Capitalizing on a crisis: a computational analysis of all five million British firms during the Covid-19 pandemic

Naomi Muggleton, Charles Rahal and Aaron Reeves

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: The Covid-19 pandemic brought unprecedented changes to business ownership in the UK which affects a generation of entrepreneurs and their employees. Nonetheless, the impact remains poorly understood. This is because research on capital accumulation has typically lacked high-quality, individualized, population-level data. We overcome these barriers to examine who benefits from economic crises through a computationally orientated lens of firm creation. Leveraging a comprehensive cache of administrative data on every UK firm and all nine million people running them, combined with probabilistic algorithms, we conduct individual-level analyzis to understand who became Covid entrepreneurs. Using these techniques, we explore characteristics of entrepreneurs—such as age, gender, region, business experience, and industry—which potentially predict Covid entrepreneurship. By employing an automated time series model selection procedure to generate counterfactuals, we show that Covid entrepreneurs were typically aged 35–49 (40.4%), men (73.1%), and had previously held roles in existing firms (59.4%). For most industries, growth was disproportionately concentrated around London. It was therefore existing corporate elites who were most able to capitalize on the Covid crisis and not, as some hypothesized, young entrepreneurs who were setting up their first businesses. In this respect, the pandemic will likely impact future wealth inequalities. Our work offers methodological guidance for future policymakers during economic crises and highlights the long-term consequences for capital and wealth inequality.

Keywords: big data; computational social science; inequality; economic sociology; Economic Sociology; Big Data (search for similar items in EconPapers)
JEL-codes: D31 G32 L20 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2025-05-31
New Economics Papers: this item is included in nep-cmp
References: Add references at CitEc
Citations:

Published in Journal of Computational Social Science, 31, May, 2025, 8(2). ISSN: 2432-2717

Downloads: (external link)
http://eprints.lse.ac.uk/127228/ Open access version. (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:127228

Access Statistics for this paper

More papers in LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library LSE Library Portugal Street London, WC2A 2HD, U.K.. Contact information at EDIRC.
Bibliographic data for series maintained by LSERO Manager (lseresearchonline@lse.ac.uk).