Tom Mould, "Overthrowing the Queen: Telling Stories of Welfare in America" (Indiana UP, 2020)

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Recorded20 Aug 2021

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It is a familiar story: A recipient of public assistance funds is caught buying expensive steaks, seafood, or other luxury foods with food stamps at the grocery store. Or they wear designer clothes and drive extravagant cars, belying the need for government assistance. Or they game the system in order to buy drugs or alcohol. Or they continue to bear multiple children in the belief that it will increase the amount of government aid they receive so that they can avoid working. Tinged with racial dog whistles and demonizations of poverty, these stories are often circulated as factual accounts of welfare fraud when they seem to operate more closely as contemporary legends – circulated stories with recurring motifs that seem as if they might have actually happened, but its veracity or falseness can be difficult to verify. Contemporary legends often include the caveat of the story happening to a “friend of a friend” though in the case of welfare legends, they often include first-person accounts. While the specific details of contemporary welfare legends are often not verified, the stories themselves are taken as accurate accounts and can impact public policy. In Overthrowing The Queen: Telling Stories of Welfare In America (Indiana University Press, 2020), Dr. Tom Mould explores these stories about welfare in the context of contemporary legends and their influence on our perceptions of public assistance. In our conversation, he explains the origins of the “welfare queen” stereotype and its connection to a singular woman who defrauded public assistance programs in the 1970s and whose story was thereafter frequently recounted in racially coded terms by Ronald Reagan during his 1976 presidential campaign. We discuss the deep connection that welfare legends have to the idea of the American Dream, often serving as counternarrative or an embodiment of the fears about its attainability. Stereotypes about welfare recipients are so deeply embedded in the American consciousness that recipients feel as they must recount their stories as exceptions to the stereotype. Such stories about people who defraud government assistance programs are compelling and memorable because they confirm preconceived biases about recipients. We talk about why such stories spread so easily and how they are spread. While contemporary legends are often told in third person, welfare legends are also told in the first person, but as Dr. Mould explains, the stereotypes are so strong that the tellers may have internalized specific details into eyewitness accounts in a move that makes for a more compelling narrative. However, even genuine eyewitness accounts do not provide the entire story – the circumstances that surround the purchase of what may be perceived come across as luxury items (perhaps a special occasion?) or why someone is driving an expensive car (a remnant from a life before falling upon difficult times?). He suggests that we take a “doubt-centered approach” when we hear such stories where we ask ourselves about the parts that may raise doubt - is it factually true? Is our interpretation true? Can this one story be generalizable to all stories about welfare recipients? Such an approach is an important addition to our understanding of how legend works. As our conversation continues, Dr. Mould suggests ways that we can and should counter the welfare legends, and why it is important to reclaim the terms “welfare” as a positive term rather than one that connotes fraud and laziness. Dr. Tom Mould is Professor of History and Anthropology at Butler University in Indianapolis, Indiana. Nancy Yan received her PhD in folklore from The Ohio State University and taught First Year Writing, Comparative Studies, and Asian American studies for several years before returning to organizing work. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/critical-theory

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