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Consumer Response to Changes in Credit Supply: Evidence from Credit Card Data

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  • David B. Gross
  • Nicholas S. Souleles

Abstract

This paper utilizes a unique new data set on credit card accounts to analyze how people respond to changes in credit supply. The data consist of a panel of several hundred thousand individual credit card accounts followed monthly for 24-36 months, from several different card issuers, with associated credit bureau data. We estimate the dynamic effects of changes in the credit limit and in interest rates, and consider the ability of different models of consumption and saving to rationalize these effects. We find that increases in credit limits generate an immediate and significant rise in debt. This response is sharpest for people starting near their limit, providing evidence that liquidity constraints are binding. However, even people starting well below their limit significantly respond. We show this result is consistent with conventional models of precautionary savings. Nonetheless there are other results that conventional models cannot easily explain, such as the fact that many credit card borrowers simultaneously hold other low yielding assets. Unlike most other studies, we also find strong effects from changes in account-specific interest rates. Debt is particularly sensitive to large declines in interest rates, which can explain the widespread use of teaser rates. The long-run elasticity of debt to the interest rate is about -1.3. Less than half of this elasticity represents balance-switching across cards, with most reflecting net changes in total borrowing. Overall, the results imply that the consumer plays a potentially important role in the transmission of monetary policy and other credit shocks.

Suggested Citation

  • David B. Gross & Nicholas S. Souleles, 2000. "Consumer Response to Changes in Credit Supply: Evidence from Credit Card Data," Center for Financial Institutions Working Papers 00-04, Wharton School Center for Financial Institutions, University of Pennsylvania.
  • Handle: RePEc:wop:pennin:00-04
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    References listed on IDEAS

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    Cited by:

    1. Orlando Gomes, 2010. "Deterministic randomness in a model of finance and growth," Journal of Evolutionary Economics, Springer, vol. 20(1), pages 95-114, January.
    2. Hang Miao & Kui Zhao & Zhun Wang & Linbo Jiang & Quanhui Jia & Yanming Fang & Quan Yu, 2020. "Intelligent Credit Limit Management in Consumer Loans Based on Causal Inference," Papers 2007.05188, arXiv.org.
    3. Morduch Jonathan, 2010. "Borrowing to Save," Journal of Globalization and Development, De Gruyter, vol. 1(2), pages 1-11, December.
    4. Andrés Murcia Pabón, 2007. "Determinantes del acceso al crédito de los hogares colombianos," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 25(55), pages 40-83, December.
    5. Cláudio Ribeiro de Lucinda & Rodrigo Luiz Vieira, 2011. "An Experimental Analysis of the Brazilian Personal Credit Market," Working Papers 10-2011, Universidade de São Paulo, Faculdade de Economia, Administração e Contabilidade de Ribeirão Preto.
    6. Balázs Égert & Ronald MacDonald, 2006. "Monetary Transmission Mechanism in Transition Economies: Surveying the Surveyable," MNB Working Papers 2006/5, Magyar Nemzeti Bank (Central Bank of Hungary).
    7. George-Marios Angeletos & David Laibson & Andrea Repetto & Jeremy Tobacman & Stephen Weinberg, 2001. "The Hyberbolic Consumption Model: Calibration, Simulation, and Empirical Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 15(3), pages 47-68, Summer.
    8. Huffman, David B. & Barenstein, Matias, 2004. "Riches to Rags Every Month? The Fall in Consumption Expenditures Between Paydays," IZA Discussion Papers 1430, Institute of Labor Economics (IZA).
    9. Edward C. Lawrence & Gregory Elliehausen, 2008. "A Comparative Analysis Of Payday Loan Customers," Contemporary Economic Policy, Western Economic Association International, vol. 26(2), pages 299-316, April.
    10. Hong Ru & Antoinette Schoar, 2016. "Do Credit Card Companies Screen for Behavioral Biases?," NBER Working Papers 22360, National Bureau of Economic Research, Inc.
    11. Alfonso-Sánchez, Sherly & Solano, Jesús & Correa-Bahnsen, Alejandro & Sendova, Kristina P. & Bravo, Cristián, 2024. "Optimizing credit limit adjustments under adversarial goals using reinforcement learning," European Journal of Operational Research, Elsevier, vol. 315(2), pages 802-817.
    12. Athreya, Kartik B., 2002. "Welfare implications of the Bankruptcy Reform Act of 1999," Journal of Monetary Economics, Elsevier, vol. 49(8), pages 1567-1595, November.
    13. Luis E. Arango & Lina Cardona-Sosa, 2015. "Determinants of consumer credit within a debt constrained framework. Evidence from microdata," Borradores de Economia 13965, Banco de la Republica.
    14. Jae Min Lee & Narang Park & Wookjae Heo, 2019. "Importance of Subjective Financial Knowledge and Perceived Credit Score in Payday Loan Use," IJFS, MDPI, vol. 7(3), pages 1-21, September.
    15. Jia, Lin & Xue, Geng & Fu, Yuwei & Xu, Longjia, 2018. "Factors affecting consumers’ acceptance of e-commerce consumer credit service," International Journal of Information Management, Elsevier, vol. 40(C), pages 103-110.

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    More about this item

    Keywords

    liquidity constraints; precautionary saving; intertemporal elasticity of substitution; consumer credit; credit cards; monetary policy; credit supply; credit channel.;
    All these keywords.

    JEL classification:

    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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