Multimodal LLMs offer a watershed change for the digitization of historical tables, enabling low-cost processing centered on domain expertise rather than technical skills. We rigorously validate an LLM-based pipeline on a new panel of historical county-level vehicle registrations. This pipeline is 100 times less expensive than outsourcing, reduces critical parsing errors from 40% to 0.3%, and matches human-validated gold standard data with an R2 of 98.6%. Analyses of growth and persistence in vehicle adoption are statistically indistinguishable whether using LLM or gold standard data. LLM-based digitization unlocks complex historical tables, enabling new economic analyses and broader researcher participation.
@article{arxiv.2505.11599,
title = {Can LLMs Credibly Transform the Creation of Panel Data from Diverse Historical Tables?},
author = {Verónica Bäcker-Peral and Vitaly Meursault and Christopher Severen},
journal= {arXiv preprint arXiv:2505.11599},
year = {2025}
}