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

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  • Arkadev Ghosh
  • Sam Il Myoung Hwang
  • Munir Squires

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.

Suggested Citation

  • Arkadev Ghosh & Sam Il Myoung Hwang & Munir Squires, 2024. "Links and Legibility: Making Sense of Historical U.S. Census Automated Linking Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(2), pages 579-590, April.
    • Handle: RePEc:taf:jnlbes:v:42:y:2024:i:2:p:579-590
    DOI: 10.1080/07350015.2023.2205918
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