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This Version: June 2011
SELF-CONTROL, FINANCIAL LITERACY AND
CONSUMER OVER-INDEBTEDNESS
by
John Gathergood
†
Abstract
This paper examines the relationship between self-control, financial literacy and
over-indebtedness on consumer credit debt among a sample of U.K. consumers
using data from a household survey. Both lack of self-control and financial
illiteracy are positively associated with non-payment of consumer credit and self-
reported excessive financial burdens of debt. Consumers who exhibit self-control
problems are shown to make greater use of quick-access but high cost credit items
such as store cards and payday loans. We also find consumers with self-control
problems are more likely to suffer income shocks, credit withdrawals and
unforeseen expenses on durables, suggesting that lack of self-control increases
exposure to a variety of risks. In most specifications we find a stronger role for
lack of self-control than for financial illiteracy in explaining consumer over-
indebtedness.
JEL classification: D03 D12 E21
Acknowledgements
I should like to thank the ESRC for supporting this research through grant: RES-
061-25-0478, and YouGov for incorporating questions into their household survey
and making the data available for the purposes of this research project.
†
University of Nottingham. john.gathergood@nottingham.ac.uk School of Economics,
University of Nottingham, Nottingham, NG7 2RD, UK
Electronic copy available at: http://ssrn.com/abstract=1873369Electronic copy available at: http://ssrn.com/abstract=1873369
2
SELF-CONTROL, FINANCIAL LITERACY AND
CONSUMER OVER-INDEBTEDNESS
What role do self-control and problems and financial illiteracy play in explaining
over-borrowing on consumer credit? The multi-billion dollar consumer credit market allows
consumers to walk around shopping malls and department stalls carrying instant access to
large amounts of readily available funds for purchases, often at rates of interest far in excess
of typical saving rates. The rise of relationship banking sees lenders commonly attach
unsolicited approvals for credit card products by default to approvals for checking and
deposit accounts. Non-prime consumer credit products targeted to the least creditworthy are
designed specifically with quick-access to the credit line in mind, such as in the payday and
home credit markets. Credit providers facilitate and promote the purchase of luxury consumer
goods by offering preferential rates of access to shoppers in particular stores or of particular
brands via store cards and mail order catalogues. These features make consumers who lack
self-control susceptible to overspending at high rates of interest by facilitating the rash
purchase decisions motivated by their own impulsiveness.
Additional features of consumer credit products make consumer financial literacy
essential for avoiding mistakes when borrowing. Due to the wide variation in repayment
terms and lending periods common across consumer credit products, lenders are typically
obliged to present the cost of credit in terms of an Annualised Percentage Rate (APR)
1
, which
consumers need to be able to interpret correctly to avoid sub-optimal borrowing decisions by,
for example, simply comparing monthly payments. Consumer debts are regularly subject to
the applications of compound interest period-by-period, such as with credit cards or bank
overdrafts. It is common for credit card products to incorporate default ‘minimum payments’
which less than clear balances month-by-month. Consumers need to be financially literate in
1
Under the U.K. Consumer Credit Act, lenders must display the APR at least as prominently as any other rate of
charge on loan advertisements. Similar provisions exist in the U.S. under the Truth in Lending Act.
3
interpreting these financial features common to consumer credit products so as to avoid over-
borrowing.
Economists have for a long time recognised that self-control might cause individuals
to engage in over-spending, resulting in under-saving and over-indebtedness (Strotz, 1956;
Thaler and Shefrin, 1981; Bernheim and Rangel, 2004; Benhabib and Bisin, 2005).
Theoretical papers on of self-control (elucidated as an intrapersonal decision time-
inconsistency problem, a conflict between ‘multiple selves’ or as cue-triggered mistakes,
among others) have commonly cited self-control problems as an explanation for high levels
of credit card borrowing (Laibson, 1997; Fehr, 2002; Heidhues and Koszegi, 2010). Recent
models of self-control in consumer credit markets show that many features of consumer
credit contracts are consistent with time inconsistent preferences on the part of consumers
(Heidhues and Koszegi, 2010; Laibson, Repetto and Tobacman, 2011)
Empirical studies on measuring self-control problems among individuals have,
however, tended to focus on the relationship between self-control and the accumulation of
wealth (Ameriks et al, 2003; Ameriks et al, 2007). There is some empirical evidence
consistent with the idea that self-control problems explain consumer behaviour in consumer
credit markets. Studies show that consumers overborrow on consumer credit products which
incorporate ‘teaser rates’, a pattern in behaviour consistent with time inconsistent choices
(Shui and Ausubel, 2004; Ausubel, 1999). Also, a recent paper by Meier and Sprenger (2009)
finds a positive relationship between lack of self-control and credit card use. However, to the
author’s knowledge, the current paper is the first study to examine the relationship between
self-control and delinquency and credit arrears in household balance sheets.
More recently, a new literature on financial literacy has demonstrated that a non-
negligible proportion of consumers lack even basic financial literacy (Lusardi, 2008; Lusardi
4
and Mitchell, 2008; Jappelli, 2010). Survey data suggest that levels of understanding of core
financial concepts such as compound interest, real versus nominal returns, portfolio
diversification and APRs are low. This is true for samples of consumers responsible for a
large part of their own retirement saving planning and also true for those with consumer
credit (Lusardi and Mitchell, 2007; Disney and Gathergood, 2011; Van Rooji et al, 2011).
Most empirical studies on financial literacy have to date examined the relationship between
financial literacy and retirement saving or portfolio diversification (Bernheim 1995, 1998;
Banks et al, 2010; Clark et al., 2011; Guiso and Jappelli, 2009; Hastings et al., 2011; Yoong,
2011). Lusardi and Tufano (2009) show that financially illiterate consumers are more likely
to report difficulty repaying their debts. To the author’s knowledge, however, this is the first
paper to examine the relationship between financial literacy and delinquency and credit
arrears in household balance sheets.
This study is based on analysis of individuals’ assessments of their own self-control in
spending decisions, financial organisation and time preference, together with standard
financial literacy measures using a household survey. The rationale for the former is that the
concept of self-control, integrated into by various means into the theoretical self-control
frameworks in the literature
2
, readily translates into a core concept – impulsiveness - which
can be elucidated in what has been described as ‘natural language’ (Ameriks et al, 2007) and
so is appropriate for elicitation via direct survey questions. On the assumption that
individuals have some self-awareness of their own control problems, it is informative to ask
individuals directly about their impulsive behaviour and use individual responses to draw
insights into the causes of over-indebtedness
3
. This is implemented in a survey of a
2
In particular, the models of Strotz (1956). Thaler and Shefrin (1981), Laibson (1997), Gul and Pesendorfer
(2001), Bernheim and Rangel (2004), Fudenberg and Levine (2004) and Benhabib and Bisen (2005).
3
The validity of this approach is, of course, central to our analysis. We provide evidence from prior studies that
this approach produces meaningful data on actual behaviours. We choose this approach over asking participants
in our survey to undertake a choice task due to the inherent difficulties in eliciting reliable measures of time
5
representative sample of the UK population. Our analysis focuses on households with
consumer credit debts. Apart from self-control, we also include measures of discounting,
confusion over financial products and financial literacy into our empirical model to shed light
on the role of these other behavioural characteristics. We also control for a broad variety of
financial shocks recently experienced by the household.
We use self-reported data on delinquency and non-payment on consumer credit
products. Studies based on household data using North American samples (the SCF, in
Zinman, 2009) and South African samples (Karlan and Zinman, 2008) suggest consumers
typically under-report their level of debt. However, we find in our sample that there does not
appear to be an under-reporting problem with consumer credit delinquency. The 3-month
delinquency rate on a broad range of consumer credit products self-reported by individuals in
our sample corresponds closely with industry estimates. Plus there are many advantages from
using household survey data in a study of this type. We are able to ask questions relating to a
broad range of characteristics, behavioural traits plus financial shocks – these data would not
be available were we to use lender-based data.
The context for this study is the United Kingdom consumer credit market. The UK
has the second largest level of non-mortgage household debt (hereafter labelled ‘consumer
debt’) in the world, after the United States, valued at the end of 2010 at close to £60bn.
Moreover, outstanding consumer debt as a proportion of household income has increased
consistently since the mid-1990s. Outstanding consumer debt as a proportion of household
income has risen from 5.3% in 1995 to 19.6% by the first quarter of 2011, illustrated in
Figure 1. Over the same period, the value of consumer debt written-off each year by lenders
as a proportion of outstanding consumer debt has increased five-fold from less than 0.5% in
preference in experimental choice settings arising from the impact of extra-experimental borrowing and lending
opportunities, on which see Coller and Williams (1999), Harrison et al. (2002), (2005),Cubitt and Read (2007).
6
1995 to over 2.5% in 2010: non-repayment of consumer debt is a growing problem in the
U.K. Over this period the U.K. consumer credit market has also seen the advent of new forms
of sub-prime credit, such as store card credit, payday lending, home credit and ‘instant access
instalment credit
4
’.
Among our sample we find that over-indebtedness, measured both as delinquency on
repayments and self-reported financial distress, occur disproportionately among individuals
who report a self-control problem, approximately 10% of our sample. We also find a positive
relationship between financial illiteracy and over-indebtedness. In contrast, heavily
discounting the future or being confused about financial products are both statistically
insignificant in our estimates. We then examine the relationship between self-control and
credit product use and show that individuals with self-control problems make
disproportionate use of quick-access credit products which facilitate impulse-driven
purchases: such individuals would benefit from restricted access to such products. However,
when we condition our models on recent financial shocks experienced by the household, we
find that the coefficients on both our self-control and financial literacy variables diminish in
their magnitude and statistical significance. This implies that individuals with self-control
problems and poor financial literacy are also more likely to suffer adverse financial shocks
and suggests that self-control problems might permeate other dimensions of economic choice
which pertain to over-indebtedness, apart from consumption / saving / borrowing decisions.
Our results contribute to the empirical literature on consumer finance by
demonstrating that behavioural characteristics of consumers have non-negligible impacts on
use and mis-use of consumer credit, choice of credit products, but also correlate with income
/ expenditure shocks. These results contribute to the understanding of consumer behaviour in
4
As an example of such, the website www.wonga.com, a ‘1% per day lender’ offers consumer short-term credit
of up to £400 repayable up to a period of 30 days at a typical APR of 4214%. Once a loan application is
approved, the funds are transferred to the applicant’s bank account within 15 minutes.
7
consumer credit markets (Agarwal et al, 2006; Campbell, 2006; Agarwal et al, 2009; Gabaix
and Laibson, 2006; Jappelli and Padula, 2011; Lusardi and Tufano, 2009; Tufano, 2009;
Stango and Zinman, 2009; Stango and Zinman, 2011; Vissing-Jorgensen, 2011) as well as
providing further insight into the drivers of over-indebtedness in the U.K. context (Bridges
and Disney, 2004; Bridges et al. 2008.)
Survey Design and Data
To implement our approach to measuring self-control, financial organisation,
discounting and financial literacy in a household survey in relation to consumer credit use
and delinquency, we partnered with the market research company YouGov, integrating our
survey questions into their consumer-credit focused DebtTrack survey. The DebtTrack
survey is a quarterly repeated cross-section survey of a representative sample of U.K.
households covering approximately 3,000 households which is conducted via the internet
5
.
For a fee, researchers can add questions to the core survey question modules, and we exploit
this provision for our research design. In this section we first describe the survey and provide
summary statistics, then introduce our survey questions on self-control and other behavioural
traits, and then describe our financial literacy questions.
Survey and sample characteristics
The core survey is comprised of approximately 85 questions covering household
demographics, labour market information, income and balance sheet details including secured
and unsecured debts and financial assets, plus information on consumer credit product
holdings, balances, monthly payments and delinquency. Respondents are asked to provide
5
We incorporated our questions into the September 2010 wave of the internet survey. There is evidence to
suggest that internet-based surveys generate less bias in responses compared with using telephone surveys
(Chang and Krosnick, 2008)
8
details at the household level, where the household unit is defined as the respondent plus
his/her partner or spouse.
The consumer credit data is particularly detailed: respondents are asked to provide
details about the number and type of consumer credit products they hold (selecting product
types from an exhaustive drop-down menu of types), outstanding balances for each item,
monthly payments, whether they are 1 month in arrears on the product, whether they are 3
months in arrears on the product, and the value of arrears. The monthly payment question
refers to the regular monthly payment or, in the case of credit products without a regular
monthly payment (such as credit cards), the payment made in the last month. Also, for credit
cards respondents are asked to give values only for ‘balances not repaid in full each month’,
so the information provided on credit card similar forms of credit reflects only outstanding
debt and not balances incurred purely for transactions purposes which are paid off within the
interest-free period without incurring any charges (typically 56 days).
Summary statistics for the survey sample are provided in Table 1. The whole sample
is comprised of 3,041 households. For our analysis we use only households with a positive
balance on at least one consumer credit item and this provides a sample size of 1,234
households. Household characteristics in the whole sample match closely those in other
household surveys which contain information on household credit and debt, including
financial characteristics. Further details on data quality, which we judge to be high, are
available in an earlier paper (Disney and Gathergood, 2011). Comparing the analysis sample
with the whole sample, households in the analysis sample typically: have a younger
respondents (67% of the analysis sample are aged under 46 compared with 47% of the whole
sample), have higher rates of employment (66% in the analysis sample compared with 57%
in the whole sample), are more than three times as likely to have dependent children (74% in
the analysis sample compared with 19% in the whole sample) and are less likely to be a
9
homeowner (59% in the analysis sample compared with 64% in the whole sample). Hence
consumer credit users are typically younger, more likely to be employed and are more likely
to have families.
Financial characteristics of the analysis sample are shown in Table 2. Mean income
among the analysis sample is £38,000. Half of households hold liquid savings, with a mean
balance of £9,500. Half are homeowners, with mean house value of a little over £200,000 and
of these approximately two-thirds have outstanding mortgage debt, with mean mortgage debt
among this group of £76,000. Mean total unsecured debt is £7,400. Data on consumer credit
holdings show that the majority of households hold at least one credit card and have access to
at least one bank overdraft. Approximately one quarter of the analysis sample have at least
one personal loan. Around one-fifth hold a store card, with slightly lower proportions for car
loans and mail order catalogues. Hire purchase, home credit, pay day loans and credit union
credit account for only very small proportions of total credit product use. Mean balances
among those holding products vary by product type. Although personal loans are held by
only one-quarter of the sample, mean loan value is relatively high at £6,700, compared with
overdraft debt held by over half of the sample buy at a mean value of only £1,200. Credit
union credit is held by only 1.5% of the sample, but the mean balance is close to £3,000.
Measures of Over-Indebtedness
We next turn to our measures of over-indebtedness. We choose to focus on indicators
of over-indebtedness which measure delinquency on debt. While over-indebtedness can
undoubtedly occur without delinquency – individuals might have too much debt relative to
their optimal level of borrowing but nevertheless find themselves able and willing to service
the cost of their debt and maintain their contractual payments – forming measures of over-
indebtedness based on debt burdens alone is problematic. For example, high debt-to-income
10
ratios might be taken as indicative of over-indebtedness. However, households expecting
high future income growth might optimally hold high levels of debt relative to their income.
Indeed, ‘official’ measures of over-indebtedness based on debt multiples, number of credit
items held or income gearing can be potentially misleading by overstating levels of over-
indebtedness.
6
Indicators of delinquency-based measured of over-indebtedness among respondents
are provided in Table 3. Three measures of over-indebtedness are presented: one month
delinquency on at least one credit item, three month delinquency on at least one credit item
and a measure of self-reported over—indebtedness based on delinquency coupled with self-
reports of ‘real financial problems’. Using this approach we are able to exploit both an
objective measure (delinquency) and a subjective measure (‘real financial problems’).
‘Delinquency’ in our data refers to a missed minimum payment on a credit/store card,
or a missed contractual payment on a repayment loan. So our delinquency measure does not
take into account any payment behaviour on bank overdrafts (unless the household has a
repayment schedule agreed with their bank to resolve the overdraft debt). In our sample, 17.5%
of households (216 observations) report being at least one-month delinquent on at least one
credit product, 10% of households (124 observations) report being at least three-months
delinquent on at least one credit product. The 10% figure for three-month delinquency closely
matches industry statistics on delinquency rates for consumer credit.
There are no official published statistics on consumer credit delinquency rates in the
U.K. The Bank of England publishes data on outstanding consumer credit and credit written-
off. The Finance and Leasing Association, the industry body for the consumer credit industry,
does not publish data on the loan books of its members. However, Moody’s rating agency
6
Elsewhere Bridges, Disney and Gathergood (2008) show that by official U.K. measures of ‘over-indebtedness’
based on such criteria, over 30% of U.K. mortgage holders and 50% of U.K. unsecured credit holders would be
considered to be over-indebted.
11
does provide data on 3-month consumer credit delinquency rates among U.K. lenders as part
of its ‘Consumer Credit Index’. In September 2009 (the month of our survey) Moody’s
reported an average 3-month consumer credit delinquency rate for the U.K. of 9.7%
7
.
The self-reported measure of over-indebtedness is constructed from the following
question, asked of all respondents in our analysis sample
8
:
A. ‘Which of the following statements best describes how well you [and your partner]
are keeping up with your credit commitments at the moment?’
1. I am/we are keeping up with all bills and commitments without any difficulties
2. I am/we are keeping up with all bills and commitments, but it is a struggle from
time to time
3. I am/we are keeping all bills and commitments, but it is a constant struggle
4. I am/we are falling behind with some bills or credit commitments
5. I am/we are having real financial problems and have fallen behind with many bills
or credit commitments
6. I/we don’t have any bills or credit commitments
7. Don’t know
From the responses to this question we identify self-reported over-indebted
households as those for which the respondent choose Statement 5. Hence our self-reported
measure of over-indebtedness incorporates both delinquencies on credit commitments
together with ‘real financial problems’ on the part of the household, in contrast to Statement 4
which involves delinquency but no financial problems.
In our analysis sample 8.5% of households (102 observations) chose statement 5.
Taking these measures together, fewer households report they are facing ‘real financial
8
This question, together with the questions on behavioural traits were asked early-on in the survey module
following the introductory section on demographics/characteristics and prior to the section on home ownership
status and mortgage / rent details. This question was asked after the questions on behavioural traits.
12
problems’ as well as delinquency (8.3% of the sample) compared with the number of
households reporting one-month or three-month delinquency (17.5% and 10% respectively).
Overall, 19% (234 households) of households in the analysis sample can be classified as
over-indebted by at least one of the over-indebtedness measures we use
9
.
Measures of self-control, heavy discounting and confusion about financial services
To measure the proportion of households with self-control problems, households who
heavily discount future consumption and are financial disorganised in the analysis sample we
employ a survey instrument whereby households are asked to identify the extent to which
their behaviour corresponds that described in a short statement. The statements used were as
follows:
i) ‘I am impulsive and tend to buy things even when I can’t really afford them’
ii) ‘I am prepared to spend now and let the future take care of itself’
iii) ‘Financial services are complicated and confusing to me’
together with the following options, from which respondents could choose one:
a) Agree strongly
b) Tend to agree
c) Neither agree nor disagree
d) Tend to disagree
e) Disagree strongly
9
This implies there are a small group of households (1.5% of the analysis sample) who chose Statement 5 from
the indebtedness question but did not identify any credit commitment on which they were at least on month
delinquent in the module on their credit commitments, which reflects a small degree of inconsistency in
respondent reports of credit delinquency within the survey.
13
f) Don’t know.
We label these the ‘impulsiveness’ statement, the ‘heavy discounter’ statement and
the ‘confused about finance’ statement respectively. There are three foundational premises
behind using this approach in our analysis: firstly, respondents are willing to self-identify
whether these statements accurately describe their behaviour; second, they are sufficiently
self-aware to be able to identify whether their behaviour is accurately described; and, third;
the types of behaviour described in the statements are analogous to the behavioural types
described in the models of self-control described in the introduction.
We argue there is sufficient evidence and precedent in previous studies for each of
these. For the first and second, two existing studies provide evidence that individuals are
willing and able to self-identify their sub-optimal behavioural traits and provide meaningful
responses which explain economic outcomes. Ameriks et al. (2003) use a series of statements
relating to financial planning activity, which also include examples where individuals are
asked to associate themselves with stated behaviours which might be perceived as sub-
optimal, such as failure to produce a plan. Ameriks et al. (2007) ask individuals in their
sample to participate in a thought-experiment whereby respondents provided their optimal
allocation of vouchers for meals at an expensive restaurant over a two-year period and then
state whether they would be tempted to deviate from that allocation and the extent of the
deviation. The authors show their measured ‘expected-ideal gap’ based on responses explains
variation in net worth across individuals in their sample.
For the third, we are confident that the behaviours described in the statements are
accurate translations of the behaviours encapsulated in models of self-control and in the
concepts of time discounting and financial sophistication. The statement on impulsive
behaviour refers specifically to purchases which the individual has some sense is
14
unaffordable to them but are motivated by impulsiveness. The statement is neither too general
(for example, referring to impulsive behaviour across an unspecified domain) not too
particular (for example, specifying a particular type or context for spending). Similarly, the
‘heavy discounter’ statement captures the concept of a strong present time preference for
consumption. It specifically refers to expenditure and refers to ideal time patterns of
expenditure which the individual would actually want to implement ‘am prepared’. The third
statement, the most straightforward of the three, captures general confusion on the part of the
respondent with regard to financial services.
Furthermore, examining the responses to our self-control statement, the proportion of
households who positively identify themselves as being ‘impulsive’ by this measure in our
analysis sample conforms to the proportion of individuals who are identified as having self-
control problems by other elicitation methods in other studies. In the analysis sample 9.2% of
respondents agree strongly or tend to agree with the impulsiveness statement. In Ameriks et
al (2007) 11.2% of their sample report a present bias in their expected compared with ideal
time allocation of restaurant vouchers, though their sample is comprised of high-wealth
individuals. Meier and Sprenger (2011) use an experiment-based measure of present bias and
find that 17% of their sample of low-income respondents exhibit present bias. Therefore, our
9.2% value of the sample of consumer credit users, who are typically mid-income, is not
inconsistent with evidence from these US studies.
Measure of financial literacy
Our measure of financial literacy is comprised of three survey questions derived from
the financial literacy literature. In this literature financial literacy is typically measured by
presenting survey respondents with hypothetical scenario-based multiple-choice questions
relating to financial concepts in which they have to, for example, correctly identify the
15
accumulated value of a debt to which compound interest has been applied or the accumulated
real value of an asset which is subject to a both interest and inflation.
Some of the concepts on which individuals are surveyed in the financial literacy
literature are more relevant to consumer debt than others. In Lusardi (2008), ‘core’ financial
literacy is comprised of the three concepts of interest compounding, real vs nominal returns
and portfolio diversification. However, in the context of overindebtedness the latter two are
not relevant, so instead we choose to introduce financial literacy questions which are
pertinent to individuals in debt. The questions and responses among our sample are provided
in Table 5. Each of the questions is framed in the context of consumer credit and involves
evaluating the cost of borrowing. The first question is the simplest and requires respondents
to correctly calculate 15% of £1000. The second and third questions are more complicated
(and are translated from Lusardi and Tufano, 2009). The second tests whether individuals can
correctly identify that the application of compound interest implies a loan principal of £1,000
at an annual rate of 20% would take less than five years to double. The third tests whether
individuals can calculate the money cost of an APR applied to a credit card and correctly
identify that the payments made to loan under considerations would be insufficient to pay
down the £3,000 debt.
A little fewer than 85% of respondents answered the first question on interest
compounding correctly, slightly fewer than 54% answered the interest compounding question
correctly and a little more than 43% answered the monthly payments question correctly. In an
earlier paper we compare respondent answers to these questions in our samples with those
from samples in the U.S. and the Netherlands (Disney and Gathergood, 2011). In general,
U.K. samples do better on average at these questions compared with U.S. samples, and worse
than Dutch samples, though differences in means of survey internet (especially internet and
telephone based surveys) might play an important part in these differences. What is clear
16
among respondents in our sample is that a significant proportion of individuals with
outstanding consumer credit debts do not answer these questions about the cost of consumer
credit correctly. Only a little more than 31% of respondents answered all three questions
correctly, with a little over 40% answering only one or fewer of the questions correctly.
Characteristics of Over-Indebted and Non-Over Indebted Households
We now compare summary statistics for households in our sample by whether they
are over-indebted or not. These are provided in Table 6. Using our measure of over-
indebtedness from Table 3, 19% of our sample is over-indebted. Based on observed
characteristics, over-indebtedness is more common among households with respondents who
are younger, unmarried with children, with less education, lower rates of employment and
higher rates of unemployment, lower rates of outright homeownership and higher rates of
private and social renting (especially social renting). Over-indebted households typically
have annual incomes of £10,000 less than non over-indebted households and unsecured debts
equivalent to one third of their annual income (compared with one-seventh for non over-
indebted households).
In terms of the behavioural characteristics of households in our sample, we compare
these by creating a series of 1/0 indicator dummy variables for whether the household is
financial literate, confused by finance, a heavy discounter or an impulsive spender. These are
constructed as follows: the financially literate dummy takes a value of 1 is the respondent
answered at least two of the financial literacy questions correctly and a value of 0 otherwise;
the other three dummies take a value of 1 is the respondent answered ‘agree strongly’ or ‘tend
to agree’ and a value of 0 otherwise. By these measures, over-indebted households in our
sample are one third less likely to be financially literate, one quarter more likely to be
confused by financial, half more likely to be a heavy discounter and more than twice as likely
17
to be impulsive spenders compared with non-over indebted households. These summary
statistics demonstrate that over-indebted households contrast with non over-indebted
households by a range of demographic, financial and behavioural characteristics.
Econometric Model and Estimation
Modelling over-indebtedness
Next we seek to model the relationship between these demographic, financial and
behavioural characteristics and over-indebtedness. The econometric model to be estimated is:
(1)
Where od is a 1/0 dummy indicator of over-indebtedness; fl, cf , hd and is are the 1/0
dummy indicator variables for financially literate, confused by finance, heavy discounter and
impulsive spender respectively, z is a vector of controls including demographic, financial and
economic variables and ε is an error term. We estimate Equation 1 using a probit model.
Estimates from a preliminary baseline specification which omits the four dummy
variables which capture the behavioural traits are shown in Table 7. In the first column we
use one month delinquency as the indicator variable for over-indebtedness, in the second we
use three month delinquency and in the third we use the self-reported measure of
overindebtedness. The baseline specification includes in the vector z a series of variables
which capture life-cycle characteristics relevant to indebtedness: age (dummies for 18-25, 26-
35, 46-55, over 55 (36-45 is the omitted group); a series of 1/0 dummy for being unemployed,
having a spouse in employment, having dependent children, being male, being married;
education leaving age in years; 1/0 dummies for being an outright homeowner, a mortgage
homeowner, a private renter (social renter is omitted group); the value of household income
in pounds sterling; the value of household liquid assets in pounds sterling.
18
Results from this preliminary specification show that: households with younger
respondents are less likely to report over-indebtedness by any of the three measures,
households with an unemployed respondent are more likely to be one-month delinquent (but
no more likely to be three months delinquent or self-report over-indebtedness), households
with an employed spouse are less likely to report one or three-month delinquency and
households with dependent children are more likely to report one or three-month delinquency.
Other variables are omitted from the table because they return statistically insignificant
coefficients. The baseline model returns values for the pseudo-R
2
of between 0.11 and 0.16,
depending on specification.
Next, we introduce the series of dummy variables which capture behavioural traits.
Table 8 presents results with one month delinquency as the indicator variable for over-
indebtedness. In Columns 1 -4 the dummy variables capturing behavioural traits each enter
alone, in Column 5 the four dummy variables enter as in the specification shown in Equation
1 above. In Column 1 the indicator variable for financially literate has a negative sign with a
value of -0.25 which is statistically significant at the 1% level. The baseline predicted
probability for the dependent variable is 0.15. The marginal effect of -0.06 implies that a
financially literate consumer is approximately 40% less likely to be one month delinquent. In
Columns 2 and 3 the coefficients on the confused by finance and heavy discounter dummies
are both positive, but not statistically significant. In Column 5 the indicator variable for being
an impulsive spender have a positive sign with a value of 0.37 which is statistically
significant at the 1% level. The baseline predicted probability for the dependent variable is
0.14. The marginal effect of 0.10 implies that an impulsive spender is approximately 70%
more likely to be one month delinquent. In Column 5 the pattern in the direction, magnitude
and statistical significance is broadly the same as in the preceding columns: being financially
19
literate is associated with a 40% lower likelihood of being one month delinquent and being an
impulsive spender is associated with a 70% higher likelihood of being one month delinquent.
In Tables 9 and 10 we replicate this analysis but change the dependent variable to an
indicator variable for being 3 months in arrears (Table 9) and for self-reporting over-
indebtedness (Table 10) respectively. The pattern in the magnitude and signs of the
coefficients on the four dummy variables is as in Table 8: there is a negative coefficient on
the financial literacy variable and positive coefficients on the other dummies, the coefficients
on the confused by finance and heavy discounter dummies are not statistically significant
from zero, the coefficients on the financial literacy variable are weakly significant in Table 9
and insignificant in Table 10, the coefficient on the impulsive spender variable is statistically
significant at the 1% level in both tables. In these specifications the marginal effects on the
financially literate dummy implies a financially literature individual is approximately 40-50%
less likely to exhibit over-indebtedness, depending on specification. The marginal effect on
the impulsive spender variable implies an impulsive spender is approximately twice as likely
to be over-indebted (in the specification show in Table 9) or three to four times as likely to be
over-indebted (in the specification shown in Table 10).
Therefore, in these probit models there is evidence of a relationship between two of
the individual traits – financial literacy and impulsiveness – and over-indebtedness, with a
non-negligible magnitude of effect in each case. However, the financially literate variable is
only statistically significant at the 5% level or lower in the specification in which the
dependent variable is one month delinquency and not in the other specifications, suggesting
that financial illiteracy is not associated with more severe levels of debt. By way of contrast,
the impulsive spender variable is statistically significant in all specifications and suggests and
sizeable effect of this individual trait on over-indebtedness
20
Modelling credit use
Why do we find this relationship between literacy, impulsiveness and over-
indebtedness? In particular, why do we find the strong relationship between impulsiveness
and problem debt? The suggestion of the literature on financial literacy and self-control is
that individuals who are financially illiterate or impulsive will make sub-optimal choices in
their borrowing decisions, tending to under-estimate the cost of credit (due to their illiteracy)
or by making excessive use of credit for impulsive purchases. In this sub-section we
investigate whether we can find evidence on this in the relationship between these individual
traits and consumer credit portfolios which are held by households in our sample. We
proceed by estimating the impact of these traits on the types of credit product held by
households. This is possible in our data because we have a record of each type of consumer
credit product held by the household. As we suggested in the introduction, different forms of
consumer credit present greater or lesser opportunities to facilitate impulse-driven purchases.
The equation to be estimated is now
(2)
Where p is a 1/0 dummy indicator value for whether the individual holds a positive balance
on at least one consumer credit product of a particular type. In our data the product types
which enter as ‘p’ in our estimates are: credit card, overdraft, personal loan, store card, car
loan, mail order catalogue, hire purchase, home credit, pay day loan, credit union loan.
Equation (2) is estimated in each case using a probit model. Results are presented in Table 11.
Results show the impulsive spender dummy is statistically significant with a positive
coefficient in models for those types of credit products which most embody the
characteristics of facilitating rash spending: store cards, mail order catalogues, home credit
21
and pay day loans. These product types have in common the features of being readily
available at the point of purchase of a good which is advertised in conjunction with the
availability of the credit facility, so allowing consumers drawn to impulsive spending to
access near-instant credit to facilitate that spending
10
. They are also higher-cost products. The
marginal effects of the coefficients in each case imply that individuals who are impulsive
spenders are, in all cases, at least twice as likely to use such products. Results also suggest
that more literate individuals are more likely to use students loans and less likely to use mail
order catalogues or credit union loans.
These results suggest the relationship between impulse spending and over-
indebtedness is at least in part mediated through the types of consumer credit used by
impulsive spenders and the contexts for their credit use which particular product types allow.
Of course, not all consumers who use these particular product types exhibit over-
indebtedness, but our results show that impulsive behaviour which is associated with over-
indebtedness is also associated with greater use of these forms of credit.
Incorporating financial shocks
A second possible explanation for the relationship between impulsive spending
behaviour and over-indebtedness is that households who are impulsive in their spending
might also be impulsive in other dimensions of their behaviour (such as in the labour market
or goods market) such that they are more exposed to income shocks or unforeseen
expenditures. Della Vigna and Paserman (2005) show that individuals who are more
impatient engage in lower quality job search in the labour market compared with more patient
10
To be specific: store cards facilitate impulse spending by being advertised and available at store checkouts,
with applications approved while the customer queues for purchase and credit available within a few minutes;
mail order catalogues are designed for consumers to order purchases from the catalogue on finance; home credit
(or doorstep credit) providers offer cash transfers to individuals on their doorstep and make loan decisions in a
short space of time at the doorstep; pay day lenders (high-street lenders) clear cash transfers in minutes and
make funds available to the shopper on the high street. Maybe it is therefore unsurprising that impulsive
spenders are shown to be more likely to use such forms of credit.
22
individuals, suggesting individuals with impulsive tendencies might engage in suboptimal
behaviour in a wider range of domains than just consumption choice (Della Vigna, 2009;
Della Vigna and Malmenider, 2004).
To incorporate financial shocks into our model of over-indebtedness we introduce
measures of four categories of the most relevant forms of financial shocks: job loss, income
fall, credit withdrawal and a major expense. These measures are derived from a series of
questions included in the survey on the recent experience of respondents in these areas. In the
case of job loss, respondents are asked whether they have recently experienced redundancy
(with or without a severance payment), their partner recently experienced redundancy (with
or without a severance payment), or ended work due to illness. In the case of income fall
respondents are asked whether they have recent experienced a ‘significant fall’ in their
income, or their partner’s income. For credit withdrawal respondents are asked whether they
have recently had their credit card withdrawn, credit limit reduced on their credit card or an
overdraft facility withdrawn. For major expenses, respondents are asked whether they have
recently incurred house repairs, replacement of a major household item due to failure or car
repairs. For each case the ‘recent’ period under consideration is set at the previous 6 months.
Respondents are asked to provide a yes/no response to each question.
Summary statistics for respondents’ answers are provided in Table 12. The most
common financial shock among the whole sample was car repairs, followed by the failure of
a major household item and a significant fall in income. For the purposes of our probit
analysis we combine responses in each category and create a 1/0 dummy variable for each
category based on whether the respondent answered yes to at least one of the questions under
that category. Table 13 compares the prevalence of financial shocks among over-indebted
and non over-indebted households. The data show over-indebted households were more
likely to have experienced each type of shock, being (approximately) four times as likely to
23
have experienced job loss, twice as likely to have experienced a fall in income, four times as
likely to have experienced credit withdrawal and one quarter more likely to have experienced
a major expense.
The revised version of the empirical model to be estimated is therefore:
(3)
Where the variables jl, if, cw and me are a series of 1/0 dummy variables which take
the value of 1 is the household reports experiencing that financial shock in the previous six
months and a value of 0 otherwise. Results are presented in Table 14. The inclusion of these
financial shock variables increases the explanatory power of the regression as measured by
the R2 value by approximately 0.05 to 0.07 percentage points depending on specification.
In all of the specifications the income fall and credit withdrawn variables are positive
and significant at the 5% level of lower. The marginal effects on these coefficients imply
large effects of financial shocks on the likelihood of over-indebtedness. The coefficient on
the impulsive spender dummy becomes statistically insignificant in Columns 1 and 2, though
remains statistically significant at the 1% level in Column 3. Hence the relationship between
impulsive spending behaviour and over-indebtedness appears in part explained by the
tendency for individuals who identify themselves as impulsive spenders to also more
commonly report experiencing a financial shock compared with individuals who do not
report they are impulsive spenders.
These results suggest the relationship between self-control and over-indebtedness
might also arise due to individuals’ self-control problems leading to patterns of behaviour
which increase their exposure to financial shocks, as well as increasing their use of expensive
credit. Such outcomes might arise due to, for example, lower quality job matches resulting in
24
greater likelihood of redundancy or income falls. Similarly, impulsiveness might lead to
lower quality product search in goods markets and lead to agents being more exposed to
expenditure shocks arising from good failing or requiring replacement. This might be
particularly relevant for durable goods, which requires patience as the utility flow is realised
over a period of time. This is how we understand these results on the relationship between
over-indebtedness, financial shocks and self-control
Conclusion
This study has examined the relationship between self-control, financial literacy and
over-indebtedness using survey data from a representative sample of U.K. households with
consumer credit debts. Measures of individual time preference, impulsiveness, and
understanding finance plus results from financial literacy survey questions were examined in
relation to delinquency on consumer credit payments and self-reported consumer credit
repayment problems. In our sample a subset of households exhibited a tendency towards
impulsive spending and heavily discounting future consumption. Levels of financial literacy
were found to be higher than those recorded in studies based on samples of consumer from
the U.S., but nevertheless low in absolute terms and two-fifths of our sample reported being
confused by finance.
We find that poor financial literacy and self-control problems are both positively
associated with over-indebtdness. There is stronger evidence for a role for self-control
problems, our measure of self-control is more significant in statistical terms and implies
stronger economic effects in all specifications. Our extensions also shed light on why
consumers with self-control problems are more likely to become over-indebted: such
consumers make more use of high-cost credit (in particular forms of high-cost credit
25
accessible at short notice and/or at the point of sale) and tend to also be more exposed to
financial shocks.
These results are important for three reasons. Firstly, they show that consumer
behavioural traits are important for explaining consumer over-indebtedness. The literatures of
financial literacy and self-control have sought to find examples of how these tenets of
consumer behaviour can be found to explain economic outcomes. We have shown that the
empirical relevance of this literature, which has focused on the accrual of wealth and
retirement saving, also extends to the issue of consumer over-indebtedness.
Secondly, our results for the relationship between self-control and over-indebtedness
suggest that consumers might benefit from less access to credit. One might argue that poor
financial literacy and poor self-control imply different remedies: whereas financial literacy
might be improved through financial education, individuals cannot be educated on self-
control. This raises the prospect that individual choices need to be restricted so as to prevent
individuals from engaging in sub-optimal behaviour. In the context of the consumer credit
market, there may be an argument for restricting credit available at the point-of-sale or
delaying access to funds so as to mitigate consumer self-control problems.
Finally, our results on the relationship between self-control and financial shocks
suggest that individuals with self-control problems have higher exposure to adverse events,
possibly due to their impulsive behaviour resulting to sub-optimal outcomes in other
dimensions of individual choice apart from intertemporal consumption/saving decisions.
Relatively little research exists of impulsive outside of the context of intertemporal
consumption choice. However, one might think that self-control problems are relevant in a
broad range of choice settings relating to consumption insurance, the composition of
consumption (purchase of durables and repairs), search in product markets, and activity in
26
labour markets. Our findings suggest the interplay between different dimensions of individual
self-control behaviour might be important for explaining economic outcomes.
27
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30
0
1
2
3
4
5
6
7
8
0
5
10
15
20
25
1995 2000 2005 2010
Outstanding Consumer Debt as % Gross Household Income (Left-Hand Scale)
Consumer Credit Write-Offs as % Loans Oustanding (Right-Hand Scale)
Figure 1: U.K. Consumer Debt and Write-Offs Since 1995
(Source: Bank of England)
31
TABLE 1
Demographic Characteristics of Survey Respondents
Whole Sample Analysis Sample
(n) (%) (n) (%)
Sample Size 3,041 100 1,234 100
Age
18 – 25 275 9.0 110 8.9
26 – 35 588 19.3 332 26.9
36 – 45 565 18.6 262 21.2
46 – 55 534 17.6 239 19.4
Over 55 1,079 35.5 291 23.6
Gender
Female 1,507 49.6 669 54.2
Male 1,534 50.4 565 45.8
Marital status
Married 1,980 65.1 385 68.8
Unmarried / divorced 1,061 34.9 849 31.2
Education leaving age
16 or under 988 32.5 383 31.0
17 – 19 770 25.3 312 25.3
over 20 1,283 42.2 539 43.7
Employment status
Employed or self-employed 1,729 56.7 814 66.0
Unemployed 132 4.3 53 4.3
Retired 602 19.8 124 10.0
Out of the labour force 578 19.0 243 19.7
Spouse employed 1,250 41.1 614 50.2
Spouse not employed 1,791 58.9 620 49.8
Dependent Children
Has dependent children 578 19.0 915 74.2
No dependent children 2,463 81.0 319 25.8
Homeownership Status
Homeowner without mortgage 862 28.4 185 14.9
Homeowner with mortgage 1,093 35.9 548 44.4
Private renter 507 16.7 265 21.5
Social renter 270 8.9 136 11.0
32
TABLE 2
Financial Characteristics of Survey Respondents
n
with positive value
%
with positive value
£ average
among those with
positive value
Household finances
Income 1,234 100 £38,000
Liquid savings 618 50.0 £9,500
Unsecured debt 1,234 100 £7,400
House value 695 46.3 £202,000
Mortgage debt 391 31.7 £76,000
Consumer credit holdings
Credit card 912 73.9 £4,400
Overdraft 695 56.3 £1,200
Personal loan 328 26.6 £6,700
Store card 224 18.1 £900
Car loan 214 17.3 £5,200
Mail order catalogue 221 17.9 £500
Hire purchase 90 7.3 £3,500
Home credit 20 1.6 £900
Pay day loan 19 1.5 £500
Credit union 18 1.5 £2,900
33
TABLE 3
Over-Indebtedness Among Survey Respondents
n
%
One-month delinquency 216 17.5
Three-month delinquency 124 10.0
Self-reported over-indebted 102 8.3
Over-indebted 234 19.0
34
TABLE 4
Responses to Behavioural Characteristics Statements
agree
strongly
tend to
agree
neither
agree
not
disagree
tend to
disagree
disagree
strongly
don’t
know
Impulsive spender
‘I am impulsive and tend to buy
things even when I can’t really
afford them’
14
(1.1)
100
(8.1)
161
(13.1)
340
(27.6)
596
(48.3)
23
(1.9)
Heavy discounter
‘I am prepared to spend now and
let the future take care of itself’
19
(1.5)
147
(11.9)
206
(16.7)
382
(31.0)
460
(37.3)
20
(1.6)
Confused by finance
‘Financial services are
complicated and confusing to me’
111
(9.0)
383
(31.0)
335
(27.2)
274
(22.2)
109
(8.8)
22
(1.8)
35
TABLE 5
Financial Literacy Question Responses
Simple Interest Question
“Cheryl owes £1,000 on her bank overdraft and the interest rate she is charged is 15% per
year. If she didn’t pay anything off, at this interest rate, how much money would she owe on
her overdraft after one year?”
(n) (%)
£850 15 1.2
£1,000 3 0.2
£1,150 1,046 84.7
£1,500 98 7.9
Do not know 72 5.8
Interest Compounding Question
“Sarah owes £1,000 on her credit card and the interest rate she is charged is 20% per year
compounded annually. If she didn’t pay anything off, at this interest rate, how many years
would it take for the amount she owes to double?”
(n) (%)
Less than 5 years 663 53.7
Between 5 and 10 years 359 29.1
More than 10 years 69 5.6
Do not know 143 11.6
Monthly Payments Question
“David has a credit card debt of £3,000 at an Annual Percentage Rate of 12% (or 1% per
month). He makes payments of £30 per month and does not gain any charges or additional
spending on the card. How long will it take him to pay off this debt?”
(n) (%)
Less than 5 years 47 3.8
Between 5 and 10 years 196 15.9
More than 10 years 232 18.8
None of the above, he will continue to be in debt 534 43.3
Do not know 225 18.2
Total Number of Questions Answered Correctly (n) (%)
0 128 10.4
1 357 28.9
2 361 29.3
3 388 31.4
36
TABLE 6
Demographic, Financial, Literacy and Behavioural Characteristics of
Over-Indebted vs non Over-Indebted
Unit Over-Indebted Non Over-Indebted
Age
18 – 25 % 6.8 9.4
26 – 35 % 23.9 27.6
36 – 45 % 26.4 20.0
46 – 55 % 21.4 18.9
Over 55 % 21.4 24.1
Male % 41.9 46.7
Married % 56.8 71.6
Education leaving age years 18.1 18.8
Employment status
Employed or self-employed % 59.4 67.5
Unemployed % 8.5 3.3
Retired % 5.6 11.1
Spouse employed % 34.6 53.3
Has dependent children % 33.7 24.0
Homeownership status
Homeowner without mortgage % 8.1 15.3
Homeowner with mortgage % 34.2 46.8
Private renter % 25.6 20.5
Social renter % 20.9 8.7
Household finances
Income £ 29,700 40,000
Unsecured debt £ 10,500 6,600
Behavioural characteristics
Financially literate % 48.2 63.6
Confused by finance % 48.7 38.0
Heavy discounter % 17.5 12.5
Impulsive spender % 17.5 7.3
37
TABLE 7
Baseline Models for Over-Indebtedness
(1)
One month
delinquency
(2)
Three month
delinquency
(3)
Self-reported over-
indebtedness
Age 18 – 25 -0.59**
(0.19)
[-0.10]
-0.70**
(0.24)
[-0.06]
-0.54*
(0.26)
[-0.01]
Age 26 – 35 -0.18
(0.13)
[-0.04]
-0.26
(0.15)
[-0.03]
-0.42*
(0.18)
[-0.01]
Age 46 – 55 -0.02
(0.14)
[-0.01]
0.04
(0.16)
[0.01]
0.25
(0.17)
[0.01]
Age over 55 0.01
(0.16)
[0.01]
0.02
(0.19)
[0.01]
0.42*
(0.19)
[0.02]
Unemployed 0.57**
(0.19)
[0.17]
0.41
(0.22)
[0.07]
0.44
(0.23)
[0.03]
Spouse employed -0.41**
(0.13)
[-0.09]
-0.52**
(0.15)
[-0.07]
-0.15
(0.18)
[-0.01]
Has dependent children 0.48**
(0.11)
[0.12]
0.45**
(0.13)
[0.07]
0.25
(0.15)
[0.01]
N 1,234 1,234 1,234
R2 0.11 0.13 0.16
LR 121.22 107.95 109.84
Prob>chi2 0.0000 0.0000 0.0000
Baseline predicted
probability
0.15 0.07 0.01
Notes: *significant at 5% level, **significant at 1% level. Variables also included in models:
gender, marital status, education leaving age, homeownership status, value of household
income, value of household liquid assets,
38
TABLE 8
Behavioural Characteristics and Over-Indebtedness: 1 Month Delinquency
(1) (2) (3) (4) (5)
Financially literate -0.25**
(0.09)
[-0.06]
- - - -0.21*
(0.09)
[-0.05]
Confused by finance - 0.16
(0.09)
[0.04]
- - 0.13
(0.09)
[0.03]
Heavy discounter - - 0.16
(0.12)
[0.04]
- 0.04
(0.13)
[0.01]
Impulsive spender - - - 0.41**
(0.14)
[0.11]
0.37**
(0.14)
[0.10]
Demographic controls Yes Yes Yes Yes Yes
Financial controls Yes Yes Yes Yes Yes
N 1,234 1,234 1,234 1,234 1,234
R2 0.11 0.11 0.11 0.11 0.12
LR 128.98 125.22 123.97 131.02 139.06
Prob>chi2 0.0000 0.0000 0.0000 0.0000 0.0000
Baseline predicted
probability
0.15 0.15 0.15 0.15 0.14
Notes: *significant at 5% level, **significant at 1% level. Variables also included in models:
gender, marital status, education leaving age, homeownership status, value of household
income, value of household liquid assets,
39
TABLE 9
Behavioural Characteristics and Over-Indebtedness: 3 Month Delinquency
(1) (2) (3) (4) (5)
Financially literate -0.19
(0.11)
[-0.03]
- - - -0.18
(0.11)
-0.02
Confused by finance - 0.02
(0.11)
[0.01]
- - 0.01
(0.11)
[0.01]
Heavy discounter - - 0.09
(0.14)
[0.01]
- -0.05
(0.16)
[-0.01]
Impulsive spender - - - 0.43**
(0.15)
[0.07]
0.44**
(0.16)
[0.07]
Demographic controls Yes Yes Yes Yes Yes
Financial controls Yes Yes Yes Yes Yes
N 1,234 1,234 1,234 1,234 1,234
R2 0.14 0.13 0.13 0.14 0.15
LR 111.02 107.99 108.39 116.00 118.65
Prob>chi2 0.0000 0.0000 0.0000 0.0000 0.0000
Baseline predicted
probability
0.07 0.07 0.07 0.06 0.06
Notes: *significant at 5% level, **significant at 1% level. Variables also included in models:
gender, marital status, education leaving age, homeownership status, value of household
income, value of household liquid assets,
40
TABLE 10
Behavioural Characteristics and Over-Indebtedness: Self-Reported Over-Indebted
(1) (2) (3) (4) (5)
Financially literate -0.14
(0.12)
[-0.01]
- - - -0.10
(0.12)
[-0.01]
Confused by finance - 0.10
(0.12)
[0.01]
- - 0.10
(0.12)
[0.01]
Heavy discounter - - 0.05
(0.16)
[0.01]
- -0.19
(0.18)
[-0.01]
Impulsive spender - - - 0.58**
(0.16)
[0.03]
0.65**
(0.17)
[0.04]
Demographic controls Yes Yes Yes Yes Yes
Financial controls Yes Yes Yes Yes Yes
N 1,234 1,234 1,234 1,234 1,234
R2 0.16 0.16 0.16 0.18 0.18
LR 111.43 110.97 110.27 123.43 126.26
Prob>chi2 0.0000 0.0000 0.0000 0.0000 0.0000
Baseline predicted
probability
0.02 0.01 0.02 0.01 0.01
Notes: *significant at 5% level, **significant at 1% level. Variables also included in models:
gender, marital status, education leaving age, homeownership status, value of household
income, value of household liquid assets,
41
TABLE 11
Behavioural Characteristics and Credit Product Usage
Panel A
(1)
Credit Card
(2)
Overdraft
(3)
Personal
Loan
(4)
Store Card
(5)
Car Loan
Financially literate 0.04
(0.09)
[0.01]
0.15
(0.08)
[0.06]
0.17*
(0.09)
[0.05]
-0.04
(0.09)
[-0.01]
-0.01
(0.10)
[-0.01]
Confused by finance -0.08
(0.08)
[-0.03]
-0.09
(0.08)
[-0.03]
-0.04
(0.08)
[-0.01]
0.03
(0.09)
[0.01]
-0.15
(0.09)
[-0.03]
Heavy discounter 0.24
(0.13)
[0.07]
0.10
(0.11)
[0.04]
-0.04
(0.13)
[-0.01]
0.06
(0.13)
[0.02]
-0.01
(0.14)
[-0.01]
Impulsive spender -0.15
(0.15)
[-0.05]
0.11
(0.14)
[0.04]
0.23
(0.15)
[0.07]
0.35**
(0.15)
[0.10]
0.22
(0.16)
[0.06]
Demographic controls Yes Yes Yes Yes Yes
Financial controls Yes Yes Yes Yes Yes
N 1,234 1,234 1,234 1,234 1,234
R2 0.08 0.03 0.08 0.06 0.07
LR 117.53 44.84 107.69 71.35 81.34
Prob>chi2 0.0000 0.0028 0.0000 0.0000 0.0000
Baseline probability 0.75 0.57 0.23 0.17 0.15
Panel B
(6)
Mail Order
Catalogue
(7)
Hire
Purchase
(8)
Home
Credit
(9)
Pay Day
Loan
(10)
Credit
Union Loan
Financially literate -0.32**
(0.09)
[-0.08]
0.03
(0.12)
[0.01]
-0.12
(0.25)
[-0.01]
-0.07
(0.25)
[-0.01]
-1.25**
(0.35)
[-0.01]
Confused by finance -0.24*
(0.09)
[-0.05]
0.07
(0.12)
[0.01]
-0.77**
(0.29)
[-0.03]
-0.02
(0.23)
[-0.01]
-0.38
(0.25)
[-0.01]
Heavy discounter 0.13
(0.14)
[-0.03]
0.09
(0.17)
[0.01]
-0.11
(0.39)
[-0.01]
0.18
(0.29)
[0.01]
-0.30
(0.44)
[-0.01]
Impulsive spender 0.50**
(0.15)
[0.14]
0.21
(0.18)
[0.03]
0.66*
(0.32)
[0.04]
0.65*
(0.29)
[0.01]
0.29
(0.40)
[0.01]
Demographic controls Yes Yes Yes Yes Yes
Financial controls Yes Yes Yes Yes Yes
N 1,234 1,234 1,234 1,234 1,234
R2 0.14 0.06 0.23 0.28 0.26
LR 156.65 36.13 39.56 53.01 47.16
Prob>chi2 0.0000 0.0294 0.0057 0.0001 0.0006
Baseline probability 0.14 0.06 0.01 0.005 0.008
42
Notes: *significant at 5% level, **significant at 1% level. Variables also included in models:
gender, marital status, education leaving age, homeownership status, value of household
income, value of household liquid assets,
TABLE 12
Financial Shocks Among Survey Respondents
Yes No
(n)
(%)
(n) (%)
Job loss 121 9.8 1,113 90.2
Redundancy – no payment 44 3.6 1,190 96.4
Redundancy – payment 24 1.9 1,210 98.1
Partner redundancy – no payment 19 1.5 1,215 98.5
Partner redundancy - payment 17 1.4 1,217 98.6
Ended work due to illness 32 2.6 1,202 97.4
Income fall 266 21.6 968 78.4
Income fallen significantly 184 14.9 1,050 85.1
Partner’s income fallen significantly 102 8.3 1,132 91.7
Credit withdrawn 102 8.3 1,132 91.7
Credit card withdrawn 21 1.7 1,213 98.3
Credit limit reduced on credit card 56 4.5 1,178 95.5
Overdraft facility withdrawn 24 1.9 1,210 98.1
Major expense 491 39.8 743 60.2
House repairs 143 11.6 1,091 88.4
Major household item 229 18.6 1,005 81.4
Car repairs 290 23.5 944 76.5
43
TABLE 13
Financial Shocks Among Over-Indebted vs non Over-Indebted
Unit Over-Indebted Non Over-Indebted
Recent shocks
Job loss % 24.3 6.4
Income fall % 38.9 17.5
Credit withdrawn % 17.5 3.8
Major expense % 47.9 37.9
44
TABLE 14
Financial Shocks, Behavioural Characteristics and Over-Indebtedness
(1)
One month
delinquency
(2)
Three month
delinquency
(3)
Self-reported over-
indebtedness
Financially literate -0.19
(0.09)
[-0.04]
-0.14
(0.12)
[-0.02]
-0.08
(0.13)
[-0.01]
Confused by finance 0.07
(0.10)
[0.02]
-0.05
(0.11)
[-0.01]
0.01
(0.12)
[0.01]
Heavy discounter 0.07
(0.14)
[0.20]
-0.02
(0.17)
[-0.01]
-0.16
(0.18)
[0.01]
Impulsive spender 0.16
(0.16)
[0.04]
0.26
(0.17)
[0.03]
0.49**
(0.18)
[0.02]
Financial Shocks
Job loss 0.53**
(0.15)
[0.14]
0.41*
(0.16)
[0.06]
0.28
(0.18)
[0.01]
Income fall 0.41**
(0.11)
[0.10]
0.29*
(0.13)
[0.04]
0.62**
(0.14)
[0.02]
Credit withdrawn 0.89**
(0.17)
[0.27]
0.70**
(0.18)
[0.12]
0.63**
(0.19)
[0.03]
Major expense 0.27**
(0.09)
[0.06]
0.25*
(0.13)
[0.03]
0.12
(0.13)
[0.01]
Demographic controls Yes Yes Yes
Financial controls Yes Yes Yes
N 1,234 1,234 1,234
R2 0.20 0.20 0.25
LR 227.63 162.47 172.53
Prob>chi2 0.0000 0.0000 0.0000
Baseline predicted
probability
0.13 0.06 0.10
Notes: *significant at 5% level, **significant at 1% level. Variables also included in models:
gender, marital status, education leaving age, homeownership status, value of household
income, value of household liquid assets,
