Credit Cards for the Poor
Ronald Mann
Discussion by
Adair Morse
University of Chicago
Vissing-Jorgensen, 2007
Hypothesis
• “…the products offered and taken up by
LMI households… differ significantly from
those used by middle-class households.”
– Compare characteristics of LMI households to
affluent ones for differences that support
different [uses]
– The paper contrasts profiles of use and users
of cc by income quintile
• Hard to get to endogenous provision of features by
cc
CC Companies
Bank
Outstandings
( $ millions)
$134,700
# Active Accounts
(1,000s)
Citigroup
115,850
47,880
MBNA (B of A)
82,118
21,119
Bank America
61,093
18,773
Capital One
53,024
24,429
JP Morgan Chase
42,996
(transactions)
(gimmicks + mix)
(“subprime”)
- Akers, Golter, Lamm, Solt 2005 FDIC Banking Review
Frame Question: Supply
Two types of Credit Cards
1. Transactions revenues
•
Still the lion share of income
2. Fee-based revenues
•
•
•
Would not offer card if depend on interest rate & transaction fee
Relieves some credit constraints
Gross Souleles (2002) $1000 added liquidity => $130 spending
– Penalty fees (Hammer Consulting Survey 2004)
•
•
•
$13 billion
39% of cc income
Increasing (Furletti Ody 2006)
– Massoud Saunders Scholnick 2006
•
•
Interest rates and fees are substitutes
(model) Cards differentiate based on demand preferences &
default probabilities
Frame question: Demand
Why do LMI use credit card debt?
1. Consumption smoothing / income shocks
2. High (inconsistent?) discount rate: tilting life
cycle consumption profile
3. Being manipulated (targeting by cc’s)
•
•
(Angie Littwin – poor borrowers have few choices)
– Does this belong in set?
– Or do cc’s offer products that { profit maximize with
respect to, market to, understand, all of these } groups
(1), (2) or both
I don’t think we even know (1) from (2), much less (3)
Not clear (1) and (2) need/use same cc features
e.g., Relating to profiles
• Age: more borrowing by young (life cycle)
• Education:
– Greater slope in income = more borrowing
early in career (life cycle)
– Sophistication = less (consumption; targeting)
or more borrowing (life cycle)
– More educ = more availability (targeting)
Empirical Thoughts
• Agarwal, Chomsisengphat, Liu, Souleles 2005
• Consumer mistakes in not taking up cc offers is a small-dollar
effect in most cases
• Data: ideally a panel of individuals
– (Ravina 2005 individual credit card balances)
– Understand their credit use “type”
– Observe patterns of spending and paying
interest/fees
– Policy: See if credit cards extract undue rents from
LMI or if fee structure is optimal way around cc “fear”
of offering high interest rate products
SCF ideas
• Characterizing income smoothers
– Normal income vs current income
• Group all those with x% lower current than normal income
– Vulnerability
• % of income that is used up in basic expenses
– Repaying balances
• Sometimes vs (always or never)
– Shop around, use of CC for vacation, jewelry
• Targeted & Temptation consumption types
– Is it important to distinguish these? Yes if there is a
distinction
– Been turned down?
– Use geography? Where do cc send most mailings?
Things to know, Differencings
– Relate (i) variance of normal to current income and (ii)
vulnerability % to (a) interest rate paid & (b) credit limit
– What is the size & rate for the balance for smoothers
• Relative to same type & income individuals who sometimes
pay off balances
• Relative to same income individuals of temptation targeting
type
– Test: estimate a propensity to default. Do a diff-in-diff
for matched group on the cost of debt.
– Who are cc targeting?
• Are lower quintile borrowers purely temptation/targeting types?
– Evidence of purely fee play?
– Want to know education levels, race, age, etc