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  1. Avatar Sam Sellers
    November 24, 2020 @ 12:25 pm

    As a CPC Alum, I really appreciate the careful, nuanced discussion of a very delicate and complicated topic. Kudos to the authors on a job well done!

    One issue that I hope the authors can speak to a bit more is the desire among scholars and advocates to make compelling claims about social disparities and how this understandable desire intersects with challenges related to statistical power, which can motivate those who do research that uses racial and/or ethnic categories to lump groups with potentially very different histories or current challenges together in order to increase statistical power. However, this can create challenges by “erasing” the identities of smaller groups in published research. As shown very nicely by Pew in its work on income inequality (https://www.pewsocialtrends.org/2018/07/12/appendix-b-additional-tables-4/), ethnic groups that are commonly combined in many demographic surveys, such as Burmese or Chinese under “Asian”, or Puerto Rican and Argentinian under “Hispanic” or “Latino/a/x” experience very different socioeconomic realities, yet members of these groups may be captured in tiny numbers in surveys where the sample size numbers in the hundreds or thousands.

    A similar set of challenges relate to gender classification, where there is a need for more research to understand disparities associated with transgender and non-binary populations. As these groups are a small share of the overall population, it can be difficult to make statistically precise claims about these groups based on data collected from standard population-based surveys unless there are efforts made to oversample these groups. Not measuring disparities can lead to those disparities being ignored in policymaking that relies on population health research. On the other hand, researchers have an ethical responsibility to report the caveats and uncertainties associated with their findings, of which there are more when sample sizes are smaller. The limitations inherent in current methods of capturing racial and ethnic (and, I would argue, gender) classification, necessitate creative strategies to measure the lived experiences and disparities experienced by identity groups that are smaller in size, which are often not captured in population-level surveys.

    Another issue that would be interesting to probe further is changing social expectations regarding how individuals “should” identify when providing demographic information to employers or researchers based on changing social norms. As the cases of Rachael Dolezal and Elizabeth Warren among others show, identifying with a racial or ethnic group that does not reflect one’s own visible identity characteristics can lead to social condemnation, even if an individual has a sincerely held belief that he or she is a member of such a group. On the other hand, there are groups of individuals who share visible identity characteristics with a particular racial or ethnic group, but who have reservations about identifying with this group. This is the case for many Jews in the United States, as nicely discussed recently in the New York Times (https://www.nytimes.com/2020/10/13/magazine/im-jewish-and-dont-identify-as-white-why-must-i-check-that-box.html), some of whom will identify as white in surveys, but who may privately identify as Jewish (as a group distinct from “white”). As noted in the Part 3 blog post, gaps between “public” and “private” identities are not uncommon, but can be difficult to measure.

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