State of the Art: Economic Development Through the Lens of Paintings

This paper analyzes 630,000 paintings from 1400 onward to uncover how visual art reflects its socioeconomic context. We develop a learning algorithm to predict nine basic emotions conveyed in each painting and isolate a context effect — the emotional signal shared across artworks created in the same location and year — controlling for artist, genre, and epoch-specific influences. These emotion distributions encode subtle but meaningful information about the living standards, uncertainty, or inequality characterizing the context in which the artworks were produced. We propose this emotion-based measure, derived from historical artworks, as a novel lens to examine how societies experienced major socioeconomic transformations, including climate variability, trade dynamics, technological change, shifts in knowledge production, and political transitions.