Thanks to recent data availability, digitized transcriptions of Victorian censuses provide unprecedented historical big data on individuals in the past, but also with new methodological challenges like the classification of otherwise underreported entrepreneurs among a population sample of millions of individuals. This paper presents a methodological solution to accomplish the task of classifying entrepreneurs. We apply machine learning, including deep learning, to outperform a standard logistic regression algorithm. Our methodological developments traverse traditional disciplinary lines using state-of-the-art artificial intelligence methods. The main conclusion of the paper is that significant gains in performance can be achieved with historical archive data through machine learning to test economic theories on historical entrepreneurship. This suggests applicability to other disciplines in information sciences.
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