This brief describes how researchers can use credit reporting data to assess social and economic mobility. Consumer credit reporting data capture individuals’ interactions with the credit market and are compiled by major credit reporting agencies into a large, consumer-level dataset covering nearly the entire US population. By linking this to census, tax, or other data sources, users can analyze factors that influence economic mobility.
How have researchers used these data?
Researchers use credit bureau data to study economic mobility by leveraging its longitudinal nature, which allows them to track individuals’ credit access, borrowing behavior, and repayment outcomes over time and across generations. These data are commonly used to measure long-run outcomes such as credit scores, credit limits, inquiries, delinquencies, and defaults and to link family credit conditions to later-life economic outcomes. Researchers also use credit data to examine how policies, financial institutions, and local market conditions shape access to credit, repayment behavior, and financial stability, particularly for low-income households.
What are these data’s limitations, and are there opportunities for increased use?
Credit bureau data provide valuable insights into consumer behavior, debt patterns, financial health, and credit access, offering researchers large, nationally representative samples that enable robust and statistically powerful analyses.
But the data are expensive, presenting a financial barrier, and they lack information on assets, income, and consumption and provide limited demographic details, making it difficult to capture overall financial well-being. Researchers often must impute or merge external datasets—an effort that can be technically complex and constrained by privacy rules. Additionally, the data’s structure, designed for lending rather than research, presents a steep learning curve. As use of credit data grows, better documentation, clearer access and pricing, and stronger researcher user groups could make these data easier and less costly to use.
