Historical Nominal Record Linkage

It depends on data quality and scale. Deterministic rules are transparent and adequate for small, clean sources but brittle to error. The probabilistic Fellegi-Sunter model handles noise principledly and is well understood. Supervised machine learning typically achieves the best precision-recall trade-off at scale but needs hand-linked training data. Modern historical practice often combines them and, regardless of choice, insists on measuring linkage error explicitly.

False matches link different people, injecting noise and bias, while selective non-linkage means the linked sample is unrepresentative, typically over-representing people with stable, distinctive names who did not migrate. Both distort downstream estimates of mobility or migration. Reporting precision and recall on a labelled sample, and analysing who gets linked, lets researchers bound or correct these biases instead of presenting linked-data results as if exact.

Blocking partitions records into groups, by name sound, birth decade, sex, within which candidate pairs are compared, avoiding the impossible task of comparing every record to every other across millions of entries. It makes linkage computationally feasible but risks discarding true matches that fall into different blocks. Designers therefore choose blocking keys that are both stable across sources and discriminating, sometimes running several blocking passes to recover otherwise lost links.