the safety of cash and debit cards (pdf: 863.0 kb)
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DNB Working Paper
The safety of cash and debit cards:
a study on the perception and
behaviour of Dutch consumers
Anneke Kosse
No. 245 / April 2010
Working Paper No. 245/2010
April 2010
De Nederlandsche Bank NV P.O. Box 98 1000 AB AMSTERDAM The Netherlands
The safety of cash and debit cards: a study on the perception and behaviour of Dutch consumers
Anneke Kosse *
* Views expressed are those of the author and do not necessarily reflect official positions of De Nederlandsche Bank.
The safety of cash and debit cards:
a study on the perception and behaviour of Dutch consumers
ANNEKE KOSSE
Cash and Payment Systems Division, De Nederlandsche Bank, the Netherlandsi
Abstract:
This paper investigates the impact of consumers’ safety perception on debit card and cash usage. A
conceptual framework of safety perception and payment behaviour is introduced and tested with 2008
consumer survey data. The results demonstrate that consumers’ payment preferences for cash and
debit cards are strongly affected by how consumers assess the likelihood and seriousness of safety
incidents related to cash, debit cards and ATM withdrawals. Risk aversion, personal characteristics
and personal experiences all play a significant role. This study underlines the importance of effective
safety measures, which minimise the risks inherent in the payment system, and of clear
communication towards consumers, so that they may continue to pay efficiently and safely in all
circumstances.
Keywords: debit card, cash, fraud, safety, payment behaviour, risk perception, risk aversion JEL-codes: C42, D12, E41
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1. INTRODUCTION
During the last decade, a reasonable amount of research was carried out in the field of retail payments
to better understand market participants’ behaviour and their underlying motivations. However,
research into consumers’ attitudes towards risks and the impact of safety perception on payment
behaviour is scarce. Several theories and studies (such as Bolt and Chakravorti 2008, He et al. 2008,
Cheney 2006, Humphrey et al. 1996 and Jonker 2007) suggest that safety is one of the drivers in the
payment choice of consumers. Others on the other hand (such as Yin and DeVaney 2001, Schuh and
Stavins 2009) find no evidence of safety playing a role in consumers’ payment choice. So whether
and how consumers’ payment behaviour is influenced by consumers’ views on safety and safety
incidents is still unclear, just as the factors underlying consumers’ safety perception, such as personal
experiences and demographics. Still, these are important issues, as changes in the (perceived) safety
level may have widespread consequences for the overall efficiency and safety of the payment system.
Debit card fraud is one of the main forms of payments fraud at points of sale (POS) and
Automated Teller Machines (ATMs) in the Netherlands. It has increased materially over the past few
years, from less than EUR 4 million in 2005 to several tens of millions of euros in 2008. Although the
financial damage is relatively small in comparison with total sales in retail POS payments (around
0.02%), the consequences might be more widespread. Safety incidents receive a fair amount of
attention from the media. This might have considerable effects on the usage of debit cards and of
electronic payment instruments in general, as consumers may shift away to other means of payment.
Cheney (2006) expresses real concern for a possible erosion of consumer confidence in electronic
payment instruments due to the increase of safety incidents. Earlier studies have demonstrated that the
debit card is often a fast and cheap way of paying (Brits and Winder, 2005; McKinsey&Company,
2006; EIM, 2007). Therefore, substitution away from debit cards could eventually erode the
efficiency of the entire payment system.
In this light it is of vital importance to have a clear understanding of how consumers assess the
safety of the different payment instruments and how this affects their payment choice. Understanding
the mechanism of safety perception and payment behaviour might help policy makers and central
bankers to preserve consumers’ confidence in the safety of the payment system as a whole and in
cost-efficient payment instruments in particular. The objective of this research is therefore to
investigate the determinants of safety perception and the impact of perceived safety on cash and debit
card usage. A conceptual framework of safety perception and payment behaviour is introduced and
tested with 2008 consumer survey data. The findings show that, in general, Dutch consumers are
positive about the safety of the Dutch POS payment system. Their safety assessment is strongly
influenced by how they assess the likelihood and consequences of possible safety incidents;
consumers who believe that the likelihood and impact of payment incidents are high, are significantly
more inclined to perceive payment instruments as unsafe. The results also point to an important role
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of risk aversion and personal characteristics in consumers’ safety assessment. This paper further
demonstrates that cash and debit card usage is significantly affected by consumers’ beliefs about
safety; people who perceive ATM withdrawals and cash to be unsafe are significantly more likely to
prefer paying by debit card.
The main features of the Dutch POS payment system in terms of payment patterns and actual
safety levels are discussed in section 2. Section 3 provides an overview of the existing literature on
the role of safety in payment behaviour and introduces a conceptual model of payments safety and
behaviour. This model will be used as a starting point for empirical analyses. Section 4 deals with the
method of data collection used and summarises the main results of the descriptive data analyses. The
results of the empirical analyses are discussed in section 5. Conclusions and policy implications can
be found in section 6.
2. MAIN FEATURES OF THE DUTCH POS PAYMENT SYSTEM
Payment behaviour at points of sale
In the 1980s, cash and guaranteed cheques were the most popular POS payment instruments in the
Netherlands. But since its introduction in the late 1980s, the debit card has rapidly gained popularity.
For several years, cheques have been phased out now and recent studies show that the substitution of
cash by debit card is still ongoing; the yearly number and value of debit card transactions is still
increasing, whereas the amount of cash withdrawn at ATMs and bank counters has stabilised (see
Figure 1). Cash is still most often used, especially for small transaction amounts, but the average debit
card transaction value is steadily decreasing. Whereas cash transactions still outnumber debit card
transactions, they lost the lead with respect to total transaction value in 2004 (Jonker and Kettenis,
2007). Usage of e-purse and credit card is low and mostly concentrates on specific segments, such as
parking, vending and catering.
The safety of POS payment instruments
It is difficult to determine the actual safety level of payment instruments. One way to do this is to
assess their normative safety; the extent to which products and systems meet applicable standards. In
January 2008, the ECB announced its oversight framework for card schemes, which lays down the
Eurosystem oversight standards for ensuring the safety and efficiency of card payment schemes
operating in the euro area. However, in spite of whether these standards are met, safety incidents still
occur and affect the actual safety level. Therefore, in this paper the actual safety level is assessed by
looking at the occurrence of safety incidents; the substantive safety level.
When using or carrying payment instruments, consumers may sustain different losses. When
carrying a payment instrument, there is a risk of loss, pickpocketing or violent robbery. In the case of
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Figure 1. Development of cash and debit card usage in the Netherlands 1997-2008
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Number (x 1,000)
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
In mln EUR
Cash withdrawals at ATMs Cash withdrawals at bank countersPOS debit card transactions
Source: De Nederlandsche Bank
cash or e-purse, these incidents result in an immediate loss of the money carried. Theft or loss of a
payment card, however, will only give criminals access to the cardholders’ bank account or credit line
if the personal identification number (PIN), if any, is captured and the cardholder is unable to block
the payment card in time. Dutch banks compensate for these financial losses with a deductible of
EUR 150 maximum when the cardholder has taken reasonable safety measures. The total costs to the
cardholders, however, are higher, when accounting for the administrative and payment
inconveniences caused. In case of a violent robbery, the loss to the consumer will be larger, since it
also involves physical or emotional loss. According to estimates based on the AD Crime Indicator
2008 (AD Misdaadmeter 2008) and Safety Monitor of the State 2008 (Veiligheidsmonitor Rijk 2008),
around 1 out of 200 Dutch inhabitants fell victim to pickpockets in 2008. The incidence of robbery is
much lower; around 1 out of 700 inhabitants was robbed in 2008. The figures show a slight increase
in pickpocketing in 2008. The number of robberies declined by more than 15% compared to 2007.
When using a payment instrument to pay at the counter or to withdraw money, there is a risk of
payment fraud. The ongoing increase in its economic value and level of acceptance has made the euro
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increasingly attractive for counterfeiting (Europol, 2007). Since 2007, the number of euro counterfeits
intercepted in the Netherlands has increased. In 2008 around 49,000 counterfeits were seized. This is
35% more than in 2007. Compared to the around 2 billion banknotes tested by the Dutch banking
sector, the likelihood of intercepting a counterfeit is relatively small, around 25 out of 1 million notes
tested. For consumers, the financial loss of receiving counterfeits equals the financial value that this
‘money’ was supposed to have, since banks do not compensate for the loss.
Most non-cash payment fraud is related to cards. Exact statistics on total card fraud are not
available, but the independent EU Fraud Prevention Expert Group reports estimates between
EUR 500 and EUR 1,000 million for the European Union. Total card fraud is often grouped in four
main categories: mail-non-receipt fraud (the physical card sent by the bank through the mail is
intercepted), lost-and-stolen card fraud (you lose your card or your card is stolen), skimming fraud
(the data on the card is copied to produce a counterfeit card), and card-not-present fraud (fraud related
to remote payments such as internet transactions). The most important form of payment card fraud in
the Netherlands is skimming. Initially, cards were mainly copied at ATMs, but since 2006, fraud has
shifted towards payment terminals in shops and petrol and railway stations. In 2008, 1 out of 450
accounts of debit card holders was attacked by skimmers. These cardholders get compensated for their
financial losses when they have taken reasonable safety measures. Besides adjusting ATMs and POS
terminals, the Dutch banking industry in particular tackles the skimming threat by introducing the
EMV technology. These new chip-based payment cards address the skimming issue and use of
counterfeit cards. The migration to EMV technology in the Netherlands is still ongoing. At the
beginning of 2009 more than 50% of all debit cards were equipped with an EMV chip. All cash
dispensers have now been modified. All POS terminals will be ready by end-2011. Until that time,
skimming is expected to remain the major form of payment fraud in the Netherlands.
Figure 2. Number of euro counterfeits seized in the Netherlands
0
10,000
20,000
30,000
40,000
50,000
60,000
2002 2003 2004 2005 2006 2007 2008
Num
ber
of c
ount
erfe
its
Source: De Nederlandsche Bank
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3. LITERATURE ON PAYMENT SAFETY AND PAYMENT BEHAVIO UR
Theoretical literature on safety and payment behaviour
The first theoretical literature on payment behaviour focused on the demand for cash as a medium of
exchange. Baumol (1952) and Tobin (1956) study the optimal amount of cash holdings by
households. They use an inventory theoretic approach and assume that consumers receive a sum of
money at the beginning of a payments period and then decide how much to hold as cash for spending
purposes and how much to save or invest for interest income. Consumers are argued to behave
rationally and to demand that amount of cash that minimises the sum of opportunity costs and
transaction costs. Baumol (1952) underlines the importance of considering safety and security as one
of the opportunity costs of holding cash. This principle is elaborated on by Alvarez and Lippi (2009),
who explicitly incorporate the probability of cash theft into the inventory model. One of the
underlying assumptions is that consumers keep smaller cash balances and increase the number of
withdrawals when the probability of theft increases.
The introduction of new electronic payment instruments gave rise to a new stream of research,
examining the choice between different payment instruments, such as cash, debit or credit cards. Each
payment instrument differs with respect to safety, anonymity, speed, retail acceptance etcetera. At the
same time, they differ with respect to the level and structure of the costs charged to consumers. Based
on the net sum of the costs and benefits of the different means of payment, consumers decide which
payment instrument to acquire and use. There are several theoretical papers on the role of pricing on
consumers’ payment decisions, such as Baxter (1983), Rochet and Tirole (2003) and Wright (2004).
There is, however, hardly any theoretical work on the impact of safety on payment choice. One of the
few are Bolt and Chakravorti (2008). When modelling merchant acceptance and consumer choice of
cash and payment cards, they include the probability of getting mugged as a proxy for the security
benefit of card payments over cash. They assume that debit cards offer consumer protection against
cash theft. In their analysis, consumers make a choice between paying a fixed fee for participating in a
card network on the one hand and the benefits of payment cards, such as being insured against theft,
on the other. In this way, the probability of theft is one of the factors that influence optimal card fees;
the higher the chance of being robbed, the higher consumers’ willingness to pay for payment cards.
He et al. (2008) take a more monetary policy perspective and develop a general equilibrium
framework with which they introduce the risk of cash theft into monetary policy. In their model,
banks supply demand deposits as a substitute for cash. Putting money in the bank and subsequently
employing it for buying purposes using debit cards, cheques or other means of payments is assumed
to reduce the risk that the money will get lost or stolen. Cash on the other hand is assumed to have the
advantage that it is less costly and allows for anonymous transactions. Kahn and Roberds (2009) also
underline the role of anonymity and signal the problem of how to prevent anonymous payment
instruments from attracting swindlers or robbers. The anonymity associated with cash payments
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makes cash attractive for illegal activities. In addition, when the sensitivity of cash to theft is
considered, account-based payment instruments are argued to be a safer alternative. Moreover, they
state that credit-based payment instruments are vulnerable to fraud in the form of identity theft, since
the level of identification is lower compared to other account-based instruments. In the end, according
to Kahn and Roberds (2009), it is about keeping fraud at a manageable level, since eliminating it
entirely would be too costly.
Empirical literature on safety and payment behaviour
The empirical literature on consumers’ payment behaviour can be divided into studies using a micro
perspective and studies applying a macro approach. The micro-oriented studies principally use
consumer survey data to explore the factors influencing consumers’ payment choice. They analyse
consumers’ stated reasons for payment choice and their perception of payment product
characteristics. The macro-oriented studies on the other hand, analyse payment usage and the
substitution process between payment methods using actual aggregate country data.
Examples of micro studies are Zinman (2009), showing that debit card usage is influenced by
relative prices, Bounie and Abel (2006), who demonstrate the importance of demographics and
transaction variables, such as the transaction amount, type of goods, spending place and acceptance,
and Borzekowski and Kiser (2008), who point at an asymmetric substitution pattern between debit
cards and other means of payment. Rysman (2007) focuses on characteristics of the payment cards
industry, such as network effects, two-sided markets and multihoming and shows that card usage is
highly correlated with the level of acceptance by retailers, too. Consumers’ behavioural change to
debit cards is studied by Keinonen (2007) in order to analyse what drives consumers when they adopt
new or other payment methods. None of these studies pay attention to the possible role of safety in
consumers’ payment choice.
Yin and DeVaney (2001) were one of the first to empirically analyse the role of safety in
consumers’ payment behaviour. When applying rational choice theory to analyse debit card usage, no
evidence is found that consumers with greater preferences for security are more likely to use debit
cards. Schuh and Stavins (2009) come to the same conclusion that safety plays a limited role in
consumers’ payment choice. Cheney (2006), however, points out that security and convenience are
the two most important factors considered when choosing a particular instrument. Jonker (2007) and
Benton et al. (2007) show that none of the POS payment instruments are intentionally chosen for
reasons of safety and that the role of safety in the decision-making process is limited to the decision of
which instrument not to use. Borzekowski et al. (2008) analyse consumers’ motivations for choosing
a particular instrument and find that only for some people the choice of whether or not to use a debit
card is determined by safety and security considerations.
In order to gain insight into the substitution process of cash by debit cards, Jonker and Kettenis
(2007) employ macro data to analyse the development of cash usage in the Netherlands between 1987
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and 2005. Aggregate macro data from 1988 to 2003 is employed by Amromin and Chakravorti (2009)
in their study of factors driving the adoption of debit cards in 13 countries and their analysis of the
impact on the demand for cash. Both studies show that the adoption of card terminals by retailers, the
number of ATMs, financial institutional branch infrastructure, wealth and interest rates influence the
substitution process between cash and debit cards. Bolt et al. (2008) find that price inducements also
play a role. No attention is paid to the role of safety and security, however, in either study. This makes
the paper by Humphrey et al. (1996) a sole exception. They study the factors influencing the
substitution between debit cards and other non-cash instruments in 13 developed countries from 1987
to 1993. A distinction is made between income and price factors, indicators of payment availability
and payment habits, and institutional influences. A measure of crime was added to the model in order
to explore the possible impact of safety and security on the payment choice. The results show that
debit card usage is negatively correlated with consumers’ security perception.
Intuition from models of food safety and purchase behaviour
The concepts of safety and consumer behaviour have been extensively studied in other research fields,
such as food science, environmental science and marketing. This literature could provide good points
of departure for modelling payment safety and behaviour. In various safety papers, the total risk of a
situation is defined as a combination of the chance that possible hazards occur and the severity of their
possible consequences (Royal Society, 1992; HMSO, 1995; Rundmo, 1997). In marketing literature,
the two-step model of risk perception comprising the likelihood of a loss occurring and the severity of
its consequences, has been generally adopted, too. However, the marketing studies state that consumer
perception is not so much influenced by technical probabilities and consequences, but rather by
consumers’ interpretation of them. Yeung and Morris (2001) for example, distinguish between
probabilities and consequences when assessing food safety risks and consumer perceptions.
Sapp (2003) examines the factors that influence consumer decision-making concerning food
technology adoption and risk perception. The results show that people’s perception of risks is strongly
affected by opinions of scientists and friends and by trust in public and private organisations. Personal
characteristics, too, appear to play an important role. Men are concluded to perceive risks to be lower
and to have a higher sense of trust. Wildavsky and Dake (1990) also analyse the impact of personal
characteristics on risk perception. Their paper shows that risk perceptions and preferences are strongly
influenced by individual differences in cultural biases, with so-called hierarchists and individualists
favouring technological risk taking and so-called egalitarians being more risk averse.
There is statistical evidence in food safety research (Huang, 1993; Eom, 1994) of risk perception
negatively influencing behaviour. More specifically, several studies show that consumers take various
actions to reduce risks; the higher the likelihood and impact of possible hazards, the more they seek
risk relief (Yeung and Morris, 2001). Especially in the context of health risks, various models have
been developed to explain why people do or do not take precautions to protect themselves. So-called
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value expectancy models for example, such as the Health Belief Model, the Theory of Reasoned
Action and the Protection Motivation Theory, state that self-protective behaviour is motivated by
people’s perception of hazards that might occur and their desire to minimise possible negative
outcomes. They assume that the motivation to take precautions depends on people’s expectation that
the precaution will reduce the likelihood and severity of the hazards and on their assessment of the
costs and benefits of taking the precaution (Weinstein, 1993).
The general concepts discussed above might be useful for understanding consumer behaviour in
the context of retail payments. Following the general notion of risk perception, consumers’ safety
perception of payment instruments may be influenced by both their perception of the likelihood of
incidents to occur when carrying or using a particular payment instrument, and by their perception of
the severity of the consequences of these incidents. Personal characteristics, personal experiences and
opinions of friends, experts and others, may play a role. Depending on their overall safety assessment,
consumers may take precautions to protect themselves by reducing the likelihood and severity of
possible safety incidents, for example by changing the payment instruments they use or by increasing
their alertness. This, combined with the general findings in payments research of personal
characteristics influencing consumers’ payment choice, allows for the construction of a simple
conceptual framework explaining the relation between safety perception and payment behaviour (see
Figure 3). This framework is used as a starting point for the empirical analyses presented in the
remainder of this paper.
Figure 3. Conceptual framework of safety perception and payment behaviour
Payments -related safety
incidents
Perceived likelihood of occurence
Perceived severity of consequences
Overall safety
perception of payment
instruments
Payment behaviour
Experiences Personal characteristics
Opinions of friends, experts and media Experiences Personal
characteristics
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4. DATA
Method of data collection
In order to test the conceptual framework presented above, a questionnaire was distributed in April
2008 among more than 2,000 Dutch household members of the so-called CentERpanel. This
questionnaire included all kinds of subjective questions related to the safety of POS payment
instruments and payment behaviour. 1,672 respondents answered the questionnaire in full,
corresponding to a 65% response rate. The data were merged with data from the 2008 DNB
Household Survey (DHS) to construct a risk aversion indicator for each respondent. The DHS is a
yearly questionnaire collecting information on assets, liabilities, work, housing, mortgages, health and
income and many subjective measures such as expectations and investment and savings motives.
Men, the elderly and higher educated people are slightly overrepresented in the sample (see Table A-5
in the Appendix). Therefore, the sample data have been reweighed by age, gender and education in
order to be representative for the Dutch population.
Descriptive statistics
98% of respondents have a debit card and a little more than 50% have a credit card and/or the Dutch
e-purse called Chipknip. The responses to the questionnaire confirm that cash and debit card are the
main payment instruments used at the counter. The majority of people pay at least once a week with
cash and debit card. E-purse and credit card are less often used (see Table 1).
Respondents were asked to rate the safety of cash, debit card, credit card and e-purse on a scale
from 1 (very unsafe) to 7 (very safe). A distinction was made between usage and carrying of each
payment instrument. The results show that, on average, Dutch consumers are positive about the safety
of the various POS payment instruments (see Table 2). Quite surprising are the little but significant
differences between scores for carrying and using; for all payment instruments consumers feel a little
less secure carrying them than using them for making cash withdrawals or POS payments (see Table
3). In general, Dutch consumers feel most secure carrying the e-purse and debit card. Cash and e-
purse are perceived to be the safest instruments to pay with. There is relatively much dissatisfaction
with the safety of credit cards and cash withdrawals at ATMs.
Table 1. Stated payment behaviour in terms of frequency
Cash Debit card E-purse Credit card
Every day 14% 10% 4% 0% A few times a week 48% 54% 10% 2% Once a week 20% 15% 7% 2% A few times a month 11% 10% 14% 9% Once a month 3% 4% 8% 9% Less than once a month 3% 4% 24% 45% Never 1% 3% 34% 32% 100% 100% 100% 100%
Note: Based on reweighed sample data
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Table 2. Safety perception of different payment aspects on a scale from 1 (very unsafe) to 7 (very safe)
Mean Standard error
No. of observations
ATM withdrawal 4.91 0.034 1656 Carrying cash 4.89 0.033 1664 Cash usage 5.37 0.032 1664 Carrying debit card 5.26 0.032 1630 Debit card usage 5.31 0.032 1624 Carrying e-purse 5.29 0.046 823 E-purse usage 5.39 0.045 793 Carrying credit card 4.93 0.045 869 Credit card usage 5.07 0.041 812
Note: Based on reweighed sample data
Table 3. Results of the two-sample t-tests
Null hypothesis P value Diff = Mean carrying cash - mean cash usage = 0 0.0000 Diff = Mean carrying debit card - mean debit card usage = 0 0.0000 Diff = Mean carrying e-purse - mean e-purse usage = 0 0.0000 Diff = Mean carrying credit card - mean credit card usage = 0 0.0000
Note: Based on reweighed sample data
When the respondents indicated that they perceived one of the aspects to be unsafe, they were asked
for an explanation. Table 4 shows that those who perceived ATM withdrawals to be unsafe, mainly
fear skimming and violent robbery. Carrying payment instruments is mainly perceived to be unsafe in
view of pickpocketing, violent robberies and loss. The fear of pickpocketing is largest with respect to
cash and e-purse. This could be due to the fact that theft of these instruments implies immediate loss
of money. The harm of debit or credit card theft is often restricted to inconvenience, as they are
protected by a PIN code or signature. Moreover, banks often compensate for the financial loss due to
fraud with stolen cards. The fear of violent robbery is largest for cash and debit cards. This may be
due to the limited amounts of money stored on the e-purse and the fact that credit cards are often left
at home. Feelings of unsafety related to the usage of payment instruments have different causes.
Paying by cash is mainly perceived to be unsafe because of the ease with which other customers can
observe how much money the purse contains. This reason is closely related to the above-mentioned
fear of theft and robbery. Card payments are mainly perceived to be unsafe because of the fear of
fraud in the form of skimming, the PIN code being spied, and deliberate erroneous debits by retailers.
Table 4. Stated reasons of unsafety perception (% of respondents feeling unsafe)
Reason 1 Reason 2 ATM withdrawal Skimming 70% Violent robbery 43% Carrying cash Pickpockets 63% Violent robbery 41% Carrying debit card Pickpockets 48% Violent robbery 47% Carrying e-purse Pickpockets 53% Loss 45% Carrying credit card Loss 57% Pickpockets 46% Cash usage Spy on content of purse 72% Counterfeits 35% Debit card usage Skimming 75% PIN spying 62% E-purse usage Deliberate erroneous debits 24% Skimming 22% Credit card usage Skimming 57% Deliberate erroneous debits 45%
Note: Based on reweighed sample data
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Table 5. Perceived likelihood and consequences of safety incidents on a scale from 1(very low) to 5 (very high)
Safety incident Perceived likelihood Perceived consequences Mean Std. Err. Obs. % dissatisfied1 Mean Std. Err. Obs. % dissatisfied2
ATM withdrawal Skimming 2.49 0.030 1587 15% 3.79 0.035 1587 66% PIN spying 2.57 0.030 1618 17% 3.50 0.034 1600 53% Cash usage Falsification 2.56 0.028 1617 16% 3.27 0.031 1616 41% Too little exchange 2.43 0.029 1649 13% 2.60 0.031 1644 17% Carrying cash Pickpockets 2.76 0.029 1631 20% 3.41 0.034 1626 47% Violent robbery 2.47 0.029 1621 13% 3.96 0.032 1638 67% Loss 2.66 0.028 1646 17% 3.17 0.032 1647 38% Debit card usage Skimming 2.25 0.026 1581 7% 3.73 0.035 1588 65% PIN spying 2.68 0.031 1614 20% 3.49 0.033 1594 52% Erroneous debits 1.91 0.027 1602 4% 3.16 0.035 1594 41% Carrying debit card Pickpockets 2.76 0.029 1631 20% 3.61 0.035 1595 56% Violent robbery 2.47 0.029 1621 13% 4.26 0.031 1596 79% Loss 2.66 0.028 1646 17% 3.22 0.034 1618 41% E-purse usage Skimming 2.07 0.041 798 7% 2.96 0.052 800 35% Erroneous debits 1.78 0.035 813 3% 2.91 0.049 802 34% Carrying e-purse Pickpockets 2.76 0.029 1631 20% 3.03 0.050 816 35% Violent robbery 2.47 0.029 1621 13% 3.68 0.050 819 59% Loss 2.66 0.028 1646 17% 2.86 0.048 828 28% Credit card usage Skimming 2.38 0.037 849 11% 3.80 0.045 853 65% Erroneous debits 2.07 0.037 857 7% 3.27 0.045 854 47% Carrying credit card Pickpockets 2.76 0.029 1631 20% 3.92 0.041 868 61% Violent robbery 2.47 0.029 1621 13% 4.19 0.041 852 77% Loss 2.66 0.028 1646 17% 3.60 0.042 869 57% 1 % of respondents that perceived the likelihood to be high or very high 2 % of respondents that perceived the consequences to be serious or very serious Note: Based on reweighed sample data
The respondents were asked to rate the likelihood of different payment incidents occurring and to rate
the seriousness of the consequences of each incident on a scale from 1 (very low) to 5 (very high).
The results are summarised in Table 5. On average, consumers believe that the chance of falling
victim to safety incidents is relatively small. The likelihood as perceived by consumers corresponds
fairly well to the real chances presented in section 2ii. The consequences, however, of possible safety
incidents are perceived to be rather serious, including the consequences of skimming. In spite of the
fact that banks in most cases compensate for damages incurred. Another surprising finding is that
consumers have a certain perception of both the likelihood and consequences of e-purse skimming
fraud, whereas in fact this type of fraud does not exist in reality. These findings could point to a
certain lack of information and knowledge among consumers on the actual probabilities and
consequences of payments-related safety incidents.
Few respondents have ever been involved in a payments safety incident themselves (see Table 6).
The most experienced incidents relate to carrying payment instruments: one in three respondents had
on one occasion lost some means of payment, and 16% had had their pocket picked. Only a small
group indicated they had ever been the victim of an incident resulting from an ATM withdrawal or a
POS payment, such as erroneous debits or PIN spying. Around 2% to 4% of respondents indicated
that they had been the victims of skimmers in the past.
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Table 6. Personal experiences with safety incidents
Payment incident
% respondents with personal experiences
ATM withdrawal Skimming 2% PIN spying 5% Carrying payment means Pickpockets 16% Violent robbery 3% Loss 31% Cash usage Falsification 12% Too little exchange 55% Debit card usage Skimming 2% PIN spying 8% Erroneous debits 5% E-purse usage Erroneous debits 2% Credit card usage Skimming 4% Erroneous debits 10%
Note: Based on reweighed sample data
The respondents were also presented with a list of possible safety measures that can be taken to
protect themselves from payment incidents. They were asked to indicate how often they took a
particular measure on a scale from 1 (never) to 5 (always). The results (see Figure A-1 in the
Appendix) show that Dutch consumers take various precautions, especially being alert to their
surroundings and carefully checking their payments. Of all respondents, 70% usually shield the cash
dispenser keys from view when entering their PIN codes and the majority checks whether the
dispenser is equipped with a special card reader guard that counters card skimming. To prevent
payment fraud and errors at the counter, 60% check the amount and authenticity of the change, 70%
shield the PIN entry pad from view and almost no one hands over their card to the cashier. Most
consumers also verify their account statements. Some indicate to really change their payment choice
by substituting one payment means or withdrawal possibility with another. About 25% tries to
circumvent ATM withdrawals once in a while by withdrawing cash at the bank or POS counter or by
withdrawing large amounts. Moreover, some consumers intentionally change from cash to debit cards
and vice versa when feeling insecure.
5. ECONOMETRIC ANALYSES
In order to assess what factors influence consumers’ safety perception, and how consumers’ payment
behaviour is affected by consumers’ beliefs about safety, the 2008 survey data are employed for
various econometric analyses, using the conceptual framework presented in section 3 as a starting
point. Given the relative small importance of e-purse and credit card in the Netherlands, we focus on
cash and debit cards only. The analysis is split up in three separate steps: 1) analysis of the relation
between safety perception and beliefs about the likelihood and consequences of incidents, 2) analysis
of the role of personal experiences and personal characteristics in consumers’ assessment of
likelihood and consequences, and 3) the impact of safety perception on payment behaviour. The
results of these separate analyses are presented and discussed below.
- 14 -
5.1 Factors affecting safety perception: consumers’ assessment of likelihood and consequences
With the aim to assess the relation between feelings of unsafety and consumers’ beliefs about the
likelihood that incidents will occur and the seriousness of the consequences, first some scatter plots
are drawn, presenting the average ‘likelihood’ and ‘consequences’ scores for cash, debit card and
ATM incidents (see Figure A-2 in the Appendix). In each plot, a distinction is made between the
average scores of the respondents who were dissatisfied about the safety level (the coloured
coordinates) and the average scores of those who felt safe (white coordinates). A comparison of the
coloured and white points shows that the ‘insecure’ respondents perceive the likelihood of the
incidents to be higher and the consequences to be more serious than those who feel safe.
In order to examine whether the differences observed in Figure A-2 are significant and whether
consumers’ safety perception is indeed influenced by consumers’ beliefs about the likelihood and
consequences of possible incidents, three Ordered Logit models are estimated with the perceived
safety level of cash (CASHSAFE), the debit card (DCSAFE) and ATM withdrawals (ATMSAFE)
being the dependent variables. These safety variables are created combining the usage and carrying
scores presented in Table 2 of section 4 and take on 7 values, ranging from 1 (very unsafe) to 7 (very
safe). They are regressed upon a vector of dummy variables indicating whether consumers perceive
the likelihood of related incidents to be high or not and whether they perceive the possible
consequences to be serious or not. An interaction term of likelihood perception and consequences
perception is added, accounting for the possibility that likelihood perception might only play a role
when the consequences are thought to be serious and vice versa. In addition, an indicator of risk
aversion is incorporated into the model to account for the fact that people might differ in their attitude
towards likelihood and consequences and in the degree to which they like to take risksiii . The results
of the Ordered Logit models are summarised in Table A-2a of the Appendix.
The estimation results show that consumers’ safety perception is very much influenced by
perceptions of the chance that incidents may occur. In the cash, debit card and ATM model, the
likelihood dummy has a significant negative sign, meaning that consumers who believe that the
chance of falling victim to cash or debit card incidents is high, are more likely to believe that these
means of payment are unsafe. The perceived seriousness of these incidents plays a role as well, but to
a lesser extent. It has a significant negative effect in the cash model at the 10 percent significance
level. In the debit card model it appears through the significant interaction term (at the 5 percent
significance level) meaning that perceived consequences only play a role when the chances are
perceived to be high. So as long as consumers believe that the chance of falling victim to a debit card
incident is small, the magnitude of the possible consequences is of no significant importance to them.
The risk aversion indicator has a significant negative impact in all three models and shows that people
who are less fond of taking risks are more likely to believe that cash, debit cards and ATM
withdrawals are unsafe, irrespective of how they assess the likelihood of occurrence and the
consequencesiv.
- 15 -
5.2 The impact of personal experiences and personal characteristics
Three Bivariate Probit models are estimated to analyse the impact of personal experiences and
personal characteristics on consumers’ assessment of the likelihood and impact of payment incidents;
a cash model, a debit card model and a model for ATM withdrawals. Each model has two dependent
variables; the perceived likelihood of safety incidents to occur and the perceived seriousness of their
consequences. For the cash model these are (LLHCASH) and (IMPCASH). LLHCASH is constructed
calculating the average of the likelihood scores of receiving counterfeits, being pickpocketed and
other cash-related incidents presented in Table 5 of section 4. This average is transformed into a
dummy variable which indicates whether consumers believe that the likelihood of cash incidents is
high or not. The dependent dummy variable IMPCASH, indicating whether the impact of cash
incidents is perceived to be serious or not, is constructed based on the average of the consequences
scores of all cash-related incidents. The same method is used to construct the dependent variables for
the debit card (ATM) model, LLHDC (LLHATM) and IMPDC (IMPATM), which indicate whether
the likelihood of debit card (ATM) incidents is perceived to be high or not and whether the
consequences of debit card (ATM) incidents are believed to be serious or not. In each model, the two
dependent dummy variables are simultaneously regressed upon a vector of dummy variables
indicating whether consumers have ever experienced payment incidents before and various personal
characteristics, such as gender, age, living environment, income and education. The results and
marginal effects of the Bivariate Probit analyses are summarised in Table A-3 of the Appendix. The
estimated rho turns out to be significantly different from zero in each model. This implies that there is
an efficiency gain in estimating three Bivariate Probit models in stead of six individual Probit models.
Consumers’ experiences strongly influence their beliefs about the chance that incidents occur and
the impact of these incidents. Those who have encountered a cash-related incident in the past tend to
rate the likelihood of cash incidents to happen to be higher than those who have not. Moreover, the
‘experienced’ appear to be more likely to believe that the consequences are serious compared to the
‘inexperienced’. The marginal effect shows that people who have experienced a cash incident in the
past are 11% more likely to perceive the chance and consequences of cash incidents to be high. The
debit card experience dummy has a strong and very significant impact in the debit card model as well.
Those who have ever experienced a safety incident with their debit card before, are 10% more likely
to assess the chances of debit card incidents to be high and the consequences to be serious. The
estimation results also point to a significant impact of past experiences with ATM-related incidents in
all three models. Next, personal characteristics have a strong and significant impact on consumers’
assessment of chances and consequences. In general, women are 7% more likely than men to estimate
the likelihood and consequences of cash, debit card and ATM-related incidents to be high and serious.
Secondly, people aged between 35 and 44 years and between 55 and 64 years tend to believe that the
consequences of incidents related to cash and ATM withdrawals are more serious than those who are
younger than 25 years. The marginal effects, however, are not that strong. Third, compared to people
- 16 -
from the lowest income category, higher income people tend to think less seriously about the
likelihood and consequences of cash incidents and about the likelihood of incidents related to debit
cards and ATM withdrawals. The marginal effects of the different income categories fluctuate
between 10% and 17%. In addition, the perceived chance of encountering a cash or debit card incident
significantly increases with the urbanisation degree increasing, with marginal effects of around 8%v.
At last, education seems to play a role. Higher educated people tend to rate the likelihood of incidents
significantly lower than less educated people. Regarding the consequences of debit card incidents,
however, they appear to be more concerned than people from the lowest education category.
5.3 The impact of safety perception on payment behaviour
Perhaps one of the most interesting questions is to what extent consumers’ daily payment behaviour is
affected by consumers’ safety perception of payment instruments. For this analysis, three types of
consumers are distinguished: (1) frequent cash users who more frequently pay cash than use their
debit card (CASHPREF), (2) frequent debit card users who pay more often by debit card than cash
(DCPREF) and (3) consumers who have no particular preference for either cash or debit card and use
both means of payment more or less to the same extent (NOPREF). 33% of all respondents seem to be
frequent cash users, 30% prefer to pay by debit card and 38% appear to have no particular preference
(NOPREF). Since there is no natural ordering between the different types of consumers, a
Multinomial Logistic model is estimated with the type of consumer (TYPE) being the dependent
variable taking on three outcomes; CASHPREF, DCPREF or NOPREF. To identify the model,
NOPREF is used as the base outcome, so that the coefficients of the CASHPREF and DCPREF
equations measure the change relative to the NOPREF alternative. As explanatory variables, three
dummy variables are used indicating whether consumers perceive cash, debit cards and ATM
withdrawals to be unsafe or not. To account for personal characteristics, gender, age, education,
income and living environment dummies are added. The Multinomial Logistic regression results are
summarised in Table A-4 of the Appendix. Overall, the results show that, after having corrected for
personal characteristics, consumers’ daily payment behaviour is strongly influenced by how they
assess the safety level of the different means of payment, with cash and debit cards being each others’
substitutes. A comparison of the predictions from the model with the actual values reveals that the
model correctly predicts 70% of the actual outcomes. Changes in the perceived safety level of cash
strongly affect consumers’ cash preferences; those who believe that paying by cash becomes unsafe,
are 16% less likely to become frequent cash users (at the 1 percent significance level). Safety
perception plays an important role in consumers’ preferences for debit cards as well. People who
think that ATM withdrawals and cash are unsafe are more likely to prefer using their debit card.
Conversely, consumers who believe that debit cards are unsafe are less likely to be frequent debit card
users. The probability of preferring debit cards decreases by 17% when consumers think that debit
- 17 -
cards become unsafe. It increases 19% when cash is perceived to have become unsafe (both at the 1
percent significance level).
5.4 Scenario analysis: 100% increase of skimming fraud
Since 2005 skimming fraud has increased rapidly in the Netherlands. Whereas the migration to the
safer EMV technology is ongoing, skimming is expected to remain the most important form of POS
payment fraud until all payment terminals are ready by end-2011. In this section, the results of the
three separate analyses presented above are brought together to analyse how Dutch payment patterns
would change if the number of skimming incidents would further increase by 100%. In 2008, 1 out of
450 debit card holders were attacked by skimmers. A 100% increase would imply that 1 out of 225
debit card holders would fall victim. Using the marginal effects obtained from the Ordered Logit,
Bivariate Probit and Multinomial Logit regressions and assuming that they are independent and
uncorrelated with each other, it is estimated how this 100% increase would affect skimming victims’
beliefs about the chances and consequences of debit card incidents and how this would finally affect
their safety assessment of debit cards and their payment preferences. Although this is a very heuristic
way of assessing the impact of fraud on victims’ payment behaviour, the results do provide a good
indication of the nature and magnitude of the consequences.
The Bivariate Probit model showed that people who have experienced a debit card incident in the
past are 9% more likely to perceive the likelihood and consequences of these incidents to be high.
Using the marginal effects of the Ordered Logit analysis (see Appendix A-2b) it can be concluded that
as a result, these people are 0.51%vi more likely to perceive debit cards to be unsafe. When
multiplying this with the marginal effect of the debit card safety dummy on payment preferences from
the Multinomial Logit model, it turns out that people who have fallen victim to debit card fraud in the
past are 0.03% more likely to become a frequent cash user and 0.087% less likely to become a
frequent debit card user. To put it differently, it could be said that around 1 out of 1,152 skimming
victims will no longer prefer paying by debit card and will instead use debit cards and cash to the
same extent, and that around 1 out of 3,278 victims will start using cash for most of their payments.
Based on these estimates, it could be concluded that a 100% increase of skimming events would
roughly generate a 0.002% decrease of frequent debit card users and a 0.001% increase of frequent
cash users. Although these are rough indications, the scenario results show that growing debit card
fraud will cause very few victims to abandon their preference for debit cards. The number of victims
that will really change their behaviour and create a distinct preference for cash is even smaller.
Overall Dutch payment behaviour will therefore hardly be affected, at least directly through changes
in the payment behaviour of victims. The indirect impact of skimming fraud however, due to media
reports or stories from family and friends, for example, might be considerable. This is something that
needs to be studied further.
- 18 -
6 CONCLUSIONS AND POLICY IMPLICATIONS
This paper investigates the impact of consumers’ safety perception on debit card and cash usage. A
conceptual framework of safety perception and payment behaviour is introduced and tested with 2008
survey data. The results demonstrate that consumers’ safety perception is strongly affected by how
consumers assess the likelihood and consequences of payment incidents; the higher the perceived
impact and chances, the less safe they feel. Risk aversion plays a significant role as well, with risk
averse people feeling less secure. Consumers’ beliefs about the likelihood and impact of possible
safety incidents are strongly influenced by past experiences and personal characteristics and
eventually affect consumers’ payment behaviour. Overall, the results show that consumers who
believe that the likelihood and impact of payment incidents are high, are significantly more likely to
perceive payment instruments to be unsafe and therefore use these instruments less often.
The results show that the current level of safety and efficiency of the Dutch retail payment system
could be maintained or even improved by minimising the risks of safety incidents occurring and by
reducing the consequences. It is, however, important to distinguish between the likelihood and
consequences perceived by consumers and the real likelihood and real consequences. Consumers
might wrongly perceive certain payment instruments to be unsafe and therefore wrongfully avoid
them. Consumers might also underestimate the likelihood and consequences of payments-related
safety incidents. Therefore, without making them feel insecure, clear communication towards the
public on the measures that are currently being taken by the different actors and on the steps that
consumers can take themselves to minimise the chances and consequences, might be of importance.
This may further increase the perceived safety of the retail payment system and further stimulate
consumers to pay efficiently and safely in all circumstances.
One important limitation of investigating consumer payment behaviour using the micro approach
is that the analysis is based on self-reported behaviour and on stated preferences. As acknowledged by
Benton et al. (2007), a crucial disadvantage of self-reporting is that it is sensitive to errors. This is
confirmed in the paper by Jonker and Kosse (2009) on how to measure the number of cash payments,
which demonstrates that it is very hard for consumers to properly recall their actual payment
behaviour. Although accurate sample selection and research methodology design might limit the gap
between actual and stated behaviour, measurement errors will never be eliminated completely.
Therefore it is recommended to further analyse the role of safety in consumers’ payment behaviour
from a macro perspective using actual transaction data, actual fraud data and data on actual media
reporting on fraud incidents.
- 19 -
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AP
PE
ND
IX
Fig
ure
A-1
. Pre
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(% o
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Figure A-2. Perceived probabilities and consequences of cash, debit cards and ATM withdrawals
Cash Debit card
1
3
5
1 3 5
Perceived risks (1 vey low - 5 very high)
Per
ceiv
ed c
onse
quen
ces
(1 v
ey lo
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Pickpockets Violent robbery Loss
Falsification Too little exchange
1
3
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Perceived risks (1 vey low - 5 very high)
Per
ceiv
ed c
onse
quen
ces
(1 v
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ery
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Pickpockets Violent robbery Loss
Skimming PIN spying Erroneous debits
ATM withdrawals
1
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Perceived risks (1 vey low - 5 very high)
Per
ceiv
ed c
onse
quen
ces
(1 v
ey lo
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5 v
ery
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)
Skimming PIN spying
- 24 -
Table A-2a: Impact of likelihood perception, consequences perception and risk aversion
Ordered logistic regressions CASHSAFE DCSAFE ATMSAFE
Likelihood of incidents is high -0.785*** -0.637** -1.367***
(0.207) (0.277) (0.377)
Consequences of incidents are serious -0.203* -0.048 -0.020
(0.110) (0.122) (0.118) Interaction term likelihood*consequences 0.133 -0.667** -0.020
(0.570) (0.296) (0.392)
Risk aversion -0.164* -0.256*** -0.217** (0.074) (0.093) (0.093)
Number of observations 1672 1672 1656
Pseudo R2 0.0135 0.0353 0.0343 Log likelihood -2409.7747 -2465.0139 -2473.3945
***,**,* Denotes significance at the 1 percent, 5 percent and 10 percent significance level respectively.
Table A-2b: Marginal effects of the debit card model (DCSAFE)
Ordered logistic regressions DCSAFE= 1
DCSAFE= 2
DCSAFE= 3
DCSAFE= 4
DCSAFE= 5
DCSAFE= 6
DCSAFE= 7
Likelihood of incidents is high 0.0004 0.0052* 0.0221** 0.0955** 0.0067 -0.1134** -0.0337**
Consequences of incidents are serious 0.0000 0.0003 0.0015 0.0071 0.0014 -0.0086 -0.0028 Interaction term likelihood*consequences 0.0004 0.0056* 0.0236** 0.1001** 0.0049 -0.1189** -0.0346**
Risk aversion 0.0001 0.0018** 0.0079*** 0.0380*** 0.0079** -0.0464*** -0.0154***
***,**,* Denotes significance at the 1 percent, 5 percent and 10 percent significance level respectively.
- 25 -
Table A-3: Impact of personal experiences and demographics
Biprobit regressions Cash Debit cards ATM withdrawals
LLHCASH IMPCASH dy/dx˚ LLHDC IMPDC dy/dx˚˚ LLHATM IMPATM dy/dx˚˚˚
Constant -0.263 0.315 -0.289 0.450* -0.312 0.432*
Experiences with ATM incidents 0.318*** -0.165 0.066* 0.592*** 0.066 0.196*** 0.874*** 0.123 0.284***
Experiences with cash incidents 0.338*** 0.247*** 0.107***
Experiences with debit card incidents 0.262*** 0.233*** 0.092*** Gender 0.162** 0.312*** 0.070*** 0.202*** 0.337*** 0.078*** 0.204*** 0.284*** 0.066***
Age:
25 - 34 years -0.291 0.183 -0.064 -0.166 0.002 -0.049 -0.047 0.276 -0.006 35 - 44 years -0.195 0.380** -0.027 -0.082 0.302 -0.013 -0.046 0.527*** -0.000
45 - 54 years -0.323* 0.239 -0.068 -0.171 0.042 -0.049 -0.048 0.264 -0.006
55 - 64 years -0.268 0.360** -0.047 -0.171 0.158 -0.044 0.075 0.246 0.029 65 years and over -0.170 0.154 -0.034 -0.012 0.026 -0.002 0.085 0.082 0.027
Living environment:
Enormously urbanised 0.264** 0.102 0.083** 0.158 -0.040 0.047 0.068 0.016 0.019 Strongly urbanised 0.264** 0.105 0.082** 0.189* 0.037 0.060* 0.155 0.124 0.048
Moderately urbanised 0.279** 0.050 0.081** 0.250** 0.044 0.080** 0.165 0.144 0.052
Little urbanised 0.184* -0.048 0.045 0.206* 0.146 0.072** 0.087 -0.021 0.023 Income:
EUR 1151 - EUR 1800 -0.557*** -0.546*** -0.169*** -0.337** -0.267 -0.108*** -0.610*** -0.156 -0.149***
EUR 1801 - EUR 2600 -0.329** -0.434*** -0.119*** -0.186 -0.199 -0.065 -0.436*** -0.024 -0.113*** More than EUR 2600 -0.414*** -0.519*** -0.151*** -0.333** -0.277* -0.113*** -0.657*** -0.142 -0.177***
Education:
Lower sec. prof. education 0.011 -0.002 0.003 -0.080 0.158 -0.017 0.014 -0.016 0.003 Higher general. / pre-university -0.221 0.091 -0.051 -0.365** 0.169 -0.098** -0.353** 0.101 -0.086**
Intermediate vocational -0.013 0.160 0.009 -0.166 0.335** -0.037 -0.025 0.155 -0.002
Higher vocational -0.313** -0.061 -0.085** -0.428*** 0.211 -0.115*** -0.164 0.050 -0.042 University -0.314* -0.074 -0.084** -0.663*** 0.260 -0.168*** -0.403** 0.148 -0.096** Number of observations 1672 1672 1672 Wald chi2 149.62 164.33 165.17 Prob > chi2 0.0000 0.0000 0.0000
Log likelihood -1984.2257 -1772.36 -1659.6724
Rho 0.3768 0.4037 0.4133 Test rho= 0 Prob > chi2 0.0000 0.0000 0.0000
***,**,* Denotes significance at the 1 percent, 5 percent and 10 percent significance level respectively. Base category: men, younger than 25 years, living in non-urbanised areas, earning less than EUR 1150 per month and only having primary education. ˚dy/dx reflects the change in Pr(LLHCASH=1, IMPCASH=1) for a discrete change of each dummy variable from 0 to 1. ˚˚ dy/dx reflects the change in Pr(LLHDC=1, IMPDC=1) for a discrete change of each dummy variable from 0 to 1. ˚˚˚ dy/dx reflects the change in Pr(LLHATM=1, IMPATM=1) for a discrete change of each dummy variable from 0 to 1.
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Table A-4: Impact of safety perception on cash and debit card usage
Multinomial logistic regression
No. of observations = 1672 LR chi2(46) = 127.01
Prob>chi2 = 0.0000 Log likelihood = -1763.3951
TYPE of consumer Coefficient dF/dx
CASHPREF (544 obs.)
Constant 0.891**
ATM withdrawal unsafe (no = 0, yes = 1) 0.398* 0.034 Debit card unsafe (no = 0, yes = 1) -0.069 0.060
Cash unsafe (no = 0, yes = 1) -0.563** -0.158***
Gender (0=m, 1=f) 0.115 0.037 Age:
25 - 34 years -0.953*** -0.199***
35 - 44 years -0.552* -0.135*** 45 - 54 years -0.211 -0.038
55 - 64 years -0.071 -0.031
65 years and over 0.223 0.034 Living environment:
Enormously urbanised 0.204 0.077*
Strongly urbanised 0.048 0.032 Moderately urbanised -0.049 0.015
Little urbanised -0.081 0.009
Income: EUR 1151 - EUR 1800 -0.448* -0.075*
EUR 1801 - EUR 2600 -0.772*** -0.144***
More than EUR 2600 -0.697*** -0.137*** Education:
Lower sec. prof. education -0.250 -0.090*
Higher general. / pre-university -0.223 -0.101** Intermediate vocational -0.345 -0.105**
Higher vocational -0.439 -0.120***
University -0.517* -0.102**
DCPREF (490 obs.)
Constant -0.379
ATM withdrawal unsafe (no = 0, yes = 1) 0.510** 0.068 Debit card unsafe (no = 0, yes = 1) -1.049*** -0.167***
Cash unsafe (no = 0, yes = 1) 0.611** 0.192***
Gender (0=m, 1=f) -0.125 -0.036 Age:
25 - 34 years 0.353 0.157**
35 - 44 years 0.302 0.115 45 - 54 years -0.081 0.002
55 - 64 years 0.169 0.042
65 years and over 0.156 0.011 Living environment:
Enormously urbanised -0.348 -0.085**
Strongly urbanised -0.231 -0.051 Moderately urbanised -0.292 -0.054
Little urbanised -0.306 -0.054
Income: EUR 1151 - EUR 1800 -0.205 -0.004
EUR 1801 - EUR 2600 -0.152 0.344
More than EUR 2600 -0.118 0.039 Education:
Lower sec. prof. education 0.410 0.112*
Higher general. / pre-university 0.590* 0.154** Intermediate vocational 0.373 0.113
Higher vocational 0.340 0.114*
University -0.017 0.040
NOPREF (638 obs.) (base outcome)
***,**,* Denotes significance at the 1 percent, 5 percent and 10 percent significance level respectively. Base category: men, younger than 25 years, living in non-urbanised areas, earning less than EUR 1,150 per month and only having primary education.
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Table A-5: Comparison survey sample with Dutch population aged 15 years and over (end 2008)
Variable
Sample
Population
Men 55% 49%
Women 45% 51%
Age:
15-24 years 5% 15%
25-34 years 12% 15%
35-44 years 16% 18%
45-54 years 22% 18%
55-64 years 23% 16%
65 years and over 23% 19%
Education:
Primary education 6% 9%
Lower secondary professional education 27% 24%
Higher general secondary / pre-university education 12% 10%
Intermediate vocational education 20% 31%
Higher vocational education 24% 16%
University education 12% 9%
Source: Statistics Netherlands.
i Contact information: Anneke Kosse, phone: +31-20-5242827, e-mail: [email protected], address: De Nederlandsche Bank, Cash and Payment Systems Division, P.O. Box 98, 1000 AB Amsterdam, The Netherlands. The views expressed in this paper are the author’s and do not necessarily reflect those of the Nederlandsche Bank or the European System of Central Banks. All remaining errors are the author’s. The author likes to thank Wilko Bolt, Hans Brits, Nicole Jonker, Maarten van Rooij and Federica Teppa, as well as seminar and conference participants at various institutions for their useful comments. ii The chance of being robbed by pickpockets is perceived to be higher than that of violent robberies. The likelihood of falling victim to skimming seems to be slightly underestimated; consumers perceive this likelihood to be smaller than violent robbery, whereas in fact it is somewhat larger. The actual skimming figures, however, correspond to 2008 in total, whereas the respondents got interviewed in the beginning of the year. The large increase in skimming fraud in the second half of 2008 is thus not accounted for in respondents’ responses. iii This indicator was taken from the DHS questionnaire. In this questionnaire, respondents were presented with the following statement: “I would never consider investments in shares because I find this too risky”. The answers to this question were coded on a scale from 1 (totally disagree) to 7 (totally agree). For the purpose of this study, this variable is transformed into a dummy variable being one if respondents agreed to the statement. iv The correlation coefficients between risk aversion, likelihood assessment and consequences assessment are very small. This indicates that the variables are really independent of each other and justifies incorporating them into this model. v This could possibly be explained by the assumption that the actual chance of safety incidents is higher in more urbanised areas. Analysis of the correlation coefficients, however, did not point at a significant correlation. vi This is the sum of the marginal effects of the significant likelihood dummies, consequences dummies and interaction terms of the first three categories that reflect feelings of unsafety, times 9%.
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