This collaborative project between US and UK-based scientists investigates the factors that impact economic prosperity across multiple study sites. The investigators employ a longitudinal research design to test and validate the extent to which variation in prosperity within sites is a function of social organization. The structures of the high dimensional, correlated data will be assessed using machine learning algorithms. A significant broader impact of data collection is the development of a robust and comparative cross-cultural dataset. The dataset is publicly available to all researchers and interested stakeholders. The research activities support the development of the scientific workforce through the training of early career researchers that include students and postdoctoral scientists. To test the impact of position in a social network on the capacity for wealth accumulation and whether the nature of social networks predicts variability, the PIs collect multilevel data. This includes individual, household, site-level and national data. The investigators also develop new statistical methods that can be applied to generalizable studies of social networks in human communities. The machine learning methods used in the analysis contribute to the development of robust methods in comparative social science research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broad