One challenge for inequality and mobility research is the limited availability of data that follows people and their descendants over the life course and across generations. Recently, restricted-use data with Social Security numbers have linked tax records across generations, but these data are restricted to recent cohorts. Some scholars have used machine learning techniques to link historical records by matching names and other identifiers.
Professors Kasey Buckles and Joseph Price will leverage data from FamilySearch, a large crowd-sourced genealogy platform which allows users to link records from multiple data sets to their profiles and to those of family members. These links, along with new ones that Buckles and Price will create using machine learning methods, will be used to explores the causes and consequences of social inequality in the U.S., both over the life cycle and across generations.