As stated previously, my sample consists of 21 women who met the following inclusion criteria:
• Have a child (or children) under the age of 6
• Worked outside of the home in their profession throughout their pregnancy
• Currently work outside of the home on a full time basis (35 or more hours per week)
• Returned to work on a full time basis within 4 months of having their child • Self-define as being “career-oriented”
• Additionally, all 21 women lived and worked in Southeast Michigan
Recruiting career-oriented working mothers of young children from the greater Detroit area elicited a relatively homogenous sample in terms of education, social class, and marital status. 29% of my sample (six women) had a Bachelor’s degree, or the equivalent. Nine women, or 43% had a Master’s Degree. Three women, or 14% had a PhD. One woman, or 5% of my sample had her JD. In sum, 86% of my sample, had a Bachelor’s degree or higher at the time of the interviews7. Inflation adjusted median household income at the end of 2011 in the United States was $51,143 (Davidson, 2012). The vast majority of my sample (around 90%) had significantly higher household incomes than the national median. Twelve women, or 57% reported household incomes in excess of $100K. 24%, or five women reported household incomes ranging between $75,000 – $99,999. 9.5%, or two women reported household incomes ranging from $50,000 - $74,999. Two women reported household incomes between and $25,000 - $49,999. All but one participant was married or cohabiting long term.
All of the participants were between the ages of 25-50, per the aforementioned screening criteria. The women in my sample ranged in age from 27 to 43. The median
age was 36. All of the women in my sample delayed childbearing until after they were established in their careers, and all of the women in my sample had children no older than six. The majority (52%, or 11 women) had two children. 38%, or eight women had one child, and two participants had three children.
All of the career-oriented women who volunteered for this study were in professional occupations, and therefore had a similar amount of education, level of economic stability, and/or income available to them. For this reason, I purposely recruited women from more than one racial/ethnic group. Fourteen women, or 67% of the women in my sample identified as White/European, and seven women, or 33% were women of color. According to 2011 census data, this is somewhat reflective of the racial/ethnic composition of Southeast Michigan8. Table 1 below provides the specific
details on reported race and ethnic identity of the women in my sample.
I surveyed women about household and child related division of labor. Eight women, or 38% of the women in my sample indicated that they do the majority of the household tasks. Four of my participants (19% of my sample) reported that someone else does the majority of the household labor. Two of those women said that they outsourced that labor on a biweekly basis, and two women had stay-at-home husbands who did the majority of the second shift work. Nine women (43% of my sample) responded that they share equally in household tasks with their husbands/partners. Interviewees were much less likely to report doing the majority of the childcare tasks than household tasks. Only four participants, or 19% of my sample, responded that they are the ones doing the majority of the childcare work. Women were much more likely to report sharing equally
in childcare tasks than they were to say that they share equally in household tasks. Fifteen women, or 71% of my sample stated that they share equally in childcare tasks. Not surprisingly, the two breadwinner participants in my sample reported that their husbands do the majority of both household, and childcare tasks.
Table 1. Participants Name Marital
Status Ethnicity Race/
Hispanic/ Latina Age # of Children Education Annual Household Income
Susan Married White/
European No 42 2 Registered Diagnostic Medical Sonographer $100K and above
Janet Married White/
European No 38 2
Masters Degree
75K- $99,999K
Bonnie Single Latina Yes 27 2 2nd year of college $49999K 25K-
Lisa Married White/
European No 30 2
Bachelor's
Degree 50K-$74,999
Kimberly Cohabiting Asian No 40 2 Masters
Degree
$100K and above
Christine Married White/
European No 36 2 Masters Degree PhD Student $75k - $99999K
Shannon Married White/
European No 37 1 PhD
$100K and above
Dawn Married White/
European No 30 1
Masters Degree
$75k - $99999K
Rachel Married European White/ No 33 3 Bachelor's Degree $100K and above
Amy Married Latina Yes 32 1
Bachelor's Degree/Some Graduate Work $100K and above
Justine Married White/
European No 44 2 JD
$100K and above
Name Marital Status Ethnicity Race/ Hispanic/Latina Age Children # of Education
Annual Household
Income Erin Married White/
European No 36 1
Masters Degree
$100K and above
Beverly Married White/
European No 32 2
Masters Degree
$100K and above
Julie Married European White/ No 38 1
MFA, Doctoral Candidate
$100K and above
Sarah Married European White/ No 34 2
BS, Some Graduate Work $75k - $99999K Dana Married Black/ African American No 37 3 MBA $100K and above Jennifer Married Black/ African American & White/ European No 29 1 HS, Some College $99999K $75k -
Nicole Married White/
European No 40 2 PhD
$100K and above
Fiona Married European White/ No 31 1 Bachelor's Degree $49999K 25K-
Gretchen Married Black/ African American No 36 2 Masters Degree, PhD Student $50K- $74,999
Concepts to Consider
I define masculine type jobs as ones in which a worker is in a male dominated profession. For example, Nicole, an Engineer, and Dana, a Marketing Development Manager, are both coded as working in “masculine type” professions. I define feminine type jobs as ones in which a worker is in a female dominated profession. Susan, a Registered Diagnostic Medical Sonographer, and Fiona, Head Teacher of an Infant/Toddler Program are two examples. When I began this research, and data analysis, that this job type coding would be sufficient in terms of locating similarities and differences in women’s appearance work, and attitudes about appearance work. I quickly learned that there were more factors to women’s appearance work experiences, particularly in workplaces – a major line of questioning in this dissertation. In order to garner a more accurate picture of women’s appearance work experiences, I also give consideration to whether, and how often performing in front of groups of “others” is a part of women’s jobs. For example, I have a disproportionate number of women who are in the teaching profession (a feminine type job) in this sample. While the grade levels and subject matter they teach vary widely, they all “perform” for a certain kind of audience each day. This matters. And it matters in ways that are different from women who work in masculine type jobs who lead business meetings. Further consideration is also given for whom the women in this sample work alongside in their workplace. Whether women work alongside primarily men, or primarily other women is likely to impact their appearance work experience and attitudes about appearance work. I provide a detailed table of women’s job title, and these concepts in Table 2.
Table 2. Women’s Career/Organizational Descriptors
Name Job Title Job Type Perform Primarily Work
Alongside Susan Registered Diagnostic Sonographer Feminine No Women Janet High School Chemistry9 and A&P Teacher
Feminine Yes Women
Bonnie Shipping Manager Masculine Occasionally Men
Lisa Assoc. Director Feminine Yes Women10
Kimberly Research
Associate Feminine No Mixed
Christine Middle School Principal Masculine Yes Women
Shannon
HS English Teacher (Private
School) Feminine Yes Women
Dawn
Associate Director of Career Counseling
Feminine Yes Mixed
Rachel
Business Analyst/Tech
Support
Masculine No Men
Amy Product Marketing Masculine Yes Men
Justine Attorney Masculine No Women
Erin Teacher (MS
Math) Feminine Yes Women
Beverly Research Project
Manager Feminine Yes Women
Julie
Graduate Student Instructor/Writing
Lab Coordinator
Masculine Yes Women
Sarah Owner, Small
Business Masculine No Women
Dana
Market Development
Manager - Americas
Masculine Yes Men
Jennifer Assistant Manager Masculine Yes Mixed
Nicole Engineer Masculine Occasionally Men
Fiona
Head Teacher/Infant Toddler Program
Feminine No Women
Gretchen Program Manager Feminine No Women11
Helen Associate Professor Masculine Yes Mixed
9
Although, Chemistry is often seen as a masculine subject.
10 Lisa often engages with high status men both in her workplace, and in the community where her
workplace is located.
11 Gretchen often engages with high status men both in her workplace, and in the community where her
A Note On Labeling
In order to contextualize my findings, I include descriptive information for each participant when I introduce them in each chapter. In Chapter 5, Career-Oriented Women’s Appearance Work, I include each participant’s name, race/ethnicity, and job title. In Chapter 6, Responding to Appearance Norms: Resistance and Accommodation, I follow the same conventions as Chapter 5, however, in the section Resistant Body Acceptance, I also include number of children. In Chapter 7: Social Locations and Appearance Work, I apply the following labeling system: Career-Oriented Mothers: job title, number of children; Reference Group Comparisons: Race/ethnicity, job title, number of children; Race/Ethnicity: race/ethnicity only; Transitions to Motherhood: number of children only; Organizational Structures: race/ethnicity, job title, job type, and work environment descriptors.