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

Selective Representation of Results

The presentation of the WI studies represents important choices about which results to emphasize, which to exclude, and how to frame the results. Consistently across the WI studies, error bars are omitted from plots,¹ resulting in plots where small, statistically insignificant differences appear important. For example, the rates of same-sex couples identifying as “husband/wife” and “unmarried partner” are compared between states with different degrees of legal recognition of same-sex couples (legal marriage, legal civil unions or domestic partnerships (CU/DP), and no legal status).² The text in the study describing these data states that the difference in rates for partnered couples is not statistically significant³ between states with and without legal CU/DP recognition. However, in a plot the raw difference is depicted without error bars that should be used to indicate the uncertainty.⁴ If the error bars were included, it would be clear that any perceived difference is not statistically significant. The lax visual representation leads the reader to conclude that legal CU/DP recognition is correlated with higher rates of same-sex couples identifying as “husband/wife,” which is not supported by the data. The study appears to use this non-significant difference to argue that legal recognition of same-sex marriage will increase identification as “husband/wife” on the US census, and thus help facilitate counting of married same-sex couples. This sort of selective representation of non-significant results is used to persuade the readers of a particular point, regardless of the empirical evidence supporting that point.

More evidence of potentially misleading choices in visual representation of data can be seen in the WI’s landmark study estimating the number of LGBT individuals in the United States.⁵ In that study, Figure 5 shows a bar plot comparing the estimated numbers of lesbian and bisexual women, gay and bisexual men, and transgender individuals. Before examining the plot, note that dividing individuals into those three separate categories is practically impossible since those identities are not mutually exclusive and are inconsistently defined, so some contrived categorization must be used. The plot shows the estimated number of individuals in each group. Using these estimates, it appears that transgender individuals make up about 8.7 percent of the LGBT population.⁶ This result is not included in the published paper, though it may be interesting because it is much higher than the survey-based estimates.⁷ Additionally, the plot representing these data appears to scale the percentage of lesbian and bisexual women by the total number of women, the percentage of gay and bisexual men by the total number of men, but the percentage of transgender people by the total number of people. These are clearly not comparable numbers, giving the transgender category roughly half the visual impact of the others, resulting in a visually powerful image where the proportion of the bar graph area representing transgender individuals is well under 8.7 percent of the total plot, leading readers to believe that transgender individuals make up a smaller percentage of the LGBT population than the data being described actually suggest.

Just as a result can be over-emphasized and interpreted (like plotting non-significant differences without error bars), some findings can be downplayed or ignored when they are inconsistent with the study objective. This is demonstrated in the previously mentioned study exploring same-sex couple identity by the lack of investigation of the evident finding that African Americans in same-sex couples are half as likely to be married or in a legal CU/DP as whites, which is a statistically significant difference.⁸ In contrast to the previous non-significant result discussed, this result is mentioned in passing in the text, but the reader must turn to the appendix to see that the difference is statistically significant and no visual figure is provided to illustrate the point. Further investigation of this significant finding might indicate that the structures of marriage and legal partnerships are not as useful or beneficial for African Americans and better serve whites, perhaps because white people are more likely to have wealth, immigration status, private health insurance, and other benefits that can be shared through marriage. However, no more attention is given to the result.

Conclusion

The mobilization of statistical data in the promotion of same-sex marriage provides a site where the rhetorical significance of empirical claims is clear. Regardless of the consistency or verifiability of the data produced, even within its own methodology, its persuasive value is significant. Given the WI’s apparent success in mobilizing its data to support its projects, its methodological errors are worth noting—not because our critique is limited to these errors, but because the errors expose the ways data operates to produce an image of a population that matches the imperatives of biopolitics. Through survey-based studies, the WI uses statistical methods that were developed for population control to justify increased legal benefits and recognition for a population it generally casts as white, able-bodied, owning-class and possessing legal immigration status. At times these methods are implemented inaccurately, clarifying the significance of their role in producing a persuasive rhetoric. Placing demands for legal equality within this framework forms the politics of equality in ways that support the broader racialized (upward) redistribution projects of neoliberal reform.⁹

All uses of statistical methods rely on making certain assumptions, and the representation of all results based in such methods is inevitably rhetorical.¹⁰ We are not suggesting that such methods must be abandoned, but rather that an awareness of their origins and an understanding of the persuasive role of “hard science” might lead us to examine how those assumptions and representations operate in contemporary advocacy projects that certainly do not consider themselves white supremacist projects or eugenics projects.

This analysis of the WI data neither attempts to be an exhaustive study of its work, nor to suggest that its practices are unusual. The link between legal rights claims and the production of statistical data, and the concerns we suggest about the production of racial-national norms that these strategies achieve, are not limited to lesbian and gay rights discourse nor to this particular think tank. Such strategies are visible across US social movements where non-profitization, among other forces, have pushed various strains of work toward “legible” demands that fit within existing economic and political arrangements.¹¹ However, lesbian and gay rights discourse is a particularly interesting location for examining these relationships because of the current visibility of quests for formal legal equality in that realm and the relatively sudden emergence of a proliferation of statistical data to support those quests. Rhetoric mobilized to support lesbian and gay rights has also become a site of articulation of key trends in neoliberal politics (pro-militarism, increased immigration control, dismantling of poverty alleviation, marriage promotion, reduced taxation for the wealthy, increased criminalization of people of color).

An understanding of how legal equality rhetoric aligns with the rehabilitation, legitimation, and expansion of racialized–gendered violence such as austerity, criminalization, and militarization provides a space to question what equality means in the context of the normalization of populations. The limitations of legal equality claims and their relationship to the collection of standardized data are interesting when considered alongside Scott’s discussion of the meaning of “equality” in the context of French Revolutionary fervor for standardized weights and measures. Scott’s analysis suggests that the “equality” articulated in nation-building documents of the mid-1700s in France and the US might be understood as an equality that was about producing a uniform relationship between certain white, male, propertied citizens and the government. Uniform weights and measures and individualized property ownership gave governments new powers to inventory, regulate, and administer and offered the promise (whether realized or not) of a new, rationalized relationship between white propertied men and the government (as opposed to the arbitrary extraction of wealth by nobles). That capacity to manage and control the population, at the level of population, expanded with the advent of eugenics and statistical methods. Eugenics rearticulated the logics that undergirded the racialized property statuses that founded the United States through the framework of scientific reason, both conceptualizing and implementing new forms of population control.

How do these genealogies still structure the frameworks of “equality” demanded through law and our ways of knowing about the population? How might this understanding of “equality” expose the ways that statistical methods and legal equality demands collaborate to sort the population and to promote the lives of those deemed worthy and diminish the lives of those framed as “threats” and “drains”?

The production of data about the gay and lesbian population (or sometimes the LGBT population) invents and describes the population in whose name rights demands are articulated. Decades of critical thinking from women of color feminism, queer theory, disability studies, and Critical Race Theory show the ways that rights claims rely on a false universalism while actually producing a narrowly articulated subject who can possess rights. Considering these insights in conjunction with the work of scholars like Scott, Foucault, and Hacking exposes the links between statistical methods, the production of normalized national populations, and the distribution of life chances. Returning to Foucault, we can begin to assess how both legal equality frameworks and the production of statistical data to support them produce images of a white settler national population that must be cultivated and protected in relation to “threats” and “drains” that must be eliminated, abandoned, or extinguished. The eugenics projects that motivated key developers of statistical methods, such as Galton, aimed to identify elements of the population that are favored and disfavored so that they can be cultivated or eradicated. The effects of these projects and methods remain with us. Examples of how lesbian and gay rights discourse develops in relation to these genealogies abound. When lesbian and gay rights discourse depicts gay and lesbian parents as “good parents” using statistical data and with the goal of winning recognition and benefits for same-sex couples, it articulates assertions about parenting that support the very racial–national norms that ensure that Black parents, parents in prison, Native parents, poor parents, and parents with disabilities will be targeted by the child welfare system.¹² When lesbian and gay rights discourse depicts lesbian and gay couples as American workers/property owners who deserve equal access to regressive tax policies, it mobilizes the same rhetorics that have produced significant growth in the wealth divide in the US in the last four decades.¹³ When lesbian and gay rights discourse depicts the police and prisons as forces that must be mobilized to save and rescue gay and lesbian people from violence through the passage of hate crime laws, it participates in expanding a system that targets people of color and poor people with homophobic and transphobic violence every day.¹⁴

Legal equality arguments tend to present existing legal structures as generally fair and neutral but for a singular exclusion and to construct the excluded group as a population that deserves inclusion. Contemporary political and economic conditions have seen the emergence of gay and lesbian rights advocacy that makes legal equality arguments and increasingly backs them up by producing a statistical picture of the population. This work constructs desirable and undesirable populations, those deserving a chance at life and reproduction and those whose exile, imprisonment, or death is acceptable or even important for the survival of the nation.

While the framings currently articulated by the most visible and well-funded lesbian and gay rights discourse makes the links between legal rights, statistical methods, and racial–national normalization relatively obvious, the methodological and strategic questions that such an analysis raises are not limited to the most blatant sites of contradiction. All resistance projects must struggle with the problem of how conceptualizing a population in order to articulate a claim involves creating an image of constitutive others who are cast as threats and drains.¹⁵ The queer and trans racial and economic justice–focused activism that operates as an alternative to the gay and lesbian rights framework must also struggle with these questions. This alternative politics also produces studies that support reform projects that, though different from the marriage/military/hate crimes reforms, still imagine a population that likely has a set of constitutive outsiders. Many formations that have sought to resist capitalism, white supremacy, ableism, heteropatriarchy, and colonialism have run up against this problem. They have produced divisions within their constituencies, required forms of violence and surveillance to manage people, and ended up disappointingly reproducing governmental functions that look all too similar to those the movements had originally opposed.

We engage this analysis not from a desire to be singular about method or strategy, but instead from an interest in tracing the relationships between certain ways of knowing and ways of governing. We are interested in tracking incentives and investments that travel with particular methods and strategies. Such relationships and incentives may root statistical methods and legal equality demands to dominant arrangements. While knowledge is implicated with power in potentially problematic ways, knowledge in the form of an active analysis of these implications is also necessary to transform those arrangements.

Acknowledgments

We thank Gary Gates and the Williams Institute at UCLA School of Law, for generously sharing the survey data analyzed here, and the individuals participating in that survey for sharing their information and experiences. We thank Britt Rusert, Craig Willse, Urvashi Vaid, Janet Jakobsen, and Soniya Munshi for editorial advice, and research assistants for citation work.

Figure 1: Gaussian distribution

A standard Gaussian distribution is shown such that a random variable following the distribution has probability proportional to the height of the curve of having any particular value. The mean (most probable value) and upper and lower tails (less probable values) are labeled.

Figure 2: Gaussian distributed random components to height

Based on the example in the text, the non-heritable random height component for a parent is unusually large (shown in the vertical line labeled parent). Assuming that the parent and child share the same genetic background, the probability that the child of the parent is taller by chance is the area under the curve to the right of the parent, and the probability that the child of the parent is shorter by chance is the area under the curve to the left of the parent.

1. See, for example: Gates, “Same-Sex Spouses and Unmarried Partners in the American Community Survey, 2008,”; Gates, “Same-Sex Couples in US Census Bureau Data”; Gates, “How Many People…?”; Herman et al., “Impact on Rhode Island’s Budget.” [↩]
2. Gates, “Same-Sex Couples in US Census Bureau Data.” [↩]
3. A finding that is statistically significant is unlikely to occur by random chance, as specified by some threshold (usually 95 percent). A finding that is not statistically significant is likely to be due to random chance, rather than some systematic trend. [↩]
4. Gates, “Same-Sex Couples in US Census Bureau Data,” fig. 7. [↩]
5. Gates, “How Many People…?” [↩]
6. With an estimated 4,007,834 lesbian and bisexual woman, 4,030,946 gay and bisexual men, and 697,529 transgender individuals, the proportion of transgender people in the whole LGBT population is 697,529 /(4,007,834 + 4,030,946) = 0.087 = 8.7% [↩]
7. The WI study reports that “[t]he 2003 California LGBT Tobacco Survey found that 3.2% of LGBT individuals identified as transgender.” [↩]
8. Recall that when we examined the data reported in “Same-Sex Couples in US Census Bureau Data,” we found that African Americans are statistically significantly less likely to identify as “husband or wife” or “unmarried partner” than Whites. (See note 85.) [↩]
9. See Lisa Duggan’s discussion of neoliberalism as being characterized by the “upward distribution of wealth,” and her useful analysis of neoliberal gay rights politics. Duggan, The Twilight of Equality? Neoliberalism, Cultural Politics, and the Attack on Democracy (Boston: Beacon Press, 2004). [↩]
10. It is worth noting that the WI publishes its own studies, eliminating any peer review or editing, which is typically required for academic publishing. Self-publishing frees the WI from the need to substantiate its analyses, but has not prevented its studies from making a significant impact in lesbian and gay rights quests. [↩]
11. Dylan Rodríguez, “Political Logic of the Non-Profit Industrial Complex.” [↩]
12. According to one Williams Institute study, “[s]ame-sex couples raising adopted children are older, more educated, and have more economic resources than other adoptive parents.” Notably, this study makes use of same-sex couples identified using census data, which, as discussed earlier, is likely to over-represent wealthier individuals. Though this study includes a discussion of possible sources of error, we still see invisibilizing of marginalized populations and a disregard for accurate statistical methodology when convenient to the political aims of the study. Gary Gates, M.V. Lee Badgett, Jennifer Ehrle Macomber, and Kate Chambers, “Adoption and Foster Care by Gay and Lesbian Parents in the United States” (2007), 16. For further reading on how child welfare systems target certain populations for family disruption, see, for example: Dorothy Roberts, Shattered Bonds: The Color of Child Welfare (New York: Civitas Books, 2002); Jane Jeong Trenka, Julia Chinyere Oparah, and Sun Yung Shin, eds., Outsiders Within: Writing on Transracial Adoption (Massachusetts: South End Press, 2006), 59–73. [↩]
13. See, for example: Michael D. Steinberger, “Federal Estate Tax Disadvantages for Same-Sex Couples,” UCLA: Williams Institute, 2009; United for a Fair Economy, Estate Tax Campaign; Beverly I. Moran, “Capitalism and the Tax System: A Search for Social Justice,” Southern Methodist University Law Review 61 (2008); Michael A. Livingston, sity LA SeKalven at 50: Progressive Taxation, ‘Globalization,’ and the New Millenium,” Florida Tax Review 4 (2000). [↩]
14. See, for example, Rebecca Stotzer, “Comparison of Hate Crime Rates Across Protected and Unprotected Groups,” UCLA: The Williams Institute, 2007; Morgan Bassichis, “‘It’s War in Here’: A Report on the Treatment of Transgender and Intersex People in New York State Men’s Prisons” (New York: Sylvia Rivera Law Project, 2007); Alexander L. Lee, “Gendered Crime and Punishment: Strategies to Protect Transgender, Gender Variant and Intersex People in America’s Prisons (Parts 1 and 2),” GIC TIP Journal (Gender Identity Center of Colorado: Summer 2004 [part I] and Fall 2004 [part II]); Alex Coolman, Lamar Glover, and Kara Gotsch, “Still in Danger: The Ongoing Threat of Sexual Violence Against Transgender Prisoners” (Los Angeles: Stop Prisoner Rape and the ACLU National Prison Project, 2005); Joey L. Mogul, Andrea J. Ritchie, and Kay Whitlock, Queer (In)Justice: The Criminalization of LGBT People in the United States (Boston: Beacon Press, 2011); Katherine Whitlock, In a Time of Broken Bones: A Call to Dialogue on Hate Violence and the Limitations of Hate Crime Laws (Philadelphia: American Friends Service Committee, 2001). [↩]
15. See Miranda Joseph, Debt to Society: Accounting for Life Under Capitalism (Minneapolis: University of Minnesota Press, 2014), discussing the violence of quantification while also cautioning against the dismissal of statistical knowledge in efforts for social justice (xx). [↩]