Race, Marital Status, Allostatic Load, and Self-Reported Health

Stand By Me: Race, Marital Status, Allostatic Load, and Self-Reported Health

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

in the Graduate School of The Ohio State University

Korrie Dchonn Johnson

Graduate Program in Sociology

The Ohio State University

2018

Dissertation Committee

Dr. Reanne Frank, Advisor

Dr. Kristi Williams

Dr. Kammi Schmeer

Dr. Cynthia Colen

Copyrighted by

Korrie Dchonn Johnson

2018

Abstract

Most studies have examined the marriage-health association for the general population, combining data for all racial groups. In doing so, the experiences of whites, the largest racial group, tends to dominate the results. Few studies have examined racial differences in the marriage-health association. Those that have are limited by small sample sizes, an inability to distinguish between different types of non-marital unions, and a failure to formally test whether racial differences are present. As a consequence, most of the previous research is inconsistent and oftentimes contradictory. This dissertation uses data from a nationally representative dataset (the National Health and Nutrition Examination

Survey (NHANES) to overcome existing limitations in the literature. I focus on how marital status is associated with two different health outcomes (self-reported health and allostatic load). The results suggest support for heterogeneity in the marriage-health association by race. Specifically, I found that marriage was associated with lower levels of allostatic load for whites but not for blacks. In the case of self-reported health, the pattern of results was more similar across groups. Both married blacks and married whites had higher odds of reporting positive self-reported health than compared to their non-married counterparts, although the magnitude of this association was slightly larger for whites than for blacks. Further analysis revealed substantial variation within the black population. I found that the marriage-positive SRH association was only present among

ii high SES blacks but not among low SES blacks. These results suggest that the marriage- health benefit is more applicable to whites and high SES blacks. This is in spite of the fact that low SES blacks consistently display worse health outcomes and are more frequently the focus of marriage promotion efforts.

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Dedication

To the black pioneers before me and to the future black leaders after me.

Acknowledgments

This dissertation would not be possible without the love and support of my family, friends, colleagues, and support system that is in place. There were some long nights and early mornings that took place during the creation of this dissertation. Thank you Dr. Reanne Frank for your unwavering support as I juggled a full-time job and a short window to complete this dissertation. I truly would not have made it this far if it was not for your support and guidance. To my mom, sister, and brother who supported me and kept me grounded, I thank you. Thank you for allowing for me to vent when times were rough and understanding the sacrifices that I had to make to accomplish this feat. To the Black Graduate and Professional Student Caucus, thank you all for holding me down! To my wonderful fiancé Ditesha, you came into my life and provided your unwavering support, I can never fully express how much that means to me. I would also like to thank my dissertation committee Dr. Kristi Williams, Dr. Kammi Scheer, and Dr.

Cynthia Colen, you feedback and support was invaluable. Dr. Robert Bennett III and the

Office of Diversity and Inclusion, thank you so much for providing me with a safe space to explore my academic agenda and research goals. Last, but not least, the crew: Robert

Jackson, Nich Harold, Guy Hudson, Andrew Miller, Frashod Barlow, Richard Lowe, and of course Darrell Lewis. I will forever be grateful for all of the support I received from everyone!

Vita

1990 ...... Born in Memphis, TN

2008 ...... Whitehaven High School

2012 ...... B.A. Criminology/Criminal Justice, University of Memphis

2014...... M.A. Sociology, University of Memphis

2014-2016...... Graduate Teaching Assistant, The Ohio State University

2016...... Graduate Teaching Associate, The Ohio State University

2016-2017...... Editorial Assistant, The Ohio State University

2017-Present...... United States Census Bureau, Statistician

Fields of Study

Major Field: Sociology

Table of Contents

Abstract ...... ii Dedication ...... iv Acknowledgments ...... v Vita ...... vi List of Tables ...... ix Chapter 1. Introduction ...... 1 Chapter 2. Literature Review ...... 9 2.1 Overview of Marriage and Health ...... 9 2.2 Marriage is associated with better health for blacks...... 11 2.3 Racial Differences in the Marriage and Health Association by Race ...... 16 2.4 Empirical Evidence of Heterogeneity in the Marriage-Health Benefit...... 27 2.5 Understanding the theoretical health benefits of marriage by race ...... 31 2.6 Overview of theoretical health benefits of marriage ...... 31 2.7 Empirical and Theoretical differences in the health benefit of marriage for blacks33 2.7.1 Variation in Union Formation ...... 33 2.7.2 Variation in Resource Accumulation ...... 36 2.7.3 Variation in Social Support ...... 37 2.8 Crisis and Strain Models ...... 38 2.9 The Strain of High SES for Blacks ...... 39 2.10 Current Project ...... 40 2.11 Research questions ...... 40 Chapter 3. Data and Methods ...... 42 3.1 Data ...... 42 Table 1: Description of NHANES Data Set ...... 44 3.2 Analytic Sample ...... 45 vii

3.3 Outcome Variables ...... 46 3.4 Key Explanatory Variable ...... 51 3.5 Additional control variables ...... 51 3.6 Analytic Strategy ...... 52 Chapter 4. Analysis Part I Blacks and Whites ...... 54 4.1 Black and White Models Combined...... 57 4.2 Separate Models by Race ...... 62 4.3 SRH ...... 63 4.4 Allostatic Load ...... 66 Table 2: Description of Key Analytic Variables...... 72 Table 3: Description of Key Analytic Variables...... 73 Table 4: Overall Ratios of Positive Self-Reported Health ...... 74 Table 5: Overall Odds Ratios of Low Allostatic Load ...... 75 Table 6: Odds Ratios of Positive Self-Reported Health by Race ...... 76 Table 7: Odds Ratios of Low Allostatic Load by Race ...... 77 Chapter 5. Analysis Part II Black Men and Black Women ...... 78 Table 8:Description of Key Analytic Variables for Blakcs ...... 89 Table 9: Description of Key Analytic Variables for blacks ...... 90 Table 10: Odds Ratios of Positive Self-Reported Health ...... 91 Table 11: Odds Ratios of Low Allostatic Load ...... 91 Chapter 6. Conclusion ...... 92 Bibliography ...... 102 Appendix A: Missing Data Comparisons ...... 111 Table 12: Description of Missing Data (Blacks and Whites) ...... 111 Table 13: Description of Missing Data (Blacks Only) ...... 111 Table 14: Description of Missing Data (Marital Status) ...... 112

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List of Tables

Table 1: Description of NHANES Data Set ...... 44 Table 2: Description of Key Analytic Variables ...... 72 Table 3: Description of Key Analytic Variables ...... 73 Table 4: Overall Ratios of Positive Self-Reported Health ...... 74 Table 5: Overall Odds Ratios of Low Allostatic Load ...... 75 Table 6: Odds Ratios of Positive Self-Reported Health by Race ...... 76 Table 7: Odds Ratios of Low Allostatic Load by Race ...... 77 Table 8:Description of Key Analytic Variables for Blakcs...... 89 Table 9: Description of Key Analytic Variables for blacks ...... 90 Table 10: Odds Ratios of Positive Self-Reported Health ...... 91 Table 11: Odds Ratios of Low Allostatic Load ...... 91 Table 12: Description of Missing Data (Blacks and Whites) ...... 111 Table 13: Description of Missing Data (Blacks Only)...... 111 Table 14: Description of Missing Data (Marital Status) ...... 112

Chapter 1. Introduction

For over a century, scholars have argued that social relationships have the potential to improve mental health and physical health. Some of the earliest work was conducted by Durkheim who associated lower rates of suicide with having strong social ties (e.g. social relationships) (Durkheim 1897). Contemporary scholars continue to argue for the importance of social relationships for health, with social relationships associated with improvements across a host of mental and physical health outcomes (e.g. Umberson and Montez 2010).

Although researchers have explored associations between health and a variety of social relationships, a particular emphasis has been given to the role of marriage, and its connection to health. People in marital unions tend to enjoy better physical and mental health compared to those who are not (i.e. those who are single, widowed, divorced or separated) (Waite and Gallagher 2000). Marriage has been empirically associated with better health and, over the past few decades, cohabitation has also been associated with improved health, although to a lesser degree (Carr and Springer 2010). Being married has been associated with lower levels of hypertension, blood pressure, depression, diabetes and lower levels of negative health behaviors such as smoking and drinking (Brown

2000; Carr and Springer 2010; Carr, Springer, and Williams 2014; House et al. 1988;

Pudrovska, Schieman, and Carr 2006; Umberson and Montez 2010; Umberson, Thomeer,

1 and Williams 2013; Williams 2003; Williams and Umberson 2004). Key mechanisms underlying the marriage-health association include higher levels of resources (e.g. income), social support, and the promotion of positive health behaviors (DeKlyen et al.

2006; Thoits 2011; Umberson et al. 2013). First, married individuals—and to a lesser degree cohabitating partners—are thought to improve health by sharing resources (Hill,

Reid, and Reczek 2013). When two individuals marry—or cohabit—it is assumed they share resources such as money, wealth, access to healthcare, and provide knowledge about health (Phelan et al. 2004; Umberson and Montez 2010). Second, married individuals—and to a lesser degree cohabitating partners—are thought to bolster/enhance outcomes by increasing an individual’s sense of social support (T. E. Seeman et al. 2014;

Sheffler and Sachs-Ericsson 2015). Perceived social support diminishes stress-induced psychological distress and physiological responses (House 2002; T. E. Seeman et al.

2014; Sheffler and Sachs-Ericsson 2015; Thoits 2011; Umberson and Montez 2010).

Third, married individuals—and to a lesser degree cohabitating partners—influence each other’s health habits. Marriage and cohabitation can instill a sense of responsibility and concern for others that then lead individuals to engage in behaviors that protect health

(Ali and Ajilore 2011; Kiecolt-Glaser, Gouin, and Hantsoo 2010; LaVeist, Zeno, and

Fesahazion 2010; Umberson and Montez 2010). There are also health straining theories.

As Carr et al. (2014) states, conceptual models of stress provide a useful framework for understanding how health is affected by family structures, relationship quality, and family transitions, (e.g. divorce or death of a spouse). Stressors may be chronic and persistent, such as strained marital quality (Anderson 2010; Williams 2003).

The literature characterizing the marriage-health association has not been without controversy. The most significant critiques involve the issue of causality. Are these associations truly causal or are there are other processes (i.e. selection) driving these patterns? Relatedly, another line of critique has raised the issue of heterogeneity of associations for different demographic sub-groups (Carr and Springer 2010; Carr et al.

2014). One of the most consequential U.S. status groupings, and the focus of the present dissertation, involves race. As Koball et al. (2010) has argued, most studies have examined the marriage-health association for the general population, combining data for all racial groups. In doing so, the experiences of whites, the largest racial group, tends to dominate the results. I argue that it is particularly important to examine the association between marital status and health for black Americans because blacks tend to experience poorer health across many outcomes and, on average, blacks spend a smaller proportion of their lives married relative to the general population.

Over the past two decades, more research has emerged on race, marriage, and health (e.g. Koball et al. 2010). For example, in 2010 the Journal of Family Issues published a volume where they specifically called attention to the gap in the literature on race, marriage and health. As Koball et al. states: “Marriage may provide different health effects for African Americans. The lack of research on this topic, prior to the current project, has prevented us from knowing whether this is the case” (Koball et al. 2010).

Yet, while the possibility of heterogeneity in the marriage-health relationship by race has received an increasing amount of attention, three common methodological issues continue to plague this emerging body of work (here I focus primarily on prior work that

3 focuses on physical health, as this is the focus of the present analysis). These include 1) dichotomized marital status groups, 2) an over-reliance on self-reported health outcomes, and 3) restrictive comparison groups (i.e. studies that only include males, older adults, or blacks). First, a large proportion of the race, marriage, and physical health studies have dichotomized marital status groups (e.g. married versus all other statuses). But prior research on marriage and health has found that—depending on the outcome—never married, cohabitating, divorced/separated, and the widowed, all differ in their associations with health—for example—the divorced/separated usually demonstrate worse on health outcomes than those who cohabit (Brown 2000; Carr et al. 2014; Mamun

2011).

Second, many of the existing studies on race, marriage, and physical health rely on self-reported physical health outcomes (Dupre 2016; Harris, Lee, and DeLeone 2010;

Roxburgh 2014; Shafer 2010). Although self-reported health outcomes are widely accepted (Carr et al. 2014), measures of physical health outcomes may suffer from underreporting (Shafer 2010). In response, a few studies have used biomarkers—which are biological indicators of health—to assess the association between marriage and health

(Carr et al. 2014; Rote 2016). Biomarkers allow researchers to more accurately examine physiological and social responses to stress, as compared to self reports (Carr et al. 2014;

McEwen 1998).

Lastly, a significant number of existing studies limit their comparison groups, either by age, race, or gender (Johnson et al. 2000; Schwandt, Coresh, and Hindin 2010;

Shortridge and James 2010). So, for instance, some studies focus on restricted age ranges

(samples younger than 25) or limit their focus to blacks with no comparison group

(Schwandt et al. 2010; Shortridge and James 2010). Because these studies are restricted to subgroups, it is unclear how these subgroups fare in comparison to other groups or the general population. For example, Schwandt et al. (2010) and Johnson (2000) both find associations between marriage and better health for blacks older that 44 but it is unclear if these findings are applicable to the entire black population or are only applicable to older blacks because no other age groups were assessed. Prior research has not yet convincingly established if marriage for blacks is associated with better physical health in the same way as it is for whites, or if marriage for blacks is associated with better physical health than the health of blacks that are not married (e.g. never married, cohabitating, widowed, or divorced/separated).

This dissertation adds to the increasing number of studies that have demonstrated that the health benefits of marriage are not universal, but are limited to particular outcomes and vary by aspects of the individual (Carr et al. 2014). The majority of this research has focused on heterogeneity by gender (Carr et al. 2014). Understanding the association between marriage and health for blacks could have important theoretical implications, adding insight to the body of literature focused on the benefits of social relationships and health. In addition to theoretical implications, understanding the association between race, marriage, and health also has the potential to influence health policies aimed at minimizing the health disparities between blacks and whites (A. J.

Cherlin 2010; Cherlin 2014).

There are strong reasons to expect that the association between marriage and physical health differs by race. First, unique stressors experienced by black Americans may undermine the ability of marriage to provide social support and alleviate stress.

(Umberson and Montez 2010), suggest that spouses provide a sense of social support— therefore alleviating stress—but to receive social support, a spouse must be available to provide support. This fundamental assumption might not be as viable for black families.

Stress is not experienced and distributed equally across different socioeconomic groups

(Lantz PM et al. 2005; Williams, Neighbors, and Jackson 2003). Stress is more likely to be associated with those who are from low income backgrounds and with low educational attainments (Lantz PM et al. 2005). In other words, those in the lowest education and income categories reported the highest prevalence of stress. With black Americans accounting for a disproportionate amount of those in the lowest income group (LaVeist

2005), blacks experience a disproportionate amount of stress compared to other racial groups (Williams et al. 1997, 2003). Racism is also a unique stressor and has been associated with poor health. As (Williams and Mohammed 2009) states: “Perceived racial or ethnic discrimination is increasingly receiving empirical attention as a class of stressors that could have consequences for health and for understanding disparities in health. This is consistent with broader interest in the role of stress as a determinant of social disparities in health.” The majority of these studies find that racial discrimination negatively impacts mental health (Hicken et al. 2013; Williams 2012; Williams and

Mohammed 2009, 2013).

Another reason to expect differences in the marriage and health relationship for

Black Americans vis a vis whites are the substantial differences in family demographic trends by race (Koball et al. 2010; Kuo and Raley 2016; Manning and Smock 2002;

Raley, Sweeney, and Wondra 2015; Smock 2000). Blacks are less likely to marry than other racial groups (Koball et al. 2010). Blacks are also more likely to divorce (A.

Cherlin 2010). The percentage of women who have never been married is higher for blacks than for Hispanics or whites (Koball et al. 2010). The percentage of men in their later 30s who have never been married is higher for blacks than for Hispanics or whites

(Koball et al. 2010). The median age at first marriage is roughly four years higher for black women than for white women: 30 versus 26 years, respectively, in 2010. At all ages, black Americans display lower marriage rates than do other racial and ethnic groups

(Raley et al. 2015). Black cohabitating couples are less likely to transition to marriage than whites (Smock 2000). If there are racial variations in union formation, this might also be indicative of differential health benefits of marriage for blacks. Another important difference in union formation patterns by race concerns differential fertility patterns.

Black women are more likely to enter a marriage with children (Raley et al. 2015). The addition of children could change how marriage is associated with health and may be a factor in any documented differences in the marriage-health association by race.

This dissertation makes multiple contributions to the literature on race, marriage, and physical health. First, this dissertation uses The National Health and Nutrition

Examination Survey (NHANES), which has a large sample size to allow for examination of multiple marital statuses (e.g. married, never married, widowed, divorced/separated,

7 cohabitating). NHANES also collects blood samples and urine samples to examine molecular biomarkers and uses trained or licensed health professionals to collect body measurements including blood pressure and body mass index. This dissertation also is able to take advantage of the large sample size of NHANES to examine different subgroups by race and gender (e.g. black men and black women). Allostatic Load is also a major contribution of this dissertation as it captures physiological stress as well as the influence of health behaviors over time (Beckie 2012).

The rest of the dissertation will be presented across six chapters. Chapter 2 will discuss the previous literature on race, marital status and physical health. Chapter 3 will describe the data used in the dissertation and the analytic approach, providing a detailed description of the models. The results will be presented in two separate chapters. Chapter

4 will compare the associations between intimate relationships and physical health for blacks versus whites. Chapter 5 will discuss the associations within the black population, paying attention to differences between black men and black women with varying levels of education. Chapter 6 will discuss the findings, conclusion, limitations and directions for future research.

Chapter 2. Literature Review

2.1 Overview of Marriage and Health

Over the past 20 years or so, scholars have begun to document heterogeneity in the marriage-health benefit (Williams and Umberson 2004); Carr, Springer, and Williams

2014). According to Carr, Springer, and Williams (2014), mounting evidence indicates that the health benefits of marriage are not universal. Instead, benefits are primarily limited to particular outcomes (such as self-reported health) and vary substantially by aspects of the individual, the marital dyad, and the larger social context. Gender differences in the marriage-health benefit have been a traditional focus of this work.

Studies focusing on physical health suggest that marriage benefits men more than women

(Gardner and Oswald 2004; Johnson et al. 2000; Manzoli et al. 2007). Studies focusing on mental health generally found that men and women manifest different symptoms, with marriage protective against women’s depressive symptoms while for men it is protective against problematic alcohol use (Strohschein et al. 2005; Williams 2003).

One of the earlier studies documenting heterogeneity in the marriage-health relationship came from Williams and Umberson’s (2004) study on gender and age differences in the marriage-health benefit. The authors used data from three waves of the

Americans’ Changing Lives survey and found that, among men, there were negative

9 physical health consequences of exiting marriage through divorce or widowhood, and these negative health consequences increased with age. But the health consequences of the transitions out of marriage more negatively affected men’s health than women’s. The authors went on to argue that prior research had missed these associations because much of the existing literature relied on cross sectional data, failed to examine the crisis model, and failed to distinguish between marital statuses. Williams and Umberson’s (2004) general conclusion was that much of the existing literature on gender, marital status and health in the early 2000s was inconsistent and contradictory, largely due to a lack of longitudinal data, issues with the conceptualization and measurement of marital status, and the lack of theoretical models (Williams and Umberson 2004). Ten years after that article was written, many of these critiques remain valid for the literature on race, marriage, and health.

Limited data and restricted marital models also plague research on marriage, race, and health. Carr, Springer, and Williams (2014) have argued that scholars have only begun to explore the ways in which SES and race condition the marriage-health relationship. They point out that studies on racial differences in the health consequences of intimate unions have generally been few and inconclusive (Carr and Springer 2010;

Koball et al. 2010; Su, Stimpson, and Wilson 2015). As Carr et al. (2014) states, “some show that marriage is equally protective for blacks and whites (Johnson et al. 2000;

Schoenborn 2004), yet others suggest that marriage is less protective for blacks because of psychological, economic, and instrumental benefits received in marriage via other social relationships (Kroeger-D’Souza 2012). This remains an important avenue for

10 future research”. In general, the studies that do exist fail to parse out the theoretical bases for potential differences by race and/or class. They are also limited by data that are frequently ill-equipped to distinguish between race, gender, and class impacts on health.

The following chapter is organized as follows: 1) I first present a summary of the literature that documents a positive association between marriage and health for blacks identifying exceptions when relevant, 2) I then summarize the literature that documents a diminished and/or negative impact of marriage on health for blacks vis a vis whites, 3) I then review some of the reasons for the identified inconsistencies in the literature, including a failure to theoretically motivate why we might expect differences in the marriage-health relationship by race. This leads to section 4) which presents a theoretical overview of the marriage-health association and lays out why we might expect it to operate differently for blacks, 5) finally I close by identifying my contribution to the literature.

2.2 Marriage is associated with better health for blacks.

A number of studies have suggested that marriage is associated with better health for blacks. Focusing on blacks exclusively, Chatters (1988) examined the impact of marital status on subjective well-being (happiness). Data came from the National Survey of Black Americans (NSBA), a national representative cross-sectional sample of black

Americans aged 18 years or older conducted during 1979 – 1980 by the Program for

Research on Black Americans at the Institute for Social Research, University of

Michigan. Models only included black Americans and were not run separately by gender.

Overall happiness was assessed by the following question: taking all things together, how

11 would you say things are these days? Would you say you’re very happy, pretty happy, or not too happy these days? OLS regression was used for this study. The findings indicated that being widowed, or separated from one’s spouse was associated with diminished happiness ratings compared to married blacks.

In another studying examining blacks exclusively, Green et al. (2012) explored the impacts of marital status on health behaviors. Data came from the Woodlawn study, which is a cohort of urban black first graders from Woodlawn (a neighborhood community on the South Side of Chicago) and followed them from age 6 to 42 (Green et al. 2012). Their sample size included 1,049 respondents; 546 black women and 503 black men. Individuals were coded as unmarried if they were never married or were living with a partner, divorced, separated, or widowed. Health behaviors were assessed through self- reports of smoking at the time of the interview, binge drinking (coded as yes or no) and past year use of illegal drugs (including hallucinogens, or heroin, as well as nonmedical use of barbiturates, tranquilizers, stimulants, amphetamines, or analgesics). The authors assessed health risk behaviors for blacks, by marital groupings, separately for males and females. The authors found that black men and women in consistently married trajectories were less likely to smoke, drink heavily, and use illegal drugs than those in unmarried or previously married trajectories (Green et al. 2012). Men and women experiencing early marital dissolution and those yet to marry had 2 to 3 times the risk of past year illegal drug use than those married. Moreover, previously married women had

3.08 times the risk of binge drinking than married women.

In another study focused only on blacks, Schwandt et al. (2010) found positive physical health outcomes among married black women relative to unmarried black women. Data came from the Atherosclerosis Risk in Communities (ARIC). The ARIC cohort was recruited from four U.S. communities: Forsyth County, North Carolina;

Jackson, Mississippi; seven northwest suburbs of Minneapolis, Minnesota; and

Washington County, Maryland. The sample was limited to those who participated in the baseline interview occurring in 1987 - 1989 (Visit 1) and first follow up occurring in

1990 - 1992 (Visit 2) (n = 3425). For marital status, they created a categorical marital status change variable to reflect the most common combinations across two time points: remained married (between Visit 1 and Visit 2), stayed single (never married, divorced, separated, or widowed between Visit 1 and Visit 2), any marital status change, never married, (divorced, separated, or widowed to married between Visit 1 and Visit 2), and married to divorced, separated, or widowed. The dependent variables were hypertension, coronary heart disease (CHD), diabetes, and death. | They found that black women who remained single between the first study visit and the follow-up were significantly more likely to develop diabetes subsequently (Schwandt et al. 2010). Single black men were

1.36 times more likely to die at a younger age if they remained single compared with married black men. Among black men, hypertension was no more common among those who remaining single compared with those who remained married or changed marital status. For black women, hypertension was significantly associated with staying single; however, the effect was rendered insignificant when demographic and health status variables were included in the fully adjusted model. Incident Coronary Heart Disease was

13 unrelated to marital status for blacks (the comparison group was black men and women of different marital statuses). Although both black men and black women who remained single were more likely to die during follow-up than those who stay married, for black women, this association was attenuated in the presence of age and education (high school vs. no high school). In other words, individual characteristics—e.g. age and education— had a stronger effect on cardiovascular disease (except diabetes for women) and death

(except for men) than marital status (Schwandt et al. 2010). This suggests that individual characteristics override the effect of marital status for blacks. It is noteworthy that black men and black women who remained single were more likely to die in subsequent waves and develop diabetes compared to their married counterparts, even after accounting for age and education.

For (Barrington 2010), marriage was found to be protective in the case of infant health. Using the Panel Study of Income Dynamics (PSID), two generations of black women who gave birth between 1967 – 2005 were examined to describe the changing relationships between marital status and low birth weight across generations. Marital status was dichotomized to those who were married at infant birth vs. those who were not married at infant birth (including those who were never married widowed divorced marriage annulled, and separated). Individual-level socioeconomic status was accounted for via maternal education, income and poverty status. Marriage was found to be protective against low birth weight among Black women. In the first-generation cohort, unmarried mothers had 1.6 higher risk of having a low birth weight infant relative to married women. In the second generation, the risk of having a low birth weight infant for

14 unmarried mothers relative to married mothers increased to 1.8. As a result, marriage became a greater protective factor for low birth weight in the second generation, with a

53% decreased risk of low birth weight among infants born to married mothers compared with infants born to unmarried mothers. Additionally, the study documented an intergenerational impact of marriage. The lowest risk for low birth weight occurred among black women who were married when they gave birth to their infants and had mothers who were married at the time they themselves were born.

A separate study restricted only to black Americans (Shortridge and James 2010) and focused on End Stage Renal Disease. Data came from the Dialysis Morbidity and

Mortality Study (Wave 2) of the U.S. Renal data System Database, a prospective study of

4,000 End-Stage Renal Disease patients. Because of missing data for some questions, there were less than 450 responses from black Americans. Marital status included never married, currently married, or previously married (divorced, widowed, or separated).

Age, sex, and level of education were available but income was not. It was tested whether currently married ESRD patients had better outcomes on all of the health-related measures relative to previously and never married patients in unadjusted models. The authors found that previously married black patients were more likely to die and less likely to receive a transplant than those who were currently married in the unadjusted models. These differences, however, were not significant in the adjusted models. The adjusted models accounted for age and education. Few of the clinical variables showed marital status to have a significant effect after adjusting for age, sex, and level of educational attainment.

2.3 Racial Differences in the Marriage and Health Association by Race

The studies reviewed so far have generally found that marriage is associated with positive health outcomes for blacks. This pattern has been particularly salient for black women with less evidence of a positive marriage health benefit for black men (Ali and

Ajilore 2011; Green et al. 2012; Schwandt et al. 2010). Another set of studies, reviewed in this section, moves beyond an exclusive focus on black Americans (intra-group differences by marital status) to focus on differences between blacks and whites (Harris et al. 2010). These assessments are important because the possibility of heterogeneity in the marriage-health relationship by race must include interracial comparisons. The majority of the positive health outcomes previously discussed were primarily, but not exclusively, found in intra-racial marital assessments (i.e. analyses that only include black Americans). In the studies in this section, the general pattern is for the observed marriage effect to be either absent or less pronounced among blacks versus whites, although there are important differences depending on gender, age, and the health outcome assessed. I turn to a discussion of these findings next.

With a focus on both black and white Americans, Williams et al. (1992) examined how marital status impacts mental health. Data came from the first wave of interviews in the Epidemiologic Catchment Area (ECA) program, which, between 1980 and 1983 surveyed 18,571 adults in five mental health catchment sites in the United States. The five sites were the Greater New Haven Area of Connecticut; Baltimore, Maryland; St.

Louis Missouri; North Carolina; and Los Angeles, California. The outcome variables

16 were whether or not respondents experienced any psychiatric disorder as assessed by the

DIS (within six months of the interview). Logistic regression models were estimated separately for each race and gender group. Marital status was divided into four mutually exclusive groups; married, widowed, separated or divorced, and never married. The findings indicate that for blacks, being married was associated with better mental health than the unmarried. Better mental health was found for both black men and women compared to their unmarried black counterparts. Better mental health was also found for both white men and women compared to their unmarried white counterparts. However, the authors observed racial differences in the marriage-mental health benefit by gender.

The magnitude of the marital benefit was higher for white married men, indicating that marriage benefits white married men more than black married men. For women, while all saw an association between marriage and better mental health, the marital status categories experiencing disadvantage differed by race. For example, widowed black women had higher odds of having any mental health disorder whereas, for white women, it was divorced or separated women who had higher odds of having any mental health disorder compared to married women.

In another study assessing the health of both black and white Americans, (Ali and

Ajilore 2011) used data from three waves of the in-home surveys of the National

Longitudinal Study of Adolescent Health (Add Health): 1994, 1996, and 2002. The marital status variable distinguished whether the individual was married or not

(dichotomous) or cohabitating or not (dichotomous). They used a propensity score matching (PSM) technique in an attempt to “isolate” the effect of marriage on their health

17 outcomes. The logic behind PSM is that it attempts to replicate the conditions of an experiment such that the treatment variable, in this case marriage, can be treated as though it occurred at random and that the individuals under analysis are homogenous on all other observed factors except the treatment variable. Separate models were run for married vs. unmarried, cohabitating vs. not cohabitating, for each race. Separate models were not run for gender. Depression, physical health, income, age, and education were all included as control variables. The authors found that, overall, marriage is associated with a reduction in risky health behaviors, specifically drinking and drug use (smoking was not significant). They also documented that while the direction of the association was similar for blacks and whites (both married groups saw a reduction in risky health behaviors relative to the unmarried), white Americans saw more of a decline in risky health behaviors. Although the magnitudes of the coefficients were compared across models, no formal statistical test was conducted to assess if the differences in magnitude were significant. The authors also ran models by cohabitation status and found no evidence for a reduction in risky health behaviors for those who cohabitated versus those who did not. They concluded that both married whites and blacks saw a decline in risky health behaviors compared to their unmarried counterparts, yet the magnitude of the decline was greater for married whites than for married blacks (Ali and Ajilore 2011).

Examining both blacks and whites, Harris, Lee, and DeLeone (2010) documented racial differences in the marriage-health association, with marriage associated with worse health behaviors for black men compared to white men and women. The study used data from the National Longitudinal Study of Adolescent Health (Add Health) (Waves I-III).

Health was set at Wave III as a function of the initial level of adolescent health at Wave I.

Health was operationalized across a range of mental health (depression), physical health

(self-reported BMI, SRH), and risk behavior measures (self-reports of binge drinking, smoking, marijuana use, and lack of physical activity). A composite measure of poor health was also assessed with a count measure of seven different health indicators

(obesity, self-reported poor health, depression index, binge drinking, smoking, marijuana use, bouts of physical inactivity). The higher the composite health score, the worse the health. Respondents were coded as married if they married between Wave I and Wave

III. Respondents that married between Wave I and Wave III were coded as married.

Never married respondents were distinguished from other marital status groups.

Separate models were run by race and gender (black women, black men, white women, white men). There was also a model ran separately by race, gender, and marital status

(married vs. cohabitating black and white men and women). Only respondents who married before the age of 25 were included. Socioeconomic measures included family structure and parental education at the individual, peer, school, and neighborhood levels.

The authors found that for black men, early marriage (marriage before the age of 25) was associated with an increase in BMI, depression, and as a result, an increase in the composite poor health measure, indicating that marriage tends to increase negative health outcomes for black men. Married black women also saw an increase in BMI but no statistically significant increase in the composite poor health score. In contrast, married white men and women experience a decrease in binge drinking which decreases the composite poor health score following marriage for both white men and women (Harris

19 et al. 2010). Both races and genders who cohabit saw an increase in the composite poor health score compared to those who did not cohabit (Harris et al. 2010). Some of these patterns are in contrast to the associations documented by Green et al. (2012) who also assessed union status and health behaviors using data from Add Health. For instance,

Green et al. (2012) found that blacks that were in consistently married trajectories were less likely to smoke and drink but Harris et al. (2010) found that there is no association for smoking and drinking for blacks who marry before the age of 25. As for the potential reasons behind these discrepancies, Green et al. suggest that it is not simply the presence of a marital union that drives associations with health, but marriage timing, transitions, and duration that contribute to health outcomes.

Another study that assessed racial differences in the marriage-health association focused on infant health outcomes. Bennet (1992) conducted a North Carolina Case

Study for the years 1968 – 1985 to assess the relationship between marital status and infant health outcomes. Data comes from the North Carolina State Center for Health

Statistics. Race categories were coded as white vs. nonwhite (93% of the nonwhite population is black). Low birth weight was confined to infants born under 2500 grams.

Very low birth weight was categorized at infant weight under 1500 grams (Bennett

1992). Marital status was categorized as married vs. unmarried. Education and income information was not included. The analysis found that for married black and white women, marriage was associated with a lower incidence of low birth weight compared to their unmarried counterparts but the differentials in birth outcomes by marital status were generally smaller for black women compared to white women. Marriage demonstrated a

20 greater perinatal health benefit for white women than for black women. No test was performed to assess statistical significance in the magnitude of difference.

In the case of perinatal health, (Barrington 2010) (discussed in previous section) found that being married and having parents that were married was associated with lower odds of low birth weight for black women. But Bennett (1992) suggests that the benefits of marriage do not correspond to equal gains for both black women and white women.

Since both of these studies (Barrington 2010 and Bennett 1992,) compared married women to their unmarried counterparts (i.e. collapsing in the unmarried category the never married, widowed, divorced or separated) this could have adversely impacted the estimate of the marriage-health benefit.

In a follow-up study, (Bennett et al. 1994) further examined racial differences in birth outcomes using national-level data, this time allowing for variation by maternal age in the association between marriage and infant health outcomes. Using nationally linked birth and infant death files from 1983 – 1985, the authors assessed the risk of infant mortality for black and white women. Models were run separately by race (married black vs. unmarried black and married white vs. unmarried white). Among black adolescent mothers, being unmarried did not significantly affect the risk of infant mortality.

However, the elevated infant mortality risk associated with being unmarried became significant for black women at ages 20-24, rose at ages 25-29, and remained consistently high for older black women. Among babies born to white teenagers who gave birth at age

17 or younger, the risk of mortality was slightly elevated among the infants of unmarried women compared to those of married women. This elevated risk among unmarried white

21 mothers appeared to rise steadily until age 30, and then to decline somewhat among older mothers. The risk associated with being unmarried was slightly elevated but non- significant among white mothers who graduated from college. According to Bennett et al.

(1994), having children at younger ages is more beneficial (health wise) for black women rather than having children at later ages.

According Johnson et al. (2000), black women’s marriage-mortality benefit differed from black men, white men, and white women. (Johnson et al. 2000) (Johnson et al. 2000)used data from the National Longitudinal Mortality Study (NLMS) to assess how mortality rates are associated with marriage among older adults. This study only included participants 45 years or older. NLMS data came from the Current Population

Survey (CPS). 281,460 respondents who were 45 years of age or older at the time of the survey were identified and followed for mortality using the National Death Index. Marital status categories included widowed, divorced or separated, married, and never married.

Cause of death was obtained from death certificates. Mortality differentials for marital status categories were determined from RRs estimated using the Cox proportional hazards regression model. Models were generally, unless otherwise noted, run separately for sex, race, and age category, and included a continuous adjustment by single years of age with age categories. For all-cause mortality, white men, white women, and black men in each of the widowed, divorced/separated, and never married categories had significantly increased risk of mortality relative to the married. In contrast, for black women, only the divorced/separated category showed significantly increased risk in all- cause mortality. For black women, being widowed or never married was not associated

22 with increased mortality rates relative to the married. This is an example of how different non-marital status groups can have varying impacts on health relative to the married. For black women, being never married and widowed is not associated with higher all-cause mortality when compared with married black women. But because they are often collapsed in the “unmarried” marital status grouping, any potential health differences between never married and/or widowed black women versus the married, are obscured.

This also highlights the varying consequences of marriage, whereas not being married is less detrimental for black women.

Su, Stimpson, and Wilson (2015) explicitly assessed heterogeneity in mortality risk between married black men and married white men. In their study, (Su et al. 2015) used data from the Health and Retirement Survey (HRS). HRS included data from 1992 –

2010. Their study was limited to white and black men (n = 3,718). The initial coding for marital status in the HRS included six categories: Married, partner, separated, divorced, widowed, and never married. These marital categories were collapsed into two groups: married and unmarried. In either racial group being married relative to being unmarried was associated with lower mortality. 68.4% of white men (n = 1,778 of 2,600) who were married in 1992 survived to 2010, as compared to 52.6% among unmarried white men.

Among black men, 57% (n = 239 of 419) who were married in 1992 survived to 2010 as compared to 46.2% (n = 108 of 234) of unmarried black men. The association turned out to be more pronounced among white men than among black men. In their survival curve models, 52% of unmarried white men were alive in 2010 compared to only 57% of married black men (Su et al. 2015). This suggests that although marriage is beneficial to

23 both black men and white men, the benefits are more pronounced for white men. Su et at.

(2015) point out a few important arguments made in this dissertation; first, little attention have been paid to the differences between blacks and whites marriage and health outcomes. Second, barriers in education, income, and discrimination limits the readiness of black adults for marriage. This, as this dissertation has argued earlier, is a product of discrimination and disparities in education, employment, income, and incarceration. As

Su et al. states, black males are six times more likely than white males to be incarcerated.

Comparing across studies, Johnson (2000), demonstrated that marriage was associated with lower risk of mortality for both black married men and white married men, yet Su et al. found that the risk of mortality was not equal between black and white men such that white married men had lower ratios of mortality compared to married black men. The work by Johnson examined the black population by gender which gave us insight into how marriage and mortality differs by gender for blacks. When black Americans were examined with attention to the magnitude of this benefit, i.e. determining whether the magnitude of the benefit was the same for black and white men, the benefits associated with marriage were found to not be as beneficial for black married men.

Examining blacks and whites, another study also used data from the Health and

Retirement Study (HRS), but only examined incidence of stroke among adults age 50 and older (Dupre 2016). Retrospective data from the HRS was used to reconstruct marital histories for each study member. The incidence of stroke was the main outcome for analysis. Socioeconomic factors include the respondents’ educational attainment, employment status, income from all sources in thousands of dollars and health insurance

24 coverage from any source. Models were not run separately for blacks and whites. The authors found that the effects of marital history were more pronounced for whites than for blacks. The odds of having a stroke were significantly higher for whites who were currently divorced, remarried, and widowed, as well as in those with a history of divorce or widowhood, compared with whites who are continuously married. For blacks, however, risk for stroke was only elevated in those who never married or who had been widowed with no significant risks attributable to divorce. Furthermore, the inclusion of more than a dozen socioeconomic, psychosocial, behavioral, and physiological variables only partially attenuated these associations. Supplemental models were run interacting age and gender but there was no evidence that the key findings differed.

A study by Shafer (2010) focused on body weight, using data from the NLSY, and found that for black women, marriage is associated with an increase in BMI and that being married is also associated with becoming obese relative to those who were unmarried. This increase is larger than that experienced by white women and men, and smaller that the increase experienced by Hispanic women and men. Only married black women were more likely to become obese. Recall that Harris et al. (2010) also found that black women who married between wave I and wave III (before the age of 25) of the Add

Health data saw an increase in their BMI.

(Roxburgh 2014) was the first study to explore how income impacted the marriage and mental/physical health benefit for blacks and whites. Data come from the

2003 National Health Interview Survey (NHIS). The NHIS is an annual cross-sectional household survey of the non-institutionalized population of the America carried out by

25 the National Center for Health Statistics. Marital Status was coded as married vs. non- married. Two measures of socioeconomic status was used; household income and education. Depression was measured using the K6, a six-item short-form scale designed for the revised NHIS and first used in 1997. Among women, both black and white, the married reported significantly lower depression and higher subjective physical health, suggesting that irrespective of race/ethnicity, women experienced significant benefit from marriage. The relationship between marriage, mental health, and class, however, was more complex for blacks than whites. Irrespective of income, married black men reported higher depression than unmarried black men, suggesting that marriage was not beneficial for the mental health of black men. For black women, marriage was associated with better mental health only among those who lived in low-income households. In average income households there was no association between marriage and mental health for black women and in high income households, as was the case for black men, marriage appeared to be a stressor. One potential explanation for this pattern given by the authors is that well-off individuals are often called upon to provide economic and social support for others. Support provision may fall particularly heavily on married black women because they have more resources than many of their kin. The relationship between depressive symptoms and marriage among whites corresponded much more closely to the pattern that has been observed in many other studies of marriage and health; generally speaking the married reported lower depression than the non-married. There was no comparison in which the non-married were less depressed than married whites, but as income increased among the married, there were increasingly smaller differences

26 between the married and the non-married. This study demonstrated that intricacies in marital status, education, income, gender and health outcome all play a complex role in conditioning the marital benefit to health by race. For whites, marriage (no matter the income) had a health benefit but the same was not true for blacks. For blacks, marriage

(at low incomes) had a health benefit but marriage among high income blacks was associated with worse physical and mental health.

2.4 Empirical Evidence of Heterogeneity in the Marriage-Health Benefit.

Overall, research that examines the association between marriage, race, and health is largely inconsistent, with some documenting a protective effect for blacks, others documenting null effects, and still others arguing that the effects vary between blacks and whites, and by gender and education. Inconsistencies in the research literature are likely due to a number of factors, including a lack of simultaneous consideration of between and within race comparisons, and differences in the conceptualization and measurement of marital status. A number of the studies described above found that marriage was associated with better health for black Americans (e.g. Ali and Ajilore (2011), Barrington

(2010), Green et al. (2012), Schwandt, Coresh, and Hindin (2010). One commonality across these studies (excluding Ali and Ajilore) is that none included cross-racial comparisons. Although these studies consistently document a marriage-health benefit among Black Americans, they do not reveal whether there were differences in the marriage-health benefit across racial groups-specifically between blacks and whites.

Without racial comparisons, it is unclear how to situate these findings (e.g. does this mean marriage is associated with better health in an identical way for blacks and

27 whites?). When studies did include racial comparisons, the general finding was that marriage did not have the same benefit for blacks as for whites ( e.g. Ali and Ajilore

2011; Williams et al. 1992 (Dupre 2016; Roxburgh 2014; Shafer 2010).

Another concern with some of the existing empirical assessments is that the comparisons were restricted to married vs. unmarried marital status groups. For instance, the Green et al. (2012) analysis focused on intra-racial comparisons and assessed how the married black population fared relative to the unmarried black population. This is one way to analyze the benefit of marriage for health, i.e. look specifically at blacks who marry vs. blacks who do not marry. However, a drawback to this approach is that the

“unmarried” category is made up of respondents who are never married, widowed, and divorced or separated. This is problematic since divorced and separated marital statues might work differently for blacks (Chatters LM 1988; Johnson et al. 2000) and divorced and separated marital statues have been associated with poor health (Amato 2010; Lorenz et al. 2006).

One reason that studies collapse marital status groups is because of concerns over small sample size. According to Schwandt et al. (2010), the failure to find an association between changes in marital status and coronary heart disease and diabetes could be the result of the need to combine multiple marital status change categories into a single variable because of limited sample size for analysis. This is where the National Health and Nutrition Examination Survey (NHANES), the proposed data for the present research, is such an advantage. The NHANES is a nationally representative program designed to assess the health of adults in the US that will provide sufficient sample size to

28 permit assessments of disaggregated marital status categories. This will allow me to avoid having to collapse together heterogeneous categories because of small sample size concerns.

Another limitation in the existing literature is the failure of some empirical assessments to account for the role of socioeconomic status (e.g. Bennett, 1992). For those studies that did take SES into account, the measurement of SES was broad and inconsistent, e.g. household income, maternal income as a proxy, poverty ratios, aggregated income from multiple sources (household and neighborhood), or education was used as a proxy for SES (Dupre 2016, Shafer 2010, Shortridge and James 2010,

Haldance et al. 2010). Income and socioeconomic status are important moderating factors of health as well as union status. Their levels also differ substantially by race/ethnicity and therefore it is crucial to consider income/SES when assessing the potential for racial/ethnic differences in the relationship between marriage and health. To address this, models were ran separately for black men and women who attended college.

The majority of the studies assessed either looked at between race differences

(Barrington 2010; Green et al. 2012; Schwandt et al. 2010; Shortridge and James 2010) or within race differences (Ali and Ajilore 2011; Bennett 1992; Bennett et al. 1994;

Dupre 2016; Johnson et al. 2000; Su et al. 2015). I argue that it is critical to make both sets of contrasts, first assess within group differences in physical health, mental health, and self-reported health to determine if there is a positive association with marriage, and, second, compare across groups to determine if the magnitude of any observed benefit (or detrimental association) is statistically different between blacks and whites. This

29 approach provides a clearer assessment of the extent that marriage is beneficial for health and for whom. For example, Barrett (1992 1994) suggests that marriage is not as protective against infant low birth weight when black women are compared to white women. Yet, Barrington (2010) found that marriage across generations was most protective against infant low birth weight for black women who married vs. black women who didn’t marry. It would be helpful if, within one study, both of these assessments were captured so that we can more accurately state in which contexts marriage is or is not beneficial for the health of blacks Americans.

Many of the existing studies on race, marriage, and physical health rely on self- reported health outcomes (Dupre 2016; Harris et al. 2010; Roxburgh 2014; Shafer 2010).

Although self-reported health outcomes are widely accepted (Carr et al. 2014), measures of physical health outcomes might be underreported (Shafer 2010). A growing number of studies are using biomarkers—which are biological indicators of health—to assess the association between marriage and health (Carr et al. 2014; Rote 2016). Biomarkers allow researchers to more accurately examine physiological and social responses to stress, as compared to self reports (Carr et al. 2014; McEwen 1998). Self-reported measures of mental health outcomes might also undermine the strain on the body from stress which this dissertation attempts to capture using allostatic load. In short, humans have the ability to respond to a threat through allostasis, but if chronically triggered through events of poverty, discrimination, or other stresses, this creates a condition called allostatic load, which has powerful negative effects on a variety of bodily systems over time.

2.5 Understanding the theoretical health benefits of marriage by race

Distinguishing between analyses that focus on variation within the black population

(within group analysis) of the marriage-health association versus cross-racial analysis of the marriage-health association, requires two theoretical and analytical approaches. The first theoretical approach should address the general association between marriage and health. The second theoretical approach should address the unique association between marriage and health for blacks. Few recent studies on race, marital status, and well-being simultaneously address both theoretical approaches. In this section, I address both theoretical concepts in order to establish a standard approach to understanding racial differences in marital status and well-being.

2.6 Overview of theoretical health benefits of marriage

People in marital unions tend to enjoy better physical and mental health compared to those who are not (i.e. those who are never married, cohabitating, widowed, divorced or separated) (Waite and Gallagher 2000). Marriage has been empirically associated with better health and there is some evidence that cohabitation is also associated with improved health, although to a lesser degree (Carr and Springer 2010). Being married is associated with lower levels of hypertension, blood pressure, depression, diabetes and lower levels of negative health behaviors such as smoking and drinking (Carr and

Springer 2010; Carr et al. 2014).

As mentioned in the introductory chapter, there are three main theories linking marriage to better health. First, spouses/partners are thought to improve health by accumulating resources. When two individuals marry they are assumed to share resources

31 such as money, wealth, access to healthcare, and provide knowledge about health (Carr,

Springer, Williams 2014). There is some empirical support for the accumulation theory

(Bierman, Fazio, and Milkie 2006; Carr and Springer 2010; Mirowsky and Ross 2003;

Umberson 1987; Umberson et al. 2013; Waite and Gallagher 2000). For instance, (Hill et al. 2013) demonstrated that the accumulation of resources among the married acted as a mechanism for improving mental health among those who were married (Drefahl 2012;

Hill et al. 2013; Phelan et al. 2004).

Second, spouses are thought to bolster/enhance health outcomes by increasing an individual’s sense of social support (Umberson and Montez 2010). Perceived social support diminishes stress-induced psychological distress and physiological responses

(Thoits 2011). As Thoits (2011) suggested, we often solicit advice from spouses for difficult situations. This advice leads to healthy solutions to difficult situations and alleviates the need for physiological responses from the body, which otherwise could lead to poorer health. This suggests that spouses moderate stressful situations by making informal recommendations on how to deal with stress. Social support is also argued to decrease the need for other coping mechanisms such as smoking and drinking

(Umberson, Crosnoe, and Reczek 2010). If spouses and partners are able to talk about their stress and de-escalate the situation, the need for alternative coping mechanisms, such as smoking or drinking, would be eliminated (Jackson, Knight, and Rafferty 2010).

This observation leads to the third mechanism linking marriage and better health which is the promotion of positive health behaviors. In epidemiologic research, poor health behaviors have been associated with poor health (Phelan et al. 2004; Riosmena,

Wong, and Palloni 2013; Ross and Mirowsky 2011). Smoking, drinking, overeating, and poor food choice all negatively impact health. It is suggested that marriage (particularly for men) allows for a spouse to monitor health behaviors (Umberson and Montez 2010,

Umberson, Crosnoe, and Reczek 2010). Having a spouse that monitors your health improves the likelihood that you will have better health habits and behaviors. On the other hand, if both spouses have poor health habits, spouses could compound each other’s poor habits and be less likely to encourage good health habits. If both spouses smoke, for example, they are less likely to quit smoking (Umberson and Montez 2010). Married men and women are less likely to smoke and drink than their single counterparts (Carr et al.

2010, 2014; Umberson and Montez 2010, Waite and Gallagher 2000).

2.7 Empirical and Theoretical differences in the health benefit of marriage for blacks

2.7.1 Variation in Union Formation

There are a number of reasons to expect theoretical heterogeneity in the marriage- better health relationship by race. First, there are substantial racial differences in union formation in prevalence, timing, and stability. Blacks are less likely to marry than other racial groups (Koball et al. 2010) and are also more likely to divorce (A. Cherlin 2010).

The percentage of women who have ever been married is lower for blacks than for

Hispanics or whites (Koball et al. 2010). The percentage of men in their later 30s who have ever been married is also lower for blacks than for Hispanics or whites (Koball et al.

2010). The median age at first marriage is roughly four years higher for black women than for white women: 30 versus 26 years, respectively, in 2010. At all ages, black

Americans display lower marriage rates than do other racial and ethnic groups (Raley et

33 al. 2015). Data from the U.S. Census Bureau’s American Community Survey for 2008–

12 showed that nearly nine out of 10 white and Asian/Pacific Islander women had ever been married by their early 40s, as had more than eight in 10 Hispanic women and more than three-quarters of American Indian/Native Alaskan women. Yet fewer than two- thirds of black women reported having married at least once by the same age (Raley et al.

2015). These differing patterns in marital activity could indicate differences in how marriage benefits health for blacks. Fewer marriages and more instability within marriage could also indicate fewer health returns to marriage for blacks. Blacks that do not marry are likely to be in cohabitating unions. 45 percent of blacks have cohabited at some point

(Smock 2000). Black mothers are more likely to be in a cohabitation relationship (Smock

2000) than other racial groups. Cohabitation is also argued to be an alternative to marriage for blacks (Smock 2000). Furthermore, cohabitation explains some of the gap between black and white marriage differences (Smock 2000).

The role of cohabitation within black unions is a complex issue and has been informed by black sexual politics often found within black feminist thought (Hare and

Hare 1989; Hunter and Robinson 2016; Johnson, Staples, and Staples 2005). Most sociological theorizing on cohabitation and blacks employs a cultural explanation suggesting that cohabitation works as an alternative family formation for blacks

(Furstenberg 2007; Manning and Smock 2002; Raley et al. 2015). But other scholars have criticized previous work that uses a cultural narrative to describe black union formation. For instance, Furstenberg (2007) argues that the cultural foundation for understanding black union formation stems from racism and stereotypes, including an

34 overreliance on the Moynihan report which concluded that black communities and families within these communities were being undermined by “a web of social pathology.”

In research dedicated to sexual politics within a union, the dialogue has begun to incorporate power dynamics, selection, and viability into the discussion of why there is a shift in the roles and values of marriage and cohabitation in family life (Cherlin 2014;

Gerson 2011), which could also be important for understanding cohabitation for blacks.

In addition to the complexities surrounding sexual politics, other scholars have suggested that blacks value marriage just as much if not more than whites (Burton and Tucker 2009;

Dixon 2008; Johnson et al. 2005). Other scholars have also suggested that the working class also values marriage—which comprises a large proportion of blacks (Edin and

Kefalas 2005; Edin and Lein 1997).

Given the complexities surrounding the perception of marriage by marginalized groups and the shifting role of cohabitation in the general U.S. population, understanding the role of cohabitation in context for blacks is not within the scope of this dissertation.

However, to begin understanding if cohabitation performs a different role for blacks than for whites, some evidence of this should be present in the analysis of marital status and health. In other words, to begin understanding the role of cohabitation in the black family and if it differs from whites, we would also expect for there to be a health benefit associated with cohabitation for blacks. The role of cohabitation will be discussed further in the results chapter of this dissertation. Research that has acknowledged cohabitation as a marital status group will be highlighted within this chapter.

2.7.2 Variation in Resource Accumulation

Another reason to expect potential heterogeneity in the marriage-better health relationship by race concerns resource accumulation. The benefits of economic accumulation via marriage might not transfer to blacks in the same way as whites given the differences in income and obstacles to economic security (which are mediated by experiences of discrimination). Additionally, there are number of stressors that are unique to blacks. With respect to economic accumulation, for black families, racial discrimination in job opportunities, education, and income (Massey 2004; Williams and

Collins 2001) undermines their ability to achieve a level of socioeconomic security, which may alter the applicability of accumulation theory for black unions (Massey 2004;

Williams and Collins 2001). Male economic circumstances have been a major predictor of transition to marriage (Manning and Smock 2002; Smock and Manning 1997). For that reason, I focus here too on the circumstances of black males in explaining the gap in marriage rates. Black men are more likely to experience job loss than other racial groups, which directly impacts their economic security (Koball et al. 2010). Black men are more likely to experience incarceration and mortality than other racial groups, which limits the number of men available for marriage (Koball et al. 2010). Furthermore, job insecurity is more common among black men than other racial groups and previous research has noted that job insecurity is a contributing factor in divorce (Amato 2010). For black men who are able to avoid incarceration, there are fewer opportunities to rent apartments, purchase a home, purchase a car, obtain a mortgage, or even obtain medical care than other racial groups (Williams and Mohammed 2013). Black men also have restricted access to high

36 paying jobs and are less likely to obtain a quality education (Williams and Mohammed

2013). Those who are able to achieve a quality education are less likely to get an equal return for their education (Walsemann, Gee, and Ro 2013; Williams and Mohammed

2013). Other research has suggested that marriage might be a stressful experience for black men because structural inequality makes it difficult to fulfill the provider role

(Bryant et al. 2010). Taken together, black men are less likely to secure the needed socioeconomic stability for marriage and those who do marry are less likely to enjoy equal returns from their education and socioeconomic status. This argument is not intended to ignore the obstacles that are faced by black women but rather is intended to call attention to the issues surrounding black men in particular (Smock and Manning

1997). Previous attempts to theorize why there is a lack of “available” black men have failed to fully account for the unique barriers impeding their socioeconomic attainment and restricting their status as “marriageable” (Cherlin 2014).

2.7.3 Variation in Social Support

In addition to the impact of socioeconomic insecurity, unique stressors experienced by black Americans could also undermine the ability of marriage to provide social support and promote positive health behaviors. Social support and positive health behaviors in marriage depends on the availability of a spouse. To receive social support and to receive positive health behaviors, a spouse must be present to provide support and promote positive health behaviors, but this fundamental assumption might not as be viable for black families. (Umberson and Montez 2010), suggest that spouses could promote positive health behaviors by monitoring their spouse’s health, but they also

37 suggest that if both spouses smoke, they are less likely to quit smoking. This assumption can also be applied to stress, if both spouses are experiencing stress, they might be constrained in their ability to provide support and promote positive health behaviors.

Stress is not experienced and distributed equally across different socioeconomic groups

(Lantz PM et al. 2005). Stress is more likely to be associated with those who are from low income backgrounds and with low educational attainments (Lantz PM et al. 2005). In other words, those in the lowest education and income categories reported the highest prevalence of stress. With black Americans accounting for a disproportionate amount of those in the lowest income group (LaVeist 2005), blacks experience a disproportionate amount of stress compared to other racial groups (Williams et al. 1997).

2.8 Crisis and Strain Models

Although this dissertation will not be exploring marital quality or marital disruption, previous research has suggested that marital status differences in health are driven by the immediate stress and trauma of marital loss (Rote 2016; Williams and

Umberson 2004). As Rote (2016) states, adjusting to new living arrangements, residence, daily routines such as household labor and financial task can become difficult and stressful. Marital disruption has been previously associated with psychological distress, which for this dissertation could mean an increase in allostatic load. This concept refers to the crisis model which suggests an immediate impact on psychological wellbeing and stress. So the differences in allostatic load and self-reported health between married widowed divorced or separated could be the result of marital disruption rather than marital benefits.

The chronic strain model indicates that marital disruptions prompt decrements in financial and social resources and set off more chronic and enduring strains which lead to poor health (Rote 2016; Williams and Umberson 2004). Long-term exposure to a marital status has been associated with low levels of social support, health compromising behaviors, a decline in income and assets, and an elevated risk in mortality (Rote 2016).

As with the crisis model, the chronic strain model could also impact allostatic load since this chronic strain would also be associated with chronic stress.

2.9 The Strain of High SES for Blacks

Other research has found that among black Americans, the impact of socioeconomic status on the stress-health nexus actually works differently than for whites. For instance, Hudson and his co-authors (2012; 2013) have found that high SES blacks have worse mental health than low SES blacks (Darrell L. Hudson et al. 2012;

Darrell L Hudson et al. 2012; Hudson et al. 2013). The authors argued that that high SES blacks were more exposed to white Americans and as such experienced more exposure to discrimination which negatively impacted mental health. Roxburgh (2014) also suggests that high SES blacks may experience higher stress levels because they are more likely to be responsible for the care of entire families (including extended families) given their high SES status compared to the rest of their family. Taken together, the additional stress experienced by black Americans, including those who are high SES, might limit the availability of social support and promotion of positive health behaviors.

2.10 Current Project

The focus of this dissertation is to expand on this relatively new literature focusing on the possibility of heterogeneity by race in the marriage and health association. The existing literature is still fairly small and often produces conflicting findings depending on methodology (i.e. how marital status is categorized and which statuses are in the reference category) and whether they use within versus between race comparisons. Existing studies are often restricted by small, non-representative samples that are limited in their ability to assess heterogeneous effects. I look to expand on this literature using data from the National Health and Nutrition Examination Survey

(NHANES), a large nationally representative dataset that will permit me to assess the association between marriage and health for black Americans while exploring both gender and class intersectionalities.

2.11 Research questions

In my dissertation, I will test whether marriage is associated with better health for blacks and how this association is moderated by class and gender. I will also examine whether these benefits are statistically different to those observed among whites.

Specifically, I test the following research questions:

Inter-group assessment:

Research Question 1: What is the overall association between marital status and well-being?

Research Question 2: Does the marriage-health association differ among blacks and whites for positive self-reported health?

Research Question 3: Does the marriage-health association differ among blacks and whites for low allostatic load? 40

Intra-group assessment:

Research Question 4: Are the marriage-health associations documented for black men and black women moderated by socioeconomic status?

Chapter 3. Data and Methods

3.1 Data

The data used for this dissertation are from the 1999 - 2016 National Health and

Nutrition Examination Survey (NHANES). NHANES is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The

NHANES program began in the early 1960s and has been conducted as a series of surveys focusing on different population groups or health topics. In 1999, the survey became a continuous program that has a changing focus on a variety of health and nutrition measurements. The survey examines a nationally representative sample of roughly 5,000 persons each year. The survey is unique in that it combines interviews and physical examinations. The NHANES interview component includes demographic, socioeconomic, dietary, and health-related questions. The examination component consists of medical, dental, and physiological measurements, as well as laboratory tests administered by highly trained medical personnel. NHANES collects biological specimens (biospecimens or biomarkers) during Mobile Examination Centers (MEC) appointments. Each MEC laboratory team includes three medical technologists and a certified phlebotomist. Staff were certified in accordance with guidelines set forth by the

American Society for Clinical Pathology. The laboratory team collected, processed, stored, and shipped biological specimens.

The laboratory component of the NHANES is critical for the present study. The vast majority of the existing studies on race, marriage, and physical health have relied on self-reported physical health outcomes (Dupre 2016; Harris et al. 2010; Roxburgh 2014;

Shafer 2010). Although self-reported health outcomes are widely utilized (Carr et al.

2014), they are ultimately subjective measures that are not as accurate as measures of physical health as those obtained by trained medical professionals (Shafer 2010). In this dissertation, I will utilize the NHANES laboratory component to obtain information on respondent biomarkers, which allow researchers to more accurately examine physiological and social responses to stress (Carr et al. 2014; McEwen 1998). Alongside the biomarker results, I will also evaluate self-reported health (SRH)—one of my interests is to compare the pattern of results from the biomarker data with the subjective reports of health status.

Table 1: Description of NHANES Data Set

3.2 Analytic Sample

Table 1 presents a description of the data set. The NHANES 1999 – 2016 component surveys were combined to produce a total sample of 92,062. Young adults aged 18 and 19 were excluded because education information is only available for adults

20 years and older. Adults over the age of 65 were also excluded. This analysis presented here focuses on the age range of 20 – 65. After restricting the age to 20 – 65 and restricting race to blacks and whites1, our analytic sample consists of 23,650 respondents.

Of these 23,650, blacks account for 35% (8,267) of the sample and whites account for

65% (15,383). Thus, the analytic sample included men and women aged 20 – 65 year old who self-identified as black or white (n=23,650).

Education is available for 99.9% (n=23,624) of the sample. Marital status is available for 98.8% (n=23,375) of the sample. Race is available for 100% of the sample.

Individuals with missing data for any component of the allostatic load algorithm were excluded. 18.8% (n = 4,451) of the sample was missing on allostatic load. Comparisons between those excluded due to missing on the allostatic load variable and those included respondents revealed no statistically significant differences in the age distribution of respondents. Excluded respondents had—on average—lower income and less education than those that were included. This was true for both combined black and white samples, as well as the black only sample. Blacks made up a larger proportion of the missing in the

1 On average, health disparities are largest between blacks and all other racial/ethnic groups (Koball et al. 2010). Accordingly, the health of Black Americans is the focus of this dissertation. While Latinos currently constitute the largest racial/ethnic group, the health of Hispanics is more similar to whites than blacks (Palloni and Arias 2004)). As such, this dissertation will focus on the health disparities between blacks and whites. 45 excluded group (42.3%) than in the included group, suggesting that blacks with the worst health are missing from the analysis. This suggests that the disparities between blacks and whites presented here are conservative estimates. For the black only models, there was no difference in the marital status distribution suggesting that the missing—who on average have lower SES—were not more likely to be unmarried (never married, widowed, divorced/separated, or cohabitating).

3.3 Outcome Variables

On the basis of previous research and data availability, 6 biomarkers were selected for inclusion in the allostatic algorithm (Brooks et al. 2014; Geronimus et al.

2006; M. Seeman et al. 2014; Seeman et al. 2002). Allostatic load is a useful concept to capture the physiological burden imposed by stress. This is often referred to the wear and tear on the body’s systems from repeated adaptation to stressors. Two of the main substances released into the body during allostasis are adrenaline and cortisol.

Chronically elevated levels of adrenaline increase the likelihood of developing hypertension. Long term exposure to cortisol is also associated with higher risks of obesity (Massey 2004). The biomarkers included capture the impact of the substances released in response to stress that result from repeated activation over time.

This dissertation relied on biomarkers that have been previously used in prior studies and that are also available for the entirety of the sampled NHANES data.

Thresholds for determining high allostatic load were based off of previous studies

(Geronimus et al. 2006 and Seeman et al. 2002, 2014). Previous research used thresholds of 3 or 4 and higher (out of 10) as a marker of high allostatic load. Although this

46 dissertation used a smaller range (0-6), this dissertation also used the threshold of 3 and higher as a marker of high allostatic load. This would make those with a “high” allostatic load consistent with previous research (Geronimus et al. 2006). As a sensitivity analysis,

I ran two sets of models, one using 3 has the threshold for high allostatic load and one using 4 as the threshold. The results were similar regardless of which threshold was used.

Systolic and diastolic blood pressures and body mass index were obtained from physician examinations. Glycated hemoglobin was collected from blood samples, albumin, and

Creatinine were collected from urine samples. It is worth noting that there are relevant critiques of AL. Previous research mainly used cross-sectional research designs with the attendant inability to examine causal relationships or temporal sequence to rule out reverse causation in which disease status would affect factors that increase stress and propagate AL. Cross-sectional data present selection issues in that with advancing age, only the healthiest remain in the population. Further, stress responses are not static and change over the life history of individuals (Beckie 2012).

Blood pressure was measured on participants 8 years and older. After resting quietly in a seated position for 5 minutes and after the participant’s maximum inflation level (MIL) had been determined, three consecutive BP readings were obtained. If a BP measurement was interrupted or incomplete, a fourth attempt may be made. All BP determinations (systolic and diastolic) were taken in the mobile examination center

(MEC). Participants with any of the following on both arms were excluded from the exam: rashes, gauze dressings, casts, edema, paralysis, tubes, open sores or wounds, withered arms, a-v shunts, radical mastectomy. Participants were also excluded if the BP

47 cuff does not fit on the arm. It is worth noting that earlier blood pressure procedures did not always collect three blood pressure readings. In some instances only one or two blood pressure readings were collected. Procedures from 1999 differ from 2015 for adults older than 50 as well. Previous research averaged the blood pressure from the second and third reading and produced their blood pressure measures. Given the missing data from earlier waves, this dissertation uses the second reading of blood pressure.

Body Mass Index (BMI) was calculated as weight in kilograms divided by height in meters squared, and then rounded to one decimal place. All survey participants were eligible for the body measures component. The body measures data were collected, in the

Mobile Examination Center (MEC), by trained health technicians. The health technician was assisted by a recorder during the body measures examination. The participant’s age at the time of the screening interview determined the body measures examination protocol. There is a concern in including BMI in the allostatic load measure, specifically as it relates to marriage. Unlike the other factors in the AL measure, BMI is positively related to marriage, that is, on average, individual gain weight upon entry into marriage.

This is particularly the case for African-American women (cite Emily ShafterAs a robustness check, I controlled for BMI in the models that predicted AL and did not find any substantive differences. As a result, I chose to include BMI in the AL measure to maintain consistency with other studies that have validated AL (Geronimus 2006;

Seeman 2014).

Glycated hemoglobin (A1C) is a measure of sugar in your blood. The higher the percentage, the higher your blood sugar levels have been: A normal A1C level is below

5.7 percent. Pre-diabetes is between 5.7 to 6.4 percent. Having pre-diabetes is a risk factor for progressing onto type 2 diabetes. Type 2 diabetes is diagnosed when A1C is above 6.5 percent. Diabetes is a leading cause of disease and death in the United States

(NHANES 2016). More than 29 million Americans are living with diabetes, and 86 million are living with pre-diabetes, a serious health condition that increases a person’s risk of type 2 diabetes and other chronic diseases (NHANES 2016). In 2014, nearly 9.3 percent of all deaths for persons over the age of 25 were among people with diabetes

(NHANES 2016).

Albumin measurements are used in the diagnosis and treatment of diseases involving the liver and/or kidneys. These measurements are frequently used to assess nutritional status, due to plasma levels of albumin being dependent on protein intake.

Decreased microalbuminuria is a sign of renal disease and may be predictive of nephropathy risk in patients with type 1 and type 2 diabetes. It is also associated with hypertension and cardiac disease.

Creatinine is produced by creatinine and creatinine phosphate as a result of muscle metabolic processes. It is then excreted by glomerular filtration during normal renal function. Creatinine may be measured in both serum and urine. Creatinine measurement is useful in the diagnosis and treatment of renal diseases, in monitoring renal dialysis, and as a calculation basis for other urinary analyses (e.g., total protein and microalbumin).

Albumin & Creatinine in previous research used biomarkers from the blood. Low levels of Albumin & Creatinine in the blood indicates that your kidneys are not

49 functioning properly since the kidneys are suppose to keep these proteins in the blood.

High levels of Albumin & Creatinine in urine represents the same kidney issue since the kidneys are leaking these good proteins into urine.

For each biomarker, I empirically determined the “high” threshold cutoff on the basis of the distribution of that biomarker in the sample. This practice follows a standard approach as performed by Geronimus et al. (2006); Seeman et al. (2014, 2002). The high- risk thresholds were defined as below the 25th percentile for Creatinine and albumin and above the 75th percentile for all others. Each participant’s biomarker reading is determined to fall either above or below each component’s assigned threshold. If the person’s reading falls in the high-risk range, they are assigned a 1, 0 otherwise. The points were then summed to obtain the allostatic load score with a maximum score of 6 possible points. In the NHANES 1999 – 2016 sample, high-risk thresholds were defined as follows: Systolic blood pressure, 130 mmHg; diastolic blood pressure, 78 mmHg; body mass index, 32.45; Creatinine, 67 mg/dL; albumin, 4.0 ug/ml; Glycated hemoglobin, 5.7%. These thresholds are very similar to those identified by Geronimus et al. (2006). Allostatic load was dichotomized, low allostatic load was considered having a score of 0, 1, 2. High allostatic load was considered a score of 3, 4, 5, 6.

A final outcome evaluated in this analysis is self-reported health. Self-reported health allows for a comparison between subjective well-being and biomarkers. The current health status questionnaire was done in the Mobile Examination Center (MEC), by trained interviewers, using the Computer-Assisted Personal Interview (CAPI) system.

It was conducted by a MEC interviewer, during the MEC Interview, for participants 12

50 and older. This dissertation used the general health condition to measure self-reported health: “Would you say your health in general is Excellent, Very Good, Good, Fair, or

Poor?” For the analysis, self reported health was also dichotomized with poor self- reported health being poor, fair, or good. Positive self-reported health was considered very good, or excellent. For consistency, both outcome variables (AL and SRH), in all of the models, the “positive” health outcome is coded as “1” and the “poor” outcome is the reference group.

3.4 Key Explanatory Variable

Marital status is asked in reference to the current marital status of the respondent.

The categories were: married at the time, never married, widowed, divorced, separated, or living with a partner. Divorced and separated individuals were combined into one marital status group. It is also worth noting that given my age cut off—65—those who are widowed are selective since the majority of the widowhood occurs after the age of 65.

3.5 Additional control variables.

This dissertation focuses on education level as the key measure of socioeconomic status. Education was dichotomized to distinguish those who have completed high school or less vs. those who have completed some college or more. Age, education, and income were used as control variables. Education was excluded from models that were restricted to one educational grouping, i.e. college educated blacks. Age was used as a control variable in all models.

3.6 Analytic Strategy

Using SAS 9.4, logistic regression models were run combined and separately for whites, blacks, black men, and black women. Separate models were also run for high SES black men and women and low SES black men and women. I begin the analysis with black and whites combined in the sample. The first model only includes marital status as the focal independent variable. The presentation of the bivariate relationship first is useful in that it establishes the baseline association between marital status and health. The second model adds race. In this way, I can assess how much of the overall relationship is impacted by racial differences in the association between marital status and health. The third model includes demographic controls (age, gender, in addition to race). I expect age to be a key characteristic impacting the aforementioned relationships. There is strong age patterning to marriage and age patterning in health. The fourth model includes socioeconomic status (education). This process was repeated for, black men, black women, high SES black men, low SES black men, high SES black women, low SES black women—excluding control variables (gender, race and education) as the models became more specific. A post estimation test was run to compare the marital status coefficients across the different models (i.e. for black vs. white). Overall, the differences between the coeffients were significant.

On the subject of weights, this dissertation does not include weights and should not include them. NHANES oversamples blacks and Latinos—which is good since the focus of this dissertation is black Americans—thus, the racial make-up in the sample is not representative of the United States proportionally. This is only a problem for studies

52 that run models based on the entire population or studies that only look at one sample year. Such studies outcomes would produce inaccurate population estimates since blacks would make up a larger proportion of the population than what is representative in the

U.S. However, this dissertation requires an oversample of blacks to run the suggested detailed analysis for high SES blacks and low SES blacks by gender. In other words, there is no need to essentially "shrink" the black population by using weights. Weights would take away from my analysis by normalizing the proportion of blacks to 13%.

Chapter 4. Analysis Part I Blacks and Whites

Chapter 4 examines the association between allostatic load and marital status for blacks and whites. Chapter 4 will also examine the association between self-reported health and marital status for blacks and whites. First, I present the descriptive statistics for the sample. Next, I present the results from the models for blacks and whites combined to assess the population average associations between marital statuses and health. I will add different variable sets sequentially to assess the impact of each set. The first model only consists of marital status. The second model includes race to examine how accounting for race impacts the association between marital status and each health outcome. The third model includes controls for age and gender. The fourth model includes socioeconomic status (measured as education). A second set of models moves beyond population averages to examine whether and how marital status is differentially associated with health for blacks versus whites. That is, the second set of models will assess the possibility of heterogeneity in the impact of marriage on health across these two racial groups.

[Table 2 About Here]

Table 2 presents the demographic profiles and socioeconomic status of blacks and whites, separately. Significant differences between the groups are indicated in the far right column. As expected, relatively fewer blacks are married (35.2%) compared to 54 whites (58.6%) in our sample although there are a larger proportion of married blacks in our sample than the national average. Nationally, only 23.8% of blacks are married compared to 50.8% of whites (U.S. Census Bureau 2016 American Community Survey).

Roughly ten percent of blacks in the NHANES data were currently cohabitating compared to 8% for whites. Both estimates are lower than the national estimate of cohabitators, which is 7% (Stepler 2017). In the NHANES sample, thirty-three percent of blacks were never married compared to 18% of whites. This is lower than the national average—49.9% for blacks and 29.8% for whites (U.S. Census Bureau). In the case of divorce, 12.2% of blacks were divorced compared to 11.8% of whites. These numbers are identical to the national average—12.0% for blacks and 11.5% for whites (U.S. Census

Bureau). With respect to separation, 5% of blacks were separated from their spouses compared to 2% of whites. These estimates were just slightly higher than the national averages of 3.7% for blacks and 1.7% of whites (U.S. Census Bureau). Four percent of blacks were widowed compared to 2% of whites. For the national average, 6.2% of whites were widowed compared to 5.6% of blacks (U.S. Census Bureau). The differences in marital status for blacks and whites were statistically significant (p<0.001).

With respect to the other demographic variables, the average age of the sample is

42.5 for blacks and 42 for whites. There is no statistically significant difference between blacks and whites in the gender distribution (the overall sample is 48% male and 52% female). In terms of the educational distribution, whites have higher levels of education and blacks have lower levels. Whereas, 50% of blacks have a high school diploma or less, only 38% of whites have that low level of education. The national average is 39.8%

55 of the U.S. has a high school diploma or less (U.S. Census Bureau 2016). At the upper end of the educational distribution, only 50% of blacks have attended some college or received a college degree compared to 62% of whites. The U.S. Census Bureau (2016) reports that those who have attended college or received a college degree account for

60.2% of the population. Fewer blacks have attended college than compared to whites.

The proportion of blacks that have attended college or received a college degree is also lower than the national average. The racial differences in education are statistically significant.

[Table 3 About Here]

Table 3 presents the mean distributions for the outcome variables utilized in the analysis, for blacks and whites separately. Overall, blacks had worse health outcomes than whites. On average, whites report better self-reported health than blacks. All biomarkers were significantly higher for blacks compared to whites. Consequently, on average, blacks had a higher frequency scoring in the “high” allostatic load category than whites. The sample average for self-reported health (range 1 {poor} – 5 {excellent}) was

3.34. Blacks had a lower mean self-reported health (3.16) than the sample average (3.34) while whites had higher self-reported health than the sample average (3.44). The dichotomized self-reported health measure displayed similar results. For blacks, 34% reported very good or excellent health compared to 49% for whites. This pattern continued for allostatic load (both for the mean of the 6 items and the dichotomized version). Systolic blood pressure, diastolic blood pressure, body mass index, Creatinine,

56 albumin and Glycated Hemoglobin, all presented higher averages for blacks than compared to whites.

4.1 Black and White Models Combined

[Table 4 About Here]

Table 4 presents the results from a set of logistic regression models estimating the association between marital status and self-reported health (good or excellent heath=1) for the black and white combined models. Model 1 is the uncontrolled model that only includes marital status. The results show that, compared to the married, all other marital statuses are associated with significantly lower odds of reporting positive self-reported health. Cohabitating individuals have 28% lower odds of reporting positive self-reported health compared to married individuals (OR=0.72). Among those not in intimate unions, widowed individuals present the greatest gap compared to married respondents, with 65% lower odds of reporting good health compared to the married (OR=0.35). Divorced or separated individuals have 51% lower odds of reporting positive SRH compared to married individuals (OR=0.49). The smallest difference is between married individuals and those who report being never married. Never married individuals have 23% lower odds of reporting great self-reported health compared to married individuals (OR=0.77).

Model 2 includes race. As expected, black individuals are significantly less likely to report positive SRH than whites. Blacks have 43% lower odds of reporting very good or excellent health compared to whites (OR=0.57). After controlling for race, the magnitude of the differences across the marital status categories are all slightly attenuated. The gaps between the married and all the other statuses decrease slightly after

57 controlling for race. Once race is accounted for, cohabitating individuals have 23% lower odds of reporting positive self-reported health (compared to 28% lower odds in model 1).

Widowed individuals continue to have the lowest odds of reporting positive SRH at 61% lower odds. The contrast most affected by accounting for race is between never married and married individuals. Once race is controlled, never married individuals only have

11% lower odds of positive SRH than those who are married (OR=0.89), compared to

23% lower odds before accounting for race (OR=0.77). Remember, black respondents are over-represented in the never married category (33% of blacks compared to 18% of whites). Clearly, the overall average associations between marital status and health is influenced by race, in particular, compositional differences between blacks and whites in martial statuses. Whether there are also heterogeneous effects of these different marital status categories by race is addressed in a subsequent section.

Model 3 includes two additional demographic controls—age and gender. As expected, the older you get, the lower the odds of reporting positive self-reported health, with each additional year associated with a 2% decrease in good health. There is no statistically significant effect of gender on SRH. After controlling for age and gender, the poorer SRH among the never married and cohabiting compared to the married becomes even more pronounced. If it were not for their more youthful age compositions, the health differences between the married and the unmarried and cohabitors would be even more deleterious for those in the latter categories. For instance, before controlling for age and gender, the never married are 11% less likely to report positive SRH (OR=0.89). After accounting for age, the never married become 31% less likely to report positive SRH

(0.69). Operating in the other direction, once we control for age and gender, the difference in health between the widowed and the married is somewhat attenuated (from

60% lower odds of good health for the widowed to 40% once age and gender are included).

Model 4 includes a control for my measure of socioeconomic status—education.

Those who attended at least some college were over two times more likely to report positive SRH health compared to those with a high school diploma or less (OR=2.11).

After the inclusion of SES, marital status and race remained statistically significant predictors of SRH. In the full model with demographic and socioeconomic controls, we continue to observe statistically significant differences in self-reported health by marital status. However, the magnitude of the gap between the married vs. other status categories decreases. Cohabitating individuals have 29% lower odds of reporting positive self- reported health compared to married individuals, which is a decrease from 36% in model

3. Among those who are not in intimate unions, widowed individuals continue to demonstrate the lowest odds of reporting positive self-reported health. Widowed individuals have 45% lower odds of reporting great self-reported health compared to married individuals (OR=0.55). In the full model, never married individuals have identical odds of positive SRH as those who cohabit (OR=0.71).

In the full model, once education is controlled, gender becomes a significant predictor of SRH. The results demonstrate that men having 9% higher odds of reporting positive self-reported health compared to women, which aligns with other work on

59 gender differences in SRH (Idler 2003). Even in the full model Blacks demonstrate 38% lower odds of reporting great self-reported health.

[Table 5 About Here]

Table 5 repeats the models presented above but this time the outcome variable is switched from self-reported health to low allostatic load. Again, model 1 presents the results from the uncontrolled model that only includes the marital status variable. There are significant differences between the married and all other status categories in predicting low AL, but the overall patterns differ from what we observed with SRH.

Unlike in the case of SRH, in the uncontrolled models, the married do not display the most positive health outcomes. In the case of low AL, those who are cohabitating have better health than the married—they have higher odds of having low allostatic load (i.e. they are more likely to display a relatively more positive health outcome which in this case is low AL) compared to those who are married (OR=1.13 for cohabiting individuals). Additionally, those who have never married have the most positive health when the outcome is measured by low AL (OR=1.23 for the never married). More in line with results of SRH, widowed individuals and those who are divorced/separated have significantly worse AL than the married. The widowed are the most health disadvantaged vis a vis the married, with 46% lower odds of having low allostatic load compared to the married (OR=0.54). The divorced or separated have 29% lower odds of having low allostatic load compared to the married (OR=0.71).

Model 2 includes race. Similar to the case of SRH, blacks are significantly disadvantaged as compared to their non-Hispanic white counterparts. Blacks have 64%

60 lower odds of having low allostatic load compared to whites (OR=0.36). Once race is added to the model, the marital disadvantage, i.e. the higher AL among the married compared to cohabitors and the never married, becomes even more pronounced. After controlling for race, the never married have 63 percent higher odds of having low AL compared to the married (OR=1.63 compared to 1.23 before controlling for race) and cohabitors have nearly 35% higher odds of having low AL (OR=1.33 compared to 1.13 before controlling for race). In contrast, the magnitudes of the difference between the married and the widowed and divorced/separated are somewhat attenuated once race is added to the model. After the inclusion of race, widowed individuals have 32% lower odds of low AL (OR=0.68), and divorced and separated individuals have 18% lower odds of having low allostatic load compared to the married (0.82).

Model 3 includes two additional demographic control variables—age and sex. As expected, each additional year in age is associated with a 4% decrease in having low allostatic load, i.e. the older you are the higher risk of a high AL. In contrast to the pattern observed in the case of SRH, where women were significantly less likely to report positive SRH, in the case of allostatic load, men display worse health. Specifically, men have 35% lower odds of having low allostatic load compared to women (OR=0.65).

There are substantial changes to the way marital status is associated with AL after age and sex are accounted for in Model 3. After adjusting for age and gender, the never married and the widowed become indistinguishable from the married in their odds of low

AL. Divorced/separated and cohabitating individuals are the only marital status groups that are significantly different from the married in their association with allostatic load.

For those who cohabit, the direction of the association changes once age and sex are adjusted. Before adjusting for age and sex, cohabitators had higher odds of having low

AL (Model 2, OR=0.96) compared to the married. But after adjustment, cohabitors are significantly less likely to have low AL (OR=0.89). This shift in direction is due to differences in age composition across the marital status groupings, with those cohabit more likely to be younger. Further analysis demonstrated that controlling for age is what renders the marital status categories insignificant (and not gender).

Model 4 includes educational level. Having attended college is associated with

28% higher odds of having low allostatic load (OR=1.28). For both self-reported health and allostatic load, education is associated with positive health. After adjusting for education, only one marital status grouping remains significantly different from the married-the divorced or separated are 10% less likely to have low AL compared to those who are married (OR=0.90). Those who cohabitate are no longer statistically different from the married in their odds of having low AL.

Even after adjusting for education, blacks continue to have 62 percent lower odds of reporting low allostatic load compared to whites (OR=0.38). Age remains statistically significant in model 4 and the odds did not change. Men continue to have lower odds of having low AL compared to women.

4.2 Separate Models by Race

The next set of analyses assesses the relationship between marital status and health separately by race. As discussed in Chapter 2, previous literature suggests that marriage might be differentially associated with health for blacks than for whites.

Specifically, it suggests that being married will be associated with better health for both blacks and whites although the magnitude will be smaller for blacks compared to whites.

One of the main contributions of this dissertation is to assess whether the relationship between marriage and health operates differently by race. While the analyses presented thus far document significant differences by race in both health outcomes (self-reported health and allostatic load), these models only examined if the outcome variable differed by race, not if the association between marriage and health differed by race. In the case of self-reported health, the NHANES data demonstrated that married individuals have the most positive health outcomes. In contrast, in the case of allostatic load, married individuals were only significantly different from divorced/separated individuals. After controlling for age and sex composition, divorced/separated individuals had 10% lower odds of low AL compared to married individuals. None of the other groups (i.e. never married, widowed, or cohabitors) were significantly different from the married in their odds of low AL. The next set of tables will parse these associations to understand if—for both health outcomes—the marital status associations differ for blacks and whites.

[Table 6 About Here]

4.3 SRH

Table 6 presents the results from a set of logistic regression models estimating the association between marital status and self-reported health separately by race. Models 1-3 present the results for blacks and Models 4-6 present the results for whites.

Models 1 and 4 are the uncontrolled models that only include marital status. We observe that marriage and its association with positive self-reported health varies by race.

Never married blacks and cohabitating blacks do not have statistically different odds of reporting positive self-reported health when compared to married blacks. Never married whites, on the other hand, reported statistically significant lower odds of reporting positive self-reported health (OR=.84). For whites, all marital status groupings report lower SRH than the married. For blacks, only divorced/separated and widowed individuals report poorer health than the married. The other groups (cohabitors and the never married) are not significantly different from the married.

Models 2 and 5 include the demographic variables age and gender. There are some interesting racial differenes in the impact of gender and age on SRH. For gender, among blacks, men are more likely to report better self-reported health than black women. Black men report 46% higher odds of reporting positive self-reported health than black women (OR=1.46).For gender among whites, men are less likely to report positive self-reported health than white women. In the case of age, it is more strongly associated with SRH among blacks than whites. For blacks, each additional year of life decreases positive health by 3% (OR=0.97) but for whites the decrease is only 1 year (OR=0.99).

The impact of adjusting for age in particular is significant. After the inclusion of age and gender, the racial difference in the way that marriage is associated with SRH becomes much less pronounced. For both blacks and whites, all unmarried categories display statistically significant lower odds of reporting positive self-reported health relative to the married. In fact, for the never married, widowed and cohabitors, the regression coefficients are nearly identical for blacks and whites. The only status where we observe a difference is with respect to the divorced/separated whose health is more disadvantaged

64 among whites than blacks. The equivalence in the associations across racial groups

(between marital status and SRH) that emerged in Model 2 is due to the joint impact of age and gender. While black men are more likely to report positive SRH and more likely to be married, the married are more likely to be older and age has a disproportionately larger impact on the health of black Americans than white Americans. All together, the result is that the association between marital status and health becomes more similar once we adjust for age and gender.

Models 3 and 6 include the socioeconomic status measure, education. The inclusion of education shows that the association between education and positive self- reported health is much stronger for whites than for blacks. Whites who attended college are nearly 2 and half times more likely to report positive self-reported health than whites who did not attend college (OR=2.46). For blacks, those who attended college only have

57% higher odds of reporting positive self-reported health than blacks who did not attend college. For blacks and whites, the magnitude of the association between marriage and positive self-reported health is only slightly attenuated once education is accounted for.

Blacks who cohabitate have 28% lower odds of reporting positive self-reported health compared to married blacks. Similar results are seen in Model 6 for whites. Whites who were cohabitating had 29% lower odds of reporting positive self-reported health compared to married whites. The odds of reporting great self-reported health for cohabitating blacks and whites only differ by 1%. This suggests that the associations between cohabitation and health for blacks and whites are nearly identical. Among individuals not in intimate unions, blacks and whites differ amongst which marital status

65 groups have the lowest odds of reporting positive self-reported health. For blacks, widowed individuals reported the lowest odds of having positive self-reported health

(46% lower odds compared to married blacks) whereas whites who were divorced or separated reported the lowest odds of having positive self-reported health (47% lower odds compared to married whites). It is worth noting that, after controlling for education, divorced or separated blacks did not report similarly low odds of positive self-reported health as divorced or separated whites. Divorced or separated blacks only had 26% lower odds of reporting positive self-reported health (OR=0.74) compared to 47% for whites

(OR=0.53). Widowed white respondents, however, do have similarly low odds of reporting positive self-reported health as blacks (0.54 and 0.57, respectively). Never married blacks have 26% lower odds of reporting great self-reported health compared to married blacks whereas never married whites had 30% lower odds of reporting great self- reported health compared to married whites. This suggests that the association between never married and self-reported health may be slightly more detrimental for whites than for blacks, although it is detrimental to both. Overall, the findings present some evidence that the health benefits associated with marriage differ for blacks and whites but not nearly as much after adjusting for demographic factors and SES.

[Table 7 About Here]

4.4 Allostatic Load

Table 7 presents the results from a set of logistic regression models estimating the association between marital status and allostatic load separately by race. Models 1-3 present the results for blacks and Models 4-6 present the results for whites.

Table 7 repeats the models presented above but this time, switches the outcome variable from positive self-reported health to low allostatic load. Again, models 1 and 4 present the results from the uncontrolled model that only includes the marital status variable. Overall, while there are differences in magnitude, the general direction of the coefficients is similar for both blacks and whites. Cohabitating blacks have 63% higher odds of having low allostatic load compared to married blacks (OR=1.63). Cohabitating whites also have higher odds of having low allostatic load compared to married whites but the magnitude of the difference is smaller than for blacks (OR=1.23). Never married blacks and whites also have higher odds of having low allostatic load compared to the married, although again the magnitude of the difference is more pronounced for blacks than whites (OR=2.06 for blacks and OR=1.40 for blacks). Widowed blacks have lower odds of reporting low allostatic load then their married counterparts (OR=0.64) but this time these lower odds are greater than for widowed whites (OR=0.73 for widowed whites). This suggests that the loss of a spouse is more detrimental for blacks than for whites in terms of SRH. One surprising difference is that divorced/separated blacks are not significantly less likely to report low allostatic load than their married counterparts.

This is contrast to the case for divorced or separated whites, who report 19% lower odds of reporting low allostatic load than their married counterparts. This suggests differences in the impact of a loss of a spouse such that blacks who are widowed have poorer health than those who are married but no such difference exists for blacks who are divorced/separated. For whites, both types of losses incur negative health impacts.

However, much cannot be interpreted from the allostatic load model since age substantially influences allostatic load.

Models 2 and 5 include the demographic variables age and gender. For blacks, the inclusion of age and gender renders all marital status groups non-significantly different from the married. Once we account for age and gender, there are no statistically significant differences in allostatic load between married and unmarried groups of blacks.

As expected, each additional age (measured in years) is associated with a decrease in odds of having low allostatic load (OR=0.95). Surprisingly, black men have 19% lower odds of reporting low allostatic load compared to black women. This is the opposite from the case of self-reported health where black men were significantly more likely to report positive self-reported health.

For whites, cohabitators, never married, and divorced or separated all have significantly lower odds of reporting low allostatic load. This is a striking difference compared to blacks who present no statistically significant differences by marital status.

As with blacks, each additional age (measured in years) is associated with a 4% decrease in odds of having low allostatic load. Also identical to blacks, white men have lower odds of reporting low AL than white women. This difference is larger for white men than for black men (OR= 0.60 for white men OR=0.81 for black men) which is exactly the opposite pattern as reported for SRH.

Models 3 and 6 include the socioeconomic measure education. We observe differences in the impact of education between blacks and whites. For whites, attending college is associated with higher odds of having low AL (OR=1.40) compared to whites

68 who did not attend college. This was not the case for blacks who see no statistically significant difference between blacks who attended college and blacks who did not attend college.

After the inclusion of education, the associations between the marital status categories and low AL are hardly altered. For blacks, there were no changes to the association between marital status (i.e. marital status is still not associated with AL). For whites, never married and divorced or separated whites remained statistically significant different from the married. Never married whites have 12% lower odds of reporting low

AL compared to married whites and divorced or separated whites have 11% lower odds of having low AL compared to whites. It is worth noting that cohabitating whites were marginally significant different from the married (p<0.06) with 12% lower odds of reporting low AL compared to married whites (OR=0.88).

Overall, these models suggest that the marriage-health association differs somewhat between blacks and whites for positive self-reported health and low allostatic load, but that the direction and extent of these differences depend heavily on demographic factors (age and sex) and to a lesser degree on SES. There are differences between blacks and whites in the associations between marriage and positive self- reported health. For whites, those who were widowed and divorced or separated had the lowest odds of reporting positive self-reported health whereas only widowed blacks reported similar odds. The gap between blacks who were married and blacks who were divorced or separated was smaller compared to the gap between whites who were married vs. divorced or separated. Among individuals not in intimate unions, blacks and whites

69 differ with respect to which marital status groups displayed the lowest odds of reporting positive self-reported health. For blacks, widowed individuals reported the lowest odds of having positive self-reported health (46% lower odds compared to married blacks) whereas whites who were divorced or separated reported the lowest odds of having positive self-reported health (47% lower odds compared to married whites). It is worth noting that divorced or separated blacks did not report similarly low odds of reporting positive self-reported health as divorced or separated whites. Overall the magnitude of the difference between married vs. unmarried was larger for whites than for blacks.

There were also differences between blacks and whites and the association between marital status and allostatic load. For whites, never married and divorced or separated whites had the lower odds of reporting low allostatic load compared to married whites. For blacks, in the fully controlled model, none of the marital status groups were significantly less likely to have low AL for blacks. Blacks who are married have the same odds of reporting low allostatic load as other marital status groups. In terms of the explanatory variables, education was not associated with allostatic load for blacks but it was protective for whites. Whites who attend college had 40% higher odds of reporting low AL while there was no statistically significance between blacks who attended college and blacks who did not attend college. In the case of gender, for both blacks and whites, men have lower odds of reporting low AL compared to their female counterparts.

Overall, these findings underscore the necessity of examining the association between marriage and health separately for blacks and whites, depending on outcome.

Clearly, this task is more relevant to the case of AL than for SRH. Blacks and whites

70 differ in the association between AL and marital status in a way that is not observed for

SRH. Married whites are more likely to report low allostatic load whereas married blacks do not see this same benefit. Table 6 and table 7 also revealed gender differences and educational differences in how these factors associate with more positive health for blacks vs. whites. The next section will focus exclusively on the black population, which has received significantly less attention in the broader marriage-health literature. I will focus specifically on heterogeneity in the associations between marital status, gender, education, and health for black Americans.

Table 2: Description of Key Analytic Variables

Table 2: Description of Key Analytic Variables Variable Name Variable Description Sample sizes, Percent, Means Total Black White Significance Married 11,636 (49.78%)2 2,877 (35.20)2 8,759 (57.62%)2 *** Never Married 5,523 (23.63%)2 2,748 (33.62%)2 2,775 (18.26%)2 *** Divorced 2,799 (11.97%)2 998 (12.21%)2 1,801 (11.85%)2 *** Separated 779 (3.33%)2 450 (5.51%)2 329 (2.16%)2 *** Widowed 621 (2.66%)2 311 (3.80%)2 310 (2.04%)2 *** Cohabitating 2,017 (8.63%)2 790 (9.66%)2 1,227 (8.07%)2 *** Age Range = 20 - 65 42.49 (SD=13.55) 42.11 (SD=13.13) *** Gender 1 = Male 1 = 11,387 (48.15%) 1 = 3,937 (47.62%) 1 = 7,450 (48.43%) 2 = Female 2 = 12,263 (51.85%) 2 = 4,330 (52.38%) 2 = 7,933 (51.57%) Education 0 = High School or Less 0 = 9,894 (41.88%) 0 = 4,094 (49.56%) 0 = 5,800 (37.75%) *** 1 = Some College or Above 1 = 13,730 (58.12%) 1 = 4,166 (50.44%) 1 = 9,564 (62.25%)

2 = % of column; *p<0.05; **p<0.01; ***p<0.001.

Table 3: Description of Key Analytic Variables

Table 3: Description of Key Analytic Variables Variable Name Variable Description Mean (Standard Deviation) Total Black White Significance Self-Reported Health 1 = Poor 3.34 (0.97) 3.16 (0.98) 3.44 (0.95) *** 2 = Fair 3 = Good 4 = Very Good 5 = Excellent Self-Reported Health 0 = Poor, Fair, Good 0.44 (0.49) 0.34 (0.47) 0.49 (0.49) *** 1 = Very Good, Excellent Allostatic Load Range 0 - 6 2.63 (1.41) 3.16 (1.34) 2.36 (1.37) *** Allostatic Load Low AL = 0, 1, 2 0.49 (0.49) 0.65 (0.48) 0.42 (0.41) *** High AL = 3, 4, 5, 6 Systolic Blood Pressure 0 = Less than 130 mmHg 0.27 (0.44) 0.35 (0.48) 0.22 (0.42) *** 1 = 130+ mmHg Diastolic Blood Pressure 0 = Less than 78 mmHg 0.32 (0.47) 0.36 (0.48) 0.30 (0.46) *** 1 = 78+ mmHg Body Mass Index 0 = Less than 32.45 0.27 (0.44) 0.34 (0.47) 0.23 (0.42) *** 1 = 32.45+ Creatinine 0 = Less than 66 mg/dL 0.77 (0.42) 0.89 (0.32) 0.71 (0.45) *** 1 = 66+ Albumin 0 = Less than 4.0 ug/ml 0.75 (0.43) 0.85 (0.36) 0.71 (0.46) *** 1 = 4.0+ Glycated Hemoglobin 0 = Less than 5.7% 0.26 (0.44) 0.40 (0.49) 0.20 (0.40) *** 1 = 5.7% or more

*p<0.05; **p<0.01; ***p<0.001.

Table 4: Overall Ratios of Positive Self-Reported Health

Table 4: Overall Odds Ratios of Positive Self-Reported Health Model 1 Model 2 Model 3 Model 4 CI Never Married 0.77*** 0.89** 0.69*** 0.71*** 0.654 – 0.773 Divorced/Separated 0.49*** 0.52*** 0.55*** 0.58*** 0.528 – 0.633 Widowed 0.35*** 0.39*** 0.49*** 0.55*** 0.441 – 0.675 Cohabitating 0.72*** 0.77*** 0.64*** 0.71*** 0.634 – 0.793 Black 0.57*** 0.59*** 0.62*** 0.582 – 0.663 Age 0.98*** 0.98*** 0.979 – 0.984 Male 1.03 1.09** 1.023 – 1.154 Attended College 2.11*** 1.980 – 2.242 *p<0.05; **p<0.01; ***p<0.001 N = 18,926

Table 5: Overall Odds Ratios of Low Allostatic Load

Table 5:Overall Odds Ratios of Low Allostatic Load Model 1 Model 2 Model 3 Model 4 CI Never Married 1.23*** 1.63*** 0.95 0.96 0.883 – 1.050 Divorced/Separated 0.71*** 0.82*** 0.88** 0.90* 0.821 – 0.984 Widowed 0.54*** 0.68** 0.98 1.02 0.830 – 1.249 Cohabitating 1.13* 1.33*** 0.89* 0.92 0.819 – 1.032 Black 0.36*** 0.37*** 0.38*** 0.348 – 0.399 Age 0.96*** 0.96*** 0.954 – 0.959 Male 0.65*** 0.66*** 0.618 – 0.699 Attended College 1.28*** 1.203 – 1.364 *p<0.05; **p<0.01; ***p<0.001 N = 18,986

Table 6: Odds Ratios of Positive Self-Reported Health by Race

Table 6: Odds Ratios of Positive Self-Reported Health by Race Blacks Whites Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Never Married 0.99 0.70*** 0.74*** 0.84*** 0.70*** 0.70*** Divorced/Separated 0.63*** 0.72*** 0.74*** 0.48*** 0.49*** 0.53*** Widowed 0.32*** 0.50*** 0.54** 0.45*** 0.51*** 0.57*** Cohabitating 0.89 0.65*** 0.72*** 0.73*** 0.64*** 0.71*** Age 0.97*** 0.97*** 0.99*** 0.99*** Male 1.46*** 1.53*** 0.88*** 0.92* Attended College 1.57*** 2.46*** *p<0.05; **p<0.01; ***p<0.001 Blacks = 6,504 Whites = 12,422

Table 7: Odds Ratios of Low Allostatic Load by Race

Table 7: Odds Ratios of Low Allostatic Load by Race Blacks Whites Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Never Married 2.06*** 1.08 1.09 1.40*** 0.88* 0.88* Divorced/Separated 0.90 1.01 1.01 0.81*** 0.86** 0.89* Widowed 0.64** 1.10 1.11 0.73* 0.98 1.03 Cohabitating 1.63*** 1.01 1.02 1.23** 0.84* 0.88t Age 0.95*** 0.95*** 0.96*** 0.96*** Male 0.81*** 0.81*** 0.60*** 0.60*** Attended College 1.08 1.40*** t=p<0.1*p<0.05; **p<0.01; ***p<0.001 Blacks = 6,318 Whites = 12,668

Chapter 5. Analysis Part II Black Men and Black Women

Chapter 4 examined the association between marital status and two health outcomes (allostatic load and self-reported health) and assessed whether there were differences in these relationships by race. Chapter 5 goes a step further and focuses exclusively on the black population to assess the possibility of intra-group differences in how marital status impacts health. I am particularly focused on whether there are differences by gender and by SES. As with Chapter 4, Chapter 5 will first present descriptive statistics for black men and black women. Next, models are presented that distinguish the population by gender and by socioeconomic status. Model 1 will examine uncontrolled marital status for high SES black men. Model 2 will examine include age as a control variable for high SES black men. Model 3 will examine uncontrolled marital status for high SES black women and Model 4 will include age as a control. Model 5 will examine uncontrolled marital status for low SES black men and model 6 will include age as a control. Model 7 will examine uncontrolled marital status for low SES black women and model 8 will include age as a control. This approach is repeated for both positive self-reported health and allostatic load.

[Table 8 About Here]

Table 8 presents the demographic profiles and socioeconomic status for black men and black women. Significant differences between black men and black women of

78 each SES group are indicated in the column for black women. We see that that black men and black women differ significantly in their demographic profiles (Columns 1 and 2). As expected, relatively fewer black women are married (30.18%) compared to black men

(40.73%). Again, reflecting the higher levels of marriage in the NHANES generally, both estimates for married black men and black women are higher than the national average for blacks (23.8%) (U.S. Census Bureau 2016). Roughly 11% of black men were cohabitating in the NHANES data compared to only 8% of black women, although both of these estimates are higher than the national estimate of 7% (Stepler 2017). The never married black men and black women make up the second largest group for each gender respectively. Never married black men account for 31.62% of the sample and never married black women account for 35.43% of the sample. These estimates are still lower than the national average, which is 49.9% for blacks overall (U.S. Census Bureau 2016).

In the case of divorce, 9.67% of black men are divorced compared to 14.51% of black women. For black men, this is lower than the national average of 12% but for black women, these numbers are higher than the national average (U.S. Census Bureau 2016).

With respect to separation, 4.5% of black men were separated from their spouses compared to 6.41% of black women. Both estimates for black men and black women were slightly higher than the national average for blacks of 3.7% (U.S. Census Bureau

2016). Only 2% of black men were widowed compared to 5.44% of black women, both of which are lower than the national average of 5.6% for blacks (U.S. Census Bureau

2016).

With respect to other demographic variables, the average age for black men is

42.82 and 42.19 for black women. There are no statistically significant differences in age for black men and black women overall. Overall, black women outnumbered black men in the sample. Black women made up over 52% of the sample whereas black men only accounted for 47% of the sample.

The next four columns differentiate the sample by both gender and SES. Columns

3 and 4 present the descriptives for high SES black men (Column 3) and high SES black women (Column 4). The last two columns focus on low SES black men (Column 5) and low SES black women (Column 6). The gender difference in the extent of marriage is visible for all SES groups. Relatively more high SES black men are married (47.35%) than high SES black women (33.03%). And this is also true for low SES men (35.04%) compared to low SES women (26.77%). In fact, a slightly higher percent of low SES black men are married than high SES black women.

Comparing within gender categories, high SES individuals have higher percents who are married. High SES married black men (47.35%) drastically outnumber low SES married black men (35.04%). For women, the difference is not as pronounced but still evident with 33.03% of high SES black women married compared to only 26.77% of low

SES black women.

Roughly 9% of high SES black men were cohabitating compared to 7% of high

SES black women. These estimates for high SES black men and women are lower than for low SES black men (13.88%) who have the highest percent in a cohabiting union.

Among low SES black women, 8.79% are currently cohabitating. For both high SES and

80 low SES black men and women, never married individuals make still account for the second largest group. However, low SES black men (33.65%) and women (36.59%) account for a larger proportion of never married when compared to high SES black men

(29.28%) and women (34.40%) who have never married.

In the case of the divorced, high SES black men (9.43%) account for a smaller proportion than high SES black women (15.58%). Divorce estimates for high SES black men (9.43%) and low SES black men (9.89%) are strikingly similar. Surprisingly, high

SES black women have higher divorce estimates (15.58%) than low SES black women

(13.26%). Although this difference is partly due to lower marriage levels among low SES black women, the same SES-divorce/separated pattern observed among black men (i.e. lower marriage among low SES men) does not translate into different divorce levels for men. With respect to separation, 3.35% of high SES black men were separated from their spouses compared to 5.95% of high SES black women. As with the divorce estimates, high SES black men have lower estimates of separation (3.35%) than low SES black men

(5.50%). High SES black women have lower estimates of separation (5.59%) than low

SES black women (6.99%). Only 1.62% of high SES black men were widowed compared to 3.64% of high SES black women. Compared to high SES black men, low SES black men have higher estimates of being widowed (2.29%). For low SES black women, their estimates of being widowed is astonishingly twice that of high SES black women (7.61% for low SES black women compared to 3.64% for high SES black women).

With respect to the other demographic variables, the average age of high SES black men is 42.08 and 41.20 for high SES black women. These are statistically

81 significant differences between high SES black men and high SES black women. The average age of low SES black men is 43.43 and 43.39 for low SES black women.

Differences among low SES black men and women are also statistically significant.

[Table 9 About Here]

Table 9 presents the mean distributions for the outcome variables utilized in the analysis for black men and women separately by education. In the case of SRH, black men report better self-reported health than black women regardless of SES. Thirty-nine percent of black men report good or excellent health compared to only 30% of black women. This gender difference persists among SES groups. Forty-three percent of high

SES black men report positive SRH, which closely matches the average for the entire

NHANES sample (44%). High SES black women have better SRH than low SES women

(36%), but it is considerably lower than high SES black men (43%). In fact, positive SRH among high SES women is more comparable to SRH among low SES black men (36% and 34%, respectively). Low SES have the lowest percent reporting positive SRH at just

22%.

Surprisingly, given their poorer SRH, black women have lower AL than black men regardless of SES. Besides BMI, all biomarkers were higher for black men compared to black women, regardless of SES. Consequently, on average, black men had a higher frequency scoring in the “high” allostatic load category than black women.

Differences between black men and black women on the individual biomarker measures were all statistically significant with the exception of albumin.

There were also statistically significant differences between high SES black men and high SES black women in the allostatic load measures. Even among high SES individuals, a gender difference favoring women is evident. The mean of the ordinal allostatic load measure was higher for high SES black men than for high SES black women. This difference between high SES black men and high SES black women is statistically significant. High SES black men had higher systolic blood pressure and diastolic blood pressure than black women but lower averages than low SES black men.

BMI was higher for black women than black men (0.41 vs. 0.24) and this gender pattern persisted within the SES comparisons. There is virtually no difference between high SES black women and low SES black women in terms of the percent in the overweight category (0.42 vs. 0.41). Creatinine was higher for high SES black women but there were no statistically significant differences in albumin levels for high SES black men and high

SES black women. Glycated Hemoglobin was higher for high SES black men than for high SES black women. Creatinine levels were higher for low SES black women than low SES black men. Albumin and Glycated Hemoglobin levels were not significantly different between low SES black men and low SES black women. Low SES black men and women had the highest levels of Glycated Hemoglobin compared to high SES black men and high SES black women and overall black men and women averages.

[Table 10 About Here]

Table 10 presents the results from a set of logistic regression models estimating the association between marital status and self-reported health for each gender/SES

83 combination for the black sample only. Model 1 is the uncontrolled model for high SES black men that only includes marital status. There are not many significant differences in

SRH across marital status categories for high SES black men. The never married, widowed, and cohabitating high SES black men do not have statistically different odds of reporting positive self-reported health when compared to married high SES black men.

Divorced or separated high SES black men, on the other hand, reported statistically significant lower odds of reporting positive self-reported health (OR=0.56).

Model 2 includes age as a control variable for high SES black men. After the inclusion of age, the marital health benefit becomes more pronounced. Cohabitating, never married, and divorced or separated high SES black men all have lower odds of reporting positive self-reported health than do their married counterparts. This suggests that there is an age component that confounds the association between marital status and health for high SES black men. Divorced or separated high SES black men report the worst odds (43% lower odds of reporting positive self-reported health), followed by cohabitating high SES black men (36% lower odds of reporting positive self-reported health), and never married high SES black men (27% lower odds of reporting positive self-reported health. Interestingly, widowed high SES black men are not significantly different in their odds of positive SRH than their married counterparts. As expected, each additional year in age is associated with a 3% decrease in odds of reporting positive self reported health for high SES black men.

Models 3 and 4 examine the association between positive self-reported health and marital status for high SES black women. Model 3 is the uncontrolled model. For high

SES black women, all union statuses with the exception of cohabitation display lower odds of positive SRH than the married. In the case of cohabitation, cohabiting high SES black women did not have significantly different odds of positive SRH than high SES black married women. High SES black women who are widowed report 49% lower odds of reporting positive self-reported health than high SES black married women. High SES divorced or separated black women report 33% lower odds of reporting positive self- reported health. The smallest difference is between high SES married women and high

SES never married women who report 21% lower odds of reporting positive self-reported health.

After the inclusion of age in model 4, the association between high SES black widowed women and lower odds of reporting positive self-reported health is accounted for and no longer displays a significant difference from the married. After accounting for age in model 4, high SES black women who cohabit become significantly different from the married. High SES black cohabitating women report 37% lower odds of reporting positive self-reported health than high SES black married women. In fact, once age is accounted for, all the marital status groups display poorer health relative to the married, except the widowed. This is the same pattern of significance that was evident for high

SES black men. However, the magnitudes differ. The differences between the married and the never married and the divorced/separated are smaller for high SES men than high

SES women. This suggests that the association between positive self-reported health and marriage is somewhat stronger for black women than for black men, which is the opposite pattern established in the general marriage and health literature. One exception

85 is in the case of cohabitation. For both high SES black men and high SES black women, the lower odds of positive SRH among cohabitors versus the married are nearly identical

(0.64 for men and 0.63 for women).

Models 5 and 6 examine the association between marital status and odds of reporting positive self-reported health for low SES black men. Somewhat surprisingly, there are no significant differences in the odds of positive health by marital status. After the inclusion of age as a control in model 6, marital status remained an insignificant predictor of SRH. It is worth noting that the direction of the associations reflect what we would expect which is that all the union statuses display lower odds of positive SRH than the married after controlling for age, although none of the findings were significant.

Models 7 and 8 examine the association between marital status and odds of reporting positive self-reported health for low SES black women. The findings mirror those for low SES black men except for the case of low SES widowed black women.

Besides the widowed, there are no significant differences in SRH by marital status for low SES black women. The exception here is widowhood. In model 7, low SES widowed black women had 66% lower odds of reporting positive self-reported health than low SES married black women. After the inclusion of age in model 8, the association remained although somewhat diminished in magnitude. In model 8, low SES widowed black women had 54% lower odds of reporting positive self-reported health.

Overall, in the case of SHR, we find evidence in support of the contention that the marriage-health association among blacks is moderated by gender and socioeconomic status. The findings demonstrate that the benefit between marriage and positive self-

86 reported health exists only for high SES black men and women. High SES unmarried, divorced/separated and cohabiting black men and women had lower odds of reporting positive self-reported health than their married counterparts. The only exception to this pattern was for high SES black widowed men and women, who were not significantly different in their odds of reporting positive self-reported health than their married counterparts. Unlike high SES black married men and women, low SES black married men and women did not see the same association with health. For low SES unmarried men and women, marital status was not significantly associated with SRH, with the exception of low SES widowed black women. Overall, the gender differences in the association between SRH and marital status were virtually non-existent for black men and women. Instead, the SES distinctions were far more salient—with high SES blacks more likely to reap a marital health benefit than their low SES counterparts. Taken together, the associations presented here underscore the heterogeneity in the black experience, at least with respect to SES and the marriage-health association.

[Table 11 About Here]

Table 11 repeats the models presented above but this time switches the outcome variable from self-reported health to low allostatic load. Again, model 1 presents the results from the uncontrolled model that only includes the marital status variable. As in the general sample, the AL patterns differ from the SRH patterns such that the married do not display the most positive health. Focusing first on high SES black men, before controlling for age there are two union statuses that display better health than the married.

First, never married high SES black men have 2 times higher odds of having low AL

87 when compared to high SES married black men. Second, cohabitating high SES black men have 59% higher odds of having low AL compared to high SES married black men.

Divorced or separated and widowed high SES black men do not have statistically different odds of having low AL when compared to married high SES black men. After the inclusion of age, the difference marriage penalty is attenuated in the case of the never married-married contrast (OR=2.58 to OR=1.34) and is not longer statistically different in the case of the cohabiting-married contrast.

We see somewhat similar patterns across all the SES-gender grouping. In the case of allostatic load, there are virtually no instances across any of the groups in which a marital benefit is evident (the only exception is in the case of the widowed for low SES black women but that effect loses its significance once age is controlled (see Models 7 and 8)). For none of the sub-segments of the black population do the married enjoy a health benefit in the form of low AL. In fact, the only groups to demonstrate a positive health benefit are the never married and cohabitors. But while these associations are evident across gender and SES groupings, none of them remain statistically significant once age is controlled in the models. The exception is among never married black men who display significantly higher odds of low AL compared to the married, even after controlling for age.

Table 8:Description of Key Analytic Variables for Blacks

Table 8: Description of Key Analytic Variables

Variable Name Variable Description Sample sizes, Percent, Means

Black Men Black Women High SES Black High SES Black Low SES Black Low SES Black Men Women Men Women Married 1,583 (40.73%) 1,294 (30.18%)*** 849 (47.35%) 772 (33.03%)*** 733 (35.04%) 521 (26.77%)***

Never Married 1,229 (31.62%) 1,519 (35.43%)*** 525 (29.28%) 804 (34.40%)*** 704 (33.65%) 712 (36.59%)*** Divorced 376 (9.67%) 622 (14.51%)*** 169 (9.43%) 364 (15.58%)*** 207 (9.89%) 258 (13.26%)***

Separated 175 (4.50%) 275 (6.41%)*** 60 (3.35%) 139 (5.95%)*** 115 (5.50%) 136 (6.99%)***

Widowed 78 (2.01%) 233 (5.44%)*** 29 (1.62%) 85 (3.64%)*** 48 (2.29%) 148 (7.61%)***

Cohabitating 446 (11.47%) 344 (8.02%)*** 161 (8.98%) 173 (7.40%)*** 285 (13.62%) 171 (8.79%)***

Age Range = 20 - 65 42.82 (13.69) 42.19 (13.42) 42.08 (13.43) 41.20 (12.93)*** 43.43 (13.88) 43.39 (13.89)***

Gender 1 = Male 1 = 3,937 (47.62%) 1 = 1,813 (43.52%) 1 = 2,121 (51.81%) 2 = Female 2 = 4,330 (52.38%) 2 = 2,353 (56.48%) 2 = 1,973 (48.19%)

2 = % of column; *p<0.05; **p<0.01; ***p<0.001

Table 9: Description of Key Analytic Variables for blacks

Table 9: Description of Key Analytic Variables

Variable Name Variable Description Mean (Standard Deviation) or Percent Black Men Black Women High SES Black High SES Black Low SES Black Low SES Black Men Women Men Women Self-Reported 1 = Poor 3.26 (0.99) 3.05 (0.95)*** 3.39 (0.93) 3.20 (0.92)*** 3.16 (1.03) 2.86 (0.97)*** Health 2 = Fair 3 = Good 4 = Very Good 5 = Excellent Self-Reported 0 = Poor, Fair, Good 0.39 (0.49) 0.30 (0.46)*** 0.43 (0.50) 0.36 (0.48)*** 0.34 (0.47) 0.22 (0.41)*** Health 1 = Very Good, Excellent Allostatic Load Range 0 - 6 3.22 (1.32) 3.11 (1.36)** 3.18 (1.32) 3.06 (1.38)** 3.24 (1.31) 3.17 (1.33)

Allostatic Load Low AL = 0, 1, 2 0.68 (0.47) 0.62 (0.48)*** 0.67 (0.47) 0.61 (0.49)*** 0.68 (0.47) 0.65 (0.48) High AL = 3, 4, 5, 6 Systolic Blood 0 = Less than 130 0.39 (0.49) 0.31 (0.46)*** 0.36 (0.48) 0.28 (0.45)*** 0.41 (0.49) 0.35 (0.48)*** Pressure mmHg 1 = 130+ mmHg Diastolic Blood 0 = Less than 78 0.40 (0.49) 0.32 (0.47)*** 0.39 (0.49) 0.32 (0.47)*** 0.40 (0.49) 0.32 (0.47)*** Pressure mmHg 1 = 78+ mmHg Body Mass 0 = Less than 32.45 0.24 (0.43) 0.41 (0.49)*** 0.26 (0.44) 0.42 (0.49)*** 0.23 (0.42) 0.41 (0.49)*** Index 1 = 32.45+ Creatinine 0 = Less than 66 0.92 (0.27) 0.85 (0.35)*** 0.92 (0.27) 0.86 (0.35)*** 0.92 (0.27) 0.85 (0.36)*** mg/dL 1 = 66+ Albumin 0 = Less than 4.0 0.85 (0.36) 0.84 (0.36) 0.84 (0.37) 0.83 (0.37) 0.86 (0.35) 0.86 (0.35) ug/ml 1 = 4.0+ Glycated 0 = Less than 5.7% 0.42 (0.49) 0.38 (0.48)*** 0.41 (0.49) 0.35 (0.48)*** 0.43 (0.50) 0.40 (0.49) Hemoglobin 1 = 5.7% or more

2 = % of column; *p<0.05; **p<0.01; ***p<0.001 90

Table 10: Odds Ratios of Positive Self-Reported Health

Table 10: Odds Ratios of Positive Self-Reported Health High SES Black Men High SES Black Women Low SES Black Men Low SES Black Women Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Never Married 1.16 0.73* 0.79* 0.62*** 1.23 0.78 1.34 0.95 Divorced/Separated 0.56** 0.57** 0.67** 0.73* 0.75 0.82 0.70 0.78 Widowed 0.54 0.71 0.51* 0.68 0.28 0.42 0.34** 0.46* Cohabitating 0.82 0.64* 0.78 0.63* 1.09 0.74 1.24 0.90 Age 0.97*** 0.98*** 0.97*** 0.97*** *p<0.05; **p<0.01; ***p<0.001 HSBM = 1,477 HSBW = 1,874 LSBM = 1,681 LSBW = 1,472

Table 11: Odds Ratios of Low Allostatic Load

Table 11: Odds Ratios of Low Allostatic Load High SES Black Men High SES Black Women Low SES Black Men Low SES Black Women Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Never Married 2.58*** 1.34t 1.83*** 1.07 2.14*** 1.09 1.71*** 0.95 Divorced/Separated 1.19 1.24 0.77 0.94 0.94 1.09 0.73 0.89 Widowed 0.51 0.81 0.69 1.30 0.68 1.32 0.51* 0.99 Cohabitating 1.59* 1.13 1.36 0.84 1.86*** 1.05 1.78** 1.05 Age 0.96*** 0.95*** 0.95*** 0.95*** t= p<0.1;*p<0.05; **p<0.01; ***p<0.001; HSBM = 1,418 HSBW = 1,850 LSBM = 1,588 LSBW = 1,462

Chapter 6. Conclusion

The association between marriage and health generally works in the direction of married individuals having better health. This is the average association. In this dissertation, I argued that racial differences are likely to be present in the way that marriage is associated with health—via differences in the accumulation of resources, spousal support, and differences in spousal influence on health behaviors between blacks and whites. On average, married individuals—and to a lesser extent cohabitating partners—are thought to improve health by sharing resources (Hill et al. 2013). When two individuals marry—or cohabit—it is assumed they share resources such as money, wealth, access to healthcare, and provide knowledge about health (Phelan et al. 2004;

Umberson and Montez 2010). Married individuals—and to a lesser extent cohabitating partners—are also thought to bolster/enhance health outcomes by increasing an individual’s sense of social support (M. Seeman et al. 2014; Sheffler and Sachs-Ericsson

2015). Perceived social support diminishes stress-induced psychological responses

(House 2002; M. Seeman et al. 2014; Sheffler and Sachs-Ericsson 2015; Thoits 2011;

Umberson and Montez 2010). It is also argued that married individuals—and to a lesser extent cohabitating partners—influence each other’s health habits. Marriage and cohabitation could instill a sense of responsibility and concern for others that then lead

92 individuals to engage in behaviors that protect health (Ali and Ajilore 2011; Kiecolt-

Glaser et al. 2010; LaVeist et al. 2010; Umberson and Montez 2010).

The aim of this dissertation was to explore the role of race and its impact on the average association between marriage and better health. In this dissertation, I have argued that the extent that this this association exists, it likely differs by race. Specifically, I suggested that barriers to marriage (e.g. education, employment, racial discrimination) and stressors unique to blacks shape the experience and benefit of marriage for blacks.

The use of data from 9 rounds of the NHANES survey in the present study represents an important improvement over past research and provides the most thorough examination to date of the association between race, marital status, allostatic load, and self-reported health. Furthermore, in response to the increased prevalence of cohabitation, my analysis considers the role of cohabitation as unique from marriage. My results lead to a number of conclusions that I detail as follows.

First, in chapter 4, my results indicate that in the case of self-reported health, there is an average health benefit for the married. All other status groupings demonstrated significantly lower odds of positive SRH relative to the married. However, I also found that there were some differences once we disaggregated the sample by race. First, in the black sample, the associations were more confounded by age and gender than was true for whites. Specifically, in the models that did not include demographic controls, there were not significant differences for blacks between the married and either the never married or cohabiting individuals. The same was not true for whites, who displayed significant differences in the odds of SRH across all marital status groupings. However,

93 once the demographic controls were included, the magnitude and direction of the associations were similar across racial groups. The marital benefit was evident for both blacks and whites in the fully controlled models and the relative strength of the marital benefit was remarkably similar. There was one remaining difference, however, in the magnitude of the associations between blacks and whites. The gap between blacks who were married and blacks who were divorced or separated was smaller compared to the gap between whites who were married vs. divorced or separated. Additionally, in the fully controlled models, blacks and whites differed in terms of which marital status groups had the lowest odds of reporting positive self-reported health vis a vis the married.

For blacks, widowed individuals reported the lowest odds of having positive self-reported health (46% lower odds compared to married blacks) whereas whites who were divorced or separated reported the lowest odds of having positive self-reported health (47% lower odds compared to married whites). It is worth repeating that divorced or separated blacks did not report similarly low odds of reporting positive self-reported health as divorced or separated whites. While they still displayed a health penalty relative to the married, it was not as large for blacks as for whites.

Overall, the results from the analysis of self rated health suggest that the association between marriage and self-reported health is somewhat stronger for whites than for blacks and less confounded by age and gender. This aligns with other research that has suggested that the association between marriage and health is stronger for whites than for blacks (Ali and Ajilore 2011; Bennett 1992; Su et al. 2015; Williams, Takeuchi, and Adair 1992).

Second, my results indicate that the marriage-health association also differs between blacks and whites in the case of allostatic load, but in different ways than for

SRH. In the fully controlled models, for whites, we observed a marital benefit but only when the married were compared to the never married or the divorced/separated—both groups were significantly less likely to report low AL than the married. This pattern is similar to what has been found in previous research on AL and marriage. For instance,

Rote (2016) also found that divorced or separated individuals had worse (i.e. higher) allostatic load levels than the married. For blacks, however, none of the marital status groups were significantly less likely to have low allostatic load, suggesting that blacks who are married have odds of reporting low allostatic load that are no different from blacks in other marital status categories. For blacks, marital status does not significantly impact AL, once age and education are accounted for. This is a new finding since Rote

(2016), who also examined marriage and AL, did not parse the models by race. While

Rote’s causal analysis was strengthened by a longitudinal analysis of marital status and health, a shortcoming was its focus on average effects that did not consider whether the marriage and health association might differ by race. I have argued as well as others

(Koball et al. 2010b), that this failure to consider the possibility of heterogeneous associations in the relationship between marriage and health prevents us from knowing whether there is a fundamental difference in the association between blacks and whites.

My results suggest that there is a clear need to revisit the theoretical and empirical frameworks linking marriage and health. As I have previously argued, the barriers to marriage (education, employment, racial discrimination) and stressors unique to the black

95 experience likely shape the experience and benefit of marriage for blacks. For instance, stress levels are higher among those who are from low income backgrounds and with low educational attainments (Lantz PM et al. 2005). Furthermore, racism is also a unique stressor and have been associated with poor health. According to Williams and

Mohammed (2009), perceived racial or ethnic discrimination is receiving increasing attention in empirical studies as a class of stressors that could have consequences for health. Overall, these models suggest a continuous need to examine the association between race, marriage and health separately.

One of the contributions of my dissertation is the examination of within racial differences along socioeconomic status lines. Few prior studies have included SES differences among blacks in their analysis of race, marriage and health (e.g. Roxburgh

2014). In chapter 5, we observed significant differences by SES in how marital status is associated with health for black Americans. In the case of self-rated health, we observed a marital benefit, but only for high SES black men and women. For low SES black

Americans, there was no significant association between health and marital status. This suggests that the marriage-health association among black men and black women is moderated by socioeconomic status. One exception to this pattern was the case of low

SES widowed black women, who did report significantly worse SRH than their married low SES counterparts. For low SES widowed black women, the chronic strain model could be a relevant tool for understanding their compromised health relative to the married (Rote 2016; Williams 2003; Williams and Umberson 2004). The chronic strain models suggests that marital disruptions prompt decrements in financial and social

96 resources and set off more chronic and enduring strains (Rote 2016; Williams and

Umberson 2004). This may be particularly relevant for low SES black Americans who have lost a spouse such that the loss translates into poorer health in a way that is not the case for their low SES counterparts in other union status arrangements.

Taken together, in the case of SRH, the associations presented in chapter 5 suggest significant heterogeneity in the black population that translates into differential associations between marriage and health, particularly by SES. I find that the health benefit associated with marriage is not conferred on low SES married blacks. Since low

SES blacks are frequently the focus of health policies that include marriage promotion, my findings suggest that this emphasis is likely misplaced and the potential health benefits of marriage promotion are likely overstated in the case of low SES blacks.

Unlike the case of SRH, for allostatic load, I did not find noteworthy differences in the association between marriage and health by SES for blacks. Instead, the general pattern was one in which marital status was not significantly associated with AL for any of the SES/gender groupings. Overall, marriage was not a significant predictor of differences in AL for any of the black sub-groups. This is in spite of significant differences in AL between blacks and whites, generally, and significant differences in AL by marital status for whites generally, but not for blacks. This suggests that in the case of blacks, other factors besides union status are having a significant impact on their elevated levels of AL. Again, I suggest that other barriers (e.g. in the realm of racial discrimination and stressors unique to blacks generally) shape their risk of elevated AL.

This dissertation underscores the need to disaggregate the association between marriage and health separately by race. Significant differences were found between blacks and whites, illuminating how the marriage-health association differs for blacks and whites. Additionally, I documented differences within the black population by gender and

SES, particularly in the case of SRH, which underscores the heterogeneity that exists in the association between marriage and health by additional factors within the black population.

Although this dissertation has contributed to the overall body of literature on marriage and health, there are several limitations that deserve mention. Because this dissertation used multiple cross-sectional data sets, casual inferences cannot be made.

This issue is relevant to a recent study by Tumin and Zheng (2018) that used data from the National Longitudinal Survey of Youth 1979 (NLSY) to show that the health benefits of marriage were 1) fairly modest and 2) not significantly different across groups defined by their likelihood of marriage. That is, any observed marital health benefit, to the extent that it existed, did not depend significantly on the likelihood of marriage in the first place.

Given what we know about significant differences across racial groups in the likelihood of marriage (as summarized in Chapter 2), the ways in which differential selection into marriage plays, or does not play, a role in the differential health benefits of marriage by race needs to be a continued focus of future research.

Additionally, the lack of marital history limits what can be said about whether marital status groupings or marital status transitions are driving the results. It is also unclear if those who were divorced were recently divorced or divorced a long time ago,

98 which could impact stress and allostatic load. Another example of this limitation is if those who are cohabitating are divorced individuals in new relationships or if those who are married have been remarried.

Another limitation was my inability to assess the role of income in the marriage- health relationship. Because income is predictive of marriage and also impacted by it, cross-sectional data would not allow me to evaluate its role. There is also a significant limitation in my ability to assess mechanisms. I have argued that identifying the broad population-level patterns with respect to racial differences in the marriage-health benefit is a key contribution of my dissertation. The NHANES data was uniquely suited for this task. But it is limited in its ability to allow for tests of the mechanisms driving the broader associations. For instance, I do have not measures of marital quality, which could impact the marital health benefit. Given that blacks have high divorce rates, poor marital quality could also be a key factor in impacting the likelihood of a marital benefit and racial differences therein. Another possibility, summarized by Tumin and Zheng (2018) is that normative differences in the meaning of marriage may play a role. They argue, “the advantages of marriage [may] erode as singlehood becomes more normative, even the groups tending to benefit the most from marriage might see these benefits decline” [12].

Although they were writing in reference to cohort differences, their suggestion remains relevant for the racial differences described in this dissertation

There are many different directions that subsequent research on racial differences in the marriage-health relationship can go. Importantly, future research should investigate what exactly SRH and AL are measuring. For example, my results suggest that some men

99 think they are healthier (i.e. according to their SRH) but actually are not as indicated by their allostatic load measure (AL). Leveraging differences between self reports of health and biological measurements of health is an exciting avenue for future work.

Additionally, I have not focused on the role of religiosity. Religiosity could imitate the health benefits of marriage. This may be particularly salient for black Americans, who routinely display higher levels of religiosity than whites. Another potential source of any observed racial differences in the marriage-health relationship concerns the issue of mass incarceration in the contemporary era, another phenomena that disproportionately impacts black Americans, and deserves further attention.

This dissertation highlighted the importance of moving beyond measures of SRH in capturing health differences. In particular, it illustrated the need to use more biomarkers to examine the marriage and health association. I found very different patterns, depending on if the outcome assessed was self-reported or was based on biomarkers. For whites, marriage was associated with allostatic load but not for blacks, although both married blacks and whites reported positive self reported health relative to the other marital statuses. These patterns suggest that, in some cases, there is a disconnect between self-reported health outcomes and biological deterioration. Future analysis should continue to utilize other biomarkers to assess allostatic load and the relationship between allostatic load and marital status. Other avenues for future research include the need to examine allostatic load differences between white men and white women, and to use marital history and marital quality to further explore the black marriage-health association. What this dissertation has made clear is that there is considerably more

100 unpacking to do if we are to more fully understand the relationship between marriage and health and how it differs across different population sub-groups.

101

Bibliography

Ali, Mir M. and Olugbenga Ajilore. 2011. “Can Marriage Reduce Risky Health Behavior for African-Americans?” Journal of Family and Economic Issues 32(2):191–203.

Amato, Paul R. 2010. “Research on Divorce: Continuing Trends and New Developments.” Journal of Marriage and Family 72(3):650–66.

Anderson, Kristin L. 2010. “Conflict, Power, and Violence in Families.” Journal of Marriage and Family 72(3):726–42.

Barrington, Debbie S. 2010. “The Increasing Protection of Marriage on Infant Low Birth Weight Across Two Generations of African American Women.” Journal of Family Issues 31(8):1041–64.

Beckie, Theresa M. 2012. "A Systematic Review of Allostatic Load, Health, and Health Disparities." Biological Research For Nursing 14(4): 311-346.

Bennett, Trude. 1992. “Marital Status and Infant Health Outcomes.” SSM Social Science & Medicine 35(9):1179–87.

Bennett, Trude, Paula Braveman, Susan Egerter, and John L. Kiely. 1994. “Maternal Marital Status as a Risk Factor for Infant Mortality.” Family Planning Perspectives 26(6):252–71.

Bierman, Alex, Elena M. Fazio, and Melissa A. Milkie. 2006. “A Multifaceted Approach to the Mental Health Advantage of the Married: Assessing How Explanations Vary by Outcome Measure and Unmarried Group.” Journal of Family Issues 27(4):554–82.

Brooks, Kathryn P. et al. 2014. “Social Relationships and Allostatic Load in the MIDUS Study.” Health Psychology : Official Journal of the Division of Health Psychology, American Psychological Association 33(11):1373–81.

Brown, Susan L. 2000. “The Effect of Union Type on Psychological Well-Being: Depression among Cohabitors versus Marrieds.” Journal of Health and Social Behavior 41(3):241–55.

102

Bryant, Chalandra M. et al. 2010. “Race Matters, Even in Marriage: Identifying Factors Linked to Marital Outcomes for African Americans.” Journal of Family Theory & Review 2(3):157–74. Burton, Linda M. and M. Belinda Tucker. 2009. “Romantic Unions in an Era of Uncertainty: A Post-Moynihan Perspective on African American Women and Marriage.” Annaameracadpoli Annals of the American Academy of Political and Social Science 621:132–48.

Carr, Deborah and Kristen W. Springer. 2010. “Advances in Families and Health Research in the 21st Century.” Journal of Marriage and Family 72(3):743–61.

Carr, Deborah, Kristen W. Springer, and Kristi Williams. 2014. “Health and Families.” Pp. 255–76 in The Wiley Blackwell Companion to the Sociology of Families, edited by J. Treas, J. Scott, and rtin Richards. John Wiley & Sons, Ltd. Retrieved February 11, 2016 (http://onlinelibrary.wiley.com/doi/10.1002/9781118374085.ch13/summary).

Chatters LM. 1988. “Subjective Well-Being Evaluations among Older Black Americans.” Psychology and Aging 3(2):184–90.

Cherlin, Andrew. 2010. “Demographic Trends in the United States: A Review of Research in the 2000s.” Journal of Marriage and the Family 72(3):403–19.

Cherlin, Andrew J. 2014. Labor’s Love Lost: The Rise and Fall of the Working-Class Family in America. Russell Sage Foundation. Retrieved April 24, 2016 (http://www.jstor.org/stable/10.7758/9781610448444).

Cherlin, Andrew J. 2010. The Marriage-Go-Round: The State of Marriage and the Family in America Today. Knopf Doubleday Publishing Group.

DeKlyen, Michelle, Jeanne Brooks-Gunn, Sara McLanahan, and Jean Knab. 2006. “The Mental Health of Married, Cohabiting, and Non–Coresident Parents With Infants.” American Journal of Public Health 96(10):1836–41.

Dixon, Patricia. 2008. “Marriage Among African Americans: What Does the Research Reveal?” Journal of African American Studies 13(1):29–46.

Drefahl, Sven. 2012. “Do the Married Really Live Longer? The Role of Cohabitation and Socioeconomic Status.” Journal of Marriage and Family 74(3):462–75.

Dupre, Matthew E. 2016. “Race, Marital History, and Risks for Stroke in US Older Adults.” Social Forces 95(1):439–68.

103

Durkheim, Émile. 1897. Suicide: a study in sociology. Place of publication not identified: Snowball Press.

Edin, Kathryn and Maria Kefalas. 2005. Promises I Can Keep: Why Poor Women Put Motherhood before Marriage. Berkeley: University of California Press.

Edin, Kathryn and Laura Lein. 1997. Making Ends Meet: How Single Mothers Survive Welfare and Low-Wage Work. New York: Russell Sage Foundation.

Furstenberg, Frank F. 2007. “The Making of the Black Family: Race and Class in Qualitative Studies in the Twentieth Century.” Annual Review of Sociology 33(1):429–48.

Gardner, Jonathan and Andrew Oswald. 2004. “How Is Mortality Affected by Money, Marriage, and Stress?” Journal of Health Economics 23(6):1181–1207.

Geronimus, Arline T., Margaret Hicken, Danya Keene, and John Bound. 2006. “‘Weathering’ and Age Patterns of Allostatic Load Scores Among Blacks and Whites in the United States.” American Journal of Public Health 96(5):826–33.

Gerson, Kathleen. 2011. The Unfinished Revolution: Coming of Age in a New Era of Gender, Work, and Family.

Green, Kerry M., Elaine E. Doherty, Kate E. Fothergill, and Margaret E. Ensminger. 2012. “Marriage Trajectories and Health Risk Behaviors Throughout Adulthood Among Urban African Americans.” Journal of Family Issues 33(12):1595–1618.

Hare, Nathan and Julia Hare. 1989. Crisis in Black Sexual Politics. San Francisco, Calif.: Black Think Tank.

Harris, Kathleen Mullan, Hedwig Lee, and Felicia Yang DeLeone. 2010. “Marriage and Health in the Transition to Adulthood: Evidence for African Americans in Add Health.” Journal of Family Issues 31(8):1106–43.

Hicken, Margaret T., Hedwig Lee, Jennifer Ailshire, Sarah A. Burgard, and David R. Williams. 2013. ““Every Shut Eye, Ain’t Sleep”: The Role of Racism-Related Vigilance in Racial/Ethnic Disparities in Sleep Difficulty.” Race and Social Problems 5(2):100–112.

Hill, Terrence D., Megan Reid, and Corinne Reczek. 2013. “Marriage and the Mental Health of Low-Income Urban Women With Children.” Journal of Family Issues 34(9):1238–61.

104

House, James S. 2002. “Understanding Social Factors and Inequalities in Health: 20th Century Progress and 21st Century Prospects.” Journal of Health and Social Behavior 43(2):125–42. House, James S., Karl R. Landis, Debra Umberson, and others. 1988. “Social Relationships and Health.” Science 241(4865):540–545.

Hudson, Darrell L. et al. 2012. “Are Benefits Conferred with Greater Socioeconomic Position Undermined by Racial Discrimination among African American Men?” Journal of Men’s Health 9(2):127–36.

Hudson, Darrell L, H. W. Neighbors, A. T. Geronimus, and J. S. Jackson. 2012. “The Relationship between Socioeconomic Position and Depression among a US Nationally Representative Sample of African Americans.” Social Psychiatry And Psychiatric Epidemiology 47(3):373–81.

Hudson, Darrell L., Eli Puterman, Kirsten Bibbins-Domingo, Karen A. Matthews, and Nancy E. Adler. 2013. “Race, Life Course Socioeconomic Position, Racial Discrimination, Depressive Symptoms and Self-Rated Health.” Social Science & Medicine Social Science & Medicine 97(1):7–14.

Hunter, Marcus Anthony and Zandria F. Robinson. 2016. “The Sociology of Urban Black America.” Annual Review of Sociology 42(1):385–405.

Idler, Ellen L. 2003. “Discussion: Gender Differences in Self-Rated Health, in Mortality, and in the Relationship Between the Two.” The Gerontologist 43(3):372–75.

Jackson, James S., Katherine M. Knight, and Jane A. Rafferty. 2010. “Race and Unhealthy Behaviors: Chronic Stress, the HPA Axis, and Physical and Mental Health Disparities Over the Life Course.” American Journal of Public Health 100(5):933–39.

Johnson, Leanor Boulin, Robert Staples, and Robert Staples. 2005. Black Families at the Crossroads: Challenges and Prospects. Rev. ed., 2nd ed. San Francisco, CA: Jossey-Bass.

Johnson, Norman J., Eric Backlund, Paul D. Sorlie, and Catherine A. Loveless. 2000. “Marital Status and Mortality.” Annals of Epidemiology 10(4):224–38.

Kiecolt-Glaser, Janice K., Jean-Philippe Gouin, and Liisa Hantsoo. 2010. “Close Relationships, Inflammation, and Health.” Neuroscience and Biobehavioral Reviews 35(1):33–38.

105

Koball, Heather L., Emily Moiduddin, Jamila Henderson, Brian Goesling, and Melanie Besculides. 2010a. “What Do We Know About the Link Between Marriage and Health?” Journal of Family Issues 31(8):1019–40.

Kroeger-D’Souza, Rhiannon Alexis. 2012. “UNION FORMATION, PARENTHOOD, AND HEALTH RISK INDICATORS AMONG THE SOCIALLY DISADVANTAGED.” The Ohio State University. Retrieved February 11, 2016 (https://etd.ohiolink.edu/ap/10?0::NO:10:P10_ACCESSION_NUM:osu13430720 19).

Kuo, Janet Chen-lan and R. Kelly Raley. 2016. “Diverging Patterns of Union Transition Among Cohabitors by Race/Ethnicity and Education: Trends and Marital Intentions in the United States.” Demography; Silver Spring 53(4):921–35.

Lantz PM, House JS, Mero RP, and Williams DR. 2005. “Stress, Life Events, and Socioeconomic Disparities in Health: Results from the Americans’ Changing Lives Study.” Journal of Health and Social Behavior 46(3):274–88.

LaVeist, Thomas A. 2005. “Disentangling Race and Socioeconomic Status: A Key to Understanding Health Inequalities.” Journal of Urban Health: Bulletin of the New York Academy of Medicine 82(S03):iii26-iii34.

LaVeist, Thomas A., Tia L. Zeno, and Ruth G. Fesahazion. 2010. “The Effects of Mother’s Marital Status on Adolescent and Young Adult Health and Economic Well-Being Among African Americans.” Journal of Family Issues 31(8):1065– 80.

Lorenz, Frederick O., K. a. S. Wickrama, Rand D. Conger, and Glen H. Elder. 2006. “The Short-Term and Decade-Long Effects of Divorce on Women’s Midlife Health.” Journal of Health and Social Behavior 47(2):111–25.

Mamun, Arif. 2011. “Cohabitation Premium in Men’s Earnings: Testing the Joint Human Capital Hypothesis.” Journal of Family and Economic Issues 33(1):53–68.

Manning, Wendy D. and Pamela J. Smock. 2002. “First Comes Cohabitation and Then Comes Marriage? A Research Note.” Journal of Family Issues 23(8):1065–87.

Manzoli, Lamberto, Paolo Villari, Giovanni M Pirone, and Antonio Boccia. 2007. “Marital Status and Mortality in the Elderly: A Systematic Review and Meta- Analysis.” Social Science & Medicine 64(1):77–94.

Massey, Douglas S. 2004. “SEGREGATION AND STRATIFICATION: A Biosocial Perspective.” Du Bois Review: Social Science Research on Race 1(01):7–25.

106

McEwen, Bruce S. 1998. “Protective and Damaging Effects of Stress Mediators.” New England Journal of Medicine 338(3):171–79.

Mirowsky, John and Catherine E. Ross. 2003. Social Causes of Psychological Distress. New York: Aldine de Gruyter.

NHANES. 2016. “NHANES 2015-2016: Glycohemoglobin Data Documentation, Codebook, and Frequencies.” Retrieved February 12, 2018 (https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/GHB_I.htm).

Palloni, Alberto and Elizabeth Arias. 2004. “Paradox Lost: Explaining the Hispanic Adult Mortality Advantage.” Demography 41(3):385–415.

Phelan, Jo C., Bruce G. Link, Ana Diez-Roux, Ichiro Kawachi, and Bruce Levin. 2004. “‘Fundamental Causes’ of Social Inequalities in Mortality: A Test of the Theory.” Journal of Health and Social Behavior 45(3):265–85.

Pudrovska, Tetyana, Scott Schieman, and Deborah Carr. 2006. “Strains of Singlehood in Later Life: Do Race and Gender Matter?” The Journals of Gerontology Series B: Psychological Sciences and Social Sciences 61(6):S315–22.

Raley, R. Kelly, Megan M. Sweeney, and Danielle Wondra. 2015. “The Growing Racial and Ethnic Divide in U.S. Marriage Patterns.” The Future of Children / Center for the Future of Children, the David and Lucile Packard Foundation 25(2):89–109.

Riosmena, Fernando, Rebeca Wong, and Alberto Palloni. 2013. “Migration Selection, Protection, and Acculturation in Health: A Binational Perspective on Older Adults.” Demography Demography 50(3):1039–64.

Ross, Catherine E. and John Mirowsky. 2011. “The Interaction of Personal and Parental Education on Health.” Social Science & Medicine 72(4):591–99.

Rote, Sunshine. 2016. “Marital Disruption and Allostatic Load in Late Life.” Journal of Aging and Health 0898264316641084.

Roxburgh, Susan. 2014. “Race, Class, and Gender Differences in the Marriage-Health Relationship.” Race, Gender & Class 21(3/4):7.

Schoenborn, Charlotte A. 2004. Marital Status and Health, United States 1999-2002. Citeseer. Retrieved February 11, 2016 (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.137.3613&rep=rep1&t ype=pdf).

107

Schwandt, Hilary M., Josef Coresh, and Michelle J. Hindin. 2010. “Marital Status, Hypertension, Coronary Heart Disease, Diabetes, and Death Among African American Women and Men: Incidence and Prevalence in the Atherosclerosis Risk in Communities (ARIC) Study Participants.” Journal of Family Issues 31(9):1211–29.

Seeman, Melvin, Sharon Stein Merkin, Arun Karlamangla, Brandon Koretz, and Teresa Seeman. 2014. “Social Status and Biological Dysregulation: The ‘Status Syndrome’ and Allostatic Load.” Social Science & Medicine 118:143–51.

Seeman, Teresa E., Tara L. Gruenewald, Sheldon Cohen, David R. Williams, and Karen A. Matthews. 2014. “Social Relationships and Their Biological Correlates: Coronary Artery Risk Development in Young Adults (CARDIA) Study.” Psychoneuroendocrinology 43:126–38.

Seeman, Teresa E., Burton H. Singer, Carol D. Ryff, Gayle Dienberg Love, and Lené Levy-Storms. 2002. “Social Relationships, Gender, and Allostatic Load across Two Age Cohorts.” Psychosomatic Medicine 64(3):395–406.

Shafer, Emily Fitzgibbons. 2010. “The Effect of Marriage on Weight Gain and Propensity to Become Obese in the African American Community.” Journal of Family Issues 31(9):1166–82.

Sheffler, Julia and Natalie Sachs-Ericsson. 2015. “Racial Differences in the Effect of Stress on Health and the Moderating Role of Perceived Social Support.” Journal of Aging and Health 0898264315618923.

Shortridge, Emily F. and Cara V. James. 2010. “Marriage and End-Stage Renal Disease: Implications for African Americans.” Journal of Family Issues 31(9):1230–49.

Smock, Pamela J. 2000. “Cohabitation in the United States: An Appraisal of Research Themes, Findings, and Implications.” Annual Review of Sociology 26(1):1–20.

Smock, Pamela J. and Wendy D. Manning. 1997. “Cohabiting Partners’ Economic Circumstances and Marriage.” Demography 34(3):331–41.

Stepler, Renee. 2017. “Number of U.S. Adults Cohabiting with a Partner Continues to Rise, Especially among Those 50 and Older.” Pew Research Center. Retrieved March 20, 2018 (http://www.pewresearch.org/fact-tank/2017/04/06/number-of-u- s-adults-cohabiting-with-a-partner-continues-to-rise-especially-among-those-50- and-older/).

108

Strohschein, Lisa, Peggy McDonough, Georges Monette, and Qing Shao. 2005. “Marital Transitions and Mental Health: Are There Gender Differences in the Short-Term Effects of Marital Status Change?” Social Science & Medicine 61(11):2293–2303. Su, Dejun, Jim P. Stimpson, and Fernando A. Wilson. 2015. “Racial Disparities in Mortality Among Middle-Aged and Older Men Does Marriage Matter?” American Journal of Men’s Health 9(4):289–300.

Thoits, Peggy A. 2011. “Mechanisms Linking Social Ties and Support to Physical and Mental Health.” Journal of Health and Social Behavior 52(2):145–61.

Umberson, Debra. 1987. “Family Status and Health Behaviors: Social Control as a Dimension of Social Integration.” Journal of Health and Social Behavior 28(3):306–19.

Umberson, Debra, Robert Crosnoe, and Corinne Reczek. 2010. “Social Relationships and Health Behavior Across the Life Course.” Annual Review of Sociology 36(1):139– 57.

Umberson, Debra and Jennifer Karas Montez. 2010. “Social Relationships and Health: A Flashpoint for Health Policy.” Journal of Health and Social Behavior 51(Suppl):S54–66.

Umberson, Debra, Mieke Beth Thomeer, and Kristi Williams. 2013. “Family Status and Mental Health: Recent Advances and Future Directions.” Pp. 405–31 in Handbook of the Sociology of Mental Health, Handbooks of Sociology and Social Research, edited by C. S. Aneshensel, J. C. Phelan, and A. Bierman. Springer Netherlands. Retrieved July 7, 2015 (http://link.springer.com/chapter/10.1007/978-94-007-4276-5_20).

U.S. Census Bureau. 2016. United States Census Bureau American Fact Finder. Retrieved (https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid =ACS_16_1YR_S1501&prodType=table).

Waite, Linda J. and Maggie Gallagher. 2000. The Case for Marriage: Why Married People Are Happier, Healthier, and Better off Financially. New York: Doubleday.

Walsemann, Katrina M., Gilbert C. Gee, and Annie Ro. 2013. “Educational Attainment in the Context of Social Inequality: New Directions for Research on Education and Health.” American Behavioral Scientist 57(8):1082–1104.

Williams, David R. 2012. “Miles to Go before We Sleep Racial Inequities in Health.” Journal of Health and Social Behavior 53(3):279–95. 109

Williams, David R. and Chiquita Collins. 2001. “Racial Residential Segregation: A Fundamental Cause of Racial Disparities in Health.” Public Health Reports (1974-) 116(5):404–16.

Williams, David R. and Selina A. Mohammed. 2013. “Racism and Health II A Needed Research Agenda for Effective Interventions.” American Behavioral Scientist 57(8):1200–1226.

Williams, David R., Harold W. Neighbors, and James S. Jackson. 2003. “Racial/Ethnic Discrimination and Health: Findings From Community Studies.” American Journal of Public Health 93(2):200–208.

Williams, David R., Yan Yu, James S. Jackson, and Norman B. Anderson. 1997. “Racial Differences in Physical and Mental Health: Socio-Economic Status, Stress and Discrimination.” Journal of Health Psychology 2(3):335–51.

Williams, Kristi. 2003. “Has the Future of Marriage Arrived? A Contemporary Examination of Gender, Marriage, and Psychological Well-Being.” Journal of Health and Social Behavior 44(4):470–87.

Williams, Kristi and Debra Umberson. 2004. “Marital Status, Marital Transitions, and Health: A Gendered Life Course Perspective.” Journal of Health and Social Behavior 45(1):81–98.

Williams and S. A. Mohammed. 2009. “Discrimination and Racial Disparities in Health: Evidence and Needed Research.” Journal of Behavioral Medicine 32:20–47.

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Appendix A: Missing Data Comparisons

Table 12: Description of Missing Data (Blacks and Whites)

Table 12: Description of Missing Data

Variable Name Variable Description Mean or Percent

Blacks and Whites included in analysis Blacks and Whites not included in analysis

Allostatic Load

Income 7.54 7.19 Education 3.64 3.55

Age 42.24 41.23 Self-reported Health

Income 7.54 7.33 Education 3.64 3.57

Age 42.24 40.97

Table 13: Description of Missing Data (Blacks Only)

Table 13: Description of Missing Data

Variable Name Variable Description Mean (Standard Deviation) or Percent Blacks included in analysis Blacks not included in analysis

Allostatic Load

Income 6.83 6.64 Education 3.41 3.31

Age 42.49 42.15 Self-reported Health

Income 6.83 6.57 Education 3.41 3.30

Age 42.49 40.99

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Table 14: Description of Missing Data (Marital Status)

Table 14: Description of Missing Data

Variable Name Variable Description Percent in each Marital Status Group (excluding refused, don’t know, or missing) Blacks included in analysis Blacks not included in analysis

Allostatic Load

Married 35.18% 34.99% Widowed 3.80% 4.31%

Divorced 12.20% 12.45% Separated 5.50% 6.04%

Never married 33.60% 33.42% Living with partner 9.66% 8.63%

Self-reported Health Married 35.18% 32.22%

Widowed 3.80% 4.13%

Divorced 12.20% 11.32% Separated 5.50% 7.01%

Never married 33.60% 35.33% Living with partner 9.66% 9.94%

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