Peiyuan Li
Peiyuan Li
Assistant Professor of Economics at Duke Kunshan University
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My research is at the crossroads of Economic History, Political Economy, and Text Analysis.
My research is at the crossroads of Economic History, Political Economy, and Text Analysis.
My doctoral dissertation, "Collective Action in State and Society: 19th and 20th Century China," has been awarded the NSF Doctoral Dissertation Grant # 2214884. This project offers a new micro-level interpretation of China’s political transition in the 20th century—tracing its path from empire to nation-state, from Kuomintang rule to Communist governance—by drawing on large-scale historical data and exploiting local variation in state-building efforts, social mobilization, and land reform.
My doctoral dissertation, "Collective Action in State and Society: 19th and 20th Century China," has been awarded the NSF Doctoral Dissertation Grant # 2214884. This project offers a new micro-level interpretation of China’s political transition in the 20th century—tracing its path from empire to nation-state, from Kuomintang rule to Communist governance—by drawing on large-scale historical data and exploiting local variation in state-building efforts, social mobilization, and land reform.
More broadly, my work seeks to bridge the gap between qualitative historical narratives and quantitative social science. I am especially interested in developing new methods to extract structured information from unstructured sources such as local gazetteers, genealogies, and historical newspapers. To this end, I explore the use of AI and natural language processing (NLP) techniques for analyzing archival material at scale.
More broadly, my work seeks to bridge the gap between qualitative historical narratives and quantitative social science. I am especially interested in developing new methods to extract structured information from unstructured sources such as local gazetteers, genealogies, and historical newspapers. To this end, I explore the use of AI and natural language processing (NLP) techniques for analyzing archival material at scale.
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