Proposal preview

The big data revolution in economic history

Economic history has always been a strongly data-oriented field of research.
As such, the big data revolution should have profound implications for economic historians. Increases in computing power, advances in distributed computing, and new methods developed in the fields like machine learning and natural language processing are changing the landscape. Data has become far larger in volume, is more varied, and is routinely linked to other datasets. It is widely believed that these developments will revolutionise the process of scientific discovery (King 2011).

The creation of these large volumes of data is a recent phenomenon though, so it is not evident that a field studying the past should be part of that development. However, historians are now also faced with new kinds of data such as large volumes of text or data gathered by crowd-sourcing. Moreover, detailed, promising data on new regions is becoming available (Dong et al. 2015; Fourie, 2016) and research is becoming ever more interdisciplinary (Turchin et al. 2015).

Indeed, a number of highly successful big data projects exist in economic history. Clio-Infra has brought together historical macro-data to analyse global inequality and wellbeing (Van Zanden et al. 2014). The census microdata from the North Atlantic Population Project the Integrated Public Use Microdata project (Ruggles et al. 2011; Ruggles et al. 2015) are key to recent economic historical research (e.g. Abramitzky et al. 2014; Long and Ferrie 2013). To an extent, even crowd-sourcing has a long tradition in economic history, with volunteer work on the English parish records allowing the construction of the famous Cambridge family reconstitution data (Wrigley and Schofield 1989).

While the field’s record is strong, it is important to ensure that economic history benefits from the new possibilities that are opening up in a world of big data. To this end, this session will bring together scholars working on large datasets and new technologies in economic history and adjacent fields. It will have both methodological papers to reflect and disseminate best practice as well as research papers focusing on the actual answers these new techniques can provide.


Abramitzky, Ran, Boustan, and Katherine Eriksson. 2014. “A Nation of Immigrants: Assimilation and Economic Outcomes in the Age of Mass Migration.” Journal of Political Economy 122 (3): 467–506.
Dong, Hao, Cameron Campbell, Satomi Kurosu, Wenshan Yang, and James Z. Lee. 2015. “New Sources for Comparative Social Science: Historical Population Panel Data From East Asia.” Demography, May, 1–28.
Long, Jason, and Joseph Ferrie. 2013. “Intergenerational Occupational Mobility in Great Britain and the United States Since 1850.” The American Economic Review 103 (4): 1109–37.
Fourie, Johan. 2016. “The Data Revolution in African Economic History.” Journal of Interdisciplinary History 47 (2): 1–20.
King, Gary. 2011. “Ensuring the Data-Rich Future of the Social Sciences.” Science 331 (6018): 719–21.
Ruggles, Steven, Steven, Katie Genadek, Ron Goeken, Josiah Grover, and Matthew Sobek. 2015. Integrated Public Use Microdata Series: Version 6.0 [Machine-Readable Database]. Minneapolis, Minn.: University of Minnesota.
Ruggles, Steven, Evan Roberts, Sula Sarkar, and Matthew Sobek. 2011. “The North Atlantic Population Project: Progress and Prospects.” Historical Methods: A Journal of Quantitative and Interdisciplinary History 44 (1): 1–6.
Turchin, Peter, Rob Brennan, Thomas Currie, Kevin Feeney, Pieter Francois, Daniel Hoyer, Joseph Manning, et al. 2015. “Seshat: The Global History Databank.” Cliodynamics: The Journal of Quantitative History and Cultural Evolution 6 (1).
Wrigley, Edward Anthony, and Roger S. Schofield. 1989. The Population History of England 1541-1871. Cambridge University Press.


  • Auke A Rijpma, Universiteit Utrecht,,
  • Pieter PW Francois, University of Oxford and University of Hertfordshire,,

Session members

  • Johan Fourie, Stellenbosch University,
  • James Z Lee, Hong Kong University of Science and Technology,
  • Patrick Manning, University of Pittsburgh,
  • Sangkuk Lee, Ajou University,
  • Evan Roberts, University of Minnesota,
  • Mikolaj Szoltysek, University of Warsaw,
  • Jan Luiten Van Zanden, Utrecht University,

Proposed discussant(s)

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