Examining case outcomes in US transgender homicides: an exploratory investigation of the intersectionality of victim characteristics

Abstract

Empirical research has yet to explore the intersections of victim characteristics on case outcomes (e.g. guilty, not guilty, unsolved) in transgender homicides. Drawing on queer criminological and intersectional frameworks, both theoretically and methodologically, we explore this relationship using data from 105 transgender homicide cases in the United States between 2010 and 2016. Although only an exploratory study due to our small and sparse sample, both bivariate and multivariate analyses reveal that case outcomes are patterned on the intersections between trans homicide victim race, gender identity, and age at death. We find that while Black trans women are the most common group of trans homicide victim, their cases are among the least likely to result in a guilty verdict. We also find that age plays a unique role in the likelihood of conviction creating an eldership advantage for some race and gender identity intersections but serving as a disadvantage for others.

Notes

Data availability statement

The data that support the findings of this study are available at https://unerased.mic.com/.

Notes

1 Transgender is an umbrella term that refers to individuals whose gender identity does not align with their assigned birth sex (e.g. a person assigned female at birth and who identifies as a man).

2 Throughout the paper, trans is used as a shortened umbrella term for transgender people.

3 Guadalupe-Diaz (2019:75) asserts, “interpersonally, genderism manifests in how we police and regulate the ‘natural’ order of two genders with masculinity as a privileged expression.”

4 Heteronormativity can be described as “formal and informal systems of cultural bias that favor heterosexuality” (Buist and Lenning 2016:xvii).

5 The term cisgender refers to individuals whose gender identity is congruent with their assigned birth sex (e.g. a person assigned female at birth and who identifies as a woman).

6 “Approximate average annual rate for ages 15–34, according to Mic data and an average of transgender population estimates” (Talusan 2016a).

7 “Approximate average annual rate for ages 15–34, 2010–2014, based on data from the National Center for Health Statistics” (Talusan 2016a).

8 For research on gender see Albonetti (1991, 2002), Forsterlee et al. (2004), Steffensmeier (1980) and Steen, Engen, and Gainey (2005). Race and ethnicity were the focus in Albonetti (1991, 2002), Farrell and Swigert (1986), Kramer and Ulmer (2002), Miethe and Moore (1986), Spohn and Holleran (2000), Steffensmeier and Demuth (2001), Ulmer (1997), Unnever (1982), and Zatz (1984).

9 See the report by Talusan (2016a) for more detail on the collection and fact-checking of the data: https://unerased.mic.com; We also conducted our own cross-reference of cases for 2010 and 2015 via media and internet sources, along with a report by the Human Rights Campaign (HRC) and the Trans People of Color Coalition (TPOCC) for 2015 cases: see Addressing Anti-Transgender Violence: Exploring Realities, Challenges and Solutions for Policymakers and Community Advocates (http://hrc-assets.s3-website-us-east-1.amazonaws.com//files/assets/resources/HRC-AntiTransgenderViolence-0519.pdf). Additionally, we searched for updated case information for several unsolved cases across all years and cross-checked the status of all pending cases as of September 2020. Our final data reflect any changes in case outcomes for pending or unsolved cases as a result of our searches. Thus, our case outcomes may be different from those initially reported by Mic.

10 We also tried other binary categorizations of our dependent variable for the regression analyses that follow. These included comparing unsolved cases to all other case outcomes as well as excluding pending cases from our analyses. Models estimated with these alternative dependent variable categorizations either did not converge or meet the assumptions of our estimation procedures. Therefore, we can only present the results from our guilty/non-guilty operationalization.

11 According to Talusan (2016a) the category gender non-conforming is primarily composed of individuals who identify as neither male nor female but present as feminine.

12 See also the work of Heinze (2006) and colleagues (Heinze and Schemper 2002; Heinze, Ploner, and Beyea 2013) for more detailed applications of the Firth procedure to logit regression.

13 We thank several anonymous reviewers for many of these suggestions.

14 “California became the first state to ban this type of defense in their AB-2501 revision of the Section 1 F1 and F2 Voluntary Manslaughter legislation which states that the defendant may not even attempt to convince a jury that the victim’s gender and/or sexual orientation may be used as a grounds for justifiable homicide” (Jamel 2018:63–64).

Additional information

Notes on contributors

Rayna E. Momen

Rayna E. Momen is a doctoral student in sociology at West Virginia University where they were awarded a W.E.B. DuBois Fellowship. Momen’s research examines the criminalization of transgender people from a queer criminological and intersectional lens. Other research interests include anti-racist and abolitionist pedagogy, educational access for incarcerated people, rural criminology, and resilience among underrepresented people in Appalachia. Momen received instructor training through the Inside-Out Prison Exchange Program and is a longtime volunteer with the Appalachian Prison Book Project.

Lisa M. Dilks

Dr. Lisa M. Dilks is an Associate Professor of Sociology at West Virginia University. Dr. Dilks is a structural social psychologist, a branch of microsociology that studies the impact of external social factors on individuals’ behaviors and interactions with others. Under this theoretical framework, her research falls into two specific but interrelated areas: (1) the creation of and remedies to social inequalities resulting from status differences among individuals, with (2) a specific focus on gender inequalities at both the individual and structural level. Dr. Dilks’s ongoing projects explore the applicability of both status and gender theories from structural social psychology to criminological phenomena, including punishments and arrest likelihoods for both street and white-collar crime. Her research is primarily quantitative, employing experimental and quasi-experimental methods along with the analysis of large secondary data sets to answer questions in both these areas.