Learning in the Credit Card Market Sumit agarwal, John C. Driscoll. Xavier Gabaix, and David Laibson* pril23,2011 Abstract Agents with more experience make better choices. We measure learning dynamics using a panel with four million monthly credit card statements. We study add-on fees specifically cash advance, late payment, and overlimit fees. New credit card accounts generate fee payments of S15 per month. Through negative feedback-ie paying a fee consumers learn to avoid triggering future fees. Paying a fee last month reduces the likelihood of paying a fee in the current month by about 40%. Controlling for account fixed effects, monthly fee payments fall by 75% during the first three years of account life. We find that learning is not monotonic. Knowledge effectively depreciates about 10% per month, implying that learning displays a strong recency effect. The speed of net learning is about twice as great for higher-income borrowers than it is for lower- income borrowers; the rate of knowledge depreciation, or forgetting, is about half as fast for high- relative to low-income borrowers. Middle-aged borrowers have the same advantageous learning dynamics relative to older borrowers. JEL: D1, D4, D8, G2 Agarwal: Federal Reserve Bank of Chicago. sagarwalofrbchi. org. Driscoll: Federal Reserve Board, john. c. driscollafrb. gov. Gabaix: New York University and NBER, xgabaix @stern. nyu. edu Laibson: Harvard University and NBER, dlaibson harvard. edu. Gabaix and Laibson acknowledg support from the National Science Foundation(DMS-0938185). Laibson acknowledges financial support from the National Institute on Aging(RO1-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 Er icson, Mike Levere, Tom Mason and Thomas Spiller provided outstanding research assistance. The authors are grateful to the editor and the referees, and to Murray Carbonneau, Stefano dellavigna Joanne Maselli, Nicola Persico, Devin Pope, Matthew Rabin, and seminar participants at the aeA Berkeley, EUI and the NBER(Law and Economics) for their suggestions. This paper previously circulated under the title"Stimulus and Response: The Path from Naivete to Sophistication in the Credit card market
Learning in the Credit Card Market Sumit Agarwal, John C. Driscoll, Xavier Gabaix, and David Laibson∗ April 23, 2011 Abstract Agents with more experience make better choices. We measure learning dynamics using a panel with four million monthly credit card statements. We study add-on fees, specifically cash advance, late payment, and overlimit fees. New credit card accounts generate fee payments of $15 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 in the current month by about 40%. Controlling for account fixed effects, monthly fee payments fall by 75% during the first three years of account life. We find that learning is not monotonic. Knowledge effectively depreciates about 10% per month, implying that learning displays a strong recency effect. The speed of net learning is about twice as great for higher-income borrowers than it is for lowerincome borrowers; the rate of knowledge depreciation, or forgetting, is about half as fast for high- relative to low-income borrowers. Middle-aged borrowers have the same advantageous learning dynamics relative to older borrowers. (JEL: D1, D4, D8, G2) ∗Agarwal: Federal Reserve Bank of Chicago, sagarwal@frbchi.org. Driscoll: Federal Reserve Board, john.c.driscoll@frb.gov. Gabaix: New York University and NBER, xgabaix@stern.nyu.edu. Laibson: Harvard University and NBER, dlaibson@harvard.edu. Gabaix and Laibson acknowledge support from the National Science Foundation (DMS-0938185). Laibson acknowledges financial 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, Mike Levere, Tom Mason and Thomas Spiller provided outstanding research assistance. The authors are grateful to the editor and the referees, and to Murray Carbonneau, Stefano Dellavigna, Joanne Maselli, Nicola Persico, Devin Pope, Matthew Rabin, and seminar participants at the AEA, Berkeley, EUI and the NBER (Law and Economics) for their suggestions. This paper previously circulated under the title “Stimulus and Response: The Path from Naiveté to Sophistication in the Credit Card Market.” 1
Introduction Economists often motivate optimization and equilibrium as the outcome of learning Learning is a key mechanism that underpins economic theories of rational behavior. Accord- ingly, many economic studies have analyzed learning in the lab, and in the field. 2 Because of data limitations, only a few papers measure learning with household- panel data. Such household studies, usually find that households learn to optimize over time. For example, Miravete(2003)and Agarwal, Chomsisengphet, Liu and Souleles(2006) espectively show that consumers switch telephone calling plans and credit card contracts te minimize monthly bill payments. A few papers are able to identify the specific information Hows that elicit learning. For instance, Fishman and Pope(2006) study video stores, and find that renters are more likely to return their videos on time if they have recently been fined for returning them late. Ho and Chong(2003)use grocery store scanner data to estimate a model in which consumers learn about product attributes. Their learning model has greater predictive power, with fewer parameters, than forecasting models used by retailers.3 In the current paper, we study individual households that learn to avoid add-on fee the credit card market. 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 -late payment, over limit, and cash advance fees. Some observers argue that account holders do not optimally minimize such fees. We want to know whether credit card holders change the way they use their credit cards -e. g, paying For example, Van Huyck, Cook and Battalio(1994), Crawford(1995), Roth and Erev(1995),Camerer (2003), and Wixted(2004) 2For example, see Bahk and Gort(1993), Marimon and Sunder(1994). Thornton and Thompson(2001) LEmieux and MacLeod(2000) study the effect of an increase in unemployment benefits in Canada They find that the propensity to collect unemployment benefits increases as a consequence of a previous unemployment spell. Odean, Strahlevitz and Barber(2010) find evidence that individual investors tend to repurchase stocks that they previously sold for a gain. Dellavigna(2009) surveys the field evidence on behavioral phenomena 4 Ausubel(1991, 1999), Shui and Ausubel(2004)and Kerr and Dunn(2008)analyze the magnitude of interest payments and fees in the credit card market ple, Frontline reports that "The new billions in revenue reflect an age-old habit of human behavior: Most people never anticipate they will pay late, so they do not shop around for better late fees"(http://www.pbs.org/wgbh/pages/frontline/shows/credit/more/rise.html)thereisalsoanascent academic literature that studies how perfectly rational firms interact in equilibrium with imperfectly rational consumers. See Shui and Ausubel(2004), Della vigna and Malmendier(2004). Mullainathan and Shleifer (2005), Oster and Morton(2005), Gabaix and Laibson(2006), Jin and Leslie(2003), Koszegi and Rabin (2006), Malmendier and Shanthikumar(2007), Grubb(2009), and Bertrand et al.(2010). See Spiegler (2011)for an overview
1 Introduction Economists often motivate optimization and equilibrium as the outcome of learning. Learning is a key mechanism that underpins economic theories of rational behavior. Accordingly, many economic studies have analyzed learning in the lab,1 and in the field.2 Because of data limitations, only a few papers measure learning with household-level panel data. Such household studies, usually find that households learn to optimize over time. For example, Miravete (2003) and Agarwal, Chomsisengphet, Liu and Souleles (2006) respectively show that consumers switch telephone calling plans and credit card contracts to minimize monthly bill payments. A few papers are able to identify the specific information flows that elicit learning. For instance, Fishman and Pope (2006) study video stores, and find that renters are more likely to return their videos on time if they have recently been fined for returning them late. Ho and Chong (2003) use grocery store scanner data to estimate a model in which consumers learn about product attributes. Their learning model has greater predictive power, with fewer parameters, than forecasting models used by retailers.3 In the current paper, we study individual households that learn to avoid add-on fees in the credit card market.4 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 – late payment, over limit, and cash advance fees. Some observers argue that account holders do not optimally minimize such fees.5 We want to know whether credit card holders change the way they use their credit cards — e.g., paying 1For example, Van Huyck, Cook and Battalio (1994), Crawford (1995), Roth and Erev (1995), Camerer (2003), and Wixted (2004). 2For example, see Bahk and Gort (1993), Marimon and Sunder (1994), Thornton and Thompson (2001). 3Lemieux and MacLeod (2000) study the effect of an increase in unemployment benefits in Canada. They find that the propensity to collect unemployment benefits increases as a consequence of a previous unemployment spell. Odean, Strahlevitz and Barber (2010) find evidence that individual investors tend to repurchase stocks that they previously sold for a gain. Dellavigna (2009) surveys the field evidence on behavioral phenomena. 4Ausubel (1991, 1999), Shui and Ausubel (2004) and Kerr and Dunn (2008) analyze the magnitude of interest payments and fees in the credit card market. 5For example, Frontline reports that “The new billions in revenue reflect an age-old habit of human behavior: Most people never anticipate they will pay late, so they do not shop around for better late fees.” (http://www.pbs.org/wgbh/pages/frontline/shows/credit/more/rise.html) There is also a nascent academic literature that studies how perfectly rational firms interact in equilibrium with imperfectly rational consumers. See Shui and Ausubel (2004), DellaVigna and Malmendier (2004), Mullainathan and Shleifer (2005), Oster and Morton (2005), Gabaix and Laibson (2006), Jin and Leslie (2003), Koszegi and Rabin (2006), Malmendier and Shanthikumar (2007), Grubb (2009), and Bertrand et al. (2010). See Spiegler (2011) for an overview. 2
fewer fees- as they gain experience We find that fee payments are very large in the first few months after the opening of an ccount. We find that new accounts generate direct monthly fee payments(not including interest payments)that average S15 per month. b However, these payments fall by 75 percent during the first four years of account life These learning effects may be driven by many different channels. Consumers learn more about the existence and magnitude of fees when they knowingly or accidentally trigger them Painful fee payments may also train account holders to be more vigilant in their card usage As a result of many different learning pathways, card holders sharply cut their fee payments over time We find that the learning dynamics are not monotonic. Card holders act as if their knowl- edge depreciates-i.e, learning patterns exhibit a recency effect. 7 A late payment charge from the previous month engenders vigilant fee avoidance this month, and this response is much stronger than the vigilance engendered by a late payment charge that was paid further back in time. We estimate that the learning effect of a fee payment effectively depreciates at a rate of between 10 and 20 percent per month. At first glance, such depreciation may seem counter-intuitive. However, if attention is a scarce resource, attention may wander as the salience of a past fee payment fades. After making any significant mistake(e.g, getting a speeding ticket), people are likely to pay attention and avoid the mistake; however as the key event recedes into history, vigilance fades There are several papers that have also documented forgetting effects, though the sett of these papers are quite different from our credit card application. For instance, Benkard (2000)finds 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 We analyze the mechanisms that may explain the fee dynamics that we measure. W mOreover, this understates the impact of fees, since some behavior -e. g. a pair of late payments 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 affecting the card holder's credit accessability and creditworthness The average consumer has 4.8 cards and 2.7 actively used card 7See Lehrer(1988), Aumann, Hart and Perry(1997) and Besanko et al.(2010) for some theoretical models of forgetfulness
fewer fees — as they gain experience. We find that fee payments are very large in the first few months after the opening of an account. We find that new accounts generate direct monthly fee payments (not including interest payments) that average $15 per month. 6 However, these payments fall by 75 percent during the first four years of account life. These learning effects may be driven by many different channels. Consumers learn more about the existence and magnitude of fees when they knowingly or accidentally trigger them. Painful fee payments may also train account holders to be more vigilant in their card usage. As a result of many different learning pathways, card holders sharply cut their fee payments over time. We find that the learning dynamics are not monotonic. Card holders act as if their knowledge depreciates — i.e., learning patterns exhibit a recency effect.7 A late payment charge from the previous month engenders vigilant fee avoidance this month, and this response is much stronger than the vigilance engendered by a late payment charge that was paid further back in time. We estimate that the learning effect of a fee payment effectively depreciates at a rate of between 10 and 20 percent per month. At first glance, such depreciation may seem counter-intuitive. However, if attention is a scarce resource, attention may wander as the salience of a past fee payment fades. After making any significant mistake (e.g., getting a speeding ticket), people are likely to pay attention and avoid the mistake; however as the key event recedes into history, vigilance fades. There are several papers that have also documented forgetting effects, though the settings of these papers are quite different from our credit card application. For instance, Benkard (2000) finds 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. We analyze the mechanisms that may explain the fee dynamics that we measure. We 6Moreover, this understates the impact of fees, since some behavior – e.g. a pair of late payments – 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 affecting the card holder’s credit accessability and creditworthness. The average consumer has 4.8 cards and 2.7 actively used cards. 7See Lehrer (1988), Aumann, Hart and Perry (1997) and Besanko et al. (2010) for some theoretical models of forgetfulness. 3
first explore several explanations that are not consistent with our preferred explanation of learning /forgetting-for example, that card usage might be negatively autocorrelated-and find that these explanations are not consistent with the data. On the other hand, we find support for mechanisms that support the learning/forgetting interpretation. Notably, we find that in the month after paying a late fee, account holders are especially likely to make their next payment more than two weeks before the due date. This suggests that a late payment fee acts as a wake up call that induces earlier fee payment. We also find that the speed of (i) net learning, (ii) the magnitude of the recency effect, and(iii) the speed of forgetting all differ across borrower characteristics. Higher-income borrowers learn more than twice as fast, have a recency effect double the size, and forget about three-times as slowly as lower-income borrowers. Likewise, middle-aged borrowers have similar learning dvantages relative to older borrowers In summary, our findings imply that a high rate of knowledge depreciation offsets learning Nevertheless, learning dominates knowledge depreciation. On average, fees fall over the life the credit card. These learning dynamics are most advantageous for high-income and middle-aged borrowers We organize our paper as follows, Section 2 summarizes our data and presents our basic evidence for learning and backsliding/forgetting. Section 3 analyzes various alternative(non- learning) explanations for our finding Section 4 discusses extensions to our analysis on learning and forgetting, including results on the demographics of learning. In Section 5, we draw some conclusions 2 Two Patterns in Fee Payment In this section, we describe the data. We then show that fee payments decline sharply with account tenure. We also show that the learning dynamics exhibit a recency effect: a late payment charge from the previous month is strongly associated with fee avoidance this month, and this elasticity sharply declines as the time gap increases between the previous fee payment and the current period
first explore several explanations that are not consistent with our preferred explanation of learning/forgetting—for example, that card usage might be negatively autocorrelated—and find that these explanations are not consistent with the data. On the other hand, we find support for mechanisms that support the learning/forgetting interpretation. Notably, we find that in the month after paying a late fee, account holders are especially likely to make their next payment more than two weeks before the due date. This suggests that a late payment fee acts as a wake up call that induces earlier fee payment. We also find that the speed of (i) net learning, (ii) the magnitude of the recency effect, and (iii) the speed of forgetting all differ across borrower characteristics. Higher-income borrowers learn more than twice as fast, have a recency effect double the size, and forget about three-times as slowly as lower-income borrowers. Likewise, middle-aged borrowers have similar learning advantages relative to older borrowers. In summary, our findings imply that a high rate of knowledge depreciation offsets learning. Nevertheless, learning dominates knowledge depreciation. On average, fees fall over the life of the credit card. These learning dynamics are most advantageous for high-income and middle-aged borrowers. We organize our paper as follows, Section 2 summarizes our data and presents our basic evidence for learning and backsliding/forgetting. Section 3 analyzes various alternative (nonlearning) explanations for our findings. Section 4 discusses extensions to our analysis on learning and forgetting, including results on the demographics of learning. In Section 5, we draw some conclusions. 2 Two Patterns in Fee Payment In this section, we describe the data. We then show that fee payments decline sharply with account tenure. We also show that the learning dynamics exhibit a recency effect: a late payment charge from the previous month is strongly associated with fee avoidance this month, and this elasticity sharply declines as the time gap increases between the previous fee payment and the current period. 4
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 ac- count's monthly statement, including previous payment, purchases, credit limit, balance debt, amount due, purchase APR, cash advance APR, date of previous payment, and fees incurred. 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 for 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 opening. Further details on the data, including summary statistics and variable definitions, are available in the appendix. We focus on three important types of fees, described below: late fees, over limit fees, and ash advance fees. 8 1. Late Fee: a direct late fee of $30 or S35 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 al impose indirect late fees by raising the APR to over 24 percent. Such indirect fees are referred to as penalty pricing The bank may also choose to report late payments to credit bureaus, adversely affecting consumers' FICO scores. Our analysis measures only direct late fees(and therefore excludes consumer costs associated with penalty pricing) 2. Over Limit Fee: a direct over limit fee. also of S30 or $35. is assessed the first time sOther types of fees include annual, balance transfer, foreign transactions, and pay by phone. All of these fees are relatively less important to both the bank and the borrower. Fewer issuers(the most notable exception being American Express)continue to charge annual fees, largely as a result of increased competition for new borrowers(Agarwal et al., 2006). The cards in our data do not have annual fees. A balance transfer fee of 2-3% of the amount transferred is assessed on borrowers who shift debt from one card to another ers repeatedly transfer balances, borrower response to this fee will not allow us to study learning about fee payment. The foreign transaction fees and pay by phone fees together comprise less than three percent of the total fees collected by banks. PIf 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 its promotional) level
2.1 Data We use a proprietary panel dataset from a large U.S. bank that issues credit cards nationally. 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 previous payment, purchases, credit limit, balance, debt, amount due, purchase APR, cash advance APR, date of previous payment, and fees incurred. 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 for 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 opening. Further details on the data, including summary statistics and variable definitions, are available in the appendix. We focus on three important types of fees, described below: late fees, over limit fees, and cash advance fees.8 1. Late Fee: A direct 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 indirect late fees by raising the APR to over 24 percent.9 Such indirect fees are referred to as ‘penalty pricing.’ The bank may also choose to report late payments to credit bureaus, adversely affecting consumers’ FICO scores. Our analysis measures only direct late fees (and therefore excludes consumer costs associated with penalty pricing). 2. Over Limit Fee: A direct over limit fee, also of $30 or $35, is assessed the first time 8Other types of fees include annual, balance transfer, foreign transactions, and pay by phone. All of these fees are relatively less important to both the bank and the borrower. Fewer issuers (the most notable exception being American Express) continue to charge annual fees, largely as a result of increased competition for new borrowers (Agarwal et al., 2006). The cards in our data do not have annual fees. A balance transfer fee of 2-3% of the amount transferred is assessed on borrowers who shift debt from one card to another. Since few consumers repeatedly transfer balances, borrower response to this fee will not allow us to study learning about fee payment. The foreign transaction fees and pay by phone fees together comprise less than three percent of the total fees collected by banks. 9 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 its promotional) level. 5