ESSAYS ON GENDER, MARRIAGE AND INEQUALITY

A DISSERTATION

SUBMITTED TO THE DEPARTMENT OF SOCIOLOGY

AND THE COMMITTEE ON GRADUATE STUDIES

OF STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Emily Fitzgibbons Shafer

August 2010

http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/ds207kh5524

© 2010 by Emily Fitzgibbons Shafer. All Rights Reserved.

Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.

ii

http://creativecommons.org/licenses/by-nc/3.0/us/http://creativecommons.org/licenses/by-nc/3.0/us/http://purl.stanford.edu/ds207kh5524

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Paula England, Primary Adviser

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Shelley Correll

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

David Grusky

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Michael Rosenfeld

Approved for the Stanford University Committee on Graduate Studies.

Patricia J. Gumport, Vice Provost Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.

iii

iv

Acknowledgments

First and foremost, I thank Paula England, my mentor for the past eight years.

Her guidance, example and support have made my work, and this project specifically,

much stronger. I feel grateful to have been her student as her commitment to her

students and her work is unparalleled. Rarely is someone so accomplished so humble;

she is not only inspiring as a scholar, but as a person as well.

Additionally, I thank Shelley Correll for her mentorship in the past two years; I

have benefitted tremendously from it. Her energy and enthusiasm for gender research is

infectious. I only wish she had come to Stanford sooner!

I also thank my other committee members -- David Grusky and Michael

Rosenfeld -- and all who have advised me along the way.

Of course, the people who deserve the most thanks are my parents, Mary

Fitzgibbons and Charles Shafer. They have always been my biggest fans and sources of

support. I do not know what I would do without them. I hope I make them proud.

To the rest of my family and loved ones that are too numerous to mention, thank

you. I am not sure what I did to deserve to be surrounded by such amazing and

supportive people; I am very, very grateful for each one of you.

v

Table of Contents

Acknowledgements iv

List of Tables and Figures vi

Chapter 1: Wives‘ relative wages, husbands‘ paid work hours, and 1

wives‘ labor force exit

Chapter 2: The effect of marriage on weight gain and propensity 28

to become obese in the African American community

Chapter 3: Why Hillary Rodham became Hillary Clinton: The 45

correlates and consequences of surname use in marriage

Bibliography 75

vi

List of Tables and Figures

Chapter 1, Table 1: Unweighted proportions, means and standard deviations 14

of variables in person year data

Chapter 1, Table 2: Summary of logistic regression over person year data 16

predicting whether a woman is out of the labor force in the following year

Chapter 1, Figure 1: Predicted probability that a married woman will exit the 19

labor force in the following survey year by her proportion wage

Chapter 2, Table 1: Percent of sample that is obese or overweight, by race, 37

gender, and marital status

Chapter 2, Table 2: Predicted probability of becoming obese in the next survey 38

year, if an individual is not obese currently and the percentage change if

respondent is married compared with never married, living alone

Chapter 2, Table 3: Individual fixed effects regression predicting body mass 40

index (BMI) by race and gender

Chapter 3, Table 1: Experimental conditions 55

Chapter 3, Table 2: Logistic regression predicting a woman changed her name 60

in marriage, all other decisions (kept surname, new last name, hyphen)

equal zero

Chapter 3, Table 3: Mean responses by condition where last name is (a) same 63

as husband‘s, (b) hyphenated or (c) different than husband‘s

Chapter 3, Table 4: Coefficients from ordered logit analysis predicting 65

outcomes 1-6 for all respondents

Chapter 3, Table 5: Coefficients from ordered logit analysis predicting 66

outcomes 1-6 for men with a high school degree or less

1

Chapter 1: Wives‘ relative wages, husbands‘ paid work hours, and wives‘ labor force

exit

One of the largest demographic changes of the second half of the twentieth century was

the increase in the proportion of women in the labor market. The proportion of women

who worked outside their homes increased rapidly from the 1960s to the 1990s; many

believed that this trend would continue until women reached parity with men. However,

in the 1990s, women‘s labor force participation rate leveled off and even slightly

decreased in the early 2000s (Percheski, 2008; Vere, 2007). Today, women‘s labor force

participation is roughly equivalent to what it was prior to the current economic recession

(Pilon, 2010). The popular press picked up on this reverse in trend; multiple articles

described a return to the appeal of being a stay-at-home mother among women (Belkin,

2003; Story, 2007). Though the press wildly exaggerated the magnitude of the decrease

in women‘s employment, they did make one point confirmed by research—that one of

the major reasons married mothers cite for wanting to stay home is the difficulty

(anticipated or actual) in combining both paid and unpaid work (Stone, 2008).

Women‘s exit from the labor force is a concern for multiple reasons. A break in

employment can have a significant negative impact on a woman‘s earnings when she

returns to the labor force. This is true even for women who are out for a short period of

time. Hewlett and Luce (2005) estimated that a two to three year exit can cause a thirty

percent decline in earnings upon professional women‘s re-entry to the labor force. Exit is

one of the main contributors to the large gender gap in lifetime earnings (Rose &

Hartmann, 2004) and is one of the reasons why women face a decline in standard of

living after divorce, which half of married women will experience (Cherlin, 2010; Raley

2

& Bumpass, 2003). Additionally, scholars interested in gender equality within

heterosexual marriage have noted that power within the relationship flows form earnings

(Blumstein & Schwartz, 1983; England & Kilbourne, 1990; Lundberg & Pollak, 1996).

Women with no earnings may therefore face the largest inequality in power in their

marital relationships.

Despite the cause for concern about deleterious effects of women‘s

nonemployment, there is little scholarly work that focuses on identifying what

specifically predicts women‘s labor market exits. The work that has been done has

focused almost exclusively on what characteristics of women -- namely, their absolute

earnings and fertility -- promote labor force exit. In this paper I argue that more attention

should be paid to the relationships in which the women are embedded when considering

women‘s labor force participation. Specifically, the purpose of this paper is to further our

understanding of wives‘ employment exits through examining two formerly unexamined

husband influences – the percentage contribution a woman‘s wages represent of her and

her husband‘s total wages, and her husband‘s time spent in paid employment (which is

relevant because it is time when a husband is not available for domestic work).

Specifically, my research questions are: Does the proportion of total wages that a wife

brings in to a household predict whether or not she will leave the labor force, holding

constant both her and her husband‘s absolute wage? And what impact does partner‘s

hours in paid work have on her hazard of exiting?

Literature Review

The bulk of prior quantitative research on women‘s labor force participation has

focused on her fertility, her absolute earnings and her husband‘s absolute earnings. The

3

negative effect of a woman‘s fertility on the likelihood of her labor force participation is

uncontested (for a review see Brewster & Rindfuss, 2000). I do not discuss that literature

here. Instead, I outline the theoretical models that posit that wives‘ and husbands‘

absolute wages impact wives‘ labor force decisions and the empirical evidence either

supporting or negating these models. I then argue for a model that includes women‘s

wages relative to her husband‘s. Next, I outline the extensive literature on gender

inequality in home production and care work and the link to the time men spend in paid

work. I then argue that men‘s time in paid work may affect their wives‘ labor force

participation and discuss qualitative evidence that supports this idea.

Wives’ and husbands’ absolute earnings

Economic theory predicts that the greater the opportunity cost of not working

outside the home, the more likely a woman is to be employed. In other words, as a

woman‘s potential earnings increases, her opportunity cost of focusing only on unpaid

work within the house increases and she is more likely to be employed.

Cross-sectional studies of women‘s employment have estimated opportunity cost

effects by examining employment rates by educational attainment, which is a proxy for

potential wages. Indeed these studies have found that as a woman‘s education increases

the likelihood that a woman is employed also increases (Cohen & Bianchi, 1999; Cotter,

Hermsen, & England, 2009; England, Garcia-Beaulieu, & Ross, 2004).

In studies that examined women‘s participation longitudinally, higher test scores

(Budig, 2003), more education (Drobnic, Blossfeld & Rohwer, 1999; Shaw, 1985) and

larger current wage rates have (Felmlee, 1984) generally been shown to decrease the

likelihood that a woman will exit employment. Similar results have been found for

4

studies focusing only on the employment of mothers (Desai & Waite, 1991; Henz &

Sundstrom, 2001). For two studies, education was not significant (Budig 2003) or did

not work in the expected direction (Felmlee, 1984). However, these were both studies

where opportunity cost was operationalized in multiple ways (in test scores and wage

rates, respectively) and in both instances the other measure of opportunity cost positively

predicted employment.

The more money a woman earns may also impact how easy it is for her to

combine market work and work inside her home, further contributing to any effect of

potential wages on employment. Gupta (2007) found that a woman‘s own earnings

predict how much household work she does; the more she earns, the less time she spends

in housework. Gupta‘s interpretation of this finding is that women who earn more

outsource household work (for example, picking up food for dinner rather than cooking

or hiring someone to clean the house). At least one other study has found similar results

(de Ruijter, Treas, & Cohen, 2005). Additionally, mothers may weigh the amount they

earn against costs of child care, further encouraging mothers who earn more absolutely to

stay in the labor force. There is some evidence that mothers‘ labor force participation is

sensitive to child care costs (Han & Waldfogel, 2001).

Economic theory also predicts that husbands‘ absolute earnings reduce wives‘

employment. In other words, a woman whose husband earns enough money for her to

focus only on unpaid work inside the home is more likely to exit the labor force. In the

longitudinal studies of wives‘ employment exits that have controlled for some measure of

husbands‘ earnings (either through a direct measure or through a measure of household

income minus her earnings), two studies found that husband‘s income promoted exit

5

from the labor force (Budig, 2003; Shaw, 1985) but one found the effect to be reversed

(Felmlee, 1984). Articles that have focused on the impact of husbands‘ earnings on

wives‘ labor force participation over time suggest that the strength of the negative

relationship is weakening (Cohen & Bianchi, 1999; Goldin, 1990).

Relative earnings

Due to marital homogamy, the tendency of individuals to marry those of similar

education or class (for example, see Schwartz and Mare, 2005), the income and

opportunity-cost effects push the opposite way on any individual woman. In other words,

the women who can earn more relative to other women will likely be married to a man

with high earnings; the former factor makes her more likely to be employed and the latter

discourages her employment. Thus, past work has attempted to calculate which has a

greater impact on women‘s labor force participation. Cross-sectional studies examining

labor force participation have shown that opportunity cost, measured by women‘s

educational attainment, promotes women‘s employment more than other household

income hinders it (Cohen & Bianchi, 1999; Cotter et al., 2009; Goldin, 1990). This

conclusion holds true in longitudinal studies that have examined women‘s employment

(Avioli & Kaplan, 1992; Desai & Waite, 1991; Felmlee, 1984; Shaw, 1985).

However, none of the studies that attempt to determine which force predominates

in women‘s labor force participation actually interact opportunity cost (wives‘ absolute

earnings) with income effects (husbands‘ absolute earnings). Yet, we know from social

psychological literature that women make social comparisons when determining their

satisfaction with the amount they earn (Major, 1987). If women make social

comparisons when evaluating their satisfaction with their earnings, they may make social

6

comparisons when evaluating the ―worth‖ of their earnings as well. I suggest that an

obvious comparison for them to make is with the earnings of their husbands. Most wives

who exit the labor force do so to focus more energy on caring for their families and to

relieving the burden of combining job and family responsibilities (Belkin, 2003; Story,

2005; Stone, 2008). Given the expectation that wives take most of the domestic burden,

women are sometimes faced with the choice between enduring a taxing double burden

and giving up the value of their own job and paycheck. If social comparisons to their

husband‘s wages affect wives‘ assessments of what their own jobs are worth, they will

affect whether they think the job is worth enough to make bearing the work/family

burden worthwhile.

Husband’s housework, care work and paid work hours

Men have not taken up unpaid care and housework at the same rates as women

have entered employment. Wives still do more housework (Bianchi, Robinson, &

Milkie 2007; Coltrane, 2004; Sayer, 2005) and care work (for example, for children and

extended family members) (Bianchi, Robinson, & Milkie 2007; Cancian & Oliker, 2000;

Sayer, 2005) than their husbands. Gender norms are strong enough that even when

women earn more than their husbands, their husbands do not respond by doing more

housework (Bittman, England, Sayer, & Matheson 2003; Brines, 1994; Evertsson &

Nermo, 2004; Greenstein, 2000). When women earn more, couples are thought to ―do

gender‖ (West & Zimmerman, 1987) by having a more traditional arrangement in

housework (with a woman doing more than her male partner) to compensate for being

gender nonnormative in earnings. Men‘s limited contribution to household work makes

7

balancing paid and unpaid labor particularly challenging for women (Blair-Loy, 2003;

Duxbury & Higgins, 1991), and might drive women‘s exit from the labor force.

The difficulty in combining paid and unpaid labor is especially strong for women

married to men who work more than 40 hours per week. The more time husbands spend

in paid work, the less they spend in household labor (Bittman et al., 2003:211; Hersch &

Stratton, 1994). As a consequence, when husbands work more hours for pay, wives spend

more time engaged in household labor, net of other factors such as family and husband‘s

share of total earnings (Bittman et al., 2003). This is perhaps also true for the amount of

time women spend in child or kin care and other household responsibilities not typically

captured by measures of housework (for example, shopping for the household or other

errands such as picking up take-out food for dinner). These findings are consistent with

accounts that view marriage as structured to support men‘s careers, but not women‘s

(Blair-Loy, 2003; Finch, 1983; Pyke, 1996; Ridgeway, forthcoming). As Finch (1983)

observed, ―a man‘s work imposes a set of structures upon his wife‘s life, which

consequently constrain her choices about the living of her own life, and set limits upon

what is possible for her‖ (pg. 2).

Consistent with this observation, wives report making career sacrifices because of

their husbands‘ careers but not vice versa (Maume, 2006). There is an association

between husbands‘ greater work hours and wives‘ ‗career sacrifices‘ if they are defined,

for example, by refusing overtime or working fewer hours (Maume, 2006), but husbands

report making no such sacrifices for their wives. In fact, Gerson (2010) found that most

young men today expect that their wives‘ employment will ―ebb and flow depending on

8

family needs‖ while placing their career‘s demands first (pgs. 174-175). Additionally,

when dual earner couples relocate it is most often at the benefit of the husband‘s career

and the detriment of the wife‘s (Bielby & Bielby, 1992; Cooke, 2003; Shauman &

Noonan, 2007).

Recent qualitative work supports the link between a husband‘s demanding career

and a wife‘s career sacrifice. In her interviews with very elite women who had left the

labor force, Stone (2008) found that one common theme in the lives of the wives she

interviewed was that their husbands were ―absent‖ at home because of their work

commitments. She states:

And in never being around, husbands had an arguably greater effect on women‘s

decisions to quit than the more immediately pressing and oft-cited ―family‖

demands of children. … [D]eference [to their husbands‘ careers] along with its

corollaries – the exemption of husbands from domestic responsibilities and the

privileging of husbands‘ careers – was a pervasive, almost subliminal feature of

women‘s stories. [pg. 62]

This work gives strong support to the idea that husbands‘ time in paid work influences

wives‘ labor force exit, at least for elite women.

Control Variables

Women‘s labor force decisions vary by race, ethnicity and immigrant status, with

whites and nonimmigrants currently having higher employment rates (England, Garcia-

Beaulieu & Ross, 2004; Reid, 2002). Additionally, previous work has found that ―work

commitment‖ defined by desire to be employed at a certain age to be predictive of future

labor force decisions (Desai & Waite, 1991). Of course, the presence of children --

especially young children or larger numbers of children -- deters fertility (Brewster &

Rindfuss, 2000; England, Garcia-Beaulieu, & Ross, 2004). All will be important control

9

variables in my analysis. In addition, whether a woman works part-time and whether she

has had continuous employment in the previous year will be important control variables,

as they are related to wages, and thus opportunity cost, and positively affect employment

(Bardasi & Gornick, 2008; Gornick & Meyers, 2003).

Method

The data I used come from the National Longitudinal Survey of Youth

(NLSY79), a nationally representative longitudinal survey. Participants were first

interviewed in 1979 when they were ages 14-21, and followed yearly until 1994, after

which they were re-interviewed biennially. In this paper, I used the data through 2004.

By 2004, the participants were approximately age 39-46. One of the many benefits of the

NLSY is its complete marital, employment and child bearing histories.

In order to examine the effects of relative earnings and husband work hours, I

limited my sample to married women who were employed at the time of marriage.

Women who were not employed at the time of marriage but later became employed were

not included in the analysis. Limiting the sample to employed women at the time of

marriage was necessary in order to have information about women‘s wages. Overall,

women who were not employed at the time of marriage have significantly lower

cognitive test score (AFQT), years of education, and spouse‘s earnings upon marriage

than women who are employed at the time of marriage (results not shown). However, the

choice to eliminate them was also a theoretical one – I am interested in what factors

influence women‘s first labor force exit for those who begin marriage attempting to

combine work and marriage.

10

The NLSY does not provide wage or hours worked information on cohabiting

partners. Therefore I cannot include cohabiting women in addition to married women.

Additionally, I did not include women who were in the military, self-employed or

farmers. Similarly, I did not include women who were married to men who were in the

military, self-employed or farmers. This is following past work that eliminates such

individuals from analysis (England, Gornick, & Shafer, 2008; Gornick & Meyers, 2003)

because of the substantial amount of ―noise‖ in their reported hours worked and hourly

wage, my two main variables of interest.

My sample consisted of 2,262 women who were employed at the time of marriage

amounting to 8,755 person years. Of the 5,827 possible female respondents in the

NLSY, 2,051 (35%) attrite before first marriage or do not have a first marriage by 2004.

Additionally, of the 3,776 women of whom we do witness a first marriage, 1,469 (25%)

are not employed at the time of first marriage. Of the remaining 2,307 women, 45 (0.8%)

were not included in the analysis because of missing data on the variables I included in

this analysis or because of my restrictions. Of the final 2,262 women in my sample, I

observed 678 women exiting the labor force, 370 women through 2002 with no exit

throughout their marriage, and the remaining 1,214 either became divorced, attrite from

the sample or have missing data on one of my variables of interest.

I used nested discrete time hazard models which are estimated using logistic

regression over person year data. Also known as discrete approximation, the models

estimate effects on the hazard rate of being out of the labor force in the following year of

my explanatory variables of interest, given that she is currently in the labor force and

married. This method is appropriate for my analysis as I am interested in predicting exit

11

from the labor force at time t, given certain family and employment characteristics at

time t-1.

The dependent variable is whether or not a woman is out of the labor force in the

following survey year. Being out of the labor force is different than being unemployed

(i.e. without a job but looking for work). The unemployed are considered in the labor

force in my analysis. Thus, those out of the labor force are those without a job and not

looking for work. I use these terms as they are used in all U.S. government statistics.

The main independent variables of interest are a wife‘s relative wage and her

husband‘s time in paid work. Her relative wage was measured as her proportion of the

―total‖ wage of her and her husband‘s combined wage.

Relative wage =

Husband‘s time in paid work was captured categorically. I was most interested in how

husbands who work long hours affect their wives‘ labor force participation. I defined

―long hours‖ as 45 hours or more per week, following Moen and Sweet (2003). Full-time

employees who do not work long hours were defined as working 35 to 45 hours per

week. The cut-off between full-time and part-time work (at 35 hours) was based on the

U.S. Bureau of Labor Statistics Current Population Survey‘s definition of full-time

employment. Additionally, I distinguished between regular part-time workers and

marginal part-time workers (those who work less than 10 hours per week) following past

work (Bardasi & Gornick, 2008).

My primary control for opportunity cost of nonemployment was measured by

hourly wage at the woman‘s job in the previous year. I measured women‘s wage in

quintiles of women‘s wages in my sample in order to standardize across years. The

12

motivation to measure women‘s wages in percentiles of all of women‘s wages and men‘s

wages in percentiles of all men‘s wages is twofold. First, we know that women make in

group comparisons when evaluating how satisfied they are with their earnings (Major,

McFarlin, & Gagnon, 1984). Second, given gender inequality in earnings, gender-neutral

quintiles would have very few women in the top wage percentile group.

Opportunity cost was also captured by a test score measure (AFQT percentile)

and level of education, because both could predict future earnings. AFQT, the Armed

Forces Qualifying Test, is a standardized test given to all of the NLSY79 participants in

1981 and is a rough estimate of cognitive skills (Budig, 2003). Education was measured

by a series of dummy variables representing whether a woman is not a high school

graduate, a high school graduate or equivalent (GED), has complete some college, or has

a four year college degree or more. Income available to the household from the

husband‘s job were captured by husbands‘ wages (parallel to wives‘ wages, they are

measured in quintiles representing the percentile the husband falls into based on all

husband‘s earnings in the sample in that given year).

The number of weeks the respondent was employed in the past year and her

current weekly hours worked were both captured by a series of dummy variables. Like

husband‘s work hours, I distinguish between wives who work long hours (greater than 45

hours per week), regular full time hours (35-45 hours per week), regular part-time (less

than 35 hours but greater or equal to 10 hours per week.) and marginal part time hours

(less than 10 hours per week). The majority of wives (approximately 70%) were

employed all 52 weeks of the previous year; this group was used as the reference

category in my models. The remaining dummy variables represented women who

13

worked 46-51 weeks, 36-45 weeks and less than 35 weeks, and there was roughly an

even distribution (approximately 10%) of wives within each group.

I captured a woman‘s fertility with the following controls: the number of

children she has, dummy variables representing age of youngest biological child, and

whether or not she is currently pregnant. Additionally, I controlled for whether the

respondent answered that she would like to be working for pay at age 35 at the time of

first interview in 1979. This variable was constructed from two variables. The first

asked ―What would you like to be doing at age 35?‖ Their available options in response

are: 1 = present job, 2 = some occupation, 3 = married, family or 4 = other. If the

respondent replied 3 = married, family he or she was then asked if he or she would like to

be working in addition to being married/keeping house/raising a family. At least one past

study has found these items, defined to be a measure of paid-work commitment, to be

positively related to women‘s later employment (Desai & Waite, 1991).

Finally, I controlled for race and ethnicity (non-Hispanic Black, non-Hispanic

White, Hispanic, or non-Hispanic Other), age and whether the woman is a first generation

immigrant. Table 1 provides unweighted descriptive statistics on all variables used in

this analysis, calculated over person years. It also provides the proportion of the sample

of women who ever had a first drop out after marriage, and, of these, the proportion who

were pregnant or had a child under six the year before dropping out.

Results

14

Table 1: Unweighted proportions, means and standard deviations* of variables in person-year data

Standard

Mean Deviation

AFQT Score 51.42 26.75

Race and Ethnicity

White 0.68

African-American 0.17

Hispanic 0.10

Other 0.05

1st Generation Immigrant 0.06

Age 29.00 5.91

In 1979, Wanted to be Employed at Age 35 0.90

Currently Pregnant 0.11

Age of Youngest Biological Child

Younger than one 0.12

Two - five 0.25

Six - eleven 0.11

Twelve - Eighteen 0.05

Over Eighteen 0.02

Total Number of Children in the Household 0.94 1.07

Education dummies (Less than High School = Reference)

High School Only 0.38

Some College 0.26

College Degree or More 0.31

Currently in school 0.07

Weeks Worked in the Past Year 47.95 9.30

Current hours worked per week 35.96 10.73

Respondent's Wage 10.70 8.08

Spouse's Wage 14.07 10.41

Spouse's Work Hours 43.43 9.79

Less than 10 0.01

10 - 34 0.04

35-45 0.68

Greater than 45 0.27

Proportion Wage (Her Wage / (Her Wage + His Wage)) 0.46 0.14

Less than 20% 0.04

20-40% 0.29

40-60% 0.53

60-80% 0.12

80-100% 0.02

Proportion of Women Who Have a First Drop Out 0.30

Proportion of Women Who are either Pregnant or Have a Child

Less than Six in the Year Prior to Drop Out 0.58

N individuals 2037

N person-years 8204

* Note: I provide standard deviations for continuous variables only.

15

Overall, approximately 30% of my sample had a first exit from the labor force.

By first exit I mean that of the women who were employed at the time of marriage, I

observed them exiting the labor force in my data. Of those who had a first exit, 58% are

either pregnant or have a child younger than 6 in the year prior to dropout (Table 1). On

average wives‘ wage rate represented .46 of the total wage rate earned by her and her

husband. More specifically, in 53% of cases, wives had wage rates that make up 40-60%

of the total wage rate; the typical case was for wives in my sample to have wage rates that

are somewhat close to their husbands‘. Thirty-three percent of the sample had a wage

rate that represented less than 40% of the household wage and 14% of the sample has a

wage rate that was 60% or more of the total wage rate. The mean number of hours

husbands work was 43.43. Twenty-seven percent of the husbands had long hours, 68%

had full time hours and only 5% worked part time.

16

Table 2: Summary of Logistic Regression Over Person Year Data Predicting Whether a Woman is Out of the Labor Force

Predictor β S.E. e^(β) β S.E. e^(β) β S.E. e^(β)AFQT Score Percentile -0.09 0.22 0.91 -0.14 0.22 0.87 -0.12 0.22 0.89Race and Ethnicity(White = Reference)

African-American -0.20 0.15 0.82 -0.18 0.15 0.83 -0.16 0.15 0.85Hispanic 0.18 0.14 1.20 0.20 0.14 1.22 0.19 0.14 1.21Other 0.03 0.20 1.03 0.04 0.21 1.04 0.03 0.21 1.03

1st Generation Immigrant 0.08 0.19 1.08 0.09 0.19 1.10 0.10 0.19 1.11Age -0.02 0.02 0.98 -0.02 0.02 0.98 -0.02 0.02 0.98In 1979, Wanted to be Employed at Age 35 -0.21 0.13 0.81 -0.22 0.13 0.80 -0.22 0.13 0.80Currently Pregnant 1.39 0.10 4.01 *** 1.38 0.10 3.99 *** 1.39 0.10 4.01 ***Age of Youngest Biological Child

Younger than one 0.28 0.16 1.32 0.30 0.16 1.34 0.30 0.16 1.35Two - five -0.35 0.16 0.70 * -0.33 0.16 0.72 * -0.32 0.16 0.72 *Six - eleven -0.46 0.21 0.63 * -0.45 0.21 0.64 * -0.46 0.22 0.63 *Twelve - Eighteen -0.50 0.33 0.61 -0.51 0.33 0.60 -0.53 0.33 0.59Over Eighteen 0.57 0.39 1.77 0.58 0.39 1.78 0.59 0.39 1.80

Total Number of Children in the Household 0.03 0.08 1.03 0.03 0.08 1.03 0.03 0.08 1.03Education dummies (High School Degree or More = Reference)

High School Degree -0.37 0.18 0.69 * -0.38 0.18 0.68 * -0.40 0.18 0.67 *Some College -0.43 0.20 0.65 * -0.44 0.20 0.64 * -0.45 0.20 0.64 *College Degree or More -0.44 0.23 0.64 -0.45 0.23 0.63 * -0.46 0.22 0.63 *

Currently in school 0.20 0.17 1.22 0.22 0.17 1.25 0.23 0.16 1.26Weeks Worked in the Past Year (52 = reference)

Less than 35 0.84 0.13 2.32 *** 0.83 0.13 2.30 *** 0.84 0.13 2.32 ***36 - 45 0.42 0.14 1.52 ** 0.42 0.14 1.52 ** 0.42 0.14 1.52 **46 - 51 0.24 0.13 1.27 0.24 0.13 1.27 0.23 0.13 1.26

Current hours worked per week (35-45 = Reference)

Less than 10 0.77 0.18 2.16 *** 0.72 0.18 2.05 *** 0.70 0.19 2.02 ***10 - 34 0.52 0.10 1.69 *** 0.51 0.10 1.66 *** 0.51 0.10 1.67 ***Greater than 45 0.08 0.19 1.08 -0.01 0.20 0.99 -0.02 0.20 0.98

Respondents Wages (0-20th percentile = reference)

21-40th percentile -0.26 0.12 0.77 * -0.27 0.12 0.76 * -0.12 0.13 0.8941st-60th percentile -0.54 0.14 0.58 *** -0.57 0.14 0.57 *** -0.32 0.16 0.7361st-80th percentile -0.58 0.14 0.56 *** -0.61 0.14 0.54 *** -0.27 0.19 0.7681st-100th percentile -0.82 0.15 0.44 *** -0.85 0.15 0.43 *** -0.37 0.23 0.69

Spouse's Wages (0-20th percentile = Reference)

21-40th percentile -0.05 0.14 0.95 -0.04 0.14 0.96 -0.26 0.16 0.7741st-60th percentile 0.05 0.14 1.05 0.09 0.14 1.10 -0.24 0.18 0.7861st-80th percentile 0.39 0.14 1.47 ** 0.46 0.14 1.58 ** 0.01 0.20 1.0181st-100th percentile 0.69 0.13 2.00 *** 0.77 0.14 2.16 *** 0.18 0.24 1.20

Spouse work hours (35-45 hours / week = Reference)

Less than 10 -0.46 0.44 0.72 -0.46 0.44 0.6310 - 34 0.11 0.19 1.12 0.11 0.19 1.11Greater than 45 0.40 0.09 1.48 *** 0.40 0.09 1.49 ***

Proportion Wage (Her Wage / (Her Wage + His Wage)) 0.18 **

Does the model control for year?

Constant -1.76 0.58 ** -1.79 0.58 ** -0.91 0.67

Χ² 14.56 *** 8.98 *

N individuals 2254 2254 2254

N person-years 8740 8740 8740

df 50 53 54

% who exited by 2004 30% 30% 30%

***p

17

Table 2 presents results from three nested models predicting a woman being out

of the labor force in the following survey year. I present the coefficients, standard errors

and odds ratios for each model. Model 1 did not include either of the two explanatory

variables of interest and serves as a baseline model. In Model 2 I introduced the hours a

woman‘s husband works per week. Finally in Model 3 I also added her proportion of the

total wage earned by the couple. In results not shown I introduced each of my

independent variables of interest separately for a total of four models. However, the

fourth model, which included proportion wage and controls only, showed effects of

proportion wage virtually identical to that in Model 3; I therefore did not include it here.

For ease in interpretation, I will discuss the odds ratios presented in Table 2 rather than

coefficients.

In Model 1 I present results using only a wife and her husband‘s absolute wage

plus controls only to predict her exit in the following year. I found support for an

opportunity cost argument in predicting a wife‘s hazard of leaving the labor force. In

general, as a wife‘s wage per hour (expressed as her percentile among other wives

employed that year) increased, she was less likely to exit the labor force in the following

year. Specifically, wives who had wages in the 21st-40

th percentile of wages had 23%

(=|1-.77|/100) smaller odds of exiting relative to those in the 0-20th

percentile; wives

whose wages are in the 41st-60

th percentile had 42% smaller odds; wives whose wages

were in the 61st-80

th percentile had 44% smaller odds and wives whose wages were in the

81st to 100

th percentile had odds 56% smaller than the reference category. The more she

earned, the less likely she was to drop out.

18

Additionally in Model 1, a basic income effect story is apparent. That is, wives

whose husbands had a higher wage rate had a greater hazard of exiting the labor force.

More specifically, wives whose husbands‘ wages were in the 61st-80

th percentile of

husband‘s wages in a given year had 47% greater odds of dropping out relative to women

whose husbands were earning in the 0-20th

percentile of husbands in that year; for wives

whose husbands were earning in the 81st to 100

th percentile, the odds of exit were 100%

greater.

Spouse’s Weekly Hours Worked

The effect of spouse‘s weekly hours of paid work – one of the main variables of

interest in this paper – is found in Models 2 and 3. I found that when a wife‘s husband

works 46 or more hours per week she was significantly more likely to drop out of the

labor force in the following year compared to wives whose husbands work 35 to 45 hours

per week. Across the two models in which I included husband‘s work hours, wives with

husbands who worked 46 or more hours had roughly 48-49% greater odds of dropping

out of the labor force of women whose husbands worked only 35 to 45 hours per week.

This effect was present even controlling for his wages, her wages and the proportion of

wages she brought in relative to him.

Relative Wage

In Model 3 I used a wife‘s proportion wage (her wage / (her wage + her husband‘s

wage)) to predict whether she was out of the labor force in the following year.

Proportion wage was indeed a significant predictor of exit (p

19

0 % of their combined wages), and one (implying she earns all the money) are not

meaningful values of this variable, I turn toward predicted probabilities which I graphed

in Figure 1.

Figure 1 illustrates the relationship between a wife‘s relative wage and the

probability that she will be out of the labor force in the following year. The bold line

represents the predicted probability of exit by her proportion of the total (her + her

husband‘s) wages (all other variables are held at their means). The dashed lines indicate

the confidence interval for the predicted probabilities. The graph illustrates that the

probability of her exit was .10 when she brought in only 10% of the total wages. When

her wage equaled that of her husband‘s the predicted probability of her exit was reduced

Figure 1: Predicted Probability that a Married Woman Will Exit the

Labor Force in the Following Survey Year by her Proportion Wage

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Proportion Wage (Wife Wage/(Wife Wage + Husband Wage))

Pred

icte

d

Prob

ab

ilit

y

of

Exit

Predicted Probability of Labor Force Exit Confidence Interval

20

to approximately .05. When her wage represented 90% of the ―total wage‖ her predicted

probability of exit dropped to approximately .02. In other words, a wife‘s predicted

probability of exit more than doubled if her wage was only 10% of the total wage

compared to when her wage rate was 50% of the total household wage (i.e. equal to that

of her husband‘s), holding constant her actual wage rate. In a more extreme example, if

two wives had the same absolute wage, but one had 90% of the total household wage and

the second earned 10% of the total household wage, the second wife‘s predicted

probability of exit was be almost 400% higher (Figure 1).

Controlling for how much her wage rate was relative to her partner erased any

significant effect of her wage rate alone. Model 3, which included proportion wage,

shows that respondent‘s wage is no longer significantly predictive of her exit. The

disappearance or muting of the large wage effect implied that the effect of her high wages

was coming from the fact that women who earned more were more likely to earn more

relative to her husband, and it is the proportion wage that appears to have the nonspurious

effect on her exit.

Like the effect of her own wage, the effect of husband‘s wage became

nonsignificant when I control for wives‘ relative wage rate (Model 3). The formerly

significant impact of men‘s wage rate appeared to be coming from the fact that men in

these wage percentiles were simply more likely to be earning the majority of the wages

that were coming into the household. It was actually an effect of his wage relative to

hers, not his absolute wage. In the final model, with relative wage of the two spouses

controlled, if a wife‘s husband was in the highest percentiles for wages she is no more

likely to exit the labor force compared to wives whose husbands were in the lowest.

21

Controls

As expected, pregnancy had a significantly positive effect on wives‘ labor force

exit across all three models. If a woman was currently pregnant, the odds of her exiting

the labor force were approximately 300% greater than if she was not. Additionally, the

effect of having a young child approached significance (p

22

significant difference in the odds of exiting for women who work full-time (35 to 45

hours per week) or long hours (greater than 45 hours per week).

AFQT percentile, race and ethnicity, immigrant status and age had no significant

impact on a woman‘s labor force exit.

Finally, women who responded when interviewed in 1979 that they would like to

have a career at age 35, had smaller odds of dropping out of the labor force than women

who said they did not want to have a career. This effect approached significance at the

p

23

It appears that, when deciding whether or not to leave the labor force, women are

more influenced by the relative impact the lack of their earnings would have on their

household, not on how well their husbands‘ incomes can support the family, or on the

absolute amount that would be foregone if they exited. As women are socially defined to

be the primary care givers in their families (for example, see Blair-Loy, 2003), women

who earn a low proportion of their total household income may see being able to focus

their energy on only unpaid work as contributing more to their families than their wages.

Additionally, husbands‘ hours spent in paid work also have an impact on the

likelihood that a woman will exit the labor force – wives whose husbands work more

than 45 hours per week had a greater hazard of exiting compared to women whose

husbands work 35 to 45 hours per week. Wives do a disproportional amount of

housework and care work on average (Bianchi, Robinson, & Milkie, 2007; Cancian &

Oliker, 2000; Coltrane, 2004; Sayer, 2005), but this inequality in time spent on household

labor is even greater when their husbands spend more time in paid work (Bittman et al.,

2003). Therefore, women married to men who work long hours may have the most

difficulty combining work and family life. This is consistent with recent qualitative work

of elite women who have left the labor force (Stone, 2008).

While the findings call into question some key precepts in existing research on

women‘s labor force exits, it is not without limitations. First and foremost, I cannot

directly test the mechanisms through which I believe proportion wage and husband‘s

hours per week have an effect. Although I believe that wives are comparing their wages

to their husbands‘, an alternative explanation is that women and men who are egalitarian

are more likely to match up by wage rate and that egalitarian men and women are more

24

comfortable with women having higher wage rates in relationships. In this alternative

interpretation, the reason wives who have a wage rate that constitutes a small percentage

of the total household wage rate are more likely to drop out is because she and her

husband are committed to having a more traditional family-work balance. However, this

interpretation of the findings is less compelling given that I do control for whether

women want to be working at age 35, which is a measure of desire for traditional

separation of work and family by gender in one‘s own life. To further explore this

potential mechanism, in models not shown I controlled for wives‘ gender ideology and

found no difference in the effect of proportion wage rate. This makes me fairly confident

that mate selection based on gender ideology is not the primary mechanism.

It is also a limitation of my analysis that I cannot be certain that husbands who

work long hours affect their wives‘ labor force participation through their unavailability

to participate in household activities such as housework and care work. Although

alternative longitudinal data sources do ask husbands about time in paid work and

housework, none also capture time in care work or follow wives from the beginning of

their marriages. Additionally Achen and Stafford (2005) have shown that self-reports of

household work are biased (as husbands and wives disagree on time each spends on

household work) whereas reports of husbands‘ time in paid work are less biased (there

are almost no discrepancies is husband and wife reports), which is one argument for

using husbands‘ paid work as an indirect measure of their availability for household

work. This is particularly true as past research has shown that husbands‘ work hours

affected their household and care work time (Bittman et al., 2003:211; Hersch & Stratton,

1994). . Even if the mechanism through which husbands‘ hours affect wives‘ exiting the

25

labor force is not their lesser availability for household and care work, the findings here

make clear that something about husbands‘ long hours impedes women‘s employment.

A final limitation of this paper is that my results may differ if I were to have data

that covered the financial crisis that struck the U.S. in the late 2000s. Because men are

losing their jobs at a disproportionate rate, it is possible that women may be much less

likely to drop out of the labor force regardless of their proportion wage or husbands‘

work hours. The effect of proportion wage and husbands‘ work hours could therefore be

conditional on a family perceiving a husband‘s job as secure or on a husband not being in

industries that have been particularly affected by the crisis (such as manufacturing and

construction). Unfortunately I do not have access to data that extend through 2008 to the

present. Of course, this merely reinforces the idea that scholars need to be paying closer

attention to how husbands and partners affect women‘s labor force decisions.

However, despite this paper contributes to the understanding of women‘s labor

force decision making and how gender inequality and stereotypic beliefs are maintained.

I argue, and my findings support, that wives are potentially evaluating the worth of their

earnings against their husbands‘ earnings. Unfortunately, gender inequality in wages in

the economy as a whole means that wives will typically earn less than their husbands‘,

which, as I have shown, means that they will be more likely to exit the labor force. Thus,

gender inequality in earnings among the employed, by leading to more women dropping

out of employment, reinforces traditional gender stereotypes that women are not

committed to their jobs. Moreover, if power does flow from earnings in marriage, and

women‘s power is lowest when they exit the labor force and earn nothing, then this

pattern creates greater gender inequality in marriage as well.

26

This paper also contributes to the literature that suggests that wives make career

sacrifices because of their husbands‘ career demands (Bielby & Bielby, 1992; Cooke,

2003; Maume, 2006; Shauman & Noonan, 2007). When husbands‘ work long hours,

women are more likely to exit the labor force. If they never return, this is the ultimate

career sacrifice. Even if women do return to the labor market (Hewlett and Luce, 2005;

Wellington, 1994), the amount they earn will be significantly less than if they had not

exited at all.

Although recent scholarship has suggested that some men expect that their wives

will make career sacrifices if their own careers demand long hours, the majority of men,

like women, say that they would prefer an egalitarian relationship, where both they and

their partners balance career and family (Gerson, 2010). This is consistent with

scholarship that reported few men prefer working long hours (defined as longer than a 40

hour work week) (Gornick & Meyers, 2003); in fact most men would prefer to work less

than they currently are (Jacobs & Gerson, 2004). Unfortunately, many may feel that they

have no real option if their jobs‘ demand longer than their desired hours (Williams,

2000). This has real implications for gender inequality in that wives may exit the labor

force because their husband‘s careers make their attempts to combine work and family

too difficult.

The gender gap in employment and the stall in progress towards equality have

been credited to women‘s difficulty in combining both paid and unpaid work (Stone,

2008). However, past work has mainly focused on women‘s individual characteristics in

predicting their labor market decisions. I have shown that relying on economic theories

(such as opportunity cost) that assume that women are acting independently of others – at

27

least in terms of labor force decisions -- is inappropriate and can lead to biased results. In

order to understand why women have not reached parity with men in terms of

employment, we need to understand how women make labor market decisions in the

context of their relationships. And we need to also take into account how the structure of

the labor force affects men‘s time in paid work, which affects women‘s employment.

Future work is needed to understand other ways husbands, families and the labor force

interact and influence women‘s labor force exits.

28

Chapter 2: The Effect of Marriage on Weight Gain and Propensity to Become Obese in

the African American Community

Obesity is associated with a host of diseases and increased likelihood of premature death

(National Center for Health Statistics, 2006). The majority of U.S. adult citizens are

considered to be overweight and about a third are obese (National Center for Health

Statistics, 2006). African American women have higher rates of obesity at all ages

relative to African American men and women and men of other races (Burke & Heiland,

2008).

The effect of marriage on weight and incidence of obesity is somewhat unclear.

Studies have shown that marriage leads to an increase in body mass index (BMI), an

indicator of percentage of body fat, for women and men (Kahn & Williamson, 1991). But

other work has found mixed results (Sobal, Rauschenbach & Frongillo, 2003;

Rauschenbach, Sobal, & Frongillo, 1995; French, Jeffery, Forster, McGovern, Kelder, &

Baxter, 1993); one study has shown no association between marriage and weight gain

(Rumpel, Ingram, Harris, & Madans, 1994). These results, although inconsistent, are

interesting in light of the fact that marriage is generally protective of health (Waite, 1995;

Lillard & Waite, 1995; Waite & Gallagher, 2000).

Although the effect of marriage on obesity rates and BMI is uncertain, even less well

understood is the relationship between marriage and obesity in the African American

community. Although studies of weight have focused on marriage and race (Kahn,

Williamson, & Stevens, 1991; Kahn & Williamson, 1991), neither specifically looked at

the effect of marriage on weight among African Americans; therefore, whether marriage

29

affects BMI and the propensity to become obese uniquely for African Americans is

unknown.

It is important to examine the differential effects of marriage across racial groups.

Marriage rates differ widely by race, suggesting the possibility that the meaning of

marriage differs by race and might, therefore, play a unique causal role in weight change

for African Americans. For example, one study estimated that by age 40, 68% of African

American women born between 1960 and 1964 were married compared with 89% of

white women (Ellwood & Jencks, 2002). These differences in marriage rates are

generally attributed to differing structural conditions experienced by African Americans,

relative to whites, such as lower economic opportunities create barriers to facilitate

marriage (Wilson, 1987). However, quantitative studies of attitudes show that African

Americans, on average, express less desire to marry relative to whites (South, 1993;

Shafer, 2006).

More research is needed to fully understand the relationship between obesity and

marriage, especially within the African American community. (Please see the first paper

in this volume by Moiduddin, Koball, Henderson, Goesling, and Besculides for a general

overview of the research literature on the relationship between marriage and health in the

African American community.) The current paper adds to the existing literature by

addressing the following questions: Are changes in BMI associated with marriage? Are

changes in the likelihood of becoming obese is associated with marriage? And how do

these effects differ by race and gender? For each question, two methods are used to

address selection bias, which is vital given that critiques surrounding the benefits-of-

30

marriage literature indicate that such studies do not adequately control for selection

(England, 2001).

Theoretical Perspectives

Several theoretical perspectives suggest mechanisms through which marriage might

affect BMI and obesity. The first views marriage as a protective institution and credits

marriage with causing individuals to be healthier (Waite, 2000). Some researchers argue

that wives, in particular, encourage their husbands to take better care of themselves by

engaging in healthy behaviors (such as eating a more balanced diet) and avoiding

potentially unhealthy behaviors (such as smoking) (Umberson, 1992). Researchers have

argued that marriage protects women‘s health through providing them greater economic

resources, which can provide access to healthier foods or better health care, although

studies have found that married women are healthier than unmarried women even after

controlling for differences in household income (Hahn, 1993). If marriage protects

individuals in terms of BMI and weight, we would expect that marriage would have a

negative effect on whether individuals are likely to become obese.

Although marriage is associated with adults being healthier, it is also associated

with a greater number of responsibilities, such as caring for one‘s spouse and, potentially,

one‘s children (Sobal et al., 1992). For example, marriage is associated with increases in

women‘s time in housework and men‘s time in paid work (Waite & Gallagher, 2000). An

increase in the number of priorities may lead to marriage being associated with weight

gain because maintaining one‘s weight becomes less of a priority. Indeed, married

31

individuals spend less time exercising than unmarried individuals (Nomaguchi &

Bianchi, 2002).

Another theoretical perspective is that prior to marriage, men and women are in

the marriage market, searching for marriage partners. Individuals who are married are no

longer in the marriage market and may prioritize their physical attractiveness less because

they no longer have to attract potential spouses (Stuart & Jacobson, 1987).1 Weight is

one component of physical attractiveness. Averett and Korenman (1995; 1999) find that

both women and men who are obese are less likely to marry; however, their results show

obesity is a stronger barrier to marriage for women. Likely this is because men tend to

place more importance on the physical attractiveness of their partners than women do (for

a meta-analysis of this literature see Feingold, 1990). In fact, there are many critiques that

current standards of female beauty encourage an unhealthy thin ideal that is unattainable

for many women (Wolf, 1991). If women are affected by this ideal we might see an

increase in women‘s BMI after marriage because they feel less pressure to keep their

weight lower. Overall, we might therefore see that marriage has a differential impact by

gender.

Thin standards of beauty are not uniform across population groups, however. There

is evidence that African American men and women have higher BMI ideals for women

than whites do. In a review of the literature, Flynn & Fitzgibbonn (1998) report that

1 Although there are differing degrees in the desire to get married among unmarried individuals, the

normative expectation of marriage is still strong (Cherlin, 2004), although perhaps less strong for African

Americans (South, 1993; Shafer, 2006).

32

overall, African American women, relative to white women, are more likely to be happy

with their bodies and are more likely to label themselves as ―normal‖ (i.e. healthy) weight

when their BMI classification would actually be overweight. In addition, African

American men report preferences for heavier women compared to white men when rating

the attractiveness of females (Greenberg & LaPorte, 1998). In fact, Averett and

Korenman (1995; 1999) found that the negative penalty for obesity on marriage is

smaller for African American women compared with white women.

There are two ways in which a preference for women with higher BMIs among

African Americans may interact with the effect of marriage on BMI. On the one hand, the

effect of marriage on weight gain might be smaller for African Americans compared with

other racial groups, as African Americans might have felt less pressure, prior to marriage,

to conform to lower weight standards. On the other hand, being off the marriage market

might lead to weight gain, regardless of weight at marriage, thus resulting in an increased

likelihood of obesity for African American women because their premarital weights are

initially closer to being obese.

Data

The data for this project come from the National Longitudinal Survey of Youth

(NLSY79), a nationally representative longitudinal survey. Participants were first

interviewed in 1979 when they were ages 14 to 21, and followed annually until 1994,

after which they were reinterviewed biannually. Data are currently available through

2004. One of the many benefits of the NLSY is that there is an oversample of African

American and Hispanic subjects; at the initial interview wave, the sample included close

33

to 3,000 African Americans, nearly 2,000 Hispanics, and more than 6,500 non-African

American/non-Hispanic whites.

In 2004, the overall retention rate for the NLSY was 76.9%. This included 73.1% of

the 1979 Hispanic or Latino sample, 78.9% of the 1979 African American sample, and

77.0% of the 1979 non-African American/non-Hispanic sample.2

The two dependent variables are obesity and BMI.3 BMI is used by the medical

community as an indicator of body fat percentage and is based on height and weight. The

calculation for BMI, based on weight in pounds and height in inches is

Levels of BMI are classified as underweight (BMI < 18.5), normal weight (BMI in range

18.5–24.9), overweight (BMI in range 25.0-29.9) and obese (BMI ≥ 30). For example, if

an individual is five feet and nine inches tall, then his or her normal weight range is from

125 to 168 lbs; if the person weighs more than this range and less than 202 lbs, he or she

is classified as overweight. Any weight greater than 202 and he or she is considered

2 Eliminated from the denominator of these retention percentages are those individuals who were

initially interviewed and then dropped by the NLSY at some later wave. For example, after 1984 the NLSY

stopped following its military subsample. Additionally, in 1991, the disadvantage white sample was also

dropped from the survey. 3 BMI has been criticized as not being a sufficiently reliable measure for obesity; these critiques

mostly surround the fact that BMI is only an approximation of body fat. Some healthy adults with larger

predicted percentages of muscle versus fat may be classified as obese. And this distinction may have

implications for racial differences in rates of obesity in the United States—at least one paper found that

measuring body fat percentage more accurately decreased the large difference between African American

and white women in the propensity to be obese by roughly half and increased the difference between

African American and white men (Burkhauser & Cawley, 2008). African Americans are more likely than

whites to be ―falsely‖ coded as obese by BMI measures compared with the Burkhauser and Cawley

measure. Unfortunately, I will not be able to apply their measure in this paper, as it requires data that are

not available in the NLSY.

34

obese. The NLSY is a particularly good source of data for studying BMI—for each

individual in the sample (who is not missing at any interview) there are 15 measures of

weight across the survey years. Respondents are asked to report their height only four

times throughout the survey, the latest being in 1985, when the youngest respondents

were 20 years old.

Both weight and height are self-reported in the NLSY. Because there is social stigma

associated with being overweight or obese, it is possible that individuals underreport their

weight. Additionally, although height should not fluctuate much in adulthood, weight

could fluctuate a great amount; therefore there is more of a risk of inaccuracies in self-

reported weight measures. However, the literature on the validity of self-reported weight

and height show a high correlation between reported and actual weight and height

(Brener, McManus, Galuska, Lowry, & Wechsler, 2003; Villanueva, 2001; Strauss,

1999). There is some evidence that women are more likely than men to under-represent

their weight (Brener, McManus, Galuska, Lowry, & Wechsler, 2003; Villanueva, 2001;

Strauss, 1999); results are mixed as to whether there are racial differences in the tendency

to over- or under-report weight (Brener, McManus, Galuska, Lowry, & Wechsler, 2003;

Villanueva, 2001; Strauss, 1999). However, at least one longitudinal study has found high

reliability in self-reported height and weight measures, which suggests that individuals

who under- or over-report their weights may do so consistently (Brener, McManus,

Galuska, Lowry, & Wechsler, 2003). Under- or over-representing by gender or race may

therefore not pose a problem for this analysis as this focus is on change within

individuals.

35

The key independent variable is whether the respondent is married. The reference

categories are never married and not cohabiting, cohabiting, divorced, separated, or

widowed. Marital status is based on the constructed relationship status variable in the

NLSY as well as the variable capturing whether the respondent is living with an opposite-

sex person as a partner. Cohabitation is an important reference category; although it is

similar to marriage in that two people are sharing a home, in general, research has found

that it is associated with less commitment and lower relationship quality (Stanley,

Whitton, & Makman, 2004; Bennett, Blanc, & Bloom, 1988; DeMaris & Leslie, 1984).

We may therefore expect to see similar, yet muted, results of cohabitation on BMI and

the likelihood of becoming obese.

Additional controls for both men and women include age, number of biological

children, earnings, current work hours, and household income. In addition, for women

current pregnancy status is included in the model.4 In models that include individual-

stable variables (i.e., the models predicting obesity) I also include the respondent‘s

Armed Forces Qualifying Test (AFQT) score and highest level of education achieved.

Methods

In order to examine the impact of marriage on probability of obesity, I employ

lagged-y regressor models, also called conditional change models. Lagged-y regressor

models address the selection issue by reducing omitted variable bias. They include a

lagged measure of the dependent variables as an independent variable, thus the focus of

the analysis is on change within individuals (Finkel, 1995). In the models predicting

4 Pregnancy is captured in two ways. In some, but not all years, female respondents are asked if they

are currently pregnant. However, beginning in 1983, detailed fertility histories were taken of each

respondent. I use a combination of both measures to capture pregnancy.

36

obesity, all independent variables are lagged. Fixed effects are not used despite the fact

that they are a preferred method for controlling for omitted variable bias (Halaby, 2004)

because fixed effects models would include only those whose obesity status changed over

the course of the analysis (Allison, 2005). Models are estimated separately for each racial

and gender group.

In order to ascertain whether individuals are more likely to become obese in time

t+1 by marital status given that they are not obese in time t, an interaction term between

marital status and obesity status at time t is included in the model. The coefficient on

marriage, therefore, can be interpreted as the difference in log odds of becoming obese,

given that an individual is not currently obese, for individuals who are married, relative to

those who are single. For example, a significant and positive marriage coefficient in this

model means that the likelihood of becoming obese (given that an individual is not

currently obese) is greater if the individual is married versus single. It is important to note

that this model does not control for BMI beyond obesity status. Therefore, a similar

marriage effect on BMI across race and gender could have differing impacts on the

likelihood of becoming obese for certain groups. Those who have higher, nonobese

BMI‘s to begin with would be more likely to become obese than those who have lower

nonobese BMI‘s with any universal effect from marriage. It is therefore crucial to also

study the effect marriage has on BMI.

In order to examine the effect marriage has on BMI, I use individual fixed effects

models. Fixed effects models are ideal in studying the effect of marriage on BMI as they

are designed to net out any static unmeasured characteristics of individuals, as well as

37

static characteristics for each year. These models are essentially ordinary least squares

(OLS) models, based on person-year data, which include dummy variables for each

person and year (Farkas, 2002).

Results

Table 1 presents descriptive statistics of weight status (overweight and obese) by

race, sex and marital status in 1990.

The mean age of respondents is 28 in 1990. Overall it appears that the highest rates of

obesity are among African American women and the lowest are among white women.

This is consistent with Centers for Disease Control and Prevention (CDC) statistics from

this period (CDC, 2007a). Turning toward differences in rates of obesity and overweight

Table 1: Percent of sample that is Obese or Overweight,

by Race, Gender and Marital Status

Married Unmarried Married Unmarried

Obese 23% 24% 18% 13%

Overweight 31% 30% 43% 40%

Total 54% 54% 61% 53%

Married Unmarried Married Unmarried

Obese 11% 12% 13% 11%

Overweight 20% 18% 44% 35%

Total 31% 30% 57% 46%

Married Unmarried Married Unmarried

Obese 16% 19% 22% 18%

Overweight 29% 22% 44% 39%

Total 45% 41% 66% 57%

Hispanic

African American

Women Men

Women Men

Women Men

White

38

among the married versus unmarried, there are no large differences by marital status

across racial groups. However, there appears to be more overweight and obesity among

married versus unmarried men. In general, it appears that there is about an 8 to 11

percentage point difference in the percent overweight or obese among married men

compared with unmarried men.

Although the descriptive results suggest that marriage might have more of an impact

on men‘s BMI, these results could be due to selection. In other words, these results do not

take into account any other differences between married and unmarried individuals in

1990. Therefore, I turn to my regression results. I first present whether marriage has an

effect on obesity rates by race and sex. Because differences in nonmarital BMI rates

could affect the probability of becoming obese in marriage, I then turn to the effect of

marriage on BMI.

Table 2: Predicted Probability of becoming Obese in the Next Survey Year, if an Individual

is not Obese Currently and the Percentage Change if Respondent is Married compared with

Never Married, Living Alone

Never Married, Living Alone Married % Change in Probabilitiy

African American Women 8.35% 10.20% * + 22%

African American Men 6.43% 6.28% Not Signficiant

White Women 3.60% 3.84% Not Signficiant

White Men 4.14% 4.56% Not Signficiant

Hispanic Women 5.25% 6.42% Not Signficiant

Hispanic Men 6.22% 6.24% Not Signficiant

Note: Predicted probabilities based on model predicting for whether respondent is obese

at next interview. Model includes controls listed in data and methods sections.

39

Table 2 provides fitted value results from the lagged-y regressor models predicting

obesity in the next survey year separately for each race/sex group. More specifically,

Table 2 presents the predicted probability that an individual will become obese in the

next survey year, given that the individual is not obese currently, by whether the

individual is never married and living alone or married.5 I present fitted values rather than

full models for ease of interpretation. Being married is only a significant predictor of

becoming obese in the next survey year among African American women. If an African

American woman is never married and not cohabiting she has an 8.35% chance that she

will be obese in the following survey year. However, if she is married, the probability is

greater: she has a 10.2% chance of becoming obese. This represents a 22% increase in the

likelihood of becoming obese, in each year. There is no significant difference between

being married or never married and not cohabiting for any other group. It is important to

note that this model does not control for BMI, only for whether the individual is currently

obese.

It appears that marriage is associated with an increased risk of becoming obese only

for African American women. Of course, this pattern could exist even if marriage causes

weight gain regardless of race or sex. For example, if African American women are more

likely to have a higher BMI when entering into marriage, a universal marriage effect (i.e.,

a marriage effect that is the same for individuals regardless of race or sex) on BMI could

5 As I discuss in the data and methods sections, models include control variables—AFQT score,

highest education achieved, whether the individual is cohabiting with an other-sex partner, and age; if

female, whether currently pregnant, number of biological children; whether the individual had zero

earnings in the past year; if they had earnings, their earnings percentile and current work hours, whether

they had zero household income, and household income percentile—results not shown

40

lead to the results found in Table 2. Thus, models predicting change in BMI, in addition

to likelihood of becoming obese, are important; the results from these models can be

found in Table 3.

Table 3 presents results from the individual fixed effects models predicting BMI

separately by race and sex (Models 1–6). It appears that marriage is associated with an

increase in BMI for all racial and sex groups and it appears that the size of the effect is

similar across groups as well. African American women and men experience a 0.40 and

0.52 increase in their BMI due to marriage, respectively. This increase is larger than that

experienced by white women and men (0.27 and 0.36, respectively) and smaller than that

experience by Hispanic women and men (0.42 and 0.58, respectively). As an example of

Table 3: Individual Fixed Effects Regression Predicting Body Mass Index (BMI) by Race and Gender

Relationship Status (Never Married, Living without a Partner)

Married 0.40 ** 0.52 *** 0.27 *** 0.36 *** 0.42 ** 0.58 ***

Cohabiting with a partner 0.23 0.34 *** -0.03 0.06 0.14 0.35 *

Separated -0.13 0.15 -0.48 *** -0.33 *** -0.28 0.06

Divorced -0.18 0.06 -0.40 *** -0.16 * -0.30 0.30 +

Widowed 0.42 -0.69 -0.19 0.03 0.93 + -1.76 *

Age 0.40 *** 0.31 *** 0.26 *** 0.25 *** 0.34 *** 0.20 ***

Currently Pregnant 1.05 *** 1.64 *** 1.62 ***

Number of Biological Children (No Children = Reference)

One -0.01 -0.14 + 0.45 *** -0.02 0.02 0.24 *

Two 0.11 -0.07 0.37 *** -0.19 *** 0.06 0.56 ***

Three or more 0.32 0.13 0.63 *** -0.10 0.19 0.97 ***

Earnings in the Last Year

Zero -0.07 -0.17 0.23 * 0.03 0.02 0.07

Earnings Percentiles (81st - 100th = Reference)

0 - 20th -0.12 -0.38 *** 0.18 * 0.05 -0.03 -0.05

21st - 40th -0.08 -0.30 ** 0.16 * 0.00 0.00 -0.28 *

41st - 60th 0.11 -0.20 * 0.14 * 0.00 0.09 -0.18

61st - 80th 0.10 -0.06 0.01 0.11 ** 0.22 -0.19 *

Current Work Hours 0.00 0.00 * 0.00 0.00 + 0.00 0.00

Household Income

Zero -0.34 -0.54 * 0.38 0.31 * -1.04 * 0.04

Household Income Percentiles

0 - 20th 0.22 0.01 0.32 *** 0.14 ** 0.35 + -0.17

21st - 40th 0.22 0.09 0.34 *** 0.08 * 0.24 -0.39 *

41st - 60th 0.24 0.05 0.27 *** 0.03 -0.01 -0.39 *

61st - 80th 0.02 -0.03 0.18 * 0.09 0.14 -0.29 +

Constant 14.62 *** 17.09 *** 15.77 *** 18.33 *** 14.58 *** 19.67 ***

N Person Years 10,828 11,075 24,866 27,243 5,383 6,089

N Persons 1,416 1,480 3,229 3,290 666 666

Model 5 Model 6Model 1 Model 2 Model 3 Model 4

Men

African American White Hispanic

Women Women MenMen Women

41

how these changes in BMI translate into pounds for an individual, for an individual who

is five feet and nine inches tall, a 0.58 increase in BMI (the largest marriage effect in

Table 3) would translate to approximately a four pound weight gain.

The greatest difference in effect size between these race and sex groups is 0.31,

which, given that the difference between obese and normal weight is roughly 5 points, is

not a substantively significant. Using the example of a person who is five feet and nine

inches tall again, 0.25 in BMI is equivalent to a little more than two pounds. However,

the substantive differences between these groups are small. 6

Thus, roughly speaking, all

groups experience a weight gain of similar magnitude from marriage.

Cohabitation, like marriage, also appears to be associated with an increase in BMI

for African American and Hispanic men. However, the effect size for cohabiting relative

to being single is smaller than for married relative to never married and living alone in

both cases.7 These findings are consistent with marriage market explanations of weight

gain. If individuals tend to gain weight when married because they are ―off the marriage

market‖, smaller, yet significant effect of cohabitation makes sense; cohabiting

individuals might feel that they are ―off the market‖, but less so compared to married

individuals. There are no clear patterns across race and gender for how changes in being

separated, divorced, or widowed are associated with changes in BMI.

6 In results not shown, I conduct Z-tests to test whether there are significant differences in the effect

size of marriage between groups (Paternoster, Brame, Mazerolle, & Piquero, 1998). Significant differences

(p < .05) in the effect sizes of marriage exist between white women and Hispanic men and white women

and African American men. 7 The effect size is significantly smaller for men of all races. In results not shown, I vary the reference

group in each model to test this.

42

It is not surprising that age is associated with increased BMI as adults tend to gain

weight as they age (Kuczmarski, 1997).8 Also, women who are currently pregnant have

higher BMIs. Because pregnancy is associated with gaining weight, and losing weight

can be difficult, we might expect that having children would increase women‘s BMI, as is

demonstrated for white women in models 1, 3, and 5 (Rosenberg, Palmer, Wise, Horton,

Kumanyika, & Adams-Campbell, 2003; Gunderson & Abrams, 1999). It is unclear,

however, why children increase BMI only for white women. Children are also associated

with an increase in BMI for Hispanic men.

Discussion and Conclusion

Even after controlling for unmeasured, individual-level fixed characteristics,

marriage is associated with increases in BMI, regardless of race and sex. Increased BMI

is not necessarily troublesome for health —the healthy weight range for adults is nearly

6.5 points wide and the effect size of marriage presented here is roughly between one-

quarter and a little more than half a point. However, the results also show that marriage is

associated with a greater likelihood of African American women becoming obese. This

finding is a concern as obesity is associated with a host of health risks and even early

death (National Center for Health Statistics, 2006).

The work adds to the existing literature in multiple ways. The first is that the

findings suggest that the effect of marriage on BMI and obesity differs by race and

8 Changes in age and year fixed effects should be collinear in my models (they are not perfectly

collinear because age is included as a continuous variable while year fixed effects are modeled using

dummy variables). However, in results not shown, I estimate the fixed effects models without year fixed

effects. The effects of age and significance levels were nearly identical to the ones I present in this paper.

Additionally, multicollinearity is not an important concern if it is in regard only to control variables, as it

should not bias my coefficients of interest.

43

gender. Previous work has examined the effect of marriage by gender, by not by race and

gender. Results indicate a relatively consistent effect of marriage on BMI across race and

gender; however, being married is associated with a higher likelihood of becoming obese

only for African American women.

This study uses up to 15 measures of weight over time, making it more likely to be

conclusive than the majority of work in this area, which tends to use only two measures

of weight over time. These results, therefore, add to the literature that shows inconsistent

results regarding whether marriage is associated with weight increases (Sobal,

Rauschenbach, & Frongillo, 2003; Rauschenbach, Sobal, & Frongillo, 1995; French,

Jeffery, Forster, McGovern, Kelder, & Baxter, 1993; Kahn & Williamson, 1991), and

suggest more definitively that marriage leads to weight gain.

Additionally, this study compares the effects of marriage to the effects of

cohabitation on BMI, which has not been commonly done in this literature (Sobal,

Rauschenbach, & Frongillo, 2003; Rauschenbach, Sobal, & Frongillo, 1995; Kahn &

Williamson, 1991). These results show that, for men, cohabitation is associated with an

increase in BMI that is smaller than the increase associated with marriage. For women,

cohabitation is not associated with changes in BMI. Cohabitation is an important

comparison with marriage, because although it resembles marriage, cohabitors tend to

express lower levels of commitment and relationship quality than do married couples

(Stanley, Whitton, & Makman, 2004; Bennett, Blanc, & Bloom, 1988; DeMaris & Leslie,

1984). It is possible that cohabitation feels like more of a commitment to men compared

44

with women, and thus, the ―off the marriage market‖ effect of caring less about one‘s

attractiveness to potential mates might be stronger for men.

Overall, for African American men and women, marriage is associated with an

increase in BMI that is similar to the weight gain for white and Hispanic men and

women. Additionally, marriage is associated with an increased likelihood of becoming

obese for African American women, but not for white and Hispanic women or men of

any race and ethnicity. This is consistent with recent work that suggests that obesity is a

particular concern among African American women (Burke & Heiland, 2008). The

findings of this study underscore the importance of targeting nutrition and healthy

lifestyle information to married couples regardless of their race or ethnicity. The results

further suggest, given that the effect of marriage on BMI is small, that certain at-risk

groups—such as African American women regardless of marital status —should also be

part of the focus of any policy effort toward reducing the prevalence of obesity.

45

Chapter 3: Why Hillary Rodham became Hillary Clinton: The correlates and

consequences of surname use in marriage

Hillary Rodham became Hillary Clinton after suggestions that her use of her

maiden name was one