Download Decision Avoidance and Deposit Interest Rate Setting

Search Costs, Decision Avoidance and Deposit Interest Rate Setting
Robert D. J. Anderson a
John K. Ashton b
Robert S. Hudson a
a
Newcastle University Business School, Newcastle University
b
Bangor Business School, Bangor University
G21 - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
Key Words: Interest rate setting, Savings, Deposits, Decision Avoidance, Behavioural Bias,
Status Quo effects.
JEL Classification: G21, G22
Abstract
This study examines why banks offer multiple and similar deposit services with different
interest rates. This examination considers how bank deposit interest rate setting is
influenced by customers assumed to have different propensities to switch deposit accounts
according to the degree they discount the costs and benefits of decisions. It is reported
banks can enhance profits by introducing duplicate new deposit products with competitive
interest rates for new customers and lowering interest rates on existing deposit services.
Model predictions that the number of deposit accounts and level of interest rates varies by
institutional type, higher levels of deposit balances are associated with less uncompetitive
accounts and the level of interest declines with the age of the deposit account are all not
rejected.
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1.
Introduction
Why do banks offer similar financial services with different levels of interest? Facing these
circumstances customers’ would surely switch to the financial service with the highest rate
of return or lowest cost? Despite such expectations, interest rate setting for many financial
services, and particularly deposits, have been viewed as ‘sluggish’, ‘sticky’, or lagged (e.g.
Hannan and Berger 1991, De Graeve et al 2007, Fuertes and Heffernan 2009) and
characterised by high levels of variance (Ashton and Letza 2002, Martin-Oliver et al 2008),
suggesting factors other than cost influence the setting of interest rates. Previously
Heffernan (2002) examining intra-bank variation in interest rate setting reported dual peaks
of more and less competitive interest rates existed for financial services; an outcome
attributed to bank interest rate discrimination between informed and ill-informed
customers with inertia. This study explores this issue using both theoretical and empirical
approaches to assess whether the age of deposit accounts is influential in determining the
level of interest set on these accounts.
This research question is addressed using both theoretical and empirical approaches.
First a model is presented to determine the interest rates and number of deposit products
used by banks wishing to profit from customers’ unwillingness to change deposit services.
Customers are assumed to have different propensities to switch deposits accounts
according to how they discount the costs and benefits of changing deposit accounts. It is
predicted if banks are aware of such consumer behaviours, they maximise profits by
introducing new products with competitive rates for customers prepared to switch
accounts, and reducing interest rates on existing deposit products for customers who avoid
switching deposit accounts. The model predictions are empirically tested using data on UK
saving deposit accounts interest rates and characteristics over a 12 year period. A large
number of highly uncompetitive deposit accounts are observed with profit making
institutions using more duplicate products than non-profit institutions. Further the
relationships between deposit interest rates and a) the type of institution, b) the number of
deposit accounts and c) the age of the deposit account are examined and all observed to be
positive and statistically significant.
This examination contributes to literatures considering both how interest rate
rigidity may emerge and the influence of behavioural anomalies in altering customers’
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decision making in saving markets. Initially, international assessments of financial services
interest rate setting have indicated infrequent and sluggish interest rate movement is
common in response to official or wholesale interest rate changes. This slow adjustment of
retail interest rates is attributed to a number of possible factors, including the
competitiveness of retail financial services markets (Heffernan 1997, Calem and Mester
1995, and Paisley 1994); interest rate asymmetry (De Haan and Sterken 2004, Lim 2001), the
structure of the banking industry (Corvoisier and Groop 2001, Jackson 1997, Hannan and
Berger 1991, and Calem and Carlino 1991, De Graeve et al 2007), lending channel effects
(De Graeve et al 2007), credit risk premiums, (Martin-Oliver 2007), bank efficiency (Fuertes
and Heffernan 2009), macroeconomic changes (Gambacorta 2008), regulation (Chong 2010)
and the scale of base rate changes (Fuertes et al 2010). In the spirit of Ausubel (1991), Kahn
et al (2001), Ashton and Hudson (2009) and Carlin (2009) who indicate adverse selection,
interest rate clustering and market complexity influence interest rate setting, this study
indicates another behavioural explanation why rigid or sticky retail interest rates exist.
Secondly a large literature has emerged examining how individuals can be poor
money managers (see Benartzi and Thaler 2007). This study contributes to this field by
responding to concerns that firms change their behaviour to exploit observed behavioural
anomalies (Frey and Eichenberger 1994) and that financial decisions are not always
presented to investors in a manner which assists optimal decision making (Hirshleifer 2001).
The importance of behavioural anomalies in this context is not really their presence or
number, which is not addressed by this study, yet their possible use by banks when setting
interest rates. Assuming banks rationally profit maximise and are aware that customers
discount the costs and benefits of switching decisions we both predict interest rate setting
behaviours and test whether these predictions are consistent with the interest rate setting
behaviours in the UK deposit market.
This study is structured into six sections. After this introduction a brief review of
pertinent literatures is provided. In section three a model is developed for maximising bank
profits when setting deposit interest rates for customers which discount the current and
future costs and benefits of switching deposit accounts quasi-hyperbolically and
exponentially. In section four, the data and methods are outlined and section five provides
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results of the empirical assessment. Conclusions and recommendations for further research
will be provided in section six.
2.
Literature review.
This brief review considers why individuals opt to defer or avoid financial decisions,
reported evidence of this behaviour within savings markets and discounting techniques
which purport to explain this behaviour. This discussion does not aim to be comprehensive
yet seeks to map the wider contours of these interrelated yet distinct debates.
Delaying or avoiding switching decisions, decision avoidance, decision making
inertia, status quo bias, and procrastination1 have all been repeated identified as an
important influence within savings markets. In particular these behaviours are identified
within enrolment and investment choice decision for retirement savings and particularly US
401(k) employee savings.
Customer enrolment decisions consider whether an individual opts to join a savings
scheme or not. When presented by the choice to opt into such a scheme many individuals
defer or have not made decisions despite the clear financial benefits of doing so. It is
reported the enrolment of employees in defined contribution pension schemes, such as the
US 401(k) scheme, differs when enrolment is undertaken automatically on behalf of the
employee as opposed to when the employee has to actively decide to join a scheme
(Madrian and Shea 2001). While employees often eventually enrol in a savings scheme, the
requirement to make a decision can delay this outcome (Choi et al 2004). Responding to
these concerns Thaler and Benartzi (2004) propose individuals are automatically enrolled
into saving schemes to raise enrolment and the level of retirement savings.
After enrolment individuals need to decide what assets or funds should form their
portfolio. These decisions are prone to many biases and rules of thumb, including naïve
investment strategies in addition to decision avoidance. Outcomes are numerous including
investing a high proportion of savings in the employers stock, using default investment
options, and adopting a narrow frame of reference when presented with excessive choice.
For example many investors stick to the default settings issued on savings plans and avoid
1
Procrastination involves delaying an action rather than just avoiding a decision: “definitions for
procrastination tend to be almost as plentiful as the people researching the topic” (Steel 2007).
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the decision to change these attributes (Madrian and Shea 2001). In Australia, even when
individuals are unhappy with the performance of investment funds they have displayed
reluctance to change (Fry et al 2007). When assessing mutual funds, Kempf and Ruenzi
(2006) report individuals which are subject to status quo bias select sub-optimal
alternatives, as these were chosen before.
The type of individual prone to such behavioural biases within financial markets has
also been examined. Agnew (2006) reports more highly paid employees tend to make better
financial decisions and in most cases women have better financial performance and men
display more risky investment behaviours. Indeed demographic variables are related with
company stock holdings with Choi et al (2004) reporting the level of investment influences
savings decision making. Stango and Zinman (2009) examining exponential growth bias2
reports education, income, race and gender are influential in explaining this bias.
A growing, yet fragmented psychology literature has also examined why individuals
opt to make no decision in face of changing circumstances. Decision avoidance while
universally observed (Steel 2007) and a chronic condition amongst 15% to 20% of the
population, is neither a clinical disorder nor does it fit easily with other branches of
psychology resulting in this area being overlooked given the division of labour between
fields of psychology (Anderson 2003). Unexpectedly, reasons advocated for decision
avoidance are diverse and include rational and non-rational explanations (Samuelson and
Zeckhauser 1988) including emotional maintenance, and inconsistencies in the perception
of time.
Responding to time inconsistencies in decision making literature has emerged
explaining these outcomes as a result of an individuals’ discounting of costs and benefits.
Specifically, hyperbolic and quasi-hyperbolic discounting functions arose to accommodate
situations where exponential discounting approaches assuming the constant decay of future
benefits and costs, appeared inappropriate. Hyperbolic discounting assumes individuals
discount future monetary rewards at a higher rate than that used to calculate the present
value of future monetary rewards (McClure et al 2004). As people discount future
eventualities in a non-exponential manner, delayed rewards or costs are discounted by
functions which are inversely proportional to the delay, reflecting the diminishing
2
Exponential growth bias is reported to be isomorphic to hyperbolic discounting (Stango and Zinman 2009).
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impatience of humans and discounting the future at a declining rate (Hepburn et al 2010).
Quasi-hyperbolic discounting approaches employed in this study (Laibson 1997) have a
greater and variable bias towards the present, a defined break point, and adopt distinct
discount factors in different time periods (Hepburn et al 2010).
We readily acknowledge hyperbolic and quasi-hyperbolic discounting approaches
have been repeatedly challenged. Concerns have arisen with the inability of hyperbolic
discounting approaches to accommodate the size of costs or benefits, where discounting
may be more rapid for smaller amounts rather than larger amounts (Kirby 1997), the
stability of these forms of discounting over time and across populations, and the direction
and sign of discounting where discounting may be more rapid for gains rather than losses
(see Read 2001 and Soman et al 2005). Abdellaoui et al (2010) also reports that while utility
is concave for gains and convex for losses as predicted by hyperbolic discounting, the
evidence supporting the degree of curvature and evidence of immediacy bias reflected in
quasi-hyperbolic discounting functions is limited. Other approaches have also emerged
challenging assumptions central to hyperbolic discounting including future resource slack
(Zauberman and Lynch 2005), the sub-additive form of time-discounting (Read 2001) and
similarity biases (Rubinstein 2003). Notwithstanding this importance of these criticisms,
Frederick et al (2002) reports the evidence of hyperbolic discounting as robust and Harris
and Laibson (2001) indicates quasi-hyperbolic discounting approaches and the implied
variation in impatience predicts observed patterns of pre-retirement wealth accumulation
and co-movements observed within many savings markets. Wider reviews of decision
making in retirement and saving are provided by Benartzi and Thaler (2007) and Choi et al
(2004) and reviews of decision avoidance and procrastination are provided by Anderson
(2003), Andreou (2007) and Steel (2007). Reviews of time inconsistencies in decision making
are provided by Berns et al (2007) and Kalenscher and Pennartz (2008).
3.
Model
Decision avoidance and inertia in product switching decisions is also the focus of a
burgeoning theoretical literature. The issue of preference stability and deferring decisions
within a certain period of time is linked with wider literatures on time discounting and
specifically quasi-hyperbolic discounting (e.g. Laibson 1997). The model adopted in this
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study assesses whether decision avoidance has a role within deposit interest rate setting
through developing a general case from O’Donoghue and Rabin (1999) in the context of
naïve and sophisticated depositors/investors (Salop and Stiglitz 1977). This classification is
consistent with experimental evidence (Wong 2008) reporting individuals can be divided
into groups which are aware and unaware of time inconsistencies in decision making. The
model is developed in a number of steps: after model preliminaries are established cases of
individual existing, time consistent and naive depositors are established. Subsequently, the
implications for the bank and the influence of multiple deposit accounts are considered.
Lastly the model predictions and associated hypothesises to be tested are forwarded.
3.1 Model Framework: Existing depositors and time inconsistent preferences
3.1.1: Model preliminaries. The depositor
To explore depositors with and without inconsistent time preferences which lead to
decision making inertia consider an existing depositor with a deposit balance D within a
deposit product offered by a bank. The return from the existing deposit balance received by
the depositor is denoted d, and the optimal deposit return obtainable from the wider
deposit market is d*. The decision to change or switch deposit accounts is made by the
depositor and can occur in any of t time periods where t  1, 2,
, n . When undertaking
decisions the depositor is deemed to face two types of benefits or costs. Initially delay costs,
the costs of not switching deposit accounts occur and are encountered in the future.
Depositors not switching a deposit account in a particular period will lead to delay costs:
X td  dt  dt*
(1)
The marginal cost of delaying a decision to switch accounts is X dt  X td  X td1  0 for all t.
When a depositor chooses to switch deposit accounts the benefits from the decision, in
period t, are X td .
Task costs include the costs of finding an optimal alternative product and associated
psychological influences. Task costs are unknown and are encountered at the time of
switching a deposit account and have an immediate effect. In each period the depositor will
review their task costs before deciding to switch deposit accounts or not resulting in a
distribution of possible task costs over t periods; this is denoted by a cumulative distribution
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 c, c ,
c  0 or F  c   0 . In cases where a decision is deferred and immediate task costs
are not encountered, the depositor will be aware that the task cost will need to be
undertaken in the future. Therefore current task costs exist when a decision is made and
future task costs exist when a decision is deferred.
An efficient behaviour or strategy is denoted Y *  Y1* , Y2* ,
, Yt*  for t periods. The
efficient behaviour is defined as where the sum of the future delay and task costs is
minimised. This will result in decision avoidance when the sum of the future delay and task
costs are less than the sum of current task costs. Alternatively when the future delay and
task costs exceed the current task costs the depositor will switch deposit products. The bank
will be aware that a proportion of customers will defer and avoid decisions.
3.1.2: Time consistent and naïve depositors.
As outlined in O’Donoghue and Rabin (1999a and 1999b) if u t is the instantaneous utility a
person gains in period t, then her intertemporal preferences at time t, U t can be
represented by the following utility function for all t:
U t (ut , ut 1 ,......, uT )   t ut  
T
  u


(1)
t 1
where δ represents long-run time consistent (exponential) discounting. The model includes
two types of depositor – those depositors with consistent time preferences, who use
exponential discounting    1 and time-inconsistent depositors which will discount future
events more than by exponential discounting    1 . A time consistent depositor    1
will therefore maximise her utility by choosing t to maximise:
n
U ( x1 )   t  X d    c t
(3)
 t
The depositor can maximise (3) by determining whether to incur the task costs c and, if so,
when to incur them. If c is large it may not be worthwhile to undertake the switching
exercise. It is only worth switching if
n
c   X d  
(4)
 t
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If it is worthwhile to switch deposit products, by induction, this should be done as soon as
possible. If the switch were done at time b, then the resulting utility would be:
n
U ( x1 )   b  X d    c b
(5)
 b
If the switching were done at time b-1, then the resulting utility would be
U ( x1 )   b 1
n
X     c


d
b 1
(6)
b 1
As the costs and benefits of delay expand and reduce over time respectively, equation (6) is
greater than (5), it is optimal to switch at the earliest possible time t.
As task and delay costs are appreciated either immediately or in the future, naïve
depositors will discount the potential future costs of delay by   1 and not discount the
immediate task costs. For the naïve customers these relationships are similar yet influenced
by the degree of quasi-hyperbolic discounting,
  .
A time-inconsistent depositor will
maximise her utility by choosing t to maximise:
n
 d
d 
t
X

 0   X    c if t  0

 t 1
U ( x1 )   n
 t  X d    c t if t  0
 

  t
(7)
When values of  are close to zero indicating high level of discounting of future events, the
potential to switch account may never resulting in the customer holding the original deposit
account indefinitely acting as a money pump as predicted by O’Donohue and Rabin (1999).
In other situations equation (7) could indicate that it is optimal to switch but at some point
in the future, yet not immediately. The model indicates customers are more likely to switch
accounts if they are less naïve and if
n
n
 t
 t
 Xd  dt  dt* is large and the deposit account is
relatively less competitive. For the bank, ceteris paribus, lower interest rates on deposit
accounts will be more profitable.
3.2 Implications for the bank
In this section we consider the case of how a profit maximising bank will manage its
accounts to maximise its profits. We assume that the bank is not be subject to the
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behavioural biases that may affect some of its customers and, in particular, will not suffer
from time-inconsistent discounting. Further the bank is aware a proportion of its customers
will quasi-hyperbolically discount future decision costs and benefits and employs this
knowledge when managing its deposit portfolio and setting deposit interest rates to
maximise its profits.
3.2.1 Management of accounts over time
If a bank currently provides a single deposit account it can alter the interest rate it offers to
maximise its profits. For simplicity we assume than general interest rates are constant over
time and that the demand functions the bank faces are set exogenously. Further, all factors
(costs, market interest rates, demand curves etc.) except the interest rate on product i
which the bank sets endogeneously to maximise its profits are assumed to be time invariant.
The benefits appreciated by the bank from each unit of deposit provision are the current
cost (the current rate at which the bank can deploy the funds it obtains) of funds d f minus
the current returns on the deposit product offered, say d i for account i and the cost of
providing this service per unit of deposit, d c . This is represented as:
X b  d f  di  d c
(8)
Where D(d i , x) be the exogeneous demand in the market from people without the deposit
account i given interest rate d i and a vector x of other factors. If Ait is the actual amount of
funds held in account i at time t then the bank profit, for deposit product i, is given by:
(d f  dit  d c ) A(dit , x)
(9)
Assuming the bank profit maximises and accommodating of the expected behaviour of both
existing customers and potential market demand, at time t+1 the bank will set the interest
rate this account i at dit+1 to maximise profits. This will provide an overall profit of:
d
f
 dit 1  d c   D  dit 1 , x   Pi Ait 
(10)
Where Pit 1 is the probability of the original customers not switching from account i and is a
function of search costs, the size of the individual customer’s funds, the naivety of the
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customers and of the attractiveness of dit 1 compared to the market. Assuming that (10) a is
a concave function of d i the bank can chose the deposit rate that maximises, d is .
The bank can also act in a more sophisticated way to take advantage of naïve
customers by initially offering a high rate on deposit accounts to attract customers and then
reducing the interest rate. This situation can be demonstrated over a two period model.
For simplicity we neglect the time value of money when discounting profits from period 2.
The expected profitability of the deposit account over a two period life is given by:
d
f
 dit  d c  D  dit , x    d f  dit 1  d c   D  dit 1 , x   PD
 dit , x 
i
(11)
It can be show that profits will be maximised if d it , the rate in the first period, is set high
and then reduced in the second period.
3.2.2 Management of a portfolio of accounts
The case of multiple deposit account provision is considered by assessing where a bank has
one account, account 1, at time t and introduces another similar account with a different
interest rate at time t+1. If the bank does not introduce another account its overall profit at
time t+1 will be given by:
d
f
 d1t 1  d c   D  d1t 1 , x   P1 A1t 
(12)
If the bank does introduce an additional account its profit at time t+1 will be given by:
d
f
 d1t 1  d c   D  d1t 1 , x   P1 A1t    d s  d 2t 1  d c  D  d 2t 1 , x 
(13)
Where P1 is a function of search costs, the size of the individual customer’s funds, the
naivety of the customers and of the attractiveness of dit 1 compared to the market. In
equation (13) account 2 will attract customers from the general population in line with the
rate offered; account 1 will attract some customers from the general market but will also
retain some existing naïve customers.
These actions creates a moral hazard if the bank has greater knowledge of the
depositors behaviour be this individually or on aggregate than the individual customer. This
issue is particularly pertinent as naïve depositors will be unaware they have present state
biases and defer decisions due to inconsistent time preferences of which they are not fully
aware. Facing such circumstances the bank may adopt a range of strategies to maximise its
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profits. Initially the bank will attempt to increase the perception of current task costs. This
may be achieved through product obfuscation, presenting information in a challenging
manner and developing the perception of high switching costs. Secondly the bank will
ensure delay cost do not become so large that the depositor moves accounts.
3.3. Model Predictions and Hypothesises to be Tested.
In summary the theoretical findings of our models indicate five main predictions. The first
two predictions indicate, naïve depositor are less likely to switch than non-naïve depositor
and that non-naïve depositors will not procrastinate and make decisions on their benefits at
a point in time. These predictions of the naivety or sophistication of depositors requires
assessment of customer characteristics and falls outside the scope of the study. The
remaining three predictions include:
Profit maximising banks progressively reduce interest rates on existing accounts and
introducing new accounts with higher interest rates and non-profit maximizing banks less
prone to this behaviour. Therefore:

the level of interest rates will decline with the age of the deposit account,

different types of institution operating in the deposit market should offer different
proportions of uncompetitive deposit accounts resulting in the level of interest rates
and number of deposit accounts to vary by institutional type, and.
 if search costs are small relative to the deposit account income depositors will switch
for the best deal in the market. Therefore the higher the level of investment or
deposit balance to lower the number of uncompetitive accounts.
4.
Data and Methodology
4.1 Data employed in the study
The data employed in the study is obtained from the monthly Moneyfacts Magazine. This
retail interest rate data is widely used by the financial press, the financial services industry,
regulators and within academic research. The range of deposit taking institutions surveyed
by Moneyfacts represents nearly the population of retail deposit suppliers in the UK over
the sample period, January 1996 to December 2008. The data represents currently
marketed accounts, so accounts which are classed as “closed issues” will not appear in
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monthly sample. As we are using the data to examine the choice faced by an investor of
available accounts at any point in time, the exclusion of closed issue accounts does not
distort our analysis. As the immediate product purchase decision of an agent is of interest to
this assessment we limit our analysis to instant access and branch-based deposit accounts.
Only institutions which operate through-out the sample period are considered and are
shown in Table 1. Institutional groupings are used to capture the institutions’ objective
function, asset size and scope of distribution. Further details of the institutions within these
classes are provided in the Appendix.
Deposit interest rates depend upon the balance being invested, giving rise to ‘tiered’
interest rate structures. Moneyfacts records the interest rate of a particular deposit account
against a set of 6 tiers ranging from £1 to £100,000. We base our analysis around 3 of these
tiers, namely initial deposit of £500, £5k and £50k, enabling comparison of the investment
choices facing depositors.
Table 1: Institutional Groupings
Type
Bank: high
(HS)
Bank: small
Count
street 6
24
Building Society (BS): 8
high street
Building society (BS): 58
small
Converted building 9
societies
Description
Traditional ‘full service’ banks with national branch network
coverage.
Small, perhaps niche market banks, with regional or single branch
network coverage.
Those building societies with a national branch network
Regional or single-branch building societies.
Building societies that have demutualised and therefore become
banks either in their own right or as an established bank subbrands.
13
4.2
Econometric Data Analysis
Three regression models to assess the relationship between interest rates on individual
deposit accounts and the type of institution, the number of accounts offered and age of
accounts and testing of model predictions. Initially to test if interest rate levels vary by
institutional type a simple specification is employed:
IRi ,jt   0  β1 institution i  β 2 timet   i   i ,t
for i  1,
, N , t  1,
(14)
,156
j
where IRi ,t is the interest rate paid for a particular initial deposit amount, j, for a particular
product, i, at a particular time period, t. institution i is a matrix of dummy variables
pertaining to the institutional groupings displayed in Table 1 while timet is a vector of
dummy variables representing each time period. To provide more insight with this model
specification, we augment the institutional groupings with individual dummies for each
constituent high street bank and high street building society, as shown in the appendix.  i is
an account-specific unobservable effect, and  i ,t is an assumed white noise error term3. As a
constant,  0 , is included in our specification, estimated coefficients will measure differences
from a base category, which represents a particular institution group in a particular month.
Accordingly, we chose to assess differences from converted building societies in January
1996. The expected findings for 1 would be positive and significant, indicating the
institutional dummy variables explain some of the variation in deposit interest rates. The
results of estimating (14) are shown in Table 2Error! Reference source not found., panel
A.
To test that the number of deposit accounts varies by institutional type the
following model is proposed:
Ni ,t   0  β1 institution i  β 2 timet   i   i ,t
for i  1,
, N , t  1,
,156
(15)
3
Future research will look at estimating the set of equations formed from each tier as a system using
seemingly unrelated regression techniques (see Zellner 1962). At the moment this is not possible as the Rogers
(1991) correction only accommodates clustering correlations in one dimension.
14
where Ni ,t are the number of similar products to product i offered by an institution, at
particular time, t and where institution i and timet are as defined previously. Results are
shown in panel B.
The third part of the analysis considers account competitiveness directly and tests if
the level of interest rates will decline with the age of the deposit account. We quantify
competitiveness as the difference between the one-month lagged base rate and the
prevailing interest rate on that account. Factors potentially influencing this competiveness
are age of the account since inception, institution type, the number of additional similar
accounts that the institution offers above that generally offered, as well as time.
Accordingly, we propose the model shown in (16) to investigate this issue, which also
includes interaction terms to investigate joint effects.
IRDi ,jt   0  β1 institutioni  β 2 timet  3 Ni*,t  β4  institution i  Ni*,t 
  4 aget  β5  institutioni  aget    6  aget  Ni*,t    i   i ,t
for i  1,
, N , t  1,
(16)
,156
j
where IRDi ,t is the difference between the one-month lagged base rate and the prevailing
*
interest rate for product i at time period t. Ni ,t is the number of additional similar products
offered by an institution at a particular point in time. This is calculated as the number of
similar products offered by that institution, minus two, as two is found to be the general


number of similar accounts offered by an institution across all time4. institution i  N i*,t is
a set of interaction dummies which will allow for quantification of the influence of extra
accounts on each institution type. agei is defined as the number of months which the
account has been in existence which is also interacted with the number of additional
accounts and with institution type5. timet is as defined previously, while in this analysis, to
cut down on the number of interactions, institution i contains only the institution level
groupings shown in Table 1 and so does not contain specific institution indicators within any
group. The results of estimating (15) are shown in Table 3. The expected values to not reject
4
See for example the constant term in Table 2 panel B.
There is perhaps a censoring effect for this variable in that the age of accounts at the beginning of the sample
is unknown. Our future research will consider how we can correct results for this potential problem.
5
15
hypothesis 3, are positive and significant coefficients for the Age variable 4. Further 5 and
6 also consider the age of accounts as part of interaction terms.
We also examine the influence of the level of investment or deposit balance to lower
the number of uncompetitive accounts, through comparison of the findings from these
models and particularly model 3, and the scale of the constant term coefficients for these
models.
Both models 1 and 3 are also estimated over three interest rate tiers for £500,
£5,000 and £50,000. All models employ a random effects model and are estimated by OLS
using the Rogers (1991) correction to standard-errors to accommodated the correlation in
the composite residuals caused by the unobservable account-specific effects,  i .
5.
Results
5.1
Influence of Institutional type
The relationship between deposit interest rates and institutional type and deposit account
numbers and institutional are reported in Panels A and B of Table 2 respectively. Results
from panel A, provides some evidence to not reject difference between institutional type,
with half of all high street building societies with positive and significant 1 estimates.
Differences between interest rate levels and the institutional type are not seen to be
significant for other institutions with the exception of Lloyds/TSB. For all high-street banks,
there is significant rejection of the null hypothesis of pricing equivalence with converted
building societies, although the strength of this significance declines, becoming more
marginal with increasing deposit balances. There are also within-class differences, notably
Lloyds TSB for which there is evidence individually that their pricing strategy is different
from that of converted building societies at the for initial deposits of £500 and £50k. Small
banks and small building societies appear to be setting interest rates in a similar way to
converted building societies for all levels of deposit.
16
Table 2: Interest Rate Level Analysis (panel A) and Number of Products Analysis (panel B)
Panel A: Interest Rate Analysis
£500 (1)
High Street
BS
£5,000(1)
Panel B: Number of
Products Analysis
£50,000(1)
Coefficient
Std Error
Coefficient
Std Error
Coefficient
Std Error
Coefficient1
Std Error
Britannia BS
0.9964
(0.6982)
1.0845
(0.7117)
0.9964
(0.6982)
0.6114
(0.3041)*
Chelsea BS
2.2907
(0.7171)**
0.1379
(0.7939)
2.2907
(0.7171)**
-1.7584
(0.3335)***
Coventry BS
0.8509
(0.9652)
0.4694
(0.7580)
0.8509
(0.9652)
-0.1777
(0.2818)
Leeds and Holbeck BS
1.156
(0.4754)*
1.1567
(0.3101)***
1.156
(0.4754)*
1.1056
(0.3061)***
Nationwide BS
0.0642
(0.2561)
-0.381
(0.2524)
0.0642
(0.2561)
-2.4104
(0.2766)***
Portman BS
0.955
(0.2563)***
1.1806
(0.2527)***
0.955
(0.2563)***
-2.3744
(0.2775)***
Skipton BS
0.8791
(0.7215)
0.8095
(0.4199)#
0.8791
(0.7215)
-0.1767
(0.4530)
Yorkshire BS
1.0455
(0.5539)#
0.4724
(0.5488)
1.0455
(0.5539)#
1.1494
(0.3122)***
Joint Test
1882.12***
7091.49***
2491.92***
454.24***
Small BS
0.2675
(0.2677)
0.1874
(0.2614)
0.2675
(0.2677)
-0.2844
(0.3582)
Small Bank
-0.1564
(0.299)
-0.0615
(0.2964)
-0.1564
(0.2990)
-0.9795
(0.3401)***
Barclays
0.3061
(0.3932)
0.3632
(0.3882)
0.3061
(0.3932)
-1.4414
(0.3263)***
HSBC/Midland
0.0189
(0.2621)
-0.3494
(0.281)
0.0189
(0.2621)
-1.5061
(0.3176)***
LloydsTSB
0.8124
(0.3811)*
0.4575
(0.3681)
0.8124
(0.3811)*
1.3388
(0.3180)*
Natwest
-0.2422
(0.2559)
-0.5018
(0.3628)
-0.2422
(0.2559)
-0.754
(0.3160)***
Royal Bank of Scotland
-0.2277
(0.325)
-0.5549
(0.3312)
-0.2277
(0.3250)
-1.4247
(0.2941)***
TSB
0.3342
(0.2626)
-0.0615
(0.2634)
0.3342
(0.2626)
-0.2844
(0.3582)**
Joint Test
17.47***
Constant
2.3627
High Street
Bank
3.11**
(0.2884)***
3.316
2.42*
(0.2754)***
3.8359
48.34***
(0.2528)***
2.1016
(0.3006)***
Notes: The equation estimated in panel A is given by Error! Reference source not found., while the equation estimated in panel B is given by
Error! Reference source not found.. Time dummy variable coefficients and standard errors are not shown. All test statistics refer to tests of significance (null is equality
17
or joint equality with zero, against a two-sided alternative). * denotes significance at the 5% level, ** at the 1% level and *** at the 0.1% level significance.
5.2
Influence of the number of deposit accounts offered
The results from Table 2, Panel B indicate strong support for not rejecting the prediction,
that the number of deposit accounts varies by institutional type. Six of the eight high street
building societies and all high street banks and small banks have significant and positive (1)
coefficient values. Again, within the institution classes, there is variation. For high street
building societies, only the Skipton and Coventry individually offer a similar number of
deposit accounts as converted building societies. Whereas the Chelsea, Coventry,
Nationwide and Portman would appear to offer fewer deposit accounts, the Yorkshire,
Leeds and Holbeck and the Britannia would appear to offer a wider selection of deposit
accounts. In the latter case, combined with the results in Table 2 panel A, indicates the
Britannia has a wide variation in the interest rates it pays on its deposit accounts.
All high street banks other than LloydsTSB have fewer deposit accounts than
converted building societies. This is surprising as it was only LloydsTSB whose deposit
accounts perform differently from the base (reported in Panel 1, Table 2). We can speculate
LloydsTSB introduces deposit accounts which are of similar price to existing accounts, rather
than at a higher rate predicted. Small banks too generally have fewer products. In summary,
most institutional groupings offer fewer deposit accounts than converted building societies.
Further, small building societies are insignificantly different in terms of the number of
deposit products marketed.
5.4
Influence of the Age of the Deposit Account
Overall, results from Table 3 support the prediction that the age of the account is correlated
with the competitiveness of the account. Accounts which are more established have rates
which are further away from the lagged base rate and so, by our measure, are less
competitive. There is also strong evidence to suggest that these age effects are not equal
and insignificant across institution types, particularly so for higher initial deposits, although
there is no individual support of such behaviour from a particular type of institution. In all
cases asset sizes the coefficient 4 is positive and significant.. The size of the age effect
would appear to increases with the size of the initial opening balance, with approximately a
0.05% additional decrease in competitiveness, per month, for initial balances of £5,000
versus those of £500, indicating this relationship is also influenced by the size of deposit.
18
Higher opening balances are observed to improve the competitiveness of the
account, as evidenced by the constant term coefficients. This supports not rejecting the
prediction that the higher the level of investment or deposit balance to lower the number of
uncompetitive accounts.
Table 3:
Competitiveness Joint Analysis
£500
£5,000
£50,000
Coefficient
Std. Err.
Coefficient
Std. Err.
Coefficient
Std. Err.
No Prod (Cent)
-0.1579
(0.1042)
-0.0827
(0.1095)
-0.0359
(0.1006)
High Street Banks
-0.2893
(0.4626)
0.0744
(0.4299)
-0.2931
(0.4196)
Small BS
-0.2747
(0.4493)
0.0699
(0.4161)
-0.2760
(0.3993)
Small Banks
0.1472
(0.4913)
0.2136
(0.4538)
-0.1198
(0.4370)
High Street BS
-1.0609
(0.5279)*
-0.3905
(0.4678)
-0.6283
(0.4329)
Joint Test
2.83*
No Prod x HS Bank
0.0635
(0.1142)
-0.0073
(0.1203)
-0.0958
(0.1188)
No Prod x Small BS
0.0869
(0.1024)
-0.0063
(0.1086)
-0.0249
(0.0997)
No Prod x Small Banks
-0.0624
(0.1242)
-0.1004
(0.1459)
-0.0780
(0.1361)
No Prod x HS BS
0.0761
(0.1279)
-0.0698
(0.1341)
-0.0341
(0.1209)
Joint Test
1.11
Age (4)
0.0157
(0.0057)**
0.0200
(0.0048)***
0.0171
(0.0051)**
Age x HS Bank
0.0025
(0.0057)
0.0022
(0.0051)
0.0059
(0.0054)
Age x Small BS
-0.0019
(0.0055)
-0.0055
(0.0048)
-0.0049
(0.0052)
Age x Small Banks
-0.0011
(0.0062)
-0.0033
(0.0055)
-0.0050
(0.0059)
Age x HS BS
0.0039
(0.0058)
-0.0024
(0.0050)
-0.0022
(0.0056)
Joint Test
2.29#
No Prod x Age
0.0003
(0.0003)
0.0004
(0.0004)
0.0005
(0.0003)#
Constant
4.0156
(0.4483)***
2.8384
(0.4234)***
2.2986
(0.4118)***
1.18
1.22
0.35
0.36
3.29*
8.19***
Having controlled in this framework for both the effect of age and the number of
accounts, this also removes much of the variation in the interest rate caused purely by
institution type. Despite this, competitiveness for initial opening balances of £500 is still
found to be related to institution type. Individually this indicates high street building
societies price more competitively. These institutional differences are also evidenced by the
19
strong rejection of the null hypothesis that institution effects are both equal and
insignificant. In terms of the number of extra accounts marketed, there appears to be no
relationship with institution type either individually or jointly. There is perhaps erroneous
significance at the 10% level of this variable when interacted with the age of the account for
opening balances of £50,000.
In summary there is evidence to support product price heterogeneity and that this
heterogeneity is in some way dependent on the institution type. There is also support for
the idea that large building societies generally provide accounts with higher rates. However,
within firm types, there is also evidence to suggest that individual firms behave differently
from their group as a whole, and so institution type should not be used as the only relevant
signal to an investor.
Considering the number of products offered is also not rejected as significant
differences exist within and between institution types. It is acknowledged that some of the
large building societies not only offer fewer products, yet also rank favourably in terms of
the interest rate they pay. Third, a strong relationship between the competitiveness of the
account and its age, after having controlled institution and number of similar account
effects is reported. Lastly, the relationship between interest rate competitiveness and the
deposit balance is not rejected.
1.5
1
0.5
0
-0.5
-1
-1.5
-2
-2.5
-3
Date Dummy Coefficients by Deposit Balance
Feb-96
Aug-96
Feb-97
Aug-97
Feb-98
Aug-98
Feb-99
Aug-99
Feb-00
Aug-00
Feb-01
Aug-01
Feb-02
Aug-02
Feb-03
Aug-03
Feb-04
Aug-04
Feb-05
Aug-05
Feb-06
Aug-06
Feb-07
Aug-07
Feb-08
Aug-08
Rate
Figure 1:
Date
500
5k
50k
20
The coefficients on the date dummies for each tier are plotted in Figure 1. These
coefficients augment the intercept coefficient shown in Table 2 panel A for the various
months in the sample. Figure 1 suggests that in the earlier part of the sample, the difference
between the various tiers remains similar to the levels reported between tiers in Table 2
panel A. However, in the later part of the sample, these differences particularly between the
lower tier and the higher tiers become smaller. This confirms our conclusions from Table 2.
6.
Conclusions
This paper examines why banks offer similar financial services with different levels of
interest. First a model is presented to determine the interest rates employed by financial
firms who wish to profit from customers’ inertia when switching deposit accounts . This
model adopts a quasi-hyperbolic discounting approach to examine the implications of
decision avoidance. Three principal and testable predictions are made from model.
Initially, as profit maximising banks can take various actions to maximise profits and
non-profit maximizing building societies are prone to this behaviour, different types of
institution operating in the deposit market should offer different proportions of
uncompetitive deposit accounts. Secondly, profit maximising banks can take various actions
to maximise profits such as progressively reducing rates on existing accounts and
introducing new accounts with higher interest rates, an action not predicted for non profit
maximizing building societies. This indicates the level of interest rate on a deposit account
will decline with the age of the account. Lastly if search costs are small compared to the
income from the deposit account, uncompetitive levels of interest on deposit accounts
should not exist. These search costs are viewed to be influenced by the scale of deposit
balance and the overall level of interest rates prevailing.
These predictions are tested using three different regression models considering the
level of interest offered on deposits, the number of deposit products and the
competitiveness of deposit accounts. These models are then employed to test these
predictions: a) the level of interest rates and number of deposit accounts varies by
institutional type, b) the level of interest rates will decline with the age of the deposit
account, and c) the higher the level of investment or deposit balance to lower the number
of uncompetitive accounts. None of these predictions are rejected.
21
Examining why banks issue multiple and similar deposit products with distinct
interest rates is important for three reasons. Initially, the research contributes to the wider
literatures examining interest rate setting in financial services and potential causes of
interest rate variability and sluggish interest rate transmission in the deposit market. It can
be reported that decision avoidance and quasi-hyperbolic discounting by customers is
consistent with banks offering a range of duplicate deposit accounts with low and often
unchanging levels of interest. This explanation is also consistent with generalised
characteristics of interest rate transmission in deposit markets where interest rate rises are
often slower than falls in response to external shocks (e.g. De Haan and Sterken 2004, Lim
2001).
Secondly, the last decade has observed a global shift towards individuals taking
increasing responsibility and autonomy for their savings decisions (Benartzi, 2001, Benartzi
and Thaler, 2002). This movement comes at a time when savings levels in many nations
including the USA and UK have been at historically low levels (Kirsanova and Sefton 2007).
As it is often more efficient to reduce restraints to an action rather than increasing driving
forces of such an action (Kahneman 1992) challenging financial firms’ behaviours which may
make saving less attractive to customers should be an efficient policy response to this
concern. Lastly, increasing public and regulatory attention has focused on how interest rates
are set within savings markets, particularly following the adoption of relatively low interest
rates in many nations.
In light of these findings, it is important to offer potential solutions or actions which
may remedies or alleviate these adverse outcomes. An important initial challenge is how to
de-bias people to be able to resist decision avoidance. Three potential actions may be
important in this context: one, improving the ability and perception of the ease of switching
for customers. Currently the perception that switching may be challenging can encourage
decision avoidance and customer inertia to form. Clearly this an area were further
information provision to the customer may be important and concerns raised in associated
deposits (OFT 2010) with delays in switching need to be addressed across the sector.
Secondly, it is essential for customers will be able to transform themselves into more
sophisticated agents, to overcome the concerns outlined in the study. Clearly the role of
customer ignorance is central to overcoming such problems of decision avoidance. While it
22
is advocated that improved financial education and literacy is still required for those with
the least ability to make decisions, we also need to acknowledge the limitations of this
approach. The benefits of financial education can be limited by financial firms as it is
important for customers to remain uniformed to assist firm profitability (Subrahmanyam
2009). While financial education is widely advocated internationally (see Fox 2004, Erturk et
al 2007) this alone can’t resolve incentives for banks to obfuscate and withhold relevant
facts in financial services sales (Kozup and Hogarth 2008, Williams 2007). Subsequently it is
suggested that a mechanism other than formal education is important for customers.
Subsequently it is suggested amendment should be made to increase repeated market
decisions. It is proposed informing customers each year of the interest rates of the deposit
accounts that they currently hold and the other interest rates provided on similar deposit
accounts provided by the bank, would assist this process. Such information provision
allowing a comparative comparison of the banks deposit offerings and associated interest
rates would at least encourage switching of products within the bank.
This approach would be viewed as improving consumer sovereignty; the set of social
and economic arrangements that allow a consumer to freely choose the goods and services
they wish to consume, rather than be directed in their choices by firms’ persuasion. (Averitt
and Lande 1998, 2007). By improving information provision in a systematic rather than
partial manner situations where some consumers cannot make informed choices between
products and firms exploit these limitations are ‘out of head’ failures constrain consumer
benefits can be limited. As seen in this case, such market failures do occur and have
substantial costs for the least able consumers; challenging these circumstances when they
arise should be a regulatory priority.
There are a number of areas for further research. Theoretically the role of excessive
choice (Ireland 2007) and market complexity (Carlin 2009) in how both consumers and
financial firms make decisions is an important avenue for future work. For example the issue
of choice in retirement savings has repeatedly been be associated with non-rational
behaviours, such as more choice of investment funds being associated with lower
participation rates (Choi et al 2004); the degree of choice of investment funds observed to
have little influence over the funds chosen (Benartzi and Thaler 2002, Choi et al 2004,
Huberman and Jiang 2006) and a reluctance for many saving plan participants to construct
23
their own portfolio yet employ a median of other participants’ decisions (Benartzi and
Thaler 2002). Lastly decision avoidance and procrastination have been mostly viewed as a
negative behaviour (Steel 2007). It would helpful to observe if procrastination also has some
possible elements in financial decision making as once decisions have been made future and
new information may not be incorporated.
24
Appendix: Group Constituent Institutions
High Street (Total: 6)
Barclays
HSBC
Lloyds (Lloyds/TSB)
NatWest
Royal Bank of Scotland
TSB
Converted
Building
Societies (Total: 9)
Abbey National
Alliance and Leicester
Birmingham Midshires
Bradford and Bingley
Bristol and West
Cheltenham
and
Gloucester
Halifax
Northern Rock
Woolwich
High Street Building
Societies (Total: 8)
Britannia BS
Chelsea BS
Coventry BS
Leeds and Holbeck BS
Nationwide BS
Portman BS
Skipton BS
Yorkshire BS
Small Building Societies
(Total: 58)
Barnsley BS
Bath BS
Beverley BS
Buckinghamshire BS
Cambridge BS
Catholic BS
Chesham BS
Cheshire BS
Chorley and District BS
City and Metropolitan BS
City of Derry BS
Clay Cross BS
Cumberland BS
Darlington BS
Derbyshire BS
Dudley BS
Dunfermline BS
Earl Shilton BS
Ecology BS
Furness BS
Greenwich BS
Hanley Economic BS
Harpenden BS
Hinckley and Rugby BS
Holmesdale BS
Ipswich BS
Kent Reliance BS
Lambeth BS
Leek United BS
Loughborough BS
Manchester BS
Mansfield BS
Market Harborough BS
Marsden BS
Melton Mowbray BS
Mercantile BS
Monmouthshire BS
National & Provincial BS
National Counties BS
Newbury BS
Newcastle BS
Norwich
and
Peterborough BS
Nottingham BS
Nottingham Imperial BS
Principality BS
Progressive BS
Saffron Walden Herts and
Essex BS
Scarborough BS
Scottish BS
Shepshed BS
Stafford Railway BS
Staffordshire BS
Stroud and Swindon BS
Teachers' BS
Tipton and Coseley BS
Universal BS
Vernon BS
West Bromwich BS
Small Banks (Total: 24)
AIB Bank (GB)
Airdrie Savings Bank
Bank of Cyprus
Bank of Ireland (GB)
Bank of Ireland (NI)
Bank of Scotland
Cater Allen Private Bank
Citibank
Clydesdale
Co-operative Bank
Coutts and Co.
First Trust Bank
Granville
HFC Bank
Hoare and Co.
Julian Hodge Bank
Laiki Bank
National
Savings/Giro
Bank
Northern Bank
Post Office
Triodos Bank
Ulster Bank
Whiteaway Laidlaw bank
Yorkshire Bank
25
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