Doing Identity Labor
How Mixed-Race Politicians Disrupt Descriptive Representation
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As the 2024 Democratic presidential nominee, Kamala Harris stood to make history as the first Black and South Asian American woman president. Between 2008 and 2024, Americans saw multiracial candidates-Barack Obama and Harris-on four presidential tickets. They rose to power alongside the rising numbers of Americans who identify as multiracial. And yet, despite the presence of major multiracial political figures and a growing multiracial population, voters are still curious about their identities. People often ask multiracial politicians questions that amount to: "What are you?" In Doing Identi...
As the 2024 Democratic presidential nominee, Kamala Harris stood to make history as the first Black and South Asian American woman president. Between 2008 and 2024, Americans saw multiracial candidates-Barack Obama and Harris-on four presidential tickets. They rose to power alongside the rising numbers of Americans who identify as multiracial. And yet, despite the presence of major multiracial political figures and a growing multiracial population, voters are still curious about their identities. People often ask multiracial politicians questions that amount to: "What are you?" In Doing Identity Labor: How Mixed-Race Politicians Disrupt Descriptive Representation, Danielle Casarez Lemi provides one of the first book-length treatments of how multiracial politicians, across office, geography, and background, navigate identity in public life. Lemi develops an interdisciplinary theory of identity labor that explains how multiracial politicians perform their racial identities, especially according to their appearance. The book assembles a rare combination of text analysis, discourse analysis, survey analysis, experimental analysis, nationwide interviews, and case studies of multiracial politicians, including a study of Vice President Kamala Harris. Lemi's groundbreaking multidimensional analysis shows that using racial identity for political gain is neither clear-cut nor ambiguous.
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