Links and Legibility: Making Sense of Historical U.S. Census Automated Linking Methods

Arkadev Ghosh, Sam Il Myoung Hwang and Munir Squires

Journal of Business & Economic Statistics, 2024, vol. 42, issue 2, 579-590

Abstract: How does handwriting legibility affect the performance of algorithms that link individuals across census rounds? We propose a measure of legibility, which we implement at scale for the 1940 U.S. Census, and find strikingly wide variation in enumeration-district-level legibility. Using boundary discontinuities in enumeration districts, we estimate the causal effect of low legibility on the quality of linked samples, measured by linkage rates and share of validated links. Our estimates imply that, across eight linking algorithms, perfect legibility would increase the linkage rate by 5–10 percentage points. Improvements in transcription could substantially increase the quality of linked samples.

Date: 2024
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DOI: 10.1080/07350015.2023.2205918

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