Ely 1995:613-615; also see Gutek and Cohen
1987:105). One Black manager in Fernandez’s (1981:62) study explained, "I cannot afford to be average or to meet the minimum requirements for a position. It’s almost mandatory that I am from the right school with a little higher degree and be blessed with the favoritism of my boss." Third, tokens are excluded from informal networks in the workplace. For example, Kanter (1977a:227) found that women were often not invited to join important "informal pre-meeting meetings." In addition, 61 percent of the managers that Fernandez (1981:53,88) surveyed agreed that male managers often excluded female managers from their networks and 36 percent of the managers agreed that white managers often excluded Black managers from their informal networks.
14 Majority-group members may exclude tokens from their informal networks because they fear that they will associated with tokens’ negative characteristics if they include tokens in their networks.
Research has challenged Kanter’s token thesis by arguing that power differences between race-sex groups persist despite numerical equivalence (Martin
1985; Zimmer 1988). Groups who experience disadvantages because of their race or sex status are "social minorities," according Zimmer (1988:72). Even when social minorities are numerically well-represented in work organizations, they face a societal-wide system of inequality that ranks them as inferior, relative to white men.
According to Martin (1985:317), "... unless explicitly counteracted, expectations which advantage males and disadvantage females will be imported into groups and will shape interpersonal exchange." This suggests that even when white women and people of color constitute large percentages of a firm’s workforce, they may be excluded, and receive few benefits, from informal networks because they are viewed as undesirable network members.'^ In sum, Kanter’s structural-numerical perspective, and the reactions against it, suggest several possible mechanisms through which employees’ sex and race affect their likelihood of having high-status network members and of receiving help from their network members—their token status, their organizational position, and their race and sex status. Thus, research suggests that
Reviews of the token research conclude that being a numerical token is more detrimental to women in male-dominated settings than to men in female-dominated settings (Martin 1985:329; Zimmer 1988; 69).
15 race and sex may directly affect, indirectly affect, or may moderate the effects of other factors on employees’ likelihood of having high-status network members and of receiving help from their network members.
In conclusion, this research draws upon several interrelated perspectives to understand the factors that affect employees’ likelihood of having high-status network members and the amount of work help that employees receive from their network members. This study does not test these theories, but uses them as a guide to identify potential determinants of and to explain the effects of different factors on employees’ likelihood of having high-status network members and of receiving help from their network members.
16 CHAPTER 2
DATA AND METHODS
This chapter provides an overview of the company I studied and describes how its physical and social structure may affect employees’ opportunities to form informal ties with each other. I also describe my entry into the company and how my employment there influenced this study. Finally, I discuss data collection procedures, measures, and methods of data analysis.
An Overview of U.S. Finance
I studied workers employed at the home-office of a large financial services company, which I call U.S. Finance (USF). USF began as a modest business in the
1920s and has grown into a company with annual revenues over 30 billion dollars.
The company employs over 20,000 individuals and has branches throughout the
United States as well as several other nations. It ranks in the top ten among those companies that provide similar services and products (Encyclopedia of American
Industries 1994). ‘
‘ Outside sources rate USF as "superior" in terms of its leadership position in its market, franchise value, capital value, and name recognition. In addition, business analysts predict that the company will continue to improve its earnings and sustain its leadership position in the market in the fumre (1996 BEST business reports).
17 USF is similar to other large corporations in terms of the broad strucmral changes it has experienced in the last several years, including downsizing and outsourcing. Like other companies in the 1990s, USF moved to team-oriented management and work. Due to its displacement of hundreds of workers, the company has also created outplacement services for its employees.
The Physical Structure of U.S. Finance
Although the home office occupies several large buildings, the majority of employees (61 percent) are located in one building. Despite its large size (on average, there are 117 workers per floor), employees have opportunities to interact in many small settings. Some floors have snack areas with tables and vending machines and all floors have at least one conference room. The company complex contains several restaurants, and benches and tables are located throughout the complex. The company also has a large cafeteria in which employees eat, socialize, and hold meetings as well as an employee lounge, a meditation room, a physical fitness room, a craft and game room, an activities center, and a library.-
The company’s floors are organized by functional area. For instance, most sales workers are on one floor while human resource employees occupy other floors.
This physical structure most likely encourages workers to form informal ties to employees within their functional area since these are the people with whom they likely interact the most.
■ This institutionalization of benefits to elicit worker cooperation and loyalty characterizes large, bureaucratic corporations (Edwards 1979:142).
18 The Social Structure of U.S. Finance
In addition to its physical structure, a company’s formal employment policies
could influence employees’ opportunities to form informal ties at work. USF’s
employment policies have a "hands-off" approach that stress individual responsibility.
For example. USF does not put employees in touch with potential mentors or offer
training or workshops on building informal networks, although it has a handbook with general suggestions on how to find a mentor.^ My informal discussions with employees in human resources indicate that USF does not offer training on informal
networks because it does not want to be legally liable for employees’ informal work relationships.
USF conducts periodic surveys of its workers that provide clues about its
informal work climate. The last poll, conducted in 1992. included over 12,000 respondents. According to this poll, only 23 percent of employees thought that managers at USF were rewarded for developing their employees and only 26 percent of employees reported that their manager held periodic career discussions with them.
The company’s hands-off policies and supervisors’ low involvement in employee development suggests that employees may turn to their informal network members for
’ I know of no research that compares the employee-development programs (i.e.. efforts that enhance employees’ career and personal skills) across corporations.
19 help on the job/ The employee poll also indicates that USF’s climate is conducive to the formation of informal networks. Eighty percent of USF employees also said that the people with whom they worked cooperated to get the job done.^
The representation of women and people of color within USF provides some general information about employees’ opportunities to form same-race and same-sex ties and indicates which groups were numerical tokens at USF. Among USF employees at the home office, 59 percent were women and 15 percent were people of color. Specifically, 48 percent of USF employees were white women, 37 percent were white men, 11 percent were women of color, and 4 percent were men of color.
Thus, white employees may have had more opportunities than people of color to form same-race network ties and women may have had more opportunities than men to form same-sex network ties. The implications of these race and sex distributions depend upon the effect of the representation of women and people of color in employees’ networks on the status of their network members and the help they receive
^ The poll results did not have a comparison between USF employees and non-USF employees on supervisors’ involvement.
’ The poll also found that a majority of USF employees were satisfied with their jobs, co-workers, supervisors, and training. For example. 68 percent of USF employees were satisfied with their jobs and 58 percent said they were satisfied with the training they received for their present job. USF produces reports in which it compares its employee poll results with those from their industry counterparts and large corporations in different industries. USF employees were less satisfied with their supervisors and their jobs than their industry and non-industry counterparts. In addition. USF employees were less satisfied with their training than other employees in the same industry.
20 from their network members. These findings also indicate that men and women of color were numerical tokens at USF. According to Kanter, minorities’ low representation at USF should lead them to be excluded from informal networks.
The race and sex composition of occupations is important for understanding employees’ informal networks because it should influence with whom employees interact and the resources available to employees.® I examined the composition of women and minorities in several occupations, relative to their overall composition in the home office. Women and people of color were underrepresented among managers, an occupation that provides access to high-status employees and control over corporate resources. For example, 39 percent of managers at USF were women and 7.5 percent of managers were people of color. Compared to their overall representations in the home office, women and people of color were overrepresented among clerical workers, an occupation with little prestige and resource power.
Eighty percent of clerical and office workers were women and 24 percent of clerical workers were people of color. However, women were overrepresented among professional workers-65 percent of all professionals were women. While 15 percent of all home-office employees were people of color, 12 percent of all professional workers were people of color.
® I examined the composition of women and people of color within occupational categories defined by the Equal Employment Opportunity Commission (EEOC). USF provides the EEOC with annual reports on the race and sex composition of its workforce within eight broad occupational categories.
21 In sum, compared to their representation in the home office, people of color were underrepresented in two occupational categories-managers and professionals— that typically provide workers with opportunities to interact with others (especially high-status others) and to control corporate resources. In addition, women were underrepresented among managers at USF. White women and people of color were overrepresented in the low-prestige occupation of clerical work. The occupations in which white women and people of color were overrepresented may have limited their opportunities to develop ties to other workers, particularly high-status workers.
The Equal Employment Opportunity (EEC) officer at the company also helped me evaluate USF’s informal work climate for white women and minorities.^ This officer is in charge of efforts to diversify the company and to ensure that the company meets EEOC requirements. According to the EEO officer, USF has made efforts to create equitable work arrangements, but the company is not on the forefront of equity issues. This officer also reported that USF was slower to implement diversity training programs than other large corporations.“ While USF’s leaders officially support diversity initiatives, the EEO officer said that they have not been "visible" in their efforts to promote diversity. The CEO has made few diversity directives and has imposed no penalties for not following the diversity requirements established by
’ This information should be viewed with caution given that it is based on only one informant.
* Diversity training seeks to make employees sensitive to cultural and social differences between people.
92 the EEO department. In addition, the implementation of diversity efforts has been left to individual department heads, who may vary in their commitment to a diversified workforce.
Most employees at USF have participated in some type of diversity activity.^
In addition, the majority of employees believe that USF is making progress in its diversity efforts, according to the employee poll. Fifty-three percent of employees reported that the company had made progress in providing opportunities for minorities and 52 percent agreed that opportunities for advancement were equally available to all employees regardless of their gender, cultural, or ethnic background. In addition. 70 percent of employees agreed that they were valued for what they could contribute regardless of their gender, cultural, or ethnic background.'® However, most of
USF's diversity efforts have been symbolic. For instance, the EEO office’s guide to help managers increase the diversity of their workforce offered no suggestions to managers on how to recruit white women and people of color. Managers would automatically "turn off" or reject the guide if they saw references to race and sex, according to the EEO officer. While I was employed at USF, I served on a task force whose mission was to develop initiatives that would increase the number of female and minority sales agents, one of the most lucrative jobs at USF. The task
® The 1992 employee poll found that 80 percent of employees had participated in some type of diversity activity—typically diversity awareness training.
The employee poll findings were not reported separately for different race and sex groups.
23 force was given limited funds and time to complete their goals and was dismantled without specific plans to implement its recommendations. In sum, the company’s diversity initiatives and the race and sex composition of USF employees suggest that women and people of color may have informal networks that contain fewer high-status network members and bring them fewer resources than those of men and whites.
Getting In
My association with USF began while I was taking a graduate course on gender and corporations that required students to interview employees from a large corporation. My entry into USF illustrates how networks provide people with resources that would be difficult for them to obtain on their own. A colleague’s boyfriend. Jason, worked at USF as a marketing intern." Jason introduced me to a marketing manager, who introduced me to a manager in human resources. Mike.
While I was interviewing Mike for my class project, he suggested I apply for an internship in his department. I applied for the job and subsequently worked as a research consultant in the human resources department at USF from February 1994 through June 1995.
In addition to providing me with a job in the company, those initial interviews introduced me to the company’s climate and highlighted the importance of informal networks at USF. During my employment, I conducted a number of qualitative studies that facilitated my dissertation research. In one study. I interviewed 25
" I use pseudonyms to refer to the individuals who helped me obtain employment at USF for the purposes of confidentiality.
24 employees who had participated in a pilot mentor program. This research allowed me to speak with a range of employees, from entry-level employees to executives, who provided me with insights into the ways in which informal networks varied across organizational rank. I conducted telephone interviews with six companies outside of
USF and one government agency about their mentor programs.*- Those interviews highlighted how USF’s problems in recruiting and retaining minority employees were similar to other companies. In my final project as an intern. I interviewed 80 sales agents and sales managers across the country about their experiences with informal mentoring. This project demonstrated how important informal networks are for obtaining sales positions.
Because I was somewhat of an insider (in fact. I was offered a full-time job), I obtained detailed information on the company (e.g.. EEOC data and company surveys) and developed social contacts who helped convince the senior vice president of human resources that my project merited corporate sponsorship.'^ Furthermore, my observations, informal interviews, and weekly conversations with employees assisted in the construction of the survey instrument and increased my confidence in the validity of my statistical findings.
1 interviewed representatives from each company’s human resource department. My interviewees suggested that it is not uncommon for companies to exchange Information on their developmental programs.
The company sponsored my dissertation by including a letter of support in the survey packet and paying for about one-third of the survey expenses (e.g.. copying and mailing).
25 Data Collection Procedures
This study used a mail survey to obtain data on employees’ network members.
Surveys are the most common method used by researchers to obtain data on network
members (Marsden 1990:440). A survey was the most appropriate method for this
study because I was interested in demographic and behavioral characteristics of
network members and respondents. Such characteristics can be readily obtained from
a survey. Surveys are also effective at retrieving information on the structural
properties of employees’ networks (e.g., network size). A large-scale survey was
necessary to be able to systematically compare the network characteristics of white
men. white women, men of color, and women of color.
Following past research, the survey began with a name generator to elicit
employees' network members (Marsden 1987; Burt 1992; Ibarra 1995). The survey
asked respondents to think of employees at USF who had "made an effort to give
[them] job. career, or personal help" (see Appendix). In order to minimize
respondents’ recall bias. I asked about the help that employees received from network
members in the last year. The survey stated that network members could include
people from various parts of the company, co-workers, supervisors, or subordinates.
It also indicated that network members could be employees that respondents saw occasionally or daily. Respondents indicated whether or not they had received different types of help from each network member and provided descriptive
information on each network member. Ideally, the survey instrument would have allowed respondents to list as many network members as they had. but space
26 limitations prevented this possibility. The survey allowed respondents to list up to
eight network members, which is more than other major surveys. For example, the
General Social Survey allows respondents to list up to five network members (see
Marsden 1987).
Unit of Analysis
The unit of analysis in this study is the "ego network, " which refers to the
network members of sampled employees. These data are called ego network data
because they were obtained from one focal person in a network (Burt 1992). Ego
network data include only direct ties and thus exclude indirect ties (i.e., people an
employee knows through others). Ego network data can be analyzed with conventional statistical methods and collected with random sampling procedures, so
the results can be generalized to a larger population (Burt 1982:90; Marsden
1990:440).
Sampling and Survey Strategy
I based the format of the survey, in part, after Ibarra's survey (1992). I also drew upon survey instruments from Brass (1985). Campbell (1988). USF. and other work-related surveys.
27 I pretested the survey on a dozen USF employees in the summer of 1995.I organized the pretest as a focus group, in which employees discussed and critiqued the survey in detail. The completion time for the pretest survey ranged from 15 to 25 minutes. The pretest helped me to identify appropriate language and to assess the level of difficulty of the survey.
In addition to the survey, employees received a cover letter from me and a letter from the Senior Vice President of Human Resources (see Appendix). The cover letter stressed that participation in the study was voluntary and that USF would not have access to the data or know who returned their survey. The Vice President’s letter requested that employees complete the survey and also stated that participation in the research was voluntary. To increase the survey response rate, I offered employees the chance to win a $50 gift certificate to an upscale restaurant if they completed and returned the survey. Respondents mailed surveys back to the Ohio
State University to increase their confidence that USF would not have access to the data.
I mailed surveys to employees’ work addresses through the company mail system for two reasons. First. I expected that respondents’ recall would be better at work than at home because the survey was about employees’ work experiences.
Second. I expected that employees would be more inclined to complete the survey on company time, for which they would be paid, than on personal time. However, there
The survey instrument for this study was approved by the Ohio State University human subjects review board in the spring of 1995.
28 were potential drawbacks to having employees complete surveys at work. Employees may have lacked privacy when completing their survey and thus may not have answered the questions honestly. Some employees might have been embarrassed if they had no or few network members, while others might have been reluctant to admit how much help they received from others. In the first scenario, respondents may have overestimated the amount of help they received from their network members. In the second scenario, employees may have underestimated the number of network members who helped them. Another potential drawback to sending surveys to employees’ work addresses is that employees may have been concerned that they were being singled out, since surveys were not sent to all employees, and as a result refused to complete the survey. In fact, I received several phone calls from employees who received the survey and were concerned that only people of color were being studied.’^ In addition, the response rate among people of color was lower than that among whites. For example. 71 percent of white women, 67 percent of white men. 60 percent of men of color, and 58 percent of women of color returned the survey.'^ If minority respondents were significantly different from minority non respondents on key determinants of the dependent variables then my results would need to be interpreted with caution.’’
A few of the callers said that all of their non-white friends had received a survey, but most of their white co-workers had not.
The response rate for each race-sex group for the first round of surveys was as follows; 56 percent of white women. 48 percent of white men. 39 percent of women of color, and 37 percent of men of color.
” I will investigate this possibility in future analyses.
29 I distributed surveys to a stratified random sample of 1,756 full-time
employees in October 1995. I excluded contract workers, part-time employees,
independent sales agents, and subsidiary employees from the sample. I oversampled
people of color so that I could conduct statistical comparisons across race.‘*
Assuming that I would have a 50 percent response rate, I chose sample sizes for each
group that would allow me to conduct race-sex analyses. The target sample consisted
of 500 white men, 500 white women. 256 men of color (the entire population), and
500 women of color. Thus, men of color made up 15 percent of the target sample,
and each other group made up approximately 28 percent of the target sample. A
human resources employee selected a random sample of workers based on these
criteria using a random-numbers computer program. The company has a research
staff that includes many PhDs who are accustomed to drawing random samples. One
week after employees received the survey, they received a reminder postcard. I
mailed a second round of surveys to the approximately 850 non-respondents one week
after the reminder postcard.
[Table 2.1 here]
Table 2.1 presents the percentage distributions of white men, white women,
men of color, and women of color in USF's home office, in the target sample, and in
the final sample of respondents. The response rate for the first round of surveys was
51 percent (904). Approximately 252 surveys were returned in the second round, for
I should have sent surveys to all managers who were women of color, but through an oversight failed to do so.
30 a total of 1156 surveys. Once I excluded the six surveys that were unusable, the final response rate was 65 percent (1150). Of the final sample, 31 percent are white women, 30 percent are white men, 26 percent are women of color, and 13 percent are men of color. More generally. 57 percent of the sample are women and 39 percent are people of color.
Measurement of Dependent Variables
The Status o f Employees’ Network Members
Few studies have examined the status of employees’ network members so there are no standard measures of this concept. Scholars have measured network members' status with either their organizational rank or occupational prestige scores (Duncan
SEI scores). For example, Ibarra (1995:685) used the average organizational rank of respondents’ network members to measure the status of their network members.
Other scholars have used the average occupational prestige score, the highest occupational prestige score, or the range of occupational prestige scores of respondents’ network members to distinguish high-status from low-status network members (Lin and Duman 1986:372; Campbell 1988:187).
To measure the status of employees’ network members. I drew upon the thesis that status reflects resource power and organizational position (Lin, Ensel. and
Vaughn 1981:395; Campbell. Marsden. and Hurlbert 1986:98). My measure is composed of four indicators: organizational rank, ability to make major purchases, access to confidential information, and ability to make final decisions. The item for organizational rank had five response categories: non-supervisor, supervisor.
31 manager, director, or officer. The survey questions for the other three indicators were: "Does this person make major purchases ($10,000 or more) without getting permission from higher up?" "Does this person have access to sensitive or confidential company information?" "Can this person make final decisions that significantly change [USF] products, programs, or services?" (see Appendix).
Respondents indicated "yes," "no," or "don’t know" for each network member.'" I created a scale by summing the scores of respondents’ network members for each indicator. Because the indicators had different standard deviations, I standardized each one and then summed the four standardized indicators for each respondent.-'
The reliability coefficient for this scale is .81. which indicates that the scale is quite internally consistent (DeVellis 1991). The scale ranges from -4.25 to 16.47. has a mean of -.09. and a standard deviation of 3.04.”
This item assumes equal differences between organizational rank categories—each organizational rank is a one unit distance from the ones below and above it. For example, a non-supervisor counts as 1, a supervisor counts as 2. and a manager counts as 3. Thus, all else being equal, a network with 3 non-supervisors is equivalent to a network with 1 manager.
One limitation of this measure is that three of the indicators (final decisions, purchasing power, and confidential information) reflect the presence or absence of control and not the range of control that a network member has. However. I assume that respondents would not have been able to provide more specific information on their network members’ control over resources.
The standard deviations and means (in parentheses) for each of the indicators were: 6.16 for organizational rank (10.46). 1.69 for makes final decisions (1.53), 1.23 for makes major purchases (.68). and 2.25 for access to confidential information (2.65).
” The inter-item correlations ranged from .41 to .62 and the mean inter-item correlation was .52.
32 I used an additive measure of network members’ status rather than a relative one (e.g., percent of high status members in a network) because a relative measure could have been easily influenced by the status of one network member. For
instance, a relative measure would have given a respondent with one network member, who was a manager, a higher score on the dependent variable than a respondent with eight network members, of whom four were managers. The relative score for the first network would have been 100 percent, while the relative score for the second network would have been 50 percent. Thus, a relative measure would not be as valid as an additive measure for this study.
My measure of network members’ status improves upon past measures in two significant ways. First, it includes more information on the types of resources (e.g.. money and information) controlled by each network member. Social capital is more than simply having network ties—it is having network ties to employees who control resources (Lin. Ensel, and Vaughn 1981:395). Second, this measure is a more precise measure of power than are SEI scores. Duncan SEI scores reflect the prestige of occupations, not the resources that incumbents in those occupations control.
The Work Help that Employees Received from Network Members
Previous research offered little guidance for creating a measure of work help from network members. Most research has examined the help that community network members provide to people rather than the work-related help that employees give to each other. My measure expands upon Ibarra’s (1992) indicators of work- related help from network members. She asked respondents with whom they
33 discussed what was going on in the firm, whom they approached when they had a work-related problem, and who helped them with important decisions (Ibarra
1992:431).
I created a work-help scale composed of six indicators: help with a work- related problem, help in getting around bureaucratic hurdles, help in meeting employees in other areas of the company, help in meeting high-level employees (such as managers), help in getting a new position or promotion, and help in getting one’s work recognized. For each item, respondents replied either "yes," the network member provided this help, or "no," the network member did not provide this help, for each of their network members.^ I coded "yes" as I and "no" as 0. I then summed network members’ scores for each indicator. Because the indicators had different standard deviations. I standardized each one and summed the six standardized indicators for each respondent.T he alpha coefficient for this scale is
.81. The range for work help is -7.49 to 18.93, the mean is .12, and the standard deviation is 4.17, indicating a fair amount of dispersion around the mean.
One benefit of this work-help scale is that it provides an overall measure of work-related help provided by network members. Given the lack of research in this
The inter-item correlations range from .22 to .61 and the mean inter-item correlation is .42.
The standard deviations and means (in parentheses) for each of the indicators of work help were: 1.96 for work-related problems (4.75), 1.69 for bureaucratic hurdles (2.15), 1.83 for work recognition (2.52), 1.80 for meeting other employees (1.92), 1.23 for meeting high-level employees (1.06), and 1.15 for getting a new position or promotion (.97). The range for these indicators is zero (no network members provided help) to eight (eight network members provided help).
34 area, I had little theoretical guidance to predict differences between individual
indicators of work help. However, a limitation of this measure is that it does not
measure the effectiveness of work help, only whether network members made an
effort to provide employees with help. Thus, it is possible that an employee received
advice on a work-related problem from a network member but the advice was useless.
Measurement of Independent Variables
Employees’ Race and Sex
I coded female as 1 and male as 0. Fifty-seven percent of the sample are women. I created separate dummy variables for Asians, Blacks, Latinos, and racial minorities who were neither Asian, Black, or Latino. The comparison group for the dummy variables is white respondents. The sample contains 37 Asian Americans. 40
Latinos. 338 African Americans, and 36 people belonging to an other race. Thus, the majority (77 percent) of people of color in the sample are African American.
Employees’ Personal Characteristics
I used two familial measures: marital stams and the presence of young children. The codes for marital status are 1, "presently married," and 0. "never married, divorced, widowed, or separated."^ Approximately 66 percent of the sample was married at the time of the survey. The standard deviation for this measure is .47. The number of children under age 18 living at home has a range of
I coded marital status in this manner to reflect the amount of time that respondents had to spend with their network members. I assume that presently married employees have less time to interact with their network members than employees who are not presently married.
3d 0, "no children," to 3. "three or more children." The standard deviation is .97.
Approximately 54 percent of respondents had no children living at home, 21 percent had one child living at home, 17 percent had two children living at home, and 6 percent had three or more children living at home.
I measured credentials with three variables: years of education completed, completion of a financial designation, and years of company tenure. The original categories for level of education were: high school diploma or less, some college, associate degree, bachelors degree, masters degree, doctorate, and other. I converted these categories into the appropriate years of education (e.g.. bachelors equals 16 years). Years of education ranges from 12 to 21 and has a mean of 14.76. The standard deviation Is 1.95, meaning that there is limited variation in the years of education among the respondents. In fact, approximately 47 percent of the respondents went to college for at least four years. Financial designations are technical certifications in financial services. The information learned through these certifications is not typically offered in college business courses. Employees can receive a designation after passing 10 exams. The company encourages employees to earn a designation by reimbursing them for all of their designation-related expenses each time they pass an exam. These designations signal that an employee is well- rounded and can work in any part of the company. Employees told me that these credentials were an unofficial requirement for advancement. In fact, only 13 percent of non-supervisors in the sample had a designation compared to 69 percent of top
36 management. I coded respondents who had at least one designation as "1" and those who had no designations as "0. " On average, twenty-three percent of the employees in the sample had a financial designation. The standard deviation is .42.
The five categories for company tenure were: "less than 1 year," "1 to 3 years," "4 to 6 years," "7 to 9 years," and "10 or more years." The mean company tenure is 3.96—an average of 6 to 7 years. The standard deviation. 1.23. indicates that there is restricted variation in respondents’ company tenure. USF has a fairly stable workforce—49 percent of respondents had worked at USF for ten or more years.
Employees' Job Characteristics
To measure organizational rank, the survey asked respondents to place themselves in one of five categories (coded 1 to 5. respectively); non-supervisor, supervisor, manager, director, or officer and above. The average organizational rank for the sample is 1.47. meaning that most employees occupied low-ranking positions.
In fact. 77 percent of the respondents were non-supervisors. The standard deviation is .97, indicating that there is little variation in this measure. The range for organizational rank is 1 to 5.
Job power reflects employees’ control over corporate resources (Spaeth
1985:606-609).I created an index of job power composed of four indicators: respondents' ability to make purchases over $10.000, to provide input into important
According to Spaeth (1985:609). job power reflects control over monetary and non-monetary (e.g., people) resources.
37 corporate decisions (those that affected company services or products), to make final
decisions on important corporate matters, and to have access to confidential
information."’ Respondents indicated whether or not they had the ability to engage
in any of these activities. To create the job-power scale. I summed respondents’
answers for each indicator, standardized each summed indicator, and summed the four
standardized scores.'* The reliability coefficient for the scale is .59, which is
minimally acceptable (DeVellis 1991). The scale ranges from -3.11 to 7.54. has a
mean of .02 and a standard deviation of 2.63.'^
I measured job communication with the question, "How important is it for you
to communicate regularly with employees outside of your work group in order to do your current job?" The range for this measure is 0. "not at all important," to 4
"essential." The mean is 2.98 and the standard deviation is 1.17. Many employees
had jobs that involved communicating with employees outside of their work group.
For instance. 71 percent of respondents said that communication was important to their job. while 12 percent of respondents said that communication with employees outside of their work group was not important to performing their job.
■’ Approximately 6 percent of respondents could make major purchases, 17 percent of respondents could make final decisions. 58 percent of respondents had access to confidential information, and 61 percent of respondents could provide input into important decisions.
■* The standard deviations and means (in parentheses) for the indicators were: .49 for access to confidential information (.58), .49 for provides input into decisions (.61). .38 for makes final decisions (.17). and .25 for makes major purchases (.07).
The inter-item correlations ranged from .16 to .35 and the mean inter-item correlation was .27.
38 Demographic Composition o f Employees’ Work Floors
To tap into employees’ likelihood of interacting with women, people of color,
and managers I measured the percent of each of these groups on the floors in which
respondents worked.The percent of women on the floors in which respondents
worked ranges from 17 to 85 and has a standard deviation of 15. On average, 57
percent of the employees on the work floors in which respondents worked were
women. This is consistent with women’s representation in the company (59 percent
of employees were women). The percent of people of color on the floors in which
employees worked ranges from 0 to 32, with a standard deviation of 9. About 14
percent of the employees on the floors in which respondents worked were people of
color. The percent of managers on the floors in which employees worked ranges
from 0 to 41, with a mean of 18 and standard deviation of 9. The typical USF
respondent worked on a floor in which about 18 percent of the employees were
managers.
Employees' Organizational Involvement Outside of USF
Opportunities to develop network ties outside of USF were reflected in the question. "In how many organizations (e.g.. professional, charity, political) are you currently an active member (e.g., attend events or meetings)?" The range for this variable is 0. "no organizations," to 3, "three or more organizations." The standard deviation is 1.02. Thirty-five percent of employees were not involved in any
I obtained these data from the company.
39 organizations, 28 percent were involved in one organization, 22 percent were involved
in two organizations, and 13 percent were involved in three or more organizations.
Characteristics o f Employees’ Networks
I based the measures of network members’ characteristics on previous ego- network research, much of which focuses on community networks (Fischer 1982;
Brass 1985; Marsden 1987; Campbell 1988; Wellman and Wortley 1990; Campbell and Lee 1992). The measures generally sum or average the characteristics of employees' network members.
I measured network size by summing the number of respondents’ network members (range 0 to 8). Employees had an average of five people in their network.
The standard deviation for network size is 1.93, indicating that there is a fair degree of dispersion on this measure. The frequency distribution for network size, presented in Table 2.2, indicates that this variable is negatively skewed. The mode for network size is 8 network members, which suggests that some of the respondents who listed eight network members might have listed more members if they had been able. When a variable is truncated in this way its effect on the dependent variable may be underestimated.
[Table 2.2 here]
I measured women’s representation in employees’ networks by the number of female network members listed by respondents. The mean number of women in employees’ networks is 2.72 and the mode is 2. Thus, about half of respondents’ network members were women. The standard deviation, 1.90. indicates that there is
40 a fair amount of dispersion on this network measure. The range for this measure is
zero (no network members were women) to eight (eight network members were
women). The multivariate analyses take into account the percentage of women in
networks by controlling for network size.
I measured the representation of people of color in respondents’ networks by
the number of people of color listed in employees’ networks. Because of the small
number of people of color at USF’s home office (971 out of 6463 employees), there
were not enough non-white network members to distinguish between different racial
minority groups.^’ The mean number of people of color in employees’ networks is
.54. the mode is zero, and the standard deviation is .89.^- A little over half of the
sample had no people of color in their networks. The range for this measure is zero
to eight.
The measure of proximity reflects the physical distance between respondents
and their network members. Scholars have measured network proximity by the
number or percentage of network members who work outside of respondents’ business unit or work group (Brass 1985:332; Ibarra 1995:684). I created an index composed of two indicators—whether the network member had ever been employed in the same work group as the respondent and whether the network member had ever worked on
The racial breakdown of network members for respondents was: 36 Asian Americans. 39 Latinos. 61 other racial minorities. 687 African Americans, and 4904 whites. Thus, the majority of people of color in employees’ networks were African American.
Fourteen percent of all network members listed by respondents were people of color, which is close to their representation in the home office (15 percent).
41 the same floor as the respondent. I averaged each indicator (i.e.. divided by network size), standardized each average measure, and summed the two standardized scores for respondents’ network members.” The correlation between the two standardized, averaged indicators is .53. The range for the measure of physical proximity is -5.57 to 1.65. the mean is .00, and the standard deviation is 1.75.
I measured network strength with two different variables—the emotional intensity of the relationship between the respondent and each of his/her network members and how often the respondent socialized with each of his/her network members. I measured emotional intensity by the reported closeness between the respondent and their network members. Marsden and Campbell (1984:497) argue that closeness is the single best indicator of tie strength. Responses for closeness to network members ranged from I to 5: "very distant" to "very close." I averaged the closeness scores for respondents’ network members (i.e., summed the scores across respondents’ network members and divided by network size). The closeness measure ranges from 0 to 5. with an average of 3.51 and a standard deviation of .74. The average USF respondent was neither very close nor very distant with their network members.
The second variable for network strength measured how often employees socialized with their network members. Respondents indicated the frequency with which they socialized (e.g.. went out for drinks) with each of their network members
” The standard deviations and means (in parentheses) for the two averaged indicators were: .30 for working in the same work group (.71) and .26 for working on the same floor (.82).
42 on a scale that ranged from 1 to 5: "seldom to never," "several times a year." "about once a month," "several times a month," or "about once a week." This measure has a mean of 2.00, a standard deviation of .96, and ranges from 0 to 5. Employees socialized with their network members infrequently, on average.
Dyads Within Employees’ Networks
I created a data set composed of all of the dyads in the sample (6047 dyads) in order to examine employees’ relationships with each of their network members. A respondent with four network members in the original data set represents four dyads in this second data set. Because my network measures sum the characteristics of respondents’ network members, they do not allow me to examine respondents’ relationships with specific network members. For example, the measure of network closeness sums the closeness responses between the respondent and each of their network members. Therefore. I cannot assess to whom respondents had their closest ties. However. I can answer this question by examining the dyads within employees networks. For instance, the dyad data indicate that 51 percent of the dyads in the sample were either somewhat close or very close. Only 10 percent of all dyads were described by respondents as somewhat distant or very distant. While the dyad data address important descriptive questions. I do not use them to test hypotheses because my interest is in respondents’ entire networks. However. I refer to the dyad analyses to help explain some of the multivariate results.
43 Method of Analysis
I assessed the extent of race and sex differences in employees’ likelihood of
having high-status network members and in the amount of work help that employees
received from their network members with two-way analysis of variance (ANOVA), a
method that tests whether the means for more than two groups differ significantly.
This analysis helped me to identify if women and people of color had, on average,
fewer high-status network members, and if they received less work help from their
network members, than men and whites.
I also used ANOVA to examine race and sex differences in the factors that
hypothetically influenced employees’ likelihood of obtaining high-status network
members and of receiving work help from their network members. For example, I
examined whether people of color had smaller networks than whites because network
size may Influence the amount of work help that employees receive from their
network members. I also examined if women had lower organizational rank than men
because employees’ position in the organizational hierarchy should influence their
opportunity to interact with high-status network members.
The oversampling of some groups could have biased the ANOVA results.
Because I oversampled white-female managers, for instance, white women's means on
organizational rank were inflated. This, in turn, decreased the likelihood of
identifying sex differences in organizational rank. To correct for the unrepresentative distribution of sex-race groups in the sample, I assigned weights to the strata on which I sampled. The sampling weight is the inverse of the selection probability for
44 each group. The selection probability is the number of group members selected into
the sample divided by the total number of members at USF’s home office. The
weights for each group are presented in Table 2.3.^ The weight for white men
means that each white man in the sample represents 4.81 white men at USF. In
addition, each white female manager in the sample represents about nine of their
counterparts at USF. I sampled the entire population of men of color at USF’s home
office, which is why their weight is one.
[Table 2.3 here]
To analyze the potential determinants of having high-status network members
and of receiving work help from network members, I employed ordinary least squares
(OLS) regression. I regressed the likelihood of having high-status network members
on network members’ characteristics and respondents’ characteristics. Similarly, 1
regressed the amount of work help that employees received from their network
members on network members’ characteristics and respondents’ characteristics. This
method allowed me to examine the factors that affected the dependent variables, net of the other factors in the model. For instance, it allowed me to uncover whether employees’ personal characteristics affected their likelihood of having high-status network members net of their job characteristics. Thus, this method helped determine which of the factors were the most important in explaining employees’ likelihood of
I normed the weights (divided the weight variable by the mean of the weight variable) so that I would have correct standard deviations and sample sizes.
45 having high-status network members and of receiving work help from network members. The regression analysis also identified whether employees’ race or sex moderated the effects of potential determinants on the dependent variables.
The determinants in a multivariate analysis tend to be correlated. When the correlations between independent variables are high, multicollinearity can make it difficult to estimate the effects of the independent variables on the dependent variables because it produces large standard errors (Lewis-Beck 1980:59). I tested for multicollinearity using the variance inflation factor (VIF), condition numbers, and variance-decomposition proportions.^^ I found that multicollinearity was a problem in both of the network models I examined. I discuss how I dealt with this in Chapters
4 and 5.
Multicollinearity can be problematic if the VIF is above 5. if a condition number is above 30. and if the variance-decomposition proportions are .5 or higher (Belsley. Kuh. and Welsch 1980:112).
46 Home Office Target Sample Respondents White Men 37.21 28.47 29.59 (2405) (500) (337)
White Women 47.76 28.47 31.34 (3087) (500) (357)
Men of Color 4.24 14.58 13.43 (274) (256) (153)
Women of Color 10.78 28.47 25.64 (697) (500) (292)
Total 100.00 100.00 100.00 (6463) (1756) (1139)*
' This number is lower than the reported sample size because of missing data on employees' race and sex.
Table 2.1: Percentage Distributions for White Men, White Women. Men of Color. and Women of Color in USF's Home Office, in the Target Sample, and among USF Respondents (total number of employees in parentheses).
47 Number of Number of Percent of Network Members Respondents Respondents 0 8 .7 1 27 2.3 2 64 5.6 3 134 11.7 4 186 16.2 5 217 18.9 6 173 15.0 7 105 9.1 8 236 20.5
Total 1150 100.00
Table 2.2: Frequency Distribution for Respondents' Number of Network Members.
48 Group Weight
White Men 4.81
White Women 9.01 (non-managers)
White Women 1.93 (managers)
Men of Color 1.00
Women of Color 1.39
Table 2.3: Sampling Weights for White Men. White Women. Men of Color and Women of Color.
49 CHAPTER 3
A PROFILE OF USF WORKERS AND THEIR NETWORK MEMBERS:
DIFFERENCES BY RACE AND SEX
In Chapter I I raised two questions regarding the relationship between
employees’ race, sex, and their informal networks. First, I asked //there were race
and sex differences in employees’ likelihood of having high-status network members
and in the amount of work-related help that employees received from their network
members. Second, I asked how employees’ race and sex influenced their likelihood
of having high-status network members and the amount of help they received from
network members. Chapter 3 addresses the first question and suggests answers to the second question, using two-way analysis of variance (ANOVA). I first examine race and sex differences in employees’ likelihood of having high-status network members and in the amount of help employees receive from their network members and then examine race and sex differences in the factors that hypothetically influence these network outcomes. This analysis contributes to our knowledge of informal organization at work by describing sex and race differences in employees’ access to different types of informal networks. In addition, by examining race and sex
50 differences in employees’ personal, job, and outside organizational characteristics, this analysis suggests factors that may account for race and sex differences in network access.
Table 3.1 presents the ANOVA results, including the weighted means for each group, the means and standard deviations for the sample, and the ranges for the dependent and independent variables. I tested for race differences in employees’ means, for sex differences in employees’ means, and for interactions between sex and race. I combined the data for racial minorities because of insufficient sample sizes for some of the racial minority groups (see Chapter 2).
[Table 3.1 here]
High-Status Network Members and Work-Related Help from Network Members
The ANOVA results indicate that USF employees had different types of informal networks, depending upon their race and sex. For instance, the network members of whites were higher in status than the network members of people of color and the network members of men were higher in status than the network members of women. There were also significant sex and race differences in the amount of work- related help employees received from their network members. Whites received more work help from their network members than did people of color and men received more work help from their network members than did women. I found no evidence that employees' race and sex interacted in their association with either the likelihood
51 of having high-status network members or the amount of work help received from
network members. For instance, women of color did not have significantly fewer high-status network members than white women or men of color.
That the network members of people of color were less powerful and provided less help than the network members of whites is consistent with Kanter’s observations about tokens some twenty years ago. Kanter (1977b:978) argued that tokens were excluded from the informal networks in which dominant-group members exchanged information and advice, leading tokens to be on the margins of informal power centers. However, the results for employees’ sex raise questions about Kanter’s token thesis for women. Kanter (1977a) argued that numerical majorities secured more formal and informal power in corporations than numerical minorities. Yet women were less likely than men to have high-status network members and women received less work-related help from their network members than did men, despite women’s numerical dominance at USF. These results suggest that employees’ access to social capital was influenced by factors rather than their numerical representation in work organizations, such as their gender status (Martin 1985:317). Potential Determinants of Status of Network Members and of Receiving Help from Network Members
The following results provide insights into why women and people of color had fewer high-status network members and received less work-related help from their network members than men and whites. I examine race and sex differences in employees’ personal, job, network, and organizational (outside of USF) characteristics.
Employees’ Personal Characteristics
Race and sex differences in employees’ credentials could help to explain why women and people of color had network members who were lower in status and provided them with less help than men and whites. Credentials may increase employees’ bargaining power with other workers and help employees obtain high- level positions. In terms of work-related credentials, men had significantly more years of education than did women. For example, 32 percent of women had at least four years of college compared to 64 percent of men. These findings are similar to past research on sex differences in education among employees (Hodson 1989:391;
Dreher and Ash 1990: 543; McGuire and Reskin 1993:493). However, there were no significant differences between whites and people of color in years of education. This finding is due to the relatively high average years of education among Asian and
Latino respondents. Blacks completed an average of 14 years of education, Asians completed an average of 16 years of education. Latinos completed an average of 15
53 years of education, and whites completed an average of 15 years of education. In addition, the interaction between employees’ race, sex, and education was not significant.
The ANOVA results indicate that men also had more technical credentials than women, as indicated by the findings for financial designations. Thirty-one percent of male respondents, compared to 17 percent of female respondents, had at least one financial designation. Men’s greater credentials may have increased their bargaining power with other employees by signaling their competence and influence. This, in turn, may have helped men attract high-status network members and increase the amount of help they received from their network members. I found no significant race differences in having a financial designation for the same reasons why I found no significant race differences in education. Chapters 4 and 5 investigate whether sex and race differences in credentials explain why women and people of color were less likely to have high-status network members and to receive work-related help than were men and whites.
In terms of tenure, women were employed at USF significantly longer than were men. For example, 69 percent of women had worked at USF for at least 7 years, compared to 63 percent of men. In addition, whites had worked at USF significantly longer than people of color. The implications of these differences depend upon the effect of tenure on employees’ likelihood of having high-status network members and on the work help that employees received from their network members.
54 Employees’ family characteristics may influence the amount of time employees
spend with network members and thus affect the resources they receive from network
members. I found that men were more likely to be married than women and that
whites were more likely to be married than people of color. Men were also more
likely than women to have young children at home. These findings are consistent
with past research, which found that men who were employed full-time were more
likely to be married than women who were employed full-time and that male workers
were more likely than female workers to have young children (Campbell 1988:189;
Hodson 1989:391; Dreher and Ash 1990:543). This suggests that it is easier for men
than women to combine marriage, children, and paid employment. The multivariate
analyses explore whether marriage and children affect women’s networks in different
ways than men’s networks. For instance. I examine if married women receive less
work-related help from their network members than married men. net of their sex.
Employees’ Job Characteristics
The kinds of organizational positions that employees occupy should influence
their opportunity to interact with different types of employees which should, in turn,
affect the characteristics of their network members. For instance, workers who occupy positions with low rank should be less likely to interact with high-status employees than workers who occupy positions with high rank. Employees with high rank typically hold positions that encourage them to communicate with other
55 employees. Thus, high-ranking employees should be more likely than low-ranking employees to interact with managers and officers in the course of performing their jobs.
I found that employees’ organizational rank depended upon the interaction between their race and sex. This means that employees’ race was associated with organizational rank in different ways for men of color and women of color. Women of color were significantly less likely than men of color, white women, and white men to occupy a position with high rank. For example. 31 percent of white men were managers or higher (e.g., officers), compared to 15 percent of men of color. 8 percent of white women, and 5 percent of women of color. However, this was the only race-sex interaction that was significant out of the twenty interactions that I tested, suggesting that this finding may be due to chance.
There were also significant race and sex differences in respondents’ likelihood of exercising job power. Men exercised more job power than women and whites exercised more job power than people of color. These results are consistent with studies that have found that whites exercise more job authority than Blacks and that men have more job authority than women (Kluegel 1978:290; Mueller and Parcel
1986:209; Hodson 1989:391; Reskin and Ross 1992:352; McGuire and Reskin
1993;493).
56 In terms of communication channels, whites reported significantly higher levels of communication outside of their workgroup than did people of color and men reported higher levels of job communication than did women. These findings support
Kanter’s (1977a:53) claim that minorities tend to be channelled into jobs with few conununication channels.
Race and sex differences in organizational rank, job power, and communication should have implications for employees’ acquisition of high-status network members and work-related help from their network members. Workers who occupy jobs with high rank may have more oppormnities to interact with high-status employees than do workers who occupy jobs at the bottom of the corporate hierarchy.
Employees’ job power likely influences their ability to attract potential network members because job power affects the amount of resources that employees control.
Having a job that requires inter-group communication may help workers develop ties to a range of workers who provide different types of help to them. Job factors may also influence employees’ likelihood of having high-status network members and work help from network members in different ways. The multivariate analyses investigate if employees’ race and sex mediate the effects of job characteristics on their likelihood of having high-status network members and the amount of help that they receive from their network members.
57 Demographic Composition of Employees’ Work Floors
The demographic composition of the floors in which employees work may
influence with whom employees interact and thus affect the characteristics of their
network members. The percentage of women, people of color, and managers on the
floors in which employees worked was significantly related to employees’ sex. Men.
compared to women, worked on floors with smaller percentages of women, smaller
percentages of people of color, and higher percentages of managers. For instance. 52
percent of the employees on the floors in which men worked were women and 61
percent of the employees on the work floors in which women worked were other
women. This finding suggests that women were more likely than men to interact with
women on their work floors. Women also had fewer chances of interacting with
high-status employees, as indicated by the finding that the floors in which women
worked had smaller percentages of managers than the floors in which men worked.
The floors in which employees of color worked had significantly higher
percentages of women and people of color, but lower percentages of managers, than did the floors in which white employees worked. For example. 18 percent of the employees on the floors in which people of color worked were other people of color.
In contrast. 14 percent of employees on the floors in which whites worked were people of color. Compared to whites, people of color had fewer opportunities to interact with managers on their floors (i.e.. high-status employees), but more opportunities to interact with workers who were located at the bottom of the corporate hierarchy (i.e.. women and people of color).
58 Employees’ Organizational Involvement Outside of USF
Involvement in voluntary organizations outside of USF may enhance employees’ access to resources, such as business contacts, making them an attractive network member. It could also increase the amount of time employees spend with their network members and thus increase the amount of help employees receive from their network members. I found no race or sex differences in the number of voluntary organizations to which employees belonged, however. All four groups were involved in an average of one voluntary organization. These findings are consistent with McPherson and Smith-Lovin’s (1982:900) study, which found that men and women belonged to the same number of voluntary organizations. Scholars have also found that people of color and whites belong to the same number of voluntary organizations (Sundeen 1988:557, 1990:492: but see McPherson 1977:204).
However. McPherson and Smith-Lovin (1982:888) found that the organizations to which men belonged were more likely than the organizations to which women belonged to be professional- and business-oriented. Thus, women and men may receive different network benefits from being involved in voluntary organizations. I explore this possibility in the multivariate analyses by testing interactions between sex. race, and involvement in voluntary organizations.
59 Employees’ Network Characteristics
The findings for network size indicate that white women’s and minorities’ informal disadvantages at work are not due to a lack of "networking." Employees had an average of five workers in their network, regardless of their race or sex.*
Thus, race and sex differences in having high-stams network members and receiving work-related help from network members cannot be explained by race and sex differences in network size. This suggests that the unequal distribution of social capital at work is due to who you know and how you know them, rather than how many people you know.
I found that female respondents tended to have more women in their networks than did male respondents. Women had an average of 3.28 women in their networks and men had an average of 1.91 women in their networks. These findings are consistent with past research documenting that work networks tend to be sex segregated (Brass 1985:336; Scott 1996:242). For example. Brass (1985:336) found that 75 percent of male employees’ ties were to other men and 68 percent of female employees’ ties were to other women. Whites and people of color had approximately the same number of women in their networks, on average.
These results may be due. in part, to the segregation of workers by occupation, job. and organizational rank (Baron. Davis-Blake. and Bielby 1986; King
1992; Reskin and Ross 1992). Bielby and Baron (1986:777). in their study of over
' The finding for sex is consistent with studies on community and discussion networks (Moore 1990:729; Fischer and Oliker 1983:125; Marsden 1987:127).
60 200 California firms, found that there was almost complete sex segregation in jobs-
96 percent of the women or the men in their sample would have had to change jobs to equalize the sex composition of jobs. In addition. King (1992:34-35) found that approximately 32 percent of Black men would have to change to an occupation dominated by white men and 30 percent of Black women would have to switch to an occupation dominated by white women to integrate occupations by race. Formal work arrangements undoubtedly influence with whom employees come into contact and thus influence their potential network members. Such structural arrangements should be key factors in determining employees’ opportunities to interact with high- status employees and employees who can provide them with resources. Employees do not have a random pool of potential network members to choose from. Rather, formal work arrangements should influence with whom employees work, with whom they interact, and thus with whom they form network relationships.
The high representation of women in female respondents’ networks may help to explain why women had fewer high-status network members than did men and why women received less work help from their network members than men. Women occupy positions with lower organizational rank than men and exercise less authority at work than men (Kluegel 1978:290; Mueller and Parcel 1986:209: Reskin and Ross
1992:352: McGuire and Reskin 1993:493). Women are less likely than men to be high-status employees and therefore are less likely than men to be high-status network members.
61 Workers’ sex and race were related to the representation of people of color in
their networks as well. Women had more people of color in their networks than did
men and racial minorities had more people of color in their networks than did whites.
People of color were probably segregated into similar jobs and departments at USF.
As a result, people of color may have had more opportunities than whites to interact
with other people of color. The multivariate analysis in Chapter 4 and 5 help to
identify the extent to which employees’ race and sex versus the race and sex of their
network members influence employees’ access to high-stams network members and
work-related help from network members.
A noteworthy pattern in Table 3.1 is the small number of people of color in
all employees’ networks. Even the group with the most people of color in their
networks, women of color, had an average of only one minority network member.
These findings are not surprising given the organizational demography of USF—people
of color made up only 15 percent of the employees at the home office. This pattern
is also consistent with past research, which found that Black managers had fewer
same-race network members than did white managers (Thomas 1990:485; Ibarra
1995:689). While people of color may have been more likely than whites to interact
with other people of color, the majority of USF employees, and thus a majority of all employees’ network members, were white. That a majority of Blacks’ network
members were white suggests that white employees did not completely reject people of color as network members.
62 I examined race and sex differences in the physical proximity of network members because proximity may influence network members’ likelihood of giving help to employees. Table 3.1 shows that people of color were as likely as whites to have worked near their network members. This finding differs from Thomas’
(1990:486) study, which found that Blacks’ same-race mentors were more likely than whites’ same-race mentors to work outside of their immediate department. I found no significant sex differences in employees’ likelihood of having worked with their network members. Thus, the network members of whites and men were just as conveniently located to them as were the network members of people of color and women.
Past research has found that the strength of individuals’ network relationships is negatively related to their likelihood of finding a job (Granovetter 1974: Lin, Ensel. and Vaughn 1981: Drentea 1997). Tie strength may influence employees’ access to work-related resources as well. Table 3.1 reveals race and sex differences in employees’ closeness to network members, one measure of tie strength. People of color were not as close to their network members as were whites. This finding is consistent with past research on managers that found that racial minorities had fewer intimate network ties at work than whites (Ibarra 1995:691). People of color have more cross-race network members at work than whites, which tend to be less intimate ties than same-race network ties (Ibarra 1995:689).’
- This is due. in part, to organizational demography. Because of their small relative numbers, people of color are more likely to interact with numerically dominant groups than vice versa.
63 To understand the association between employees’ race and their closeness to network members I examined the racial composition of network dyads (see Chapter
2). Cross-tabular analyses revealed that people of color were closer to their minority network members than to their white network members; 29 percent of minorities' ties to people of color were very close, but only 13 percent of minorities’ ties to whites were very close (results not shown). White respondents, in contrast, were equally close to their same-race and cross-race network members. Thirty-eight percent of whites’ cross-race and same-race ties were very close. Thus, sharing the same race did not automatically lead to strong ties between employees. These race differences may be due to the token status of people of color at USF. People of color may have formed close bonds with other people of color to endure the daily frustrations of feeling invisible, devalued, and unwelcome (Cose 1993). Qualitative field research, such as that described in Chapter 6, could shed light on how tokens’ position as outsiders affected their social and professional interactions at work. If close network relationships provide workers with more work help than do weak network relationships, then race differences in closeness can help us understand why people of color received less help from their network members than did whites.
Women were closer to their network members than were men. Since the closeness between people of color and their network members depended upon the race of their network members. I examined whether the association between employees' sex and closeness to network members depended upon the sex of their network members. Analysis of the dyad data showed that women were closer to their female
64 network members than to their male network members; 24 percent of women’s same-
sex network ties were very close, compared to 14 percent of their cross-sex ties.
Given that a majority of women’s network members were other women, this finding
helps to explain why women were closer than men to their network members. Men
were about as close to their male network members as to their female network
members; 15 percent of men’s same-sex ties were very close compared to 13 percent
of their cross-sex ties. These differences may reflect women’s stams as social
minorities. Zimmer (1988) argued that social minorities are devalued by dominant
groups and treated like outsiders within their workplaces.- Thus, like people of
color, women may have formed close ties to other women in response to their
marginal status at USF.
I found no race differences in the frequency with which employees socialized
with their network members, a second measure of tie strength. People of color
socialized with their network members as often as whites, an average of several times
a year. I found no sex differences in employees’ likelihood of socializing with their
network members, in contrast to studies reporting that female employees are less
likely than male employees to socialize with their co-workers (Wellman 1985:174;
Scott 1996:239). A lack of socializing, therefore, carmot explain why women and people of color received less work help from their network members than men and whites. To further investigate race and sex differences in socializing, I examined the
^ Kanter (1977a: 147-149) observed that workers who lack opportunity are more likely to form friendship, rather than instrumental, ties at work. If that was the case then women should have formed equally close ties to their male and female network members.
65 dyad data. The results were similar to those for network closeness—women were more likely to socialize with their same-sex network members than their cross-sex network members and people of color were more likely to socialize with their same- race network members than their cross-race network members.However, in contrast to the results for closeness, the amount of time that men and whites socialized with their network members depended upon the sex and race of their network members. Approximately 12 percent of men frequently socialized with their same-sex network members, compared to 9 percent of men with cross-sex network members. In addition, 10 percent of whites with same-race network members frequently socialized with their network members, compared to 7 percent of men with cross-race network members. These findings suggest that closeness and socializing tap into different aspects of tie strength between network members.^
Discussion of Results
These results indicate that a formal and informal system of race and sex stratification exists at USF. In terms of formal stratification, women and people of color occupied jobs with lower organizational rank, less power, and less inter-group communication than men and whites. Furthermore, work floors tended to be
* Approximately 18 percent of people of color with same-race network members socialized frequently, compared to 8 percent of people of color with cross-race network members. Similarly, about 11 percent of women with same-sex network members socialized frequently, compared to 7 percent of women with cross-sex network members (results not shown).
^ For example, network members who are not close may socialize. Employees may also view socializing with network members in an instrumental, rather than expressive, manner.
66 segregated by race and sex. As a result, women and people of color were more
likely to work near other women and people of color than were men and whites.
In terms of informal race and sex stratification, women and people of color
had lower-status network members and received less help from their network
members than men and whites. As a result, women’s network members may have
been less able than men’s network members to advocate for them, to help them obtain
visible job assignments, and to divert corporate resources to them. Similarly, these
results indicate that people of color had network members who provided them with
less instrumental help than whites. However, that people of color had as many
network members as whites suggests that numerical tokens at USF did not experience complete isolation. Rather, the ANOVA results suggest that numerical tokens differed from numerical dominants in terms of the types of network members they had and in terms of the amount of help they received from their network members. While women were not numerical tokens at USF, they had network members with less status than men and had network members who provided them with less assistance than the network members of men. These results suggest that women’s numerical majority at the company did not grant them formal or informal advantages over men. Such results are consistent with Martin's (1985) and Zimmer's (1988) argument that women’s status as social minorities influences their formal and informal power apart from their numerical representation in work organizations.
67 Chapters 4 and 5 investigate the implications of these race and sex differences by examining whether personal and structural factors affect employees’ likelihood of having high-status network members and work-related help from network members.
By examining network, job, and personal factors simultaneously, these analyses identify the mechanisms through which men and whites acquired more high-status network members and work-related help at work than women and people of color.
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HhRSONAl. Years ut Educamm 14.76 15.53 14.24 15.08 14.39 12.0010 1.95 21.00
Fiiuncial 0.23 0.32 0.18 0.18 0.14 .0010 1.00 0.42 Dcsigiuiinns
Years of Tenure 3 96 3.88 4.08 3.39 3.83 1.0010 5.00 1.23
Manial Suius 0.66 0.75 0.63 0.63 0.46 .0010 1.00 0.47
Number nl Children 0.77 0.89 0.69 0.78 0.71 .0010 3 .00 0.97
JOU Ürgunuaiional 1.47 1.86 1.24 1.36 1.19 1.0010 5.00 0.97 Rank 4
Jnb Pow er 0.02 0 79 -0.36 -0.58 •0.81 •3.11 10 7.54 2.63 0\ VO Cunununicamm 2.98 3.13 2.97 2.76 2.53 .0010 4.00 1.17 Ouiside Workgroup
* 5'igiiilicaiil al llic 05 Iuvl I. 2 lailciJ lo l Tabic 3.1 coiuinucti on nc»i page.
Table 3.1 : ANOVA Results by Employees' Race and Sex. Table 3.1 (continued).
t'IwraorrlMk Variable White Men While ïujabls ganiW W o n ien t Wnnee Devlnllon
H.OOK PeicciK 111 Ü57 0 31 0.60 0.55 0.63 .1 7 1 0 .8 5 0.15 roMi’OsnoN Wiiincii uii Flmir
Pcieeiil ul People ol 0 14 0 13 0 IS 0.17 0.18 .0010 .32 0.00 Color on Floor
Pcicem of Manageii 0.18 0.20 0.17 0.16 0.16 .00 to .41 0.09 on Floor
OUTSIDE Organizalloiul III 1.16 1.08 1.03 1.13 .0010 3.00 1.02 SOCIAL TIES Involvement
NETWOKK Neiwurk Size 5 30 5.39 5.31 5.29 4,93 .00 10 8.00 1.93
Nuinlier nf Women 2.72 1.86 3.28 2.34 3.26 .00 to 8.00 1.90 in Network
Number ul People ol 0.54 0.34 0.47 1.08 1.44 .00 to 8.00 0.89 Color In Nettvork
PIty&ical Ptum nnty 0.00 4)02 0.14 4 )09 4). 10 •5.57 to 1.65 1.75
Clovenevv 3.51 3.50 3 67 3 35 3.41 .00 to 5.00 0.74
Socialize tviili 2.00 2.00 2.08 2.03 1.88 .00 to 5.00 0.96 N etw ork
* Sigitrlienitt at the .05 level, 2 tailed test. + Slgttincaiil race-scit iiilcraclinii at llie .05 level, 2-tajled lest. CHAPTER 4
MULTIVARIATE RESULTS FOR EMPLOYEES’ LIKELIHOOD OF HAVING
HIGH-STATUS NETWORK MEMBERS
In this chapter I investigate the factors that influence employees’ likelihood of having high-status network members. Network theory suggests that three conditions are needed to establish network relationships with high-status employees (Wellman
1983; Campbell. Marsden. and Hurlbert 1986; Lin and Dumin 1986; Wellman and
Wortley 1990; Campbell and Lee 1992). First, employees need the opportunity to interact with high-status employees in order to have high-status network members.
Second, employees need sufficient time to interact with high-status employees in order to develop informal relationships with them. Third, employees need to possess resources and credibility to attract high-status employees. High-status employees should be drawn to workers who appear to have something of value to exchange with them.
This research enhances our understanding of informal organizational structure in two ways. First, it helps us to understand how employees accumulate social capital at work. High-status network members constitute one type of social capital because they control resources that can help employees to perform their jobs and to acquire
71 work rewards, such as promotions. I compare how structural factors, such as the race composition of the floors in which employees work, as opposed to personal factors, such as employees’ credentials, influence workers’ likelihood of having high- status network members. Second, this research contributes to organizational and stratification theory by elucidating the mechanisms through which employees’ sex and race influence their access to high-status network members. For instance, I examine whether sex differences in organizational rank explain sex differences in employees’ likelihood of having high-status network members and if men and women receive different network payoffs for their organizational rank. I also examine whether and how the processes that generate race differences in employees’ likelihood of having high-status network members differ from those generating sex differences in employees’ likelihood of having high-status network members.
Employees’ Race and Sex
Previous studies offer conflicting accounts of how employees’ race and sex affect their likelihood of having high-status workers in their networks. This may be because there are no standard measures of network members’ status (see Chapter 2) and because most studies use non-representative samples of workers.' Some research suggests that being a woman or a person of color affects employees’ likelihood of having high-status network members, net of their job and personal characteristics
(Fernandez 1981; Brass 1985: Ohlott, Ruderman. and McCauley 1994: Scott 1996).
' For example. Ibarra (1995:682) studied managers and Campbell (1988:186) studied white-collar workers.
72 For example, Scott (1996:239) found that male corporate-govemment relations
managers had more contact with top officials than their female counterparts, net of
job title, experience, work history, and age. Studies documenting that race or sex
directly affects employees’ likelihood of having high-status network members suggest
that employees’ decisions about whom to include in their networks depend, in part,
upon the race and sex of potential network members. In contrast, other studies have
found that women and people of color have network members who are lower in status
than men and whites because the former occupy organizational positions that influence
with whom they interact (Campbell 1988:190; Ibarra 1995:690). In other words, this
second group of studies suggests that employees’ race and sex indirectly influence
their likelihood of having high-status network members by affecting their
organizational location and control over resources. In sum, research suggests that
women and people of color have network members who are lower in status than men
and whites, but it is unclear whether employees’ sex and race affect their likelihood
of having high-status network members directly or indirectly, such as through job
factors. Furthermore, no research has examined whether race and sex interact to
affect employees’ likelihood of having high-status network members. We do not
know, for instance. If women of color are less likely than white women or men of color to have high-status network members, net of their personal and job characteristics.
I mmed to Kanter’s explanations for sex differences in workers’ behavior to predict the effect of employees’ race and sex on their likelihood of having high-status
73 network members. Kanter (1977a) developed three arguments to explain women’s and men’s behavior in organizations-their organizational position, their power, and their proportional representation. Kanter (1977a: 152-161) argued that women’s organizational positions, rather than their sex, explained why they exercised less authority, acquired less power, and obtained fewer work rewards than men. She argued that sex differences in workers’ behaviors, attitudes, and aspirations were due to the divergent opportunities provided by men’s and women’s organizational positions. Kanter (1977a: 177-181) argued that women lacked power because they were less likely than men to occupy positions in which they engaged in extraordinary, visible, and relevant activities. Kanter suggests that being a woman or a person of color indirectly influences employees’ likelihood of having high-status network members by affecting their organizational positions.-
Kanter’s (1977a; 1977b) thesis on tokens offers another possible explanation for race and sex differences in employees’ likelihood of having high-status network members. Kanter suggested that even when tokens occupy high-level organizational positions they are excluded from their peers’ informal networks. Tokens’ auxiliary traits, such as their sex, are the basis for their informal exclusion, regardless of their credentials or organizational location. According to Kanter (I977b:978), "The result is quarantine-keeping tokens away from some occasions." Kanter (1977b:978) found that saleswomen were excluded from all-male clubs and informal gatherings in which
- In other words, Kanter’s organizational and power explanations both attribute men’s and women’s behavior in organizations to their structural location.
74 male workers gave each other job advice and discussed political strategies. High- status employees might avoid informal interactions with low-status employees because high-status employees believe such interactions would lower their legitimacy and power (Blalock 1967:52). Kanter’s token explanation suggests that employees’ race and sex can lower their chances of having ties to high-status employees, net of their organizational positions.
Other scholars have challenged this hypothesis by arguing that employees’ sex and race directly influence their experiences at work, regardless of their numerical representation (Martin 1985: Zimmer 1988). Such arguments suggest that even when white women and people of color constitute large percentages of a firm’s workforce they are excluded from informal networks because their race and sex signal their perceived inferiority. In sum, Kanter suggests two, somewhat incongruent, hypotheses for race and sex differences in informal networks. The first hypothesis indicates that the effect of employees’ ascriptive characteristics on their informal networks is due to their organizational characteristics, while the second hypothesis indicates that employees’ race and sex directly affect their informal networks.^
I draw upon both of Kanter’s hypotheses to explain how employees’ race and sex affect their likelihood of having network members with high status. I expect that the effects of employees’ sex and race on their chances of having high-status network members operate in two ways. First, employees’ race and sex should indirectly affect
^ In fairness to Kanter, I should acknowledge that she was not seeking to explain the composition of employees’ networks. Rather, she was attempting to provide a structural explanation for workers’ behavior and for the distribution of work rewards.
75 their likelihood of having high-status network members through their job and network characteristics. By this I mean that women and people of color should be less likely than men and whites to have organizational positions that attract high-status network members and provide them with oppormnities to interact with high-status employees.
Second, I expect that women and people of color reap fewer network benefits, in the form of high-status network members, than men and whites from similar organizational positions. In other words, I expect that the effects of employees' personal and job characteristics on their likelihood of having high-status network members depend upon their race and sex. For example, even when white women and minorities occupy high-ranking positions, I expect that white men have more opportunities to interact with high-status employees and thus are more likely than white women and minorities to have high-status network members. These hypotheses maintain Kanter’s emphasis on structural factors, but also recognize that employees’ race and sex may affect the relationship between organizational location and informal networks.'*
Employees’ Personal Characteristics
Social exchange theory highlights how employees use their personal resources, such as their credentials, to influence their bargaining power in transactions and to attract potential network members. For example, many USF employees display their degrees, certificates, and awards on their desks. Social exchange theorists would
■* Kanter (1977) is certainly not the only scholar to highlight the role of structural factors in determining employees’ interactions, but she was among the first to seriously consider the relationship between gender and organizational structure.
76 argue that this public display of credentials increases employees’ bargaining power with other workers by increasing their perceived competence/ Admittedly, degrees and awards may not increase workers’ competence. Nevertheless, people associate competence and power with such credentials. Thus, social exchange theory suggests that employees’ credentials influence their ability to form ties to high-status employees by increasing their attractiveness to high-status network members.
Network studies offer no clear indication of how employees’ credentials affect their likelihood of having high-status network members.® Ibarra (1995:690) found no association either between managers’ education or tenure and their likelihood of having high-status network members. Yet, job-search research has found that individuals’ level of education is positively associated with the occupational status of their job contact (Lin, Ensel, and Vaughn 1981:401). Some research also suggests that women’s and racial minorities’ credentials convey less influence in their informal interactions than men’s and whites’ credentials, suggesting that men’s and whites’ credentials generate more network benefits than women’s and minorities.’ White women’s and minorities’ credentials may bring them less power than white men’s credentials because of stereotypes about women’s and Blacks’ incompetence (Kanter
® One way that workers assess the competence of their co-workers is by their co workers’ credentials (Rosenbaum 1989:333-334).
® Community research has consistently found a positive association between educational level and network size (Fischer 1982: Moore 1990:731; Campbell and Lee 1992). Thus, credentials may indirectly affect employees’ likelihood of having high- status network members by increasing network size.
77 1977a:236; Pettigrew and Martin 1987:57-58)/ For example. Cose (1993:49-51)
argued that black skin was associated with incompetence and inferiority in the
business world, regardless of Blacks’ credentials. In other words, the effect of
employees’ credentials on their chances of having high-status network members may
depend upon their race and sex.
Workers also require sufficient time with high-status employees to form a
network tie with them. One factor that should influence employees’ time for informal
interactions is their family characteristics. I expect that employees’ family
characteristics interact with their sex to affect employees’ chances of having ties to
high-status employees. Marriage and children should diminish women’s likelihood of
having high-status network members but not men’s likelihood of having high-status
network members. Women’s double duty in the family and in the labor market leaves
them with potentially little time to socialize after hours and to be actively involved in
business and professional organizations.* Numerous studies have found that
employed women perform the majority of housework and childcare in their homes
(for a review, see Thompson and Walker 1991). For instance, employed women
spend an average of 35 hours per week doing housework, while employed men spend
^ Research has demonstrated that individuals hold negative stereotypes about women and people of color even when presented with evidence that challenges such beliefs. For example, in experiments on college students, white subjects evaluated Blacks' performance more negatively than whites’ performance, even though both groups engaged in identical behaviors (Pettigrew and Martin 1987:58).
* No study, to my knowledge, has examined how much of employees’ socializing time with network members occurs outside of work.
78 an average of 21 hours per week doing housework (Shelton 1992:73). Thus,
marriage and children should limit women’s time to form relationships with high-
status employees more than men’s time.
The little research that has examined the effect of marital status on employees’
informal networks supports this hypothesis. Campbell (1988:193) found that female
employees with young children were less likely to have high-status network members
than female employees without young children.^ Male employees’ marital status, in
contrast, had no effect on their likelihood of having high-status network members,
according to Campbell (1988:193). Wellman (1985:174) found that employed
married women were also less likely to socialize with their co-workers after work
than employed married men. In sum, I assume that employees’ family characteristics
affect the amount of time that employees have to interact with potential network
members. As a result, I expect that married women have lower-status network
members than married men and that employed mothers have network members who
are lower in status than employed fathers, net of employees’ sex and other relevant
variables.
Employees’ Job Characteristics
Occupying a position with high organizational rank, such as that of manager or
officer, should increase employees’ chances of having high-status network members
because these positions typically involve communication with many employees (Kanter
^ Campbell (1988) examined employees’ personal networks, which included network members outside of the workplace.
79 1977a; Jackal! 1988). As a result, workers with high rank should be more likely to meet high-status employees than workers with low rank. Most large corporations are also structured to limit contact between low-status and high-status employees. For example, executives’ offices at USF are located at the top of a building where there is little employee traffic. Executives also have their own entrance to the building and their own dining room. In addition, USF sponsors many exclusive sporting events in which it gives free tickets to, and reserves special seats for, managers and officers.
In sum. employees with low rank have fewer opportunities than employees with high rank to interact with high-status employees. As a result, employees at the bottom of the corporate hierarchy should have network members who, like themselves, are lower in status than the network members of employees who occupy high-level positions.
Research on job authority'" indicates that the benefits of occupying a position with high organizational rank depend upon workers’ race and sex. For instance, even when women and people of color occupy managerial positions, they exercise less authority than men and whites (Kluegel 1980; Mueller and Parcel 1985; McGuire and
Reskin 1993). Female managers are less likely than their male counterparts to make decisions about hiring and promotions and to make final decisions (Reskin and Ross
1992:352). Black managers are less likely than white managers to control a budget, to influence hiring and promotions, and to exercise job discretion (Fernandez
1972:175; Greenhaus, Parasuraman, and Wormley 1990:76). Female and Black
Job authority reflects supervisory power, such as hiring and firing.
80 executives also tend to have jobs in personnel, public relations, and other support
functions that are non-profit and non-production (Reskin and Ross 1992:350; Collins
1997:140). As a result, female and Black managers are less likely than are male and
white managers to control resources that high-status employees want, which should
make female and Black managers less likely than male and white managers to attract
high-status network members.
Research on managers provides support for the hypothesis that women and people of color receive fewer network payoffs than men and whites for occupying positions with high organizational rank. One study found that female managers were less likely than male managers to feel included in informal networks and to have supportive co-workers (Ohlott, Ruderman, McCauley 1994:62). Fernandez (1982:53) found that 71 percent of Black male managers and 59 percent of Black female managers felt that minorities were excluded from informal networks at work (also see
Cose 1993:16.46). In sum. high organizational rank should bring white women and people of color fewer high-status network members than white men.
The amount of job power that employees exercise at work should influence their chances of having high-status network members by affecting their ability to attract high-status employees." Social exchange theorists would predict that employees with high job power have access to many corporate resources and thus have something of value to exchange with high-status employees. High-status
" The indicators for respondents’ job power are almost identical to those for network members' status. However, for the sake of clarity, I use the term "job power" to refer to respondents’ status.
81 employees have more to gain by interacting with powerful workers than with workers
whose jobs lack power. In addition, high-status employees have more power than
low-status employees to choose with whom they interact and to initiate network
relationships. However, women and people of color should have lower-status
network members than men and whites even when they have high levels of job power.
Women and people of color are segregated into occupations and jobs with less
influence and prestige than those occupied by men and whites (England 1992; Reskin
1993). For example, controlling a budget in human resources confers less prestige
and thus brings workers less bargaining power than controlling a budget in sales.'-
Jobs that require incumbents to communicate with employees from a variety of
work groups should affect employees’ likelihood of having high-status network
members in one of two ways. First, they could indirectly affect the chances of
having high-status network members by increasing the size of employees’ networks.
Communicating with employees outside of one’s work group should lead a worker to
come into contact with many employees, increasing his/her chances of interacting
with a manager or director. Second, they could directly affect employees’ access to
high-status employees. Workers who occupy jobs requiring communication with a
wide range of workers should obtain new information quickly. According to social exchange theorists, that information should increase employees’ usefulness in the eyes
■- Jobs are also a signal of employees’ competence. For instance, employees who enter a company through finance or sales jobs are perceived as more competent by other workers than employees who begin their employment in personnel (Rosenbaum 1989:333).
82 of high-status employees. In fact, workers who have network members outside of
their work group are perceived as more powerful by their colleagues than employees
who only have network members within their work group (Ibarra 1993:61).
Because women and people of color are more likely than men and whites to
hold jobs with low prestige, flexibility, and complexity (Parcel and Mueller
1983b: 174; Hodson 1989:391; Glass 1990:787-790), they are likely to receive fewer benefits from occupying a job that encourages inter-group communication than men and whites. For the most part, white women and people of color in high- communication jobs probably obtain routine, unimportant information from predominantly low-status employees. Thus, jobs that encourage inter-group communication should bring white men higher-status network members than white women and people of color.
Demographic Composition of Employees’ Work Floors
The demographic composition of employees’ immediate work environment should influence the characteristics of their network members. Work floors at USF. for example, are organized in terms of functional areas, such as sales and marketing.
1 assume that a large portion of workers’ daily interactions occur on the floors in which they work. 1 hypothesize that the race and sex composition of the floors in which employees work indirectly affect employees’ likelihood of interacting with high-stams employees by influencing the representation of women and people of color in their networks. Employees who work on floors with many people of color should have more people of color in their networks than employees who work with few
83 people of color because the former have more opportunities to interact with people of color. The more people of color that employees have in their networks, the more likely they are to have low-status network members. Similarly, the more women that employees have on their work floors, the greater their opportunity is to have women in their networks and the less likely they are to have high-status network members.
The opportunity to interact with high-status employees may depend upon the number of managers on the floors in which employees work. I expect that the more managers that employees have on their work floors, the more likely they are to have high-status network members because they should have more opportunities to interact with managers during the course of their work day.
Employees’ Organizational Involvement Outside of USF
To the extent that involvement in voluntary organizations increases workers’ resources and visibility, voluntary organization membership should enhance employees’ attractiveness to high-status employees (Epstein 1971; Alderfer 1987).’^
Involvement in voluntary organizations may also provide workers with the opportunity to meet high-status employees that they might not meet in the course of their work.
For example, one employee whom I interviewed told me that her involvement in a charity event allowed her to meet a vice president outside of her area. In addition, I interviewed three human resource employees at USF who said they encouraged
Some research also suggests that organizational involvement increases the size of individuals’ networks, and thus may indirectly increase employees' likelihood of having high-status network members. For example, Fischer (1982:109) found that individuals involved in many voluntary organizations had more non-kin network members than individuals involved in few organizations (see also Campbell and Lee 1992:1094).
84 membership in voluntary organizations as a strategy to meet potential network members.''* While meeting a person hardly guarantees that he/she will become a member of your network, it is a necessary condition.
Employees are involved in many kinds of voluntary organizations that bring them into contact with different types of potential network members. For instance, managers are likely to be involved in organizations whose members are other managers, while non-managers are likely to be involved in organizations whose members are other non-managers.'^ Given that USF is structured to limit contact between high-ranking and low-ranking employees (see Chapter 2), I suspect that such segregation spills over into employees’ non-work activities. In fact, belonging to exclusive voluntary organizations may be one way that managers and officers distinguish themselves from low-ranking employees.'® Thus, managers’ organizational involvement should allow them to interact with more high-status employees than non-managers’ organizational involvement. This, in turn, should lead managers to have network members who are higher in status than non-managers.
Involvement in voluntary organizations may be less beneficial to women than men because men and women belong to different types of voluntary organizations.
For instance, McPherson and Smith-Lovin (1982:890) found that the voluntary
'"* USF encourages its employees to engage in philanthropic work by offering them gift certificates, time off from work, and company recognition for their charitable efforts.
I know of no research that supports this claim.
'® In fact, the correlation between employees’ organizational rank and involvement in voluntary organizations is .22 (see Table 4.1).
85 organizations that men belong to are significantly larger than women’s, net of work
status (e.g., full-time versus part-time). Sex differences in organizational size are
mainly due to the types of organizations to which men and women belong. Men are
most likely to belong to professional, labor, and business organizations, while women
are most likely to belong to social, church, and community organizations (McPherson
and Smith-Lovin 1982:896).*’ McPherson and Smith-Lovin (1982:901) concluded
that "men are located in positions in the voluntary network which are much more
likely to provide access to information about possible jobs, business opportunities, and
chances for professional advancement. " In other words, the voluntary organizations
to which men belong are more likely than the voluntary organizations to which
women belong to have instrumental business contacts who provide them with
resources. In fact. Ibarra (1992:439) found that men received more network benefits
for their professional activity than women.*® Research also indicate that whites and
Blacks belong to different types of voluntary organizations (see McPherson
1977 :203). I also expect that men and women of color belong to racially-segregated voluntary organizations that bring them into contact with other people of color who are less likely than whites to have high status. In sum. I expect that employees' race and sex moderate the effects of voluntary organizational involvement on their
” Men were five times more likely than women to have the opportunity to meet Job- related contacts through their voluntary organizations (McPherson and Smith-Lovin 1982:901)
Ibarra (1992) examined network centrality, which reflects how important an employee is in their network (e.g.. how many network members rely on him/her for informal help).
86 likelihood of having high-status network members.
Characteristics of Employees’ Networks
The size of employees’ networks should be associated with their likelihood of
having high-status network members. Having a large network increases the likelihood
of having heterogeneous network members and such diversity should increase the
chances that employees’ networks will include high-status members (Campbell.
Marsden, and Hurlbert 1986:102). While network size may not be associated with
the average stams of network members it should be associated with the summed
statuses of network members. In my measure of network members’ status, all
network members have some degree of status. Thus, adding a member to employees’
networks increases employees’ score on the dependent variable. I consider network
size to be a control variable because of the manner in which I measured the dependent
variable.
The number of women and people of color that employees have in their
networks should be related to their chances of having high-status network members
because women and people of color tend to occupy low-level positions in work organizations. For example, women and people of color tend to have less job authority and organizational rank than men and whites (Kluegel 1978:290: Mueller and Parcel 1986:209; Hodson 1989:391; Reskin and Ross 1992:350-352; McGuire and Reskin 1993:493). In addition, EEOC reports'^ from USF show that women
USF files annual reports on the race and sex composition of occupations to the Equal Employment Opportunity Commission.
87 and people of color are less likely to occupy managerial positions and more likely to
occupy clerical positions than men and whites. For example, only 39 percent of all
managers at USF were women, whereas 80 percent of all clerical workers were
women. Furthermore. 7 percent of all managers at USF were people of color and 24
percent of all clerical and office employees were people of color. Thus, women and
people of color at USF were underrepresented in high-status occupations and
overrepresented in low-status occupations. As a result, I expect that the more women
and people of color that employees have in their networks, the more likely they are to
have low-status network members. In fact. Brass (1985:336) found that women in
sex-integrated work groups had more access to top executives than women in female-
dominated work groups (also see Ibarra 1992:439).
Multivariate Results
I present the correlation results between the independent and dependent
variables in Table 4.1, but do not discuss them in detail. Table 4.2 presents the
unstandardized and standardized OLS regression coefficients for employees’ likelihood of having high-status network members. The dependent variable measures the
following characteristics of network members: organizational rank, ability to make
major purchases, access to confidential information, and ability to make final decisions. I used one-tailed tests of significance for all hypotheses. I tested all race and sex (two-way and three-way) interactions with employees’ credentials, organizational rank. Job power. Job communication, and involvement in voluntary organizations. I also tested interactions between being female and both of the family
88 characteristics. I tested an interaction between organizational rank and involvement in voluntary organizations as well. I reported only those interactions that reached statistical significance at the .05 level (i.e., non-significant interactions were dropped from the final model).
[Table 4.2 here]
Employees’ Sex and Race
I hypothesized that women would have lower-status network members than men but that being a woman would not affect employees’ likelihood of having high- status network members, net of other relevant factors. The correlation results indicate that women were less likely to have high-status network members than men
(r= -.07; Table 4.1). However, the non-significant coefficient for being female in the multivariate analysis indicates that women were not significantly less likely than men to have high-status network members, net of their personal, job, organizational, and network characteristics (see Table 4.2).-° This finding suggests that women were not excluded from networks of high-status employees simply on the basis of women’s sex status.-' Rather, women’s network disadvantage was due to their disproportionately occupying positions in the corporation in which they controlled few resources and interacted with low-status employees. Chapter 3 showed that women had fewer credentials and lower organizational positions than men, which decreased
-° The regression coefficient for being female was non-significant in a model that contained only the coefficients for employees’ race (results not shown).
-‘ I discuss the difficulty of defining exclusion in Chapter 6.
89 workers’ likelihood of having ties to high-status employees. In fact, while 59 percent
of USF employees were women, only 39 percent of managers were women. Contrary
to my hypotheses, the effects of network, job, and personal characteristics on
employees’ likelihood of having high-status network members did not depend upon
employees’ sex. Thus, I found no evidence that men’s credentials and organizational
positions brought them significantly more high-status network members than women.
Blacks were less likely than whites to have high-status network members
(r= -.19; Table 4.1). However, the multivariate analysis in Table 4.2 indicates that
this negative relationship is explained by the fact that Blacks were less likely than
whites to occupy organizational positions that would have increased their opportunity to interact and thus form trusting relationships with high-status employees. In addition. Blacks were less likely than whites to control corporate resources that high- status employees would want.
The multivariate results indicate that Asians were not significantly less likely than whites to have high-status network members, net of other relevant factors."
The main reason why Asians were less likely than whites to have high-status network members was because they, like Blacks, were less likely than whites to occupy the organizational positions that would have increased their access and attractiveness to high-status network members. For instance, whites’ average organizational rank was
1.80. while Asians’ average organizational rank was 1.35 (results not shown).
" The non-significant correlation between being Asian and employees’ likelihood of having high-status network members was -.01 (see Table 4.1).
90 Similarly, Asians exercised less job power than their white counterparts. Whites had an average job power score of .46, while Asians had an average job power score of
-.96.
Latinos were less likely than whites to have high-status network members because they too were less likely than whites to hold organizational positions that gave them the chance to form trusting exchange relations with powerful employees.^ For instance, whites’ average organizational rank was 1.80, while Latinos’ average organizational rank was 1.30. Thus, Latinos were less likely than whites to regularly interact with high-status employees as part of their job. In addition, whites had an average job power score of .46, while Latinos had an average job power score of
-.90. As a result, high-status employees had less of an incentive to interact with
Latinos than with whites.
The results for being a member of a racial minority group other than Asian,
Black, and Latino are parallel to those for Blacks and Latinos.-^ Being a member of this other minority group did not significantly decrease employees’ likelihood of having high-status network members, net of other relevant factors. Furthermore, such minorities did not receive fewer network remms to their personal or job characteristics, relative to whites.
The non-significant correlation between being Latino and employees’ likelihood of having high-status network members was .01 (Table 4.1).
Thirty-six employees placed themselves in this category, which includes Native Americans and mixed-raced individuals.
91 In sum, these results provide partial support for the hypotheses regarding the effects of employees’ race and sex on their likelihood of having high-status network members. Consistent with my prediction, the negative effect of being female on employees’ likelihood of having high-status network members was explained by the fact that women were less likely than men to have the characteristics that would have increased their access and attractiveness to high-status employees. In other words, women did not have fewer high-status network members than men simply because of their sex status. However, contrary to what I expected, employees’ sex did not moderate the effects of employees’ personal and job characteristics on their chances of having high-status network members. For instance. I found no evidence that women’s job power made them less attractive network members to high-status employees than men’s job power. Similarly. Blacks. Asians. Latinos and other minorities (i.e.. those who were not Asian. Black or Latino) had lower-status network members than whites because they were less likely than whites to have the job and network characteristics that would have increased their access to high-status employees. Contrary to my hypotheses. I found no evidence that people of color received fewer network returns to their personal, job, and network characteristics than whites. Thus, I found only partial support for Kanter’s token thesis—numerical minorities were more likely than whites to be on the fringes of powerful corporate networks. However, the reasons for these race differences are likely due to structural, rather than personal, exclusion.^
^ Structural exclusion refers to how organizational arrangements and the allocation of jobs limit employees' opportunities to form network ties, while personal exclusion refers to individuals’ attempts to prevent an employee from participating in a network.
92 By this I mean that minorities’ structural characteristics precluded them from
interacting with high-status employees. Had high-status persons excluded people of
color from their networks, I would have expected that being a person of color would
have directly diminished employees’ likelihood of having high-status network
members, regardless of their organizational positions. However, because I did not
collect data on personal exclusion, this interpretation is purely speculative.
Employees’ Personal Characteristics
Neither education, financial designations, nor company tenure directly affected
employees’ likelihood of having high-status network members, net of other factors.
In addition, the effects of employees’ credentials on their chances of having high-
status network members did not depend upon their race or sex. However, employees'
credentials influenced their access to high-status network members indirectly through
their effects on job characteristics. Respondents’ years of education, years of tenure, and financial designations significantly enhanced their likelihood of having high-status
network members before I included the job characteristics into the model (analysis not
shown). Thus, employees’ credentials were important in terms of the organizational positions and power that they helped workers to obtain.
An example of personal exclusion is when a high-status employee does not invite an employee to lunch with his/her other network members (for example, see Granovetter [1995:172-175] and Thomas [1990:189]). Structural exclusion can occur when employers segregate workers into low-ranking positions that limit their opportunity to interact and thus form relationships with high-status employees (see Kanter 1977a). It is possible that USF leaders and employees personally exclude people of color from organizational positions that could increase minorities’ opportunities to have high-status network members, but 1 did not collect data that can address such a possibility.
93 Neither measure of family status, marital status and number of children, significantly affected employees’ likelihood of having high-status network members.
In addition, employees’ sex did not moderate the effects of family characteristics on their likelihood of having high-status network members. Thus, these results provide no support for the argument that marriage and children decrease women’s, but not men’s, likelihood of having high-status network members. That employees’ sex did not interact with their family characteristics may also be to a selection effect among my sample of full-time workers. I may have only sampled those women who had devised solutions to their work-family conflicts (e.g.. hired a maid).
Employees’ Job Characteristics
Employees’ organizational rank was critical in determining their likelihood of having high-status network members. Managers and officers were more likely than non-supervisors to have high-status network members, consistent with my hypothesis.
Employees with high rank likely have many opportunities to interact with high-status employees during the course of their work day and have access to resources that high- status employees want.
The multivariate analysis reveals that employees’ job power was positively related to their likelihood of having high-status network members. The more job power employees had. the more likely they were to have high-status network members. Employees with high job power controlled important corporate resources and thus were beneficial to high-status employees. High-status employees who provided help to employees with high job power could expect something of value in
94 return for their efforts. Contrary to my expectations, however, the effect of job power on employees’ likelihood of having high-status network members did not depend upon their race or sex. Thus, once women and people of color acquired jobs that gave them control over corporate resources they were no less likely than men and whites to develop ties to high-status employees.
In sum. employees’ job characteristics played critical roles in their likelihood of having high-status network members. Before the inclusion of the interaction terms, job power and organizational rank had the largest standardized coefficients in the model (excluding network size). These findings lend support to network scholars’ argument that organizational position is pivotal in determining employees’ informal interactions.
Demographic Composition of Employees’ Work Floors
Neither the race nor sex composition of the floors in which employees worked had significant effects on employees’ likelihood of having high-status network members, net of the included variables."* In addition, the managerial composition of the floors in which employees worked did not significantly influence employees’ likelihood of having network ties to high-status employees. However, the percentage of people of color on the floors in which employees worked was negatively related to
-* 1 found multicollinearity between the total number of employees on workers’ floors of employment and the total number of women on employees’ work floors, which were the original measures of floor composition. To correct this problem. 1 used proportional measures of race and sex floor composition (e.g.. the percentage of female employees on the floors in which employees worked). The results for the proportional measures were no different from the results for the absolute measures.
95 employees’ likelihood of having high-status network members in a model that did not
include the network characteristics (network size, number of women in network, and
number of people of color in network). Employees who worked on floors with a
large percentage of people of color tended to have smaller networks than employees
who worked on floors with a small percentage of people of color.-’ Thus, the race
composition of the floors in which employees worked was indirectly related to
employees’ chances of having high-status network members.
Employees’ Organizational Involvement Outside of USF
Being involved in voluntary organizations increased employees’ likelihood of
having high-status network members. Social exchange theorists would argue that
employees' involvement in voluntary organizations gave them access to novel
resources, which increased their bargaining power with high-status employees. Being
involved in voluntary organizations may have also increased employees’ opportunity
to build trusting relationships with high-status employees. However, to distinguish
between these two explanations I require information on what employees do in their
voluntary organizations and what they receive from participating in such
organizations. Contrary to my hypothesis, the effect of employees’ involvement in
voluntary organizations on their chances of having high-status network members did
” In addition, working on a floor with a large percentage of people of color was negatively related to years of education, having financial designations, the number of organizations in which employees were involved, inter-group communication, organizational rank, and job power. In addition, women. Blacks, Latinos, and other minorities (excluding Asians) were likely to work on floors where a high percentage of the workers were people of color (see Table 4.1).
96 not depend upon their race or sex. In contrast to what I expected, I found no evidence that high-ranking employees gained more network benefits from their
involvement in voluntary organizations than other employees.
Characteristics of Employees’ Networks
The multivariate results indicate that the characteristics of employees’ networks were important in explaining their likelihood of having high-status network members. The number of network members that employees had was positively associated with their likelihood of having high-status network members. Thus, employees with large networks tended to have network members with higher status than employees with small networks.-* In addition, the representation of women in employees’ networks was negatively related to their chances of having network ties to high-status employees. The representation of women in employees’ networks had the same effect on employees’ likelihood of having high-status network members for white men. white women, men of color, and women of color. This finding does not mean that the addition of a woman to employees’ networks diminished their chances of having high status network members because the addition of a woman increased network size. To illustrate this point, 1 substituted respondents’ mean values on the
'* 1 ran an OLS regression model with network size as the dependent variable, given the magnitude of its effect on employees’ likelihood of having high-status network members (adjusted R-square = .05). Years of education, involvement in voluntary organizations, a job involving inter-group communication, and job power were positively related to network size (significant at the .05 level, one-tailed tests), net of other personal and job characteristics. Thus, while education and inter-group communication did not directly affect employees’ likelihood of having high-status network members, they helped employees indirectly by increasing their number of network members. In addition, being Latino was positively associated with network size (two-tailed test of significance).
97 independent variables into the regression equation for employees’ likelihood of having high-status network members. An employee with average values on all of the determinants had a value of 5.49 on the dependent variable. If this employee added one woman to his/her network, the value of the dependent variable became 6.04. If this employee added one man to his/her network instead of one woman, the value of the dependent variable became 6.31. Thus, the addition of a woman to an employee’s network increased his/her chances of having a high-status network member, but not as much as the addition of a man.
Some scholars might interpret this finding to mean that to maximize employees’ networks we should dissuade employees from forming informal ties to women at work. There are two problems with such an argument. First, it assumes that women, rather than the social characteristics associated with women, lowered employees’ chances of having high-stams network members. In fact, we know that women occupy organizational positions that grant them less prestige, authority, and power than men (Mueller and Parcel 1985; England 1992; McGuire and Reskin 1993;
Tomaskovic-Devey 1993). Instead of suggesting that employees limit their contact with women, a long-term solution to maximizing employees’ networks would be to enhance women’s organizational power. The second problem with this argument is that the avoidance of women would not guarantee that employees would be welcomed into men’s networks. Thus, avoiding women could lead an employee to have fewer network members and thus be more isolated.
98 The representation of people of color in employees’ networks was not significantly related to their likelihood of having high-status network members, contrary to my hypothesis. This finding is surprising given that people of color at
USF were less likely than whites to hold high-level positions. While 18 percent of whites were managers, only eight percent of people of color were managers. Thus, employees had a small pool of high-status people of color to draw upon as network members. This non-significant coefficient could be due to two factors. First, the number of people of color in employees’ networks was so low and varied so little that it was unlikely to affect employees’ likelihood of having high-status network m em bers.Second, employees may be highly selective when it comes to including people of color in their networks. If people of color at USF are tokens, employees might only include exceptional people of color in their networks. Kanter (I977b;974) argued that tokens face greater performance pressures than majority groups. In one study, for example. 83 percent of Black managers believed that minorities had to perform at higher levels than whites (Fernandez 1981:62). A white manager in
Fernandez’s (1981:62) study observed that. "Most minority executives are superminorities-mediocrity is the privilege of the white male."
The mean number of people of color in employees’ networks was only .54. the standard deviation was .89. and the range was zero to eight.
99 Discussion of Results
In the introduction to this chapter I claimed that an examination of employees’ likelihood of having high-status network members would contribute to our understanding of informal organization at work in two ways: by explaining how employees acquired high-status network members and by elucidating the mechanisms through which employees’ sex and race influenced their likelihood of having high- status network members. I discuss each of these contributions in turn.
This analysis revealed that job and network factors were more important than personal characteristics in affecting employees’ likelihood of having high-status network members. For instance, job power and organizational rank had the largest standardized coefficients in the model before the inclusion of the interaction terms.
Furthermore, the inclusion of the three network factors significantly Increased the explanatory power of the model. The adjusted R-square went from .46 before the inclusion of the network characteristics to .63 when these factors were included in the model (analysis not shown).In contrast, employees’ credentials did not directly influence their likelihood of having high-status network members, which suggests that employees’ credentials did not enhance their bargaining power with high-status employees. Rather, employees’ organizational positions gave them the opportunity to interact with and to attract high-status network members. Because high-status network members are only one type of social capital at work, we need analyses that examine whether the determinants identified in this study influence other types of
Most of this increase was due to network size.
100 social capital. Chapter 5 helps to meet this need by examining how organizational
location and personal characteristics influence the amount of work-related help
employees receive from their network members.
These findings also enhance our understanding of how employees’ race and sex
affect their access to informal networks. I found that employees’ race and sex were
consequential for their likelihood of having high-status network members. The
bivariate results indicate that women and Blacks were less likely than men and whites
to have high-status network members. However, being a woman or a person of
color, in and of itself, did not directly prevent employees from forming network
relationships with high-stams employees. Instead, the effects of employees’ race and
sex on their likelihood of having high-stams network members operated through
intervening mechanisms, such as their organizational rank, job power, and
representation of women in their networks. In addition, I found no evidence that employees’ race and sex interacted to affect employees’ likelihood of having high-
stams network members. For instance. Black women and Black men did not significantly differ in terms of their network remms to their job power. However,
recall from Chapter 3 that women of color had significantly lower organizational rank than white men. white women, and men of color. Therefore, women of color were more disadvantaged than the other race-sex groups in that they were less likely to occupy high-ranking organizational positions, a key determinant of having high-stams network members.
101 These results also offer insights into developing equitable employment practices in work organizations. First, they suggest that efforts to improve minorities’ informal networks through networking seminars or assertiveness training would have limited success. Workers acquire social capital by occupying jobs that offer them the opportunity to interact with other employees and to control corporate resources. If companies are serious about equalizing access to informal networks, they would provide white women, women of color, and men of color with more opportunities to interact with high-status employees. One strategy to achieve this goal would be to give white women and people of color cross-functional assignments that allow them to interact with employees in different divisions, increasing their exposure to high-status employees.
Second, they suggest that increasing minorities’ numbers in work organizations would have beneficial, although limited, effects on minorities’ social capital. Women appear to have benefited from their large numbers at USF insofar as their proportional representation in high-level positions was greater than that of their minority counterparts. While 11 percent of women at USF were managers, 8 percent of people of color at USF were managers.^' In contrast, 25 percent of men were managers and 18 percent of whites were managers. Thus, women, particularly those who were white, had more opportunities than people of color to interact with high- status employees. However, despite their greater numbers, women had lower-status
In addition, only 6 percent of women of color were managers and 14 percent of men of color were managers.
102 network members, on average, than men. This is because women were segregated into organizational positions that diminished their ability to attract and to interact with high-status network members. For instance, women were less likely than men to work on floors with high percentages of managers. Thus, increasing racial minorities’ numbers in corporations would have limited effects on their likelihood of having high-status network members if they are segregated into positions that give them little to no opportunity to work with high-status employees and to control information, decisions, and people.
Third, desegregating jobs and departments could equalize the distribution of social capital by encouraging white men. white women, men of color, and women of color to interact in small groups on similar goals. This would provide workers with more opportunities to develop trusting cross-race and cross-sex relationships
(Pettigrew and Martin 1987). Work organizations should also pay more attention to how their physical arrangements influence cross-race and cross-sex interactions.
Rather than organizing work floors by functional area, like USF does, organizations could vary the departmental composition of workers on floors to increase the likelihood that employees of different races and sexes would interact. Thus, we need to put minorities into positions where they interact with high-status employees but we also need to provide them with opportunities to form relationships with high-status employees. Employees are most likely to form relationships with each other when they regularly interact in a cooperative environment.
103 In addition to increasing white women’s and minorities’ access to beneficial network members, employers need to reduce their reliance on informal methods to allocate jobs, promotions, and other work rewards. Corporations rely on informal mechanisms of evaluating and rewarding employees, despite the availability of formal, standardized methods, such as written performance evaluations (Kanter 1977;
Braddock and McPartland 1987; Pettigrew and Martin 1987).^- For example, hiring managers often rely upon their informal networks to identify job candidates (Kanter
1977a; Jackall 1988). From the managers’ and the company’s point of view, this is an efficient and inexpensive way to fill job vacancies. My interviews with managers and officers at USF indicate that they regularly use informal methods to fill job vacancies. One upper-level manager at USF said that when people rose to positions of power "they seem to be appointing their friends or people that have worked for them before, to other higher positions. And it looks, um, parochial, it looks closed.
[But ajfter having observed that for a long time, 1 don’t think there’s anything really sinister about it . . . You know the safest thing to do is to take people whose work 1 know . . . and who 1 know 1 can count on and who won’t let me down .... It’s safer to do that than to try somebody new." Given that informal networks tend to be segregated by sex and race, the use of informal networks to fill jobs and determine work rewards is most likely to benefit workers who already occupy high-level positions in work organizations (Brass 1985:336; Scott 1996:242). In fact, Drentea
For example, Jackall (1988:33) found that managers often made hiring decisions based upon how comfortable they felt around a candidate and their "gut feelings" about a candidate.
104 (1997:20) found that women who used informal search methods found jobs that were
more segregated by sex than women who used formal search methods. Furthermore,
she found that the use of informal job search methods decreased women’s earnings
but had no effect on men’s earnings (Drentea 1997:23).^^
There are a range of formal methods that employers can use to evaluate workers, to allocate jobs, and to distribute work rewards. Companies could employ corporate-wide job postings so that white women and people of color have the opportunity to apply for jobs that they might not hear about through their informal networks. Employers also need to develop performance evaluation procedures that accurately reflect job skills.Many evaluation systems assign less weight to the skills found in women’s jobs than men’s jobs or disregard the skills involved in women's jobs altogether (Phillips and Taylor 1980: Jacobs and Steinberg 1990;
England 1992). These changes could increase managers’ confidence in, and thus use of, formal evaluation procedures. Furthermore, the use of formal evaluation methods could enhance managers' ability to recognize and reward white women’s and minorities’ skills and thus increase their attractiveness to high-status network members. However, it would be foolish to expect that formal work practices could ever completely replace informal ones. People are, after all, social beings who are
” Women who used informal job search methods earned an average of $13,566 per year, while women who used formal job methods earned an average of $17,821 per year (Drentea 1997:23).
’■* Employers could look to states that have adopted comparable worth policies to develop more equitable job evaluation systems.
105 bound to be influenced by the informal ties they form with each other. Nevertheless, companies can take steps to enhance the social capital of disadvantaged groups by increasing their access to different network members and by adopting more formal work procedures.
106 i 2 I 4 £ 6 2 & 2 1 2 1 1 12 u 11 11
1-Woman 1.00 .16" .00 -.03 .01 -.22" .07- .12" -.16" 09- .02 -.07" -.10- -.12" .07"
2-Black 1.00 ..1 2 - .12" .12" .16- -.15" -.07- -.19" .00 .01 -.18" -.2 3 - -.16" .18-
3-Asian 1.00 -.03 -.03 .11- -.01 -.15" .01 05 -.05 -.03 -.04 .07- -.03
4-Latino 1.00 .03 .03 -.02 -.06 .02 -.02 -.08" -.07" -.05 -.06" .07"
5-Other 1.00 -.03 -.02 .01 .01 .01 .01 .00 -.W -.0» .09" .Mibority
6-Education 1.00 .30- -.17" .01 -.01 .18- .17- .38" .29" -.36-
7-Fmaocial 1.00 .12- .10" .04 .17" .10" .42- .28" -.14- Designations
B-Tenure 1.00 .16" .11- .02 .11- .21- .20" .01
9-Marttal 1.00 .32- -.00 .12" .11- .09- -.03 Status
lO-Nunbcr ot 1.00 -.02 .04 .01 .02 .01 QiiUren
II Voluntary 1.00 10- .22- .24- -.09" Organizations
12- 1.00 .25" .35- -.21" Coaununicate Outside Croup
13-Rank t.OO 50" -. 21"
l4'/ob Power 1.00 -.23"
15-Race 1.00 Comp, of Floor
* Significant at the .05 level. Table 4 .1 continued on next page.
Table 4.1: Correlations Between Independent and Dependent Variables.
107 Table 4.1 (continued).
12 IS 12 a 21 a 21 a a
UWonun .30- -.07- -0 4 31" .14- .02 .07" -.01 -.07" -.08"
2-6tack .14- .15- -.13" .10" .39- -.06- -.11" -.06" -.19" -.15"
Asian -0 5 -.04 05 -.01 -.01 .03 -.08" -.00 -.01 -.02
4* Latino .01 .02 05 .02 .00 -.02 .01 -.02 .01 .03
S ^ b e r .06 -.05 .01 .03 .11" .02 .00 .03 -.06 -.03 .Minority
6> Education -.09- 36- .17- -.01 -.09- -.06" .04 .07" .30" .20-
7>Financial -07- .28" .05 -.06 .10- -.05 .02 .00 .27" .09- Desipxatiofts
8-Tenure -.04 -.03 -.03 -.04 -.10- .02 .01 -.08" .11" -.06
9-8(ariiaJ -0 9 - .07" 07- -.08" -.15- .03 .00 -.02 .09" .06- Scants
10-Number of -.04 -.00 -.01 -.06" -.03 .04 .04 .01 .01 .01 Children
II Voluntary .02 .12" .12- .08" .05 -.04 .03 .06" .28" .21- Organizations
12- - 12- 14- .13- -.01 -.11" -.00 .12- .09" .27" .22" Cotntnunicate Outside Group
12-Rank -.06 36- 13- -.11" .15" -.13" .04 -.01 .57" .26"
14-lob Power - 07- .21- 16- -.01 .12- -.02 .11- .07" .57- 31"
IS-Race .31- -.50" .12" .07" 21" -.05 -.07" -.04 -.25" -.15" Cienp. of Floor
16-Sex Comp, 1.00 -.26" .03 .35" .16- .04 -.02 -.05 -.06" -.10" of Floor
17-Manager t.OO .07" -. 10- .17- -.06 .03 .02 .27- .16- Comp, of Floor
18-Network 1.00 .56" .20" -.03 .04 -.00 J l " 65 - Size
19-Number of 1.00 .30" .02 .06 -.01 .13" .30- Women
2(VNumber of t.OO -.08" .04 .04 -.05 .09- People of Color
21-Proximity 1.00 .13" .13" -.12" -.03
22-Closeness 1.00 .49" .09" .22"
23-Sncializing 1.00 .02 18"
24-Network 1.00 61- Member Status
25-Work Help 1.00 from Network
• Sttnifiont u the .05 level.
108 Characteristic Variable V n A W ard W h standardized Hypothesized (interactions italicized) (Standard error) Effect
SEX AND RACE OF W oman {l=woman, 0=man) 0.321 0.049 RESPONDENT (.150)
Black (1 =Black, 0=white) 0.175 0.024 (.172)
Asian (1= Asian, 0=white) 0.106 0.006 (.365)
Ladno (I=Latino, 0=white} 0.714 0.042 (.347)
Other race (I =other, O^whüe) -0.428 - 0.020 (.448)
PERSONAL Years o f Educadon -0.054 -0.032 + (.042) Financial Designadons 0.017 0.002 + (.172) Years of Company Tenure -0.061 -0.024 + (.058)
Marital Status -0.134 - 0.020 - for women (.150)
Number of Children at Home 0.092 0.027 - for women (.072) JOB Organizadonal Rank 1.040* 0.332 + (.086) Job Power 0.383* 0.318 + (.030) Communicadon Outside of 0.020 0.007 + W ork Group (.060)
' Significant at the .05 level. 1-tailed test. Table 4.2 condnued on next page.
Table 4.2: OLS Regression of Likelihood that Employees have High-Status Network Members (N=986).
109 Table 4.2 (continued).
Characteristic Variable Standardized Hypothesized Effect (Standard error)
FLOOR COMPOSITION Percent of Women 0.882 0.040 (.527)
Percent of People of Color -0.867 -0.024 (.921)
Percent o f M anagers 1.343 0.037 + (.925)
OUTSIDE SOCIAL TIES Outside Organizational 0.297* 0.096 Involvement (.065)
NETWORK Number of Women in Network -0.266* -0.152 (.050)
Number of People of Color in -0.053 -0.018 Network (.069)
Network Size 0.825* 0.491 (.045)
Constant -5.391* (.819)
Adjusted R-square = .627
' Significant at the .05 level. 1-tailed test.
1 1 0 CHAPTERS
MULTIVARIATE RESULTS FOR WORK-RELATED HELP FROM
NETWORK MEMBERS
Network scholars often refer to informal networks as social resources or social capital because they channel the flow of information, influence, power, and resources in organizations (Campbell, Marsden, and Hurlbert 1986:98; Lin, Ensel, and Vaughn
1981:395; Lin and Dumin 1986:365-366). These resources are social in that they are a product of relationships, rather than of individuals (Lin 1982:132; Campbell,
Marsden, and Hurlbert 1986:98). These resources are also social in that the relationships from which they stem are conditioned by individuals’ social statuses.
Social networks help people to acquire resources that would difficult to obtain on their own. In fact, informal networks are the most common method of finding a job, and workers who use informal search methods find jobs with higher pay and status than workers who use formal search methods (Lin, Ensel, and Vaughn 1981:397; DeGraaf and Flap 1988:461; Granovetter 1995:11; but see Drentea 1997:23).
While research has documented how informal networks influence workers’ entry into work organizations, we have little knowledge of the resources that workers receive from their network members once they are inside a work organization (Cook
1 1 1 and Whitmeyer 1992; Nohria 1992; Granovetter 1995).* As a result, we lack information about the informal mechanisms through which resources are distributed at work. I address this scholarly gap by examining the factors that influence the amount of work-related help that employees receive from their network members.^ Building upon the theoretical framework presented in Chapter 4, I examine how employees’ personal, job, network, and outside organizational characteristics influence the amount of work-related help they receive from their network members.
In Chapter 4 I described three factors that are important in explaining the characteristics of employees’ informal networks-the opportunity to interact with others, the time to develop relationships, and the resources to attract network members. The network perspective indicates that two additional factors should influence the help that workers receive from their informal network members—the strength of employees’ network relationships and the physical proximity of employees’ network members. The strength of a network relationship should influence a network
‘ Research has explored structural characteristics of workers’ networks (Brass 1984; Ibarra 1995). For instance, Ibarra (1995:432) examined network centrality in workers’ communication, advice, support, influence, and friendship networks. However, few smdies have examined the factors that influence the amount of help that workers receive from their network members.
^ My work-help measure is composed of six indicators: help with a work-related problem, help in getting around bureaucratic hurdles, help in meeting employees in other areas of the company, help in meeting high-level employees, help in getting a new position or promotion, and help in getting one’s work recognized (see Chapter 2).
1 1 2 member’s motivation to help an employee. The physical proximity of a network member should affect how accessible he/she is to an employee and thus the frequency with which he/she provides help to an employee.
This study lends insight into our understanding of social capital by identifying the personal, job, and network characteristics that increase the amount of help that employees receive from their network members. For instance, it informs us as to whether personal resources help employees to acquire social capital even when they occupy low-level Jobs. It also extends Granovetter’s (1973; 1995) strength-of-weak- ties thesis by examining how tie strength affects the provision of help by network members in the workplace. Finally, it helps to identify the conditions under which employees’ sex and race influence the benefits they receive from their network members. Because I examine several disadvantaged groups, this study provides a rare account of informal stratification in a work organization.
Employees’ Race and Sex
Most research has not examined the effects of employees’ sex and race on the work-related help they receive from their network members. I formed hypotheses by drawing upon the same theoretical perspectives that I used to hypothesize the effects of employees’ race and sex on their likelihood of having high-status network members. I expect that employees’ sex and race affect the work help they receive from their network members in two ways. First, employees’ race and sex should indirectly affect the work help they receive from their network members. I expect that white men are more likely than white women and people of color to have the
113 organizational positions and network characteristics that increase the amount of help they receive from their network members. Second, I expect that employees receive different amounts of work help from their job and network characteristics, depending upon their race and sex. Specifically, I expect that white women and people of color receive fewer network benefits (i.e., work-related help) than white men from their job and network characteristics.
Employees’ Personal Characteristics
Social exchange theory suggests that employees’ credentials influence their attractiveness to potential network members by increasing employees’ perceived competence and influence.^ This perspective implies that employees’ credentials directly influence the amount of help they receive from their network members by increasing their bargaining power. However, I know of no research that supports this hypothesis. Chapter 4 indicated that employees’ credentials indirectly influenced their likelihood of having high-status network members by affecting their job characteristics. Thus, I expect that employees’ credentials have a positive, but indirect, effect on the amount of work-related help they receive from their network members. I also expect that the positive effect of company tenure on the work help employees receive from network members is weaker both for employees with very short and very long company tenure than for employees with moderate company tenure. Employees with short tenure have not been at the company long enough to
^ This perspective differs from human capital theory, which assumes that employees’ credentials actually increase their productivity.
114 establish trusting relationships with their network members, which should lead them to receive less work help from their network members than workers who have been at
USF for a moderate number of years. Workers with long company tenure may not need as much help from their network members as do workers with moderate company tenure because of their experience and thus may receive less work-related help from their network members than employees with moderate company tenure.
Senior employees may give more help than they receive from network members.
Employees’ family characteristics may affect the amount of time they have to interact with their co-workers. Being married and having children may diminish employees’ ability to socialize with their network members and to join voluntary organizations. Family characteristics should be particularly important in explaining sex differences in the amount of help that employees receive from their network members. Given that women perform a majority of household labor (see Thompson and Walker 1991 for a review), women should have less time to interact with their network members than men, thus decreasing the amount of help that women receive from their network members. In sum, I expect that employees’ family characteristics indirectly affect the amount of work help they receive from their network members by both affecting the amount of time that employees have to socialize with their network members and by affecting the number of voluntary organizations in which employees are involved."*
■* To test this possibility I examine whether employees’ family characteristics affect work help from their network members before I included employees’ involvement in voluntary organizations and amount of socializing with network members into the model.
115 Employees’ Job Characteristics
Chapter 4 indicated that employees’ organizational rank and job power influenced their likelihood of having high-status network members by increasing employees’ opportunity to interact with and to attract high-status employees. These results suggest that organizational rank and Job power indirectly affect the amount of help that employees receive from their network members. However, high rank and job power may also directly influence assistance from network members by providing employees with resources that increase their bargaining power and thus increase the amount of help they obtain from their network members. Workers with high rank and high job power should be sought after by other employees because of their ability to reciprocate in social exchanges. However, past research suggests that network returns to organizational rank and job power are greater for men and whites than for women and people of color (see Chapter 4).
Job communication did not directly influence employees’ likelihood of having high-status network members (see Table 4.1). However, it may affect the amount of work help that employees receive from their network members. Employees who interact with workers outside of their immediate work group should have more diverse networks than workers who interact mainly with members of their work group. Heterogeneous networks should provide employees with more work-related help than homogeneous networks because the former should have more resources
(Granovetter 1982; 1995). I also expect that the network returns to job communication depend upon employees’ race and sex, given that white women and
116 people of color tend to hold jobs with low prestige, authority, flexibility, and
complexity (Parcel and Mueller 1983b: 174; Hodson 1989:391; Glass 1990:787-790).
Even when white women and people of color occupy high-communication jobs, the
resources they control should be less lucrative than the resources white men control in
high-communication jobs because these groups occupy different types of high-
communication jobs.
Demographic Composition of Employees’ Work Floors
The sex, race, and managerial composition of the floors in which employees
work should influence with whom workers come into contact because floors at USF
are organized by functional area (e.g., marketing) and I assume that a majority of
employees’ interactions occur on the floors in which they work.^ This, in turn,
should affect the characteristics of workers’ networks. I expect that the race, sex,
and managerial composition of work floors indirectly affect the amount of work-
related help that employees receive from their network members by affecting the
characteristics of their networks.
Employees’ Organizational Involvement Outside of USF
Employees’ outside organizational involvement should affect the help they
receive from their network members for the same reasons that it affected workers’
likelihood of having high-status network members. Being involved in voluntary organizations should increase workers’ resources which, in turn, should increase their
^ I have no evidence to support this assumption, however. My future qualitative research will explore with whom employees regularly interact.
117 ability to obtain aid from their network members. However, employees with high rank should be more likely than employees with low rank to be involved in organizations that have instrumental business contacts. As a result, managers’ organizational involvement should lead them to receive more help from their network members than non-managers’ organizational involvement. I expect that white men receive more network resources by being involved in voluntary organizations than white women and people of color because they belong to different types of voluntary organizations (see Chapter 4).
Characteristics of Employees* Networks
Large networks tend to be more diverse than small networks and therefore should bring employees both a wider range of work-related help and more work- related help than small networks (Campbell, Marsden, and Hurlbert 1986:102). Most of the research on the association between network size and help from network members has focused on social support from community networks rather than work- related help from network members. Community research supports my proposed hypothesis that larger networks provide more support than smaller networks (For a review, see Walker, Wasserman, and Wellman 1993:81). I consider network size to be a control variable because of the way I measured work-help from network members; adding a network member increases the dependent variable in most cases
(see Chapter 2).
118 The representation of women in employees’ networks could affect the work
help that employees receive from their network members in two ways. First, it could
indirectly influence how much help employees receive from their network members
by affecting other aspects of their networks. For instance. Chapter 4 revealed that the
number of women in employees’ networks was negatively associated with their likelihood of having high-status network members. Thus, employees with many women in their networks should receive less work-related help from their network members than employees with few women in their networks because the former should tend to have low-status network members. Second, some research suggests that the representation of women in employees’ networks directly affects the amount of work-related help that employees receive from their network members.
Community studies have found that women provide more emotional support to their network members than men (Wellman 1990:64; Wellman and Wortley 1990:576).
Women and men also report that they prefer to receive emotional support from women (Wellman 1990:68). Thus, employees may request more emotional than instrumental aid from female network members. As a result, the number of women in employees’ networks may directly affect the amount of work-related help employees receive from their network members.
The representation of people of color in employees’ networks had no effect on their likelihood of having high-status network members. I argued in Chapter 4 that employees may be highly selective about which people of color they include in their
119 networks. This suggests that the number of people of color in employees’ networks should not influence the amount of work-related help employees receive, net of other relevant factors.
High-status network members should provide employees with more work- related help than low-status network members because of their influence over people, decisions, and information (Lin, Ensel, and Vaughn 1981; Lin 1982; Campbell,
Marsden, and Hurlbert 1986; Aiderfer 1987). In addition, high-status employees are more likely than low-status employees to have a diverse network of workers
(Campbell, Marsden, and Hurlbert 1986:99). According to Lin and Dumin
(1986:376), " ...the strength of a higher position is due to its access to higher occupations while maintaining its access to lower occupations. The advantage is being able to reach upward in the occupational structure, without sacrificing access to lower positions." Such diversity should increase the amount of resources that high- status network members provide to workers. In fact, individuals who use high-status job contacts find better jobs than those who use low-status job contacts (Lin, Ensel, and Vaughn 1981:398; Lin and Dumin 1986:366; DeGraaf and Flap 1988:463).
Brass (1985:338) also found that employees who were connected to high-status network members were more likely to be promoted than those who were not connected to high-status network members.®
However, some studies suggest that the benefits of having high-status network members vary by employees’ race and sex. Ibarra (1995:693) found that white
® This finding was based upon correlation analysis.
1 2 0 managers perceived their high-status network members as more useful in their careers than minority managers. This may be because race and sex segregation lead white women and minorities to interact with high-status network members who, like themselves, are segregated in departments that confer little prestige and influence.
Even when white women and people of color have high-status network members they may receive less work-related help from them than white men. High-status network members may assume that white women and people of color are less likely to benefit them than white men and thus may put less effort into helping them than white men.
In sum, I expect that having high-status network members increases the amount of work-related help that employees receive from their network members, but more so for white men than for white women and people of color.
Much of the research on the effects of tie strength compares the benefits of strong ties to weak ties in terms of finding jobs (Lin, Ensel, and Vaughn 1981; Lin and Dumin 1986; Granovetter 1995). Granovetter (1982; 1995) explained that weak ties are more helpful than strong ties in individuals’ job searches because weak ties connect individuals to people who possess novel information. According to
Granovetter (1982:112), "the significance of weak ties is that they are far more likely to be bridges than are strong ties." For example, Lin and Dumin (1986:377-381) found that weak job contacts led individuals to a wider range of occupations and to higher-stams occupations than strong ties. In sum, job-search research suggests that a
1 2 1 network composed mainly of weak ties provides more instrumental help to employees than a network composed mainly of strong ties (Lin and Dumin 1986; Granovetter
1995; also see Burt 1992).
Granovetter (1982) acknowledged that strong ties might be more important than weak ties in crises and in uncertain situations (also see Krackhardt 1992).
Groups that are economically insecure are more likely to rely upon strong ties than weak ties because the former are more reliable and readily available, according to
Granovetter (1982:114-115). Network members who have strong ties have greater motivation and commitment to help each other than those who have weak ties
(Granovetter 1982; 1995). For example, Stack (1974:33) found that poor urban Blacks relied upon an extensive network of close ties for services and resources. Strong ties may be important to USF employees, given the recent changes in workers’ security.
Like other large corporations, USF has downsized, cut employee benefits (particularly for non-supervisory employees), and increased its reliance on outsourcing. Because of the lack of research on the effect of tie strength on work-related help from network members, I test whether strong or weak network ties provide more work-related help to employees.
Some scholars speculate that strong ties provide more instrumental benefits to white women and minorities than white men. Burt (1995:38) claimed that white women and minorities are viewed as suspect in the workplace and thus have to rely
1 2 2 upon strong ties, such as sponsors or mentors, to succeed at work.’ In fact, Burt
(1992:148,166) found that female managers’ and entry-rank male managers’ advancement relied upon having one or two strong ties.® The people to whom white women and racial minorities are weakly connected may be more susceptible to sex and race stereotypes because weak ties tend to lack personal information on each other (Ibarra 1993:73). As a result, the instrumental benefits of weak ties for disadvantaged groups may be offset by weak ties’ greater potential for stereotyping.
Thus, white women and people of color may receive more work help from strong network ties than white men.
Network members who are physically proximate should be more likely than network members who are physically distant to provide help to each other. While little research has examined how the proximity of network ties affects the exchange of work-related support, proximity is associated with the exchange of social support in community networks, such as lending household items, helping with repairs, and providing childcare (Wellman and Wortley 1990:569).^ Wellman and Wortley
(1990:570) found that even neighbors who did not like each other exchanged services because of their physical accessibility. I expect that workers also rely upon network members who are conveniently located to them for work-related help.
’ Kanter (1977:184) also speculated that sponsors were more important for female employees than male employees because women were viewed by other employees as less competent and trustworthy than men.
® Aside from Burt (1992), there have been few empirical examinations of this claim.
’ Ibarra and Andrews (1993) examined how network proximity influenced employees’ work-related perceptions, but not their exchange of resources.
123 Multivariate Results
Table 5.1 presents the unstandardized and standardized OLS regression coefficients for work-related help that employees receive from their network members. I tested all race and sex (two-way and three-way) interactions with employees’ organizational rank, job power, job communication, involvement in voluntary organizations, network closeness and likelihood of having high-status network members. I also tested interactions between being female and both of the family characteristics and I tested an interaction between organizational rank and involvement in voluntary organizations. I used one-tailed tests of significance for all hypotheses and reported only those interactions that reached statistical significance at the .05 level.
[Table 5.1 here]
Employees’ Sex and Race
Women did not receive less work-related help from their network members than men, net of their personal, job, organizational, and network characteristics. This means that the significant correlation between being female and work help from network members (r= -.083; Table 4.1) was due to women’s lower likelihood of possessing characteristics that attracted network members and of occupying organizational positions that allowed them to interact with network members who had access to corporate resources. The negative effect of being female on work-related help became non-significant when I included employees’ personal characteristics (i.e., credentials and family characteristics) into the model. The effects of network, job.
124 and outside organizational characteristics on the work help received from network members did not depend upon employees’ sex. Thus, contrary to my hypotheses, women did not receive significantly less work help for their organizational positions and network characteristics than men. Furthermore, women of color were not less likely white men, men of color, and white women to receive work-related help from their network members (i.e., the interaction between being female and being Black was not significant).
The mechanisms that generated race differences in the amount of work-related help that employees received from their network members were similar to those that generated sex differences. For instance, Asians were not less likely than whites to receive work-related help from their network members once other factors were taken into account. In fact, the effect of being Asian on work-related help from network members was not significant in a model that only included employees’ race and sex.
However, the effect of being Asian on work-related help was negative and significant in a model that excluded the coefficients for tie strength. Thus, Asians received less work-related help from their network members than whites, in part, because they were less likely than whites to have strong ties to their network members. In addition, Asians were less likely than whites to possess the resources and the positions
125 that would have increased their bargaining power with network members and provided them with opportunities to interact with network members who controlled many resources.
The bivariate analyses revealed that Black employees received significantly less help from their network members than white employees (r= -.15; Table 4.1).
However, the multivariate analyses indicate that the negative effect of being Black on work-related help from network members was explained by Blacks’ location within
USF—the negative effect of being Black became non-significant in a model that included employees’ job and personal characteristics (results not shown).
I found no evidence that Latinos received less work-related help than whites from their network members, net of other relevant variables." In fact, the effect of being Latino on work-related help from network members was not significant in a model that only included employees’ race and sex.
Employees who belonged to a racial minority group other than Asian, Black, and Latino received less work-related help from their network members than whites, net of personal, job, network, outside organizational characteristics.*^ Thus,
Asians’ average score on work help from network members was -.44 and whites’ average score on work help from network members was .51 (results not shown).
" The correlation between being Latino and work-related help was not significant (see Table 4.1).
This finding is difficult to interpret because the specific composition of this group is unknown. The company employs a small number of Native Americans. This group could also include mixed-race individuals. Thirty-six employees placed themselves in this category.
126 contrary to my hypotheses, I found one instance in which employees’ race directly influenced the amount of work-related help they received from their network members.
In sum, employees’ race and sex were important in determining the amount of work-related help they received from their network members in that they influenced employees’ credentials, job characteristics, and network characteristics. Thus, my prediction regarding the indirect effects of race and sex on work-related help from network members was partly supported. Women received less work-related help than men because they were less likely than men to hold the organizational positions that would have increased their access to resource-rich network members. Similar network and job differences between whites and people of color explained why
Asians, Blacks, and other minorities received less work-related help from their network members than whites. However, contrary to my predictions, I found no evidence that women and people of color received less work-related help from their personal, job, and network characteristics. For instance, those women and people of color who occupied high-ranking positions were not less likely than men and whites to receive work help from their network members. I also predicted that neither employees’ sex nor race would directly influence the amount of help that employees received from their network members. However, being a racial minority other than
Asian, Black, or Latino was negatively related to the amount of network help employees received, net of other relevant factors.
127 Employees’ Personal Characteristics
Neither education, financial designations, nor company tenure directly affected the amount of work-related help employees received from their network members, net of other factors. However, employees’ years of education significantly enhanced the work help ± at employees received from their network members before I included the job factors into the model (analysis not shown). Thus, being highly educated helped workers to obtain organizational positions which, in turn, allowed them to attract and to interact with network members.
The effect of company tenure on work-related help depended upon how many years respondents were employed at USF, as indicated by the significant coefficient for tenure squared. To interpret this nonlinearity, I substimted different values of company tenure, ranging from one to five, into an equation that included the derivatives of the tenure coefficient and the squared tenure coefficient.'^ The effect of tenure on work-related help was positive for individuals who had been employed at the company for relatively few years (three years and under), but was increasingly negative for individuals who had been employed at the company for a moderate number or many years (over three years). For example, the effect of tenure for individuals who had been employed at USF for less than one year was .37. The effect of tenure for individuals who had been employed at USF between one and three years was positive, but weaker than that for employees with less than one year of
I used the following equation: .61-2(.12)Tenure = .61-.24(Tenure).
128 tenure. Among individuals who bad been employed at USF for more than three years, the effect of tenure on work-related help was increasingly negative. For example, the effect of tenure on work-related help for employees with seven to nine years of tenure was -.35 and for employees with ten or more years of tenure was
-.59. This finding suggests that, contrary to my hypothesis, very junior employees relied upon network members for work-related help more than workers who had been employed at USF for a moderate or high number of years. One explanation for this finding is that junior employees’ lack of experience led them to be more dependent upon their network members than senior employees. It is also possible that the types of help that the survey asked about were most relevant to junior employees (e.g., help in meeting other employees).
Employees’ family characteristics did not significantly influence the amount of work help they received fi’om their network members—the coefficients for marital status and number of children were not significant. These results suggest that being married and having children did not decrease employees’ interaction time with their network members. In sum, no personal characteristic directly affected the amount of help that employees received from their network members.
Employees* Job Characteristics
To understand how employees’ organizational rank affected the amount of work-related help they received from their network members, we must take into account employees’ involvement in voluntary organizations. The interaction between organizational rank and involvement in voluntary organizations means that employees
129 with high rank received a larger network payoff than employees with low rank for being involved in voluntary organizations. For example, the effect of organizational rank on work-related help for non-supervisors who were involved in one voluntary organization was -.25, while the effect of organizational rank for managers who were involved in one voluntary organization was -A l}^ The effect of being involved in three voluntary organizations was -.13 for non-supervisors, while the effect of being involved in three voluntary organizations was .45 for managers.
The principles of social exchange help us to understand why having outside organizational resources benefited workers, particularly high-ranking ones. Workers who control unique resources outside of their company are in a good position to obtain work help from their network members because they have something of value to exchange with their network members. The resources that high-ranking employees obtain through their voluntary organizations likely increased their attractiveness to— and bargaining power with-network members. As indicated earlier, the effect of organizational rank also depended upon whether an employee was Latino or white.
However, employees’ organizational rank did not significantly interact with being female, Asian, Black, or a member of a minority group other than Asian, Black, or
Latino. In addition, the effect of employees’ involvement in voluntary organizations on the work help they received from their network members did not depend upon
" I used the following equation to determine these effects: -.31 (Rank) . 14(Organizational involvement) +.20(Rank*Organizational involvement).
130 their race or sex. This means that being active in voluntary organizations did not produce significantly different network benefits for white men, white women, men of color, and women of color.
Employees’ job power did not significantly affect the amount of help they received from their network members, net of other factors. However, employees’ job power was positively associated with the amount of help that employees received from their network members in a model that excluded the likelihood of having high-status network members. Thus, exercising job power indirectly increased the help that employees received from their network members by increasing employees’ likelihood of having high-status network members. The effect of employees’ job power on the work help they received from network members did not depend upon their race or sex, contrary to my hypothesis.
Employees who communicated with workers outside of their work group received more instrumental help from their network members than employees whose interactions were restricted to their work group. I expect that employees with high external job communication formed network relationships with a diverse group workers which, in turn, increased the amoimt of help they received fi"om their network members. Holding a job that requires inter-group communication may also help workers to obtain unique resources that increase their ability to bargain with network members. In contrast to what I predicted, occupying a high extemal- communication job produced the same network benefits for white men, white women, men of color, and women of color.
131 Demographic Composition of Employees’ Work Floors
The percentage of women, people of color, and managers on the floors in which employees worked did not significantly affect how much assistance employees obtained from their network members. This suggests that the race and sex segregation of work floors did not directly influence the benefits that workers received from their network members. In fact, the composition of employees’ work floors did not significantly affect the amount of help that employees received from their network members even before the network factors were included in the model.This suggests that, contrary to my expectations, employees’ networks were not substantially constrained by the demographic composition of their immediate work environment.
Characteristics of Employees’ Networks
Network scholars claim that structural and compositional characteristics of networks should influence what workers receive from their network members
(Campbell, Marsden, and Hurlbert 1986; Wellman and Wortley 1990). In fact, employees with many network members received more work-related help than employees with few network members. However, this significant finding is due, in part, to the way in which I measured the dependent variable.
However, the percentage of women on the floors in which employees worked was negatively related to the work help they received from their network members in a model that excluded only the tie strength measures. Thus, workers on floors with a high percentage of women tended to form weak ties to their network members, which decreased the amount of help they received from them.
132 The representation of women in employees’ networks did not significantly affect how much assistance employees received from their network members. In other words, I found no evidence that female network members provided employees with less help at work than male network members. The number of women in employees’ networks did not significantly affect work-related help in a model that excluded the status of network members and did not depend upon employees’ sex.
The number of people of color in employees’ networks did not significantly affect the amount of work help that employees received from their network members.
This finding is not surprising given that the representation of people of color did not influence employees’ likelihood of having high-status network members. As stated in
Chapter 4, I expect that this non-significant coefficient is due, in part, to employees’ strict criteria for including minority network members. The effect of the number of people in employees’ networks on work-related help from network members did not depend upon employees’ race, meaning that minority network members were not a greater benefit (in terms of the amount of help they provided) to minority employees than to white employees, net of other factors.
Employees with high-status network members obtained significantly more help from their network members than employees with low-status network members. The standardized coefficient for status of network members indicates that it was the most important determinant in the model, assuming that network size is a control variable.
Given the small number of high-status people of color at USF, it is possible that high-status minorities serve as network members for many employees. Interviews with high-status minorities at USF would help to determine if this speculation is true.
133 This finding is consistent with the fact that high-status employees influence corporate
decision-making, the distribution of resources, and the allocation of rewards. For
example, a vice president whom I interviewed for a company study said that he
helped obtain promotions for the employees in his network by calling other vice
presidents and asking them to put his network members on their candidate lists for
jobs. He said that vice presidents routinely exchanged such favors and that only
"losers" went to personnel or human resources to participate in formal developmental
programs. Managers in charge of hiring decisions want reliable and detailed
information on job candidates, which is often difficult to obtain from formal
evaluations. In fact, according to human resource analysts at USF, there is little
variation in employees’ performance appraisals—most are rated above average.
These performance appraisals, therefore, are probably not very helpful in managers’
hiring decisions.
I had predicted that network members who were physically proximate to
employees would have been more likely to provide work help to employees than
network members who were physically distant from employees. The multivariate
analysis provided no support for this prediction, as indicated by the non-significant coefficient for proximity. Thus, the physical accessibility of a network member did not directly influence his/her likelihood of providing aid to an employee.
In fact, the correlation between physical proximity and work-related help from network members was not significant (r= -.03; Table 4.1). Physical proximity was significantly and negatively related to two indicators of work help from network members—help in meeting other employees and help in meeting other managers (results not shown).
134 However, physical proximity was positively related to work-related help from network members in a model that excluded the two tie-strength measures. Thus, close physical proximity indirectly increased work help by strengthening the ties between network members.
The strength of employees’ ties to their network members was positively related to the amount of work-related help they received from their network members, as indicated by the significant coefficients for closeness to network members and for socializing with network members. Having close ties to network members increased the amount of help that employees received from their network members. However, contrary to my hypotheses, neither employees’ race nor sex moderated the effects of closeness on the work help that employees received from their network members.
Thus, 1 found no support for Burt’s (1992) claim that disadvantaged groups gain more instrumental benefits from strong network ties than dominant groups.
The amount of time that employees socialized with their network members also influenced how much help they received from their network members. Employees who frequently socialized with their network members obtained more work-related help from their network members than employees who infrequently socialized with their network members. Socializing increased employees’ time with their network members, increasing their likelihood of obtaining help from them. For example, an employee who spends a few hours on the golf course with a network member increases his/her chances of obtaining information from that network member. In
135 fact, a vice president interviewed in a study on banking said, "I took those damn golf lessons. I don’t like the game and I did it . . . because in banking golf is the name of the game" (Bird 1990:164). Socializing also fosters trust between people, increasing a network member’s motivation to help an employee.**
Discussion of Results
These results contribute to our understanding of at least three aspects of informal organizational life. First, they enhance our imderstanding of the factors that help employees to acquire resources and services from each other. More broadly, they help us to understand the informal distribution of resources and thus social capital in organizations. Second, they expand our understanding of race and sex stratification in organizations by illuminating the mechanisms that generate race and sex differences in employees’ informal networks. Third, they extend our knowledge of how tie strength influences employees’ access to resources at work. The results from this study also have implications for creating policies that can help to equalize the benefits that employees receive from their networks.
These analyses indicate that strucmral factors were more important than personal factors in determining employees’ work-related help from network members, consistent with the results from Chapter 4. For example, credentials were not
'* While most network scholars view tie strength as causally prior to work-related help (for example, see Wellman and Wortley 1990), it is possible that these variables have reciprocal effects (i.e., they influence each other). For instance, a network member to whom an employee is close probably provides him/her with more help on the job than a network member to whom an employee is not close. However, that exchange of help may enhance the closeness of their relationship which, in turn, increases the amount of help the network member provides to the employee in the future.
136 directly relevant for the amount of help that employees obtained from their network members. Rather, employees’ credentials helped them to obtain organizational positions which, in turn, influenced with whom they formed informal ties and thus the amount of help they received from their network members. In contrast, employees’ job characteristics were important in determining how much help they received from their network members. For instance, employees who occupied Jobs that encouraged them to communicate with workers outside of their work group had network members who provided them with more instrumental aid than employees who occupied Jobs in which they rarely communicated with employees outside of their work group.
However, the most important determinants of work-help from network members were characteristics of network members themselves. For instance, the status of network members was the most important predictor of how much help employees received from their network members, net of network size.
These results also indicate that the distribution of informal resources at work depend, in part, upon employees’ sex and race. I found that women and people of color received less work help from their network members than men and whites.*’
Women and people of color (except for minorities who were neither Asian, Black nor
Latino) received less work-related help than men and whites because the former tended to occupy positions at USF that decreased their opportunity to form informal ties to employees who could help them. Thus, these findings provide no evidence
*’ However, employees of color received different amounts of work-related help from their network members. The average work-help score for Asians was -.44, for Blacks was -1.02, for Latinos was .64, and for other racial minorities was -.75.
137 that the same types of network members provided women and Asians with less help than men and whites. These results also highlight the relationship between formal and
informal stratification at work. For instance, one of the main reasons that women obtained less work help from their network members than men was because they were less likely than men to have high-status network members. I expect that this was a result of their segregation in female-dominated jobs that offered them relatively few opportunities to interact, and thus form relationships, with high-status employees.
This study extends our knowledge of tie strength at work by illustrating how tie strength affects certain types of instrumental help that employees receive from network members. I found that strong network ties were more helpful than weak network ties in gaining work-related help from network members. I expect that my findings differ from job-search studies because giving someone information about a job opening does not require significant time or effort. However, giving someone help on the job takes time away from an employee’s work and it might involve putting his/her reputation on the line. For example, if a network member helps an employee to secure a promotion and that employee performs poorly in the new position, their poor performance raises doubts about the judgment of the network member who recommended them. Therefore, providing work-related help to another employee requires more motivation than giving someone information about a job opening. Network members to whom employees have strong ties are more motivated to help them than network members to whom employees are only acquainted.
Furthermore, strong ties are more reliable and accessible than weak ties. If an
138 employee needs immediate help with a time-consuming problem they probably feel more comfortable requesting such help from a network member to whom they have a strong tie than one to whom they have a weak tie. In other words, employees can impose upon network members to whom they are strongly tied more than those to whom they are weakly tied. Finally, while scholars have suspected that minority groups gain more work-related benefits from strong ties than dominant groups, I found no support for this hypothesis. Thus, whites’ strong network ties were just as likely to provide them with work-related help as minorities’ strong network ties.
However, race played an important role in regards to workers’ tie strength because people of color had fewer strong ties at work than whites, which diminished the amount of help they received from their network members. Organizational demography probably limited the number of same-race ties and thus strong ties that people of color formed.
There are several policy implications that I draw from these results. That the status of network members was key in determining the amount of work help employees received from their network members suggests that policies need to provide white women and minorities with more opportunities to interact with high- status employees. Regular interaction with high-status network members would allow white women and racial minorities to develop the tmst, commitment, and familiarity that is necessary for creating helpful network relationships. The long-term solution to increasing women’s and minorities’ access to high-status employees is to desegregate jobs. However, given the slow pace with which job desegregation occurs (see Reskin
139 1993:245-246), employers need to take other immediate steps to increase interaction between white women, minorities, and high-status employees. One strategy would be to develop mentor programs that facilitate interaction between high-status and low- status workers. Admittedly, formally-mandated relationships probably do not attain the same degree of trust and comfort as informal relationships. However, these programs are one vehicle through which minorities could gain access to high-status employees. In my study of USF’s pilot mentor program, employees cited many benefits from their formal mentoring relationships.^® Mentors gave proteges^* information that proteges’ managers were unlikely to provide them. For instance, one mentor cautioned his protege that some managers are reluctant to promote good employees because such employees help their departments to look good. Many mentors gave their proteges "off the record" information on different employees in the company and told them who they should and should not trust. Another mentor brought her protege on "high-level sightings," or to places where high-status employees socialized. In order for mentor programs to be successful, however, high- status mentors need an incentive to provide help to white women and racial minorities and proteges need assurance that they will not be stigmatized by obtaining special help.
^®I conducted semi-structured interviews with 24 participants, mentors and proteges, in the mentor program. Interviews lasted between 45 to 60 minutes. All of the mentors occupied high-ranking positions (manager or above) and a majority of the proteges were non-supervisors.
Proteges are employees who are mentored.
140 Characteristic V ariab le Unstandardized b Standardized Hypothesized (interactions italicized) (Standard error) Effect
SEX AND RACE OF Woman (I =woman, 0=mm) -0.330 -0.039 RESPONDENT (.213)
A sian (I =Asian, Q=\vhice) -0.657 -0.029 (.521)
Black (I=Black, 0=white) 0.088 0.010 (.245)
Latino (I=Latino, 0=white) -0.348 -0.016 (.500)
Other race (1 =other, 0=white) -1.395* -0.049 (.641)
PERSONAL Years of Education -0.002 - 0.001 (.060)
Financial Designations -0.268 -0.028 (.241)
Years of Company Tenure 0.610 0.187 + (.487)
Years of Tenure Squared -0 . 122* -0.259 (.070)
Marital Status -0.078 -0.009 (.211)
Number of Children at Home 0.070 0.016 (.102)
OUTSn>E SOCIAL TIES Outside Organizational -0.145 -0.037 + Involvement (.164)
JOB Organizational Rank -0.313 -0.077 + (.180)
Rank*Outside Organizational 0.197* 0.130 + Involvement (.085) Job P ow er 0.033 0.021 + (.045)
Communication Outside of 0 .202 * 0.058 + W ork G roup (.084)
* Significant at the .05 level. 1-tailed test. Table 5.1 continued on nest page. ** 145 cases were lost because of missing data on floor composition.
Table 5.1: OLS Regression of Amount of Work-Related Help From Network Members (N=986**).
141 Table 5.1 (continued).
Characteristic Variable Unstandardized Standardized Hypothesized k Effect (Standard error)
FLOOR COMPOSITION . Percent of Women - 1.001 -0.035 (.744)
Percent of People of Color 1.730 0.036 (1.317)
Percent o f Managers 1.495 0.032 + (1.309) NETWORK Network Size 0.968* 0.447 + (.075)
Number of Women in Network 0.021 0.009 (.072)
Number of People of Color in .048 -0.013 Network (.099)
Status of Network Members 0.446* 0.343 + (.047) Physical Proximity of Network 0.030 0.012 + M em bers (.056)
Closeness to Network Members 0.738* 0.129 + o r - (.149) Amount of Socializing with 0.338* 0.078 + Network Members (.111)
Constant -8.625 (1.414)
Adjusted R-square = .558
’ Significant at the .05 level, I-tailed test
142 CHAPTER 6
CONCLUSION
Contributions of Research
Social networks consist of patterned, organized interactions between people who know one another (Scott 1991; Wellman 1992; Ibarra 1993).' Because network interactions are often informal, they engender what Kanter (1977:164) called the
"shadow structure, " or informal social structure, in organizations. Thus, these structured interactions are one of the components that make organizations organized.
Social networks are also social resources because they channel the flow of information, influence, power, and resources in organizations (Campbell, Marsden, and Hurlbert 1986:98; Lin, Ensel, and Vaughn 1981:395; Lin and Dumin 1986:365-
366). The study of social networks, therefore, provides a glimpse of how power and influence is distributed behind the organizational chart.
This study examined two aspects of employees’ networks, the likelihood of having high-status network members and the amount of help received from network members, to understand how resources were distributed within a firm’s informal
' Social networks can also exist between groups, organizations, communities, and states.
143 social structure. The results of this investigation contribute to the sociology of work and organizations by comparing the effects of personal and structural factors on characteristics of employees’ networks, by examining the effect of tie strength on work-related resources and by enhancing our understanding of race and sex stratification in organizations’ informal structure.
Employees’ personal characteristics played an indirect role or no role or in their likelihood of acquiring high-status network members and in receiving work- related help from network members, according to my results. For instance, I found no evidence that women’s family responsibilities posed significant barriers to their access to high-status network members or limited the amount of help they received from their network members. These findings suggest that employees’ family characteristics do not affect the amount of time that employees spend with their network members. However, these results may mask selection effects, in that the fully-employed women in my sample may have had the resources to resolve their work-family conflicts. The women who could not resolve their work-family conflicts likely worked part-time or left the labor force and thus were not in my sample. I also found no evidence that credentials affected employees’ bargaining power with network members, as social exchange theorists might predict. Instead, credentials influenced employees’ likelihood of having high-status network members and of receiving help from their network members by affecting employees’ job characteristics and network
144 size. Credentials may be important in the initial stages of network formation insofar as they help individuals to acquire organizational positions that enhance their ability to interact with and to attract potential network members.
Employees’ job characteristics, in contrast to their personal characteristics, played direct roles in determining the status of their network members and the amount of help they received from their network members. For example, exercising job power and holding high-ranking positions increased employees’ likelihood of having high-status network members. In addition, employees’ ability to communicate with workers outside of their work group affected how much assistance they received from their network members. Network theory suggests that such job characteristics influenced employees’ opportunities to interact with potential network members and increased employees’ bargaining power with network members by providing them with control over corporate resources. These results indicate that the resources associated with employees’ organizational positions, as well as the resources that employees brought with them to work, influenced their likelihood of having high- status network members and the amount of help they received from their network members.
Employees gained more work-related help from their strong network ties than from their weak network ties. My findings differ from job-search studies because providing help at work requires more effort and motivation than does providing information on a job opening. Individuals who are weakly tied to each other are not emotionally close and interact infrequently. The benefit of such distant ties is that
145 they are likely to provide an individual with novel information because they are not a regular member of an individual’s social circle. The people to whom we have strong ties, in contrast, are those to whom we share trusting, intimate relationships. Thus, the people to whom employees have strong ties are more motivated to provide employees with work-related help than those to whom they have weak ties. These results suggest that the bridging benefit of weak ties is relevant for obtaining resources that do not require much effort or risk on the part of the provider, whereas strong ties are critical for employees’ ability to perform their jobs and thus succeed within work organizations.
Qualitative data lend support to these quantitative findings. For instance, several of the high-status employees whom I interviewed said that they shared their previous mistakes with their junior network members in order to help prevent them from making similar errors. The sharing of potentially damaging information is unlikely between acquaintances or people to whom employees are weakly tied. Thus, one benefit of strong network ties Is that their mumal trust leads them to share sensitive information that can be useful in each other’s careers. In addition, I interviewed several employees who reported that their close network members provided them with honest criticism on their performance, which was difficult to obtain from their colleagues and managers. The people to whom employees have strong ties are more likely to provide candid feedback because they probably know each other very well and because they trust and respect each other.
146 I also uncovered a system of informal stratification in which power and
resources were unequally distributed that was based, in part, upon employees’ race
and sex.^ Women and people of color were less likely than men and whites to have
high-status network members and to receive work-related help from their network
members. As a result, women’s and minorities’ network members may have been
less able than men’s and whites’ network members to advocate for them, to help them
obtain visible job assignments, and to divert corporate resources to them. However,
employees’ race and sex did not interact in their effects on the two network outcomes
I examined. For example, women of color did not receive significantly less work-
related help from their network members than men of color or white women. These
race and sex differences in workers’ access to different kinds of networks suggest that
informal networks are an avenue through which race and sex inequities are maintained
at work. For example, that women and people of color have less access to high-status
network members than men and whites may diminish their ability to obtain promotions, to hear about new positions, and to keep their jobs amidst company downsizing.
These results provide mixed support for Ranter’s token hypothesis. My
findings are consistent with Ranter’s predictions that numerical minorities’ experience
informal disadvantages at work in that people of color had less access to high-status
network members and received less instrumental aid from their network members than
^ Because I collected detailed information on employees’ network members and oversampled people of color, this study was among the first to examine how employees’ race and sex simultaneously affected characteristics of their networks.
147 whites. Consistent with Ranter’s (1977a) findings, my research suggests that people of color were on the margins of the informal power structure at USF. However, my results raise doubts about Ranter’s predictions regarding the benefits of large group size for historically-disadvantaged groups. Despite women’s numerical majority at
USF, for example, they had fewer high-status network members and obtained less work-related help from their network members than men. These results suggest the need for research that is able to isolate the effects of group size from those of employees’ race and sex on employees’ access to work-based networks.
This research also helped to reveal the mechanisms responsible for race and sex differences in employees’ likelihood of having high-status network members and of receiving help from their network members. Race and sex differences in employees’ credentials, organizational location and network composition largely explained why women and people of color were less likely than men and whites to have high-status network members and to receive work-related help from their network members. These results suggest that the processes generating these race and sex differences in employees’ networks began well before employees’ developed their base of network ties. For instance, employees’ credentials, in combination with the pre-existing system of sex and race segregation, likely influenced the type of jobs that employees acquired, which in turn affected the type of potential network members with whom they interacted.
That employees’ sex and race (in all but one case) did not directly influence employees’ likelihood of having high-status network members and the amount of work
148 help they received from their network members does not mean that race and sex are irrelevant for employees’ networks. Rather, these analyses indicate that employees’ race and sex affected these two network outcomes indirectly through employees’ organizational location and the characteristics of employees’ network members. If we define exclusion to include both intentional and unintentional actions, then these results suggest that race and sex differences in the two network outcomes I examined were due to the structural exclusion of women and minorities from positions that had power, wide communication channels, and resources. This point was brought home to me on many occasions during my employment at USF, including a training session for "high-potential" managers that I helped to conduct. The dozen or so participants were chosen based upon their credentials, their leadership potential, and recommendations from high-status employees who knew them. All of the participants were white men who appeared to be in their thirties. The training session, which took several days, gave participants important managerial skills, but it also provided them with the opportunity to form ties to other rising stars. The participants did not have the chance to include or exclude white women and people of color from this emerging network because there were no white women or people of color in the potential network pool.^ Few white women and people of color occupied the
^ The only women in attendance at the training were the human resource employees who were helping to conduct the training.
149 managerial positions that could have provided them with the opportunity to be labeled
by others as "high potential" (e.g., those in which they could perform extraordinary
tasks).
However, one minority group did receive less instrumental help from their
network members than whites, even after controlling for the factors that should have
accounted for the amount of help that employees received from their network
members. Racial minorities who were neither Asian, Black, nor Latino, received less
work-related from their network members than whites, net of their personal, job, and
network characteristics. As 1 discuss shortly, I require additional data to determine if
this finding reflects the personal exclusion of this minority group from helpful
networks.
The instances in which I found no evidence of race and sex disadvantages are as important as the instances of disadvantages for our understanding of race and sex stratification. For example, white men, white women, men of color, and women of color had approximately the same number of network members, demonstrating that minority groups were not completely excluded from informal networks at USF. In addition, the network payoffs to employees’ organizational positions and network characteristics were not statistically different between race-sex groups. For instance,
I found no evidence that women’s organizational rank gave them less access to high- status network members than men’s organizational rank. In addition, having high- status network members did not influence the amount of work-help that employees received from their network members in significantly different ways for Blacks,
150 Latinos, and Asians, relative to whites. Thus, the factors needed to acquire high- status network members and work-related help from network members operated similarly for white men, white women, men of color, and women of color. White women and people of color were, nevertheless, less likely than men and whites to have beneficial networks because of USF’s formal system of stratification. Women and people of color tended to occupy positions with low organizational rank, job power, and job communication, which decreased their ability to form ties to high- status network members and to receive help from their network members. This suggests that the formal system of stratification at work largely determines the informal one.
Limitations of Study
The cross-sectional design of this study prevented me from analyzing the ways in which employees’ network relationships fluctuated over the course of their careers.
For example, this study could not determine how often employees added new members to their networks or what happened to network members who lost their usefulness to employees. Furthermore, this study could not investigate if the use of strong ties and weak ties varied across employees’ careers. I also did not examine how the types of resources that employees received from their network members changed over the course of their company tenure. This limitation does not reduce my confidence in this study’s findings because it refers to a different set of questions about employees’ informal networks than the ones I addressed in this study.
151 My use of a mail survey restricted my study to self-reported acts of help, limiting my data to employees’ perceptions of help from network members. One potential problem with self-reported data is that employees may not be aware of the times and the ways in which they were helped by their network members. For example, one manager whom I interviewed described how she was recommended for a promotion and did not find out until much later that two senior female officers had worked behind the scenes to get her this new position. Employees may have also overestimated or underestimated how much help they received from network members. As long as employees did not vary in their overestimation or underestimation of the help they received,'* this limitation should not have influenced the general patterns I found in this study (e.g., the importance of job and network factors).
The survey instrument was also limited in that it asked respondents about only the presence and absence of help, rather than the effectiveness of the help given by network members. For instance, I found that being in a high-communication Job increased the amount of help that employees received from their network members, but this help may not necessarily have benefited employees. Knowing the effectiveness of help from network members is important in order to assess the conditions under which informal networks serve as social capital to workers. Because
■* For instance, if men are more likely than women to underestimate the amount of help they receive from their network members then my conclusions would be more tentative because I may have over-looked sex differences in the determinants of work- related help.
152 I lacked such information, I may have overlooked differential returns to job and network characteristics. For instance, I found no evidence that high-status network members provided whites with more instrumental help than Blacks. However, if
Blacks are perceived by high-status employees as untrustworthy and incompetent, then high-status employees may not provide the same quality of help to Blacks as they do to whites. For instance, one Black manager I interviewed said that his white peers spoke to him in passing, but he believed that they felt uncomfortable interacting with people of color. It could also be the case that minorities do not exploit their networks as much as whites do. For example, the highest-ranking woman of color at USF told me that she received more informal requests for help from non-minorities than minorities in the company. She speculated that people of color relied too much on formal chaimels rather than informal channels to get their work done. In fact, one of the minorities who participated in USF’s pilot mentor program told me that she rarely called her high-status mentor because she believed she should complete her work on her own. In sum, because I do not have data on the effectiveness and extent of help from network members, I may have overlooked race and sex differences in the quality of employees' networks.
One goal of this study was to better understand the mechanisms that maintain race and sex inequality in work organizations. According to Kanter (1977a), one way that minority groups are denied power by dominant groups is through their exclusion from informal networks. While my study provided hints about exclusion from informal networks, it did not directly measure network exclusion. As a result, this
153 study was limited in its ability to explain why women and people of color were less
likely than men and whites to have high-status network members and to receive
instrmnental help from their network members. This limitation is one that this study
shares with other studies due to the difficulty of defining exclusion. For instance, is
an act that unintentionally prevents minorities from forming networks ties a type of exclusion? Granovetter (1995:173) observed that, for some employees, "Their motive
is probably that of conserving opportunities for those within [their own group]; but
the aggregated result of such action is little different than if exclusion had been the principal intent. " Exclusion is particularly difficult to define when work networks overlap with friendship networks. For example, one white female manager whom I
interviewed told me that the three male managers in her department went to lunch together every day without inviting her. I imagine that these male managers perceive their lunches as friendly excursions rather than as something that excludes their female peer from informal socialization. Future research needs to explore different ways to conceptualize exclusion from informal networks, including intentional and unintentional exclusion as well as personal and structural exclusion.
Another limitation of this research was its focus on work-related help from network members, which ignored other important benefits of informal networks, including encouragement, moral support, help with family-work conflicts, and strategies for dealing with sexism and racism (for example, see Scott 1996:244-245).
White women and people of color are disadvantaged in terms of obtaining instrumental support from their network members, but they may be successful in
154 obtaining non-instrumental support from their network members. In fact, my
interviews with participants in the pilot mentor program uncovered numerous
instances in which mentors provided suggestions to proteges on resolving work-family conflicts, particularly among female mentors paired with female proteges. One
female protege described how her male colleagues were giving her a hard time for taking time off from work because of her pregnancy. Her mentor helped her resolve the situation without alienating her male colleagues by using humor. In addition, several of the women of color whom I interviewed spoke about the importance of having other women of color in their networks with whom they could "vent" their frustrations. Fortunately, the data I collected contain information on emotional support from network members, which I will analyze in future research. This limitation suggests that we need to be cautious about generalizing these results to o±er network outcomes.
Another drawback of this research was that the survey did not ask employees to identify the specific voluntary organizations in which they were involved or to explain how they benefited from their participation in voluntary organizations.
Because people belong to a wide variety of voluntary organizations, it was difficult to interpret the positive effects of voluntary organizational involvement on the likelihood of having high-status network members and of receiving help from network members.
For instance, this study could not determine if employees’ involvement in voluntary organizations provided them with unique resources that made them attractive network members to high-status employees or if employees actually interacted with more high-
155 status employees in the organizations in which they were involved. This omission does not challenge my basic finding that voluntary organizational involvement is important for employees’ networks. However, having such information would have enabled me to more precisely identify the mechanisms through participation in voluntary organizations benefited employees’ networks.
One of the most critical limitations of this study was that I did not have data on employees’ jobs, only characteristics of their jobs. As a result, I had difficulty distinguishing between structural and personal exclusion. For instance, job-level data would have helped me to better understand if high-ranking Latinos were personally excluded from instrmnental networks or if their jobs decreased their contact with employees who could provide them with help. Such information would not have changed my findings, but may have changed my interpretation of the findings.
Similarly, job data could have helped me to rule-out structural exclusion as an explanation for why minorities who were not Asian, Black, or Latino received less work-related help from their network members, net of other relevant factors.
Information on employees’ jobs may have also helped this study to distinguish between the processes identified by network theory. For instance, it may have allowed me to assess the degree to which employees’ time with network members, opportunities to interact with employees, and attractiveness to potential network members influenced the characteristics of employees’ network members and what employees received from their network members. For instance, did exercising job power make employees attractive network members to high-status employees because
156 they controlled resources or did employees with high job power have more opportunities to serve on committees and teams with high-status employees, which increased their opportunity to form relationships with them? In sum, the lack of job data in this study does not raise doubts about the robusmess of my findings, but it does raise questions about my interpretation of these results.
Future Research
One important area in need of future research is employees’ harmful and ineffective network members. For instance, scholars do not know the extent to which employees receive misinformation from their network members or experience career set-backs because of their network members. Workers’ jobs compel them to interact with people who are not necessarily helpful or with whom they would prefer not to interact. In fact, Wellman (1982:79) found that a fourth of people’s community network ties were to individuals who they did not like and with whom they would not voluntarily interact. Neighbors formed network relationships based, in part, upon physical proximity and convenience. According to Wellman (1982:79), "Such
’structurally embedded’ ties become involuntary parts of network membership packages." Thus, the saying, "you can’t choose your family" is also applicable to many network relationships in that structural constraints limit individuals’ network choices. For example, one female employee commented to me of her current manager, "... I don’t think [he] has a clue what’s going on here and doesn’t have the intellectual skills that challenge me. He doesn’t have the political skills at all."
This same employee told me that she believed she was denied a promotion because
157 her mentor had burned so many bridges because he lacked political skills (e.g., he
called someone a "jerk" in a meeting). The study of detrimental ties could enhance
our understanding of informal stratification in work organizations. For example, I
found no race or sex differences in network size, but white women and people of
color may have more detrimental ties than white men. In sum, a complete
understanding of employees’ informal networks requires an examination of their
liabilities as well as their benefits.
Another area in need of research is the processes through which employees
initially form informal network ties at work. Investigating the context in which employees develop network relationships (e.g., on the job or through social activities)
could help us to understand why employees have different types of network members and receive different benefits from their network members. For instance, scholars do
not know if the network ties that employees form outside of work provide them with more on-the-job benefits than the network ties that employees form in the course of performing their jobs. Granovetter (1995:154) speculated that the network ties that people form early in their career have cumulative advantages and disadvantages over time. For example, employees who use informal contacts within a company to obtain their first job may have an advantage over those who enter the company through formal means because the former have a base of ties from which they can build their informal networks within the company.
This study also suggests the need for case studies on work organizations to investigate how an organization’s work culture, physical organization, and
158 employment policies influence workers’ ability to form network ties and thus acquire social capital. Unlike my survey, a case study could investigate the degree to which an organization’s corporate culture (e.g., values and informal norms) is hospitable to white women and people of color. It could also investigate the degree to which white women and racial minorities are socially integrated in a work organization, such as how active they are in company-sponsored events, sports teams, and team outings.
By examining the organization as a whole, a case study could better determine which groups are tokens and social minorities.
Several other aspects of employees’ informal networks merit future attention as well. We require studies that compare work organizations with different structural characteristics to better understand the determinants of employees’ informal networks.
The use of team work, the number of employees, and the race and sex composition of employees in work organizations may influence workers’ networks. For instance, scholars do not know if the use of formal work teams encourages the development of informal ties between workers. It is also unclear if the processes through which employees form network ties are different in small and large firms. Furthermore, are strong ties even more important for acquiring help in small firms than large firms?
Comparing organizations with different numbers of women and people of color could help to explain the relationship between employees’ sex, race, token status, and informal networks. Such an investigation could disentangle the extent to which people of color are informally disadvantaged because of their social stams (e.g., they are considered inferior because of their race) versus their numerical status.
159 Future research should also examine the role that informal networks play in employees’ work rewards. For instance, smdies need to investigate if the work- related help that employees receive from their network members increase their earnings and chances for promotion. Research could investigate whether the effect of workers’ human capital on their earnings depends upon their network characteristics.
Such an investigation could help us discern the relationship between human capital, social capital, and work rewards. For instance, do the benefits of having a college degree depend upon the people to whom an employee is connected? Under what conditions is social capital more important than human capital in employees’ work rewards?
In conclusion, the study of employees’ informal networks promises to offer insights into the distribution of resources in work organizations and thus informal stratification at work. In fact, Granovetter (1995:141) observed that, "... despite modernization, technology, and the dizzying pace of social change, one constant in the world is that where and how we spend our working hours, the largest slice of life for most adults, depends very much on how we are embedded in networks of social contacts." While much is unknown about employees’ informal networks,^ what is clear is that informal networks occupy a central role in individuals’ experiences at work.
® In fact, it was not until 1985 that a representative sample of Americans was asked about their informal networks in the General Social Survey (Bernard and Shelley 1987:50).
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171 APPENDIX
COVER LETTERS AND SURVEY INSTRUMENT
October 16, 1995
Dear Mr./Ms. XX,
I want to personally thank you for participating in this study and assure you of several things. First, your responses will be completely confidential. Completed surveys will be sent directly to the Ohio State University. No one at USF will see your answers, have access to the data, or know who returned the survey. USF and survey respondents will receive a summary report in which all results are grouped together.
Second, participating in this study is voluntary. Third, the survey takes only 20 minutes or less to complete, so please fill it out today. Finally, the number on your survey is for mail and raffle purposes.
If you have any questions feel free to contact me at the above address. Thank you for helping to make this research possible.
Sincerely,
Gail McGuire Researcher
* I have altered the wording of the survey to camouflage USF's identity
172 Dear USF employee.
The enclosed survey is part of a study that is being sponsored by USF. The study focuses on the namre of cooperation and exchange between employees, which is important information to have as we move to a team environment. These findings will also help enhance our career development services.
Because the survey is being sent to a small sample of employees who were selected at random, the research depends upon each individual filling out and returning their survey. While I encourage you to take the survey, doing so is voluntary.
All surveys and data will be kept at the Ohio State University. USF will only see group data contained in a report that will be prepared by the researcher. No one at USF will have access to any individual data.
Thank you in advance for your time and support of this important research.
Sincerely,
Senior Vice President Human Resources
173 THE HELP YOU RECEIVE
Please think of the employees who have made an effort to give you job. career, or personal help since the beginning of the year (January 1995). For instance, which employees have helped you solve a work-related problem, coached you. or encouraged you? The people who have helped you could include someone from a different part of the company, a co-worker, supervisor, or someone who reports to you They could be people you see occasionally or daily.
Write the initials of your helpers in the upper right hand comer of the survey. Each space is meant for the initials of one helper. There is space for up to 8 helpers, but you do not have to use them all. You will be referring to these initials throughout the survey.
After each question circle an answer for each helper directly under their Initials. If you circle "1" this means a helper did the activity for you this year. If you circle "2" it means a helper did qqI do the activity for you this year. An example completed by the researcher is below.
BES.EARCÜER’5. EXAMPLE:
Initials of researcher's heloers
Has this person helped you with a work- related problem or question? Y E S 1 1 1 1 1 1 1 1
N O 2 2 2 2 2 2 2 2
Has this person helped you get around bureaucratic hurdles or cut through red ta p e ? YES 1 1 1 1 1 1 1 1
N O 2 2 2 2 2 2 2 2
(This means that only WS. BR. and PM have helped the researcher with a work-related problem, and that only BR has given the researcher suggestions an how to cut through red tape).
174 0 1 Has mis person neipeo you witn a work-related problem or question'’ YES 111111
NO 2 2 2 2 2 2
02 Has mis person Helped you get around tHireaucratic hurdles or cut mrough red tape? YES 1111111
NO 2 2 2 2 2 2 2
0 3 Has this person neiped you m eet employees outside of your area (e g . Human resources, daim s)? YES 1111111
NO 2 2 2 2 2 2 2
04 Has mis person Helped you meet high-level employees. such as diredors or olficers? YES 1 1 1 1 1 1 1
NO 2 2 2 2 2 2 2
05 Has iHis person given you encouragement and moral support? YES 1111111
NO 2 2 2 2 2 2 2
06 Has this person given you suppon with a personal problem? YES 1111111
NO 2222222
Q? Old mis person Help you get your first USF |0b le g leii you me io 0 was open put m a good word for you)? YES
NO
0 8 Has mis person Helped you get a new position gr promotion at USF ai any «me? YES 1
NO 2
0 9 Has mis person Helped you gam recognition for your woth? YES 1111111
NO 2 2 2 2 2 2 2
If you recall another employee who has helped you feel free to add their initials to the list above.
175 THE HELP YOU GIVE
Undtr lach h tlp cft initials, cire/a tha numbar of tha answar that dascribas wliat veu hmv» danm for a ac h halp ar.
010 Have you neiped mis person witn a work-relaled problem or question') YES i i 1 t t ' i i
NO 2 2 2 2 2 2 2 :
011 Have you neioed mis person meet otner employees'’ YES i 1 i i i i i ■
NO 22222222
012 Have you given mis person encouragement and moral Support'’ YES 1111111'
NO 2 2 2 2 2 2 2 2
013 Have you neiped mis person get a new |ob g promotion? YES 11111111
NO 22222222
014 Have you neiped this person gain recognition lor meir wonc? YES 1111111-
NO 22222222
DESCRIBING YOUR RELATIONSHIPS Under aach helper's initials, c/rcla tha numbar of tha answer that bast describes your relationship. An axampta completed by me e se a rc h er is below.
RESEARCHER’S EXAMPLE: Initials of researcher's helpers
How long nave you known this person? LESS THAN 1 YEAR 1 1 1 1 1 1 1 1
1-3 YEARS 2 2222222
4-8 YEARS 33333333
MORE THAN 6 YRS 4 4444444
IThis means mat me nseaicher has known WS and BR Ibr 4-6 ye»n and has known AN, PM. and MM1-3 for years) ______
176 0 1 s HOW long nave you Known inis person'’ LESS t h a n 1 YEAR 1 1 1
1-3 YEARS 2 2 2 2 2
4.6 YEARS 3 3 3 3 3
MORE THAN 6 YRS 4 4 4 4 4
0 <6 How often 00 you talk to llus person (m person ohone e-mail. etc)'’ ABOUT EVERY DAY 1 1 1
AT l e a s t o n c e a WEEK 2 2 2 2 2
AT l e a s t o n c e a MONTH 3 3 3 3 3
LESS THAN ONCE A MONTH 4 4 4 4 4
0 1 7 How often 00 you get together socially With this person (e g .lunch, dnnks. goll)? ABOUT ONCE A WEEK 1 1 1 1 1 1 1 1
SEVERAL TIMES A MONTH 2 2 2 2 2 2 2 2
ABOUT ONCE A MONTH 3 3 3 3 3 3 3 3
SEVERAL TIMES A YEAR 4 4 4 4 4 4 4 A
SELDOM OR NEVER 5 5 s 5 s 5 5 5
H ow C lose 00 you feel to this person? VERY CLOSE 1 1 1 t 1 1 I 1
SOMEWHAT CLOSE 2 2 2 2 2 2 2 2
n eith er CLOSE NOR DISTANT 3 3 3 3 3 3 3 3
SOMEWHAT d is ta n t 4 4 4 4 4 4 4 A
VERY d is ta n t 5 s s s 5 5 5 5
Has this person taken you "under their wing" by Showing a special interest m your career? YES 1 1 1 1 t 1 , ,
NO 2 2 2 2 2 2 2 2
For Question 2 0 circle the number of y o u r a n s w e r .
020 For this Question tnmk aoout the retanonshio Between me heloers you listed. Which of the following statements oest oescnoes now many 01 your neipers Know one another^ 1 ALL OF t h e p e o p l e t LISTED KNOW ONE ANOTHER 2 MOST OF THE PEOPLE t LISTED KNOW ONE ANOTHER 3 ONLY A FEW OF THE PEOPLE I LISTED KNOW ONE ANOTHER 4 NONE OF THE PEOPLE I LISTED KNOW ONE ANOTHER 5 DON'T KNOW
177 DESCRIBING THE PEOPLE WHO HELP YOU
Undtr each helper's initials, pleas# circle (he number of the answer that best describes each helper.
0 2 1 How long nas m s person worKed at USF compared to you? MORE YEARS THAN ME 1 1 1 1 1 1 t ’
ABOUT THE SAME YEARS AS ME 2 2 2 2 2 2 2 2
LESS YEARS t h a n ME 3 3 3 3 3 3 3 3
022 What'S this person's position at USF? NON^UPERVISOR 1 1 t 1 t t 1 t
SUPERVISOR 2 2 2 2 2 2 2 2
m a n a g e r 3 3 3 3 3 3 3 3
DIRECTOR 4 4 4 4 4 4 4
OFFICER O R ABOVE s 5 s 5 5 5 5 5
023 Has this person ever been employed in the same area (e g daims underwnang) as you? YES 1 1 1 1 1 1 1 .
NO 2 2 2 2 2 2 2 2
02a Has ms person ever worked on the same Hoor as you? YES 1 1 1 t t , 1 ,
NO 2 2 2 2 2 2 2 2
025 Has IhiS Person ever been m the same work group as you? YES 1 1 1 1 1 , •
NO 2 2 2 2 2 2 2 2
026 Has this person ever been your supervisor or manager? YES 1 1 1 I ,
NO 2 2 2 2 2 2 2 :
02? Can this person make final decisions that significantly Change USF products, programs, or services? YES 1 1 1 1 1 1 ,
NO 2 2 2 2 2 2 :
DOfifT KNOW 3 3 3 3 3 3 :■
178 Initials of Your USF Helpers:
028 Can tnrs person make ma|or purchases 1*10.000 or morel wiinout getting permission from higher up? YES 1 1 1 1 1 1 ’
NO 2 2 2 2 2 2 2 2
OONTKNOW 3 3 3 3 3 3 3 3
029 Ooes this person nave access to sensitive or confidential company information^ YES 1 1 1 1 1 1 t ,
NO 2 2 2 2 2 2 2 2
OONTKNOW 3 3 3 3 3 3 3 3
030 Ooes this person socialize with higfwevet employees (e g directors, officers)'’ YES 1 1 1 1 1 t 1 t
NO 2 2 2 2 2 2 2 2
OONTKNOW 3 3 3 3 3 3 3 3
031 What IS this person s sex’’ FEMALE 1 1 1 1 1 I I t
f/ALE 2 2 2 2 2 2 2 2
032 Wnat 'S tnis person s race’ ASIAN 1 1 1 1 I , , ■
BLACK (AFRICAN AMERICAN) 2 2 2 2 2 2 2 2
HISPANIC (LATINO/A) 3 3 3 3 3 3 3 3
WHITE (NON-HISPANIC) 4 4 4 4 4 4 4
OTHER s 5 s 5 5 5 5 5
179 FINAL SUMMARY INFORMATION
Please circit the number of the answer that tiest describes you. or write your answ er in the blank provided All information will oe stnctiy confidential.
0 33 HOW many years have you worked for USF? 1 LESS THAN 1 YEAR 2 1-3 YEARS 3 4-6 YEARS 4 7-9 YEARS 5 10 OR MORE YEARS
034 How long have you had your Q£S]ficI job at USF? 1 LESS THAN 1 YEAR 2 1-3 YEARS 3 4-6 YEARS 4 7-9 YEARS 5 10 OR MORE YEARS
035 What IS the highest level of school you have finished? 1 HIGH SCHOOL OIPLOMA OR LESS 2 SOME COLLEGE 3 ASSOCIATE DEGREE 4 BACHELOR DEGREE (B A . B.S.) 5 MASTER DEGREE (M A. M S.. M.B A ) 6 DOCTORATE (PH 0 . ED 0 . M D . J D ) 7 o t h e r (SPEO FY ) ______
036 Do you have any professional designations (e g . CPCU. CPA CLU)? 1 YES 2 NO
037 In wnicn of me following areas are you currently employed? 1 ACTUARIAL 2 CLAIMS 3 communications AND EXTERfW. RELATIONS 4 FACILITIES MANAGEMENT 5 f in a n c e /a c c o u n t in g 6 HUMAN RESOURCES 7 LEGAL/REGULATORY 8 m a r k e t in g a n d s a l e s 9 SYSTEMS AND DATA PROCESSING 10 UNDERWRITING 11 o t h e r ISREC'FY) ______
038 In now many oifferent areas (e g . human resources, daims) have you been employed at USF? 1 ONE 2 TWO 3 t h r e e OR MORE
039 What IS your position at USF? 1 n o n -s u p e r v is o r 2 s u p e r v is o r 3 MANAGER 4 DIRECTOR 5 OFFICER OR ABOVE
180 Q40 In now many ot^anizaticns (e g . grofessional. cnanty. ooiiocal) are you currently an active member (e g . attend events or meetings)'’ 1 NONE 2 ONE 3 rwo 4 THREE OR MORE
0 4 1 Oo you currently nave som eone outSKleofUSF wno h as taken you "under ttteir wing* by snowing a speoal interest m your career’ 1 YES 2 NO
042 wnat se» are you’ 1 FEMALE 2 m ale
043 wnat race do you consider yourself 1 ASIAN 2 BLACK (AFRICAN AMERICAN) 3 HISPANIC (LATINO/A) 4 WHITE (NON-HISPANIC) 5 o t h e r (SPECIFY) ______
044 Aooui now many times did you work witn a member ol a work group other than your own last w eek’ 1 0 Tim e s 2 1-2 TIMES 3 3-4 TIMES 4 5 OR MORE TIMES
0 45 How important is it for you to communicate regulaity with em ployees outside of your work group in order to oo your current |00’ 1 e s s e n t ia l 2 VERY im p o r t a n t 3 SOMEWHAT IMPORTANT 4 not VERY important 5 NOT IMPORTANT AT ALL
046 Have you ever provided mom into deosions that significantly changed USF programs, products, or services’ 1 YES 2 NO
0 4 7 Have you ever m ade linal decisions that signilicanlty changed USF programs, products, or services’ 1 YES 2 NO
348 Have you ever nao access lo sensitive or confidential company mformation’ 1 YES 2 NO
349 Have you ever made major purchases (S10.CXX} or more) without getting permission from higher up’ 1 YES 2 NO
181 QSO HOW wouW you acscnoe your career progress since you starteo wiin USF') 1 1 h a v e a d v a n c ed RAPIDLY 2 I HAVE MADE STEADY ADVANCES 3 I HAVE s ta y e d AT ABOUT THE SAME LEVEL 4 I h a v e LOST SOME GROUND
OS 1 How satisSeo are you wiin me amount of help mat you nave received from me USF neipers ytxj listed) 1 VERY SATISFIED 2 s o m e w h a t SATISFIED 3 SOMEWHAT DISSATISFIED 4 VERY DISSATISFIED
052 How saasSed are you wim me amount of help mat you nave received from your direct suomvisnro t VERY SATISFIED 2 s o m e w h a t SATISFIED 3 s o m e w h a t dissatisfied 4 VERY DISSATISFIED
053 Overall, now saosSed are you in your USF job') 1 VERY SATISFIED 2 SOMEWHAT SATISFIED 3 SOMEWHAT dissatisfied 4 v e r y dissatisfied
0 5 4 In w nat y e a r w ere you twrn")
055 wnat IS your current mantal status') 1 m a r r ie d 2 WIDOWED 3 DIVORCED 4 SEPARATED 5 NEVER MARRIED
056 HOW many cniioren under me age of 18 do you have Innng at home? t NONE 2 ONE 3 TWO 4 three OR more
057 In wnicn ol me following caiegones did your 1994 USF earnings fall (liefore taxes)') 1 UNDER $19 999 2 BETWEEN $20,000-29.999 3 BETWEEN $30,000-39.999 4 BETWEEN $40,000-49,999 5 BETWEEN $50,000-59.999 S BETWEEN $60 000-69 999 ) BETWEEN $70,000-79.999 9 BETWEEN $80,000-89 999 9 BETWEEN $90,000-99 999 10 $100 000 or more YOU ARE DONE I Your help in this researchIs greatly apprsciafed. Please place your survey in tfie envelope addressed to the Ohio State University it you w ould like a summtry report print your name and address on the back o f (hereturn envelope (not on the survey) and write 'reouest 'esuits ~ If you w ould like to com m ent on th e survey please turn (hepage.
182 Is there anything else you would like to say about the kinds of help you get or give at work? if so. you can use tne soace below for that purpose Also, please share any comments you have on how to better understand the ways m which people do or ao not help each other at work
183