# **Introduction**

Each year in the U.S. we see an increase in ads and TV spots during Pride month or at megaevents, such as the Super Bowl or the Grammys, that feature happy, wealthy same-sex - couples. Seeing these ads, <u>reading newspaper articles about the spending power of same sex couples</u> and consuming popular images of lesbian, gay and trans people on television or in movies, it would be easy to assume that LGBTI people are better off than most. However, appearances can be deceiving and what the media portrays is often far from the lived reality of most LGBTI people, in the United States and beyond. There is a tendency to conflate the experience of one subgroup, wealthier gay men, with the experience of the others tied up in the “LGBTI” acronym, when their opportunities and outcomes are quite different, and what we know about each subgroup varies significantly.

The little rigorous data that are available suggest that <u>LGBTI people are often among the most vulnerable in society</u> and on average fare worse than non-LGBTI people.<sup>1</sup> Unfortunately, these data are patchy and so it is hard to say for certain - though theory and qualitative research support this overall finding. Of course, large differences exist across contexts. The scarce quantitative data that do exist are mostly limited to developed countries, and focus on urban and cisgender<sup>2</sup> male populations. The invisibility in the data makes it more difficult to persuade policy makers that LGBTI people should be specifically included in development programs, know how to target such programs to best address their needs, and measure whether such programs are working.

To encourage development professionals to address the data gap, this paper provides an overview of, and some reflections on, existing quantitative data collection efforts.<sup>3</sup> The goal is to stimulate and inform future knowledge creation and avoid some obstacles previous studies faced. The paper addresses issues of **defining LGBTI communities** , **estimating the LGBTI population** , the **representativeness of samples,** (including comparing outcomes to non-LGBTI populations), using **big data** and **experimental techniques** . Primarily, we hope to reach empirical experts and researchers who are new to the topic to encourage more SOGIE inclusive, rigorous data collection. Secondarily, we hope that those with a background in SOGIE issues might read this and thus be more open to placing an emphasis on quantitative data. By highlighting the specific development outcomes of LGBTI people, policy makers who otherwise would not have recognized LGBTI people as an area of concern might be convinced to design more inclusive development programs.

> 1 Valfort, Marie-Anne. 2017. LGBTI in OECD Countries: A Review. OECD Social, Employment and Migration Working Papers, No. 198.

> 2 Cis gender refers to a person whose self-identity conforms to their biological sex, that is, not transgender. 3 Both qualitative and quantitative data are necessary to make informed policy decisions, as well as create the political space to act. Existing data collection efforts have focused more heavily on qualitative data, and we therefore address quantitative data here as one way of encouraging researchers to address this imbalance.

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