Minimum Required Payment and Supplemental Information Disclosure Effects on Consumer Debt Repayment...

7/31/2019 Minimum Required Payment and Supplemental Information Disclosure Effects on Consumer Debt Repayment Deci

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DANIEL NAVARRO-MARTINEZ, LINDA COURT SALISBURY, KATHERINE N. LEMON,

NEIL STEWART, WILLIAM J. MATTHEWS, and ADAM J.L. HARRIS

Repayment decisionshow much of the loan to repay and when tomake the paymentsdirectly influence consumer debt levels. The authors

examine how minimum required payment policy and loan information dis-closed to consumers influence repayment decisions. They find that whilepresenting minimum required payment information has a negative impacton repayment decisions, increasing the minimum required level has apositive effect on repayment for most consumers. Experimental evidencefrom U.S. consumers shows that consumers propensity to pay the mini-mum required each month moderates these effects; U.K. credit card fielddata indicate that borrowers credit limit and balance due also moderatethese effects. However, increasing the minimum level is unlikely to com-pletely eliminate the negative effect of presenting minimum payment infor-mation. In addition, disclosing supplemental information, such as futureinterest cost and time needed to repay the loan, does not reduce the neg-ative effects of including minimum payment information and has no sub-stantial positive effect on repayments. This research offers new insights

into the debt repayment process and has implications for consumers,lenders, and public policy.

Keywords: consumer debt, credit cards, financial decision making,information disclosure, public policy

Minimum Required Payment andSupplemental Information DisclosureEffects on Consumer Debt RepaymentDecisions

*Daniel Navarro-Martinez is Postdoctoral Research Fellow, Depart-

ment of Economics, University of Warwick (e-mail: D.Navarro-Martinez@

warwick.ac.uk). Linda Court Salisbury is Assistant Professor of Mar-

keting, Carroll School of Management, Boston College (e-mail:

[email protected]). Katherine N. Lemon is Accenture Professor

and Professor of Marketing, Carroll School of Management, Boston

College (e-mail: [email protected]). Neil Stewart is Professor of Psy-

chology, Department of Psychology, University of Warwick (e-mail:[email protected]). William J. Matthews is a lecturer, Depart-

ment of Psychology, University of Essex (e-mail: [email protected]). Adam

J.L. Harris is Teaching Fellow, Division of Psychology and Language Sci-

ences, University College of London (e-mail: [email protected].) The

first two authors are listed alphabetically and contributed equally to the

research. The third and fourth authors are listed alphabetically and also

contributed equally. The authors thank Ian Shayler at the U.K. Cards Asso-

ciation for his assistance in compiling the data set used in Study 2. This

work was partially funded by the Economic and Social Research Council

(U.K.) grant RES-000-22-3339. Gal Zauberman served as associate editor

for this article.

Consumer debt levels are high, with many consumersstruggling to pay down their debts. In the United States, totalconsumer debt was $2.4 trillion in late 2010, $806 billionof which was revolving debt. The average U.S. householdhas an estimated $7,300 in revolving debt, and credit cardloan charge-off rates have more than doubled since 2007(Federal Reserve Board 2009, 2011). In the United King-dom, credit cards account for 63.9 billion of consumerdebt. Approximately 47% of U.K. credit cardholders and56% of U.S. cardholders carry a balance (Department forBusiness, Innovation and Skills 2010; Federal Reserve Bankof Boston 2008). In addition, an estimated 13% of U.S.credit cardholders and 14% of U.K. credit cardholders payonly the minimum required amount each month on theircredit cards (Department for Business, Innovation and Skills2010; Federal Reserve Bank of Boston 2008). Revolvingdebt (e.g., credit card debt) grows when consumers bor-row more funds, but debt also grows as consumers rate of

2011, American Marketing Association

ISSN: 0022-2437 (print), 1547-7193 (electronic) S60Journal of Marketing Research

Vol. XLVIII (Special Issue 2011), S60S77


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Consumer Debt Repayment Decisions S61

repayment slows. Our focus in this research is on under-standing debt repayment behavior.

In financially developed countries, credit cards consti-tute one of the main devices for consumers to engagein unsecured borrowing. Typically, each month a bor-rower makes a decision about how much of his or herloan to repay. An important mechanism that credit cardfirms employ to prevent borrower default is the minimum

required payment policy, which includes disclosing infor-mation about the required minimum payment in borrowersmonthly credit card bills. Currently, virtually every lendinginstitution operates under some minimum payment policy.Monthly minimum payments are typically calculated as asmall percentage of the outstanding balance or a small fixedamount, whichever is larger. For example, in the UnitedKingdom, a minimum of 2.5% or 5 is typical. Cardhold-ers who fail to repay the minimum are often charged a fee,and the slow repayment leads to greater (compounding)interest costs.

To illustrate the impact of minimum payment policy ondebt, imagine a U.K. cardholder with an outstanding bal-ance of 2,500 under a standard interest rate of 17% annual

percentage rate (APR) and a minimum payment policy of2% or 5, whichever is larger. The time it would taketo repay the debt and the total interest charged over thatperiod for four different monthly repayment strategies areas follows:

Repay minimum requiredevery month

38.4 years topay off

5,283 totalinterest

Repay 60 every month 5.3 years topay off

1,221 totalinterest

Repay 120 every month 2.1 years topay off

434 totalinterest

Repay 240 every month 1 year topay off

183 totalinterest

In the case in which only the minimum is repaid, the debtdecreases extremely slowly, and the total interest chargesare extremely high. Thus, debt can easily get out of controlif consumers make only minimum repayments. The situ-ation changes dramatically if consumers instead repay amoderately higher amount than the minimum each month.From the lender perspective, these consumer repaymentdecisions can also significantly influence the firms rev-enues and profitability.

Minimum payments on credit card debt are controver-sial and have also been the focus of several regulations.Some U.S. and U.K. institutions have recently proposedincreases in minimum payments. A prominent example isthe change from 2% to 5% in minimum payments that

Chase Bank, one of the largest credit card issuers in theUnited States, implemented in 2009. Another case is that ofa white paper the Department for Business, Innovation andSkills (2009) presented to the British Parliament, in whichit proposed a mandatory increase in minimum payments tothe level of 5% (the proposal has since been rejected). Therecently enacted U.S. Credit Card Accountability, Respon-sibility and Disclosure (CARD) Act of 2009 (H.R. 627,S.414) requires lenders to include a minimum paymentwarning in all monthly credit card statements. This warn-ing discloses information to consumers on every monthlycredit card statement, including the total interest cost and

the amount of time it will take to pay off the card balance ifconsumers only make minimum monthly payments (here-inafter referred to as supplemental information). Bothtypes of interventionsraising the minimum payment leveland disclosing supplemental informationaim to increasemonthly repayment amounts. We examine the efficacy ofeach approach in this research.

Little research has examined the effects of minimum

payment policies and loan cost information disclosure onindividual-level consumer repayment decisions. It seemslikely that increasing minimum requirements or provid-ing supplemental loan cost information would influenceconsumers who would otherwise repay only the mini-mum. However, minimum payment policies and cost infor-mation could also have unintended effects on the largergroup of consumers who tend to make higher repaymentsthan the minimum. Stewart (2009) finds that the merepresence of minimum required payment information on amonthly credit card statement leads consumers to makelower monthly credit card repayments than if the informa-tion were not present. This finding (which we replicate withU.S. consumers) suggests that the current practice of requir-

ing a minimum payment has a negative effect on consumerrepayment behavior, and it calls into question whether orhow the presence of other relevant information will affectdebt repayment.

To understand how minimum payment policy and loaninformation influence consumers repayment decisions, weexamine the effect of varying the level of minimum pay-ment required and disclosing three specific types of infor-mation: the minimum payment required, loan interest costs,and length of time to pay off the loan. Employing a mul-timethod approachtwo controlled experiments with U.S.adult consumers and field data from 11 U.K. lenderswe find that increasing minimum required payment levelshas positive effects on repayment decisions for most con-

sumers. These effects are moderated by borrowers creditlimit, balance due, and propensity to pay the minimumrequired. Our experimental findings also suggest, however,that increasing the minimum payment level is unlikely toeliminate the strong negative effect of merely presentingminimum payment information. Finally, we find no evi-dence that disclosing supplemental loan information to con-sumers yields positive effects on repayment decisions.

This research offers important implications for market-ing practice and public policy by providing insights intothe following questions: What information should lendersdisclose or make salient to consumers? What minimumpayment policies might reduce the firms credit risk byreducing default rates? and How can public policy mak-

ers strengthen interventions to improve consumer financialdecision making?

CONCEPTUAL BACKGROUND

Debt repayment decisions entail the consumer choosingboth how much and when to repay a loan. A relativelysmaller payment allows the borrower to have more cash onhand to use for other purposes in the current month but,because of compounding interest rates, increases the future(and, thus, total) cost of the loan. A relatively larger pay-ment leaves the consumer with less money available in thecurrent month but reduces the future cost of the loan. Thus,


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S62 JOURNAL OF MARKETING RESEARCH, SPECIAL ISSUE 2011

the consumer must decide whether to repay more now(decreasing current utility) or repay more later (decreasingfuture utility). As such, the debt repayment decision can beconceived of as a trade-off between the desires to minimizecurrent cost and minimize future, total cost over the lifeof the loan. Thus, salience of these costs should have animportant impact on the repayment decision.

Salience can be influenced by the decision context, such

as the type of information available to the consumer at thetime the decision is made, which, in the case of revolv-ing debt, is typically once per month. In addition to min-imum payment information, credit card lenders typicallyprovide interest cost information in terms of compoundAPR, but research shows that consumers have difficultyunderstanding interest compounding (Eisenstein and Hoch2007; Stango and Zinman 2009), and a surprisingly largenumber of consumers have poor knowledge of the cost ofcredit (Lee and Hogarth 1999). This is particularly prob-lematic for borrowers of flexible loans that do not requirethe loan to be paid off within a fixed period. Estimatingflexible loan duration is difficult for consumers, thoughinterest cost information improves accuracy in some cases

(Ranyard and Craig 1993).Stewart (2009) reports results from a U.K. credit cardrepayment experiment, which shows an important, unex-pected association between minimum payment and repay-ment behavior. In a hypothetical scenario-based experiment,using fictitious credit card bills presented to cardholders,he finds that omitting minimum payment information fromthe statements led to significantly higher repayments. Stew-art links these patterns with the psychological phenomenonof anchoring (Tversky and Kahneman 1974; for a simi-lar suggestion, see Thaler and Sunstein 2008), suggestingthat the presence of minimum payment information leadsconsumers to anchor on that small amount and thus make asmaller repayment.

Just as consumers use the credit limit information sup-plied by credit card lenders as an informative signal oftheir future earnings potential and ability to repay (Somanand Cheema 2002), so may debtors interpret minimum pay-ment information as a signal of the amount that wouldbe appropriate for repaying the loan in a timely manner.Such information might also be construed as a paymentrecommendation or implicit payment norm (McKenzie,Liersch, and Finkelstein 2006; Wansink, Kent, and Hoch1998). Alternatively, increased attention to minimum pay-ment information may increase salience of current costsand the desire to minimize them, leading to lower repay-ment. Each of these perspectives offers possible contrib-utory mechanisms for the negative effects of minimum

payment information. Although testing alternative explana-tions is outside the scope of this research, teasing apartthese contributing factors is an important avenue for furtherresearch.

This research aims to identify practical public policy orlender interventions that have the potential to mitigate thenegative effects of disclosing minimum payment informa-tion on repayment. One potential approach is to increasethe minimum required level, effectively increasing the sizeof the low anchor. This approach is likely to increaserepayment, especially for borrowers prone to paying theminimum required amount each month. If the minimum

payment acts as a signal or recommendation, increasing itsvalue is likely to increase the repayment amount.

A second approach is to disclose other supplementalloan information, such as future interest cost and time topay off the loan, similar to that reported in the CARDAct. A wealth of research in intertemporal decision makinghas shown that for decisions involving trade-offs of costsand benefits over time, consumers typically weigh imme-diate outcomes more heavily than distant-future outcomes(for reviews, see Frederick, Loewenstein, and ODonoghue2002; Read 2004). This might be the case for the con-sumers who pay only the minimum amount required everymonth, allowing the future cost of the loan to increase. Ifconsumers weigh current (versus distant) outcomes moreheavily, disclosing information that makes future outcomesmore salient may lead them to shift more weight towardconsideration of future costs. Information about future loaninterest cost should bring debtors attention to the long-termcosts of paying only the minimum required. This would shiftrepayment considerations away from the minimum pay-ment, potentially increase consideration to minimize futurecosts, and dampen the negative effect of minimum paymentinformation on current repayment amount. The anchoringperspective would suggest a similar prediction, in which useof a consider-the-opposite strategy might reduce anchor-ing (Mussweiler, Strack, and Pfeiffer 2000).

Prior research also suggests that disclosing the timeneeded to pay off the loan affects repayment decisions.As goal proximity increases, effort toward attaining thegoal increases (Kivetz, Urminsky, and Zheng 2006). Thus,prompting consumers to look ahead to how far they arefrom paying off their loansuch as time needed to payoff a credit card balance when only the minimum requiredamount is paid each monthcould affect their motivationand effort to repay the debt (Cheema and Bagchi 2011). Ifthe time to repay the loan is proximal, this would increasemotivation to pay off the balance due and increase current

repayment amount. For borrowers with high balances andlow minimum required payment levels, such informationwould indicate a very distant time horizon toward achievingtheir goal, reducing effort and repayment amount.

In summary, minimum payment policy and the infor-mation disclosed to the consumer at the time of repay-ment should affect repayment decisions. We examine theeffects of varying minimum required payment levels, aswell as providing supplemental information, on the con-sumer repayment decision. We expect the presence of min-imum payment information to have a negative impact onrepayment, as in prior research, but we also expect thateffect to be moderated by increasing minimum requirementlevels as well as disclosing supplemental information about

future loan cost and time required to pay off the loan bal-ance. We conducted studies to test these ideas: two exper-imental studies of U.S. consumers and a field study thatexamines credit card customers in the United Kingdom.

STUDY 1: EXPERIMENTS

Prior research with U.K. consumers reveals a nega-tive effect of presenting minimum payment information onrepayment amount (Stewart 2009). Our goal in Study 1 isto examine the effects of minimum required payment infor-mation and policy on U.S. consumers and also to exam-ine the effects of disclosing other loan cost information


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to borrowers. To this end, we conducted two controlledexperiments that explored three questions: (1) Does thepresence of minimum payment information have a neg-ative effect on repayment for U.S. consumers? (2) Doesincreasing the minimum required payment level affectrepayment amount? and (3) Does presenting supplemen-tal informationnamely, loan interest cost and payoff timeduration informationmitigate or enhance the effects of

minimum payment information on consumers debt repay-ment decisions?

Study 1a: Presenting Minimum Payment Information to

U.S. Consumers

We tested whether the negative effect of presenting min-imum payment information observed with U.K. consumers(Stewart 2009) would be replicated for a sample of U.S.adult consumers and for a (higher) credit card balance duelevel closer to the U.S. average. To accomplish this, weconducted a two-cell between-subjects experiment similarto Stewart (2009).

A random sample of 127 U.S. adult consumers werepresented with one of two hypothetical monthly credit

card statements and asked to make a repayment deci-sion. Participants were randomly assigned to each condi-tion: (1) the minimum payment absent condition, whichincluded information about the balance due and the APRand (2) the minimum payment present condition, whichalso included minimum required payment information (seeFigure 1).

Information on the statements. We chose the informationon the statements to be consistent with values typical atthe time of the study. The same account balance was pre-sented to all participants: $1,937.28. We chose this valuebecause it represented the average credit card balance forU.S. households at the time, and we wanted to avoid usingany round numbers or other values that would not be

typical in a credit card statement. We set the APR valueat 14%, which represented an approximate average APR atthe time of the study (Federal Reserve Board 2010); thiswas also presented to all participants. We calculated the

Figure 1STUDY 1A: MINIMUM PAYMENT INFORMATION PRESENT

CONDITION

Credit Card Statement

April 2009

Account Number

4321 5678 9012 3456

Annual Percentage Rate (APR): 14.0%

Current Total Account Balance: $1,937.28

Minimum Required Amount: $38.74

How much will you pay (in dollars)?

$

minimum required payment amount as 2% of the balancedue, or $38.74, which again reflects common practice forU.S. credit card lenders at the time; this appeared only inthe minimum payment present condition.

Procedure. A random sample of adult consumersreceived an e-mail informing them of the study and invitingthem to participate by clicking on the URL link embed-ded in the invitation. Participants completed the repayment

decision task before the rest of tasks presented to them. Inboth conditions, they read the same instructions, as follows:

Imagine you have a credit card and received yourmonthly credit card statement this morning. On the nextscreen, you will see the credit card statement, and you willbe asked to make your payment. Please consider how muchyou can afford to pay, and treat your payment decision asyou would in your everyday life.

Participants then saw a credit card statement (such as thatin Figure 1) and entered their desired repayment amount atthe bottom of the statement.

Measures. The repayment decision is influenced by theextent to which a consumer considers and comprehendscurrent and future loan costs. Thus, a persons temporal

orientation and knowledge of interest compounding shouldinfluence repayment behavior. We measured both after thedecision task to control for their impact on repaymentbehavior in our analyses.

Prior research has shown that decision makers predis-position toward considering more immediate concerns ver-sus future concerns influences credit card debt (Joireman,Kees, and Sprott 2010) and fiscal responsibility (Joireman,Sprott, and Spangenberg 2005). We measured predisposi-tion toward consideration of future consequences (CFC)using Strathman et al.s (1994) 12-item scale (Cronbachs = 81). Each scale item used a seven-point Likert for-mat (1 = extremely uncharacteristic, and 7 = extremely

characteristic); scale items appear in Appendix A.Financial knowledge is also likely to influence repay-ment decisions because it affects a persons ability to com-prehend loan cost information. Evidence suggests that asurprisingly large number of consumers have poor knowl-edge of the cost of credit (e.g., Lee and Hogarth 1999).Revolving debt growth is directly influenced by interestrates and the speed of repayment relative to compound-ing interest growth. Thus, we were specifically interestedin controlling for consumers knowledge of interest com-pounding, which prior research has shown to be lack-ing (Eisenstein and Hoch 2007; McKenzie and Liersch2011; Stango and Zinman 2009). We measured objec-tive knowledge of interest compounding using three quiz-

style questions in a manner similar to Lipkus, Samsa, andRimers (2001) numeracy measure. Knowledge values canrange from 0 to 3; the questions appear in Appendix A.Finally, we measured annual household income, using a15-point scale ranging from 1 (less than $20,000) to 15($150,000 or greater), increasing in $10,000 increments.Table 1 provides a description of our consumer sample andsummary measures.

Results. A one-way analysis of variance (ANOVA), withlog-transformed repayment amount as the dependent vari-able, revealed a significant, negative effect of the presenceof minimum payment information on repayment amount


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Table 1STUDY 1: CONSUMER SAMPLE DESCRIPTION AND SUMMARY

MEASURES

Summary Measures Study 1a Study 1b

Sample size 127 481

Percentage of men 45.9 46.4

Percentage of women 54.1 53.6

Median age 4554 years 3544 yearsMedian education Some college Some college

Median income $40,000$49,999 $50,000$59,999

Average number of

credit cards

3.5 3.3

Average current

balance, most

frequently used card

$1,671.36 $1,776.65

Knowledge of interest

compounding,

proportion (%) at

levels {0, 1, 2, 3}

{20.5, 27.0,

27.0, 25.4}

{17.1, 29.9,

28.5, 24.5}

Proportion who paid

full balance due

13.39% 27.03%

Proportion who paid

minimum requiredor less

9.45% 8.52%

Average repayment, all $436.77 $667.01

Average repayment,

revolvers only

$204.87 $181.84

Notes: Proportion who paid minimum required or less assumed a

minimum amount of $38.74 for all conditions except the 5% minimum

required condition in Study 1b, which assumed $96.86. Revolvers refers

to participants who paid less than the full balance due. Education level was

measured with ordinal categories; some college was the median value.

(F1112 = 620, p < 02) across all participants.1 When weexcluded participants who paid the full balance, the neg-ative effect was even stronger (F196 = 995, p < 003).Consistent with prior research (Stewart 2009), participantstended to pay less when minimum payment informationwas present (versus absent), after we controlled for dif-ferences in temporal orientation, knowledge of interestcompounding, and income level. Participants in the con-dition with minimum payment information present paidan average $119.82 less than those in the condition forwhich the information was absent (MMR present = $37639,MMR absent = $49621), and a similar difference in medianvalues occurred ($50 vs. $175, respectively). Finally, a non-parametric MannWhitney test confirmed significant dif-ferences between the two conditions (z = 249, p < 02).

Thus, the negative effect of minimum payment informationon repayment amount that Stewart (2009) finds with U.K.consumers also occurred in our sample of U.S. consumers.

Study 1b: Potential Lender Interventions

Study 1a confirms that presenting minimum paymentinformation has a negative impact on repayment for U.S.consumers, and this negative impact is robust to controlling

1We use the logarithmic transformation of observed repayment amount

to reduce positive skewness; for ease of exposition, we refer to this simply

as repayment amount throughout the remaining discussion.

for key differences across consumers. At the same time, asa practical matter, both lenders and policy makers wouldlikely counter that some minimum required payment is nec-essary. Thus, the goal of Study 1b is to test two types ofpotential lender interventions aimed to mitigate the neg-ative effect of minimum payment information on repay-ment decisions: increasing the minimum required paymentamount and disclosing supplemental loan cost information.

We conducted a between-subjects experimental design witha U.S.-based online consumer panel. A random sample of481 U.S. adult consumers was presented with one of sevendifferent hypothetical monthly credit card statements andmade a repayment decision. Figure 2 illustrates an exampleof one condition: time to pay off the loan. We designed theseven information conditions to enable testing of five spe-cific interventions: increasing the minimum required pay-ment level (Conditions 13) and disclosing future interestcost, disclosing time to pay off the loan, disclosing futureinterest cost and time to pay off together, and disclosingtime to pay off with information about the monthly repay-ment amount needed to pay off the loan in three years(Conditions 1, 2, 47).

Participants were randomly assigned to one of thefollowing information conditions (Conditions 27 alsoincluded balance due and APR information, just as in thecontrol):

1. The control condition included information about the bal-ance due and the APR;

2. The 2% minimum condition included information aboutthe minimum required payment amount, using 2% of thebalance due as the minimum required level;

3. The 5% minimum condition included information aboutthe minimum required payment amount, using 5% of thebalance due as the minimum required level;

4. The future interest cost condition included the 2% mini-mum payment information and information about the futuretotal interest cost that would be incurred if the 2% min-imum payment were made every month until the balancewas paid off;

Figure 2STUDY 1B: TIME TO PAY OFF CONDITION

Credit Card Statement

December 2010

Account Number

4321 5678 9012 3456

Current Total Account Balance: $1,937.28

Annual Percentage Rate (APR): 14.0%

Minimum Required Amount: $38.74

Number of years until Balance is

paid off if you pay the minimum

requ ired amou nt e ach mont h: 19 ye ars

How much will you pay (in dollars)?

$


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5. The time to pay off condition included the 2% minimumpayment information and information about the number ofyears needed to pay off the balance if the 2% minimumpayment were made every month;

6. The future interest cost and time to pay off conditionincluded the 2% minimum payment information, future totalinterest cost information, and time to pay off information;

7. The time to pay off and three-year payoff conditionincluded the 2% minimum payment information, time to

pay off information, and information about the monthlyrepayment amount needed to pay off the current balance inthree years (described in more detail subsequently).

Information manipulation values. Our informationmanipulation values were consistent with Study 1a; someconditions included additional loan cost information as wedescribe here. All participants saw the same account bal-ance due, $1,937.28, and APR value, 14%. We calculatedthe minimum required payment amount as either 2% of thebalance due, or $38.74, as in Study 1a, or 5% of the bal-ance due, or $96.86 (for the 5% minimum condition only).We calculated both the future interest cost, $2,159.20, andthe number of years to pay off the balance, 19 years,assuming monthly interest compounding and a 2% mini-mum payment, or $10, whichever is larger, is paid in eachfuture month. Finally, mimicking the CARD Act mini-mum payment warning, the time to pay off and three-yearpayoff condition included information about the monthlyrepayment amount required to pay off the current balancein three years: $66.21 every month for three years.2 Theexperimental procedure was identical to that in Study 1a.

Measures. As in Study 1a, we measured individual dif-ferences in CFC (Cronbachs = 84), objective knowl-edge of interest compounding, and income.3 In addition,we assessed whether our information manipulations affectedparticipants differently depending on past repayment behav-iors with their own credit card loans. We asked participants

the extent to which they tended to pay only the minimumrequired amount each month for their own credit cards,using a seven-point Likert scale (I often make only theminimum payment on my credit card bills; 1 = stronglydisagree, and 7 = strongly agree). Table 1 provides adescription of the consumer sample and summary measures.

Results: effects of increasing minimum payment level.We examined the impact of increasing the requiredminimum payment level. We analyzed the 2% minimum,5% minimum, and control conditions to estimate the effectof varying minimum payment requirements on repaymentbehavior (controlling for CFC, knowledge, and incomein all analyses). Using a one-way ANOVA, with log-transformed repayment as the dependent variable, we founda significant effect of the minimum payment manipula-tion (F2181 = 470, p < 02). Furthermore, an ordinaryleast squares regression analysis indicated that, after we

2The CARD Act minimum payment warning includes information about

the monthly payment amount required to pay off the entire balance in three

years. Given the $1,937.28 balance due and 14% APR in our scenario,

the monthly amount would be $66.21.3We also measured consumers attitude toward debt and found that

it was statistically insignificant in all our analyses; thus, for the sake

of parsimony and brevity, we omit if from our discussion and analyses

reported here.

controlled for the presence of minimum payment informa-tion, no statistically significant main effect of increasing theminimum required level occurred (presence of minimumpayment information: t = 221, p < 03; minimum pay-ment level: t = 70, p > 48). Table 2 illustrates the meandifferences between conditions. A nonparametric KruskalWallis test confirmed the statistically significant differencesacross the three conditions (22 = 7537, p < 03), and a

MannWhitney test revealed statistically significant differ-ences between the control and 2% minimum conditions(z = 2229, p < 03) but no statistically significant differencebetween the 2% minimum and 5% minimum conditions(z = 161, p > 87).

We also assessed whether borrower heterogeneity has amoderating influence on minimum payment level effects.More specifically, the effect of increasing the minimumrequired payment amount may vary for consumers withdifferent inclinations to repay only the required minimum.We tested this prediction by adding an additional indepen-dent variable to our regression: participants self-reportedpropensity to make the minimum required payment everymonth (recoded to range from 0 to 6, for analysis purposes).The results, summarized in Table 3, show a significant,negative effect of propensity to pay the minimum (PPM:t = 403, p < 001); this is the simple effect of propen-sity to pay the minimum for the case in which no mini-mum payment information is present. More important, wefound a significant, negative interaction between propen-sity to pay the minimum and the presence of minimumpayment information (MR PPM: t = 200, p < 05) anda significant, positive interaction with minimum requiredlevel (MR5PPM: t = 300, p < 004).

The MR5PPM interaction coefficient in Table 3 esti-mates the extent to which the effect of increasing theminimum level from 2% to 5% varies with borrowerspropensity to repay only the required minimum. We con-ducted a spotlight analysis to unpack this moderatingeffect. We summarize the estimated effect of increasing the

Table 2STUDY 1B: REPAYMENT BY INFORMATION CONDITION

Proportion Proportion

Paying 2% Paying 5% Proportion Mean Mean

Information of Balance of Balance Paying Repayment Repayment

Condit ion or L ess or Less Full Balance A ll ($) R evolvers ($)

Control 145 15.94 36.23 875.83 266.52

2% minimum 588 33.82 29.41 695.03 177.42

5% minimum 00 18.84 21.74 535.89 146.60

Future interest

cost (IC) 1618 42.65 23.53 577.47 150.53

Time to pay

off (TP) 725 30.43 21.74 580.44 203.54

IC and TP 571 42.86 24.29 662.38 169.62

TP and three-year

payoff payment 441 45.59 32.35 742.29 170.79

Notes: Revolvers refer to participants who paid less than the full

balance due.


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Table 3STUDY 1B: THE EFFECT OF MINIMUM PAYMENT LEVEL ON

REPAYMENT

Model Variable Coefficient SE t p-Value

Minimum payment information

present (MR) 495 .237 209 .038

Minimum increase from 2% to

5% (MR5)

320 .253

126 .208Propensity to pay minimum

required (PPM) 296 .073 403 .000

MRPPM 272 .136 200 .047

MR5PPM 424 .141 300 .003

CFC 159 .108 148 .142

Knowledge of interest

compounding 375 .089 421 .000

Income 044 .023 194 .054

Intercept 4803 .597 804 .000

Notes: The dependent variable is the logarithmic transformation of

repayment; model R2 = 393. Independent variables, MR and MR5, are

coded as 01 indicator variables. PPM required is recoded to range from 0

to 6 for this analysis. Boldface indicates statistical significance at the 5%

level.

minimum level from 2% to 5% (MR5) at low, moderate,and high PPM levels as follows:

Estimated Effect of Spotlight Test ofPPM MR5 on Repayment Significance

0 320 t = 126, p > 203 320 + 4243 = 952 t = 265, p < 016 320 + 4246 = 2224 t = 299, p < 01

Consistent with Figure 3, Panel A, increasing minimumpayment level is estimated to have a positive effect for bor-rowers with a moderate (t = 265, p < 01) to high (t = 299,

p 20) for borrowers with a low propensity to pay theminimum required. A similar spotlight analysis of the esti-mated effect of presenting minimum payment information(MR) at low, moderate, and high propensity to pay theminimum levels indicates significant, negative effects at alllevels (low/0: b = 495, t = 209, p < 04; moderate/3:b = 1311, t = 364, p < 001; high/6: b = 2127, t =290, p < 01).4

These results indicate that presenting minimum paymentinformation has a negative effect on repayment amount,and this effect increases in magnitude (i.e., becomes morenegative) as the debtors propensity to pay the minimum

required increases. In addition, the estimated effect ofincreasing minimum payment level is positive for borrow-ers whose propensity to pay the minimum is moderateto high. This is not the case for borrowers with a lowpropensity to pay only the minimum; this group of borrow-ers does not increase repayment with minimum payment

4Casual observation suggests a smaller simple effect of the presence

of minimum payment information at high (56) than at moderate (24)

propensity to pay the minimum in Figure 3, Panel A, which is contrary to

the larger effect size reported for high propensity to pay the minimum in

our spotlight analysis. This contrast is due to the use of log(repayment)

in the regression analysis, but not in Figure 3, Panel A.

Figure 3STUDY 1B: EFFECTS OF MINIMUM LEVEL AND

SUPPLEMENTAL INFORMATION

B: Proportion of Participants Repaying the Minimum or Less

A: Mean Repayment Amount by MinimumPayment Condition and PPM

0

2

4

6

8

10

12

14

16

18

Control MinimumPayment

Future

Cost (IC)

Time to

Pay

Off (TP)

IC + TP TP + 3-Yr

Payoff

P

roportionPayingMinimum

orLess(%)

0

200

400

600

800

1,000

1,200

01 (Low) 24 (Moderate) 56 (High)

MeanRepaymentAmount($)

PPM

Control2% Minimum

5% Minimum

Only

Interest

Notes: All conditions, except Control, have 2% minimum payment

information.

level. The nonsignificant decrease in simple mean repay-ment depicted in Table 2from $695.03 to $535.89 for allparticipants and from $177.42 to $146.60 for revolversmasks this moderating effect of propensity to repay theminimum on the relationship between minimum paymentlevel and repayment amount.

Results: disclosing supplemental information. We testedwhether presenting supplemental interest cost informationand/or payoff duration information would mitigate the neg-ative effect of minimum payment information. To this end,we performed analyses of six of the experimental condi-tions: 2% minimum, future interest cost, time to pay offthe loan, future interest cost and time to pay off, time topay off and three-year payoff information, and the controlcondition. All but the control condition included a 2% min-imum payment level (a typical level for U.S. lenders).

Unexpectedly, our analysis did not uncover any sig-nificant mitigating effects of additional information on


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repayment (see Figure 3). A one-way ANOVA, with log-transformed repayment amount as the dependent variable,showed a significant effect of information manipulationacross conditions (F9355 = 284, p < 02). This is con-sistent with a nonparametric KruskalWallis test indicat-ing significant differences across the six conditions (25 =15257, p < 01). However, this significant effect reflectsonly the impact of including a minimum payment on the

bill (replicating Study 1a). An ordinary least squares regres-sion, parsing out the effects of minimum payment infor-mation from other information types, showed a significant,negative effect of presenting minimum payment (t = 216,

p < 04), but after we controlled for minimum paymentinformation, no significant effects of future interest cost,time to pay off, or three-year information occurred (all

p > 18). Similarly, a KruskalWallis test for just the fivesupplemental information conditions indicated no signif-icant difference across conditions (24 = 2398, p > 66).Finally, we tested propensity to pay the minimum as amoderator of information effects but found no significantinteraction effects.

Our analyses thus far suggest that none of the sup-plemental information manipulations reduced the nega-tive effects of including minimum payment informationon the bill. Therefore, we conducted additional analysesto examine more closely whether supplemental informa-tion affects debtors likelihood of repaying the minimumrequired amount, making a partial repayment, or repayingthe full balance due. We examined this question using aseries of three logit models. First, we estimated an orderedlogit model, with three (ordered) payment categories asthe dependent variable: repay the minimum required orless, repay more than the minimum but less than the fullbalance (a partial repayment), and repay the full balance.The observed portion of participants repayment decisionsfalling into each of the three categories was 6.90%, 66.26%,and 26.85%, respectively. The model results reveal a sig-nificant, negative effect of future interest cost information(Table 4; p < 04) and a larger significant, positive inter-action effect between future cost information and time topay off information (p < 03). The presence of future costinformation decreased the likelihood of a participant beingin a higher repayment category, but the effect was mit-igated when time to pay off information appeared with

Table 4STUDY 1B LOGIT MODEL ESTIMATES: SUPPLEMENTAL INFORMATION EFFECTS

Ordered Logit: Likelihood of Binary Logit: Likelihood of

{Minimum, Partial, Full} Repaying Minimum

Model Variable Coefficient SE p-Value Coefficient SE p-Value

Minimum payment information present 593 .397 .135 1279 1179 .278

Future interest cost (IC) 885 .422 .036 1469 702 .036

Time to pay off (TP) 643 .412 .119 624 771 .418

IC TP 1350 .588 .022 2373 1046 .023

Three-year payoff 764 .411 .063 758 776 .328

CFC 599 .158 .000 292 283 .302

Knowledge 324 .122 .008 336 222 .130

Income 146 .033 .000 1689 081 .036

Intercept 1489 1611 .355

Notes: The ordered logit log-likelihood is 253.237; the binary logit log-likelihood is 78.479. Minimum refers to minimum required payment or

less. Boldface indicates statistical significance at the 5% level.

future interest cost. We found a marginally positive effectof disclosing three-year payoff information, but the effectdid not achieve statistical significance (p > 06). The effectsof all other information types, including minimum paymentinformation, were nonsignificant.

We followed up the ordered logit analysis with two binarylogit models, one that predicts the likelihood of paying theminimum required amount (or not) and one that predicts the

likelihood of paying the full balance due (or not). We foundno statistically significant effects of our information manip-ulations on the likelihood of paying the balance in full (all

p > 09). However, we found a significant, positive effectof future interest cost information on the likelihood of pay-ing only the minimum required amount (Table 4; p < 04).This effect was moderated by a significant, negative interac-tion effect with time to pay off information (p < 03): Wheninformation about time to pay off the balance appeared withfuture interest cost, the effect of future interest cost wasmitigated. Thus, participants were more likely to pay theminimum required when future interest cost was disclosedbut not if the information was also accompanied by time topay off information (see Figure 3, Panel B).

Results summary. The information available to con-sumers at the time they decide influences their repay-ment decisions. We examined whether increasing minimumrequired payment level or presenting supplemental loancost information would have a positive effect on repay-ment and mitigate the negative effects of minimum pay-ment information on repayment. We found a significantinteraction between minimum payment level and propensityto pay the minimum required amount: The results indicatea positive effect of increasing the minimum level for bor-rowers with a moderate to high propensity to pay the min-imum and a nonsignificant effect for borrowers with a lowpropensity to pay the minimum. These findings highlightthe importance of considering borrower heterogeneity in

setting minimum payment policy.We also found that, overall, disclosing supplemental

information about interest cost and time to pay off theloan did not significantly attenuate the negative effectsof presenting minimum payment information. Furthermore,disclosing future interest cost information increased thelikelihood that consumers would pay only the minimum


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amount required. However, when information about time topay off the balance appeared together with future interestcost information, the effect was mitigated. This finding sug-gests a cautionary note regarding the effectiveness of reg-ulation requiring disclosure of similar types of loan costinformation for changing borrower behavior.

STUDY 2: CREDIT CARD REPAYMENT FIELD DATA

Study 1 suggests that the effect of minimum requiredpayment level on repayment amount decisions is positivefor some consumers, but the effect is moderated by con-sumers propensity to pay the minimum each month. Thisunderscores how different types of consumers can behavedifferently under the same intervention. Our goal in Study 2is to further examine the effects of minimum requiredpayment policy using real credit card repayment transac-tions for U.K. credit card customers. Specifically, we exam-ine the extent to which variation in minimum paymentsis related to real-world repayment behavior. In doing so,we also explore whether borrowers credit limit and loanbalance moderate the influence of minimum payment on

repayment decisions. We begin with a description of thedata, followed by our modeling approach and results.

Data

We use credit card transaction data anonymously pro-vided by 11 different U.K. credit card providers throughthe UK Cards Association in September 2009 (hereinafterdenoted as Providers A through K). The data sampleincludes 955,014 credit card statements from 106,554 dif-ferent credit cardholders, and the records span a period of21 months. We excluded cardholders with accounts paid byautomatic (predetermined) repayment and those with pro-motional rates and balance transfers from the sample to

avoid mixing genuine monthly repayment decisions withother forms of repayment less relevant for our purposes. Ouranalyses focus on four variables: loan balance, minimumpayment, credit limit, and actual repayment amount. Theseare the relevant variables for which we received full infor-mation from all the credit card companies. No demographicinformation was provided for the consumers in our data set.

Minimum payment policy structure. Minimum paymentpolicies consist of a small predetermined percentage(between 1% and 5%) to be applied to the outstanding bal-ance, or a small fixed amount (most often 5) to be paid ifthe corresponding percentage falls below the fixed amount.An important consequence of this is that if a particular

cardholder has, for example, a low outstanding balanceof 20, under a minimum payment policy of 2% or 5,whichever is larger, the cardholder will face an actual min-imum of 5, which represents 25% of the balance (not thepredetermined 2%). Portions of our credit card statementshave these small fixed amounts, representing widely vary-ing percentages of the outstanding debt, and 99% of thosestatements have a minimum payment policy with the samefixed amount minimum of 5. In addition, the percentagesof the balance these fixed amount minimums represent areperfectly correlated with the size of the balance. For thesereasons, we focus our analysis on the repayment decisions

made when borrowers were presented with the predeter-mined percentages as minimums (n = 602850).5

Credit card provider types. Table 5 provides the numberof observations for each of the different minimum pay-ment policies and providers contained in our data set. Notethat most of the variability in minimum payment policiesoccurs between providers; single-policy providers con-stitute the majority of our observations. However, three

providersB, F, and Hhave a considerable amount ofwithin-provider variation in their policies. These policy-varying providers are also distinct in that their overallcorrelation between stipulated minimum payment level andcredit limit is quite high, negative, and statistically sig-nificant ( = 43, p < 001). That correlation falls drasti-cally among the single-policy providers ( = 07, p < 001).Credit limit can serve as a proxy for key indicators, such ascredit score and income, or as an internally determined riskmeasure by the financial institution (Scholnick, Massoud,and Saunders 2008). These factors suggest that the policy-varying providers are using criteria related to credit limitsto classify more risky customers into policies with higher

minimum payment levels. Such a segmentation scheme cangreatly distort the actual relationship between minimumpayment levels and repayment behavior. Thus, the anal-yses separately consider these two groups: single-policyproviders, which do not vary their minimum payment pol-icy across customers, and policy-varying providers, whichdo vary their minimum payment policies across customers.

Descriptive Statistics

Figure 4 displays the overall distribution of outstand-ing balances, credit limits, and relative repayments (actualrepayment/balance) for all statements and also for thoseexcluding fixed amount minimums. The distribution of out-

standing balances is strongly positively skewed (Figure 4,Panel A). Credit limit displays a more normal-like distri-bution, though it is somewhat positively skewed as well(Figure 4, Panel B), and credit limits are considerablyhigher than the outstanding balances. Panels C and D inFigure 4 exhibit the typical distribution of borrowers rel-ative repayments. A sizable proportion of the cardholders(more than 50%), represented by the rightmost spike ineach graph, repay their outstanding debt in full. At the otherextreme, a small proportion (less than 5%) does not repayanything at all. That is represented by the small leftmostspike in each graph. The rest of the consumers pay variousamounts in between. The distribution of partial repayments

is markedly positively skewed and shows clear bounds, orjumps, at the minimum required payments. Repaymentsthat are equal to the minimum payment are represented bythe slightly higher spikes at the corresponding levels (e.g.,.02 for a minimum of 2%, .03 for a minimum of 3%) onthe left-hand regions of the graphs.

5We also estimated a model for the statements with fixed amount

minimum payments; however, the results of such estimation do not mean-

ingfully inform us about the effects of varying the fixed amount minimum,

and thus we do not report them here. The results are available on request.


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Table 5STUDY 2 FIELD DATA: SAMPLE SIZE BREAKDOWN AND DESCRIPTIVE STATISTICS

Credit Card Provider

Minimum Payment Policy A B C D E F G H I J K Total N

1% or interest + 5 0 8507 0 0 0 0 0 0 0 0 0 85072% or 5 0 55440 93805 0 0 5284 0 0 0 0 0 1545292.25% or 5 160006 36589 0 0 0 0 0 0 0 0 0 196595

2.25% or interest + 5 0 0 0 0 0 0 0 1214 0 0 0 12142.5% or 5 0 0 0 0 0 0 118515 0 118233 0 149639 3863872.75% or interest + 5 0 0 0 0 0 0 0 196 0 0 0 1962.85% or interest + 5 0 0 0 0 0 0 0 6 0 0 0 63% or 25 0 0 0 0 0 0 0 1891 0 0 0 18913% or 5 0 0 0 11237 785 35049 0 0 0 115895 0 1629663.25% or interest + 5 0 0 0 0 0 0 0 108 0 0 0 1084% or 5 0 0 0 0 0 24 0 0 0 0 0 245% or 10 0 0 0 0 0 0 0 11344 0 0 0 113445% or 25 0 0 0 0 0 0 583 0 0 0 0 5835% or 5 0 0 0 0 0 17183 0 0 0 0 0 171835% or interest + 10 0 0 0 0 0 0 0 8 0 0 0 8

Unknown % or 5 0 9729 0 0 3744 0 0 0 0 0 0 13473Total N 160006 110265 93805 11237 4529 57540 119098 14767 118233 115895 149639 955014

Descriptive Statistics

Mean balance () 929 798 936 1044 429 1368 1830 414 845 751 657 970Mean credit limit () 6184 6234 6330 4526 3671 9120 7144 5343 5496 4867 5991

Proportion Paying Full BalanceAll .58 .88 .61 .59 .71 .16 .57 .63 .61 .62 .73 .62Predetermined percentage .45 .83 .47 .47 .18 .10 .53 .17 .49 .52 .63 .52

Notes: Providers B, F, and H systematically vary their minimum payment policy across customers. Credit limit data were unavailable for Provider E.

The observations labeled as unknown or 5 are cases with a fixed minimum payment of 5 for which the official percentage in the policy could not

be determined. Note also that some of the policies add the interest to the fixed amount.

Figure 4STUDY 2: DISTRIBUTION OF BALANCES, CREDIT LIMITS, AND REPAYMENTS

A: Balances B: Credit Limits

Balance

Frequency

0 1,000 3,000 5,000

0

50,000

150,000

Frequency

0

50,000

150,000

Credit Limit

0 5,000 10,000 20,000

C: Repayments, All

Relative Repayment

Percentage

.0 .2 .4 .6 .8 1.0

0

10

30

50

Percentage

0

10

30

50

Relative Repayment

.0 .2 .4 .6 .8 1.0

D: Repayments, Predetermined Percentage


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Modeling Approach

We model the distribution of relative repayments as amixture of three probability distributions: a point distribu-tion representing the probability of a full repayment, a pointdistribution representing the probability of a minimumrepayment, and a beta distribution capturing the locationand dispersion of partial repayments. As Panels C and D inFigure 4 illustrate, these component distributions together

characterize the overall distribution of repayments. Mini-mum payment policies are likely to have distinct effectson these different components of repayment practices, andconsequently an appropriate analysis of repayment behav-ior should take this into account.

Our dependent variable in the model, R, is a modifiedversion of the relative repayments (repayment/balance), inwhich all the repayments equal to the minimum or lowerare set to 0. Consequently, R is 1 if a full repayment ofthe outstanding debt is made, 0 if the repayment made isat the minimum or below, and between 0 and 1 if a par-tial repayment is made. For example, R = 2 means thata partial repayment representing 20% of the outstandingbalance was made. We model R as a mixture of three dif-ferent distributions: (1) a point mass at R = 0, which we callthe minimum repayment component (MRC) and whichcaptures the probability of making a minimum repayment;(2) a point mass at R = 1, which we call the full repay-ment component (FRC) and which captures the probabil-ity of making a full repayment; and (3) a continuous betadistribution of partial repayments, which we call the par-tial repayment component and which captures the location(LOC) and dispersion (DISP) of the partial repayments.The specification for each component of the repaymentmodel appears in Appendix B.

The explanatory variables included in the model arecredit card balance, relative minimum (i.e., minimum pay-

ment/balance), credit limit, the interactions between thesevariables, and a set of 19 dummy variables to control forpossible effects of the different months in the sample.6

Provider-specific dummy variables do not appear in themodel because, as we explained previously, in our data setthe differences in minimum payment levels come largelyfrom the differences between the providers (see Table 5).For the sake of brevity, we report only the results for themain variables and their interactions here.7 We standard-ize all the independent variables, subtracting the mean anddividing by the standard deviation in the sample to whichthe model is applied, to make the results more meaningfulwhen the interaction terms are included. We estimated themodel by maximum likelihood with statistical software R

(R Development Core Team 2007). (The code used is avail-able on request.)

Results: Single-Policy Providers

Overall, for single-policy providers, the results suggesta mostly positive association between minimum paymentlevels and actual real-world repayment practices, whichis broadly consistent with Study 1b. Higher minimum

6We cluster the last two months together because of the reduced number

of observations in the final month.7A more detailed description of the results is available on request.

payments were significantly associated with considerablyhigher proportions of full repayments of the outstandingdebt, higher (and less dispersed) partial repayments, andmoderately lower proportions of repayments at the mini-mum or below. These effects were moderated by the levelof balance and credit limit, but they remained positive forthe vast majority of cardholders.

Effects of increasing minimum payment level. Table 6

summarizes the model estimation results for single-policyproviders and displays the estimated coefficients for eachof the four different aspects captured by the model: thefull repayment component (FRC), the minimum repaymentcomponent (MRC), and the location (LOC) and dispersion(DISP) of the beta distribution of partial repayments. Theresults indicate a significant, positive main effect of min-imum payment level on the proportion of full repayments(b = 102, p < 001);8 a significant, positive main effecton the location (or size) of partial repayments (b = 059,

p < 001); and a smaller but also significant, negative effecton their dispersion (b = 016, p < 001). We also found asignificant, positive main effect on the proportion of repay-ments at the minimum or below (b = 038, p < 001). Note,however, that the results for the minimum repayment com-ponent should not be directly interpreted from the estimatedcoefficients in Table 6, because these refer to the condi-tional probability (not the unconditional or absolute prob-ability) of making a minimum repayment (for the modelspecification, see Appendix B). Panels AC in Figure 5illustrate the actual effect on the proportion of minimumrepayments. The largest effect of the minimum paymentlevel is that on the proportion of full repayments. In addi-tion, the results in Table 6 show a series of significant inter-actions between minimum payments and the other predictorvariables.

To further illustrate these findings, we use the estimated

model to predict the effects of different minimum pay-ment levels in any possible scenario. Figure 5 illustratesthe predicted effects of an increase in minimum paymentsfrom 2% to 3% and 5% (chosen because essentially noprovider has a 4% minimum percentage), assuming a bal-ance and a credit limit that are at the median (761 and5,700, respectively). In line with the results from Table 6,there is a strong positive effect of minimum payment onthe proportion of full repayments (22% overall; Figure 5,Panel C). Partial repayments shift to higher levels, and theirdispersion decreases, as the minimum payment increases(Figure 5, Panel B). Note also that the distribution of partialrepayments includes fewer repayment decisions as the levelof the minimum increases because of the increase in theproportion of full repayments. Finally, Figure 5 reveals thatthe absolute effect of the increase in minimum paymentson the proportion of repayments at the minimum or belowis small and negative (3% overall; Figure 5, Panel A). Wealso illustrate the overall positive effect of minimum levelon repayments in the plot of mean relative repayments inFigure 5, Panel D. This highlights the broadly consistentresults between these findings and those in Study 1b.

8For ease of exposition, we use the term minimum payment to refer

to the model variable relative minimum in our discussion of the results.


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Table 6STUDY 2: MODEL ESTIMATES FOR SINGLE-POLICY PROVIDERS

Minimum Repayment Full Repayment

Component (MRC) Component (FRC)


Balance (B) 310 .005 .000 1544 .008 000

Relative minimum (RM) 038 .006 .000 102 .004 000

Credit limit (CL) 168 .008 .000 793 .005 000

BRM 002 .006 .388 158 .008 000

BCL 016 .001 .000 128 .002 000

RMCL 079 .007 .000 232 .006 000

BRMCL 019 .004 .000 005 .007 245

Intercept 1319 .036 .000 106 .022 000

Location (LOC) Dispersion (DISP)


Balance (B) 470 .004 .000 235 .004 000

Relative minimum (RM) 059 .003 .000 016 .004 000

Credit limit (CL) 257 .005 .000 165 .005 000

BRM 051 .004 .000 054 .005 000

BCL 081 .002 .000 031 .002 000

RMCL 016 .004 .000

006 .005 104BRMCL 032 .002 .000 037 .002 000

Intercept 1169 .020 .000 797 .023 000

Notes: The overall log-likelihood of the estimated model is 288,993. Boldface indicates statistical significance at the 1% level.

Figure 5STUDY 2: PREDICTED EFFECTS OF INCREASING MINIMUM PAYMENTS, SINGLE-POLICY PROVIDERS

A: Minimum Repayments B: Part Repayments Distribution C: Full Repayments D: Mean Repayments

2 3 5

Minimum Payment (%)

ProportionofStatements(%)

0

20

40

60

80

0 20 40 60 80 100

Size of PartialRepayment (%)

Frequency

5%

3%

2%

2 3 5

Minimum Payments (%)


0

20

40

60

80

2 3 5

Minimum Payments (%)

MeanRelativeRepayment

.0

.2

.4

.6

.8

1.0

Notes: Predictions assume the median balance (761) and median credit limit (5,700).


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Figure 6STUDY 2: PREDICTED PROPORTIONS OF FULL REPAYMENTS BY BALANCE AND CREDIT LIMIT

D: Policy-Varying, CL = 2,850 E: Policy-Varying, CL = 5,700 F: Policy-Varying, CL = 8,550

l

l

l0.0

0.2

0.4

0.6

0.8

1.0

Percent Minimum

ProportionFullRepayments

h

h

h

2 3 5

Balance Levels:

200 (l.)5008001,1001,4001,7002,0002,300 (h.)

0.0

0.2

0.4

0.6

0.8

1.0

l

l

lProportionFullRepayments

h

h

h

2 3 5

Percent Minimum

0.0

0.2

0.4

0.6

0.8

1.0


l

l

l

Percent Minimum

h

h

h

2 3 5

A: Single-Policy, CL = 2,850

l ll

h

h

h

0.0

0.2

0.4

0.6

0.8

1.0


Percent Minimum

2 3 5

B: Single-Policy, CL = 5,700

l

l

l

h h h

0.0

0.2

0.4

0.6

0.8

1.0


2 3 5

Percent Minimum

C: Single-Policy, CL = 8,550

l

l

l

h

h

h

0.0

0.2

0.4

0.6

0.8

1.0


Percent Minimum

2 3 5

Notes: The credit limits (CL) illustrated here represent one-half of the median credit limit (2,850), the median credit limit (5,700), and the median

plus one-half of the median (8,850). The balances go from 200 to 2,300, which encompasses 89% of all the statements.

Moderating effects of balance and credit limit. Figure 6illustrates the role of balance and credit limit in moderatingthe effect of minimum payments on full repayments andshows predicted effects of an increase in minimum pay-ments on the proportion of full repayments at different lev-els of balance and credit limit (Panels AC). Each panelplots a different level of credit limit, and each line rep-resents a different level of balance. In line with the coef-ficients in Table 6, the influence of increasing minimum

payments on full repayments is mostly positive. This effectis qualified by the level of balance and credit limit, so that itbecomes moderately negative for statements with low creditlimits and high balances but positive for the vast major-ity of statements. Though not illustrated here, balance andcredit limit also moderate the effect of minimum paymentson partial repayments and minimum repayments.

Results: Policy-Varying Providers

Overall, for policy-varying providers, our estimationresults indicate a strong negative relationship between min-imum payment level and repayment among these providers.

This relationship is directionally opposite to the effectswe found for single-policy providers, suggesting that thesecompanies may be segmenting consumers by classifyingmore risky customers into policies with higher minimumpayment levels.

We found a significant and strong negative main effectof minimum level on the proportion of full repayments(b = 1650, p < 001; see Table 7); a significant, negative

main effect on the location of partial repayments (b = 083,p < 001); a negative effect on their dispersion (b = 122,p < 001); and a significant and strong positive main effecton the proportion of repayments at the minimum or below(b = 406, p < 001). All these patterns are confirmed in thepredictions at the median balance and median credit limitdisplayed in Figure 7. The predicted effects of increasingminimum payments on full repayments (Panel C) and onminimum repayments (Panel A) are quite extreme. PanelsDF in Figure 6 also reaffirm these results, showing a gen-eral and strong negative effect on the proportion of fullrepayments, for any level of balance and credit limit.


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Table 7STUDY 2: MODEL ESTIMATES FOR POLICY-VARYING PROVIDERS

Minimum Repayment Full Repayment

Component (MRC) Component (FRC)


Balance (B) 290 .015 .000 2088 .024 000Relative minimum (RM) 406 .019 .000 1650 .021 000

Credit limit (CL) 551 .020 .000 515 .012 000BRM 159 .018 .000 490 .039 000BCL 017 .007 .009 252 .018 000RMCL 281 .019 .000 300 .017 000BRMCL 061 .010 .000 108 .027 000Intercept 1894 .058 .000 966 .068 000

Location (LOC) Dispersion (DISP)


Balance (B) 552 .008 .000 498 .011 000Relative minimum (RM) 083 .009 .000 122 .010 000Credit limit (CL) 376 .008 .000 370 .010 000BRM 052 .009 .000 125 .011 000BCL 106 .005 .000 098 .006 000RMCL 016 .009 .036 131 .011 000

BRMCL 051 .005 .000 043 .007 000Intercept 1615 .028 .000 1446 .038 000

Notes: The overall log-likelihood of the estimated model is 34,418. Boldface indicates statistical significance at the 1% level.

These results for policy-varying providers are consis-tent with segmentation in which more risky customers areassigned higher minimum payment levels. This confirmsthe account of these providers discussed previously andalso reaffirms the significance of the results obtained for thesingle-policy providers. Specifically, when the relationshipbetween minimum payment levels and repayments is pre-determined by consumer segmentation, the relationship isnegative, with higher minimum payment levels associatedwith lower proportions of full repayments, lower partial

repayments, and higher proportions of minimum repay-ments. These findings are the opposite of what we foundin the main analysis with the single-policy providers.

Potential Field Data Limitations

One important potential concern with our field dataresults is that the effects of minimum payments obtainedmight be confounded with the effects of the interest ratesconsumers face. For example, it is plausible that settinghigher minimum payments is associated with also settinghigher interest rates, and if that is the case, the observedeffects of higher minimum payments could just be effectsof higher interest rates. As explained subsequently, we can

rule out such an account.We should clarify that we have access only to par-

tial interest datathe total interest charges incurred eachmonthnot the official interest rate policies set byproviders. Consequently, we do not know the interest ratesfor a large proportion of statements that did not incur inter-est charges. In addition, we find that inferring annual inter-est rates from the total interest charges is unreliable becausethese amounts can include different charges unrelated toapplying a fixed interest rate to the unpaid debt in the pre-vious month. For these reasons, we do not include interestrates in our main analyses.

Despite these limitations, we can construct monthly inter-est rates for a subset of the statements from the informationwe have and analyze how they correlate with minimum pay-ment levels. This exercise produces clear results. A highpositive correlation exists between interest rates and mini-mum payments levels among the policy-varying providers( = 46, p < 001), but that correlation disappears amongthe single-policy providers ( = 02, p < 001). This result isin line with our findings and largely rules out confoundingeffects of interest rates. It also shows that the policy-varying

providers, which segment consumers and give more riskycustomers higher minimum payments, also tend to givethem higher interest rates. With these providers, however,higher minimum payments are associated with lower repay-ments (see Figure 7), presumably because the effect ofthe segmentation exceeds any other effects of minimumpayments or interest rates. In contrast, with single-policyproviders, there is no association between a higher mini-mum payment and a higher interest rate, so there is no roomfor distorting effects of interest rates.

Finally, as with all uncontrolled real-world data, otherrelevant selection effects not contemplated in the modelsmay exist. For example, it might be that providers withdifferent minimum payment levels also have other char-

acteristics (unknown to us) that lead to different types ofconsumers self-selecting into or differentially qualifying forcredit card companies with different minimum paymentpolicies. Another aspect that we could not account for withour data is that some consumers have a debt portfolio,which can include, for example, spending on more thanone credit card. Understanding how consumers deal withthe debt in different components of their portfolios mightshed new light on the role of minimum payment levels inrepayment decisions. We considered as many aspects ofrepayments as the data allowed, and these issues remain asimportant questions for further research.


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Figure 7STUDY 2: PREDICTED EFFECTS OF INCREASING MINIMUM PAYMENTS, POLICY-VARYING PROVIDERS

D: Mean RepaymentsA: Miniinum Repayments B: Part RepaymentsDistribution C: Full Repayments

2 3 5

Minimum Payment (%)


0

20

40

60

80

0 20 40 60 80 100

Size of PartialRepayment (%)

Frequency

5%

3%

2%


0

20

40

60

80

2 3 5

Minimum Payment (%)

2 3 5

Minimum Payment (%)

MeanRelativeRepayment

.0

.2

.4

.6

.8

1.0

Notes: Predictions assume the median balance (761) and median credit limit (5,700).

Results Summary

The results of Study 2 show that there is a mostly pos-itive association between minimum payment levels andactual real-world repayment practices, which is broadlyconsistent with our findings in Study 1. Our main results,for single-policy providers, show that higher minimum pay-ments are significantly associated with considerably higherproportions of full repayments of the outstanding debt,higher (and less dispersed) partial repayments, and moder-ately lower proportions of repayments at the minimum orbelow. These effects are moderated by the level of balanceand credit limit, but they remain positive for the vast major-ity of cardholders. The moderating effects we uncoveredhere and in Study 1 (for propensity to repay the minimum)highlight the need for lenders to consider consumer hetero-geneity when setting minimum payment polices.

Our estimation results for policy-varying providers (B, F,and H) suggest that these companies are segmenting con-sumers by classifying more risky customers into policieswith higher minimum payment levels. This creates a strongnegative relationship between minimum payment level andrepayment among these providers: a negative effect on like-lihood to pay in full, a negative effect on location and dis-persion of partial repayments, and a positive effect on likeli-hood to pay the minimum. These findings clearly reflect the

nature of the segmentation undertaken by credit card com-

panies and reaffirm the significance of the results obtainedamong the single-policy providers.

DISCUSSION AND CONCLUSION

We examined the effects of varying the level of mini-mum required payment and disclosing supplemental loaninformation on debt repayment decisions. Our findings haveimplications for public policy, consumers, and financial ser-vices firms. Our experiments with U.S. consumers con-firm that including minimum payment information in creditcard bills has a strong negative effect on repayment behav-ior (see also Stewart 2009). Given this effect, our studiesshed light on the question whether increasing the percent-

age of the outstanding balance required to be paid eachmonth would increase repayments. Our field study sug-gests that such a change would have mostly positive effectson consumers repayment practices, and these effects aremoderated by the level of balance and credit limit. Ourexperimental results also show that borrowers propensityto repay the minimum moderates the effect of increasingthe minimum required level. More important, our experi-ments suggest that increasing the minimum payment levelis unlikely to offset the strong negative effect of present-ing minimum required payment information. This aspect isdifficult to judge from the field data. To the best of our


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knowledge, no credit card providers offer credit cards with-out minimum payments or do not include minimum pay-ment information in their bills. Consequently, the effect ofnot including minimum payments in real credit card billsis an empirical question yet to be answered.

Assuming that the presence of minimum payments incredit card debt is a given, our findings suggest that increas-ing their level could reduce this debt for consumers and

help achieve higher repayments (with the caveats explainedpreviously). As we indicated at the beginning of the arti-cle, such a measure is in line with the change from 2%to 5% in minimum payments carried out by Chase Bankin August 2009, as well as the (rejected) proposal for amandatory increase in minimum payments to 5% presentedto the British Parliament by the Department for Business,Innovation and Skills (2009). This last measure assumesthat consumers would benefit from such an increase, mainlyin the sense that cardholders who opt to repay the mini-mum would be better protected against the risks of increas-ing debt because they would presumably repay a largerportion of their debt each month. Our analysis also sug-gests that increasing minimum payments could benefit the

majority of the larger group of customers who opt to repaytheir debt in full, though this might negatively affect theminority. From the perspective of financial services firms,the moderating effects of the level of balance and creditlimit in the field study and of the propensity to repay theminimum in the experimental results suggest that a seg-mentation approach that accounts for these individual dif-ferences could be worth exploring when setting minimumpayment levels.

The second category of potential interventions that weexamined involved disclosing supplemental informationabout the time to pay off the loan if only the mini-mum is repaid, the future interest cost incurred, and themonthly repayment required to pay off the loan in three

years. Overall, disclosing such supplemental informationdid not significantly mitigate the negative effects of present-ing minimum payment information. Moreover, we foundthat disclosing future interest cost information significantlyincreased the likelihood of paying only the minimumrequired, though this effect was attenuated when informa-tion about the time to pay off the balance also appeared.These three types of information (time to pay off the loan,future interest cost, and repayment needed to pay off thebalance in three years) are exactly what the recently enactedCARD Act requires lenders to include in their bills. Ourresearch suggests that relying on this information disclo-sure alone is not likely to offset the negative effects ofincluding minimum payment information in the bills and

probably will not increase debtors monthly repayments tothe levels expected. Additional forms of intervention, suchas credit counseling services or financial literacy education,may be required to improve repayment practices. Recentresearch on savings growth (McKenzie and Liersch 2011)shows that interventions at the time of the repayment deci-sion that highlight the exponential growth of debt due tocompound interest might be effective.

We are not implying that lower debt levels are alwaysbetter for all the agents involved in financial transactions.The functioning of the financial system is a complex issuethat involves parties with fundamentally conflicting interests

and goals. From one perspective, statistics show that someU.S. consumers are carrying unhealthy levels of debt, andmeasures that nudge them into healthier repayment practicescould be beneficial. Recent industry statistics report aver-age outstanding credit card debt for households with creditcards as a high as $10,679, with approximately 13.9% ofconsumers disposable income going to service credit carddebt in the fourth quarter of 2008 (Nilson Report 2009; U.S.

Congress Joint Economic Committee 2009, cited in Wilcox,Block, and Eisenstein 2011). Alternatively, there are situ-ations in which consumer debt may actually be beneficialor necessary (to make ends meet). In addition, the finan-cial firms that set the minimum payment policies have as aprimary objective to increase their profits, which under theright conditions can help the financial system as a whole.Policy makers and regulators should ideally aim to max-imize overall consumer welfare, which involves makingcomplex decisions about which agents to favor or protect.Rather than prescribing specific policy interventions, ourfindings provide relevant insights into the credit card debtrepayment process that can be useful for policy makers,financial institutions, consumers, and researchers.

Our results suggest that neither including minimum pay-ment information in credit card bills nor disclosing addi-tional information (e.g., as mandated by the CARD Act)works as intended, and therefore we advocate a clini-cal trials approach to regulation in which further changesare tested experimentally before a broader introduction.A recent memorandum from the U.S. Office of Manage-ment and Budget (Sunstein 2010) also advocates suchan approach.

Great potential exists for further research in this area.A fruitful next step would be to conduct additionalexperiments to further examine the effects of the types ofinformation mandated by the CARD Act and compare themwith alternative approaches, such as more thorough finan-

cial education. As part of this approach, it would also beuseful to elicit consumers beliefs about the consequencesof credit and analyze whether these beliefs change afterconsumers receive different types of information. Furtherresearch could also examine the effects of anchors thatare higher than the typical minimum payments consumersfacefor example, different levels of repayment recom-mendations. It would also be worthwhile to examine fur-ther actual credit card repayment behavior using lendersmonthly credit card statement transaction data (similarto our U.K. field data) to compare, for example, repay-ment behavior before and after the CARD Act regulationswent into effect in 2010. Further research addressing theseimportant issues is critical for both consumers and finan-

cial service providers well-being.

APPENDIX A: MEASUREMENT SCALES (STUDY 1)

CFC (Strathman et al. 1994)

1 = extremely uncharacteristic, and 7 = extremelycharacteristic.

Items 3, 4, 5, 9, 10, 11, and 12 are reverse scored ().

1. I consider how things might be in the future, and try toinfluence those things with my day to day behavior.

2. Often I engage in a particular behavior in order to achieveoutcomes that may not result for many years.


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3. () I only act to satisfy immediate concerns, figuring thefuture will take care of itself.

4. () My behavior is only influenced by the immediate out-comes of my actions (that is, outcomes occurring in a matterof days or weeks).

5. () My convenience is a big factor in the decisions I makeor the actions I take.

6. I am willing to sacrifice my immediate happiness or well-being in order to achieve future outcomes.

7. I think it is important to take warnings about negative out-comes seriously even if the negative outcome will not occurfor many years.

8. I think it is more important to perform a behavior withimportant distant consequences than a behavior with less-important immediate consequences.

9. () I generally ignore warnings about possible future prob-lems because I think the problems will be resolved beforethey reach crisis level.

10. () I think that sacrificing now is usually unnecessary sincefuture outcomes can be dealt with at a later time.

11. () I only act to satisfy immediate concerns, figuring thatI will take care of future problems that may occur at a laterdate.

12. () Since my day to day work has specific outcomes, itis more important to me than behavior that has distant out-comes.

Knowledge of Interest Compounding

Knowledge score is calculated by allocating 1 point foreach correct answer (underlined); score range is 03.

1. Do you believe the following statement is true or false?A loan with a 3% monthly interest charge is a better dealthan a loan with a 20% annual interest charge. {True; False;Not sure}

2. Which of the following do you think would be worth morein two years?

{$100 received today, and then put in a savings accountat an interest rate of 10%, compounded annually; $120 re-ceived two years from today; They would be worth thesame amount; Im not sure which would be worth more.}

3. If you borrowed $100 today at an APR of 12%, approx-imately how much interest would you owe at the end of1 month? {$2.00; $12.00; $1.00; $4.00; Im not sure.}

APPENDIX B: MODEL SPECIFICATION (STUDY 2)

The minimum and full repayment components (MRCand FRC) of the model simply assume, respectively, thatrepayments are at R = 0 (the minimum or lower) or at R = 1(full repayment). The probability of a full repayment, pFR,is given by

log

pFR

1 pFR

= XFRCFRC(A1)

where XFRC is the matrix of independent variables and FRCis the vector of coefficients. To ensure a well-behaved like-lihood function, we estimate the probability of a minimumrepayment conditional on the cardholder repaying less thanthe full balance, as obtained from

log

pMR R


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Lee, Jinkook and Jeanne M. Hogarth (1999), The Price ofMoney: Consumers Understanding of APRs and ContractInterest Rates, Journal of Public Policy & Marketing, 18 (1),6676.

Lipkus, Isaac M., Greg Samsa, and Barbara K. Rimer (2001),General Performance on a Numeracy Scale Among HighlyEducated Samples, Medical Decision Making, 21 (1), 3744.

McKenzie, Craig R.M. and Michael J. Liersch (2011), Misunder-standing Savings Growth: Implications for Retirement SavingsBehavior, Journal of Marketing Research, 48 (Special Issue),S1S13.

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Mussweiler, Thomas, Fritz Strack, and Tim Pfeiffer (2000),Overcoming the Inevitable Anchoring Effect: Considering theOpposite Compensates for Selective Accessibility, Personalityand Social Psychology Bulletin, 26 (9), 114250.

Nilson Report (2009), Credit Card Outstandings, 923 (April),[available at http://www.nilsonreport.com/issues/2009/923.htm].

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