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State of the Art: Economic Development Through the Lens of Paintings Clement Gorin, Stephan Heblich & Yanos Zylberberg Share X LinkedIn Facebook Bluesky Threads Email Link Working Paper 33976 DOI 10.3386/w33976 Issue Date June 2025 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. * Acknowledgements and Disclosures We would like to thank Dionissi Aliprantis, Laure Athias, Sascha Becker, Karol Borowiecki, Nicolas Baumard, Davide Cantoni, Don Davis, Shari Eli, Oliver Falck, Oded Galor, Ed Glaeser, Claudia Goldin, Erik Hornung, Robert Johannes, Hubertus Kohle, Ruru Hoong, Michael Hutter, Bob Margo, Stelios Michalopoulos, Petra Moser, Nathan Nunn, Omer Ozak, Dominic Rohner, Stefanie Schneider, Munir Squires, Marco Tabellini, Mathias Thoenig, Daniel Trefler, Matt Turner, Hans-Joachim Voth, Fabian Waldinger, and participants at Brown, the Cleveland Fed, Cologne, Harvard/ BU, LMU, Paris Saclay, Paris Dauphine, Ecole Normale Superieure de Lyon, the University of Bristol, the University of Toronto, UBC, Warwick, and the ACEI 2023, the 2024 CUER-UBC workshop, ENS-PSL, the 2024 German Economists Abroad conference, the 2024 IEB Workshop on Urban Economics, the Microdata in Economic History Workshop, and the NBER SI for helpful comments. Leyi Wang provided excellent research assistance. We acknowledge the research support from the ORA Grant ES/V013602/1 (MAPHIS) and the Social Sciences and Humanities Research Council of Canada (SSHRC, Insight Grant 435-2025-0725). The usual disclaimer applies. 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