Disaggregating data by race allows for more accurate research

The term ‘women of colour’ was introduced as a symbol of political solidarity, but its evolution to a biological term encompassing all non-white women has resulted in aggregation of data from diverse ethnic groups. Breaking out statistics by race, ethnicity and gender is therefore crucial for researchers who are committed to inclusion, argues Rhonda V. Sharpe.

In 1977, a group of black women activists and scholars from Washington, DC, travelled to Houston, Texas, to attend the National Women’s Conference. Concerned that the conference agenda that had been created for Black women was inadequate, they presented the Black Women’s Agenda, a platform that they thought better addressed the needs of African American women. When other non-white women saw it, they wanted their concerns to be included—and they were—but that meant the name had to change to something more inclusive. Thus, the term ‘women of colour’ was born.

In the 40 years since this conference, the term women of colour has become synonymous with non-White women. And the use has expanded so that, today, we routinely use ‘people of colour’ to designate any non-White identifying person. That may be convenient, but when it comes to research, it’s also lazy, and it wreaks havoc on the accurate collection and analysis of data. I often see research or data divided into two groups—‘women’ and ‘women of colour’. This is an example of the intersection of institutional racism and sexism. It suggests that ‘women’ is not inclusive of Asian, Black, Hispanic, Native American and multi-racial women. A ‘woman’ is assumed to be white.

“Using the term ‘woman of colour’ erases identity and ignores a wealth of information about the unique experience of each group of women.”

The term women of colour was coined as an expression of political solidarity, according to activist Loretta Ross, who was part of that discussion in 1977. It represented a commitment to collaborate politically with other minoritized women to address common oppressions. But the term has evolved from a political term to a biological one. Now it is used as a shorthand to refer to any and all non-white racial or ethnic groups, and it is often used even when the specific race or ethnicity is known. This evolution from political to biological has sanctioned the aggregation of Asian, Black, Hispanic, Native American and multi-racial women as a homogeneous group despite their distinct differences. Using the term ‘woman of colour’ or ‘people of colour’ erases identity and ignores a wealth of information about the unique experience of each group of women.

Researchers must understand that shared experiences among various groups of women may not lead to similar outcomes. Take the recent reports about sexual harassment and discrimination among female economists. Women of all ethnicities and races have these experiences. However, in my 2018 National Economic Association Presidential Address, I explained that these shared experiences have different consequences on the undergraduate economics pipeline. Black women seem to be most affected by the hostile environment of the economics profession. How do I know? I looked at the disaggregated data.

Disaggregating data by gender for Asians, Blacks, Hispanics, Native Americans and multiracial women is crucial for effective public policy. For example, data on fertility rates that examine ‘women of colour’ as a group do not take into account that Asian and Hispanic women have higher fertility rates than White or Black women. Thus the lack of population data delineated by race and ethnicity plus gender may result in parental rights, reproductive rights and childcare policies that are not inclusive of the needs of these women who are more likely to be the most vulnerable.

The US Census provides population projections by race or ethnicity, but not by race or ethnicity plus gender; therefore, data on the future demographic positions of women are not available. Not only do population projections need to be made based on gender, but they also need to be broken down by race or ethnicity plus gender (intersectional projections). This lack of data treats gender as inconsequential for policy planning and assumes only race or ethnicity is important. Trivializing race–gender and ethnicity–gender data has implications for policy planning.

Diversity is a dynamic descriptor that pivots on difference. Diversity has expanded to include difference by geography, gender identification, physical ability, religion and sexual orientation. But the role of difference is precarious because although difference characterizes diversity, it is also the basis for exclusion. Institutionalized exclusion is not just entrenched in the equitable access to quality education, employment, healthcare, housing and a host of other goods and services, but is also rooted in how data are collected and reported. If the data are collected and reported without taking a true diversity of experiences into consideration, it can’t be used to create a more inclusive society.

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Correspondence to Rhonda Vonshay Sharpe.

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Sharpe, R.V. Disaggregating data by race allows for more accurate research. Nat Hum Behav 3, 1240 (2019). https://doi.org/10.1038/s41562-019-0696-1

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