ISBN 978-1-932795-51-6
Debit vs. Credit: How People Choose to Pay
ideas grow here
PO Box 2998
Madison, WI 53701-2998
Phone (608) 231-8550
PUBLICATION #172 (10/08)
www.filene.org
ISBN 978-1-932795-51-6
Debit vs. Credit:
How People
Choose to Pay
Victor Stango, PhD
Graduate School of Management
University of California, Davis
Jonathan Zinman, PhD
Department of Economics
Dartmouth College
Debit vs. Credit:
How People
Choose to Pay
Victor Stango, PhD
Graduate School of Management
University of California, Davis
Jonathan Zinman, PhD
Department of Economics
Dartmouth College
Copyright © 2008 by Filene Research Institute. All rights reserved.
ISBN 978-1-932795-51-6
Printed in U.S.A.
ii
Filene Research Institute
Deeply embedded in the credit union tradition is an ongoing
search for better ways to understand and serve credit union
members. Open inquiry, the free flow of ideas, and debate are
essential parts of the true democratic process.
The Filene Research Institute is a 501(c)(3) not-for-profit
research organization dedicated to scientific and thoughtful
analysis about issues affecting the future of consumer finance.
Through independent research and innovation programs the
Institute examines issues vital to the future of credit unions.
Ideas grow through thoughtful and scientific analysis of toppriority consumer, public policy, and credit union competitive
issues. Researchers are given considerable latitude in their
exploration and studies of these high-priority issues.
The Institute is governed by an Administrative Board made up
of the credit union industry’s top leaders. Research topics and
priorities are set by the Research Council, a select group of
credit union CEOs, and the Filene Research Fellows, a blue
ribbon panel of academic experts. Innovation programs are
Progress is the constant developed in part by Filene i3, an assembly of credit union
replacing of the best there is
executives screened for entrepreneurial competencies.
with something still better!
— Edward A. Filene The name of the Institute honors Edward A. Filene, the “father
of the U.S. credit union movement.” Filene was an innovative
leader who relied on insightful research and analysis when
encouraging credit union development.
Since its founding in 1989, the Institute has worked with over
one hundred academic institutions and published hundreds of
research studies. The entire research library is available online at
www.filene.org.
iii
Acknowledgments
Thanks to Carrie Jankowski for research assistance, George
Hofheimer for helpful comments, and the Filene Research
Institute for financial support.
v
Table of Contents
ix
Executive Summary and Commentary
xiii
About the Authors
Chapter 1
Introduction
1
Chapter 2
Background and Previous Work on the
Debit/Credit Choice
7
Chapter 3
The Data
13
Chapter 4
Differences across Transactions and Consumers
19
Chapter 5
Classifying Panelist Types
23
Chapter 6
Debit Use, Credit Use, and Economic Welfare
29
Chapter 7
Conclusions
33
Appendix
Evidence on Credit Union Member
Characteristics
37
References
39
vii
Executive Summary and Commentary
By George A. Hofheimer,
Chief Research Officer
It may shock you to discover consumers sometimes act in an irrational manner. Take, for example, my coworker’s decision to buy
gas from a particular filling station because he likes the company’s
commercials and green signage but disregards the 10 cent per gallon price premium. Or, witness my teenage son’s “need” to spend
his hard-earned babysitting money on the latest version of the
Madden NFL video game even though this year’s version is materially the same as last year’s version. Finally, scrutinize the inertia
that keeps many consumers with the same auto insurance provider
year after year despite the 10 minutes they’d need to spend to get a
better deal that could save them hundreds of dollars a year. These
situations drive economists crazy since their classical models weigh
heavily on the concept of utility maximization. They argue, for
instance, the value of 10 minutes spent searching for a better auto
insurance package is far less than the value of savings a consumer
will reap with a cheaper insurance package. Rationally, the consumer’s utility is maximized by getting the new insurance package, so
the consumer will get the new insurance package. In the real world
many of us are trapped by inertia and maintain our current auto
insurance coverage.
Although these classical economic models are extremely useful, the
messy concepts of psychology and behavior creep into every decision we make, resulting in seemingly irrational actions. Do we throw
away the traditional economic theories, or do we modify our understanding of what is rational? A new, emerging branch of economics
called behavioral economics attempts to take these complex factors
into consideration and develop new models to understand how
people make economic decisions. This report examines one such
decision that is especially germane to credit unions: debit or credit?
We believe understanding “how people pay” has implications for
financial institution strategy, economic theory, and public policy
considerations.
What Did the Researchers Discover?
A pair of not-so-traditional economics professors—Victor Stango,
PhD, of the University of California, Davis, and Jonathan Zinman,
PhD, of Dartmouth College—explores this topic using a new dataset
that tracks transaction-level choices consumers make between debit
and credit, as well as detailed information on consumer characteristics such as income and creditworthiness. They present a number of
ix
new facts about the debit/credit choice, including the following key
findings:
• Most people “single-home” when they pay: They tend to
use nearly all debit or nearly all credit when paying for retail
purchases.
• Payment choices are influenced by retail purchase characteristics,
such as transaction size, but even controlling for those characteristics there is a clear “propensity to use debit” that varies across
consumers and is stable over time. Furthermore, it is fairly easy to
classify people as either “debit users” or “credit users.”
• Debit users and credit users are similar in some ways and different in others. There are only small differences in income and total
spending. But, debit users tend to be less creditworthy than credit
users, and their credit cards have higher interest rates.
Stango and Zinman also explore whether psychological models of
mental accounting are useful descriptions of consumer behavior and
whether mental accounting benefits those using it. Their key findings
include:
• Persistent debit card use is not fully explained by the most
important economic factor that should affect the costs of debit
vs. credit: whether the consumer is carrying a credit card balance.
This finding suggests that some consumers have other behavioral
or psychological motives for using debit that do not subscribe to
traditional economic theories.
• Credit users pay far less in account fees (across all their accounts)
than debit users or “mixers” (those who have both debit and
credit transactions). This finding suggests that those who use
credit systematically are more financially sophisticated than those
who use debit systematically.
• Mixed evidence signifies debit use is a useful way of moderating
overall spending, as would be suggested by psychological theories
of the debit/credit choice.
Practical Implications
Credit unions looking for ways to better understand member behavior will find this report extremely useful. Findings from this unique
data set give credit unions a lot to think about in terms of segmentation, member behavior, product development, and the whole
concept of consumers’ “irrational” behaviors. Perhaps most useful
is the finding that across demographic segments, many consumers
tend to rely on only one payment choice type. This finding creates
an opportunity for credit unions to broaden their thinking about
how to segment their membership. Although credit unions currently
segment members along demographic lines such as age and income,
x
this report and previous research indicate that a behavioral approach
to segmentation may be a promising approach for credit unions to
consider.1 Payment choice seems to be an interesting segmentation
candidate to consider. Testing this segmentation approach could lead
to a deeper understanding of debit consumer needs and potentially
translate into the next whiz-bang product in the financial services
landscape.
On a broader level, this report tackles an important and promising
topic in economics: how psychology influences consumer actions.
Projects like this one, which wade into the field of behavioral economics, benefit the business community by recognizing that consumers don’t always act in a rational manner. Trying to understand
and create a narrative around why a consumer may not act in his
or her “economic” best interest is an emerging skill, and one that
may give your organization an advantage in the future. Rather than
throw our hands up in the air and say consumers are irrational, it is
much more productive to analyze the decision-making process of a
consumer and understand why, for example, he or she uses debit or
credit payment methods. Although we are a long way from completely understanding consumer behavior and decision making, taking the first few steps in this journey can only benefit credit unions.
Years from now, as the concepts of behavioral economics become
mainstream, perhaps I’ll have an answer as to why my grandson
desperately wants Madden 2038.
1
Stephanie Norvaisas and Jay Russo, Why Choose a Credit Union: An Ethnographic Study of Member Behavior (Madison, WI: Filene Research
Institute, 2007).
xi
About the Authors
Victor Stango, PhD
Victor Stango is assistant professor of economics in the Graduate
School of Management at the University of California, Davis. He has
been on the Davis faculty since 2008. He previously held academic
positions at Dartmouth College and other schools and a nonacademic position as a senior economist in the Federal Reserve system.
Professor Stango’s research focuses on retail financial services. His
current research, “Fuzzy Math and Household Finance,” documents
cognitive biases in how consumers interpret loan and saving terms
that involve compounding. His work with Jon Zinman shows that
these biases have economically substantive effects on household
financial outcomes. In a related work, Professors Stango and Zinman
are investigating whether households’ short-term financial decisions
are well described by models of rational economic behavior. That
work tests whether psychology-based models of “mental accounting”
can improve on pure economic theories of household finance.
Professor Stango’s previous work has focused on credit card and ATM
markets and has appeared in the American Economic Review, the Journal of Law and Economics, the Review of Economics and Statistics, and
other academic journals. He is coeditor (with Shane Greenstein) of
Standards and Public Policy (Cambridge University Press).
Jonathan Zinman, PhD
Jonathan Zinman is an assistant professor of economics at Dartmouth
College. He joined the faculty in 2005 after working as an economist
at the Federal Reserve Bank of New York. Dr. Zinman obtained his
PhD in economics from the Massachusetts Institute of Technology,
and a BA in government from Harvard. In addition to his teaching
and research, Professor Zinman also serves as a visiting scholar at the
Federal Reserve Bank of Philadelphia, a member of the Behavioral
Finance Forum, a research associate at Innovations for Poverty Action,
and a Research Advisory Board member of stickk.com.
Professor Zinman’s research focuses on consumer and entrepreneurial choice with respect to financial decisions. His substantive interests focus on testing economic theories of how firms and
consumers interact in markets, and on testing the merits of incorporating specific features of psychology into economic models. His
methodological interests focus on developing randomized-control
field experiments and survey designs that generate clean tests of
economic theories and related policy questions. He has papers
published in or under revision for several journals, including the
American Economic Review, Econometrica, the Journal of Finance, the
xiii
Review of Financial Studies, the Journal of Banking & Finance, and
the Review of Income and Wealth.
Professor Zinman applies his research by working with financial
institutions to improve pricing, product development, marketing,
and risk assessment strategies. He works directly with institutions
around the globe to identify and test innovations that are profitable
for firms and beneficial to their clients.
xiv
CHAPTER 1
Introduction
Consumers make more than 40 billion
debit and credit transactions every year in the
United States alone, and for financial service
providers the relative costs and benefits of
those transactions can be very different. So,
understanding payment choices is critical for
banks, credit unions, and other providers of
retail financial services.
Understanding how households make financial decisions is a foundational piece of economic knowledge, but many questions about
household finances remain unanswered. One recurring question is
whether standard economic models can fully describe how households make decisions, or whether economic models that incorporate
psychology perform better.
A striking example of this debate is raised by a simple question:
debit or credit? In the United States, well over half of debit and
credit transactions are now made with debit cards, which immediately deduct the transaction amount from the consumer’s deposit
account.2 But most consumers also carry credit cards, which are a
cheaper alternative if consumers do not carry balances. Economists
are often puzzled by debit’s popularity.3
One possibility, and the subject of some conjecture but little hard
research to date, is that the debit/credit choice is motivated by psychology as well as economics. If people have self-control problems,
they may use debit cards as part of a “mental accounting” plan to
moderate spending. This psychology-based theory of spending asserts
that payment choices may depend on something other than purely
economic costs and benefits associated with card use.
Interest in these questions goes far beyond academic circles. Consumers make more than 40 billion debit and credit transactions every
year in the United States alone, and for financial services providers
the relative costs and benefits of these transactions can be very different. So, understanding payment choices is critical for banks, credit
unions, and other providers of retail financial services. Merchants
and other parties in the retail supply chain also face costs and benefits associated with payment choices. Policymakers care too; both
debit and credit markets have open policy questions.4
2
See Federal Reserve System (2007) for some summary evidence, which we discuss in detail later in the report.
3
See Zinman (2004) for a discussion of these issues.
4
The debit and credit card industries have long public policy histories. Disclosure regulation has always been a contentious issue in credit
cards, continuing to the present (see, e.g., the recently introduced Credit Card Accountability, Responsibility and Disclosure Act). Both the
debit and credit card industries have faced antitrust scrutiny as well.
2
Academic research using detailed real-world data on payment choices
has been scarce until now, because the data one would want for such
research are hard for academic researchers to get.5 Ideally, a researcher
would want to see transaction-level choices by a large set of consumers, tracking them over time. One would also want to see a comparative analysis identifying different types of consumers and using data
from a broad range of financial institutions. But most existing work
has used either survey responses or aggregate data to examine payment choices, making it difficult to draw real conclusions. The lack of
such academic work leaves a knowledge gap for industry practitioners as well, particularly those at smaller financial institutions (such as
credit unions) with only limited resources for intense data analysis.
In this report, we present the first wave of research findings from a
new data set that goes far beyond what academic researchers previ-
Consumers make more than 40 billion debit and credit transactions every year in the United
States alone, and for financial services providers the relative costs and benefits of these transactions can be very different.
ously had at their disposal. Lightspeed Research (formerly Forrester
Research) collected our data as part of its comprehensive consumer
survey system. The data we employ for the report track roughly
1,000 individuals for an entire year (2006). We observe all of the
retail debit and credit card transactions of each individual.6 The
data come from individual checking and credit card statements. We
observe transaction dates and amounts, and whether the individual
used a debit or credit card in the transaction. Beyond these data we
also observe a wealth of account and consumer-level information.
On debit card (checking) accounts, we observe all of the explicit
fees on the account. On credit cards we observe all fees and interest
charges. The consumer-level information includes not only standard
demographic information (income, education, etc.) but also credit
bureau data (FICO score, open debt accounts, etc.). This level of
richness is available to few other researchers.7
5
The interested reader can find accessible surveys summarizing academic research on payments in various issues of the Review of Network
Economics (www.rnejournal.com).
6
We also observe other transactions (checking, ATM, Automated Clearing House [ACH]) but do not discuss them, in order to focus in more
detail on the debit/credit margin. In future work we plan to expand the scope of the analysis.
7
The data held by Sumit Agarwal, an economist at the Federal Reserve Bank of Chicago, are comparable to ours (and superior in that they
cover many more consumers) but differ in two key ways. First, the debit card accounts are from only one financial institution. Second, they
contain only partial credit card information (limited to credit cards from that same financial institution). See www.chicagofed.org/economic_
research_and_data/econ_index.cfm for a list of working papers using those data.
Chapter 1
3
The first thing we do with the data is to make a simple description
of payment choices: How many transactions does the typical individual make in a month? In a year? Are most transactions made with
debit or credit, and how does that choice correlate with transaction
amounts? What we find is that individuals vary greatly in how many
transactions they make. We also find a clear relationship between
transaction size and the debit/credit choice.
We also examine how individuals vary in their choices of debit vs.
credit. Do most people specialize, choosing the same method almost
all the time, or do transaction characteristics dictate a mix for most
people? If someone uses a debit card for almost all his or her transactions during the month, does that mean he or she will continue to do
so, or do people switch between intense use of one choice and intense
use of another? Our findings are quite strong for these measures. Most
people primarily use one type of card and do not change their status
as a debit user or credit card user during the sample period.
The report then develops an individual-level measure of the propensity to use either debit or credit. It is a systematic and transactionindependent measure of whether an individual simply prefers to use
debit or prefers to use credit. Even this simple measure is in principle an important test of how well economics can explain payment
choices. A strict interpretation of the economic theory says that only
transaction- and payment-specific choices should matter to people
when they choose; there should not be any systematic preference for
one payment choice that operates independent of the economic costs
and benefits. We find strong evidence of such a preference: We can
clearly distinguish debit users from credit users and a group of mixers, who vary in their card choice.
After describing our measure of debit propensity, we ask a number
of questions about it: How is debit propensity correlated with other
consumer characteristics? Are those who concentrate on debit richer
or poorer, more or less creditworthy, or different in some other
way from those who specialize in credit? Again, this is a new area
of inquiry because we can match our transaction-level data with
information about each panelist—including income and credit score.
What we find is that there are essentially no differences in average
income for debit users vs. credit users, but that debit users tend to
have lower credit (FICO) scores. This suggests that debit users are
less sophisticated financially than credit card users.
We also attempt to shed some light on the economics-psychology
linkage. Economic theory does a poor job of explaining why people
concentrate on debit if they have the ability to use a credit card
and do not carry credit card balances. So, we can ask whether those
who use debit cards are carrying credit card balances. We find that
4
carrying balances is inversely related to debit card use; that is, those
for whom debit is relatively cheaper actually use it more. This is a
puzzling result and suggests that a psychological motive may explain
persistent debit card use.
We also ask the most important question: If people who can use
credit choose instead to use debit, do they benefit from this? We
take two complementary approaches to the question. First, we ask
whether concentrating on debit is associated with higher or lower
overall fees and costs on all payment accounts. We find that debit
users pay substantially higher fees than credit card users. In fact,
despite their markedly less intense credit card use, debit users end up
incurring interest costs as high as those incurred by credit card users,
and they incur substantially higher late and over-the-limit fees on
their credit cards. Because they also pay higher bounced-check fees,
debit card users end up paying annual costs some 2 1/2 times greater
than those paid by credit card users. Those who “mix” by making
more equal transaction shares on both types of cards are closer to
debit card users in terms of the fees they pay.
Economic theory does a poor job of explaining why people concentrate on debit if they have
the ability to use a credit card and do not carry credit card balances. So, we can ask whether
those who use debit cards are carrying credit card balances.
The general thrust of these findings is that debit card users appear to
be substantially less sophisticated financially than credit card users.
This belies a pure psychology-based story for debit card use—that
it is a helpful way of moderating a lack of self-control. Nor do our
findings fit with a story that credit card users are primarily borrowers
who have less liquidity than those who use debit cards systematically.
One limitation of our findings is that we do not observe the actual
benefits to any one individual of concentrating on debit cards. It may
be, for example, that while debit users overall are less financially
savvy than credit card users, an individual with self-control problems
may do better using a debit card rather than a credit card to make a
purchase.
We also examine a secondary implication of the “mental accounting”
story of debit use: Specializing on debit use may moderate overall
spending. To examine this hypothesis we ask whether debit users
spend more or less than credit users, controlling for differences in
income. The evidence on this point is mixed and depends on consumers’ income and FICO score. Among consumers with high FICO
scores (and presumably high financial sophistication), there is only a
weak relationship between debit use and overall spending, although
there may be a small moderating effect for high-income consumers.
Chapter 1
5
But among consumers with low FICO scores, the results differ.
Consumers with low income and low FICO scores who use debit
spend far more per month than those who use credit. But consumers
with high income and low FICO scores who use debit spend far less.
This suggests that the relationship between debit use and spending is
complex and varies by the type of consumer being examined.
We conclude by discussing what our findings might imply for those
managing financial institutions. We also outline some useful ways
that smaller financial institutions might construct measures similar to
ours, in order to incorporate them into decisions.
6
CHAPTER 2
Background and Previous Work
on the Debit/Credit Choice
Because paying with credit or debit is more
convenient for consumers, and generally
cheaper for merchants and financial
institutions, the use of debit and credit at
retail points of sale has exploded. By 2006,
debit and credit together represented more
than 50% of all noncash transactions, and
the share made by check had fallen to less
than 33%.
Research on payment choices has exploded in recent years, primarily
because electronification has changed how consumers pay for goods
and services. Consumers can now choose between the “old” way of
paying for things—cash or check—and two new ways, debit cards
and credit cards. Because paying with credit or debit is more convenient for consumers, and generally cheaper for merchants and financial institutions, the use of debit and credit at retail points of sale has
exploded. By 2006, debit and credit together represented more than
50% of all noncash transactions, and the share made by check had
fallen to less than 33% (Federal Reserve System 2007).
Two puzzles emerged from this regime change. One is that by international standards, the United States lags far behind other countries
in how quickly electronic payments have taken hold in the market.8
This finding has prompted research trying to understand why the
United States might be different. It has also motivated policy discussions about whether the government should mandate faster adoption
of electronic payments.
Another puzzle, also specific to the United States, is that the debit
card has overtaken the credit card as the primary form of electronic
payment. As recently as the mid-1990s, nearly all card payments
were credit card transactions, but in 2006 the debit card overtook
the credit card as the more popular card choice. Figure 1 shows some
summary data on this point.
Some view the rapid growth in popularity of debit use as a puzzle in
economic terms because, by many measures, the debit card is a more
costly means of payment. It is this puzzle that we focus on here.
Explaining Debit and Credit Choices at
the Point of Sale: The Economics
Given the facts, what could explain payment choices—and in
particular the rise in popularity of debit cards? Economic theory
8
In 1993, for example, only 20% of transactions in the United States were electronic, whereas in the European Union and Japan, the figures
were 61% and 78%, respectively.
8
Figure 1: Share of Payment Transactions Made with Debit and
Credit Cards, 2003 and 2006
Year
2003
2006
Debit card
15.60
25.30
Credit card
19.00
21.70
Debit card
45%
54%
Credit card
55%
46%
Total transactions (billions)
Share of debit/credit total
Source: Federal Reserve System, The 2007 Federal Reserve Payments Study.
suggests that if consumers face different costs and benefits associated
with payment instruments, they will choose the instrument with the
greatest net economic benefit.9 As Jevons (1918) pointed out long
ago, the costs and benefits might be pecuniary or nonpecuniary.
Nonpecuniary influences on payment choice include acceptance,
security, portability, and time costs. For some choices (like cash vs.
check), differences in these nonpecuniary costs can be substantial.
Cash, for example, has universal acceptance, whereas personal checks
may be declined by many merchants.
But for the debit/credit choice, these nonpecuniary differences are
minimal; acceptance differed until recently. In our sample year
(2006), debit and credit enjoy nearly identical acceptance.10 Most
debit cards now bear the VISA or MasterCard logo, making the
equivalence exact for those cards.11 Security is also nearly equal.
Debit and credit now offer comparable fraud protection, and hence
offer similar theft risk compared to cash or check. The two choices
also have similar time costs. From the consumer’s vantage point,
debit and credit transactions are typically processed exactly the
same way, using either a point-of-sale terminal or a signature-based
transaction. These methods may be more or less time consuming
than cash or check depending on the situation (Klee 2006), but the
difference between them is small. Nor is portability an issue. Both
debit and credit cards offer identical advantages over bulkier cash and
checkbooks.
The pecuniary differences might matter; thus, these are the ones
that we focus on. It is important to note that if explaining payment
9
See, e.g., Whitesell (1992) or Santomero and Seater (1996) for models of consumer payment choice.
10 Shy and Tarkka (2002) view them as identical.
11 There are a few exceptions; e.g., some merchants take only PIN (“online”) debit, and following the Walmart settlement in 2003 some
merchants take credit but not signature debit. Hayashi, Sullivan, and Weiner (2003) describe the debit card industry’s institutions and
operations.
Chapter 2
9
choices at the point of sale is the objective, then we can disregard the
fixed costs of debit and credit cards—things like annual fees. Assuming that every individual has both a credit and debit card (and in
our data, that is true), those fixed costs are irrelevant when thinking
about whether to use debit or credit for the next transaction. The
only costs that should matter are marginal costs.
Even the marginal pecuniary costs and benefits associated with debit
vs. credit are often implicit rather than explicit. For instance, generally speaking, the explicit cost per transaction is typically zero for
either type of card.12 Differences exist in the implicit costs, however. Using a debit card typically involves removing funds from a
non-interest-bearing account, meaning that the consumer does not
forgo any interest income by using the funds (economists would say
that the opportunity cost of the transaction is zero). But the implicit
costs of using credit cards are not zero. If the consumer is not carrying a credit card balance, then the implicit marginal cost of using
credit is actually negative, because the consumer “floats” the balance
until the next credit card bill is due; the card issuer effectively loans
the customer money. So, anyone not carrying a balance should prefer
credit cards to debit cards. On the other hand, any customer carrying a balance should prefer to use debit because credit card charges
increase credit card balances and end-of-month interest charges. The
upshot of all this is that a simple to measure (and in our data, easy to
observe) variable indicating whether a consumer is carrying a credit
card balance should usefully explain credit and debit use based on
economic costs and benefits.13
Another factor that may affect the debit/credit choice and that is
based on economic costs and benefits is liquidity. A debit transaction
can impose a large and direct cost when the account has insufficient
funds and the transaction causes a checking account overdraft. The
cost of overdrafting is often quite high, so if a consumer is uncertain
about his or her account balance but knows it is low, the risk of overdrafting might deter debit card use.
There are other pecuniary, and even less direct, costs and benefits
associated with the debit/credit choice; most make using credit
more attractive. Credit cards often have rewards programs (such as
cash back or frequent flyer miles) that increase the marginal benefit
12 Only about 14% of (large) debit issuers charge fees, and the median nonzero fee is about 75 cents (Board of Governors of the Federal
Reserve 2003).
13 Shop: The Card You Pick Can Save You Money, the biannual publication of the Federal Reserve Bank of San Francisco (1998, 8), states:
“Under nearly all credit card plans, the grace period applies only if you pay your balance in full each month. It does not apply if you carry
a balance forward.” Nationally representative surveys have found that most credit card holders are cognizant of the interest rates charged
on their plans; e.g., Durkin (2000) reports that at least 85% are aware of their APRs, and Durkin (2002) reports that 54% of holders consider rate information the “most important” disclosure, with 78% of holders responding that the APR is a “very important” credit term.
10
of using credit. These incentives typically can be valued at approximately one cent per dollar charged for the 50–60% or so of card
holders earning rewards.14
In short, the nonpecuniary costs of using debit vs. credit are unlikely
to be different. The pecuniary cost differences are implicit but probably well captured by two pieces of information. One is whether the
individual uses a credit card to borrow money. If so, using debit is
cheaper on the margin. If not, credit is probably cheaper, particularly
if it offers other benefits via rewards programs. The other useful piece
of information is liquidity. We should expect that consumers with
low deposit account balances should turn to credit cards in order to
avoid paying bounced-check fees.
Explaining Debit and Credit Choices at
the Point of Sale: The Psychology
Psychology offers different explanations as to why consumers might
choose debit over credit. This is not to say that psychologists think
that people ignore economic costs and benefits; they merely propose
other influences on decisions, influences that can often push against
the direction of economic incentives. The most well-known class of
psychological explanations is that involving mental accounting.
Thaler (1999, 183) defines mental accounting as “the set of cognitive operations used by individuals and households to organize,
evaluate, and keep track of financial activities.” Mental accounting
in payment choice can take several forms, but for the debit/credit
choice it involves what we might call “debit as discipline.” A large
body of work in psychology finds that many people have self-control
problems that cause them to do things that they later regret. In
household finance, this often means buying something and later
regretting the purchase. So, households might use debit to discipline
their behavior.15 Committing (mentally) to always purchasing with
debit, even if it is more costly in economic terms, helps people control
their spending in two ways. First, and most directly, it simply prevents them from spending money they do not have. Paying immediately (via debit) rather than later also makes the expenditure more
salient. If people feel the pain of paying, they might defer purchases
that they would later regret. Salience might also help consumers
track their spending. All of these factors can produce a pattern of
paying exclusively with debit at the point of sale, even when paying
with credit would yield benefits such as a “float” during the grace
period or frequent flyer miles.
14 The December 1996 Survey of Consumers found that 56% of credit card holders had a card with rewards.
15 See Prelec and Loewenstein (1998) for a discussion of mental accounting and debit use.
Chapter 2
11
A less discussed issue is that psychology may also push consumers
to overborrow.16 People may have biases that push them to consume
more today. Paying with credit further biases toward impulse purchases because it decouples payment from consumption and generates greater pleasure. This strategy could lead to overspending.
Finally, there is a version of mental accounting that drives consumers
to mix their payment choices: “I use my debit card for groceries and
my credit card for gas.” Again, the mental accounting story motivates this behavior as something that helps people with budgeting or
controls impulse buying.
A large body of work in psychology finds that many people have self-control problems that cause
them to do things that they later regret. In household finance, this often means buying something
and later regretting the purchase. So, households might use debit to discipline their behavior.
We don’t focus much on the second explanation, since economists
typically take the view that there is too much debit use rather than
too little, and the first mental accounting story explains that pattern. But both stories are important, in that they enrich a model of
the debit/credit choice. And despite the broad intuitive appeal of the
mental accounting story, there has been virtually no empirical work
really testing the theory in the context of household finance,17 and
there has been none testing its relevance for the debit/credit choice at
the point of sale.18
16 See Ausubel (1991) and Prelec and Simester (2001) for discussions of overborrowing, and Laibson (1997) and Thaler and Benartzi (2004)
for discussions of undersaving. Mental accounting has also been offered as an explanation for long-standing “puzzles” in realms such as
life-cycle wealth accumulation (Bernheim, Skinner, and Weinberg 2001) and portfolio choice (Gross and Souleles 2002).
17 The bulk of empirical support for mental accounting models has come from laboratory experiments—see Thaler (1999) and Soman (2001)
for reviews. There are relatively few experiments that directly test the impact of budgeting processes on spending (Heath and Soll [1996] is
an exception). Several field studies have found evidence consistent with an important role for mental accounting (via loss aversion) in asset
sale decisions (Odean 1998; Genesove and Mayer 2001; Haigh and List 2005).
18 Zinman (2007, Forthcoming) finds that debit use is largely consistent with economics-based theories rather than psychology-based theories but uses only household-level data rather than transaction-level data.
12
CHAPTER 3
The Data
While the financial institutions themselves are
not the primary focus of the analysis, the set is
large in the full data, and representative. The
data contain customers/members of the largest
national banks, smaller regional banks, and
credit unions. Credit union members make up
roughly 10% of all panelists.
We take our data from a nationally representative consumer panel
assembled by Lightspeed Research. The panel consists of over 8,000
households, although we use only a subsample for this report (for
reasons we detail below). The pool of panelists is drawn from a larger
pool who participate regularly in other consumer surveys. All of our
data are from 2006.
At sign-up, each panelist is required to register two payment
accounts with Lightspeed. The payment accounts may be deposit
(checking or savings) accounts or credit card accounts. Registration
requires a one-time revelation of account log-in and password information. Once the panelist supplies that information, Lightspeed uses
it to access the accounts daily, to obtain two types of information via
electronic “scrapes.” One scrape collects account data, which consists
of information about the account that is updated daily. It includes
available balances and recent transactions. The second type of scrape
collects statement data by accessing and downloading monthly
account statements. The statement data vary only monthly and list a
full transaction history as well as other information (such as the APR
if the account is a credit card).
Two other sources of information complement the statement and
account data. One is a Lightspeed-administered survey collecting demographic information such as income and household size.
The second is a credit report, typically “pulled” when the panelist
registers. In this report we use several pieces of information from
the credit report, but the most important is the reported number of
“active” deposit and credit card accounts. Lightspeed requires only
a minimum number of accounts rather than the complete set. In
many instances we observe that while a panelist has registered only
one credit card, his or her credit report lists more than one active
card. To be as accurate as possible about measuring the full set of
transactions, we therefore restrict the sample in this report to the set
of people whose account registrations for Lightspeed closely match
their credit bureau information. We also restrict the analysis to those
panelists reporting at least one deposit account and at least one
credit card account.
14
The restriction we impose for account matching across the two
records (Lightspeed and credit bureau) is that each panelist must
have a number of Lightspeed-registered credit card accounts that
is no more than one less than the number reported to the credit
bureau. For example, a panelist who registers two cards with Lightspeed will be in the subsample if he or she has two or three cards
reported to the credit bureau, but not if he or she has four or more.
This restriction is important because it ensures that the transactions
we observe are as close as possible to the full set of card transactions
made by panelists.
Figure 2 lists summary data regarding our subsample. We have
roughly 1,000 panelists meeting our selection criteria. Nearly all of
the panelists are in the sample for all 12 months;
the average number of months per panelist is
Figure 2: Summary Data on Panelists
11.3. For all panelists in the sample, the average
number of deposit accounts reported is 1.30.
Customers
994
This is slightly below the average number of
Customer-months
11,218
checking accounts listed on their credit bureau
Deposit accounts/panelist
reports. The numbers of credit card accounts are
In data
1.30
also quite close. One point related to the results
Credit bureau
1.89
that follow is that there is probably some selecCredit card accounts/panelist
tion bias in this sample. Because people tend to
In data
2.32
register fewer credit cards than they have, and
Credit bureau
2.58
because we need to restrict the analysis to those
registering most or all of their card accounts, we
are probably weighting the sample toward lighter users of credit card
debt. We address this issue a bit later.
While the financial institutions themselves are not the primary focus
of the analysis, the set is large in the full data, and representative. The
data contain customers of the largest national banks, smaller regional
banks, and credit unions. Credit union members make up roughly
10% of all panelists.
Transactions
The Lightspeed data contain information about all financial transactions made in deposit or credit card accounts. In the data, a transaction is defined as any change to the account’s balance. This includes
both retail purchase transactions and any changes occurring because
of deposits, fees, interest charges (on credit cards), and other inflows
or outflows for other reasons.
Figure 3 summarizes data on transaction frequencies in our sample.
The top pane summarizes the distribution of transactions across the
accounts for the entire 12-month sample period. The median number of transactions is 595. The interquartile (25th–75th percentile)
range is 313–931, and the 90th percentile is 1,297. Most of these are
Chapter 3
15
retail purchase transactions, which include not only debit and credit
card charges but also checks and other payments (such as automated
transfers and bill payments). The median number of retail purchase
transactions per month is 460, with an interquartile range of 228–
746 and a 90th percentile of 1,054. Many of these are checks, meaning that the number of retail “card” transactions is lower (shown in
the next row).
The breakdown of debit and credit card retail purchase transactions
follows in the next two rows. The medians for debit and credit cards
are 72 and 54, respectively, and the ranges are quite large. There are
two interesting patterns here. One is that the dispersion—moving
from the low percentile to the high percentiles—has a very large
range. This suggests that many people cluster on one type of payment at the point of sale. The other interesting fact is the split of
debit vs. credit. The numbers of debit vs. credit transactions are
roughly similar in each cell, with people making slightly more than
half of their transactions on debit cards. This is roughly in line with
industry data that suggest that just more than half of all transactions
are made with debit cards. This is encouraging because it suggests
that our sample may indeed be representative of the population at
large.
The bottom pane of Figure 3 shows similar data, but by month
rather than for the entire year. Most of the patterns are similar. The
median numbers of total and retail purchase transactions are 51
and 40, respectively, amounting to just more than one transaction
per day. The median number of card transactions per month is 21.
Again, there is substantial dispersion in the data between credit card
and debit card.
Figure 3: Summary Data on Transactions
Percentile
Transactions per panelist
10th
25th
Median
75th
90th
Total transactions
All
142
313
595
931
1,297
Retail purchase
86
228
460
746
1,054
Retail card purchase
35
103
253
449
719
Debit card purchase
0
5
72
265
476
Credit card purchase
0
12
54
190
431
All
6
23
51
84
121
Retail purchase
3
16
40
67
100
Retail card purchase
1
6
21
42
67
Debit card purchase
0
0
3
23
47
Credit card purchase
0
0
3
17
41
Transactions per month
16
The overall picture presented in Figure 3 is of tremendous heterogeneity across individuals in their purchase patterns. Some panelists
make large numbers of transactions, while some make very few. More
important, there seems to be important variation across individuals
in the way that they pay for transactions.
Figure 4 presents data on spending per month by panelists in the
sample, as well as account balances. The median monthly expenditures on debit and credit cards are $829 and $104, respectively, with
substantial dispersion; the 90th percentiles of each are $3,112 and
$1,592, respectively. There are also many consumers who make very
few transactions overall, spending less than a few hundred dollars per
month on their cards.
The last two rows show the distribution of average monthly account
balances. The median deposit account balance is $1,204, with the
median credit card balance being slightly lower. There is also substantial dispersion in each. The credit card balance figure, it is important to note, is the average monthly balance on the card before any
monthly payments are made. It may include balances that are not
incurring interest charges.
Figure 4: Summary Statistics on Monthly Spending and Available Credit ($)
Percentile
Category
10th
25th
Median
75th
90th
Mean
12
330
3,864
9,458
14,713
6,023
Total retail debit card spending
5
228
829
1,764
3,112
1,336
Total retail credit card spending
0
0
104
759
1,592
535
Deposit account balance
92
394
1,204
3,234
7,116
2,671
Credit card balance
60
349
1,074
3,012
6,904
2,559
Total retail card spending
Chapter 3
17
CHAPTER 4
Differences across Transactions
and Consumers
In this section we first show how transaction
characteristics determine payment choices, and
then discuss systematic panelist-level differences
in payment choice.
We are interested in understanding what determines how transactions are made, with a primary focus on the debit/credit choice.
Thus we first show how transaction characteristics
determine payment choices, and then discuss
Figure 5: Distribution of Retail Card Transacsystematic panelist-level differences in payment
tion Amounts
choice. The goal is to develop a panelist-level
measure of the propensity to choose debit or
.6
credit that operates independently of transaction
characteristics.
.4
Share
A key aspect of transactions is their size. Figure 5
is a histogram showing the distribution of retail
transaction size. Most transactions are small in
dollar terms, with the vast majority being less
than $100.
Figure 6 shows how transaction size is related
to the debit/credit choice. The figure creates
dollar-value bins for transaction amount and
shows the share of transactions made in each
bin on debit and credit cards. The smallest bin
includes transaction amounts less than $10.00. In
this bin, which comprises over one-quarter of all
.2
0
0.00
100.00
Category
1
Minimum ($)
Maximum ($)
Debit card
Credit card
Percentage
of all
transactions
—
10.00
0.59
0.41
25.48
2
10.01
25.00
0.56
0.44
29.10
3
25.01
50.00
0.53
0.47
24.11
4
50.01
100.00
0.50
0.50
12.58
5
100.01
250.00
0.46
0.54
6.64
6
250.01
500.00
0.32
0.68
1.46
7
500.00
none
0.00
1.00
0.63
20
300.00
Transaction amount ($)
Figure 6: Retail Purchase Transaction Type by Transaction
Amount Decile
Transaction share
200.00
400.00
500.00
Figure 7: Panelist-Level Debit Share of Retail
Transactions
.2
Share
.15
.1
.05
0
0
.2
.4
.6
.8
1
Share of transactions on debit cards
Figure 8: Panelist-Level Debit Share of Retail
Spending
.3
Share
.2
.1
0
0
.2
.4
.6
.8
1
retail transactions, nearly 60% of all transactions
are made with debit cards. The share of transactions made with debit cards is also greater than
50% for transactions in the next two bins (up
to a transaction value of $50), comprising over
two-thirds of all retail card purchase transactions.
Larger transactions tend to be made with credit
cards, and in our sample nearly all transactions
over $500 are made with credit cards.
Because of these differences—the small size
of most transactions, and the fact that smaller
transactions are more likely to be paid with debit
cards—most panelists tend to concentrate their
purchases on their debit cards. Figure 7 shows the
distribution of panelist-level transaction shares on
debit over the sample period. The vast majority
of panelists use debit cards for more than 80%
of their transactions. When the transactions are
weighted by purchase amounts (see Figure 8), the
picture changes somewhat. Because most large
transactions are made with credit cards, spending by each panelist is more concentrated toward
credit cards.
Another interesting feature of Figure 8 is that
it clearly shows that in terms of spending, most
panelists specialize in their payment choices: They
tend to concentrate most purchases on either
debit or credit cards, rather than choosing something in an intermediate range. This is important
for our purposes, as it implies that classifying
panelists as “debit users” or “credit users” may be
informative.
Share of total spending on debit cards
Chapter 4
21
CHAPTER 5
Classif ying Panelist Types
We find evidence of behavioral differences
between debit users and credit users; by our
measures, heavy credit card users are more
responsible than heavy debit users. They have
better credit, indicating a stronger history of
financial decision making.
As we stated earlier, one of the key questions in understanding payment choices is whether they are fully described by economic characteristics associated with panelists and transactions, or whether there are
noneconomic (psychology-based) preferences for
debit or credit as payment choices. In this section
Figure 9: Panelist-Level Max-Average Debit
we describe a method for answering this question.
Share Difference
We take as a starting point the information in the
previous section, which shows that most people
concentrate their transactions on one type of card,
and that such choices seem to be influenced by
transaction characteristics.
.3
Share
We then ask whether the share of transactions made
on debit cards is stable over time for each panelist, or
whether it varies much based on changes over time
in transaction size or other changes in shopping patterns. Figure 9 shows a histogram of the difference
between each panelist’s average share of debit transactions over the year and each panelist’s maximum
share of debit transactions in any one month.
.4
.2
.1
0
0
.2
.6
.8
Variability over time in debit card share
If transaction choices vary a lot from month to
month within panelists, these differences should
be large, but they are small. For most panelists,
the share of transactions made on debit is therefore fairly stable over
time. This is again useful information, as it seems plausible that
something systematic about panelists drives the debit/credit choice.
Constructing an Individual-Level
Measure of Debit Propensity
Because the data in Figure 9 are merely suggestive, we construct a
more detailed measure of the panelist-level propensity to use debit
or credit. This involves a regression-based method that we do not
describe in detail here but that has a simple intuition.19
19 A full description of the regression model and the results is available from the authors upon request.
24
.4
1
What we are interested in identifying is a panelist-level characteristic
that drives debit choice and that is constant over time. We could construct a useful measure of this simply by using our transaction-level
data to calculate the share of each panelist’s transactions made on
debit rather than credit and using those shares as a measure of debit
propensity. But we also know that other factors influence the debit/
credit choice. For example, we know from the previous section that
transaction size in dollars affects the debit/credit choice. Also affecting
the debit/credit choice is whether the panelist is currently revolving a
credit card balance or has enough liquidity to use a debit card even if
that is the first choice. So, for example, two panelists might have different shares of all transactions made using debit cards simply because
one makes a lot of small transactions and the other makes just a few
large transactions. Or, they might have different debit shares because
one carries a credit card balance and the other does not. What we
would like to do is distinguish those differences from those stemming
from an underlying intrinsic preference for debit or credit that is not
described by the economic characteristics of the panelist or transaction. A multiple regression model can control for all these factors and
separately identify the panelist-level constant propensity to use debit
that interests us. One can think of the measure that we estimate as an
individual-level measure of the inherent probability that the panelist
chooses to make any transaction using a debit card.
One can also use this model to assess the significance of important
economic characteristics (such as dollar transaction size or whether the
panelist is revolving a credit card balance). Suppose that we observe the
pattern in Figure 9, which suggests a high level of concentration on debit
cards. If this relationship is completely driven by economic transaction
or panelist characteristics, then our regression model should not be able
to identify any systematic debit propensity, because all of the variation in
actual debit use will stem from the other controls in the model.
Figures 10 and 11 show the results of this method for estimating
debit propensity. Figure 10 is from a simple model that uses only
transaction size to account for the debit/credit choice. The debit
propensities are all between zero and one. One can view these as
probabilities; a number close to one means that holding transaction
size constant, the panelist has close to a 100% chance of choosing debit for any given transaction. A panelist with a value close to
zero will choose credit for an equivalent transaction. And one with
a value close to the middle sometimes chooses debit and sometimes
chooses credit. The pattern suggests a clear difference among types
of panelists; there are substantial numbers who prefer to use debit
and substantial numbers who prefer to use credit, but there are very
few in between. This indicates that there is a strong individual-level
component to the debit/credit choice.
Chapter 5
25
Figure 10: Panelist-Level Propensity to Use
Debit Card (Simple Model)
.3
.2
Share
Figure 11 shows another set of estimated debit
propensities. These are derived from a model
that controls for whether the transaction is made
by a panelist with a revolving credit card balance and whether the panelist is forced to use
credit because of low deposit account balances.
The model still yields significant variation across
customers in debit propensity, and a tendency for
consumers to concentrate their purchases on one
type of card.
.1
Interpreting the Debit
Propensity Results
0
0
.2
.6
.8
1
Debit propensity
Figure 11: Panelist-Level Propensity to Use
Debit Card (Full Model)
.3
.2
.1
Further evidence that psychology matters comes
from the correlations between the debit/credit
0
choice and our measures of economic cost and
0
.2
benefits. Panelists who are revolving credit card
users should be less likely to use credit and more
likely to use debit on their next transaction,
because revolving makes using credit more costly.
But this is not the case; revolving credit users are
more likely to use credit than debit. A similarly counterintuitive pattern exists for our liquidity measure. One would expect that consumers with low deposit account balances would use credit more often, to
avoid the possibility of a checking overdraft. But low deposit account
balances are in fact correlated with more intense debit use. There may
be a mechanical influence to the relationship (using debit more often
necessarily reduces deposit balances), but that still means that the economic costs are not a dominant influence on the debit/credit choice.
26
.4
Share
Economic theory suggests that the single greatest difference between debit and credit should
occur when consumers are carrying debt on their
credit cards. This implies that once we account for
that, as well as other things such as liquidity and
transaction size, there should be no systematic
differences across customers in their preference for
debit. That is not true in Figure 11. This contradicts a pure economics-based theory of the debit/
credit choice and suggests a psychological motive
for payment choices. It is also possible, of course,
that there are unobserved differences across consumers in the economic costs and benefits of the
debit/credit choice, but this is unlikely given that
we can accurately measure the central economic
influences on the debit/credit choice.
.4
.6
Debit propensity
.8
1
Who Uses Debit?
We now turn to the question of how those who prefer debit differ
from those who prefer credit. Figure 12 classifies panelists into three
groups: those who prefer debit, those who prefer credit, and those
who “mix” by indicating a preference in the middle. We classify
“credit users” as panelists whose
debit propensity is less than
Economic theory suggests that the single greatest difference
10% (0.10), and “debit users”
between debit and credit should occur when consumers are
as those whose debit propensity
carrying debt on their credit cards.
is greater than 90% (0.90); the
remainder are labeled “mixers,”
meaning that they sometimes prefer to use debit and sometimes
prefer to use credit. The figure presents data on monthly spending
in each category for each type, as well as shares of spending on debit
and credit cards.
The top row shows median and mean total monthly spending by
type of user. There is essentially no difference among the types of
users, with each type spending a median value of roughly $1,000
Figure 12: Debit Card Propensity and Household Characteristics
Type of consumer
Variable
Credit user
Mixer
Debit user
Total
Monthly retail card
spending ($)
Median
1,104
924
1,065
1,021
Mean
1,705
1,481
1,398
1,513
Monthly debit card
spending ($)
Median
Monthly credit card
spending ($)
Debit card spending
share
Median
Mean
0.01
0.41
0.92
0.47
Debit card transaction
share
Median
0.31
0.73
0.95
0.78
0.33
0.69
0.93
0.68
Average deposit
account balance ($)
Median
1,856
1,926
1,045
1,569
Mean
4,416
5,106
3,225
4,277
Average credit card
balance ($)
Median
1,686
1,780
997
1,474
Mean
3,117
3,556
2,799
3,180
Average credit limit ($)
Median
14,067
8,964
2,433
7,726
Mean
19,123
12,139
5,604
11,780
Median
$45,000–55,000
$45,000–55,000
$45,000–55,000
$45,000–55,000
Mean
$55,000–65,000
$45,000–55,000
$45,000–55,000
$45,000–55,000
Median
753
700
625
693
Mean
735
691
632
683
Median
16.32
17.79
18.60
17.52
Mean
16.17
17.48
18.68
17.51
Household income ($)
FICO score
Credit card APR
0
306
992
249
26
540
1,296
660
Median
1,093
354
0
192
Mean
1,679
941
103
852
0.00
0.39
0.96
0.45
Mean
Mean
Chapter 5
27
on cards of all types. As the next four rows indicate, there is, by
construction, a substantial difference in the composition of spending
across types: debit users spend on their debit cards, whereas credit
users use their credit cards.
The next two rows show average balances in deposit accounts and on
credit cards over the sample period. Credit users have higher deposit
balances because they retain their cash for a longer period of time
before paying their bills; debit users have a lower average deposit
balance. Credit users also have higher credit card balances and limits,
of course, although this does not necessarily imply higher interest
payments, because they may pay their bills in full.
The last three rows show income, creditworthiness, and the cost of
credit. There are essentially no differences in average income (which
is measured only in categories) across the groups. This suggests, along
with the total spending figures, that heavy debit or credit use is not
simply an indication of some general difference in the set of people
in each category.
There are substantial differences in creditworthiness, however. The
median FICO score among credit users is 753, a level that is quite
high and certainly in the “best” category denoted by lenders. With
a median of 700, mixers have slightly worse creditworthiness, and
debit users have creditworthiness that is still worse, with a median of
625. These differences carry over to credit card rates; debit users pay,
on average, interest rates that are over 200 basis points higher than
credit users.
These data show two things. First, in terms of income and total
spending, the differences between debit users and credit users are
really quite small. It is not the case that debit users are observably all
that different from credit users. This is important because it further suggests that heavy debit use is something intrinsic to panelists, rather than an indication of some other unobserved difference
across panelists; such a difference would probably be correlated with
income or spending. Second, we do find evidence of behavioral differences between debit users and credit users; by our measures, heavy
credit card users are more “responsible” than heavy debit users. They
have better credit, indicating a stronger history of financial decision
making. This is evidence in support of the view that heavy credit
users do not use credit simply because they are cash-constrained
or otherwise in financial difficulty. They choose to do so, perhaps
because they pay their bills in full and are cognizant that credit cards
are cheaper than debit cards in economic terms.
28
CHAPTER 6
Debit Use, Credit Use, and Economic Welfare
Credit users pay remarkably less in deposit fees
than either mixers or debit users. The average
credit user pays $9.29 in overdraft fees over
our sample, while the average debit user pays
$210.96; at a typical overdraft fee of $35, this
represents roughly seven overdrafts per year.
The key question raised by the differences among consumers is
whether “homing” on debit or credit is something that materially
affects economic welfare. Do credit users recognize that credit is
superior in economic terms and save money by using it? Or, is debit
used by some households as a self-control mechanism to moderate
impulse buying? We now pursue these fundamental questions.
Panelist Type and Fees on Card
Accounts
We first ask whether debit and credit users incur substantially different costs of making transactions. Figure 13 tabulates these costs
by panelist type. We examine overdraft fees and other deposit fees,
which are typically monthly service charges. We also examine credit
card interest payments, late/over-the-limit fees on credit cards, and
other fees (again, typically monthly charges or annual fees).
The differences are striking. Credit users pay remarkably less in deposit
fees than either mixers or debit users. The average credit user pays
$9.29 in overdraft fees over our sample, while the average debit user
pays $210.96; at a typical overdraft fee of $35, this represents roughly
seven overdrafts per year. Mixers are in the middle, with an average of
$109.60. There are similar differences in other fees. Debit users pay an
average of $62.25, while credit users pay $12.98. On the whole, then,
debit users pay substantially higher fees associated with debit card use.
Figure 13: Fees on Deposit and Credit Card Accounts by
Consumer Type ($)
Type of consumer
Variable
Overdraft fees
Credit user
Mixer
Debit user
Total
9.29
109.60
210.96
117.33
Other deposit account fees
12.98
43.98
62.25
41.90
Credit card interest
99.86
210.27
94.25
140.93
Late/over-the-limit fees
37.65
81.40
96.00
74.64
Other credit card fees
22.32
29.30
15.66
22.76
182.10
474.56
479.12
397.56
All fees
30
The pattern on the credit card side is also interesting. For credit card
interest there is little difference between debit and credit users—each
pays slightly less than $100 per year in credit card interest. Mixers, in
fact, incur the highest interest charges—over $200 per year. In terms
of other fees, both mixers and, surprisingly, debit users pay the highest fees. For example, the average late/over-the-limit fee total over the
year is $96.00 for debit users, but only $37.65 for credit users.
Credit users pay remarkably less in deposit fees than either mixers or debit users. The average credit user pays $9.29 in overdraft fees over our sample, while the average debit user pays
$210.96; at a typical overdraft fee of $35, this represents roughly seven overdrafts per year.
There are substantial differences in total fees. On average, credit users
pay roughly $182 per year in fees across all of their accounts. Debit
users and mixers pay nearly $500. This is a clear pattern; heavy credit
card use is less costly because heavy credit card users avoid checking
overdrafts and they do not pay much more in credit card interest
because they tend to pay their balances in full more often.
Panelist Type and Total Spending
It appears that heavy debit users pay a price for the use of debit, in
the form of higher fees. But do they get benefits? One such benefit
might be that debit use moderates impulse spending. We examine this in Figure 14 by asking how
Figure 14: Consumer Type and Overall
total spending is related to debit or credit use. We
Spending
also show differences based on credit score.
Average total monthly spending ($)
Variable
Credit user
Mixer
Debit user
All consumers
Income category
Low
849
847
895
Medium
1,615
1,315
1,235
High
2,598
2,240
1,732
FICO score > 692
Income category
Low
1,079
1,025
1,019
Medium
1,688
1,587
1,329
High
2,551
2,181
2,270
458
710
865
FICO score < 692
Income category
Low
Medium
1,303
907
1,189
High
2,957
2,353
1,351
The top three rows show total monthly spending
for three income categories: low, medium, and
high. We then show average monthly spending
for each group. The results do show a relationship: In the medium- and high-income groups,
monthly spending is much lower for debit users
than for credit users or mixers.
When the consumers are stratified by FICO score
(above/below the sample median of 692), a more
complex pattern emerges. Among consumers with
high FICO scores, there are only small differences
in spending based on debit use. For consumers with low FICO scores, there is a nonlinear
relationship. Among low-income consumers with
low FICO scores, debit use is positively correlated
with total monthly spending. The relationship
reverses for high-income consumers.
Note: “Low” income is <$45,000 annually, “medium” is $45,000–$100,000, and “high” is
>$100,000.
Chapter 6
31
The results are mixed overall, perhaps as they should be. The overall
pattern is suggestive of a useful role for debit use as a moderating
influence on spending. But for consumers with low FICO scores
who also have low income, the opposite may be true. This bears
investigation in future work, and it provides a provocative set of facts
that can be used going forward.
32
CHAPTER 7
Conclusions
We find that debit users and credit users are
similar in some ways and different in others.
There are only small differences in income and
total spending. But, debit users tend to be less
creditworthy than credit users, and they have
credit cards with higher interest rates.
This report presents the first evidence using high-frequency decisions
on the debit/credit choice in retail purchases. We are interested in
several questions: What determines how people pay? Does economic
theory describe payment choices, or is there a psychological component? And how does the payment choice affect consumers?
We find that most people “single-home” when they pay: They tend
to use nearly all debit or nearly all credit when paying for retail
purchases. These differences are influenced by retail purchase characteristics, such as transaction size, but even controlling for these
characteristics there is a clear “propensity to use debit” that varies
across consumers and is stable over time.
We also find that debit users and credit users are similar in some ways
and different in others. There are only small differences in income
and total spending. But, debit users tend to be less creditworthy than
credit users, and their credit cards have higher interest rates.
We also find some provocative results concerning the effects of being
a debit or credit user. Credit users pay far less in account fees across all
their accounts than debit users
or mixers. This suggests that
Debit users tend to be less creditworthy than credit users,
heavy credit card users are in fact
and their credit cards have higher interest rates.
sophisticated consumers. We also
find an effect that might suggest a benefit from heavy debit use: It may moderate spending overall.
Thus, heavy debit use may have both costs and benefits.
How can these findings be useful to financial institution executives?
We see a few promising avenues. One insight is that even among customers who are in obvious ways similar—e.g., on income or FICO
score—there may be dramatic differences in payment choices. So, the
debit/credit propensity provides a new metric on which customers
differ.
The relevance of that difference, of course, is that the revenue streams
from debit users and credit users are very different. This is particularly true if one focuses on deposit account revenue: A typical debit
user generates more than 10 times the deposit account revenue per
34
year than a credit user generates. While it is beyond the scope of
this report (and the expertise of the authors) to advise on marketing, targeting marketing strategies to debit users rather than credit
users might bear fruit on the bottom line. There may be other costs
for acquiring or serving those members, of course, but taking the
differences in revenue that we identify seems like a promising starting
point.
While much work remains, we view these data as offering many new
insights into retail payment choices. The patterns we find can prove
useful not only to other academics researching payment choices but
also to practitioners and policymakers interested in retail household
finance.
Chapter 7
35
Appendix
Evidence on Credit
Union Member
Characteristics
While the primary purpose of this report is to exploit the richness
offered by data on customers of all financial institution types, readers
of Filene reports understandably take an inherent
interest in facts about the credit union system.
Figure 15: Panelist-Level Propensity to Use
Therefore, in this appendix we discuss along two
Debit Card (Credit Union Members Only,
important lines some results for the subsample of
Simple Model)
our data who are credit union members.
There is a key difference between bank customers
and credit union members in debit propensities.
Figure 15 shows the distribution of debit propensities for credit union members. Nearly 60% of
credit union members are credit users, whereas
roughly 20% are debit users; this contrasts to the
figures for the bank sample, in which less than
30% are credit users (compare with Figure 10).
Although we cannot fully explain this difference,
it is worth noting.
.6
Share
.4
.2
0
0
.2
.4
.6
Debit propensity
The other major difference between the bank and
credit union samples lies in revenue for the different member types. Figure 16 shows data on fee
revenue for credit union members only. Among
credit union members, fee revenue is $278 per
year, substantially less than the $398 figure shown in Figure 13.
This disparity is almost exclusively due to differences in deposit (or
.8
1
Figure 16: Fees by Customer Type (Credit Union Members
Only)
Type of consumer
Variable
Overdraft fees
Credit user
Mixer
Debit user
Total
28.83
0.00
16.86
19.14
4.79
52.57
130.00
40.36
Credit card interest
199.51
74.97
0.00
130.28
Late/over-the-limit fees
104.94
45.57
28.00
75.30
16.87
5.57
15.80
13.74
354.94
178.68
190.66
278.82
Other deposit account fees
Other credit card fees
All fees
Appendix
37
share draft) account fees; credit card account fees are nearly identical for the two groups. This may reflect a combination of generally
lower account fees at credit unions and different usage patterns by
customers.
Another interesting difference across the two samples is the relative
size of revenue streams from the two types of users. Among bank
customers, it is debit users who generate the greatest overall revenue,
whereas among credit union members it is credit users. These differences are particularly relevant given that many credit union members
do not hold a credit card from their credit union but rather from a
bank. Thus, the credit user revenue stream for credit union members
may actually flow to a bank or other credit card issuer.
Again, we hesitate to speculate too strongly on these results, but we
hope that they prove intriguing to those with particular interest in
the credit union system.
38
References
Ausubel, L. 1991. “The Failure of Competition in the Credit Card
Market.” American Economic Review 81(1): 50–81.
Bernheim, Douglas, Jonathan Skinner, and Steven Weinberg. 2001.
“What Accounts for the Variation in Retirement Wealth among U.S.
Households?” American Economic Review 91(4): 832–57.
Board of Governors of the Federal Reserve. 2003. Annual Report to the
Congress on Retail Fees and Services of Depository Institutions. Washington, DC.
Durkin, Thomas A. 2000. “Credit Cards: Use and Consumer Attitudes.” Federal Reserve Bulletin 86(9): 623–34.
———. 2002. “Consumers and Credit Disclosures: Credit Cards and
Credit Insurance.” Federal Reserve Bulletin 88(4): 201–13.
Federal Reserve Bank of San Francisco. 1998. Shop: The Card You Pick
Can Save You Money, www.frbsf.org/publications/consumer/shop.pdf.
Federal Reserve System. 2007. 2007 Federal Reserve Payments
Study. Available at www.frbservices.org/files/communications/pdf/
research/2007_payments_study.pdf.
Genesove, David, and Christopher Mayer. 2001. “Loss Aversion and
Seller Behavior: Evidence from the Housing Market.” Quarterly Journal
of Economics 116(4): 1233–60.
Gross, Davis, and Nicholas S. Souleles. 2002. “Do Liquidity Constraints
and Interest Rates Matter for Consumer Behavior? Evidence from Credit
Card Data.” Quarterly Journal of Economics 117(1): 149–85.
Haigh, Michael, and John List. 2005. “Do Professional Traders Exhibit
Myopic Loss Aversion? An Experimental Analysis.” Journal of Finance
60(1): 523–34.
Hayashi, Fumiko, Richard Sullivan, and Stuart E. Weiner. 2003. A Guide
to the ATM and Debit Card Industry. Federal Reserve Bank of Kansas City.
Heath, Chip, and Jack Soll. 1996. “Mental Budgeting and Consumer
Decisions.” Journal of Consumer Research 23(1): 40–52.
Jevons, W. Stanley. 1918. Money and the Mechanism of Exchange. Vol. 3
of The Bankers Practical Library. New York: Appleton.
References
39
Klee, Elizabeth. 2006. “Paper or Plastic? The Effect of Time on Check
and Debit Card Use at Grocery Stores.” Working Paper, Federal
Reserve Board of Governors, Washington, DC.
Laibson, David I. 1997. “Golden Eggs and Hyperbolic Discounting.”
Quarterly Journal of Economics 112: 443–77.
Odean, Terrance. 1998. “Are Investors Reluctant to Realize Their
Losses?” Journal of Finance 53(5): 1775–98.
Prelec, Drazen, and George Loewenstein. 1998. “The Red and the Black:
Mental Accounting of Savings and Debt.” Marketing Science 17(1): 4–28.
Prelec, D., and D. Simester. 2001. “Always Leave Home Without It.”
Marketing Letters 12: 5–12.
Santomero, Anthony M., and John J. Seater. 1996. “Alternative Monies and the Demand for Media of Exchange.” Journal of Money, Credit
and Banking 28(4): 942–60.
Shy, Oz, and Juha Tarkka. 2002. “The Market for Electronic Cash
Cards.” Journal of Money, Credit and Banking 34(2): 299–314.
Soman, Dilip. 2001. “Effects of Payment Mechanism on Spending
Behavior: The Role of Rehearsal and Immediacy of Payments.” Journal
of Consumer Research 27(4): 460–74.
Thaler, R. H. 1999. “Mental Accounting Matters.” Journal of Behavioral Decision Making 12(3): 183–206.
Thaler, R. H., and S. Benartzi. 2004. “Save More Tomorrow™: Using
Behavioral Economics to Increase Employee Saving.” Journal of Political Economy 112: S164–S187.
Whitesell, William. 1992. “Deposit Banks and the Market for Payment Media.” Journal of Money, Credit and Banking 24(4): 483–98.
Zinman, Jonathan. 2004. “Why Use Debit Instead of Credit? Consumer Choice in a Trillion-Dollar Market.” Federal Reserve Bank of
New York Staff Reports, July.
———. 2007. “Household Borrowing High and Lending Low Under
No-Arbitrage.” Working Paper, Dartmouth College, Hanover, NH.
———. Forthcoming. “Debit or Credit.” Journal of Banking and
Finance.
40
ISBN 978-1-932795-51-6
Debit vs. Credit: How People Choose to Pay
ideas grow here
PO Box 2998
Madison, WI 53701-2998
Phone (608) 231-8550
PUBLICATION #172 (10/08)
www.filene.org
ISBN 978-1-932795-51-6
Debit vs. Credit:
How People
Choose to Pay
Victor Stango, PhD
Graduate School of Management
University of California, Davis
Jonathan Zinman, PhD
Department of Economics
Dartmouth College