About the Data | Accessing the Data | Fees | FAQ | Approved Projects
The University of California Consumer Credit Panel (UC-CCP) is a new dataset of anonymized consumer credit information, created for the purpose of studying consumer financial well-being and identifying trends among California households related to credit, debt, income, and mobility. The UC-CCP was created in 2020 through a partnership between the California Policy Lab, the Student Borrower Protection Center, and the Student Loan Law Initiative. The dataset is designed for use by researchers affiliated with the University of California or the California Policy Lab. The data can inform research on a variety of topics including economic mobility, health and financial well-being, social mobility, the impact of student debt, California’s housing challenges, and more.
More About the Dataset
The UC-CCP is a longitudinal panel of approximately 40 million consumers starting in 2004 and continuing quarterly through the present. Updates to the data on a quarterly basis are anticipated but will depend on funding. The sample comprises anonymized credit records of a nationally representative 2% sample of US adult consumers with credit records along with 100% of Californians with credit histories. The dataset also includes records from consumers that shared an address or an account (e.g., co-signers) with those in the sample. Data elements includes demographic and geographic information about consumers, 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, including collections and deferments.
While the UC-CCP is similar to existing credit panels by the Federal Reserve Bank of New York and the Consumer Financial Protection Bureau, it also has three distinct advantages for researchers:
1. The size of the sample and the oversampling of California consumers.
2. The granularity of the data (some other panels lack the raw account-level data).
3. A streamlined process (through CPL) for potentially linking the UC-CCP data with other person-level or address-level data.
The data originates from one of the three nationwide consumer reporting agencies. Before being provided to the UC-CCP, the data was stripped of any information that might reveal consumers’ identities, such as names, addresses, and Social Security numbers.
Here is a public deck describing the data.
Accessing the Data
The UC-CCP is hosted on CPL’s Secure Data Hub, which is a virtual enclave environment designed for secure analysis and research of sensitive administrative microdata. Only approved users have access and only for approved projects. User activities are monitored, logged, and audited.
Potential users should note that not all requests may be approved. We have usable annual data from 1Q 2004 through the present. We usually receive the new quarter’s data within 1-2 months after the quarter’s end. The data in 2020 is monthly; all other years have quarterly data. Pending funding availability, we hope to continue purchasing quarterly data on an ongoing basis.
Data Access Fees
We charge a per-project access fee that helps recoup our costs of hosting, curating, and cleaning the data, and maintaining good documentation for users. There is an additional fee assessed for each project requiring an individual-level linkage. This covers CPL’s costs in facilitating the linkage and writing code to hash/link the data, as well as costs that the bureau charges us for each linkage. These rates are subject to change and tend to change on July 1st of each year, in alignment with our costs. The linkage fee is assessed on top of the basic access fee, for those requesting linkages.
Basic access fee: $8,522
Linkage fee: $13,030
UC Berkeley authors: Starting 10/1/24, and continuing for two years, projects with a UC Berkeley PI will have the basic access fee waived because those fees have been covered by a generous gift from the James Irvine Foundation. In your application, where it asks if you have funds available, please mark that you are applying for CPL funding.
Frequently Asked Questions
Can you describe the data in more detail? For example, what variables are in the data?
Here is a public deck describing the data. More detailed data documentation is available to share with approved applicants, including variable lists, a glossary of codes for each field, and detailed documentation about how to best use the data from other users, including code snippets.
Our data is similar to credit panels held by the Federal Reserve Bank of New York and the Consumer Financial Protection Bureau, so it may help to read up on those data. The 2024 NBER working paper, Consumer Credit Reporting Data, provides an in-depth guide on the data and research possibilities using it, including linking it with other data.
The NY Fed describes their data here: An Introduction to the New York Fed Consumer Credit Panel. One main difference from the NY Fed’s data, besides sampling, is that the UC-CCP contains tradeline-level information from the credit bureau, in addition to person-level data.
Here is a sampling of research using consumer credit panel data:
Pandemic Patterns: California is Seeing Fewer Entrances and More Exits (2021) (note: this research used UC-CCP data)
CalExodus: Are People Leaving California (2021) (note: this research used UC-CCP data)
The Credit Consequences of Unpaid Medical Bills (2018)
Credit Invisibles and the Unscored (2016)
The Effect of Debt Collection Laws on Access to Credit (2018)
The Financial Crisis at the Kitchen Table: Trends in Household Debt and Credit (2013)
‘No More Credit Score’: Employer Credit Check Bans and Signal Substitution (2016)
What Determines Consumer Financial Distress? Place- and Person-based Factors (2020)
Overview
For each consumer in each archive, there are four files: one on consumer characteristics, one on tradelines, one on inquiries, and one on public records.
- Consumer characteristics include credit score, geography, gender (estimated), race/ethnicity (estimated), month and year of birth, marital status (estimated), occupation and education codes (estimated), household count (estimated), and an indicator of homeownership status (estimated). Some of these variables do not come directly from the credit record and are instead modeled/estimated by the credit bureau, with varying levels of missingness and reliability.
- “Tradelines” are loans or other reported credit products, and we receive several variables describing that tradeline, such as loan type, balance amount, minimum payment, credit limit, open and closure dates, and multi-year monthly payment history.
- Hard inquiries for credit (i.e., credit checks) are tracked by date, dollar amount, and type of business.
- Public records include bankruptcy records, including the type of bankruptcy, the filing date, and the amounts of assets and liabilities.
Can you identify individuals in the data?
No. The data are anonymized so that consumer privacy is maintained. There are no names, addresses, social security numbers, birth dates, or other personally identifying information in the data.
Do you know the race or ethnicity of individuals in the data?
Starting in June 2010, and continuing annually to present, the bureau provides us with an estimate of race/ethnicity for all consumers in the panel, using the BISG method.
For what years do you have the data?
We have quarterly extracts going back to 2004. The first archive is from March 2004, and we are receiving archives through present from March, June, September, and December of each year. We plan to continue purchasing the data going forward, pending funding availability.
Some of the demographic data is unavailable for archives before June 2010.
In order to better study the COVID-19 pandemic, we obtained monthly data for the year 2020.
Going forward we expect to receive quarterly data about one month after each quarter ends.
What is the most detailed geography for which you have data?
Each consumer record has a 5-digit ZIP. After June 2010, the UC-CCP has census geography information for ~80% of records, down to the census block-level.
Can you describe the sampling methodology in greater detail?
There are two samples, one nationwide and one from California.
The National Sample: For each archive, the bureau first selects all records with a “consumer pin” ending in one of two two-digit numbers (e.g., 24 or 56). The consumer pin is assigned sequentially by the bureau and we have conducted testing to ensure that the pins are as good as random, thereby creating a representative nationwide sample.
The California Sample: The bureau provides records for everyone that has lived in California since 2004. We also receive records after a consumer leaves California, and for those who arrived before 2020, we receive their records from their prior locations as well.
Household Members and Associated Borrowers: For both the National and California Samples, we also have data for consumers who share the same address (max of 8 co-habitants) during that archive (Household Members). And we also have data for consumers who are on the same tradelines, such as co-signers (Associated Borrowers). These Household Members and Associated Borrowers are distinguished within the data, and we only have data for them during the archives in which they are associated with the main sample members.
Can the UC-CCP data be linked to other data? If so, how?
Yes, in some circumstances.
The UC-CCP data can readily be linked with other data at varying geographies (ZIPs, census tracts, counties, etc).
In addition, we have arranged for a streamlined process by which UC-CCP data can be linked with other data at the person- or address-level. This process requires that each data provider encrypt, in the same manner, identifiers that can then be anonymously matched and linked on CPL’s servers, without ever seeing the identifiers. The process requires an additional fee (listed below) and is subject to approval by the data providers and by CPL. The queue for such linkages can be long and wait times have been several months.
In some circumstances, projects may be able to reuse existing linkages with approvals from the relevant data owners. This is not always possible, but when it is, they would not incur an additional linkage fee, but there may be other costs if the reuse would require investment of CPL’s staff time. As of September 2024, UC-CCP data have been linked to data from the California Student Aid Commission, the California Social Services Department, the Alameda County Probation Department, and the U.S. Patent Office. In the near- to medium-term, we have plans to link the UC-CCP data to energy bill data, parcel data, voter data, hospitalization data, birth data, and student-loan servicer data.
Can surveys be conducted off the UC-CCP? If so, how?
This is feasible, but can be expensive.
It is possible to select consumers from the UC-CCP and have a third party administer a mailed survey to those people, compile the responses, and return the responses linked to the UC-CCP records. CPL does not see any individual identifiers as part of this process.
If you are interested in this, please reach out to ucccp@capolicylab.org to discuss.
Who is eligible to access the data?
Faculty, students, and employees of the University of California are eligible to access the data, but their specific use of the data must first be approved by the credit bureau and CPL. UC authors may have non-UC co-authors, subject to approval by CPL and the credit bureau. If such non-UC co-authors want to access the data, their institution must sign an additional sub-agreement with UC.
What is the approval process for accessing the data?
If you are interested in conducting research with the UC-CCP, please apply using this link.
Not all research projects may get approval. Projects will generally be reviewed within 6 weeks of submittal, so please be patient if you’ve submitted an inquiry before that time. Please do not check-in with us before 6 weeks unless there is some unusual urgency.
Even those who are approved for access may experience delays in getting access to the data. We appreciate your patience; we are a small team and there is a large volume of requests.
Approved UC-CCP projects
(Most of these projects are hosted, though some are CPL-led projects.)
(Note, August 2024: This data is in the process of being updated, for example, we will be adding publication links for projects that have them in the near future.)
Project Number | Project Name | All Team Members |
---|---|---|
2020-001 | Financial shock from extreme heat events | Alan Barecca,Jisung Park,Bret Stevens,Katrina Jessoe |
2020-002 | Young CA migrants | Lily Nienstedt |
2020-003 | Camp Fire effects | Izzy Clayter |
2020-004 | Student loan repayment metrics | Shuo Chen |
2020-005 | Neighborhood effects of foreclosures | Stefan Klos |
2020-006 | Minimum wage effects on financial distress | Anna Larson |
2020-007 | Who borrows student loans in CA | Ismael Soto |
2020-008 | Income-driven repayment building block | Kara Segal |
2020-009 | Mobility during housing crisis | Kendle Kuechle |
2020-010 | Auto loans and recessions | Jacob Wasserman,Evelyn Blumenberg |
2020-011 | College choice and family finances | Rob Fairlie, Sandra Black, Jeff Denning, Steve Ramos, Oded Gurantz, William Sullivan, Michael Jensen |
2020-012 | Smoothing income fluctuations using credit | Niklas Flamang |
2020-013 | Correlates of income-driven repayment | Brian Galle |
2020-014 | Financial, labor, and household effects of IDR enrollment | Lesley Turner |
2020-016 | Effect of income-driven repayment on behavior and residency | Daniel Collier |
2020-017 | Racial disparities in student debt | Laura Hamilton,Adam Goldstein,Charlie Eaton |
2020-018 | Repayment trajectories for student borrowers of color | Raphael Charron-Chenier |
2020-019 | Student Supports (college safety net) | Johanna Lacoe, Jesse Rothstein, Elise Dizon-Ross, Alan Perez, Jennifer Hogg, Kassandra Hernandez, Jamila Henderson, Sarah Hoover, Monica Saucedo Hernandez, Justine Weng, Sam Ayers, Huizhi Gong, Gaby Lohner, Cara Tan |
2020-020 | The impacts of tax credits on financial health | Jesse Rothstein,Krista Ruffini |
2020-021 | Infogroup cross-validation | Tim Thomas,Evan White |
2020-023 | The effect of lender leniency on household finances | Evan White |
2020-024 | How does rent control impact financial well-being? | Nicole Montojo,Steve Raphael |
2020-027 | CalExodus | Natalie Holmes,Evan White,Rebecca Brough,Vikram Jambulapati |
2020-147 | Adult Fines and Fees | Jennifer Skeem, Luyi Jian, Miray Salman, Aldazia Green |
2021-002 | The impact of student loans on college major choice | Alice Li |
2021-003 | The cost of financial product bundling | Jack Liebersohn, Anthony Zhang, Constantine Yannelis |
2021-004 | Present Bias in Consumer Borrowing Decisions | Xiao Yin |
2021-005 | The effect of juvenile fee repeal in Alameda County | Jennifer Skeem, Jaclyn Chambers, Luyi Jian |
2021-006 | Impact of residential electricity affordability programs | Jesse Buchsbaum, Meredith Fowlie, Paula Meloni |
2021-007 | Student Debt and High-Skill Worker Location Choice | Zachary Sauers, Paola Giuliano |
2021-009 | Effect of public lending subsidies on household balance sheets | Manisha Padi, Alexander Stratton |
2021-010 | The distributional impacts of wildfires on household finance | Woongchan Jeon |
2021-011 | Student Loan Forebearance Ending | Niru Ghoshal-Datta |
2021-012 | Co-signing and intergenerational wealth transfer | Michelle Belon |
2021-013 | Debt Collection Lawsuits in California | Dalie Jimenez, Claire Johnson Raba, Claire Johnson Raba, Dalie Jimenez, Prasad Krishnamurthy, Luis Faundez Chacon, Cindy Xu |
2021-016 | Higher Ed Access and Innovation Disparities | Alex Bell, Vikram Jambulapati |
2021-018 | The Impact of Financial Stability Regulation on Households | Manisha Padi, Brittany Lewis, Erica Jiang |
2021-019 | Why is financial distress so seasonal? | Niklas Flamang |
2021-020 | The Effects of Expanded UI on Local Financial Conditions | Niklas Flamang, Niklas Flamang, Sree Kancherla |
2021-022 | Student Loan Payment Pause | Vikram Jambulapati, Evan White |
2021-023 | California Credit Dashboard | Evan White, Sarah Hoover, Steve Ramos |
2021-024 | Pandemic Effects on Financial Health | Brett Fischer, Evan White |
2022-001 | Moralizing the Repayment of Time-Barred Debt | Malena De La Fuente, Franklin Shaddy, Brett Hollenbeck, Poet Larsen, Eitan Rude |
2022-002 | Student Loan Payment Pause and Forgiveness | Dalie Jimenez,Sultana Fouzia,Annemarie Schweinert,Laura Boisten |
2022-003 | Bargaining with Distressed Borrowerrs | Arka Prava Bandhyopadhyay,Manisha Padi |
2022-004 | Minimum wages and consumer loans | Michael Reich,Carl McPherson,Justin Wiltshire,Denis Sosinskiy |
2022-005 | Credit supply shocks and household defaults | Mikhail Mamonov |
2022-006 | Effects of Buy Now, Pay Later on Financial Well-Being | Sarah Papich |
2022-007 | Elimination of State Aid to For-Profit Colleges | Shayak Sarkar,Oded Gurantz,Ryan Sakoda |
2022-008 | Differential exposure to Great Recession | Jesse Rothstein,Sandra Black |
2022-117 | Financial Conseq of Stud Loan Default and Servicer Quality | Meredith Welch,Benjamin Kaufman |
2022-118 | Potential Effects of the New IDR Plan | Chris Bamona,Evan White |
2022-120 | Between Recessions: Hshld Fin Stability & Monetary Policy | Valerie Boctor |
2022-219 | Student Loan Debt in Bankruptcy | Benjamin Kaufman,Belisa Pang,Dalie Jimenez |
2022-222 | Bank Branch Closings and Consumer Financial Access | Prasad Krishnamurthy,Manisha Padi,Abhay Aneja |
2022-240 | Data Privacy Regulation and Financial Intermediaries | Jinyuan Zhang,Erica Jiang,AJ Chen |
2022-242 | Student Debt in the South | Mark Huelsman,Mike Pierce,Benjamin Kaufman,Sultana Fouzia,Katherine Welbeck |
2022-246 | The Long-run Impact of the Great Recession on Student Debt | Marshall Steinbaum,Sergio Pinto |
2022-247 | The effect of the pandemic student loan repayment pause on financial wellbeing | Dalie Jimenez,Cindy Xu,Jonathan Glater,Marshall Steinbaum,Sergio Pinto,Sultana Fouzia,Diego Briones,Axel Morales Sanchez,Belisa Pang |
2023-248 | Relationship Between Consumer Financial Health and Safety Net Enrollment | Brett Fischer, Jesse Rothstein, Erika Brown, Sarah Hoover, Jennifer Hogg, Evan White, Nikta Akhavan, Huizhi Gong, Vikram Jambulapati |
2023-250 | Percolation of natural disaster related credit shocks through networks | Palaash Bhargava,Shreya Chandra |
2023-252 | Disparate Risk and Burden in the Student Loan System | Jonathan Glater,Raj Darolia,Ron Zimmer,Regina Lewis |
2023-255 | Low-income housing, low-income households, and residential mobility | Mike Lens,Jose Loya,Paavo Monkkonen,Lillian Liang,Dan Rinzler,Matt Nissen,Gregory Preston,Aaron Barrall |
2023-256 | Health Shocks and Financial Well-being | Marion Aouad,Michael Fitzpatrick |
2023-259 | Repeat bankruptcy filers and student loans | Belisa Pang |
2023-260 | The Migratory Impacts of Wildfires | Victoria Wang |
2023-261 | Consumer Credit Panel Data and Joint Liability | Asia Bento,Vellore Arthi,Brianna Rodgers |
2023-262 | Summer Institute 2023 | Evan White,Vikram Jambulapati,Cindy Xu,Dickson Chung,Daniel Lopez-Orozco,Emily Wang,Helena Hu,Andrea Bizarro,Rohan Bijukumar,Quyen Le,Daniel Perez,Trevan Nguyen,Sukham Sidhu,Ramona Mukherji |
2023-263 | Student Loan Debt, Migration, and State Investment in Higher Education | Johnathan Conzelmann |
2023-264 | Estimating household wealth by ZIP-code | Gabriel Zucman,Wouter Leenders |
2023-265 | Mortgage Interest Rates and Housing Lock | Jesse Rothstein,Jack Liebersohn,Hannah Case,Rina Nagashima |
2023-266 | Causal Effects of Changes in Medical Debt Removal Eligibility in California | Tiffany Taylor |
2023-267 | Impacts of Health Care Mergers, Acquisitions, and Pricing Reforms on Debt | Adam Leive,Ambar La Forgia,Alexander Adia,Elena Prager |
2023-269 | The effect of credit access on migration | Jack Liebersohn,Greg Howard,Flavio Rodrigues |
2023-275 | Credit Status and Rental Housing Access in Northern California | Alex Ramiller |
2023-276 | Disaster rebuilding costs and climate adaptation and mitigation | Bhavyaa Sharma,Galina Hale,Ted Liu |
2023-278 | Socioeconomic Implications of Extreme Weather Events | Jeremy West,Ethan Raker,Qianping Ren,Rongjin Zhang,Gonzalo Respighi Grasso,Weishi Steadmon |
2023-279 | Evaluating the Effect of SNAP Benefits Cliff on Financial and Labor Outcomes | Jesse Rothstein,Erika Brown,Karla Palos Castellanos,Sarah Hoover,Nikta Akhavan,Brett Fischer |
2023-283 | The Effect of SNAP on Financial Health among Hispanic Communities in California | Min Lee,Jason Somerville,Tatiana Homonoff,Katherine Meckel |
2023-287 | Health Provider Concentration and Medical Debt | Alaa Abdelfattah,Sergio Pinto,Marshall Steinbaum,Alaa AbdelFattah,Justin Wiltshire |
2023-288 | Effects of CalFresh Emergency Allotments on Financial Health | Katherine Meckel,Tatiana Homonoff,Jason Somerville,Min Lee |
2023-294 | UC-CCP Code Review | Shogher Ohannessian |
2023-295 | The Impacts of Parent PLUS Access on Postsecondary Enrollment | William Sullivan, Sandra Black, Palaash Bhargava, Steve Ramos, Jeff Denning, Oded Gurantz, Sandra Black, Rob Fairlie, Michael Jensen |
2023-300 | The impact of legalized sports gambling on consumer credit outcomes | Brett Hollenbeck,Poet Larsen,Davide Proserpio |
2023-301 | Prepaying mortgages | Vikram Jambulapati,Jack Liebersohn,Michael Fitzpatrick |
2023-303 | Experience Effect of Homeownership Choice | Junru Lyu,Gene Kang,Sharath Sonti |
2024-315 | Pregnancy and Financial Outcomes | Victoria Wang,Lei Ma,Letian Yin |
2024-316 | Institutional Segregation, Financial Aid Policies, and Student Debt | Marshall Steinbaum,Sultana Fouzia,Eduard Nilaj,Sergio Pinto,Jack Landry,Laura Beamer |
2024-318 | Income-Driven Repayment Plans, Insurance, and Borrower Outcomes | Crossan Cooper |
2024-324 | Financial Literacy Education | Elaine Shen, Junru Lyu, Stefano DellaVigna |
2024-333 | Buy now, forgiven later? Policy adoption uncertainty and personal finance: Evid | Jean Donovan Rasamoelison |
2024-334 | Trajectories of residential migration and financial well-being among residents | Gregory Preston,Katherine Chen,Ya-Chin Tina Shih,Michael Lens |
2024-335 | Student Loan Debt and the Transmission of Monetary Policy to Consumption | Molly Shatto |
2024-337 | Retrospective Medical Debt Analyses | Adam Goldstein,Charlie Eaton |
2024-341 | When the Credit Dries Up: Examining the Effect of Water Utility Surcharges on Co | Steve Ramos,Hannah Farkas |
2024-342 | Partisan Costs of Unfulfilled Student Loan Forgiveness | Michael Span |
2024-343 | Can High School Financial Education Reduce the Black-White Wealth Gap? | Shogher Ohannessian,Melody Harvey,Carly Urban |
2024-344 | Summer Institute 2024 | Andy Yang,Seetal Neelon,Celine Phan,Genie Kwak,Aaditya Borse,Jamie Hui,Tomas Lopez,Halle Strause,Mario Jimenez,Wei Teo,Elizabeth Franck,Bobby Zhu |
2024-351 | Shared Appreciation Loans, Housing Investment, and Household Finances: Evidence | Dustin Swonder |
2024-364 | The Economic Effects of Bank Mergers on Consumers | Tanya Paul, Rui Shi, Robert Haffner |
2024-366 | Impacts and Implications of California Housing and Transportation Costs | Jesus Barajas, Maxwell Waechter, Md Musfiqur Bhuiya |
2024-367 | Household Finance and Bankruptcy | Dalie Jimenez,Ben Tilkin,Belisa Pang,Abbye Atkinson,Edward Morrison,Samuel Lisner |
2024-368 | Impact of CalFresh on Financial Stability (No CDSS data)) | Tatiana Homonoff,Nikta Akhavan |
2024-371 | Medical Debt, Negative-Only Credit Reporting, and Credit Scores | Liz Laderman,Carolina Reid,Candice Wang,Sara Weiss |
2024-374 | Examining the computational origins of unpredictability of adverse life outcomes | Rediet Abebe, Avi Feller, Andre Cruz |
2024-375 | Understanding Student Debt Choice | Elaine Shen, Tudor Schlanger, Dmitry Taubinsky |
2024-376 | equity in disaster and forced migration | Ethan Sharygin, David Swanson, Cindy Chen |
2024-377 | Impacts of Telecommuting and Remote Services on Transportation, Land Use, And Cl | Fynnwin Prager, Michael McNally |
2024-378 | Student Loan Debt and Voter Turnout | Jacob Grumbach, Maria Krupenkin, Rachel Fordham |
2024-379 | Public Goods, Credit, and Migration | Jeremy West, David Schönholzer, Gonzalo Respighi Grasso |
2024-384 | Student Debt and Lawyers' Careers | Meghan Dawe |
2024-388 | Parental Cosigning and Homeownership | Elin Colmsjoe |