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How Well Do Automated Linking Methods Perform? Lessons from U.S. Historical Data.

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Widely-used record linkage algorithms often produce errors, impacting data quality and research findings. Careful consideration of algorithm assumptions is crucial for accurate historical data analysis.

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Area of Science:

  • Social Sciences
  • Computer Science
  • Statistics

Background:

  • Historical record linkage is vital for social science research, enabling longitudinal and cross-sectional data analysis.
  • Existing record linkage algorithms vary in their assumptions and performance, with limited understanding of their impact on data quality.

Purpose of the Study:

  • To evaluate the performance of common record linkage algorithms using high-quality datasets.
  • To assess the direct effects of algorithm choices and assumptions on data quality and research inferences.

Main Methods:

  • Literature review of historical record linkage in the U.S.
  • Performance evaluation of record linking algorithms using two hand-linked datasets and one synthetic dataset.
  • Analysis of linking errors and their relationship to sample characteristics.

Main Results:

  • No single algorithm, including manual linking, consistently yields representative samples.
  • 15-37% of algorithm-generated links were identified as errors by human reviewers.
  • False links systematically correlate with baseline sample characteristics, introducing potential measurement error.

Conclusions:

  • Linking errors can significantly attenuate research findings, such as estimates of intergenerational income elasticity.
  • Current trends towards automated linking and higher link rates may exacerbate the impact of errors.
  • Recommendations are provided for reducing linking errors and future research directions.