Combining Family History and Machine Learning to Link Historical Records / Joseph Price, Kasey Buckles, Jacob Van Leeuwen, Isaac Riley.

Format:

Book

Author/Creator:

Price, Joseph.

Contributor:

National Bureau of Economic Research.

Buckles, Kasey.

Van Leeuwen, Jacob.

Riley, Isaac.

Series:

Working Paper Series (National Bureau of Economic Research) no. w26227.

NBER working paper series no. w26227

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2019.

Summary:

A key challenge for research on many questions in the social sciences is that it is difficult to link historical records in a way that allows investigators to observe people at different points in their life or across generations. In this paper, we develop a new approach that relies on millions of record links created by individual contributors to a large, public, wiki-style family tree. First, we use these "true" links to inform the decisions one needs to make when using traditional linking methods. Second, we use the links to construct a training data set for use in supervised machine learning methods. We describe the procedure we use and illustrate the potential of our approach by linking individuals across the 100% samples of the US decennial censuses from 1900, 1910, and 1920. We obtain an overall match rate of about 70 percent, with a false positive rate of about 12 percent. This combination of high match rate and accuracy represents a point beyond the current frontier for record linking methods.

Notes:

Print version record

September 2019.