This website uses cookies.

By clicking the "Accept" button or continuing to browse our site, you agree to first-party and session-only cookies being stored on your device to enhance site navigation and analyze site performance and traffic. For more information on our use of cookies, please see our Privacy Policy.

How Well Do Automated Linking Methods Perform? Lessons from US Historical Data

  • Martha J. Bailey
  • Connor Cole
  • Morgan Henderson
  • Catherine Massey
Journal of Economic Literature
vol. 58, no. 4, December 2020
(pp. 997-1044)
Download Full Text PDF

Abstract

This paper reviews the literature in historical record linkage in the United States and examines the performance of widely used record-linking algorithms and common variations in their assumptions. We use two high-quality, hand-linked data sets and one synthetic ground truth to examine the direct effects of linking algorithms on data quality. We find that (i) no algorithm (including hand linking) consistently produces representative samples; (ii) 15 to 37 percent of links chosen by widely used algorithms are classified as errors by trained human reviewers; and (iii) false links are systematically related to baseline sample characteristics, showing that some algorithms may introduce systematic measurement error into analyses. A case study shows that the combined effects of (i)–(iii) attenuate estimates of the intergenerational income elasticity by up to 29 percent, and common variations in algorithm assumptions result in greater attenuation. As current practice moves to automate linking and increase link rates, these results highlight the important potential consequences of linking errors on inferences with linked data. We conclude with constructive suggestions for reducing linking errors and directions for future research.

Citation

Bailey, Martha J., Connor Cole, Morgan Henderson, and Catherine Massey. 2020. "How Well Do Automated Linking Methods Perform? Lessons from US Historical Data." Journal of Economic Literature, 58 (4): 997-1044. DOI: 10.1257/jel.20191526

Additional Materials

JEL Classification

  • C45 Neural Networks and Related Topics
  • C81 Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
  • J62 Job, Occupational, and Intergenerational Mobility; Promotion
  • N31 Economic History: Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy: U.S.; Canada: Pre-1913
  • N32 Economic History: Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy: U.S.; Canada: 1913-