A Cross-Cultural Study of Variability in Economic Prosperity using Machine-Learning and Statistical Analysis

The causes and consequences of material wealth inequality are topics of primary interest across the social sciences, and of great relevance to policymakers and the public. Longstanding hypotheses assert the importance of the social determinants of inequality, delineating the routes through which individuals and households rely on their social networks to buffer subsistence risks or leverage their positions within these networks for personal gain.

This project advances a cross-cultural, longitudinal study, collecting comprehensive economic, demographic, and social support network data in over fifty communities in over thirty countries around the globe. The core aim of this research is an empirical test of theories derived from economics and sociology, which predict that a household’s material wealth---as indexed by the value of its material possessions---is influenced both by its social connections and the structure of its local community. With such tests, this study elucidates sources of variation in economic prosperity within and between this sample of communities.

This project is already underway, with the first and some of the second wave of data collection completed. This funding enables the completion of the second wave of data collection in the remaining communities and the integration of these data with previously collected data facilitate longitudinal analysis. The study sites are extremely varied, so analyzing these data entails novel analytical challenges. For this reason, a second objective is to apply and develop new machine learning and statistical methods that ensure meaningful comparison and inferences with the multilevel, high-dimensional data. The funding also facilitates an interdisciplinary workshop to bring together statisticians, economists, and network scientists with the anthropologists who collected the primary data. Another objective is to prepare the full dataset for deposition in an open-access repository, thereby offering a valuable...