Selected Readings on the LGTBQ+ Community and Data

By Uma Kalkar, Salwa Mansuri, Marine Ragnet and Andrew J. Zahuranec

Photo by Cecilie Johnsen/Unsplash is licensed under CC0.

As part of an ongoing effort to contribute to current topics in data, technology, and governance, The GovLab’s Selected Readings series provides an annotated and curated collection of recommended works on themes such as open data, data collaboration, and civic technology.

Around the world, LGBTQ+ people face exclusion and discrimination that undermines their capacity to live their lives and succeed. Together with allies, many LGBTQ+ people are fighting to exercise their rights and achieve full equality. However, this struggle has been undermined by a lack of specific, quantifiable information on the challenges they face.

When collected and managed responsibly, data about sexual and gender minorities can be used to protect and empower LGBTQ+ people through informed policy and advocacy work. To this end, this Selected Reading investigates what data is (and is not) collected about LGBTQ+ individuals in the areas within healthcare, education, economics, and public policy and the ramifications of these outcomes. It offers a perspective on some of the existing gaps regarding LGBTQ+ data collection. It also examines the various challenges that LGBTQ+ groups have had to overcome through a data lens. While activism and advocacy has increased the visibility and acceptance of sexual and gender minorities and allowed them to better exercise their rights in society, significant inequities remain. Our literature review puts forward some of these recent efforts.

Most of the papers included in this review, however, conclude with similar findings: data for about LGBTQ+ communities is still lacking and as a result, research on the topic is often times also lagging behind. This is particularly problematic, as detailed in some of our readings, because LGBTQ+ populations are often at the center of discrimination and still face disparate health vulnerabilities. The LGBTQI+ Data Inclusion Act, which recently passed the US House of Representatives and would require over 100 federal agencies to improve data collection and surveying of LGBTQ communities, seeks to address this gap.

We hope this selection of readings can provide some clarity on current data-driven research for and about LGBTQ+ individuals. The readings are presented in alphabetical order.

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Selected Reading List (in alphabetical order)

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Annotated Selected Reading List (in alphabetical order):

D’Ignazio, Catherine, and Lauren F. Klein. Data Feminism. MIT Press, 2020. https://mitpress.mit.edu/books/data-feminism.

  • D’Ignazio and Klein investigate how data has been historically used to maintain specific social status quos. To overcome this challenge, they approach data collection and uses through an intersectional, feminist lens that identifies issues in current data handling systems and looks toward solutions for more inclusive data applications.
  • The editors define data feminism as “power, about who has it and who doesn’t, and about how those differentials of power can be challenged and changed using data.” The book centers around seven principles that identify and challenge existing power structures around data and seek pluralist, context-based data processes that illuminate hidden and missed data.

Giblon, Rachel, and Greta R. Bauer. “Health care availability, quality, and unmet need: a comparison of transgender and cisgender residents of Ontario, Canada.” BMC Health Services Research 17, no. 1 (2017): 1–10. https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-017-2226-z.

  • Canada boasts a universal healthcare and insurance system, yet disparities exist between the treatment quality, services, and knowledge about transgender patients.
  • Data collection on transgender, non-binary, and intersex individuals is not conducted in Canadian health surveys, making it difficult to compare and contrast the healthcare provided to transgender people with that provided to cisgender people. Moreover, a lack of physician knowledge about trans needs and/or refusal to provide hormone therapy/ gender-affirming procedures result in trans individuals explicitly avoiding medical services. The lack of services, comfort, and data about transgender people in Canada demonstrate their severely “unmet health care need.”
  • Using data about Ontario residents from the Canadian Community Health Survey and the Trans PULSE survey, the researchers find that 33% transgender Ontarians had an unmet health need that would not be unmet if they were cisgender. As well, transgender men and women found the quality of healthcare in their community to be poor than compared to cisgender individuals. Twenty-one percent of transgender people avoided going to emergency rooms because of their gender identity.

Bowleg, Lisa, and Stewart Landers. “The need for COVID-19 LGBTQ-specific data.” American Journal of Public Health 111, no. 9 (2021): 1604–1605. https://pubmed.ncbi.nlm.nih.gov/34436923/.

  • The adage “no data, no problem” has been magnified during the pandemic, highlighting gaps around data collection for LGBTQ communities, which often intersect with other communities who are disproportionately at-risk for COVID-19, such as minority populations in the service industry and those who smoke.
  • Despite concerns about the stigma facing LBGTQ communities, data collection from these demographics has been relatively feasible, with federal governments drastically increasing their data collection from LGBTQ communities.
  • However, the lack of direction and guidance at a federal level to collect sexual and gender minority data has stunted information about how this demographic has experienced COVID-19 when compared to cis-gender, heterosexual groups. The authors stress the need for data collection from LGBTQ communities and advocacy to encourage these practices to help address the pandemic.

Marshall, Zack, Vivian Welch, Alexa Minichiello, Michelle Swab, Fern Brunger, and Chris Kaposy. “Documenting research with transgender, nonbinary, and other gender diverse (trans) individuals and communities: introducing the global trans research evidence map.” Transgender Health 4, no. 1 (2019): 68–80. https://www.liebertpub.com/doi/10.1089/trgh.2018.0020.

  • Marshall and colleagues study a series of 15 academic databases to assemble a dataset describing 690 trans-focused articles. They then map where and how transgender “have been studied and represented within and across multiple fields of research” to understand the landscape of existing research on transgender people. They find that research around the trans community focused on physical and mental healthcare services and marginalization and were primarily observational research.
  • The authors found that social determinants of health for transgender people were the least studied, along with ethnicity, culture, and race, violence, early life experiences, activism, and education.
  • With this evidence map, researchers have a strong starting point to further explore issues through a LGBTQ lens and better engage with trans people and perspectives when looking at social problems.

Medina, Caroline and Lindsay Mahowald. “Collecting Data about LGBTQI+ and Other Sexual and Gender-Diverse Communities.” Center for American Progress, May 26, 2022. https://www.americanprogress.org/article/collecting-data-about-lgbtqi-and-other-sexual-and-gender-diverse-communities.

  • The paper argues, that despite advances “a persistent lack of routine data collection on sexual orientation, gender identity, and variations in sex characteristics (SOGISC) is still a substantial roadblock for policymakers, researchers, service providers, and advocates seeking to improve the health and well-being of LGBTQI+ people.”
  • Even though various types of data are integral to the experiences of LGBTQI+ people, the report narrows its focus to data collection in two forms of environments: general population surveys & surveys regarding LGBTQI+ people. Specific population surveys such as the latter provide significant advantage to capture specific and sensitive data.
  • It argues that a range of precautions can be adopted from a research design perspective to ensure that personal data and information is handled with care and matches ethical standards as outlined in the Data Ethics Framework of the Federal Data Strategy ranging from privacy and confidentiality to honesty and transparency.

Miner, Michael H., Walter O. Bockting, Rebecca Swinburne Romine, and Sivakumaran Raman. “Conducting internet research with the transgender population: Reaching broad samples and collecting valid data.” Social science computer review 30, no. 2 (2012): 202–211. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3769415/.

  • The internet has the potential to collect information from transgender people, who are “a hard-to-reach, relatively small, and geographically dispersed population” in a diverse and representative manner.
  • To study HIV risk behaviors of transgender individuals in the U.S., Miner et al. developed an online tool that recruited individuals who frequent websites that are important for the transgender community and used quantiative and qualitative methods to learn more about these individuals. They conclude that while online data collection can be difficult to ensure internal validity, careful testing and methods can overcome these issues to improve data quality on transgender people.

Pega, Frank, Sari L. Reisner, Randall L. Sell, and Jaimie F. Veale. “Transgender health: New Zealand’s innovative statistical standard for gender identity.” American journal of public health 107, no. 2 (2017): 217–221. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5227923/.

  • Pega et al. discuss New Zealand’s national statistical standard for gender identity data collection, the first of its kind. More governments in Australia and the United States are now following suit to address the health access and information disparity that transgender people face.
  • Data about transgender people has advanced progressive policy action in New Zealand, and the authors celebrate this statistical standard as a way to collect high quality data for data-driven policies to support these groups.
  • While this move will help uncover LGBTQ individuals currently hidden in data, the authors critique the standard because it does not “promote the two-question method, risking misclassification and undercounts; does promote the use of the ambiguous response category “gender diverse” in standard questions; and is not intersex inclusive.”

Ruberg, Bonnie, and Spencer Ruelos. “Data for Queer Lives: How LGBTQ Gender and Sexuality Identities Challenge Norms of Demographics.” Big Data & Society 7, no. 1 (June 18, 2020): 205395172093328. https://journals.sagepub.com/doi/full/10.1177/2053951720933286.

  • Drawing from the responses of 178 people who identified as non-heterosexual or non-cisgender in a survey, this paper argues that “dominant notions of demographic data, […] that seeks to accurately categorize and “capture” identity do not sufficiently account for the complexities of LGBTQ lives.”
  • Demographic data commonly imagines identity as fixed, singular, and discrete. However, the researchers’ findings suggest that, for LGBTQ people, gender and sexual identities are often multiple and in flux. Most respondents reported their understanding of their identity shifting over time. For many, “gender identity was made up of overlapping factors, including the relationship between gender and transgender identities. These findings challenge researchers to reconsider how identity is understood as and through data.” They argue that considering identities as fixed and discrete are not only exclusionary but also do not wholly represent the dynamic and fluid nature of gender identities.
  • The piece offers several recommendations to address this challenge. Firstly, the researchers argue to remove data discreteness, which will enable users to select multiple identities rather than choose one from a drop-down list. Secondly, create communication and feedback channels for LGBTQ+ to express whether surveys and other data collection methods are sufficiently inclusive and gender-sensitive.

Sell, Randall L. “LGBTQ health surveillance: data = power.” American Journal of Public Health 107, no. 6 (2017): 843–844. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5425894/.

  • Sell recounts his motto: ‘data = power;’ ‘silence = death’ and how LGBTQ people have been victims of this situation. He argues that health research and surveillance has systemically ignored sexual and gender minorities, leading to gaps in administrative understanding and policies for LGBTQ population.
  • He laments that very few surveys on American health collect sexual and gender orientation data, and the lack of standardization around this data collection muddies researchers’ ability to collate and utilize the information meaningfully.
  • He calls for legislation that mandates the National Institutes of Health to include sexual and gender minorities in all publicly funded research similar to the specific inclusion requirement of women and racial and ethnic minorities in studies. Despite concerns about surveillance and targeting of LGBTQ minorities, Sell argues that data collection is imperative now for a long-scale understanding of the needs of the community, transcending political terms.

Snapp, Shannon D., Stephen T. Russell, Mariella Arredondo, and Russell Skiba. “A right to disclose: LGBTQ youth representation in data, science, and policy.” Advances in child development and behavior 50 (2016): 135–159. https://pubmed.ncbi.nlm.nih.gov/26956072/.

  • Despite significant and positive reforms such as the legalization of same-sex marriages and protection from intersectional sexual harrasment (Webb, 2011) in the United States, there is a striking gap in literature on evidence-based practices that support LGBTQ+ Youth (Kosciw & Pizmony-Levy, 2013). The lack of data-driven solutions stifle the creation of inclusive environments where members of the LGBTQI+ community feel heard and seen. There is a striking gap in literature on evidence-based practices that support LGBTQ+ Youth (also see Kosciw & Pizmony-Levy, 2013; Mustanski, 2011).
  • At present federal and local state data-states do not include SOGI (Sexual Oreintation & Gender Identity) in demographic questions. Data sets that do have spaces to disclose SOGI are largely in a health-related setting such as the Centre for Disease Control or Youth Risk Behavior. As such learning and education disparities and outcomes are not accurately measured.
  • Missing systematic SOGI data renders members of the LGBTQ+ community invisible and sidelined. As such several members of civil society have therefore demanded for the need to gather SOGI data in the Department of Health, Education & Justice. Such data is therefore central to holistically encapsulate the discriminatory experiencees LGBTQ+ Youth face in an education setting, integral to well-being and development. Scholars and research teams have thusfar overcome the barriers of data reliability and validity (see Ridolfo, Miller, & Maitland, 2012) by collating the most effective methods for data collection (Sexual Minority Assessment Research Team, 2009).

Wimberly, George L. “Chapter 10: Use of large-scale data sets and LGBTQ education.” LGBTQ issues in education: Advancing a research agenda (2015): 175–218. https://ebooks.aera.net/LGBTQCH10.

  • This book chapter highlighs the importance of large-scale data sets to gain understanding about LGBTQ students, school experiences, and academic achievement.
  • Young people who identify as LGBTQ tend to be generalized and ways that LGBTQ identification questions are asked by surveys change across years, making it important to disaggregate large-scale data for more granular knowledge about LGBTQ people in education.
  • Wimberly provides information about multiple datasets that collect this information, how they ask questions on LGBTQ identity, and ways in which the datasets have been used or have the potential to be leveraged for a more comprehensive understanding of students. He also points out the limitations of existing data sets, namely that they tend to be retrospective of the LGBTQ adolescent experience and collected from convenience samples, such as college students. This limitation also impacts the external validity of the data, especially with regard to rural, racialized, and lower-income LGBTQ students.

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