Compare methods

	Historical Event-History Demography	Historical Nominal Record Linkage
Field	Historical Demography	Historical Demography
Family	Survival analysis	Process / pipeline
Year of origin	2004	2016
Originator	Eurasian Population and Family History Project (Tommy Bengtsson, Cameron Campbell, James Lee and collaborators)	Ivan Fellegi and Alan Sunter (probabilistic theory); James Feigenbaum, Ran Abramitzky, Leah Boustan (historical ML methods)
Type	Hazard regression on longitudinal micro-data	measurement-linkage
Seminal source	Bengtsson, T., Campbell, C., & Lee, J. Z. (2004). Life under Pressure: Mortality and Living Standards in Europe and Asia, 1700-1900. MIT Press. ISBN: 9780262025515	Abramitzky, R., Boustan, L., Eriksson, K., Feigenbaum, J., & Perez, S. (2021). Automated Linking of Historical Data. Journal of Economic Literature, 59(3), 865-918.
Aliases	Historical hazard analysis, Eurasian Project event-history, Survival analysis of vital events, Micro-level demographic response models	Record linkage, Census linking, Fellegi-Sunter matching, Historical individual linkage
Related	4	3
Summary	Historical event-history demography applies the statistical machinery of survival and hazard analysis to longitudinal individual-level historical data, modelling the risk that a person experiences a demographic event—death, marriage, migration, or a birth—as it varies with their changing circumstances. Pioneered by the Eurasian Population and Family History Project, whose comparative findings Bengtsson, Campbell and Lee synthesised in Life under Pressure (2004), the approach exploits population registers and reconstituted families that record events with precise dates alongside time-varying covariates such as grain prices, household composition and social standing. Its signature contribution is measuring the short-term demographic response to economic stress: how mortality, fertility and marriage reacted, differentially by class and household position, to harvest failure and price spikes. By moving from aggregate correlations to individual hazards, it reveals who bore the brunt of subsistence crises and how families buffered, or failed to buffer, their most vulnerable members.	Historical nominal record linkage is the task of recognising when records in different sources, two censuses, a census and a draft register, a baptism and a marriage, refer to the same person, even though no shared identifier exists and names are misspelled, ages misreported, and places renamed. Linkage is the engine behind longitudinal historical micro-data: it builds the life-course panels that underpin studies of migration, mobility, mortality, and the long-run effects of early-life conditions. Three families of methods dominate. Deterministic linkage applies hand-crafted rules; the probabilistic Fellegi-Sunter framework weights field agreements and disagreements by their discriminating power; and supervised machine learning, trained on hand-linked examples, learns to classify candidate pairs. Modern historical practice, led by Abramitzky, Boustan, Feigenbaum, and collaborators, emphasises transparent, replicable algorithms and, crucially, explicit measurement of linkage error, since false matches and missed links can bias every downstream estimate.