who makes credit card mistakes?

Who Makes Credit Card Mistakes?

Nadia MassoudSchool of Business, University of Alberta, Canada

Anthony SaundersStern School, NYU, NY, NY

Barry ScholnickSchool of Business, University of Alberta, Canada

Paper Presented at the FDIC 2006 Fall Workshop, Washington D.C.

Preliminary – Please do not quote

•Thanks to the anonymous Canadian deposit taking institution for providing the proprietary data. •Thanks also to FDIC Center for Financial Research (CFR) for Funding.

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Campbell, JF, 2006 “Household Finance”

• “Evidence …suggests that many households invest efficiently, but a minority make significant mistakes. This minority appears to be poorer and less well educated”

Abstract from AFA Presidential Address (JF, 2006, p1553)

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Campbell (2006, JF)Conclusion

– “Poorer and Less Educated households are more likely to make (Investment) mistakes than wealthier and better educated households. This pattern reinforces the interpretation of nonstandard behavior as reflecting mistakes rather than nonstandard preferences.” (p. 1590)

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Less Education - More Mistakes

• Equity Ownership (Campbell, 2006, Guiso, Sapienza and Zingales, 2005)

• Portfolio Diversification (Campbell, 2006, Calvet et al, 2006)

• Mortgage Refinancing (Campbell, 2006)

Low Education Causing Mistakes is Different from “Nonstandard Preferences” (i.e. Psych/Behavioral Explanations)

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THIS PAPER

1. Measure Financial Mistakes in CREDIT CARD usage.

2. Who makes these Credit Card Mistakes?(Income, Education, etc)

i.e. We ask the same question as Campbell, 2006 – but examine a different kind of mistake

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The Credit Card Debt Puzzle..

“conventional models cannot easily explain, for example, why so many people are borrowing on their credit cards, and simultaneously holding low yielding assets”

Gross and Souleles QJE 2002 (p. 149)

“Borrow High Lend Low” (BHLL) Zinman (2006)

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Existing Explanations for Credit Card Debt Puzzle (BHLL)

1. Rational - Precautionary Balances

Zinman , 2006

Telyukova and Wright, 2006• C Cards and Deposits not perfect substitutes.• Need Deposit Balances to pay for things that will

not accept Credit Cards.• Therefore hold deposits and don’t fully pay down

Credit Card Debt

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Behavioural Theories of BHLL

2. Self Control

(Bertraut and Haliassos (2002), Haliassos and Reiter (2005), )– hold Credit Card Balances to stop you

spending MORE on Credit Cards– Accountant-Shopper Framework (Two

Spouses in a household)

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Behavioural Theories of BHLL (cont)

3. Hyperbolic Discounting (Laibson, Repetto and Tobacman, 2005)– Patient over long term; Impatient over Short

term – therefore hold LT Assets and ST Credit Card

debt.– More applicable to LT Assets (Houses, Stock

Portfolio etc) not Demand Deposits

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This Paper…

Demographic Explanations for BHLL• Income• Education• Unemployment• House Ownership• Immigrant

Same explanation as Campbell (2006) – but different financial mistakes (BHLL)

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Our Data

• Unique Proprietary Data Base

• Single Canadian Deposit-Taking Institution

• Confidentiality Agreement

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Our Proprietary Banking Data

• ~100 000 Bank Consumers

• 19 Months (Panel)

• ~1.5 Million person/month data points

• Credit Card and Deposit Data

• Taken Directly from Monthly Statements

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Matching Credit Card and Deposit Data

Match

(1) Credit Card Monthly Statement Data

with

(2) Deposit Account Monthly Statement Data

Match using consumer Social Insurance (SIN) # and Name

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Demographic Data

Our data has POSTAL CODE of each individual. (In Canada ~ 50 Households in each Postal Code)

Match with

Statistics Canada Census Data: Smallest Geog Area =Dissemination Area (DA). Approx 200 Households in each DA

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Matching Bank and Census Data

• Using Postal Code, we can EXACLY MATCH Each Bank Consumer with appropriate DA Census Data (Average from ~200 Households)

• Income• Unemployment • Education• Home Ownership• Immigrant

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US Zip Code vs. Can Post Code

US Census by ZIP Code (5 Digit)

Available for 28 785 ZIP CODES

Average: >10 000 people per Zip Code

Canada Census by Post Code (6 Digit)

DA Available for ~ 200 Households

(~ 600 people)

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Methodology

• Credit Card Mistakes are our Dependent Variables– THREE different kinds of Credit Card

Mistakes (i.e. 3 Different models)

• Demographic Data (Income, Education etc) are our Independent Variables

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DEFINING MISTAKES (Dep Vars)

• Zinman (2006) Framework • “Borrow High Lend Low” (BHLL).

Unadjusted Wedge=

min [Credit Card Debt, Demand Deposits]

Unadjusted COST=

max[0, Unadjusted Wedge*(r_card-r_dep)]

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Our First Measure of Mistakes• “Bad Habit Persistence”

Rational Expectations Theory: Agents should not make REPEATED mistakes

Test BHLL every month. Stronger test than just a single month (we have 19 months data)

Our Measure: What proportion of months do you BHLL

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Model 1. Repeated BHLL

• Our Data: 19 Months

• Estimate if BHLL for each consumer for each month.

• If BHLL = 1, if NOT BHLL = 0.

• Proportion of Months that each Consumer BHLLs

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Bad Habit Persistence 1=BHLL every month, 0=no month

Never BHLL in Any Month

BHLL Every Month

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Second Measures of MistakesRepeated BHLL (as in 1 above) adjusted for

Precautionary Balances*

(*As defined in Zinman, 2006 and Telyukova and Wright, 2006 and Telyukova 2006)

• Assume that Credit Cards and Deposits are

NOT perfect substitutes.• Assume that its Rational for Consumers Hold

“Excess” Deposits for Precautionary Reasons

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Measuring “Precautionary” Balances

• We assume a “Value at Risk” Framework

• How much $$ do you have to hold in deposits in case of large future shocks?

• We assume a confidence level of 1 standard deviation of deposits (measured over 19 months)

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Bad Habit Persistence Adjusted for Precaut Bal (-1sd)

Never BHLL in Any Month

BHLL Every Month

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Third Measure of Mistakes

Consumer is Delinquent or Overlimit on Credit Card – but still has deposits that could pay outstanding Credit Card debt.

• Delinquent – don’t pay minimum balance• Overlimit – Charge over preset limit

• Very Costly to be delinquent and overlimit – pay fee AND impacts future credit rating.

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Model 3:

• BHLL AND Delinquent / Overlimit

• Measure: Multiply two (0,1) measures• (Delinq/overlimit=1, else=0) * BHLL• Measure = 1 if both delinq/olimit AND BHLL

• Measure for every month (not proportion of months – i.e. don’t look at repeat errors)

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Delinquent/Overlimit AND BHLL (All Months) (LOGIT)

Delinquent/Overlimit AND BHLL (total consumer/months)

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Independent (Demographic) Variables

1. Ln(Average Income)2. Unemployment Rate %3. Education

1. % High School 2. % Some Post Secondary Education3. % University Grad

4. Own Home %5. Immigrant

1. Developed Countries2. LDCs

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Fico (Beacon) Score

• Created by Independent Credit Bureau

• Includes data from ALL Debt Accounts (not just Credit Cards)

• Based on Lagged Data

• Include as control for Banks ex ante assessment of credit risk

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Credit Score – FICO (Beacon)

ApplicationCut Off

Excellent Risk Score

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RESULTS

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Table 1: (Model 1) Dep Var: Proportion of months each consumer BHLL.(Positive Coefficient implies more mistakes)Tobit Model (Dep Var from 0 to 100%)

Coeff t stat

FICO Score -0.00194 -31.75***

ln(Income) -0.07399 -1.37

Unemployment % 0.004677 1.79*

High School % 0.008163 3.95***

Some Post Sec Ed % 0.013451 9.34***

University Grad % -0.00694 -6.47***

Own House % -0.00161 -3.41***

Immigrant-Developed % -0.01381 -4.54***

Immigrant-LDC % -0.00152 -1.2

Constant 1.82082 3.49***

Sample Size 61971

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Table 2: (Model 2) Dep Var: Proportion of months each consumer BHLL (adjusted for Precautionary Balances). (Positive Coefficient implies more mistakes)Tobit Model (Dep Var from 0 to 100%)

Coeff t stat

FICO Score -0.00084 -18.02***

ln(Income) -0.11883 -3.04***

Unemployment % 0.000244 0.13

High School % 0.00624 4.19***

Some Post Sec Ed % 0.007224 6.95***

University Grad % -0.00185 -2.37***

Own House % -0.00062 -1.81*

Immigrant-Developed % -0.00627 -2.85***

Immigrant-LDC % 0.000532 0.58

Constant 0.94075 2.48***

Sample Size 59971

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Table 3: (Model 3) Dep Var: Consumers who BHLL AND are delinquent/overlimit.(All Months, not proportion of months)(Positive Coefficient implies more mistakes)Logit Model (Dep Var = 0 or 1). Control for Clustering

Coeff z stat

FICO Score -0.00519 -54.5***

ln(Income) -0.02206 -0.54

Unemployment % 0.002719 1.43

High School % 0.003352 2.2***

Some Post Sec Ed % 0.004095 3.84***

University Grad % -0.0045 -5.53***

Own House % -0.00162 -4.64***

Immigrant-Developed % 0.000531 0.23

Immigrant-LDC % -0.00079 -0.81

Constant 1.972561 4.9***

Sample Size 1004516

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Who Makes LESS mistakes

• University Grad (All 3 models – all *** )

• Own House (All 3 models)

• Immigrant-Developed Countries (2 models)

• High Income (1 model)

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Who Makes MORE mistakes

• High School Only (All 3 models – all ***)

• Some Post Sec (All 3 models - all ***)

• Unemployed (1 model)

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Implications

1. Demographics Matter

2. Education Really Matters

• Sign Changes: University = Less Mistakes, High School/Some Pst Sec = More Mistakes

• Highly significant (***): across all 3 types of mistake, and all 3 levels of education

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Implications for Theory

• Same argument as Campbell (2006 JF)

• Demographic Explanation (especially education) reflects “non-standard Behaviour” NOT “non-standard Preferences”

• i.e. Behavioral Explanations based on non-standard preferences need to explain why mistakes occur in some demographic groups, and not others.