why are (some) consumers (finally) writing fewer checks?: the role of payment characteristics scott...
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Why Are (Some) Consumers (Finally) Writing Fewer Checks?:
The Role of Payment Characteristics
Scott Schuh and Joanna StavinsFederal Reserve Bank of Boston
October 26, 2007
Economics of Payment SystemsTelecom Paris
2
Total Volume of Paper Checks in the US
0
10
20
30
40
50
60
1969 1971 1974 1976 1979 1981 1984 1986 1989 1991 1994 1996 1999 2001 2004 2006
Billi
ons (
4QM
A,a
nn.)
Actual Survey Data
Projections
Published in 1981
Published in 2002
Published in 2005
200
6 in
Pro
gres
s
Published in 1983
50
42
37
37
SOURCE: Benton, Blair, Crowe, and Schuh. (2007) “The Boston Fed Study of Consumer Behavior and Payment Choice: A Survey of Federal Reserve System Employees.” Federal Reserve Bank of Boston Public Policy Discussion Paper #07-1.
3
Shift to Electronics
SOURCE: Survey of Consumer Finance (1995, 2004).
0
20
40
60
80
100
Credit Card Debit Card ACH Online Bill Payment
Per
cen
t o
f al
l Res
po
nd
ents
0
20
40
60
80
1001995
2004
Consumers are shifting from paper checks to electronic payments
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Motivation & Overview• Limited research on consumer payment choice
• Most econometric studies:
• Schuh and Stavins econometric study:
• Few other studies also use payment characteristics to explain payment choice (Carow and Staten 1999, Jonker 2005, Klee 2006, Borzekowski, Kiser and Ahmed 2007), but focus on subset of payments, small set of characteristics, lack of individual data.
ij iY DEMOG
ij i ijY DEMOG CHAR
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Payment Choice Variables (Yij)
where ijjij i ij
i
nS N n
N
ADOPTION (0 or 1, logit):
USE/SHARE (OLS):
1 if consumer has adopted payment 0 otherwiseij
i jA
number of payments per monthijn
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Consumer Payment Data Surveys• Partial characteristics data:
– Boston Fed (FRS employees)– Boston Fed/AARP (U.S. consumers)
• Complete characteristics data:– Boston Fed/RAND survey (U.S. consumers)
• To be collected in 2007-08– Other sources
• Dove Consulting/ABA (U.S. consumers)• FirstData (U.S. consumers)• Jonker (2005) (Dutch consumers)
• Existing data on consumer payment behavior are inadequate for testing models of payment demand– Public data: few, infrequent, limited payments variables– Private data: proprietary or expensive (or both), not representative
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Federal Reserve System Survey Data: Demographics
• 6 age categories:
<25, 25-34, 35-44, 45-54, 55-64, over 65
• 4 education categories:
HS or less, some college, college, post-graduate
• 2 homeownership (“wealth”) categories:
own, rent
• 4 income categories:
<$50K, $50-75K, $75-100K, over $100K
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Federal Reserve System Survey Data: Payment Characteristics
• We measure consumers’ assessments of:– Cost (out-of-pocket only)– Convenience (or ease)– Safety– Privacy– Errors– Timing/control– Record keeping
NOTE: 2007-08 RAND Survey will have expanded, refined list
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Payment Characteristics
• Relative CHAR for each payment method:– Credit cards vs. checks– Debit cards vs. checks– ACH vs. checks– Online banking vs. checks– Stored value cards vs. checks
• Asked if better (+1), worse (-1) or same (0) as check for each characteristic type
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Payment Characteristics• We DERIVE characteristics (k) relative to other payment methods
(j, j’) from OBSERVED characteristics relative to checks:
• DERIVED relative characteristics may not reveal valid differences when payment methods have the same OBSERVED characteristic rating relative to check (see diagonal below)
, _ , _ , _k j j k j CK k j CKP P P
start with ...
-1 0 1
subtract from it …
-1 0 1 2
0 -1 0 1
1 -2 -1 0
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Example: Online Bill Payment
Adoption
0
10
20
30
40
50
60
25-34 35-44 45-54 55-64 Over 65
Pe
rce
nt
0
10
20
30
40
50
60
SOURCE: AARP (2006).
Use (by Adopters)
0
1
2
3
4
5
6
7
8
9
10
25-34 35-44 45-54 55-64 Over 65
Nu
mb
er
of
Pa
ym
en
ts
0
1
2
3
4
5
6
7
8
9
10
Unconditional age profiles of adoption and use differ
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Example: Online Bill Payment
SOURCE: AARP (2006).
Average use is similar across ages but varies widely within age;
characteristics help explain this large within-group variation
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Econometric model summary
• CHAR add a lot
– Higher R2 when CHAR included• True for observed, derived or both types
– Tests show that all CHAR should be included• Especially in share regressions
– CHAR reduce significance of demographics
ij ij ij ijY DEMOG CHAR
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Model Evaluation: Model FitAdoption of Payment Methods (Pseudo R^2)
Payment Type Observations Full Model Original
CHAR OnlyDerived CHAR
Only DEMOG Only
Credit Card 1189 .31 .17 .17 .13
Debit Card 1189 .38 .29 .24 .03
ACH 1192 .41 .33 .21 .08
Internet Banking 1192 .36 .30 .17 .06
Share of Payment Methods (R^2)
Payment Type Observations Full Model Original
CHAR OnlyDerived CHAR
Only DEMOG Only
Check 1182 .37 -- .33 .09
Credit Card 1182 .28 .14 .21 .09
Debit Card 1182 .27 .17 .20 .06
ACH 1182 .17 .12 .09 .02
Internet Banking 1182 .19 .15 .11 .02
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Payment characteristics reduce significance of demographics
Significance in Econometric Model of Adoption
Without Characteristics With Characteristics
Credit Card
Debit Card ACH
Online Banking
Credit Card
Debit Card ACH
Online Banking
Age
Education
Income
Percent of Data Explained
12 5 4 5 31 37 43 37
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Model Evaluation: Restriction TestsAdoption of Payment Methods (P values)
Payment Type ObservationsExclude DEMOG
Exclude Derived CHAR
Exclude Derived and
Original CHAR
Exclude Original CHAR from
model of DEMOG and Original
CHAR
Credit Card 1189 .00 .15 .00 .00
Debit Card 1189 .72 .00 .00 .00
ACH 1192 .05 .22 .05 .00
Internet Banking 1192 .15 .21 .00 .00
Share of Payment Methods (P values)
Payment Type ObservationsExclude DEMOG
Exclude Derived CHAR
Exclude Derived and
Original CHAR
Exclude Original CHAR from
model of DEMOG and Original
CHAR
Check 1182 .00 .00 -- --
Credit Card 1182 .00 .00 .00 .00
Debit Card 1182 .00 .00 .00 .00
ACH 1182 .24 .01 .00 .00
Internet Banking 1182 .28 .32 .00 .00
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Adoption Regression Results
• Demographics:– very few significant variables
• OBSERVED characteristics (relative to checks):– ease (+) and timing (+) highly significant– cost (+) significant for CC, OBP but not DC, ACH– safety (+) significant for DC only
• DERIVED characteristics (relative to other methods)– ease– timing– cost– record keeping
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Use Regression Results
• Demographics:– Age: young use fewer checks, more CC, DC– Education: graduate degrees use fewer checks, more CC
• OBSERVED characteristics (relative to checks):– ease (+) important, especially for DC– record keeping (+) important for all but DC– errors (-) important for CC
• DERIVED characteristics:– ease; cost; record keeping; timing– privacy and safety only important in OBP
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Assessments of Characteristics
Much Worse
Worse Same Better Much Better
SOURCE: Benton et al. (2007).
Characteristic
Adopters Non-adopters
CreditCard
DebitCard ACH
Stored-Value Card
Online BillPay
CreditCard
Debit Card ACH
Stored-Value Card
Online Bill Pay
Cost
Convenience
Safety
Privacy
Errors
Timing
Recordkeeping
Assessments vary widely between adopters & non-adopters
(Relative to paper checks)
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Econometric Concerns
ijt ij ijt ijtY DEMOG CHAR
0c cij ijE
*ijt ijt ijtCHAR C
, * are simultaneously determinedij ijY C
Potential problems:
Model with time (subscript t):
* is endogenous for some ijtC j
for several potential reasons
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Endogeneity of Characteristics
• Likely endogenous:– Cost
• Likely exogenous:– Errors, timing/control
• Mixed?:– Convenience, security, privacy, record
keeping
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Econometric Solutions
1. Instrumental variable (IV) estimation– Shortage of IV candidates
• A few survey questions may be valid
– DEMOG not promising IV’s
2. Data collection from multiple surveys?– Import instruments from other payments data– LHS, RHS variables from different surveys (a
solution used in marketing literature)
23
DEMOG as Instruments?
• CHAR not explained well by DEMOG in 1st stage:
• R2 are all below 0.05 DEMOG explain very little cross-section variation in CHAR (same as Jonker (2005))
• cost and ease slightly better explained by demographics; AGE almost uniformly significant, INCOME not usually significant
• DEMOG mostly unimportant for ACH, OB
ij iCHAR DEMOG
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IV Estimation Results
• Point estimates generally remain about the same as in non-IV estimation but…
• Not much is statistically significant (as is usual for IV estimation)
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Theoretical Musings
Key questions to be answered by theory:
What are the primary payment methods?
What are the main payment characteristics?
How do we model payment demand?
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Conclusions
• Payment characteristics are much more important than demographics in explaining consumer payment demand
• Consumer payment decisions consistent with their assessments of characteristics
• Existing data on consumer payment behavior are inadequate for testing models of payment demand
• Need to develop better theory and data for research on consumer payment demand– Boston Fed/RAND new-and-improved survey (2007-08)