Winners and losers in health insurance: access and type of coverage for women in same-sex and opposite-sex partnerships.

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Winners and Losers in Health Insurance: Access and Type of Coverage for Women in Same-Sex and Opposite-Sex Partnerships a

Heili Pals PhD & Warren Waren PhD

a

a

Sociology Department, Texas A&M University, College Station, Texas, USA Accepted author version posted online: 08 Jan 2014.Published online: 12 Mar 2014.

To cite this article: Heili Pals PhD & Warren Waren PhD (2014) Winners and Losers in Health Insurance: Access and Type of Coverage for Women in Same-Sex and Opposite-Sex Partnerships, Women & Health, 54:2, 94-114, DOI: 10.1080/03630242.2013.870633 To link to this article: http://dx.doi.org/10.1080/03630242.2013.870633

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Women & Health, 54:94–114, 2014 Copyright © Taylor & Francis Group, LLC ISSN: 0363-0242 print/1541-0331 online DOI: 10.1080/03630242.2013.870633

Winners and Losers in Health Insurance: Access and Type of Coverage for Women in Same-Sex and Opposite-Sex Partnerships HEILI PALS, PhD and WARREN WAREN, PhD Downloaded by [University of Connecticut] at 11:30 11 October 2014

Sociology Department, Texas A&M University, College Station, Texas, USA

Using data from the American Community Survey, 2009 (N = 580,754), we compared rates of health insurance coverage and types of coverage used between women in same-sex and opposite– sex partnerships. This large, national dataset also allowed us to investigate regional variation in insurance coverage for women in same-sex partnerships by comparing “gay-tolerant” states versus other states. Multivariate analyses revealed that women in same– sex partnerships consistently had lower rates of health insurance coverage than married women in opposite-sex partnerships, but always more than unmarried women in opposite-sex partnerships. We also found that state-level variation in gay tolerance did not contribute to the access or type of coverage used by women in same-sex partnerships. KEYWORDS lesbian, health insurance, same-sex partner, homosexual tolerance, unmarried partners

INTRODUCTION One of the main concerns facing same-sex partners in the United States is access to health insurance (Mayer et al., 2008; Buchmueller & Carpenter, 2010; Committee on Lesbian Gay Bisexual Transgender Health Issues and Research Gaps and Opportunities, 2011; Dean et al., 2000). Two important national studies, the National Health Interview Survey (1997–2003; Heck, Sell, & Gorin, 2006) and the Current Population Survey (CPS; 1996–2003; Ash Received March 5, 2013; revised November 6, 2013; accepted November 14, 2013 Address correspondence to Heili Pals, PhD, Sociology Department, Texas A&M University, 311 Academic Bldg, College Station, TX 77843. E-mail: [email protected] 94

Downloaded by [University of Connecticut] at 11:30 11 October 2014

Winners and Losers in Health Insurance

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& Badgett, 2006), revealed a substantial gap in access to health insurance coverage. Regional studies report inconsistencies in the disparity between lesbian and heterosexual women in terms of access to health insurance. For example, a California study found that partnered lesbian women are more than twice as likely to be uninsured as married heterosexual women and, thus, are more likely to have had difficulty obtaining needed medical care (Ponce et al., 2010). However, a large study in Massachusetts found no differences in insurance coverage between straight and lesbian women (Conron, Mimiaga, & Landers, 2010); health insurance is mandated by the state in Massachucetts, which should, in theory, lead to more equal distribution of health insurance in general. Such regional inconsistencies reported in health insurance coverage opens the door to new research regarding the role of state legislation in creating disparity in access to coverage and the types of insurance used by same-sex partners. For example, do state policies such as constitutional amendments banning same-sex marriage encourage, hamper, or have no effect on lesbian access to healthcare within that state? Do those policies have any effect on the type of coverage used by lesbians in the state? This article addresses a critical gap in the current literature on the health care coverage of women in same-sex partnerships, bringing the latest national data to bear on rates of coverage, state variations in coverage, and the types of coverage used or avoided by same-sex female partners. We compared the national rates of health insurance coverage among women in same- and opposite-sex partnerships to determine if women in same-sex partnerships had lower coverage in states that were not “gay tolerant” and to ascertain if some types of health insurance were harder to obtain for women in same-sex partnerships. Health care in the United States is unique among Western industrialized countries in that it is financed, primarily, by private, for-profit insurance companies. This situation developed in the 1950s as the interests of various stakeholders converged in an effort to keep universal health care from becoming the law of the land (Quadagno 2004; Thomasson 2002). That distant political battle led to the current dominance of employer-provided private health insurance, with the alternatives of public health insurance and individually owned private health insurance relegated to weak supporting roles. As of 2011, 55% of people in the United States had employer-provided health insurance, 32% had public health insurance, only 10% had individually owned insurance, and 15.7%—48.6 million people—were uninsured (DeNavas-Walt et al., 2012). These rates do not add up to 100% because only 72% of the people were insured by one type of insurance, while others are either uninsured or insured by multiple types of coverage. Perhaps as a holdover from the changes to the industry in the 1950s, marriage provides a substantial premium in terms of health insurance coverage. Unmarried women are among those least likely to have insurance coverage. Buchmueller and Carpenter (2010) found that although same-sex partners had lower rates of coverage than others, “men and women in


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same-sex relationships were actually more likely to be insured than were unmarried individuals in different-sex relationships” (p. 493). That finding echoed the results of an earlier, national sample using different data (Ash & Badgett, 2006). Previous research into the specific health issues facing sexual minorities has identified structural barriers to health care through the systematic denial of care for same-sex dependents and the threat of insurance loss if an employee is identified as a sexual minority and is fired (Badgett, 2006; Bradford, Ryan, & Rothblum, 1994; Committee on Lesbian Health Research Priorities, 1999; Mayer et al., 2008). Another important consideration identified for further investigation is “compensation discrimination,” which includes withholding noncash benefits, such as pensions and family leave, from those in same-sex relationships (Badgett 2006). Because the legal status of same-sex partnerships is still being debated in the United States, individuals in these partnerships are left without legal protection. Individual states have varied widely in their response to the claims of same-sex partners for civil rights or the benefits of legal marriage (Ponce et al., 2010). State variation in health insurance rates between lesbian and heterosexual women provides an indication as to whether and how much legislation regulating same-sex relationships affects the disparity. For example, the 2004 California Insurance Equality Act extended spousal dependent coverage to domestic partners (Gorn, 2010). We tested whether states that actively oppose civil rights for same-sex partners (i.e., “not gay tolerant”) had a larger gap in rates of insurance coverage and more variation in types of insurance used between women in same-sex and opposite-sex relationships. Employers in states with restrictive legislation might be less likely to offer benefits voluntarily to same-sex partners than employers in states without such laws. This means that if state legislation limits the civil rights for same-sex couples, employers have very little incentive to extend the partner coverage to same-sex couples or to any couples outside of traditional heterosexual married couple. In previous research, this type of variation between states has been shown to affect wages for women in same-sex relationships, but not earnings (Pals & Waren, 2011). Three large, national studies relying on pooled data over a number of years have supported the earlier calls for research and reveal a substantial gap in access to health insurance coverage between straight and lesbian women. Heck et al. (2006) used the National Health Interview Survey (1997–2003) to confirm that women in same-sex relationships were less likely to have health insurance. Ash and Badgett (2006) used the 1996–2003 waves from a supplement of the CPS to find that nearly twice as many same-sex partnered households as married households had no insurance coverage—and that women in unmarried opposite-sex partnerships were almost three times as likely to have no insurance coverage. A more recent national study based on state-level data from the Centers for Disease Control and Prevention (CDC) for 2000–2007 showed that the gap



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in insurance coverage did not fully explain the lack of use of health services by lesbian women (Buchmueller & Carpenter, 2010). Although these surveys were relatively large in size and national in scope, they had relatively small samples of lesbian women. It was necessary in each of these surveys to pool data over several years to build up large enough samples of same-sex partners for meaningful statistical comparison— a frequent problem facing research on sexual minorities. For example, the CPS study had a sample size of just 478 women in same-sex partnerships accumulated over an 8-year period (Ash & Badgett, 2006). The study from the state-level data from the CDC yielded 2,881 female same-sex partnered households over the course of 8 years—around 360 respondents per year of the study (Buchmueller & Carpenter, 2010). Pooling data can lead to problems with enumeration of same-sex households, because attitudes toward homosexuality have changed rapidly over the study period (Baunach, 2011). Because homosexuality has become more acceptable, larger proportions of same-sex households may self-identify to researchers, effectively diluting the pool in early years and concentrating effects in later years (see Ash & Badgett, 2006; Pals & Waren, 2011). To counter such conceptual problems found when using small sample sizes, we used the American Community Survey (ACS) that allows for a much larger sample of the population of interest. Our study had 5,900 women in same-sex partnerships for a single year, 2009. We used the ACS to compare the national rates of health insurance coverage among women in same- and opposite-sex partnerships among a large national sample that has not been pooled over multiple years. Due to the structural disadvantages faced by many same-sex partners in the United States, we hypothesized that partnered women in same-sex relationships would have higher rates of being uninsured than partnered women in opposite-sex relationships (Hypothesis 1). We also hypothesized that because women in same-sex partnerships will not be offered employer coverage similarly to married women, partnered same-sex women would use public health coverage (Hypothesis 2) and private coverage (Hypothesis 3) more than partnered women in opposite-sex relationships. Also, we tested for variation across the country to determine if women in same-sex partnerships had lower coverage (Hypothesis 4) or different types of coverage in states that were not gay tolerant (i.e., in states that had restrictive legislation to limit the civil rights to same-sex couples).

METHODS Data Source We used the ACS public use microdata sample from 2009. The ACS is a nationwide, ongoing survey collected by the U.S. Census Bureau as a

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replacement of the former “long-form” of census (U.S. Census Bureau, 2008). The ACS interviews about 2 million people each year. The sample is a random sample of all housing units in the United States. In the 2009 ACS, 1,917,748 people were interviewed. The response rate was very high: 98% in 2009 (U.S. Census Bureau, 2012). Such a high response rate is possible partly because the U.S. Census uses three different modes of data collection: mail, telephone, and personal visit (U.S. Census Bureau, 2009a) and partly because of the mandatory nature of the survey (similar to U.S. Census). Approximately 47% of the interviews were conducted by mail, 10% via telephone, and 41% via face-to-face interviews (the remaining 2% were non-responses; U.S. Census Bureau, 2009a).

Sample Selection and Sexual Orientation A formidable limitation faced by researchers in the field of sexual minority health is enumeration of the population of interest (Badgett, 2006; Mayer et al., 2008; Pals & Waren, 2011). At this early point in the development of the literature on same-sex couples, two methods have been adopted: self-identification and a “couples-based” strategy. Self-identification of sexual minority status allows respondents to self-categorize, sidestepping inferences from researchers, and it permits individual-level analysis. Unfortunately, at present, only a few large-scale surveys ask direct questions about sexual orientation. The alternative couples-based method relies on inferences from multiple questions in a survey regarding the sex and relationship status between individuals within the same household. This approach allows large samples of “same-sex, unmarried, partnered” households to be enumerated within datasets frequently used by social researchers (Black et al., 2007; Buchmueller & Carpenter, 2010; Walther, Poston, & Gu, 2011). The couplesbased method was used in all three of the national surveys discussed previously (using data from the National Health Interview Survey, the CPS, and the CDC) and was the enumeration method used in our analysis with the ACS. The weaknesses of this approach include the validity of focusing only on partnered individuals and reliability concerns when enumeration and measurement are based on inferences from the researchers and not directly from respondents. In spite of these legitimate arguments, the couples-based (or sometimes referred to as “population-based”) method has yielded the most quantitatively rigorous research on sexual minorities to date. Thus, the ACS does not directly ask questions concerning sexual orientation or sexual identity; however, it allows us to identify lesbian women by their relationship to the householder and the sex of that householder. Black et al. (2000) found that only about .4% of those who claimed to be in an unmarried relationship with a same-sex partner had mistakenly reported their status and were not gay or lesbian. Gates (2010)



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reported that 90% of same-sex couples chose either the “husband/wife” or the “unmarried partner” options on the Census 2010 form (however, because same-sex marriage was illegal at the time of the census, the “married” status was later changed to “unmarried” for all same-sex partners). Thus, by identifying unmarried, same-sex partners as lesbians, about 10% of the data was lost for partnered lesbians who may have noted their status as something other than “married” or “unmarried” partner (e.g., “roommate”). Therefore, in our analysis, we focused only on partnered women (the analysis does not include women living without a partner) and were able to compare lesbian partnered women (women in same-sex relationships) with married and unmarried partnered heterosexual women (women in opposite-sex partnerships). For clarity, we refer to these groups based on their relationship status and not based on the inferred identity. We included all women aged 18–65 years who were either householders or married or unmarried partners to householders. The total sample size for this group of partnered women was 580,754 women. The majority were married women in opposite-sex relationships (90.2%), followed by unmarried women in opposite-sex relationships (8.8%), and women in same-sex relationships (1%). As expected, the percentage of women in samesex relationships was rather small, but due to large general sample size, 5,913 women reported being in same-sex relationships. For the analysis, we created two dummy variables, leaving women in same-sex relationships as the reference category. This allowed us to compare both groups of women in opposite-sex partnerships directly against women in same-sex relationships.

Dependent Variables The ACS began gathering health insurance information in 2008 (Lynch, Boudreaux, & Davern, 2010). Health insurance has been captured by a single question that asked whether respondents had any of the seven types of coverage at the time of survey: (1) health insurance through a current or former employer or union; (2) insurance purchased directly from an insurance company; (3) Medicare, (4) Medicaid, Medical Assistance, or any kind of government-assistance plan for those with low incomes or disability; (5) TRICARE or other military health care; (6) Veteran’s Affairs (VA); or (7) Indian Health Service. We used two different dependent variables created from these questions. The first dependent variable measured whether the respondent had any health insurance. About 12% of the sample did not have any type of health insurance (a total of 66,826 partnered women). The second dependent variable differentiated between different types of health insurance. We identified five categories: (1) about 71% of women had only employer insurance (employer coverage includes TRICARE—military health

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care [http://www.military.com/benefits/tricare]); (2) about 8% of women had direct private coverage and no employer insurance; (3) almost 4% of women had both direct private coverage and employer coverage; (4) just above 6% of women had only public coverage (including Medicare, Medicaid, government assistance plan, VA, and Indian Health Service); and (5) almost 12% of women had no insurance (Table 1).

TABLE 1 Means and Proportions for the Dependent and Independent Variables (Partnered Women Aged 18–65 Years; N = 580,754)


Opposite-Sex Partnership

Dependent Variables No insurancea Insurance typea Employer coverageb Direct private coverage Both employer and direct Only public coverage No health insurance Independent Variables Statea Ban same-sex relationshipb No prohibition Other states Age in years (18–65) Years of education (0–24) Disabled Racea Whiteb Black Hispanic Other race Presence of childrena Annual individual income in thousands (–$10 to $500) Type of worka Private for profitb Private not for profit Government Self-employed Unemployed Not in labor force Percent Valid N

Married

Unmarried

Same-Sex Partnership

Total

9.97

27.05

13.39

11.51

72.79 8.24 3.80 5.20 9.97

48.04 6.59 1.89 16.44 27.05

68.41 7.54 3.33 7.32 13.39

70.57 8.09 3.62 6.21 11.51

.39 .15 .46 45.96 18.49 .08

.35 .17 .48 36.31 17.77 .09

.34 .19 .47 43.78 19.31 .11

.38 .16 .46 45.09 18.44 .08

.76 .06 .11 .07 .48 44.77

.69 .09 .16 .06 .42 34.78

.80 .06 .09 .05 .27 77.98

.76 .06 .11 .07 .48 44.23

.42 .09 .15 .07 .10 .17 90.19 523,805

.59 .07 .11 .04 .10 .09 8.79 51,036

.48 .12 .18 .08 .06 .08 1.02 5,913

.43 .09 .15 .07 .10 .16 100 580,754

Note. Source: U.S. Census Bureau (2009b). a The means for dichotomous variables denote proportions in the category. b Categories that in multivariate analysis were used as reference categories.

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Independent Variables


GAY-TOLERANT STATES,

NOT

GAY-TOLERANT STATES,

AND

OTHER STATES

We categorized states into three groups based on information from the National Gay and Lesbian Task Force (2009). Their research indicated those states in 2009 that prohibited same-sex relationships by statute and/or constitutional amendment (National Gay and Lesbian Task Force 2009). We separated the three groups of states: (1) gay tolerant, (2) not gay tolerant, and (3) the remaining states. At one extreme were gay-tolerant states— states that had no prohibition against same-sex relationships. At the other extreme were not gay-tolerant states—states that in 2009 had constitutional amendments and statutes banning same-sex marriage and other same-sex relationships. The third group was comprised of states that having one piece of legislation (either constitutional amendment or a statue) banning samesex marriage only. Two of the states in the third group (Nebraska and Wisconsin) had a broad constitutional amendment banning same-sex marriage and other relationships. We included these states in the third group because they had only one piece of legislation banning these relationships, as opposed to multiple pieces of legislations, as in not gay-tolerant states. Based on this, a total of 12 states (Connecticut, Delaware, Iowa, Maine, Massachusetts, New Hampshire, New Jersey, New Mexico, New York, Rhode Island, Vermont, and Washington, DC) were categorized as gay-tolerant and 19 states (Alabama, Alaska, Arkansas, Florida, Georgia, Idaho, Kansas, Kentucky, Louisiana, Michigan, Montana, North Dakota, Ohio, Oklahoma, South Carolina, South Dakota, Texas, Utah, and Virginia) were categorized as not gay-tolerant states. We included two dummy variables in our analysis, leaving the not gay-tolerant states as the reference category. About 16% of the sample lived in gay-tolerant states; 39% lived in not gay-tolerant states; and about 46% lived in the rest of the states. CONTROL VARIABLES We controlled for age, education, race/ethnicity, disability status, the presence of children, type of work, and income. Age varied from 18 to 65 years; the mean age in our sample was 45 years. Education was measured in years; the average woman in our sample had 18 years of education. We distinguished between four race and ethnicity categories: White non-Hispanic (reference category, 76% of women), Black non-Hispanic (6%), Hispanic (11%), and other race (about 7%). Disability status was measured by a dichotomous indicator. About 8% of the women in the sample had some kind of disability. Presence of children was measured by a dichotomous measure indicating whether the household has children under the age of 18 years. A total of 48% of the sample has children younger than 18 years living in their household. We included two variables about the respondent’s work life. The

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first identified the type of work place. We distinguished between six different categorizations: (1) private for profit (reference category; 43%); (2) private not for profit (about 9%); (3) government employee (15%); (4) self-employed (about 7%); (5) unemployed (10%); and (6) not in labor force (16%). We also controlled for individual income measured in thousands of dollars, which varied from −$10,000 to $500,000 annually (income was measured as all earnings and also captured a possible loss). The average annual individual income in our sample was $44,000.


Data Analyses In our bivariate analysis, we used one-way analysis of variance when comparing the means of a continuous variable across the three groups of women (married opposite-sex relationship, unmarried opposite-sex relationship, and unmarried same-sex relationship) and chi-square tests when comparing the proportion in a categorical variable across these three groups of women. In our multivariate analysis, we used binary logistic regression to estimate the odds of not having health insurance coverage and multinomial logistic regression for estimating the odds of using different types of insurance coverage (see the discussion independence of irrelevant alternatives [IIA] in the Appendix), while adjusting for potential confounding variables that were included based on theory and past research. Thus, we controlled for age, education, race/ethnicity, disability, presence of children, type of work, and income (DeNavas-Walt et al., 2012). The relationship status dummy variables helped establish whether the rate of having health insurance or the rates of various insurance types differed by relationship status. Model fit was estimated by an incremental increase in likelihood ratio chi-square. To investigate whether the differences between relationship statuses were the same across states, interaction terms were used between the relationship dummy variables and the state variables (whether a state was gay-tolerant). Effect sizes were measured using odds ratios in both logistic models and by calculating and graphing the predicted probabilities for both being uninsured and for the different types of health insurance.

RESULTS Bivariate Analysis The bivariate rate of being insured varied by relationship status (p < .001; Table 1). Unmarried women in opposite-sex relationships had the highest rate of not being insured (27%), followed by women in same-sex relationships (13%). Married women in opposite-sex relationships had the lowest rate of not having health insurance (about 10%).



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Similarly, the types of coverage differed by relationship status. In bivariate analysis, unmarried women in opposite-sex relationships had the highest rate of having only public coverage (16%; compared to 5% for married women and 7% for women in same-sex relationships) and the lowest rate of employer coverage (48%; compared to 72% for married women and 68% for women in same-sex relationships). Based on the bivariate analysis, unmarried women in opposite-sex relationships were younger than the other two groups on average. They also were the least educated (17.7 years of education, compared to 18.5 for married women and 19.3 for women in same-sex relationships). The rate of disability was the highest among women in same-sex relationships (11%; as compared to 8% among married women and 9% among unmarried women in opposite-sex relationships). More Hispanic women were among the unmarried women in opposite-sex relationships than in the other two groups. While in both groups with opposite-sex partners more than 40% of households had children (48% for married women and 42% for unmarried women), only 27% of households of women in same-sex relationships had children younger than 18 years of age. Also, at the bivariate level, women in same-sex relationships had a much higher average individual income than both groups with opposite-sex partners ($77,000; as compared to $45,000 for women in married heterosexual relationships and $35,000 for women in unmarried heterosexual relationships). This is partly due to differences in the labor force status. Women in same-sex relationships had a lower rate of being out of the labor force (8%; as compared to 17% for married women in opposite-sex relationships) and a lower rate of being unemployed (6%; as compared to 10% for both groups in opposite-sex relationships). Women in same-sex relationships were also more frequently self-employed. Interestingly, women in same-sex relationships were also more frequently working at government jobs (18% of women in same-sex relationships were working in government sector, compared to 15% for married and 11% for unmarried women in opposite-sex relationships).

Health Insurance or Not Next, we estimated a series of binary logistic regressions of not having health care coverage (see Table 2). The first model in Table 1 included all of the main effects. We found that the odds of being uninsured were 26% higher for unmarried women in opposite-sex relationships than for women in same-sex relationships. Married women in opposite-sex relationships had about half the odds of being uninsured than women in same-sex relationships. Several control variables affected the likelihood of having health insurance. Older people and people with more education and higher income had lower likelihood of not having health insurance. All minority racial and

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TABLE 2 Odds Ratios from Binary Logistic Regression of No Health Care Coverage (Partnered Women Aged 18–65 Years; N = 580,754)

Relationship (ref. same-sex relationship) Unmarried opposite-sex Married opposite-sex Age in years Years of education Disabled Race (ref. white) Black Hispanic Other race Presence of children Annual individual income (in thousands, $) Type of work (ref. private for profit) Private not for profit Government Self-employed Unemployed Not working State (ref. ban same-sex relationship) No prohibition Other states State X relationship No prohibition X married Other states X married No prohibition X unmarried Other states X unmarried Constant Likelihood ratio chi-square Degrees of freedom Change in LR chi-square Degrees of freedom

Model 1

95% CI

Model 2

95% CI

1.26∗∗∗ 0.49∗∗∗ 0.97∗∗∗ 0.88∗∗∗ 0.92∗∗∗

1.16–1.36 0.45–0.53 0.97–0.98 0.88–0.88 0.90–0.95

1.32∗∗∗ 0.49∗∗∗ 0.97∗∗∗ 0.88∗∗∗ 0.92∗∗∗

1.16–1.50 0.43–0.56 0.97–0.98 0.88–0.88 0.90–0.95

1.49∗∗∗ 2.76∗∗∗ 1.24∗∗∗ 0.92∗∗∗ 0.98∗∗∗

1.44–1.55 2.70–2.82 1.20–1.29 0.90–0.94 0.98–0.98

1.49∗∗∗ 2.76∗∗∗ 1.24∗∗∗ 0.92∗∗∗ 0.98∗∗∗

1.44–1.55 2.70–2.82 1.20–1.29 0.90–0.94 0.98–0.98

0.63∗∗∗ 0.40∗∗∗ 1.83∗∗∗ 1.10∗∗∗ 0.91∗∗∗

0.61–0.66 0.39–0.42 1.77–1.90 1.07–1.13 0.89–0.94

0.63∗∗∗ 0.40∗∗∗ 1.83∗∗∗ 1.10∗∗∗ 0.91∗∗∗

0.61–0.66 0.39–0.42 1.77–1.90 1.07–1.13 0.89–0.94

0.50∗∗∗ 0.73∗∗∗

0.49–0.52 0.72–0.75

0.51∗∗∗ 0.75∗∗

0.39–0.67 0.63–0.89

0.96 0.99 0.92 0.99 11.84∗∗∗ 75978.88∗∗∗ 21 8.16 4

0.76–1.29 0.83–1.18 0.73–1.26 0.77–1.10

11.99∗∗∗ 75970.72∗∗∗ 17

Note. Source: U.S. Census Bureau (2009b). Model 1 includes all of the main effects. Model 2 adds interaction terms between state’s gay tolerance and relationships statuses. CI = Confidence Intervals; LR = Likelihood Ratio. ∗∗ p < .01; ∗∗∗ p < 0.001 (two-tailed tests).

ethnic groups were more likely to be uninsured than were White women. Both being disabled and having kids in household lowered the likelihood of being uninsured. Those self-employed and unemployed had a higher likelihood of being uninsured than people in the private for-profit sector. With a large sample, even small differences can become significant. This led us to direct our attention to effect size. To understand better the effect size, we calculated and graphed the predicted probabilities of not having health insurance based on the variables of model 1 in Table 2 (Figure 1).

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Winners and Losers in Health Insurance .14 .12 .112

Predicted Probability

.10 .090 .08 .06 .046

.04


.02 .00 Same-sex

Unmarried opposite-sex

Married opposite-sex

Relationship

FIGURE 1 Predicted probability of no health care coverage by partnership status (based on model 1 in Table 2).

We graphed both the point-estimate of the predicted probability (horizontal line) and the confidence intervals (vertical line). In Figure 1, none of the confidence intervals overlapped; thus, we observed a significant difference among all of the graphed predicted probabilities. Married women in opposite-sex relationships were the best off, with only .05 probability of not having health insurance. Women in same-sex relationships had .09 probability of not having health insurance, followed by unmarried women in opposite-sex relationships whose predicted probability of not having health insurance was .11. Thus, women in same-sex relationships and unmarried women in opposite-sex relationships had about twice as high a probability of not having health insurance as married women.

Health Insurance by State In model 2, we used interaction effects to estimate whether the likelihood of having health insurance differed by the gay tolerance of the state. However, although we had a very large sample size and a nationally representative sample, the interaction effects between relationship status and state variables were not statistically significant. This meant that the rate of not having health insurance was the same in states that have banned same-sex relationships and in states that have no prohibition against same-sex relationships. In other words, regardless of the state, married women in opposite-sex relationships always had the lowest rate of being uninsured, followed by women in same-sex relationships and—worst off—unmarried women in opposite-sex relationships. Because interaction effects in model 2 were not significant, and

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main effects cannot be directly interpreted with interaction terms retained, model 1 in Table 2 should be used to interpret results.


Types of Health Insurance Multinomial logistic regressions estimated whether a certain type of health insurance was more or less likely for women in same-sex relationships. Our dependent variable, health insurance type, had five categories: (1) employer insurance; (2) direct private insurance (and no employer insurance); (3) both direct private and employer insurance; (4) only public coverage; and (5) no health insurance. We used the most frequent category—employer insurance—as our reference group. In the first column of Table 3 are the odds of having direct private coverage compared to the odds of having employer coverage, which was 46% higher for unmarried women in opposite-sex relationships than for women in same-sex relationships. Married women, however, had 16% lower odds of having direct coverage compared to having employer coverage than women in same-sex relationships. Thus, unmarried women in opposite-sex relationships had the highest likelihood of having direct coverage, followed by women in same-sex relationships, and last, married women. No differences were observed among the three groups in having both direct coverage and employer coverage. However, as expected, large differences appeared in having only public coverage and not being insured. Unmarried women in opposite-sex relationships had 103% higher odds than women in same-sex relationships of having only public coverage as compared to having employer coverage. At the same time, married women had 66% lower odds than women in same-sex relationships of having only public coverage. Based on this, married women were the best off with lowest likelihood of only public coverage, followed by women in same-sex relationships, and—worst off—unmarried women in opposite-sex relationships. The pattern of no insurance coverage was similar to the previous results. Married heterosexual women had the lowest odds of not having health care coverage, followed by women in same-sex relationships. Unmarried heterosexual women had the highest odds of no health care coverage among the three groups of women. To understand these results better, we calculated the predicted probabilities of health coverage type by partnership status. In Figure 2, the first panel provided the predicted probability of employment coverage, and the second panel showed the same for other coverage types. Married women in opposite-sex relationships had the highest probability of employer coverage (.83), followed by women in same-sex relationships (.75) and unmarried women in opposite-sex relationships (.66). Panel B provides similar results as those provided in Table 2: Married women in opposite-sex relationships had the lowest probabilities and

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1.31 − 1.63 0.76 − 0.93 1.10 − 1.10 .99 − .99 .90 − .97 0.59 − 0.67 0.79 − 0.85 1.10 − 1.19 0.82 − 0.86 1.00 − 1.00

0.74 − 0.81 0.44 − 0.48 6.23 − 6.62 2.43 − 2.59 2.69 − 2.85 0.06 − 0.08

0.63∗∗∗ 0.82∗∗∗ 1.14∗∗∗ 0.84∗∗∗ 1.00∗∗∗

0.78∗∗∗ 0.46∗∗∗ 6.42∗∗∗ 2.51∗∗∗ 2.77∗∗∗ 0.07∗∗∗

95% CI

1.46∗∗∗ 0.84∗∗∗ 1.01∗∗∗ .99∗∗∗ .93∗∗∗

Note. Source: U.S. Census Bureau (2009b). CI = Confidence Intervals. ∗∗ p < .01; ∗∗∗ p < 0.001 (two-tailed tests).

Relationship(ref. same-sex relationship) Unmarried opposite-sex Married opposite-sex Age in years Years of education Disabled Race (ref. white) Black Hispanic Other race Presence of children Annual individual income (in thousands, $) Type of work (ref. private for profit) Private not for profit Government Self-employed Unemployed Not working Constant Likelihood ratio chi-square Degrees of freedom

Odds ratio

Direct private (no employer)

0.96 1.14∗∗∗ 1.49∗∗∗ 1.11∗∗∗ 1.25∗∗∗ 0.03∗∗∗ 177437.22∗∗∗ 60

1.93∗∗∗ 1.10∗∗∗ 1.21∗∗∗ 0.97 1.00∗∗

0.89 0.99 1.02∗∗∗ .98∗∗∗ 1.37∗∗∗

Odds ratio

0.92 − 1.02 1.10 − 1.18 1.41 − 1.58 1.05 − 1.17 1.20 − 1.31 0.02 − 0.04

1.84 − 2.03 1.04 − 1.16 1.15 − 1.28 0.94 − 1.01 1.00 − 1.00

0.76 − 1.04 0.86 − 1.14 1.02 − 1.02 .97 − .99 1.31 − 1.44

95% CI

Both direct private and employer

0.73∗∗∗ 0.56∗∗∗ 2.13∗∗∗ 2.57∗∗∗ 2.96∗∗∗ 6.08∗∗∗

2.13∗∗∗ 1.45∗∗∗ 1.85∗∗∗ 1.56∗∗∗ 0.98∗∗∗

2.03∗∗∗ 0.34∗∗∗ 0.98∗∗∗ 0.84∗∗∗ 4.87∗∗∗

Odds ratio

0.69 − 0.78 0.53 − 0.59 2.01 − 2.25 2.47 − 2.67 2.85 − 3.07 5.28 − 7.01

2.04 − 2.23 1.40 − 1.50 1.77 − 1.93 1.52 − 1.60 0.98 − 0.98

1.81 − 2.28 0.31 − 0.38 0.98 − 0.98 0.83 − 0.84 4.74 − 5.02

95% CI

Only public coverage

0.63∗∗∗ 0.40∗∗∗ 2.78∗∗∗ 1.48∗∗∗ 1.29∗∗∗ 30.56∗∗∗

1.77∗∗∗ 2.82∗∗∗ 1.34∗∗∗ 0.97∗∗∗ 0.98∗∗∗

1.57∗∗∗ 0.42∗∗∗ 0.97∗∗∗ 0.84∗∗∗ 1.42∗∗∗

Odds ratio

0.61 − 0.66 0.38 − 0.42 2.69 − 2.88 1.43 − 1.52 1.25 − 1.33 27.40–34.08

1.70 − 1.83 2.75 − 2.89 1.30 − 1.39 0.95 − 0.99 0.98 − 0.98

1.47 − 1.71 0.38 − 0.46 0.97 − 0.97 0.84 − 0.84 1.37 − 1.46

95% CI

No health care coverage

TABLE 3 Odds Ratios from Multinomial Logistic Regression of Health Insurance Type, Compared to Employer Coverage (Partnered Women Aged 18–65 Years; N = 580,754)


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(a)

(b)

FIGURE 2 Predicted probability of types of health care coverage by partnership status (based on Table 3). a. Employment coverage. b. Direct coverage, employment direct coverage, public coverage, and no health care coverage.

unmarried women in opposite-sex relationships had the highest probabilities for public coverage and no coverage. Unmarried women in opposite-sex relationships were also the group most likely to rely on only direct private coverage (probability of .10), while they were less likely to have both direct private and employer coverage (probability below .03). Next, we estimated multinomial logistic regression models of health insurance type by adding interaction effects between gay-tolerant states and relationship status (table available from authors upon request). None of the


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interaction effects were statistically significant. The three groups of women were the same in terms of health insurance type regardless of whether the state was gay-tolerant.


DISCUSSION Through the use of the ACS, this study has provided the largest sample of same-sex female partnered households in one year available for research in the United States. Our results focused on comparison of rates of no health insurance coverage, a comparison of types of coverage used by different groups of women, and a comparison of the rates of health care coverage for women in same-sex relationships by gay-tolerance of the state. Health insurance for many women in the United States is based on partnership status. Gay tolerance at the state level is a viable indicator of the extent to which employers might extend their partner coverage to same-sex partnered women. If states and employers do not recognize these partners structurally, then, theoretically, it would be easy to discriminate against samesex partners. Because same-sex marriage is not legal in most U.S. states and because of the prevalence of other legal prohibitions against same-sex relationships, it is logical to assume that among women, women in same-sex partnerships would have the lowest rate of health insurance (a similar result was found in Ash and Badgett [2006], Buchmueller and Carpenter [2010], Heck et al. [2006], and Ponce et al. [2010]), the highest rate of public health insurance, and lowest rate of employer-provided insurance (reflected in Hypotheses 1, 2, and 3, respectively). However, our analyses showed a different picture. Consistently, women in same-sex relationships were less likely than married women in opposite-sex partnerships to have health insurance—but more likely than unmarried women in opposite-sex partnerships to have health care coverage. Unmarried women in opposite-sex partnerships were more likely than women in same-sex relationships to be uninsured and to have public insurance. They were less likely than women in same-sex partnerships to have employment coverage. Consistently, the order from having health insurance to not having it was: married women, women in same-sex partnerships, and then unmarried women in opposite-sex partnerships. The marriage premium for women’s health insurance coverage was thus strongly confirmed in our results. Unfortunately, the penalty for those not married— particularly for women in opposite-sex relationships—was quite severe. Our results, both on rates of no insurance coverage and the consistent ordering among the types of insurance (married with highest use; unmarried partnered with lowest use; and women in same-sex relationships in between), led to two observations. First, most of the differences in health care coverage were explained by marital status and not by sexual orientation,


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a finding that echoed that of Buchmueller and Carpenter (2010). Consistently, the difference in the rates was smaller between women in same-sex relationships and unmarried women in opposite-sex partnerships than between women in same-sex partnerships and married women in opposite-sex partnership. A policy of legally recognizing domestic partnerships for both same-sex and opposite-sex partnerships might equalize the picture of health care coverage among U.S. women by extending benefits enjoyed by married couples to all partnered women. Second, women in same-sex partnerships were slightly better off than unmarried women in opposite-sex partnerships in terms of health care coverage. This could mean that, due to the known legal disadvantage, women in same-sex partnerships were better prepared to obtain insurance than unmarried women in opposite-sex partnerships. Our final focus for these analyses applied to the variation in health care coverage rates between the states (Hypothesis 4). We separated out three groups of states: (1) gay-tolerant states (states that had no prohibition against same-sex relationships); (2) not gay-tolerant states (states that had constitutional amendments and statutes against same-sex relationships); and (3) the rest of the states. Our analyses showed no difference in rates of having health insurance across these three groups of states, nor were any differences observed in rates of the types of health care coverage used by partnered women in these three groups of states. Thus, regardless of how gay-tolerant a state was, women in same-sex and unmarried women in opposite-sex partnerships were still less likely than married heterosexual women to have health care coverage. This finding of no state-level variation in health care coverage is not conclusive that structural discrimination does not exist (significant differences still exist among the groups in our study). Rather, it may be that the structure causing the differences is at the national level—an interpretation with important implications for future policy on women’s access to health insurance.

Limitations It would be interesting to see whether state-level differences would appear if we separated out states with legalized same-sex marriage. However, this type of analysis was not possible because in 2009, very few states had legalized same-sex marriage: Massachusetts legalized same-sex marriage in 2004; California briefly legalized same-sex marriage in 2008 (it was also abolished in 2008), Connecticut legalized same-sex marriage in 2008, and others, such as Iowa and Vermont, legalized same-sex marriage in 2009 (Garvey et al., 2012). Even in those states that legalized samesex marriage, it had been institutionalized only recently and did not allow time to see the impact on health care coverage. Therefore, similar analysis should be replicated with newer data if more states legalize same-sex marriage.



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The ACS used in this data had several benefits: large sample size, national representation, no known participation bias, and measurement of different types of health insurances. However, ASC also has its limitations. First, ASC did not allow for control of health behaviors (e.g., smoking behavior, obesity, and exercise) or self-rated health (we did not control for whether or not respondent has some kind of disability). The health behaviors and the self-rated health can affect people’s need for health insurance. However, as long as the health behaviors do not affect the need for health insurance differently for people with different sexual orientation and marital statuses, it should theoretically not change the results of this analysis. It is conceivable that the reason for lower rates of health care coverage for unmarried heterosexual women and lesbians is the lack of spousal or partner coverage—insurance provided by an employer for the coverage of an employee’s spouse or partner. This type of care is routinely denied to unmarried women and directly affects lesbian partners who cannot marry in almost all states in the United States (Badgett, 2006). In California, partnered lesbians are estimated to be one fourth as likely (28%) as married heterosexual women to get partner coverage (Ponce et al., 2010). Using pooled data from the CPS between 1996 and 2003, Ash and Badgett (2006) reported that less than 5% of unmarried partnered households (gay, lesbian, or straight) had partner coverage, whereas 36% of married households use partner care coverage. Unfortunately, the ACS data used in this article did not allow us to estimate the rates of partner coverage directly (the survey did not include such detailed questions on insurance). It did, however, allow us to examine differences in the use of private, public, and employer-based types of insurance. It is possible that the rate of private or public insurance utilization among women in same-sex partnership is higher because of the lack of partner coverage. Studies of health insurance in the United States (including this study) lack an important aspect: a measure of the quality of health insurance. With ACS, we were only able to measure whether a person had health insurance and the type of insurance it was, but we were not able to compare which health insurance was better. Yet, the quality of insurance might represent a crucial difference between couples in opposite-sex and same-sex relationships. Imagine a married heterosexual couple in which both partners work. This couple has a choice: each partner can either take their own employer’s insurance or they can decide to sign both partners into the best plan of the two offered. If we imagine a similar unmarried (same-sex) couple who both work at a place that has employer-provided health insurance, they do not have a choice: each has to accept their respective employer-provided health insurance. Thus, because same-sex couples have less to choose from, the eventual health care coverage they use may be worse than that of the heterosexual married couple.


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ACS is a cross-sectional study and thus did not allow us to follow the over-time trend of the decision of selecting health insurance and jobs. It is possible that women in same-sex relationships were aware of their inability to qualify for partner coverage and, therefore, planned accordingly in their job search to ensure that they had a job that provided health insurance. Using ACS data we are unable to identify these types of protective behaviors. ACS allowed us to identify the sexual orientation only through partnership with the householder limiting our sample to only partnered women. Often, surveys that directly ask about sexual orientation have had a much smaller sample size, making the analyses performed here difficult to conduct. However, if large-scale surveys were to incorporate direct questions about sexual identity, similar analyses should be repeated with other definitions of sexual orientation. In June 2013, Defence of Marriage Act (DOMA) was ruled unconstitutional by the U.S. Supreme Court (United States v. Windsor, 2013). As a result, federal employees in same-sex couples will be able to include their spouses on their health insurance plan. Changes in enforcement, including taxation of health insurance contributions, are still being debated (Andrews, 2013; Bernard, 2013). Newer data should continue this analysis with comparison of federal and other employees to demonstrate whether and how much this legislation contributes to equality in terms of health insurance.

REFERENCES Andrews, M. 2013. Same-sex couples seeking insurance wait for IRS rules. Shots: Health News from NPR. http://www.npr.org/blogs/health/2013/08/13/ 211633947/same-sex-couples-seeking-insurance-wait-for-irs-rules. Ash, M. A., and M. V. Lee Badgett. 2006. Separate and unequal. Contempy Econ Policy 24:582–99. Badgett, L. 2006. Discrimination based on sexual orientation. In Handbook on the economics of discrimination, ed. W. M. Rodgers, 161–86. Northampton: Edward Elgar Publishing. Baunach, D. M. 2011. Decomposing trends in attitudes toward gay marriage, 1988–2006. Soc Sci Quart 92:346–63. Bernard, T. S. 2013. How the court’s ruling will affect same-sex spouses. New York Times, June 26. http://www.nytimes.com/2013/06/27/your-money/how-thesupreme-court-ruling-will-affect-same-sex-spouses.html?pagewanted=all&_r=0. Black, D., G. Gates, S. Sanders, and L. Taylor. 2000. Demographics of the gay and lesbian population in the United States. Demography 37:139–54. Black, D., G. Gates, S. Sanders, and L. Taylor. 2007. The measurement of same-sex unmarried partner couples in the 2000 U.S. Census. http://papers. ccpr.ucla.edu/papers/PWP-CCPR-2007-023/PWP-CCPR-2007-023.pdf Bradford, J., C. Ryan, and E. D. Rothblum. 1994. National Lesbian Health Care Survey. J Consult Clin Psych 62:228–42.



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APPENDIX One of the limitations while using multinomial logistic regression is that this method assumes the independence of irrelevant alternatives (IIA): the odds of one outcome should not depend on the other available outcomes. Often, when working with nominal dependent variables, the statistical tests for the independence of irrelevant alternatives show dependence. In health insurance coverage, none of the insurance options (i.e., employment coverage, direct coverage, and public coverage) are similarly equal (Long & Freese, 2006). This gave us confidence that, theoretically, IIA should hold in the case of health care coverage. Regardless, we formally tested the IIA assumption using the Hausman and Small-Hsiao tests for IIA. The Hausman test indicated that we violated the IIA assumption, while Small-Hsiao test gave conflicting results, showing that we only partly violated the IIA assumption. The IIA tests often give conflicting conclusions and based on Monte Carlo experiments the properties of these tests can be rather poor (Cheng & Long, 2007). In case of conflicting results, Long and Freese (2006, p. 243) suggested to refer to an early statement by McFadden (1973) that multinomial logistic regression can be appropriate when the alternatives “can plausibly be assumed to be distinct and weighted independently in the eyes of each decision maker” (p. 113). We felt that we could plausibly assume that the women who bought insurance saw these categories (employment coverage, direct coverage, and public coverage) as distinct and made decisions based on personal benefit.

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