Black and Indigenous Inequality in Mexico: Implications for Multiracialism and Intersectionality Research

Abstract

In recent decades, an increasing number of Latin American countries have included ethnoracial questions on their censuses, giving rise to unprecedented data on monoracial and multiracial forms of classification. In Mexico, the government launched a count of its black population for the first time in the nation’s history in 2015, in addition to its long-standing practice of enumerating its indigenous population. Most recently in 2018, it conducted a survey, again asking about both black and indigenous identification. Within this short time span, the black population grew from 1.8 percent to 5.9 percent, becoming a sizable, statistically visible minority. A large majority of black individuals also identified as indigenous, revealing an important form of dual-minority multiracialism. In this article, we analyze these unprecedented data, detailing the size, composition, and growth of these populations. We use the Mexican case to illustrate the potential implications of measuring ethnoracial inequality using single- versus dual-category approaches. We find that black disadvantage is considerably more pronounced when explicitly allowing for multiracial classification. Methodologically, our findings contribute to nascent conversations about how to incorporate the new social and statistical realities of multiracialism in inequality analyses. Theoretically, we expand the multiracialism literature from its traditional focus on part-white mixtures, to a focus on overlapping minority classification. Finally, we build on theories of intersectionality, which generally focus on intersections of oppression across multiple “master statuses” (e.g., race, class, and gender), by also examining intersecting oppressions within the single master status of race.

Classification Algorithm for Skin Color (CASCo): A new tool to measure skin color in social science research

“…However, the topic of political representation remains severely understudied. Researchers have reported a lack of reliable and standardized data on the matter (Htun 2015; Sue, Riosmena, and Telles 2021; Telles 2004) as one of the obstacles preventing this research. CASCo can contribute to redress this situation by harvesting data in a quick and reliable manner.…”