PDF《Learning in the Credit Card Market》

7 Moreover, this understates the impact of fees, since some behavior C e.g. a pair of late payments C not only triggers direct fees but also triggers an interest rate increase, which is not captured in our $15 calculation. Suppose that a consumer is carrying $2,000 of debt. Changing the consumer'

s interest rate from 10% to 20% is equivalent to charging the consumer an extra $200. Late payments also may prompt a report to the credit bureau, adversely a?ecting the card holder'

s credit accessability and creditworthness. The average consumer has 4.8 cards and 2.7 actively used cards.

8 See Lehrer (1988), Piccione and Rubinstein (1997) and Aumann, Hart and Perry (1997) for some theo- retical models of forgetfulness.

3 between

10 and

20 percent per month. At ?rst glance, this ?nding seems counter-intuitive. But there are actually several examples of papers that have found such forgetting e?ects. For instance, Benkard (2000) ?nds evidence for both learning and forgetting ― that is, depreciation of productivity over time ― in the manufacturing of aircraft, as do Argote, Beckman and Epple (1990), in shipbuilding. Our ?ndings imply that learning is very powerful, but that depreciation partially o?sets learning. Nevertheless the net e?ect of learning is clear. Learning generates an overwhelm- ing net reduction in fee payments. The paper has the following organization. Section

2 summarizes our data and presents our basic evidence for learning and backsliding. Section

3 presents a model for those pat- terns. This model is estimated with the Method of Simulated Moments in section 4. Section

5 discusses alternative explanations for our ?ndings. Section

6 concludes.

2 Two Patterns in Fee Payment In this section, we describe the dataset and present two sets of reduced-form analyses of the data. 2.1 Data We use a proprietary panel dataset from a large U.S. bank that issues credit cards na- tionally. The dataset contains a representative random sample of about 128,000 credit card accounts followed monthly over a

36 month period (from January

2002 through December 2004). The bulk of the data consists of the main billing information listed on each account'

s monthly statement, including total payment, spending, credit limit, balance, debt, purchase and cash advance annual percentage rate (APR), and fees paid. At a quarterly frequency, we observe each customer'

s credit bureau rating (FICO score) and a proprietary (internal) credit '

behavior'

score. We have credit bureau data about the number of other credit cards held by the account holder, total credit card balances, and mortgage balances. We have data on the age, gender and income of the account holder, collected at the time of account

4 opening. Further details on the data, including summary statistics and variable de?nitions, are available in the data appendix. We focus on three important types of fees, described below: late fees, over limit fees, and cash advance fees.9 1. Late Fee: A late fee of $30 or $35 is assessed if the borrower makes a payment beyond the due date on the credit card statement. If the borrower is late by more than

60 days once, or by more than

30 days twice within a year, the bank may also impose '

penalty pricing'

by raising the APR to over

24 percent. The bank may also choose to report late payment to credit bureaus, adversely a?ecting consumers'

FICO scores. If the borrower does not make a late payment during the six months after the last late payment, the APR will revert to its normal (though not promotional) level. 2. Over Limit Fee: An over limit fee, also of $30 or $35, is assessed the ?rst time the borrower exceeds his or her credit limit. The same penalty pricing as in the late fee is imposed. 3. Cash Advance Fee: A cash advance fee of the greater of