This award is an Early-concept Grant for Exploratory Research (EAGER). The project will provide the first data closely exploring the changing meanings that people attach to being overweight ("fat stigma") in the developing world. The PIs are building a conceptual model of the relationship between obesity stigma and upward mobility as mediated by symbolic body capital that can be applied cross-culturally. They hypothesize a tipping point of economic prosperity where the idea of fatness as an undesirable personal and social characteristic begins to proliferate. In this phase of the project they will evaluate ethnographically the key concepts that that they will test with more extensive survey-based research in a later phase.
The growing global prevalence of obesity is associated with increased engagement in the global economy, affecting diet and work patterns. At the same time there appears an increase in stigma against overweight even in societies that traditionally valued larger bodies. Little is known about the predictors and processes of the rise of fat stigma. Even less is understood about changing understandings of shifts in body size, body size ideals, and fat stigma. The researchers will collect ethnographic data in a typically upwardly mobile segment of these populations -- university students -- as well as among their non-student age-matched peers. They will conduct participant-observation on university campuses and in community sites in Belize, Jamaica, and Nepal, three countries that vary in obesity rates, wealth, and culture. Interviews will be conducted with infomants matched by age (18-25) and gender in each of the six sites (for a total of 120 interviews). They will also conduct a survey of a larger sample.
Findings from this research will contribute to theory on embodiment, moral personhood, and social transition. They will also inform health-promotion programs and provide timely opportunities for two-way international educational collaborations in cultural anthropology. Three graduate students and two undergraduate students will be engaged in data collection, and two graduate students and two undergraduate students will assist in data coding and analysis.
