# Presentation: 440656

## 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- Preliminary – Please do not quote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.

## 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

## 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

## 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

## 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

## 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

## 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

## 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

## 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

## 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

## Our Data

- Unique Proprietary Data Base
- Single Canadian Deposit-Taking Institution
- Confidentiality Agreement

- Massoud Saunders Scholnick

## 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

## 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

## 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

## 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

## 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

## 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

## 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

## 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

## 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

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

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

- Never BHLL in Any Month

- BHLL Every Month

- Massoud Saunders Scholnick

## 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

## 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

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

- Never BHLL in
- Any Month

- BHLL Every Month

- Massoud Saunders Scholnick

## 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

## 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

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

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

- Delinquent/Overlimit AND BHLL
- (total consumer/months)

- Massoud Saunders Scholnick

## 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

## 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

## Credit Score – FICO (Beacon)

- Application
- Cut Off

- Excellent Risk Score

- Massoud Saunders Scholnick

## RESULTS

- Massoud Saunders Scholnick

## Slide 32

| 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 |  |
|  |  |  |

- Massoud Saunders Scholnick

## Slide 33

| 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 |  |
|  |  |  |

- Massoud Saunders Scholnick

## Slide 34

| 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 |  |
|  |  |  |

- Massoud Saunders Scholnick

## 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

## Who Makes MORE mistakes

**High School Only (All 3 models – all ***)**
**Some Post Sec (All 3 models - all ***)**
- Unemployed (1 model)

- Massoud Saunders Scholnick

## 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

## 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