A wealth of research identifies industrial structure as a central correlate of place-level poverty and suggests that changes in and the clustering of industry contribute to the spatial clustering of poverty over time. However, few studies have investigated the spatial and temporal dimensions simultaneously, and none have effectively examined spatio-temporal interactions. Consequently, a core tenet of theory on poverty in place has not been adequately examined. To address this limitation, we explicitly test hypotheses about systematic variation in the poverty-industry relationship over time and across space using a new method to quantify dynamic associations by simultaneously accounting for spatial and temporal autocorrelation and relationship heterogeneity. The Upper Midwest is our study site given dramatic regional changes in dominant industries (i.e., manufacturing, services, and agriculture) and poverty during the past several decades. We find that the specific character of the poverty-industry relationship systematically varies along both the temporal and spatial dimensions: industry is more protective in certain periods than in others according to sector trends, and is more protective in certain places than others conditional on sector dependence. Our approach yields a more precise and reliable understanding of the long reach of local industrial structure on the spatial clustering of poverty.