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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. 1 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’ 2 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 3 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). 4 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). 5 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 6 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 td1 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 7 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 8 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 Xd 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 9 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 10 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 11 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 12 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 Coefficient1 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 References Abdellaoui, M, Attema, A. E. And Bleichrodt, H. (2009). “Intertemporal Tradeoffs for Gains and Losses: An Experimental Measurement of Discounted Utility”, The Economic Journal, vol.120, pp.845866. Agnew, J. R. (2006). “Do Behavioural Biases Vary Across Individuals: Evidence from Individual Level 401(k) Data?”, Journal of Financial and Quantitative Analysis, vol.41, no.4, pp.939-962. Anderson, C. J. (2003). “The psychology of doing nothing: Forms of Decision Avoidance result from Reason and Emotion”, Psychological Bulletin, vol.129, pp.139-167. Ausubel, L. M . (1991). “The Failure of Competition in the Credit Card Market”, The American Economic Review, Vol. 81, No. 1, pp. 50-81 Andreou, C. (2007). “Understanding Procrastination”, Journal of the Theory of Social Behaviour, vol.37, no.2, pp.183-193 Ashton, J. K. (2009) “Synchronisation and Staggering of Interest Rate Change by UK Financial Service Firms”, International Review of Applied Economics, vol.23, no.1, pp. 55-69. Ashton, J. K. and Hudson, R. S. (2008). “Interest rate clustering in UK financial services markets”, Journal of Banking and Finance, vol. 32, pp. 1393-1403. Ashton, J. K. and Letza, S. (2003). “The Differential Returns Offered by Mutually Owned and Proprietary UK Depository Institutions: 1993-2000”, The Annals of Public and Cooperative Economics, vol.74, no.2, pp.183-204. Averitt, N. W. and Lande, R. H. (1998). “Consumer Sovereignty: A Unified Theory of Antitrust and Consumer Protection law!”, Antitrust Law Journal, vol.65, pp.713-756. Averitt, N. W. and Lande, R. H. (2007). “Using the Consumer Choice Approach to Antitrust Law”, Antitrust Law Bulletin, vol.175, pp. 175-264. Benartzi, S. (2001). “Excessive Extrapolation and the Allocation of 401(k) Accounts to Company Stock”, Journal of Finance, vol.61, no.6, pp.1747-1764. Benartzi, S. and Thaler, R. H. (2002). “How Much Is Investor Autonomy Worth”, The Journal of Finance, vol. 62, no.4, pp.1593-1616. Benartzi, S. and Thaler, R. H. (2007). “Heuristics and Biases in Retirement Savings Behaviour”, Journal of Economic Perspectives, vol. 21, no.3, pp.81-104. Berns, G. S., Laibson, D. and Loewenstein, G. (2007). “Intertemporal choice – towards an integrative framework”, Trends in Cognitive Sciences, vol.11, no. 11, pp.482-488. Calem, P.S. and Carlino, G.A. (1991) The Concentration / Conduct Relationship in Bank Deposit Markets, The Review of Economics and Statistics, 73(2), pp.268276. Calem, P.S. and Mester, L.J. (1995) Consumer Behaviour and the Stickiness of Credit-Card Interest Rates, The American Economic Review, 85(5), pp.13271336. Carlin, B. I. (2009). “Strategic price complexity in retail financial markets”, Journal of Financial Economics, vol.91, pp. 278-287. Chernev, A. (2004). “Goal Orientation and Consumer Preference for the Status Quo”, Journal of Consumer Research, vol.31, pp.557-565 Choi, J. J., Laibson, D. and Madrian, B. C. (2004). “Plan Design and 401(k) Savings Outcomes”, National Tax Journal, 62, no.2, pp.275-298. 26 Chong, B.C. (2010). “Interest rate deregulation: Monetary policy efficacy and rate rigidity”, Journal of Banking and Finance, vol.34, pp. 1299-1307. Chorvat, T. (2007). “Tax Compliance and the Neuroeconomics of Intertemporal Substitution”, National Tax Journal, vol.60, no.3, pp.577-588. Corvoisier, S. and R. Gropp. (2002). “Bank Concentration and Retail Interest Rates”, Journal of Banking and Finance, 26, (2002), 2155-2189. De Graeve, F., De Jonge, O. and Vander Vennet, R. (2004) The Determinants of Pass-Through of Market Conditions to Bank Retail Interest Rates in Belgium, National Bank of Belgium Working Paper Series, 47, (Brussels, National Bank of Belgium). De Haan, L. and Sterken, E. (2005) Asymmetric Price Adjustment in the Dutch Mortgage Market, DNB Working Paper Series, 61, (Amsterdam, De Nederlandsche Bank). Erturk, I., Froud, J. Johal, S., Leaver, A. and Williams K. (2007). “The democratization of finance? Promises, outcomes and conditions”, Review of International Political Economy, vol.14, no.4, pp.553-575. Fox, L. (2004). “Federal Reserve Personal Financial Education Initiatives”, Federal Reserve Bulletin, Autumn, pp.447-457. Frederick, S., Lowenstein, G. and O’Donoghue, T. (2002). “Discounting and Time Preference: A Critical Review”, Journal of Economic Literature, vol.40, no.2, pp.351-401. Frey, B. S. and Eichenberger, R. (1994). “Economic Incentives transform psychological anomalies”, Journal of Economic Behaviour and Organization, vol.23, pp.215-234. Fry, T., Heaney R. and Mckeown, W. (2007). Will investors change their superannuation fund given the choice?”, Accounting and Finance, vol.47, pp.267-283. Fuertes, A. M. and Heffernan, S. A. (2009). “Interest rate transmission in the UK: A comparative analysis across financial firms and products”, International Journal of Finance and Economics, vool.14, pp.45-63. Fuertes, A. M., Heffernan, S. A., and Kalotychou, E. (2010). “How do UK banks React to Changing Central Bank Rates?”, Journal of Financial Services Research, vol. 37, no.2-3, pp. 99-130. Gambacorta, L. (2008). “How do banks set interest rates”, European Economic Review, vol.52, pp.792-819. Harris, C. and Laibson, D. (2001). “Dynamic Choices of Hyperbolic Customers”, Econometrica, vol.69, no.4, pp.935-957. Hepburn, C., Duncan, S. and Papachristodoulou, A. (2010). “Behavioural Economics, Hyperbolic Discounting and Environmental Policy”, Environmental and Resource Economics, vol.46, pp.189-206. Hannan, T. H. & Berger, A. N. (1991) The Rigidity of Prices: Evidence from the Banking Industry, The American Economic Review, 81(4), pp.938-945. Heffernan, S.A. (1997) Modelling British Interest Rate Adjustment: An Error Correction Approach, Economica, 64(254), pp.211-231. Heffernan, S. A. (2002) How Do UK Financial Institutions Really Price Their Banking Products?, Journal of Banking and Finance, 26(10), pp.1997-2016. Huberman, G. and Jiang, W. (2006). “Offering versus Choice in 401(k) Plans: Equity Exposure and Number of Funds”, The Journal of Finance, 61, no.2, pp.763801. 27 Hirshleifer, D. (2001). “Investor Psychology and Asset Pricing”, The Journal of Finance, vol.56, no.4, pp.1533-1597. Ireland, N. J. (2007). “Posting Multiple Prices to Reduce the Effectiveness of Consumer Price Search”, The Journal of Industrial Economics, vol.55, no.2, pp.235-263 Irons, B. and Hepburn, C. (2007). “Regret Theory and the Tyranny of Choice”, The Economic Record, vol.83, no.261, pp.191-203. Jackson, W. E. III. (1997). “Market Structure and the Speed of Price Adjustments: Evidence of Non-Monotonicity”, Review of Industrial Organization, vol. 12, pp.37-57. Kahn, C., Pennacchi, G., Sopranzetti, B., 1999. Bank Deposit Rate Clustering: Theory and Empirical Evidence. The Journal of Finance, vol.54, pp.21852214. Kahneman, D. (1992). “Reference Points, Anchors, Norms and Mixed Feelings”, Organizational Behavior and Human Decision Processes, vol.51, pp.296-312 alenscher, T. And Pennartz, C. M. A. (2008). “Is a bird in the hand worth two in the future? The neuroeconomics of intertemporal decision making”, Progress in Neurobiology, vol.84, pp.284-315. Karlsson, A. and Nordén, L. (2007). “Home sweet home: Home bias and international diversification among individual investors”, Journal of Banking and Finance, vol.31, pp.317-333. Kempf, A and Ruenzi, S. (2006), “Status Quo Bias and the Number of Alternatives: An Empirical Illustration from the Mutual Funds Industry”, The Journal of Behavioural Finance, vol.7, no.4, pp.204-213. Kiranova, T. and Sefton, J. (2007). “A comparison of national saving rates in the UK, US and Italy”, European Economic Review, vol. 51. pp. 1998-2028. Kirby, K.N. (1997). “Bidding on the Future: Evidence Against Normative Discounting of Delayed Rewards”, Journal of Experimental Psychology: General, vol.1236, no.1, pp.54-70. Kozup, J. and Hogarth, J. M. (2008). “Financial Literacy, Public Policy and Consumers’ Self Protection – More Questions, Fewer Answers”, The Journal of Consumer Affairs, vol.42, no.2, pp.127-136. Laibson, D. (1997). “Golden Eggs and Hyperbolic Discounting”, The Quarterly Journal of Economics, Vol.112, No.2, pp.443-477. Lim, G. (2001) Bank Interest Rate Adjustments: Are They Asymmetric, The Economic Record, 77(237), pp.135-147. Madrian, B. C. and Shea, D. F. (2001). “The Power of Suggestion: Inertia in 401(k) Participation and Savings Behaviour”, The Quarterly Journal of Economics, vol.116, no.4, pp1149-1187. Martin-Oliver, A, Salas-Fuma, V, and Saurina, J. (2008). “Search Cost and Price Dispersion in Vertically Related Markets: The Case of Bank Loans and Deposits”, Review of Industrial Organization, vol.33, pp.297-323. Martin-Oliver, A. (2007) “A test of the Law of One Price in retail banking”, Journal of Money, Banking and Credit, vol.39*, no.8, pp. 2021-2040. McClure, S. M., Laibson, D. I, Loewenstein, G. and Cohen, J. D. (2004). “Separate Neural Systems Value Immediate and Delayed Monetary Rewards”, Science, vol.306, pp.503-507. 28 Mittelhammer, R.; Judge, G.; Miller, D. (2000) “Econometric Foundations”, Cambridge: UK Morrin, M, Broniarczyk, S. Inman, J. J. and Broussard, J. (2008). “Saving for Retirement: The Effects of Fund Assortment Size and Investor Knowledge on Asset Allocation Strategies”, The Journal of Consumer Affairs, vol. 42, no.2, pp.206-222 O’Donoghue, T. and Rabin, M. (1999a). “Doing It Now or Later”, The American Economic Review, vol.89, No.1, pp.103-124. O’Donoghue, T. and Rabin, M. (1999b). “Incentives for Procrastinators”, The Quarterly Journal of Economics, vol.114, no.3, pp769-816. O’Donoghue, T. and Rabin, M. (2001). “Choice and Procastination”, The Quarterly Journal of Economics, vol.116, No.1, pp.121-160. Office of Fair Trading. (2010). Cash ISAs: Response to supercomplaint by consumer Focus, OFT1246, London. Paisley, J. (1994). A model of building society interest rate setting. Bank of England Working Paper Series, 22, (London: Bank of England). Read, D. (2001). “Is Time-Discounting Hyperbolic or Subadditive?”, Journal of Risk and Uncertainty, vol.23, no.1, pp.5-32. Rogers, W. (1991) “Regression Standard Errors in Clustered Samples”, Stata Technical Bulletin, vol.13, available in Stata Technical Bulletin Reprints, vol.3, 89-94. Rubinstein, A. (2003). “Economics and Psychology”? The Case of Hyperbolic Discounting”, International Economic Review, vol.44, no.4, pp.1207-1216. Salop, S. and Stiglitz, J. (1977). “Bargains and Ripoffs: A Model of Monopolistically Competitive Price Dispersion”, Review of Economic Studies, vol. 44, no.3, pp. 493-510. Samueulson, W. and Zeckhauser, R. (1988). Status Quo Bias in Decision Making”, Journal of Risk and Uncertainty, vol.1, pp.7-59. Sander, H. and Kleimeier, S. (2004). “Convergence in euro-zone retail banking? What interest rate pass-through tells us about monetary policy transmission, competition and integration”, Journal of International Money and Finance, vol.23, pp.461-492. Shiwakoti, R.; Keasey, K. and Hudson, R. (2008) Comparative Performance of UK Mutual Societies and Stock Retail Banks: Further Evidence, Accounting and Finance, vol.48, pp. 319-336. Soman, D., Ainslie, G., Frederick, S., Li, X. Lynch, J. Moreau, P., Mitchell, A., Read, D., Sawyer, A., Trope, Y., Wertenbroch, K. and Zauberman, G. (2005). “The Psychology of Intertemporal Discounting: Why are Distant Event Valued Differently from Proximal Ones?”, Marketing Letters, vol.16, issue 3/4, pp.347-360. Stango, V. and Zinman, J. (2009). “Exponential Growth Bias and Household Finance”, Journal of Finance, vol.64, no.6, pp.2807-2849. Steel, P. (2007). “The Nature of Procrastination: A Meta-Analytic and Theoretical Review of Quintessential Self-Regulatory Failure”, Psychological Bulletin, vol.133, no.1, pp.65-94. Subrahmanyam, A. (2009). “Optimal Financial Education”, Review of Financial Economics, vol.18, pp.1-9. 29 Thaler, R. H. and Benartzi, S. (2004). “Save More Tomorrow: Using Behavioural Economics to Increase Employee Saving”, Journal of Political Economy, vol.112, no.1, pp.s164-s187. Williams, T. (2007). “Empowerment of Whom and for What? Financial Literacy Education and the New Regulation of Consumer Financial Services”, Law and Policy, vol.29, no.2, pp.226-256. Wong, W. K. (2008). “How much time-inconsistency is there and does it matter? Evidence on self-awareness, size and effects”, Journal of Economic Behaviour and Organization, vol. 68, pp.645-656. Zellner, A. (1962) An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias, Journal of the American Statistical Association, 57(298 - June), 348-368. 30