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

Martha Bailey^1,2, Connor Cole¹, Morgan Henderson¹

¹University of Michigan.

Journal of Economic Literature

|July 23, 2021

Summary

This summary is machine-generated.

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.