Fashion is intertwined with external cultural factors, but identifying these links remains a manual process limited to only the most salient phenomena. We propose a data-driven approach to identify specific cultural factors affecting the clothes people wear. Using large-scale datasets of news articles and vintage photos spanning a century, we introduce a multi-modal statistical model to detect influence relationships between happenings in the world and people's choice of clothing. Furthermore, we apply our model to improve the concrete vision tasks of visual style forecasting and photo timestamping on two datasets. Our work is a first step towards a computational, scalable, and easily refreshable approach to link culture to clothing.