PDF《Learning in the Credit Card Market》

Learning in the Credit Card Market Sumit Agarwal, John C.

Driscoll, Xavier Gabaix, and David Laibson? December 1,

2007 Abstract Learning through experience facilitates optimization. We measure learning dynamics using a panel with four million monthly credit card statements. We study add-on fees, speci?cally cash advance, late payment, and over-limit fees. New credit card accounts generate fee payments of over $16 per month. Through negative feedback ― i.e. paying a fee ― consumers learn to avoid triggering future fees. Paying a fee last month reduces the likelihood of paying a fee this month by about 40%. Controlling for account ?xed e?ects, monthly fee payments fall by 75% during the ?rst three years of account life. We ?nd that learning is not monotonic. Knowledge depreciates about 10% per month, implying that learning displays a strong recency e?ect. JEL classi?cation: D1, D4, D8, G2. Keywords: credit cards, feedback, learning, learning-by-doing, recency. ? Agarwal: Federal Reserve Bank of Chicago, [email protected]. Driscoll: Federal Reserve Board, [email protected]. Gabaix: New York University and NBER, [email protected]. Laibson: Harvard University and NBER, [email protected]. Gabaix and Laibson acknowledge support from the National Science Foundation (Human and Social Dynamics program). Laibson acknowledges ?nancial support from the National Institute on Aging (R01-AG-1665). The views expressed in this paper are those of the authors and do not represent the policies or positions of the Board of Governors of the Federal Reserve System or the Federal Reserve Bank of Chicago. Ian Dew-Becker, Keith Ericson and Tom Mason provided outstanding research assistance. The authors are grateful to Murray Carbonneau, Stefano DellaVigna, Luigi Guiso, Joanne Maselli, Nicola Persico, Matthew Rabin, and seminar participants at the AEA, Berkeley, EUI and the NBER for helpful discussions and suggestions. This paper previously circulated under the title Stimulus and Response: The Path from Naivete to Sophistication in the Credit Card Market.

1 1 Introduction Economists believe that learning through experience underpins optimization and gener- ates technological progress. Large literatures measure learning dynamics in the lab,1 and in the ?eld.2 However, because of data limitations, relatively few papers measure learning in the ?eld with micro-level (household) data. Among such household studies, most show that households learn to optimize over time.3 Moreover, a few papers are able to identify the speci?c mechanisms and information ?ows that elicit learning. For instance, Fishman and Pope (2006) study video stores, and ?nd that renters are more likely to return their videos on time if they have recently been ?ned for returning them late. Ho and Chong (2003) use grocery store scanner data to estimate a model in which consumers learn about product attributes. They ?nd that the model has greater predictive power, with fewer parameters, than forecasting models used by retailers.4 The current paper studies the process by which individual households learn to avoid add-on fees in the credit card market.5 We analyze a panel dataset that contains three years of credit card statements, representing 120,000 consumers and 4,000,000 credit card statements. We focus our analysis on credit card fees C late payment, over limit, and cash

1 For example, Van Huyck, Battalio and Beil (1990, 1991), McAllister (1991), Crawford (1995), Roth and Erev (1995), VanHuyck, Battalio and Rankin (2001), Anderson (2000), Camerer (2003), and Wixted (2004a, 2004b).