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Who Makes Credit Card Mistakes?

Who Makes Credit Card Mistakes?. Nadia Massoud School of Business, University of Alberta, Canada Anthony Saunders Stern School, NYU, NY, NY Barry Scholnick School of Business, University of Alberta, Canada Paper Presented at the FDIC 2006 Fall Workshop, Washington D.C.

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Who Makes Credit Card Mistakes?

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  1. Who Makes Credit Card Mistakes? Nadia Massoud School of Business, University of Alberta, Canada Anthony Saunders Stern School, NYU, NY, NY Barry Scholnick School 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University of Alberta, Canada Paper Presented at the FDIC 2006 Fall Workshop, Washington D.C. Preliminary – Please do not quote Thanks to the anonymous Canadian deposit taking institution for providing the proprietary data.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.

  2. 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) Massoud Saunders Scholnick

  3. 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) Massoud Saunders Scholnick

  4. 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) Massoud Saunders Scholnick

  5. THIS PAPER • Measure Financial Mistakes in CREDIT CARD usage. • 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 Massoud Saunders Scholnick

  6. 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) Massoud Saunders Scholnick

  7. 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 Massoud Saunders Scholnick

  8. 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) Massoud Saunders Scholnick

  9. 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 Massoud Saunders Scholnick

  10. This Paper… Demographic Explanations for BHLL • Income • Education • Unemployment • House Ownership • Immigrant Same explanation as Campbell (2006) – but different financial mistakes (BHLL) Massoud Saunders Scholnick

  11. Our Data • Unique Proprietary Data Base • Single Canadian Deposit-Taking Institution • Confidentiality Agreement Massoud Saunders Scholnick

  12. 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 Massoud Saunders Scholnick

  13. 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 Massoud Saunders Scholnick

  14. 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 Massoud Saunders Scholnick

  15. 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 Massoud Saunders Scholnick

  16. 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) Massoud Saunders Scholnick

  17. 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 Massoud Saunders Scholnick

  18. 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)] Massoud Saunders Scholnick

  19. 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 Massoud Saunders Scholnick

  20. 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 Massoud Saunders Scholnick

  21. Bad Habit Persistence 1=BHLL every month, 0=no month Never BHLL in Any Month BHLL Every Month Massoud Saunders Scholnick

  22. Second Measures of Mistakes Repeated 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 Massoud Saunders Scholnick

  23. 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) Massoud Saunders Scholnick

  24. Bad Habit Persistence Adjusted for Precaut Bal (-1sd) Never BHLL in Any Month BHLL Every Month Massoud Saunders Scholnick

  25. 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. Massoud Saunders Scholnick

  26. 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) Massoud Saunders Scholnick

  27. Delinquent/Overlimit AND BHLL (All Months) (LOGIT) Delinquent/Overlimit AND BHLL (total consumer/months) Massoud Saunders Scholnick

  28. Independent (Demographic) Variables • Ln(Average Income) • Unemployment Rate % • Education • % High School • % Some Post Secondary Education • % University Grad • Own Home % • Immigrant • Developed Countries • LDCs Massoud Saunders Scholnick

  29. 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 Massoud Saunders Scholnick

  30. Credit Score – FICO (Beacon) Excellent Risk Score Application Cut Off Massoud Saunders Scholnick

  31. RESULTS Massoud Saunders Scholnick

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  35. Who Makes LESS mistakes • University Grad (All 3 models – all *** ) • Own House (All 3 models) • Immigrant-Developed Countries (2 models) • High Income (1 model) Massoud Saunders Scholnick

  36. Who Makes MORE mistakes • High School Only (All 3 models – all ***) • Some Post Sec (All 3 models - all ***) • Unemployed (1 model) Massoud Saunders Scholnick

  37. Implications • Demographics Matter • 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 Massoud Saunders Scholnick

  38. 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. Massoud Saunders Scholnick

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