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.
The full population of England and Wales employers and own-account business proprietors is estimated using population censuses 1851-1911. The main contribution of the article is a method of mixed single imputation to overcome the challenge of non-responses to the census 1851-1881. This method is compared with alternatives. Downloads of all data allow replication. The method is used to track trends in proprietor numbers and entrepreneurship rates to reassess the 'decline of Victorian entrepreneurship' , onset of the 'U'-shaped trough of the twentieth century, the 'climacteric' of 1901, and compositional changes by sector and sex. There is strong sector and gender diversity, with changes in female participation major drivers of overall trends. Proprietor numbers show slow increases of employers, and rapid rise and then decline of own-account, with a turning point after 1901. The methodology and turning point is compared and confirmed against the 1921 census and national and local trade directories.
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