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Through a relationship with TransUnion, the Kilts Center makes available an archive of anonymized historical, longitudinal consumer credit data that began in July 2000.

These data are available to Chicago Booth and University of Chicago researchers, making possible cutting-edge research in marketing, finance, economics, and more.

Chicago Booth and University of Chicago faculty and PhD students interested in submitting a research proposal should contact Please note that co-authors outside Chicago Booth or University of Chicago are prohibited.


More about TransUnion Data

The TransUnion data include a simple 10% random sample from the United States, including new entrants.

Dataset Details

Years available: Monthly snapshots from July 2000 through January 2021, with periodic updates
What is included: Consumer- and account-level variables
Types of files: Five distinct record types (segments) appear for each consumer, each month:

  1. Header: in-file since date, date of birth (if available)
  2. Trades (types of loans/credit): type, status, usage, payment
  3. Collections: amount, status, payment
  4. Public Records: type, amount, status
  5. Custom: consumer-level attributes, credit score

February 2021

Competition and Selection in Credit Markets
Constantine Yannelis and Anthony Lee Zhang
In more competitive markets, lenders have lower market shares, and thus lower incentives to monitor borrowers. Thus, when markets are competitive, all lenders face a riskier pool of borrowers, which can lead interest rates to be higher, and consumer welfare to be lower.

March 2020

What Determines Consumer Financial Distress? Place- and Person-Based Factors
Benjamin J. Keys, Neale Mahoney, and Hanbin Yang
Financial distress evolves when people move to places with different levels of financial distress. For collections and default, there is only weak convergence following a move, suggesting these types of financial distress are not primarily caused by place-based factors (such as local economic conditions, loan supply, and state laws) but instead reflect person-based characteristics (such as financial literacy and risk preferences). 

December 2019

Financing the Gig Economy
Gregory Buchak
The gig economy is uniquely sensitive to household borrowing constraints on the extensive margin: When finance is unavailable to low-income households, these gains evaporate.

March 2019

Credit Supply and Housing Speculation
Atif Mian and Amir Sufi
The surge in private label mortgage securitization in 2003 fueled a large expansion in mortgage credit supply by lenders financed with non-core deposits.

February 2019

How do Americans repay their debt? The balance-matching heuristic
John Gathergood, Neale Mahoney, Neil Stewart, and Jörg Weber
By studying credit card repayments using linked data on multiple cards from the United Kingdom, the authors showed that individuals did not allocate payments to the higher interest rate card, which would minimize the cost of borrowing, but instead made repayments according to a balance-matching heuristic under which the share of repayments on each card is matched to the share of balances on each card.

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