Legal Equality, Gay Numbers and the (After?)Math of Eugenics

The Deployment of Empirical Data for Legal Equality

Gay and lesbian or LGBT legal equality discourse has been specifically critiqued for articulating a rights-centered politics that manifests significant problems and limitations. While maintaining neoliberal multiculturalism, it has articulated a white, wealthy, able-bodied, gender-conforming citizen as the purported universal subject of gay rights, seeking reforms that fail to disrupt and often support or exacerbate arrangements of maldistribution and state violence. It has participated in narratives of deservingness and undeservingness in order to shore up claims for “equality” that have made it complicit in and a site of reproduction of anti-immigrant, anti-poor, racist, and ableist logics. ¹ In recent years, its efforts have increasingly relied on the production of data that articulates and categorizes sexual orientation (and sometimes gender identity) within the confines of a racial/national project that supports neoliberal arrangements, including the expansion of criminalization, privatization, austerity, and warfare. This politics of inclusion and recognition endeavors to make propertied/professional white lesbian and gay people (and to a lesser degree propertied/professional white trans people) into junior partners in white supremacy, settler colonialism, and heteropatriarchy. ² “Gay and lesbian equality” advocacy often employs very conservative contemporary political frameworks: calls to law and order, calls to manage the harms of capitalism inside the marital family as poor relief programs are dismantled, and calls to invade and occupy purportedly “backward” countries portrayed as more homophobic and sexist than the US. ³

The use of statistical analysis is essential to producing an idea of a LGBT population that meets racial national norms and that should be protected from national enemies alongside other proper citizens. The project of identifying and defining populations, or sorting the population, as Scott might say, produces narrow and exclusive categories. In the context of rights deservingness, and of seeking affirmation in American legal structures, that project is a racial one. Its reliance on statistical data roots it to a methodology designed to promote valued populations and diminish those cast as threats. Looking briefly at some of the ways the Williams Institute, a preeminent producer of such data, conceptualizes lesbian and gay (and sometimes LGBT) populations in order to support various legal equality claims illustrates the relationship between legal equality claims, statistical methods, and the production and circulation of racial–national norms.

In the following analysis we examine the implementation of statistical methods in several studies published by the WI. Our analysis of the problems within the WI’s deployment of statistical methods aims not to suggest that they should do their statistical analysis “right,” but rather to demonstrate that the desire for quantitative data to back up the LGBT legal equality advocacy agenda is so strong, and the deployment of numbers so requisite in policy work directed to the state, that the methods need not even be employed faithfully or consistently in order to be produced as persuasive evidence. We observe some examples of flaws in the WI’s work to show how advocacy goals, and the kinds of LGBT populations those goals aim to portray, govern the deployment of statistical methods. Not only are statistical methods intertwined with racialized–gendered population management projects in a way that raises questions about their use in advocacy, but the effects of those projects on knowledge formation can be so strong that they overwhelm even the consistent application of these methods.

First, we examine how the WI’s use of census data over-represents people in marriages or marriage-like relationships in accord with the central prioritization of same-sex marriage advocacy in the WI’s work. Next, we critically examine the WI’s estimate of the number of LGBT people in the United States. We then consider a particular study in the WI’s series of analyses estimating the economic impact of legalizing same-sex marriage, again focusing on assumptions made and examining how those assumptions might be convenient to the political aims of the WI’s legal equality agenda. Finally, we consider how results are reported (or excluded) across studies by the WI, noting how this influences reader interpretation.

Who Gets Counted?

Many studies published by the WI rely on US census-style data, that is, household surveys with individual information regarding categorical race and ethnicity, binary gender, identification of “head of household,” relationship to the “head of household,” birth date, and sometimes additional information. When considering census-style data, the WI typically limits their analysis to “same-sex couples,” which are defined as pairs of cohabiting individuals, where one is the “head of household” and the other defines their relationship to the “head of household” as either “married” or “unmarried partners,” and both mark the same gender box. ⁴ This method of data collection and classification establishes a narrow definition that many people who are targeted by homophobia and/or transphobia and/or identify as LGBT fall outside of if they do not organize their households or family lives according to the norms the WI is deploying to conceptualize the population. This group excludes individuals not in a cohabiting partnered relationships, couples living in a larger extended family structure (when neither couple member is the “head of household”), and individuals cohabiting with a partner using a different gender marker. In excluding these groups, many gender variant and trans people, people of color, and poor people are systematically excluded from study, and owning-class White same-sex couples are over-represented. ⁵) Specifically, because of market forces at play in couple formation, poor people and people of color are less likely to be in a cohabiting relationship. ⁶ Individuals belonging to these same groups are also more likely to live in larger family structures, making them less likely to be named “head of household.” ⁷ Even if gender variant and transgender individuals are “heads of households” in cohabiting sexual relationships, they will only be counted if their partner marks the same gender category. In addition to this disproportionate representation, this study design is only capable of seeing and counting people who are married or in marriage-like relationships, which can cause an overrepresentation of higher-income people since marriage is correlated with higher wages. ⁸

The WI’s focus on producing statistical data to support the campaign to legalize same-sex marriage is an incentive to design studies that depict same-sex couples as married or in marriage-like relationships, and to exaggerate the value of marriage to populations facing homophobia and transphobia and erase people who do not fit the WI definition of same-sex couples. Indeed, a notable portion of the WI’s funding is provided by donors for whom legal marriage equality has been a top priority, such as the Gill Foundation, the Arcus Foundation, and the Wellspring Foundation. ⁹ The WI’s prioritization of marriage is reflected in their 48 policy studies concerning the economic impact of extending marriage to same-sex couples (which, in almost every study, predict a net economic gain for the state), as compared to four policy studies published on HIV/AIDS and three on immigration. ¹⁰ The focus on marriage inclusion and neglect of other key survival issues aligns with the legal equality agenda that the WI’s data support.

A few studies published by the WI are based on data outside the census style. For example, in a study meant to explore which groups are over- and under-represented by the same-sex couple definition outlined above, an online survey was used to collect data through a third-party survey company. ¹¹ The survey company enlists volunteers to take online surveys about a variety of subjects. People polled in this way are more likely to have easy internet access and time and energy to spend taking surveys, probably resulting in an underrepresentation of poor people, single parents, people of color, and people with less education access. ¹² Perhaps because they were aware of these trends, the survey was targeted to an audience over-representing people of color. However, after all the data was collected, it was statistically re-weighted to resemble national demographic proportions, resulting in the down-weighting of survey responses from people of color. ¹³ The WI’s efforts to count a lesbian and gay or LGBT population constructs that population in ways that produce and enforce racial, gender, class, and family formation norms in order to produce data that fits legal equality arguments centered in racialized–gendered images of national citizenship.

Defining and Counting an LGBT Population

In 2011, the WI published a study entitled “How Many People Are Lesbian, Gay, Bisexual and Transgender?,” boldly declaring an estimate of the LGBT population in the United States. ¹⁴ The estimate is a composite figure drawn from eleven surveys performed between 2002 and 2011 by a variety of agencies across the United States and internationally that included questions regarding self-identified sexual orientation and/or gender identity. The surveys were administered using diverse methods (mail-in survey, phone interview, etc.) in a variety of geographic locations.

Several things about the study stand out. First, membership in the LGBT population is determined by self-identification, without regard to who may and may not self-identify in the categorical LGBT groups (typically constructed around a white middle-class norm and lacking other identifying terms that may have more resonance in populations of color, among indigenous people, and among people in street economies). Additionally, self-identification is likely to produce a lowered count of people engaged in same-sex sexual behavior or desire or gender nonconforming behavior or desire, as is acknowledged, but not examined, by the study. ¹⁵ This sort of population count may be useful to support arguments about the economic impact of legalizing gay marriage, but it is unclear that population counts of self-identified LGBT individuals are useful in the struggles against discrimination or health discrepancies, the other reported uses of this data. For instance, in addressing homophobia and transphobia-related health disparities, merely counting the number of self-identified LGBT individuals may not be as useful as collecting information from people about sexual practices, access to health care, experiences with health care providers and insurance companies, risk behaviors of various kinds, and other key factors. In fact, those people facing barriers to health care related to same-sex desire or practice or gender nonconforming desire or practice who do not self-identify as LGBT may be those who are least reached by interventions aimed at addressing homophobia and transphobia as barriers to health information and health care.

The estimate of the number of transgender individuals included in this report raises additional concerns, not least of which are how the surveys compiled to create the estimate define trans identity. ¹⁶ The estimate of the trans population is arrived at by compiling data from two prior surveys that used varying language to define transgender to survey participants. Both of these surveys, though in different ways, generally used self-identification as the indication of trans identity. ¹⁷ The WI authors characterize these surveys as using “questions that implied a transition or at least discordance between sex at birth and current gender presentation,” (emphasis added). ¹⁸ The authors then compare the estimate they have arrived at with an estimate from another study in an effort to use the consistency between the studies to suggest accuracy. ¹⁹ The study to which they compared their estimate articulated trans identity as “actually tak[ing] steps to transition from one gender to another.” This approach to defining a trans population utilizes key tropes of transphobia, including a reduction of trans identity to certain body modification practices and an erasure of trans people who do not engage in them. The authors’ characterization of these survey results in a way that emphasizes “transition” and identifies those who have not engaged in “transition” as meeting a somehow lesser criteria, alongside their uncritical invocation of an estimate that utilizes medical criteria for trans identity, has several concerning implications. It affirms transphobic understandings of trans identity that emphasize medical authority and exclude people who do not desire or cannot afford medical treatment from membership in the trans population. These exclusions are highly racialized and classed given the fact that gender confirming health care for trans people is mostly excluded from Medicaid programs nationally and the racial wealth divide means people of color have less access than white people. ²⁰ This is particularly concerning given that these estimates of the population are explicitly being used to shape advocacy efforts. Additionally, the report’s comparison of its own estimate with the estimate arrived at by the survey using medical criteria suggests a willingness to under-represent or a failure to recognize the problem with these results if they appear consistent despite using significantly different criteria for defining the category. These issues indicate both a willingness to employ and rely on statistical data regardless of its obvious inaccuracy, and a lack of concern for key issues in trans politics, particularly those impacting low-income trans people and trans people of color.

These problems can also been seen in how, in interpreting some of the survey results, inconsistent assumptions are made that cause trans identity to be collapsed into LGB identities. The study attempts to estimate the proportion of the general population that is transgender. To create this estimate, it is assumed that all transgender people identify as LGB (so will be implicitly counted in surveys used to estimate the LGB population proportion). Many trans people who are not bisexual, lesbian, or gay may answer surveys in ways that do not identify them as such. These people will not be counted in the LGB estimate. Starting from the problematic assumption that transgender people are all included in LGB, the study then estimates the percentage of the total population that is transgender as the product of the estimated proportion of the total population which is LGB and the estimated proportion of the LGB population which is transgender. ²¹ In doing so, transgender people who do not identify as LGB are erased from this count, which also lowers the count of trans people in the general population. All of these awkward and inappropriate assumptions may seem reasonable when attempting to estimate the number of transgender individuals forced through a binary gender framework. Notably, using this method, the total population estimated percentage of transgender individuals is five times lower than the same percentage estimated directly in a different survey. ²²

The implications of the WI’s methods of defining the transgender population and its relationship to the LGB population could be the subject of an entire article, but a few implications are worth noting here. In addition to citing survey data that relies on transphobic definitions of trans identity that center medical authority rather than self-identity, ²³ “How Many People Are Lesbian, Gay, Bisexual and Transgender?” conflates transgender identity with LGB identity in ways that directly disserve trans and gender nonconforming populations. Trans and gender nonconforming people have been consistently marginalized in lesbian and gay rights politics, sometimes through explicit exclusion and sometimes because resources are consistently devoted to issues impacting lesbian and gay people, while issues, often of great urgency, impacting trans and gender nonconforming people are ignored. ²⁴ Conflating “T” with “LGB” supports the erasure of the specificity of harm and violence faced by trans people, whose struggles with a range of legal systems and administrative programs (prisons, foster care, homeless shelters, hospitals, immigration, Medicaid) that enforce binary gender norms often suggest quite different political priorities than the “marriage equality”-centered agenda promoted by the WI. Making many trans and gender nonconforming lives illegible and folding trans and gender nonconforming people into a politics centered on an exceptionally narrow understanding of what constitutes an LGB population and the interventions that population might seek perpetuates the constitutive racialized and classed transphobia of lesbian and gay rights politics.

Finally, as mentioned earlier, the inconsistency in research methods, sample sizes (number of individuals surveyed), and social contexts across the surveys contributing to the cumulative study “How Many People Are Lesbian, Gay, Bisexual and Transgender?” may dramatically affect the resulting estimates. For a rigorous analysis of diverse estimates, these factors must be taken into account. However, in the published study, equal weight is given to each of these surveys in the cumulative estimates, resulting in somewhat arbitrary figures. Despite this lack of rigor, because the desire for such numbers is so strong, these estimates were announced to much fanfare. ²⁵ Their persuasive value is directly related to the perception of them as “scientific,” yet they fail to measure up even to the problematic statistical methods they purport to employ. These failures, however, point to the underlying investments of the counting project, the populations it cultivates and disposes of, and the racialized and gendered norms it produces and reproduces.

Alternate Estimate of the Gay Male US Population

To further illustrate the process by which an estimate of the number of LGBT people in the US such as the one engaged by the WI “makes up people,” to use Hacking’s words, we present a similar estimate made with alternate data sources more closely tied to sexual behavior. The exercise of coming up with a different estimate, using a different set of criteria for gay identity and different available data sets, illustrates how such projects invent, rather than discover and count, a population. As of August 2011, an estimated 2.08 million residents of the United States had profiles on Manhunt.net, an online cruising site targeted towards men who want to have sex with men. ²⁶ This is more than half the WI estimate of 4.03 million total gay men residing in the United States ²⁷ and continues to grow by about 8,000 individuals each week.110 If one could learn what proportion of men who want to have sex with men also use Manhunt.net, the estimated number of Manhunt.net users in the United States could be divided by that proportion to get an estimate of the total number of men who want to have sex with men in the United States. That proportion is unknown. However, without differing from the strategies of assumption and speculation often employed by WI studies, we can manufacture an estimate. According to a survey of 609 men during Atlanta gay pride in 2002, 34 percent of the total sample reported having met a sexual partner through the internet. ²⁸ If we ignore issues of sample bias and chronological inconsistency, ²⁹ and speculate that half of men who find male sexual partners online use Manhunt.net specifically, ³⁰ we arrive at a total estimate of 12.24 million men desiring sex with men residing in the United States, more than three times the WI estimate. If we were to assume that, given the existence of many competing online websites for men who want to have sex with men, the proportion using Manhunt.net might be even smaller, we would reach an even larger estimate of the number of men who want to have sex with men in the US.

To be clear, we do not propose to have accurately estimated the number of gay men or men who want to have sex with men in the United States. In fact, our estimate aims to expose the dangers of such endeavors. However, by using a different approach which, like the WI study, takes existing data and extrapolates from it to produce a particular gay population, we reveal the subjectivity of such a process. Obviously, while membership in Manhunt.net is likely correlated with sexual behavior or desire, perhaps more so than self-identification, it does not define sexuality. This method is also subject to sample bias since Manhunt.net users do not uniformly represent the population of gay men or men who want to have sex with men, ³¹ so certain groups will be over and under-represented based on these data. However, this back-of-the-envelope calculation can be contrasted with the WI estimate to illustrate problematic assumptions required to categorize LGBT and non-LGBT individuals and to extrapolate from limited data to describe a larger group. It is perhaps especially useful as a foil because, while the WI’s marriage-focused approach to producing statistics about LGBT people has often enforced family norms by over-representing people in marriages or marriage-like same-sex relationships and under-representing single people and people living in extended families, our use of Manhunt.net data centers sexual practice or desire, imposing a different definition of gay identity that is also arbitrary and narrow. The contrast between the two approaches exposes how the purportedly objective task of counting populations is actually a task of defining populations and proposing interventions—the task of normalization or, in Hacking’s term, of “making up people.”

The Economic Impact of Marriage for Rhode Island

The WI has published many studies examining the economic impact of legalizing same-sex marriage, almost always finding it to be beneficial. ³² Predicting the economic ramifications of legalizing same-sex marriage requires many assumptions and estimations. These studies typically use methods that consider those populations most benefited by existing social and legal structures that distribute wealth and life chances, like legal marriage, in ways that rely on “family values” rhetoric often used to support racist, anti-poor agendas. ³³ A useful example is the WI study of the economic impact of legalizing gay marriage in Rhode Island. ³⁴ In this study, census-identified “married” or “unmarried partner” same-sex cohabiting couples are considered, and it is found that legalizing same-sex marriage will increase revenue to the state due to increased taxation of these couples, increased taxable wedding spending of same-sex couples, and decreased public benefits distributed when these individuals combine their incomes. Many problematic assumptions are made to produce statistical support for these assertions.

As explored earlier, the census-based methodology utilized by the WI produces a likely over-representation of wealthier same-sex couples. By using data that disproportionately represents couples with more disposable and taxable income, estimates of increased tax revenue to the State are likely be inflated. As a part of the calculations, estimates were made for changes in public benefits distribution by the State due to legalizing gay marriage. In these estimates, same-sex couples are assumed to receive public benefits at the same rate as different-sex couples, as assumption that discounts the economic impact of homophobia. In fact, a different WI study shows that same-sex couples experience poverty and receive public benefits at higher rates than their different-sex counterparts. ³⁵ In estimating reduced rates of public benefits towards legally married same-sex couples, an implicit assumption is made that individuals in same-sex couples receiving public benefits are less likely to be eligible for those benefits when legally married. The assertion seems to rest on an idea that individuals will somehow become wealthier by marrying. It is difficult to understand the basis for this assumption. Perhaps it is assumed that poor people will marry people of greater means? However, research suggests that people are more likely to marry others with similar socioeconomic status. ³⁶ The assertion that legalizing same-sex marriage in Rhode Island would decrease public benefits reliance is especially concerning because of how it aligns with anti-poor rhetoric in the US and eugenic ideas of using marriage policy and family law to eliminate undesirable populations. Attacks on welfare and other poverty programs in the 1990s and 2000s have used gendered-racialized images of poverty, like the trope of the “welfare queen,” to assert a notion that poverty is a result of moral laxity and is best solved by eliminating benefits programs and promoting marriage. Such assertions have deep roots in the use of marriage promotion of various kinds as a part of social control programs aimed at people of color in the US, particularly Black people. ³⁷ The desire of same-sex marriage advocates to frame marriage as a cure for poverty has disturbing overlap with right-wing “family values” politics that has targeted people of color as in need of state intervention to promote marriage and was taken up in recent years under George W. Bush’s administration and continued by Barack Obama’s through the implementation of “marriage promotion” policies in social welfare programs. ³⁸ In addition to all of these concerns, this advocacy echoes the eugenic principle that statistics can be used to measure a population and develop policy reforms that will improve the population by eliminating poor people cast as “dependent” and “draining.” In this case, legalizing gay marriage is proposed to decrease the number of individuals receiving public benefits, transforming them into members of married couples who somehow become more economically secure.

Other problematic assumptions are visible in this report as well. The study assumes that a tax structure that would cause legally married individuals to pay more taxes than unmarried individuals will not affect marriage rates, despite citing a study showing that women are significantly less likely to marry when that would cause them to incur a tax penalty. ³⁹ Additionally, the spending of wedding guests is estimated with remarkable precision, but based on a series of arbitrary assumptions possibly inflating the final estimate, ⁴⁰ so that the precision is not meaningful, revealing its use as a rhetorical device rather than an accurate estimate. Finally, potential harm to same-sex couples is ignored when wedding spending (whether by reducing savings or by increasing debt) is valued as increased revenue for the state. Given the significance of American consumer debt, unemployment, and the rising cost of weddings, these arguments raise concerns about the alignment of wedding spending arguments for legalizing same-sex marriage with a concern for the well-being of same-sex couples. ⁴¹ The combined arguments that legalizing same-sex marriage will increase consumption and tax revenue and decrease public benefits reliance relies on harmful framings of same-sex couples as possessing inflated wealth and of marriage as a solution to poverty.

1. Mattilda Bernstein Sycamore, ed., That’s Revolting! Queer Strategies for Resisting Assimilation (Brooklyn: Soft Skull Press, 2004); Ian Barnard, “Fuck Community, or Why I Support Gay-Bashing,” States of Rage: Emotional Eruption, Violence, and Social Change, ed. Renée R. Curry and Terry L. Allison (New York: New York University Press, 1996), 74–88; Cathy J. Cohen, “Punks, Bulldaggers, and Welfare Queens: The Radical Potential of Queer Politics?” GLQ: A Journal of Lesbian and Gay Studies 3.4 (1997), 437–465; Ruthann Robson, “Assimilation, Marriage, and Lesbian Liberation,” Temple Law Review 75 (2002), 709; Anna M. Agathangelou, D. Morgan Bassichis, and Tamara L. Spira, “Intimate Investments: Homonormativity, Global Lockdown, and the Seductions of Empire,” Radical History Review 100 (2008), 120; Christina B. Hanhardt, “Butterflies, Whistles, and Fists: Gay Safe Streets Patrols and the ‘New Gay Ghetto’ 1976–1981,” Radical History Review 100 (2008), 61; “Is Gay Marriage Racist?” A Conversation with Marlon M. Bailey, Priya Kandaswamy, and Mattie Udora Richardson in Sycamore, That’s Revolting!; Kenyon Farrow, “Is Gay Marriage Anti-Black?,” March 5, 2004; Chandan Reddy, “Time for Rights? Loving, Gay Marriage and the Limits of Legal Justice,” Fordham Law Journal 76 (2008), 2849.[↑]
2. Frank B. Wilderson III, “The Prison Slave as Hegemony’s (Silent) Scandal,” Warfare in the American Homeland: Policing and Prison in a Penal Democracy, ed. Joy James (Durham: Duke University Press, 2007), 23–34.[↑]
3. Jasbir Puar and Amit Rai, “Monster, Terrorist, Fag: The War on Terrorism and the Production of Docile Patriots,” Social Text 20.3 (Fall 2002): 117–148; Jasbir Puar, Terrorist Assemblages: Homonationalism in Queer Times (Durham: Duke University Press, 2007); Katherine Franke, “Dating the State: The Moral Hazards of Winning Gay Rights,” Columbia Human Rights Law Review 44.1 (2012); Sarah Schulman, “Israel and ‘Pinkwashing’,” The New York Times, November 22, 2011; Pinkwatching Kit, Pinkwatching Israel, May 24, 2012; Tom W. Smith, “Cross-National Differences in Attitudes about Homosexuality,” April 2011 (a WI-supported study finding that “ex-socialist” countries are more homophobic).[↑]
4. Gates, “Same-Sex Spouses and Unmarried Partners in the American Community Survey, 2008,” UCLA: The Williams Institute, 2009; Gates, “Same-Sex Couples in US Census Bureau Data: Who Gets Counted and Why,” UCLA: The Williams Institute, 2010; Herman et al., “Impact on Rhode Island’s Budget.”[↑]
5. We re-examined the data presented in Gates, “Same-Sex Couples in US Census Bureau Data: Who Gets Counted and Why,” UCLA: The Williams Institute (2010), which includes demographic information for LGBT individuals in cohabiting sexual relationships and their status as counted or not counted using the definition above. Comparing individuals who are counted and not counted, we found that counted individuals are statistically significantly (p = .008) higher income than people not counted.
   – We used a one-sided Student’s t-test to test the null hypothesis that the average income for counted individuals (people who answered the census with “husband or wife” or “unmarried partner” [q810] and also one partner of which is “head of household” [q805]) is lower than uncounted individuals (people who answered census with “other nonrelative” or “housemate or roommate” or where neither partner is “head of household”). We considered income with the available binned discretized data (q462) and assumed that the income of each individual was the mid-point of their bracket and people making “$250,000 or more” were making $300,000.
   African Americans are statistically significantly (p = .005) less likely to identify as “husband or wife” or “unmarried partner” than Whites.
   – We used a one-sided Fisher exact test against the null hypothesis that the odds ratio is more than 1.0 on a contingency table comparing race marker (q485, “White” and pooled “Black” and “African American”) and relationship (q810, pooled “husband or wife” and “unmarried partner” and pooled “other nonrelative” and “housemate or roommate”).
   African Americans are nearly statistically significantly (p = .06) less likely to be counted than Whites. If there was more data available from African American individuals, the statistical power of the test would increase and the difference might become statistically significant.
   – We used a one-sided Fisher exact test against the null hypothesis that the odds ratio is more than 1.0 on a contingency table comparing race marker (q485, “White” and pooled “Black” and “African American”) and counted and uncounted status (as defined above[↑]
6. A 2002 survey of US residents 15–44 years of age found higher marriage rates for White people than Black people and found that poor people have lower marriage rates than people who are not poor. The survey found that 45.4 percent of Latina women, 37.2 percent of White women, and 25.8 percent of African American women were currently married. Similarly, 42.7 percent of Latino men, 44.4 percent of White men, and 31.5 percent of African American men were currently married. It also found that 35.8 percent of women and 39.5 percent of men at or under the poverty line were married, as opposed to 60.7 percent of women and 52.0 percent of men at or over one and a half times the poverty line. Centers for Disease Control and Prevention, “Fertility, Family Planning, and Reproductive Health of U.S. Women: Data from the 2002 National Survey of Family Growth,” series 23, number 25 (December 2005), table 46; Centers for Disease Control and Prevention, “Fertility, Contraception, and Fatherhood: Data on Men and Women from Cycle 6 (2002) of the National Survey of Family Growth,” series 23, number 26 (May 2006), table 29. See also, “How Your Race Affects the Messages you Get,” OkTrends blog, OkCupid.com, October 5, 2009; Hongyu Wang and Grace Kao, “Does Higher Socioeconomic Status Increase Contact between Minorities and Whites? An Examination of Interracial Romantic Relationships among Adolescents,” Social Science Quarterly 88 (2007): 146–166.[↑]
7. “Preparing for Investments along the University Corridor: Income, Race, and Family Structure in the University Corridor,” Institute on Race and Poverty, http://www.irpumn.org/website/projects/index.php?strWebAction=project_folder&intDocFolderID=17, last visited 2011.[↑]
8. Hal R. Varian, “Analyzing the Marriage Gap,” The New York Times, July 29, 2004; Kate Antonovics and Robert Town, “Are All the Good Men Married? Uncovering the Sources of the Marital Wage Premium,” The American Economic Review, May 2004; Hyunbae Chun and Injae Lee, “Why Do Married Men Earn More: Productivity or Marriage Selection?,” Economic Inquiry 39.2 (New York: Oxford University Press, 2001), 307–19; but see Ronald Mincy, Jennifer Hill, and Marilyn Sinkewicz, “Marriage: Cause or Mere Indicator of Future Earnings Growth?,” Journal of Policy Analysis and Management 28.3 (2009): 417–39 (finding that marriage promotion activities targeting low-income people and supported by research suggesting that marriage raises wages may be ineffective).[↑]
9. Personal correspondence with Urvashi Vaid, former executive director of the Arcus Foundation and board member of the Gill Foundation, February 18, 2013 and June 2, 2013.[↑]
10. These counts of studies are based on what studies had been posted to the WI website through the end of October, 2015.[↑]
11. Gates, “Same-Sex Couples in US Census Bureau Data.”[↑]
12. It is well-documented that internet access is not uniform over individuals with different ethnicities, ages, incomes, or education backgrounds. Karen Mossberger, Caroline J. Tolbert, and Michelle Gilbert, “Race, Place and Information Technology,” Urban Affairs Review 41 (2006): 583–620; Paul G. Harwood and Wayne V. McIntosh, “Virtual Distance and America’s Changing Sense of Community,” in Democracy Online: The Prospects for Political Renewal through the Internet, ed. Peter M. Shane (Routledge: Psychology Press, 2004): 209–224; “Internet and American Life Project: Demographics of Internet Users,” Pew Research Center; Steven P. Martin and John P. Robinson, “The Income Digital Divide: Trends and Predictions for Levels of Internet Use,” Social Problems 54 (2007): 1–22.[↑]
13. Gates, “Same-Sex Couples in US Census Bureau Data.”[↑]
14. Gates, “How Many People are Lesbian, Gay, Bisexual and Transgender?,” UCLA: The Williams Institute, April 2011.[↑]
15. The bulk of the WI’s studies use census data and study cohabiting same-sex couples as a proxy for LGB populations. Even given the limits of self-identification data, this study could be used to contrast the different populations studied under each definition of LGB. However, no such analysis is presented, perhaps because it might raise challenges regarding sample bias.[↑]
16. Clearly, from our perspective, the entire project of defining trans identity is problematic and concerning. The term transgender, transsexual, and trans are used with an enormous variety of meanings by various individuals, regional groups, subcultural groups, and language groups around the US. Other terms are also used extensively to indicate behavior or identification that exceeds or violates gender norms and makes those behaving or identifying in particular ways vulnerable. Transgender advocacy and scholarship has extensively examined debates about line-drawing around trans identities and the dangers and harms that come with rigid or narrow definitions that usually operate to exclude or deny many of those facing the worst violence of coercive gender systems and that often affirm authority and control for knowledges and institutions dominated by white professionals. Dean Spade, “Documenting Gender,” Hastings Law Journal 59 (2008): 731; Pooja Gehi and Gabriel Arkles, “Unraveling Injustice: Race and Class Impact of Medicaid Exclusions of Transition-Related Health Care for Transgender People,” Sexuality Research and Social Policy: Journal of NSRC 5.1 (March 2008): 7; Riki Anne Wilchins, Read My Lips: Sexual Subversion and the End of Gender (1997; repr. New York: Riverdale, 2013), 51; Franklin Romeo, “Beyond a Medical Model: Advocating for a New Conception of Gender Identity in the Law,” Columbia Human Rights Law Review 713 (2005).[↑]
17. The study, “Transgender Health in Massachusetts” asked this question: “Some people describe themselves as transgender when they experience a different gender identity from their sex at birth. For example, a person born into a male body, but who feels female or lives as a woman. Do you consider yourself to be transgender?” The California LGBT Tobacco Survey asked the question this way: “We are also interested in speaking with adults who consider themselves to be transgender, or transsexual in any way. By this, I mean people who have a gender identity or presentation that is different from what society says you should have for your birth sex. Would you include yourself in this group?” K.J. Conron, G. Scott, G.S. Stowell, and S. Landers, “Transgender Health in Massachusetts: Results from a Household Probability Sample of Adults,” forthcoming; Field Research Corporation, “Lesbians, Gays, Bisexuals, and Transgender: Tobacco Use Survey, 2004,” California Department of Health Services (2004). Clearly, these questions are different in important ways. The first question much more closely adheres to an understanding of trans identity that is focused on transition from one distinct binary gender category to another, and treats binary sex as a natural fact rather than a social construct. Certainly, many gender nonconforming people whom we might imagine should be “counted” if the data is to be used to consider barriers and harms facing people because of gender nonconformity would not describe themselves in the narrow terms of being female but born in a male body or vice versa. However, a detailed analysis of the assumptions in these questions is beyond the scope of this article. Rather, we primarily mean to point to the difference between these questions and an approach that defines trans identity as requiring desire for and access to specific medical care.[↑]
18. Gates, “How Many People…?,” 5.[↑]
19. Although the estimates they compare vary by an order of magnitude.[↑]
20. Dean Spade with Gabriel Arkles, Phil Duran, Pooja Gehi, and Huy Nguyen, “Medicaid Policy and Gender-Confirming Health Care for Trans People: An Interview with Advocates,” Seattle Journal for Social Justice 8 (Spring/Summer 2010): 497; Spade, “Documenting Gender”; Gehi and Arkles, “Unraveling Injustice.”[↑]
21. For those inclined towards mathematical equations, this reasoning can be represented as P(T) = P(LGB) P(T|LGB). This equation is only correct if all T are included in LGB. If there are T outside LGB, the equation would need to sum over all possible conditions (for example non-LGB).[↑]
22. The direct estimate of the percentage of transgender individuals in the Massachusetts Behavioral Risk Factor Surveillance Survey is 0.5 percent, while the estimate arrived at by combining data from the California LGBT Tobacco Survey and the California Health Interview Survey using the logic described is 0.1 percent.[↑]
23. For a more detailed analysis of why evidence of medical treatment should not be used to define trans identity and the significant harm that is caused by the use of such criteria in numerous policies and programs, see Spade, “Documenting Gender,” 731; Gehi and Arkles, “Unraveling Injustice.”[↑]
24. Shannon P. Minter, “Do Transsexuals Dream of Gay Rights? Getting Real About Transgender Inclusion,” in Transgender Rights, ed. Paisley Currah, Richard M. Juang, and Shannon P. Minter (Minneapolis: University of Minnesota Press, 2006), 141–170; Sylvia Rivera, “Queens in Exile, the Forgotten Ones,” in Genderqueer: Voices from Beyond the Sexual Binary, ed. Joan Nestle, Riki Wilchins, and Clare Howell (Los Angeles: Alyson Books, 2002), 67–85; Dean Spade, “Fighting to Win,” in Sycamore, That’s Revolting!, 31–38.[↑]
25. The estimate of nine million LGBT people in the US contained in the WI’s study, “How Many People are Lesbian, Gay, Bisexual and Transgender?,” for example, has been cited so frequently that the WI does not track the number of citations. A Google search for “9 million” and “gay” yields over two million citations. Email between Cathy Renna, communications consultant to the WI, and Alex West, research assistant to Dean Spade, September 12, 2011.[↑]
26. As of August 2011, Manhunt.net reports having over 6.5 million user profiles (http://www.online-buddies.com/products.htm). According to the internet use research company Alexa, 32 percent of the traffic at Manhunt.net originates from the United States (http://www.alexa.com/siteinfo/manhunt.net). Assuming that traffic is uniform per user across countries, we take 32 percent of the total 6.5 million profiles to get an estimated 2.08 million profiles by US residents.[↑]
27. Gates, “How Many People…?”[↑]
28. Jonathan Elford, “The Internet and Gay Men,” Social Research Briefs 1 (2003); E. Benotsch, S. Kalichman, and M. Cage, “Men Who Have Met Sex Partners via the Internet: Prevalence, Predictors, and Implications for HIV Prevention,” Archives of Sexual Behavior 31 (2002): 177–183.[↑]
29. At the time of the survey, Manhunt.net was a new website used primarily by people in and around Boston.[↑]
30. Note that, in fact, this is an arbitrary decision, so labeling it as “optimistic” is somewhat nonsensical. However, this kind of reasoning is common in WI study estimates.[↑]
31. Even among the general internet population, Manhunt.net receives more visitors who are aged 25–54, making more than $60,000/year, African American, White, or Latino, and fewer visitors who are older than 55, making less than $60,000/year, or Asian (http://www.alexa.com/siteinfo/manhunt.net). The representation of transgender people on Manhunt.net is not clear. This is in addition to sample bias inherent in considering internet users.[↑]
32. Of 48 such studies published as of October 2015, almost all predicted that he state will cumulatively gain money if legal marriage were extended to same-sex couples.[↑]
33. A press release from the WI dated May 28, 2011 suggested another explicit use of the organization’s empirical methods to support an agenda that explicitly endorses capitalism and derides alternatives. The press release was entitled “New Study Shows Vast Majority of Countries Have Become More Accepting of Homosexuality; Trend Slower or Reversed in Russia and Other Ex-Socialist Countries.”[↑]
34. Herman et al., “Impact on Rhode Island’s Budget.”[↑]
35. Randy Albelda, M.V. Lee Badgett, Alyssa Schneebaum, and Gary J. Gates, “Poverty in the Lesbian, Gay, and Bisexual Community,” UCLA: The Williams Institute, 2009.[↑]
36. It is clear that complex market forces based on class background, race, religion, education, and other factors greatly influence dating and marriage partner choice, usually resulting in homogamous couples. “How Your Race Affects the Messages You Get,” OkTrends; Wang and Kao, “Does Higher Socioeconomic Status Increase Contact between Minorities and Whites?”; Wendy Manning and Pamela J. Smock, “First Comes Cohabitation and then Comes Marriage?” Journal of Family Issues 23 (2002): 1065–87; Matthijs Kalmijn, “Intermarriage and Homogamy: Causes, Patterns, Trends,” Annual Review of Sociology 24 (1998): 395–421; Debra L. Blackwell and Daniel T. Lichter, “Mate Selection among Married and Cohabiting Couples,” Journal of Family Issues 21(2000): 275–302.[↑]
37. Gwendolyn Mink, “The Lady and the Tramp: Gender, Race and the Origins of the American Welfare State,” in Women, the State and Welfare, ed. Linda Gordon (Madison: University of Wisconsin Press, 1990), 92–122; Holloway Sparks, “Queens Teens and Model Mothers: Race, Gender and the Discourse of Welfare Reform,” in Race and the Politics of Welfare Reform, ed. Sanford F. Schram, Joe Soss, and Richard Fording (Ann Arbor: University of Michigan Press, 2003), 188–189; Daniel Patrick Moynihan, The Negro Family: The Case for National Action (Washington, D.C.: Office of Policy Planning and Research, US Department of Labor, 1965); Personal Responsibility and Work Opportunity Reconciliation Act of 1996, Pub. L. No. 104–193, 101 (1996); Neubeck and Cazenave, Welfare Racism.[↑]
38. See Robert Rector and Melissa Pardue, “Understanding the President’s Healthy Marriage Initiative,” Heritage Foundation, March 26, 2004; Robert Pear and David D. Kirkpatrick, “Bush Plans $1.5 Billion Drive For Promotion of Marriage,” The New York Times, January 14, 2004; Phoebe G. Silag, “To Have, To Hold, To Receive Public Assistance: TANF and Marriage Promotion Policies,” Journal of Gender, Race, and Justice 7 (2003): 413, 419 (describing West Virginia’s $100 bonus to public assistance recipients who are married); Sarah Olson, “Marriage Promotion, Reproductive Injustice, and the War Against Poor Women of Color,” Dollars & Sense, January–February 2005, 14 (describing marriage promotion programs that provid[e] extra cash bonuses to recipients who get married, [deduct] money from welfare checks when mothers are living with men who are not the fathers of their children, [and] increase[e] monthly welfare checks for married couples”).[↑]
39. James Alm and Leslie A. Whittington, “For Love or Money? The Impact of Income Taxes on Marriage,” Economica 66 (1999): 309–310.[↑]
40. First, it is assumed that weddings in Rhode Island will attract the average of number of out-of-town wedding guests observed in same-sex weddings in Massachusetts. Then it is assumed that half the number of guests will rent hotel rooms at the average rate in Rhode Island. In a different part of the study, it is assumed that wedding spending for same-sex couples is one quarter that of different-sex couples. These assumptions are somewhat arbitrary and raise questions such as: Do same-sex wedding guests stay in luxury hotels and business-rate hotels included in the average hotel figure? Does that estimate inflate the estimated amount spent? Do same-sex wedding guests ever stay with friends or family or share hotel rooms among more than two people? Does that estimate overestimate hotel revenues? How much do same-sex couples spend on weddings? Is a quarter of different-sex couples spending an overestimate? Will the states that recognize same-sex marriage later attract as many weddings as the first few states to do so, or did people flock to the first states to recognize same-sex marriage? These sorts of questionable assumptions are used consistently to set up an estimation concluding that legalizing same-sex marriage will economically benefit the state.[↑]
41. “Our Wedding Put Us in Debt!,” Oprah.com, April 27, 2004; “Average Wedding Debt ‘Takes Almost 3 Years to Pay Off’,” GregoryPennington.com, May 3, 2011; Ellie Omahoney, “Wedding Debt Outlives Marriage by Five Years,” Marie Claire UK, July 29, 2009; G.E. Miller, “What Does the Average Wedding Cost,” 20 Something Finance, March 14, 2015.[↑]