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Credit Card Debt and Consumer Payment Choice: What Can We Learn from Credit Bureau Data?

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We estimate a two-stage Heckman selection model of credit card adoption and use with a unique dataset that combines administrative data from the Equifax credit bureau and self-reported data from a representative survey of consumers. Higher-income consumers carry higher credit card balances, but they tend to repay those balances each month. Credit card revolvers have lower income and are less educated. Revolvers are twice as likely to use debit cards as credit cards for payments, but they carry much higher balances on their credit cards. The high cost of paying off credit card debt likely exacerbates existing inequalities in disposable income. Unlike the mortgage market, we find no evidence for lenders’ cutoff between subprime and prime consumers in the credit card market.

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  1. The SCPC questionnaire and data are available at

  2. See

  3. The two-stage Heckman model cannot be estimated here, because the entire sample consists of credit card holders. Thus, credit card adoption = 1 for everyone in the sample, and so stage 1 of Heckman (adoption) cannot be identified.

  4. Federal Reserve Economic Data, see


  6. RD analysis includes several steps. In step 1, we plot the relationship between the outcome variable and the rating variable to investigate what functional form to use. The data fit a 2nd degree polynomial with no discontinuity at 650. In step 2, we select a bandwidth based on minimizing MSE and test the validity of RD. The bandwidth is 60, or consumers with risk score between 590 and 710. RD is tested by examining if consumers can cross the 650 threshold and if the density of the variable is continuous. The test showed no statistical evidence of systematic manipulation of the score variable. In step 3, we estimate the treatment effect using observations within the chosen bandwidth [590, 710] using the specification: Credit _ Balance = α + β0Ti + β1Ri + β2RiTi + β3DEMi + εi where Ti is the treatment effect indicator, and Ri is the rating variable included to correct for selection bias (Heckman and Robb 1985). The coefficient on the treatment effect indicator Ti is not significant.


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The author thanks José Fillat, Joe Peek, and an anonymous referee for helpful comments, and Allison Cole and Liang Zhang for excellent research assistance. The views expressed here are those of the author and do not necessarily represent the views of the Federal Reserve Bank of Boston, the Board of Governors, or the Federal Reserve System.

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Correspondence to Joanna Stavins.

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Stavins, J. Credit Card Debt and Consumer Payment Choice: What Can We Learn from Credit Bureau Data?. J Financ Serv Res 58, 59–90 (2020).

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