Stigma is a major social problem that hinders efforts to control communicable diseases. Much of the previous research from the medical and social sciences focuses on individual experiences with stigma and pays little attention to how and why stigma develops. The proposed research takes a new approach and conceptualizes stigma as a community-level phenomenon. Drawing on recent advances in economics, a structural theory is developed (Aim 1) which argues that, at least initially, stigma serves to benefit the community by reducing the spread of infection. Once a treatment has been developed, stigma nevertheless persists in some communities, giving rise to a dynamic inefficiency, while it dissipates in others. This theory will be tested using unique primary and administrative data from rural India, the site of an ongoing NIH-funded study (HD 058831) that is evaluating the impact of community volunteers (village or caste members) on adherence to treatment among TB patients. The first step in the empirical analysis will be to establish the definition of community (village, caste, or caste within village) that is relevant for the analysis of stigma in the Indian context (Aim 2). Th second step will be to establish that stigma is a community-level rather than individual-level phenomenon (Aim 3). The theory indicates that once a treatment is developed, communities will either remain in the stigmatizing equilibrium or move to a non-stigmatizing equilibrium, depending on their fundamental characteristics. Communities above a threshold level of wealth or education, which benefit disproportionately from having healthy members, will readily become non-stigmatizing. Communities above a threshold level of access to health services, which lowers the cost of treatment, will also become non- stigmatizing. However, the stigma norm will persist in communities below these thresholds. The third step in the empirical analysis will be to statistically identify the threshold at which communities switch discontinuously from the stigmatizing to the non-stigmatizing equilibrium (Aim 4). The final step will be to examine whether health outcomes - testing delays and non-adherence to treatment for TB - change discontinuously at the same threshold, which would provide support for the theory and the causal relationship between stigma and those health outcomes (Aim 5). Much of the data for the proposed analysis is already being collected, e.g., a range of stigma measures, community characteristics, and treatment outcomes. The supplemental project will collect additional TB testing data from centers throughout the study area. Overall, this innovative research brings a fresh community-based theoretical perspective and state-of-the-art non-linear econometric techniques to the analysis of stigma. Apart from the contribution to basic social science, the results will inform future policy interventions that seek to reduce stigma and improve health outcomes by (1) showing that the optimal design of these interventions should be at the community (rather than individual) level and (2) identifying specific communities that could most easily be pushed from the stigmatizing to the non-stigmatizing equilibrium.