Transforming gender data collection methods

If your company wants data-driven Trans inclusion efforts, there's an often overlooked question that should be in your engagement surveys and HR information systems: ✅ "Do you currently, or have you ever, identified as Transgender?" 🔲 Yes 🔲 No 🔲 Prefer Not to Say The "or have you ever" piece is critical because it's not an uncommon experience for people to transition genders and drop the "Trans" identifier or identify with it less afterwards. If your surveys ask for gender identity and employees can only select one option, for example... 🔲 Man 🔲 Woman 🔲 Nonbinary 🔲 Transgender ... some Trans men and Trans women will select the "Man" or "Woman" box because that's what they identify as first. Companies are then at risk of not reaching a statistically significant response rate of Trans employees and/or losing trust with Trans employees because it's clear that you're not in the loop enough with the Trans experience. Some surveys have gender identity options that employees don't understand or they list so many options that they risk failing to meet statistical significance. For example they give options for Cisgender Man, Cisgender Woman, Transgender Man, Transgender Woman, etc. While these answer options together are totally inclusive and technically accurate, I can tell you as someone who trains thousands of people on gender inclusion that the majority of cisgender people don't know what "Cisgender" means. So, to collect gender identity information and Trans status that balances data cleanliness, inclusivity and accuracy while maximizing our chances of reaching statistical significance, I would recommend the following: ✅ "What is your gender identity?" (Select all that apply, if possible) 🔲 Man 🔲 Woman 🔲 Nonbinary 🔲 Agender 🔲 A gender identity not listed (write in, if possible in your system" 🔲 Decline to answer ✅ "Do you currently, or have you ever, identified as Transgender?" 🔲 Yes 🔲 No 🔲 Decline to answer These questions are usually optional for all employees. Companies that have strong data governance and privacy and high levels of trust can require employees to answer these questions so long as there is a "decline to answer" option. The cultural conversation around gender is evolving and many non-LGBTQ people are realizing that they have been socialized into gender categories. You should expect that how we ask these questions and the answer options we give employees will evolve too. We should take that as a welcome sign that our organizations are becoming more inclusive and GENDER EXPANSIVE rather than seeing it as the LGBTQIA+ mafia changing the acronym yet again. UGH that dang gender mafia 😉 . Trans and nonbinary people deserve to be counted accurately and to have their voices heard. There is no organizational accountability on DEI without good data, ambitious goals and a willingness from leaders to look Trans people in the eye.

Gender-sensitive data collection and estimation are essential for producing statistics that reflect the realities of both women and men. This training module was developed under the Asia-Pacific Network of Statistical Training Institutes to provide statisticians, researchers and civil society with practical guidance on integrating gender perspectives into data processes, from collection to estimation and analysis . This module covers the following key aspects: – Rationale and learning objectives for mainstreaming gender in data systems – Integration of gender considerations in censuses, administrative records, registries and household surveys – Specific guidance for time-use surveys and violence against women surveys, addressing design, sampling and interviewer training – Common gender biases in data processes and strategies to minimise them through careful design and training – Methods for gender data estimation, including identifying research questions, applying international standards and developing tabulation plans – Use of internationally agreed metadata and repositories (UNSD, ILO, WHO, UNESCO, FAO) to align concepts and methods – Recommendations for multi-level sex disaggregation and intersectional analysis across population groups The content emphasises that gender must be integrated at all stages of statistical work—from questionnaire design and sample selection to interviewer training and coding—to avoid bias and ensure relevance. By using international standards, engaging gender specialists and applying careful disaggregation, the module equips practitioners to generate more accurate, inclusive and policy-relevant gender statistics that can inform sustainable development and social equity.

Excited to share our new National Academy of Medicine report on how sex and gender identity influence disability evaluations by the Social Security Administration (SSA). We were commissioned by SSA & took part in a 1+ year process to report on the unique health challenges faced by Transgender and Gender Diverse (TGD) individuals, as well as those with Variations in Sex Traits (VSTs). We developed conclusions that could make for a more inclusive and accurate assessment process when people apply for disability benefits. **Key Takeaways:**   1️⃣ **TGD and VST Populations:** The report underscores that individuals from these populations often face significant barriers to healthcare, which can delay disease detection and worsen long-term health outcomes. These health disparities can directly impact their eligibility for disability benefits.     2️⃣ **Inclusive Language**, particularly for conditions like reproductive cancers and HIV: By removing gendered terms and focusing on the condition itself, disability evaluations can be inclusive for all individuals, regardless of gender identity. 3️⃣ **Data Collection:** Routine collection of data on gender identity, sex recorded at birth, and relevant care (e.g., gender-affirming treatments) is vital for an accurate evaluation. These data are lacking in many healthcare systems, but incorporating them into disability applications could help lead to more accurate determinations.  4️⃣ **Special Considerations:** The report highlights the need for careful evaluation of sex-specific diagnostic criteria for conditions like pulmonary function and kidney disease. In some cases, the sex recorded at birth may be the appropriate reference point, but in others—particularly for those receiving gender-affirming hormones—additional or alternative metrics may be necessary. One option is to use the measurement most likely to support an individual's disability application in cases where both 'male' and 'female' reference ranges are considered. 5️⃣ **Training for Disability Adjudicators:** Given the complexities in assessing disability claims for TGD individuals and those with VSTs, staff can receive training on the unique health and social challenges faced by these populations. This could ensure more accurate, fair, and compassionate disability determinations. By improving how disability claims are evaluated for TGD and VST individuals, we can move closer to a system that truly supports everyone. https://lnkd.in/egUKPbHT #SSA #HealthEquity #healthcare

What gets counted, counts. Structural inequalities often seem anecdotal until we see their full scale. Data helps reveal the bigger picture. But how we collect data matters just as much as what we measure. Far too often, inequities in how information is gathered distort the picture. If you work in the social impact sector, this really matters. Why? Well, when data practices are flawed or extractive, they lead to flawed programs and interventions. For true social impact, we need equitable, thoughtful approaches to data collection, ones that centre communities, honour lived experiences, and prioritise accuracy over convenience. Without this, we risk building social interventions on shaky foundations. Responsible data practices require context, power analysis, and prioritizing those most affected That's why I find myself returning time and time again to "Data Feminism" by Catherine D'Ignazio and Lauren Klein (link to their open source book down below) According to them, an "intersectional feminist approach to counting insists that we examine and, if necessary, rethink the assumptions and beliefs behind our classification infrastructure, as well as consistently probe who is doing the counting and whose interests are served." For example, they argue that Data feminism requires us to challenge the gender binary, along with other systems of counting and classification that perpetuate oppression. Counting gender, after all, is never just a technical decision, it’s deeply ethical and tied to power. Questions about counting must be accompanied by questions about consent, as well as of personal safety, cultural dignity, and historical context. It is complex, but it’s complexity we must embrace if we want to drive real, equitable change. That is why Design for Social Impact Lab offers practical ways to discuss and design inclusive, equity-centered data practices that amplify diversity within communities, challenge biases, and drive systemic change. I'd love to hear from you ( please feel free to add comments, links in the chat): * When was the last time you questioned how or why something was being measured in your field? What did you learn? * In your experience, what’s the most powerful example of data being used to challenge structural inequalities? _______________________________________________________________________ Want to learn more? Check out * Take a look at some of the considerations in the free handout below * Read Data Feminism: https://shorturl.at/HxftF * Sign up for Design for Social Impact Lab upcoming Research Design for Social Impact course : https://shorturl.at/IFzzd #nonprofits #socialimpact #data