University of California Consumer Credit Panel
The University of California Consumer Credit Panel (UCCCP) is a new dataset of anonymized consumer credit information, created for the purpose of studying consumer financial well-being. The UCCCP was created in 2020 through a partnership between the California Policy Lab, the UCI School of Law, and the Student Borrower Protection Center.
The UCCCP is a longitudinal panel of approximately 40 million consumers starting in 2004, continuing quarterly through 2019, monthly in 2020, and quarterly thereafter. Updates to the dataset are expected to continue, pending funding availability. The sample comprises credit records of a nationally representative 2 percent sample of U.S. adult consumers with credit records and a 100 percent sample of consumers residing in California. The dataset also includes household members and co-signers of those in the sample. The data originates from Experian, one of the three nationwide consumer reporting agencies. Before being provided to the UCCCP, the data was stripped of any information that might reveal consumers’ identities, such as names, addresses, and Social Security numbers.
The UCCCP is similar to other consumer credit panels built by the Federal Reserve Bank of New York, the Consumer Financial Protection Bureau, and other universities. The UCCCP is different in four main ways:
1. The dataset is designed for use by researchers affiliated with the University of California and the Student Borrower Protection Center;
2. The sample size is quite large, and California consumers are oversampled;
3. The data are as granular as are available; and
4. There is a streamlined process for potentially linking the UCCCP to other data, especially administrative data in California.
Data elements include demographic and credit information about consumers. Demographic information (starting in 2010) includes gender, age, geography, and household count. Credit information includes credit scores and raw tradeline-level information about each loan or collections item, including payment history, credit limits and balances, and various information about the type and status of those tradelines.
These data provide a powerful platform for researching student loans. Though the identities of lenders are masked, each loan is linked to a lender ID that can be tracked across borrowers. This allows researchers to group loans by originator or servicer. The detailed loan-level credit information allows researchers to track the status of repayment over time, including deferments and forbearances.
Moreover, characteristics such as income-driven repayment plan participation can be imputed from the data based on existing models. The full credit file of these borrowers allows researchers to investigate relationships between student loan debt and other tradelines on their credit bureau file (e.g., revolving debt, mortgages). Finally, the time period of the panel covers a period of substantial growth in student lending, including the growth in for-profit college lending and a major financial recession.
Student Loan Servicer Historical Dataset
A large servicer of Federal Family Education Loans (FFEL) has provided SLLI/CPL with a historical dataset of loans that are no longer in the servicer’s portfolio due to repayment, default, consolidation, or the like. All records are anonymous and do not contain the borrower’s name, address, social security number, or any other personal identifiers. The current sample includes all the FFEL lender California loans and a 100,000-person nationwide random sample.
Data elements include loan type, school code, disbursement date, graduation/separation date, repayment date, interest rate and type, original loan balance, principal paid by the borrower, interest paid by the borrower, whether the loan was rehabilitated or consolidated, the most recent payment amount, and the most recent payment plan. Demographic data elements include imputed gender and race, age, and census tracts.
It may be possible to link this file to the UCCCP and/or to other California administrative records (such as tax records).
Please direct inquiries regarding dataset access here.