The Dynamics of Bank Location Decisions in Australia1
Christopher Heard, Flavio M. Menezes2 and Alicia Rambaldi School of Economics, The University of Queensland
Abstract: This paper exploits a large panel to study trends in, and determinants of, the decisions made by the four largest Australian banks about whether to establish or maintain branch‐ and ATM‐ level presence in a local market between 2002 and 2013. These decisions are potentially important for competition in local banking markets. Our analysis suggests that past presence is the most important factor for explaining current presence in a particular local market. Moreover, we present evidence that the four largest banks co‐locate branches. The impact of the location of other (smaller) banks on the location of the four largest banks is less clear; there is some limited evidence that this impact is negative for two of the four largest banks. Our results also suggest that the four largest banks responded differently to the GFC in terms of their branch location decisions. Our analysis of ATM location decisions reveals that the four largest banks follow different strategies. These results suggest that Australian banks did not shy away from competition, either before or after the GFC.
Keywords: branch location; exit; entry; banking.
1 The authors acknowledge the financial support of the Australian Institute of Business and Economics. Flavio Menezes acknowledges his senior research fellowship with AIBE. 2 Corresponding Author. Email: [email protected]. 1
1. Introduction
This paper studies the trends in, and determinants of, the decisions made by the four largest Australian banks about whether to establish or maintain branch‐ and ATM‐level presence in a local market. The aim of our analysis is to understand how competition between the four largest banks manifests itself through their location decisions after controlling for population characteristics and the presence of other financial institutions. This aim can be seen as complementary to a more direct investigation of competition. A secondary aim of this paper is to determine whether some of the Australian Senate’s concerns about competition more broadly and, in particular in regional areas, at least as measured by the patterns of location decisions, are supported by the data.
There are many aspects to studying competition in the banking sector. From a consumer’s perspective, bank competition can have a direct impact on the costs associated with some of the most important individual lifetime financial decisions such as obtaining a mortgage. From an industrial organisation perspective, the banking sector is particularly affected by technological change, such as automation from the 1970s with the advent of automatic teller machines (ATMs), online banking from the 1980s and internet banking from the mid‐1990s, to more recent innovations in payment systems platforms for smart phones.
Importantly, the interest in banking competition is necessarily intertwined with concerns about financial stability. The potential trade‐off between competition and financial stability is well‐known and has been widely studied. As Allen and Gale (2014) point out, ‘greater competition may be good for (static) efficiency, but bad for financial stability’.
In Australia, this trade‐off between competition and financial stability is behind the ‘Four Pillars’ policy. This policy aims to maintain competition by preventing mergers between the four largest institutions (collectively referred to as the ‘Big 4’): the Australia and New Zealand Banking Group (ANZ), the Commonwealth Bank of Australia (CBA), National Australia Bank (NAB) and Westpac Banking Corporation (Westpac).
The Four Pillars policy is, however, controversial (Davis (2011)) and is varyingly credited with helping to shield Australia from the Global Financial Crisis (Lewis (2013)) and with reducing competition and harming consumers (CIFR (2015)). An Australian Senate (2011) inquiry into competition within the Australian banking system also raised concerns about a reduction in banking competition in the wake of the Global Financial Crisis (GFC).3 In addition, the Committee’s Final Report highlighted the importance of branch networks as a potential barrier to entry (p. 63) and raised concerns that ‘banks may not be competing to provide a good service in remote areas” (p. 70).
The importance of branch location decisions for bank strategy and the analysis of competition is well recognised (See Meidan 1984 and Amel, Kennickell & Moore 2008). Branch location decisions may be driven by a number of strategic considerations. For example, Chang, Chaudhun and Jayarantne
3 “…policymakers in Australia, as prudence demanded, accorded greater priority to the immediate stability of the financial system. The net effect, together with other forces at play during this period (including the relative inability of smaller institutions to access funds at a competitive price) has led to a more concentrated banking market than existed prior to the financial crisis, with the 'big four' banks increasing their dominance across most if not all banking markets.” (p. xv).
2
(1997) propose a model of ‘rational herding’ in which banks locate branches close to the branches of other banks due to lack of information about likely performance, even though aggregate results from clustering may be suboptimal. They use a cross‐sectional model and data from New York City to find evidence consistent with rational herding. The link between branch location and competition is also explored by Cohen and Mazzeo (2010). These authors find that competition and potential competition with multimarket banks induce banks of all types to expand their branch networks, suggesting that this is consistent with strategic attempts to deter entry.
In this paper we focus exclusively on entry and exit decisions with a view to understanding their dynamics. Our approach is to first document how the branch‐level presence of the Big 4 banks has changed over time. Then we estimate a series of dynamic panel probit regressions, following Wooldridge (2005), to explain the entry and exit decisions of the Big 4 banks. Our analysis, however, takes into account the impact of the demographic characteristics of the population on banking location decisions. 4
By and large our analysis suggests that, perhaps not surprisingly, past presence is the most important factor for explaining current presence in a particular postcode. Moreover, we present compelling evidence that the Big 4 co‐locate branches. The impact of the location of non‐Big 4 banks on the location of the Big 4 banks is less clear; there is some limited evidence that this impact is negative for two of the Big 4 banks. This limited evidence does not seem to be enough to give credence to concerns about competition in areas where larger banks are less likely to locate, due to, for example, a small population base or a low level of economic activity. Our results also suggest that the Big 4 responded differently to the GFC in terms of their branch location decisions.
Our analysis of ATM location decisions reveals a divergence of behaviour among the Big 4, with different banks following different strategies. These include targeting postcodes that are poorly served by non‐Big 4 institutions with larger populations and less income reported from businesses, to targeting postcodes with larger populations and higher average taxable income. This diversity of strategies also undermines concerns about lack of competition by the Big 4 in the location of ATMs. It suggests that the Big 4 react to each other’s location decisions in different ways, which is often a characteristic of competitive markets. 5
This paper is organised as follows. Section 2 describes some standard measures of competition in the banking sector and explains how our approach complements existing measures of competition. Section 3 provides an analysis of recent trends in the location of bank branches in Australia, relating these to the trends in the location of ATMs. Section 4 presents the data and empirical approach. Section 5 presents the empirical results while Section 6 concludes with a discussion of bank behaviour in terms of entry and exit.
2. Measuring competition in the Australian banking sector
Competition analysis traditionally focused on measures of concentration, such as market shares, as proxies for competition (in banking see for example Cetorelli and Strahan, 2006). The more recent
4 This is similar to the approach of Huysentruyt, Lefevere and Menon (2013). 5 For example, competition analysis often relates the existence of a maverick firm, which exhibits strategic behaviour that diverges from other firms in the market, to the prevalence of vibrant competition. In particular, mergers that involve a maverick firm often receive closer scrutiny from competition authorities. See Breunig and Menezes (2008). 3
approaches, however, recognize that market shares are of limited use in understanding how competition constrains the behaviour of firms in a market and, instead, focus on direct measures of market power such as the Lerner index (Lerner, 1934), the adjusted‐Lerner index (Koetter, Kolari and Spierdijk, 2012), the H statistic (Panzar and Rosse, 1987), and the profit elasticity (Boone, Griffith and Harrison, 2005).
However, directly measuring competition (or market power) in the banking sector is not straightforward given that prices, quantities and costs data, the backbone of most studies of competition, are usually not publicly available. Thus, often, the degree of competition is measured indirectly. For example, in a seminar paper, Keeley (1990) developed a theoretical framework, based on the ‘charter value’ of a bank, to measure the market power of banks. He then used this framework to show empirically that the US banking regulation of the 1970s and 1980s had increased competition and led to a reduction in monopoly rents.
More recently, the availability of some data on prices and quantities (e.g., through Bankscope) in the banking sector has allowed some inroads in the direct estimation of market power. Nevertheless, it remains the case that data on marginal costs needs to be estimated econometrically. For example, Clerides et al. (2015) estimate the degree of competition in the banking sectors of 148 countries over the period 1997–2010 using three methods: the Lerner index, the adjusted Lerner index, and the profit elasticity. Marginal cost estimates required for all methods are obtained using a flexible semi‐parametric methodology. These authors show that all three indices point to a deterioration of banking competition during the period 1997–2006, an improvement until 2008, and further deterioration post GFC.
Clerides et al. (2015) show that the levels of competition differ across regions and income groups, but there is gradual convergence over time. For Australia, for example, their estimated Lerner Index is reproduced in Table 1 below.
Table 1: Lerner Index for the Australian Banking Sector 1997 1998 1999 2000 2001 2002 2003 2004 1997‐ 2010 mean 0.253 0.248 0.211 0.285 ‐0.085 0.225 n.a. n.a. 0.209 2005 2006 2007 2008 2009 2010 0.250 0.233 0.218 0.165 0.250 0.251 Source: Clerides et al. (2015)
This suggests that the mean mark‐up over marginal costs (as a percentage) for the 1997‐2010 period is equal to 20.9%. Given the reliance on estimates of marginal costs, it is difficult to know how accurate such predictions are. Taking this evidence at face value, it suggests that the Australian banking sector is on average more competitive than the UK and US sectors but less competitive than Canada’s. We note, however, that the profitability of the banking sector implied by these numbers is consistent with that observed in practice. For example, the Reserve Bank of Australia reports that the major banks’ return on assets averaged around 0.9 per cent and the post‐tax return on equity averaged about 15 per cent from 1992 to 2010.6 The analysis in this paper can be seen as complementary to the direct estimation of measures of market power. For example, if the Big 4
6 See http://www.rba.gov.au/publications/submissions/inquiry‐comp‐aus‐bank‐sect‐1110.pdf. 4
were to systematically avoid co‐locating, this could be construed as circumstantial evidence of lack of competition. The analysis in Sections 4 and 5 below reveals a pattern of location decisions that is consistent with the prevalence of competitive behaviour, with the largest banks’ likelihood of entering a market increasing with the number of competitors.
3. Data and Trends in the number of bank branches
3.1 Data
Our analysis exploits a new panel dataset based on points of presence data at postcode level collected by APRA. These data are provided to APRA by ADIs and released annually on the APRA website. Postcodes in Australia are spatial units7 defined by Australia Post to facilitate delivery of postal services. They are also widely used in the collection of data and are generally the smallest spatial units for which statistical data are available.8 For each postcode, our panel contains measures of ADI presence derived from the APRA data set, as well as various demographic characteristics taken from Australian Tax Office (ATO) data. 2,705 distinct postcodes appear at least once in the APRA data, of which 2,241 have demographic data available.
Our analysis assigns presence to postcodes and uses postcodes as the basic unit of analysis. This precludes an effective spatial approach because spatial analysis using arbitrarily imposed spatial units leads to discretization bias (Huysentruyt et al. 2013). It is also unclear how proximity could be meaningfully understood in the context of postcodes, some of which are very large. Previous studies have overcome this by using point‐pattern analysis (Duranton & Overman 2005) or similar methods (Huysentruyt et al. 2013). These solutions are limiting for our dataset as they require identification of precise location (through, for example, addresses). In future work we will seek to augment the database to add the addresses of branches and their actual geographic location in latitude and longitude.9
For each postcode, the annual APRA releases contain raw data which consists of entries corresponding to each of the different types of point of presence of an institution in a particular postcode. Each entry gives a name for the point of presence type and includes a description of the service offered. APRA compiles this information from surveys by ADIs in which they report all of their points of presence. ADIs are required to provide this information in accordance with APRA Reporting Form ARF 396.0 (APRA 2008). ADIs are responsible for naming and describing reported points of presence, although there are some guidelines (for example, ADIs report whether a point of presence offers a specified minimum range of services considered to be ‘branch‐level’).
7 Excepting a small number of postcodes with no spatial correspondence (used, for example, for specific government functions). 8 This varies between datasets. For example, the census includes data at postcode level, but other statistics released by the ABS are typically not available at postcode level. Instead, specially defined Statistical Areas are used to overcome some of the shortcomings of postcodes as spatial units. In particular, postcode boundaries are not fixed and do change over time, particularly in regional areas. Additionally, ‘smallest’ is potentially misleading because postcodes in remote parts of the country can be very large. 9 Huysentruyt et al. (2013) assign location based on address, but only for branches (not for ATMs, which may be more difficult to locate). Their analysis is only for three years and 233 neighbourhoods. By contrast we use data from 12 years with over 2,000 postcodes. 5
Each annual release contains raw data from the year of release and the two preceding years. Inconsistencies between data for the same year from different releases are resolved by using the data from the most recent release. Points of presence are reclassified into 16 groups. There are many cases of ambiguity to resolve. These are limited for ATMs, and assignment of branches is aided by the branch‐level service indication. Assignment of other points of presence is challenging as clear descriptions are not always available. There is also evidence of inconsistent reporting of some point of presence types (for example, one ADI may report a large number of one point of presence type, while a similar institution reports none or very few). This is particularly true for points of presence for which the meaning of location is unclear (e.g. EFTPOS).
To characterise the level and diversity of ADI presence the panel includes the dummy variables anzpresent and anzatmpresent. The former takes the value 1 if ANZ has at least one branch‐level point of presence in a postcode and 0 otherwise, while the latter takes the value 1 if ANZ has at least one ATM in the postcode. There are analogous variables for the CBA (cbapresent, cbaatmpresent), NAB (nabpresent, nabatmpresent) and Westpac (wespresent, wesatmpresent). Presence of non‐Big 4 ADIs is captured by noothpresent and noothatmpresent which record the number of non‐Big 4 ADIs with a branch‐level point of presence or ATM in a postcode, respectively. In late 2008, the non‐Big 4 banks Bank of Western Australia (operating as Bankwest) and St George were acquired by the CBA and Westpac respectively. We treat the acquired banks’ points of presence as belonging to the relevant acquiring bank from 2009 onwards.
This results in a panel describing the presence of all ADIs, with each row representing a Year, Postcode combination. We used this panel to produce the geographic visualisations in Section 3.2. The panel is unbalanced as many postcodes do not appear in all 12 years. It is unsatisfactory to treat these as ‘missing’ observations because ADIs are required to report points of presence only in postcodes where they are present. They do not report those postcodes where they are absent. This means a ‘missing’ observation is likely to be a postcode where no ADIs are present in the relevant year. Consequently, we insert zeros for the presence variables, which makes the panel balanced. When a postcode has no points of presence of any kind in any year, it is excluded from the data. In the absence of a record of postcodes in each year our approach has the benefit of minimising the risk of falsely omitting a large number of postcodes, which could lead to biased estimates, although we recognise that the approach potentially ignores the possibility that new postcodes are introduced or old postcodes retired.
3.2 Trends in the number of bank branches
Bank branch numbers in Australia rose throughout the 1970s and 1980s, largely due to the entry of new institutions, then declined from the late 1980s into the 1990s (RBA 1996). Despite the increase in branch numbers until the 1980s, the number of bank branches per head of population declined steadily between the early 1970s and mid‐1990s, excepting a short period of levelling off in the late 1980s (RBA 1996).
Figure 1 shows the number of branches present Australia‐wide for the 2002‐2013 period, based on APRA data. ‘AI’ refers to all Authorised Deposit Taking Institutions (ADIs), and ‘B4’ refers to Big 4 banks. Although the numbers are not directly comparable to those from earlier RBA reports (slightly different institutions are included), it is clear that branch numbers have ended the downward trend observed in the 1990s and have instead stabilised. There is a spike in branch numbers for non‐Big 4 institutions in 2005‐06, but otherwise there appears to be a gradual upward trend. Big 4 branch 6
numbers have remained stable, increasing slightly in 2005‐06 and in 2008‐09 (the latter increase corresponds to the acquisitions of The Bank of Western Australia and St George, two smaller banks, by the CBA and Westpac respectively).
Figure 1: Number of bank branches in Australia
8000
7000
6000
5000
4000
Branches 3000
2000 AI Branches 1000 B4 Branches
0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Source: APRA
The decline in the number of branches per head of population also appears to have ended. Figure 2 shows the number of branches per million inhabitants of Australia from 2012‐2014. Branches per head of population is stable for the Big 4 over the period, and although a spike is observed for non‐ Big 4 institutions in 2005‐06 the ratio gradually returns to the 2002 level by 2014. This suggests that, at least at an aggregate level, the 2000s and early 2010s have been a period of stability in contrast to the large changes of the late 20th century.
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Figure 2: Number of bank branches per population (million)
350
300
250
200
Ratio 150
100 AI Branches 50 B4 Branches
0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Source: APRA
In its 1996 bulletin, the RBA also noted that non‐metropolitan areas have historically recorded more branches per head of population than metropolitan areas, but that this difference narrowed throughout the 1970s to the 1990s.
The APRA points of presence data include location information (postcodes), allowing us to study whether this trend has continued. Figures 3 and 4 show maps of Australia for 2002 and 2011 respectively with postcodes coloured by the number of branches per (thousand) head of population. Postcode boundaries are taken from the 2006 census data. Postcodes coded as missing may have no observed points of presence in the relevant year, may have been lacking population data in the relevant census, or may have had zero population recorded in the census. Maps are also presented of the Sydney and Melbourne metropolitan areas.
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Figure 3: Branches per Population (thousands) in 2002
Sydney
Melbourne
Source: APRA
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Figure 4: Branches per Population (thousands) in 2011
Sydney
Melbourne
Source: APRA
Regional postcodes in Queensland, New South Wales, Victoria and Southern Western Australia continue to have relatively high numbers of branches per head of population compared to metropolitan areas. The distribution of branches does not appear to have changed significantly over the 2002‐2011 period. Figure 5 shows the total percentage change in branches per head of population from 2002 to 2011. We code postcodes with 0 branches in 2002 and 2011 as having a 0% change and postcodes with 0 branches in 2002 and a positive number of branches in 2011 as having a 100% increase. Overall, there is no clear direction of change in either regional or metropolitan areas. Metropolitan postcodes are very mixed, decreases in branches per head of population in regional Queensland contrast with increases in branches per head across regional New South Wales.
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Figure 5: Percentage Change in Branches per Population (thousands) Between 2002 and 2011
Sydney
Melbourne
Source: APRA
While population is certainly a driver for branch location, economic activity also ought to matter for location decisions. In 2006 the ABS provided retail trade data at postcode level. These data record the number of businesses from different sectors in groups based on their number of employees and their annual turnover. By assuming that each firm in a group has employees and turnover equal to the midpoints of the upper and lower limits of that group, it is possible to obtain approximate measures of total employment and total turnover for each postcode.
Figure 6 shows postcodes coloured by the number of branches in 2006 per billion dollars of annual turnover. Equivalent images for number of branches in 2006 per thousand employed persons were produced but are omitted because they were very similar, with both measures of economic activity being highly correlated (r=0.91).
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Figure 6: Branches per Annual Turnover ($ billion) in 2006
Sydney
Melbourne
Source: APRA and ATO
As with branches per head of population, branches per turnover is relatively high in regional areas compared to metropolitan areas. Within metropolitan areas less variability is observed, with the great majority of postcodes having fewer than 11 branches per billion dollars of annual turnover (and most having fewer than 3). Notably, CBD postcodes no longer stand out from surrounding postcodes. This suggests that, at least within metropolitan areas, branches cluster in postcodes of high economic activity, rather than postcodes with large populations.
Finally, we document the relationship between the evolution of branches and ATMs. In 1996 the RBA noted the increasing importance of ATMs as an alternative service delivery channel to branches, reporting that the number of ATMs first exceeded the number of branches in that year (RBA 1996).
Figure 7 compares the number of branches and number of ATMs per head of population over the 2002‐2013 period. It is clear that the number of ATMs increased rapidly in the late 1990s and early 2000s, from comparable to the number of branches in the mid‐1990s (RBA 1996) to over twice the number of branches in 2002. From 2002 to 2013, however, the ratio of ATMs to branches was quite stable. The number of ATMs appears to have increased slightly faster than branches up to 2010, but declined more rapidly from 2011 to 2013. The overall result is that, in 2013, the ratio of branches to ATMs was virtually unchanged from the ratio in 2002. The rapid growth in ATM presence, and any
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associated displacement of branch banking, appears to have ended. Figure 7 also highlights that, at least at an aggregate level, the effect of the 2007‐08 GFC on the Australian retail banking sector was relatively mild. This is consistent with observations made by the RBA (Donovan & Gorajek 2011). Figure 7: Branches and ATMs per million people
900 800 700 600 AI Branches 500 AI ATMs
Ratio 400 300 200 100 0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Source: APRA
4. Empirical Approach
Our empirical approach consists of estimating a number of versions of an econometric model designed to study whether postcode demographics and levels of local competition predict entry and exit dynamics. The basic framework is the dynamic panel probit regression of Wooldridge (2005). The panel dimension allows us to control for unobserved effects.
A Probit model entails a non‐linear relationship, and thus first‐differencing cannot be used to remove any unobserved effects. A common solution then is to assume that the unobserved effect is a function of an independent variable. Adding the independent variable controls for the unobserved effect. This approach is often referred to as the Mundlak‐Chamberlain method (Mundlak (1978), Chamberlain (1980)). Wooldridge (2005) uses Mundlak’s (1978) approach to design an estimator for the dynamic panel Probit regression. Like Heckman’s (1981) it also builds on the random effects (RE) specification. However, Woolridge’s is simpler as it can be estimated with standard RE software. The instruments are the set of non‐redundant explanatory variables in all periods. That is, strictly exogenous variables that vary over time. The initial condition is assumed fixed, and the dynamics correctly specified.
While this framework allows us to control for unobserved postcode level effects, we are unable to estimate the effect of time‐invariant variables. The approach taken is to suppose that institutions decide each year whether or not to have a branch‐level presence in a particular postcode (a binary variable equal to 1 if the institution is present and 0 otherwise). We include as explanatory variables
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a full set of time dummies, the lagged presence variable for the institution of interest, the number of the other Big 4 with a branch‐level presence in the postcode (nob4, 0‐3) and the number of other (non‐Big 4) institutions with a branch‐level presence in the postcode (nooth). In addition, a number of demographic variables are included in the model as controls (totind, meantaxinc and nbusincloss). These are discussed below
The econometric model is of the following form:
, , , 4 ,…, 4 , ,…, , 1 ,…, , , … , , , … , , Φ η 4 , 1, … , 1 Where t=1 corresponds to 2003 and t=T corresponds to the final year in the sample (2013 in this case). The initial condition is given by , . The unobserved effect, ci, is assumed to satisfy the assumptions outlined in Wooldridge (2005). The ηt are a set of year intercepts. To account for unobserved heterogeneity we include nob4, nooth and the demographic variables for all periods between 2003 and 2013 inclusive.
We then estimate analogous regressions for ATM presence. To improve efficiency and to formally test for inter‐bank heterogeneity, we then estimate pooled models (combining all Big 4 into a single regression), allowing us to use Wald tests for coefficient differences. We then extend the analysis by studying whether there are changes in dynamics due to the acquisitions in late 2008 or in the post‐ GFC period. This is achieved by adding interactions between the presence variables and the appropriate year dummies.
Demographic Control Variables and Descriptive Statistics of Model Data
The presence data (see Section 3.1) are supplemented by demographic data from the ATO which reports information about personal taxation at postcode level on an annual basis. The variables included are the sum of taxable and non‐taxable residents (totind), mean taxable income (meantaxinc) and total net income or loss reported from business sources (nbusincloss). The first is included to control for population (totind and population reported in the census are highly correlated with r=0.99) and the second to control for socioeconomic factors. nbusincloss is included to attempt to control for economic activity, since geographic visualisations suggest that economic activity may be important for explaining branch presence. Unfortunately the data on which the visualisations are based are not available at postcode level annually, so cannot be included in our regression. We include nbusincloss under the rationale that business owners may live close to their businesses, and therefore business income or loss reported in a postcode may be correlated with economic activity in that postcode. Both nbusincloss and meantaxinc are adjusted for inflation using ABS CPI data. All values are in 2013 Australian dollars
Table 1 reports descriptive statistics for the variables included in the model. The large standard deviations suggest a high level of inter‐postcode variability. There are as few as 5 individuals and as many as 59,765 and mean taxable income ranges from less than $7,000 to over $250,000, so there is considerable variation in demographic characteristics.
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Table 1: Descriptive Statistics
Variables Mean (s.d.) 2002 2007 2013 Overall anzpresent 0.24 (0.43) 0.26 (0.44) 0.25 (0.43) 0.25 (0.43) cbapresent 0.30 (0.46) 0.31 (0.46) 0.32 (0.47) 0.31 (0.46) nabpresent 0.24 (0.43) 0.26 (0.44) 0.25 (0.44) 0.25 (0.43) wespresent 0.20 (0.40) 0.27 (0.44) 0.32 (0.46) 0.27 (0.44) anzatmpresent 0.24 (0.43) 0.34 (0.47) 0.39 (0.49) 0.33 (0.47) cbaatmpresent 0.38 (0.49) 0.38 (0.49) 0.40 (0.49) 0.39 (0.49) nabatmpresent 0.25 (0.43) 0.26 (0.44) 0.28 (0.45) 0.28 (0.45) wesatmpresent 0.27 (0.44) 0.28 (0.45) 0.36 (0.48) 0.31 (0.46) noothpresent 0.69 (1.84) 0.98 (1.95) 0.83 (1.67) 0.85 (1.81) noothatmpresen 0.90 (1.48) 1.35 (1.81) 0.88 (1.39) 1.13 (1.64) totind 4294.68 4929.42 5208.78 4897.96 (5468.09) (6289.05) (6805.56) (6320.04) meantaxinc 50281.61 56425.08 50229.18 53203.80 (11643.10) (16370.33) (15449.19) (14793.85) nbusincloss 6454372.03 8673935.67 10554487.04 8802126.16 (8809295.40) (12441606.42) (16108877.59) (13000802.60)
One main object of the study is the decisions to enter or exit (or stay) in a market by the Big 4. To this end, Figure 8 shows the frequencies of entry and exit for each of the Big 4 from the raw data. Generally, entry frequencies were relatively high (compared with exit frequencies) in earlier years, while exit frequencies were relatively high after 2007. The exceptions are CBA and Westpac in 2008‐ 09, when these banks acquired The Bank of Western Australia and St George respectively. The spike in entries for the CBA in 2008‐09 is followed in 2009‐10 by a spike in exits, perhaps suggesting consolidation following the merger. Note the important difference in scale in Figure 8. ANZ and the CBA had comparable volatility (all frequencies between 0% and 1.8%), NAB had several years where transition frequencies were between 1% and 3.5%, and Westpac’s trends were dominated by very large spikes in entry frequency in 2005‐06 and 2008‐09 (8.4% and 5.3% respectively). These results highlight the differences between the behaviour of the Big 4, suggesting that entry and exit decision making may differ across institutions.
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Figure 8: Big 4 Entry and Exit Frequencies 2002‐2013
5. Empirical Results
Table 2 reports the maximum likelihood estimates of equation (1). The coefficients of the nob4, nooth and demographic variables from all periods which are included to account for unobserved heterogeneity, as well as the coefficients of the year dummies are excluded to simplify the output (these coefficients are not of interest).
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Table 2: Branch Regressions ML estimates (** indicates significance at the 5% level, * at the 10% level)
Explanatory variable ANZ CBA NAB Westpac presentt‐1 4.592** 3.689** 4.091** 4.334** (0.216) (0.190) (0.158) (0.093) nob4t 0.261** 0.681** 0.548** 0.210* (0.128) (0.146) (0.120) (0.107) nootht ‐0.011 ‐0.178* 0.024 ‐0.171** (0.071) (0.093) (0.069) (0.059) totindt 0.00002 0.0004** 0.00005 0.00004 (0.00006) (0.00008) (0.00006) (0.00005) meantaxinct ‐0.00002* 0.000007 ‐0.00001 0.00000006 (0.00001) (0.00001) (0.00001) (0.000009) nbusinclosst 0.000000002 ‐0.00000003 ‐0.00000002 0.00000004** (0.00000002) (0.00000002) (0.00000002) (0.00000001) present0 0.476 2.564** 1.926** 0.880** (0.600) (0.563) (0.437) (0.144) constant ‐4.240** ‐3.506** ‐3.500** ‐2.863** (0.539) (0.464) (0.421) (0.226) 0.205 0.787 0.775 0.00001 (0.503) (0.163) (0.148) (0.0003) Log‐likelihood value ‐678.367 ‐682.966 ‐877.957 ‐1319.441
Recall that in this framework, the exit probability is the probability that the institution chooses not to have a branch‐level presence in a postcode in year t, conditional on having a branch‐level presence in the postcode in year t‐1. Similarly, the entry probability is the probability that the institution chooses to have a branch‐level presence in the postcode in year t, conditional on not having a branch‐level presence in year t‐1.
Table 2 reveals that lagged presence has a large, positive and significant coefficient for all of the Big 4, and the magnitude of the coefficients is comparable across institutions. There is also evidence that the Big 4 co‐locate branches, since the coefficients of nob4t are positive and significant for all four banks. The coefficients for the CBA and NAB are larger than those for ANZ and Westpac.
We find that the coefficient of nootht is negative for Westpac (at the 5% level of significance) and there is some evidence (significant at the 10% level) of a negative effect for CBA. The negative coefficients are similar in size, but in the Westpac regression the coefficient is about four‐fifths the magnitude of the coefficient on nob4t, while for CBA the nob4t coefficient is almost four times as large.
Moreover, totindt has a positive coefficient in the CBA regression (and not significant in the other regressions) and nbusinclosst has a positive coefficient in the Westpac regression. That is, CBA displays a preference for postcodes with relatively high populations, while Westpac displays a preference for postcodes with more economic activity (at least as measured by personal income from businesses). These results may suggest differences in the strategic behaviour of different banks.
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Statistical comparison between the banks and more efficient estimation can be achieved by pooling all four institutions together and estimating a combined regression. Table 3 reports the maximum likelihood estimation of the combined regression. The regression equation itself is the same as the equation above with the addition of dummy variables for CBA (cba), NAB (nab) and Westpac (wes), and interactions of those dummies with the year dummies, nob4, nooth and totind. As there are a large number of coefficients, the columns of Table 3 give the coefficients on the interaction terms.
Table 3: Pooled Branch ML estimates (** indicates significance at the 5% level, * at the 10% level)
Interaction Explanatory variable *cba *nab *wes presentt‐1 4.422** ‐0.516** ‐0.175 ‐0.279 (0.174) (0.219) (0.208) (0.195) nob4t 0.330** 0.246 0.145 ‐0.055 (0.120) (0.169) (0.158) (0.166) nootht ‐0.014 ‐0.141 0.044 ‐0.223** (0.078) (0.116) (0.102) (0.102) totindt 0.00003 0.0003** ‐0.00002 0.0001 (0.00005) (0.00007) (0.00007) (0.00007) meantaxinct ‐0.000006 ‐ ‐ ‐ (0.000006) nbusinclosst 0.0000000009 ‐ ‐ ‐ (0.000000008) present0 1.104** 0.682** 0.173 0.506** (0.280) (0.239) (0.218) (0.245) constant ‐4.096** 0.410* 0.701** 0.847** (0.267) (0.244) (0.235) (0.235) 0.560 (0.090) Log‐likelihood value ‐3606.904
The coefficients of the combined regression are similar to those from the separate regressions. Wald tests on the interaction terms indicate that there is no difference in the state dependence (the coefficient on lagged presence) between the Big 4 (p=0.10), or in the effect of the number of other Big 4 banks present (p=0.25). There is a significant difference between the effects of the number of non‐Big 4 banks present (p=0.02), the number of taxable and non‐taxable individuals (p=0.0001) and the year dummy variables (p<0.0001). 5.1 ATM Presence
Besides branches, the other major point of presence for the Big 4 banks is ATMs. As discussed above, the number of ATMs in Australia grew rapidly in the late‐1990s, but appears to have stabilised since. In this section, we repeat the analysis above using ATM‐level presence rather than branch‐level presence. The regression equations are unchanged, except that the criteria for presence is now that an institution reports at least one ATM in a postcode.
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Table 4 reports the maximum likelihood estimates of separate regressions for each of the Big 4 banks (analogous to the results in Table 2 above). As before, we omit the coefficients on the variables included to control for unobserved heterogeneity and year dummies in order to simplify the output.
Table 4: ATM ML estimates (** indicates significance at the 5% level, * at the 10% level)
Explanatory variable ANZ CBA NAB Westpac presentt‐1 3.132** 2.876** 2.738** 3.149** (0.075) (0.084) (0.072) (0.110) nob4t 0.096* 0.216** ‐0.084 0.202** (0.056) (0.063) (0.052) (0.074) nootht ‐0.072** ‐0.0007 0.013 ‐0.126** (0.035) (0.044) (0.035) (0.050) totindt 0.0002** 0.0001** ‐0.00001 0.0003** (0.00005) (0.00006) (0.00004) (0.00007) meantaxinct 0.000007 0.00002** ‐0.00002** ‐0.00002 (0.000006) (0.000007) (0.000007) (0.000009) nbusinclosst ‐0.00000002* ‐0.000000002 ‐0.00000002 0.00000002 (0.000000009) (0.00000001) (0.000000009) (0.00000002) present0 0.526** 1.311** 1.425** 1.985** (0.126) (0.171) (0.135) (0.293) constant ‐2.438** ‐3.297** ‐3.428** ‐3.817** (0.183) (0.308) (0.247) (0.347) 0.486 0.880 0.626 0.791 (0.069) (0.083) (0.064) (0.111) Log‐likelihood value ‐2597.901 ‐1846.946 ‐2220.716 ‐1463.351
The ATM regression results suggest that ATM presence is more sensitive to market characteristics than branch presence (measured by the number of demographic variables with significant coefficients). It is possible to construct profiles of the postcodes where each of the Big 4 is most likely to enter or least likely to exit.
ANZ appears to target postcodes that are poorly served by non‐Big 4 institutions, with larger populations and less income reported from businesses. CBA appears to target postcodes where the population is larger and income is higher. By contrast NAB prefers postcodes with lower taxable income. It is interesting that NAB, alone among the Big 4, does not appear to be affected by the presence of other Big 4 banks when making ATM location decisions. Finally, Westpac appears to target postcodes with fewer non‐Big 4 institutions present and larger populations (similar to ANZ). We have also run a pooled regression. While we do not report the detailed results here, the regression results confirm that the lagged presence, number of other Big 4 with ATMs present, total number of individuals and number of non‐Big 4 institutions with ATMs present affect ATM location decisions differently across the Big 4 banks.
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5.2 Acquisitions
One possible explanation for the negative coefficients on nootht in the Westpac and CBA results is the acquisitions. The acquisitions mean that, in 2009, Westpac and CBA branches ‘enter’ in postcodes where nooth decreases (because St. George or The Bank of Western Australia branches ‘exit’). To test whether this is the cause of the result, we re‐estimate the CBA and Westpac regressions adding an interaction between the 2010 dummy variable and nooth. The rationale is that the coefficient on these interactions should capture any change in the nooth coefficient due to the immediate effects of the acquisition (distinct from gradual changes such as consolidation). Table 5 reports the maximum‐likelihood estimates.
The addition of the acquisition interaction leaves the coefficients unchanged for CBA branches and ATMs and for Westpac branches. The interactions themselves lack significant coefficients in these regressions. The interaction does have a significant coefficient for Westpac ATMs, but it is positive, and including the interaction term increases the magnitude of the negative coefficient on noothatm. The negative effect of non‐Big 4 presence on CBA branch presence and on Westpac branch and ATM presence cannot be explained by the acquisitions.
Table 5: Regressions with Acquisition Interactions (** indicates significance at the 5% level, * at the 10% level)
Explanatory variable CBA Branch Westpac Branch CBA ATM Westpac ATM presentt‐1 3.694** 4.333** 2.876** 3.187** (0.190) (0.093) (0.084) (0.110) nob4t 0.675** 0.209* 0.217** 0.192** (0.146) (0.107) (0.063) (0.074) nootht ‐0.188** ‐0.172** ‐0.001 ‐0.163** (0.094) (0.060) (0.044) (0.050) η7*nootht 0.074 0.007 ‐0.028 0.237** (0.119) (0.070) (0.088) (0.064) totindt 0.0004** 0.00004 0.0001** 0.0002** (0.00008) (0.00005) (0.00006) (0.00007) meantaxinct 0.000007 0.00000006 0.00002** ‐0.00001 (0.00001) (0.000009) (0.000007) (0.000009) nbusinclosst ‐0.00000003 0.00000004** ‐0.000000002 0.00000002 (0.00000002) (0.00000001) (0.000000001) (0.00000002) present0 2.546** 0.880** 1.311** 1.869** (0.562) (0.144) (0.171) (0.287) constant ‐3.491** ‐2.861** ‐3.300** ‐3.680** (0.463) (0.227) (0.308) (0.336) 0.783 0.009 0.881 0.747 (0.163) (0.081) (0.083) (0.111) Log‐likelihood value ‐682.775 ‐1319.436 ‐1846.896 ‐1456.364
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5.3 Other Points of Presence
The APRA data contain a number of points of presence which are not branches or ATMs. These include agencies of various types, business development managers and offices (e.g. head offices without branch facilities), as well as facilities such as EFTPOS and electronic banking (e.g. phone and internet banking).
One challenge with analysing the data is that reporting of these points of presence appears to be inconsistent, and the boundaries between different point of presence types can be ill‐defined. Consequently, analysis focusing on single point of presence types may lead to misleading conclusions. However, it is possible that important trends in bank behaviour cannot be seen in branches and ATMs alone. To investigate this possibility, we estimate another group of regressions based on presence of any of several point of presence types. In particular, we repeat the analysis above for branches and ATMs, but now define an institution as being present if it has as least one point of presence which is one of several types. We exclude ATMs and also exclude points of presence for which location is not meaningful (e.g. electronic banking).
Table 6 reports the maximum likelihood estimates of separate regressions for each of the Big 4 banks (analogous to the results in Table 4 above). As before, we omit the coefficients on the variables included to control for unobserved heterogeneity and year dummies in order to simplify the output.
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Table 6: Non‐ATM Regressions (** indicates significance at the 5% level, * at the 10% level)
Explanatory variable ANZ CBA NAB Westpac presentt‐1 4.041** 3.417** 2.809** 2.537** (0.161) (0.065) (0.113) (0.117) nob4t 0.044 0.283** 0.459** 0.201** (0.095) (0.085) (0.095) (0.097) nootht 0.116* ‐0.117** ‐0.070 ‐0.387** (0.069) (0.050) (0.062) (0.071) totindt 0.00002 0.0003** 0.0003** ‐0.000004 (0.00006) (0.00005) (0.00006) (0.00007) meantaxinct ‐0.00003** ‐0.0000001 ‐0.00002* 0.00001 (0.00001) (0.000006) (0.00001) (0.00001) nbusinclosst 0.00000002 ‐0.00000002* ‐0.00000004** 0.00000005** (0.00000002) (0.00000001) (0.00000001) (0.00000002) present0 1.513** 1.352** 4.102** 5.267** (0.421) (0.098) (0.307) (0.460) constant ‐4.533** ‐4.619** ‐6.321** ‐3.428** (0.459) (0.225) (0.523) (0.451) 0.655 0.820 1.375 1.231 (0.154) (0.052) (0.111) (0.188) Log‐likelihood value ‐884.325 ‐3227.075 ‐1409.494 ‐1271.588
The results reported in Table 6 reveal that, as with branches and ATMS, past presence is the key determinant of the decision to remain present in a given location. We have also run a pooled regression, which is omitted but confirms that coefficients differ between the Big 4 banks for lagged presence, number of other Big 4 banks present, number of non‐Big 4 banks present and total number of individuals. These results (beyond just past presence) are broadly similar to those obtained for branches, which suggests that there are unlikely to be dramatically different patterns in other points of presence that warrant further investigation.
5.4 The impact of the GFC Australian ADIs appear to have been relatively mildly affected by the GFC, at least at the levels of aggregate ATM and branch presence. The most notable consequences are the two large acquisitions in late 2008,10 which have already been analysed. However, Australian ADIs were not immune to profit downturns and funding challenges after 2008 (Donovan & Gorajek 2011). To examine whether dynamics changed in the post‐GFC environment, we create a new variable gfc which takes the value 1 if the year is 2008 or later and 0 otherwise. We then run new regressions adding interactions between gfc and both nob4 and nooth. The results are reported in Tables 10 (for branches) and 11 (for ATMs). There is evidence of different post‐GFC behaviour for branch placement for all of the Big 4 except NAB. ANZ showed clustering before the GFC (nob4 had a positive coefficient), but this
10 The CBA’s acquisition of The Bank of Western Australia can be directly linked to difficulties faced by the latter’s foreign owner during the GFC. St George was in a stronger position, but the GFC did strengthen Westpac’s advantageous position which arose from its access to cheap funds (Senate Standing Committees on Economics, 2009). 22
ceased after the GFC. nooth does not have significant coefficients in the CBA or Westpac regressions before the GFC but has negative coefficients after the GFC.
For ATM placement there is evidence of changed behaviour for all of the Big 4 except Westpac. ANZ located ATMs away from other Big 4 before the GFC, but not afterwards, while the CBA responded negatively to nooth only after the GFC. NAB presence was negatively affected by other Big 4 before the GFC but not after, when nooth instead becomes important (with a negative coefficient). Although there are some cases where pro‐competitive behaviour appears to have been blunted by the GFC (notably the placement of ANZ branches), there does not appear to have been a systematic retreat from competition.
As the acquisitions occurred in 2008‐09, it is not possible to discriminate between the ongoing effects of the acquisitions and the effects of the broader post‐GFC environment. Having acquired The Bank of Western Australia, with its extensive network of regional branches and ATMs, the CBA might have been expected to consolidate in following years by closing some regional points of presence or slowing the rate at which new ones were opened. If regional postcodes have higher levels of non‐Big 4 presence, this consolidation would be consistent with the pattern of coefficients in the CBA regressions. A similar explanation may be behind the Westpac branches result. Note, however, that the implications of the acquisitions for competition overall are unclear; CBA and Westpac may be closing branches in regional areas during consolidation (which could be bad for competition), but at the same time the acquisitions themselves may mean a Big 4 presence in markets previously served only by smaller banks. Also, if Bankwest was in a weak position then its ability to be a robust competitor might have been in question anyway. Table 7: GFC Branch Regressions
Explanatory variable ANZ CBA NAB Westpac presentt‐1 4.696** 3.633** 4.098** 4.359** (0.153) (0.193) (0.163) (0.135) nob4t 0.637** 0.435* 0.885** 0.473** (0.192) (0.222) (0.210) (0.241) nootht ‐0.041 0.142 0.001 0.121 (0.110) (0.162) (0.123) (0.118) totindt 0.000006 0.0004** 0.00008 0.00003 (0.00006) (0.00009) (0.00007) (0.00006) meantaxinct ‐0.00002* 0.000006 ‐0.00001 ‐0.000003 (0.00001) (0.00001) (0.00001) (0.00001) nbusinclosst 0.000000007 ‐0.00000003 ‐0.00000002 0.00000004** (0.00000002) (0.00000002) (0.00000002) (0.00000002) present0 0.338** 3.341** 2.106** 1.318** (0.150) (0.627) (0.483) (0.409) gfc*nob4t ‐0.754** 0.210 ‐0.496 ‐0.268 (0.294) (0.389) (0.330) (0.344) gfc*nootht 0.184 ‐0.637** 0.099 ‐0.492** (0.182) (0.248) (0.195) (0.178) constant ‐4.064** ‐3.982** ‐3.524** ‐3.032** (0.298) (0.561) (0.453) (0.291) 0.004 1.011 0.834 0.420 (0.037) (0.174) (0.162) (0.171) Log‐likelihood value ‐664.339 ‐650.491 ‐864.528 ‐1227.464 23
Table 8: GFC ATM Regressions
Explanatory variable ANZ CBA NAB Westpac presentt‐1 3.152** 2.908** 2.711** 2.995** (0.078) (0.085) (0.073) (0.116) nob4t ‐0.314** 0.200 ‐0.319** 0.151 (0.112) (0.129) (0.103) (0.149) nootht ‐0.099* 0.064 0.091 ‐0.018 (0.060) (0.076) (0.066) (0.096) totindt 0.0002** 0.0001** ‐0.00001 0.0003** (0.00006) (0.00006) (0.00004) (0.00008) meantaxinct 0.000006 0.00001** ‐0.00002** ‐0.00001 (0.000006) (0.000007) (0.000008) (0.00001) nbusinclosst ‐0.00000002* ‐0.000000002 ‐0.00000002** 0.00000002 (0.00000001) (0.00000001) (0.00000001) (0.00000002) present0 0.542** 1.311** 1.520** 2.480** (0.130) (0.172) (0.142) (0.335) gfc*nob4t 0.509** 0.011 0.348** 0.033 (0.147) (0.171) (0.139) (0.192) gfc*nootht 0.018 ‐0.245** ‐0.179** ‐0.145 (0.086) (0.109) (0.087) (0.125) constant ‐2.438** ‐3.317** ‐3.509** ‐4.245** (0.187) (0.311) (0.259) (0.409) 0.486 0.873 0.664 0.971 (0.073) (0.084) (0.066) (0.123) Log‐likelihood value ‐2578.357 ‐1828.047 ‐2196.057 ‐1429.032
5.5 Estimates of Entry and Exit Probabilities
Further evidence of the divergence of behaviour by the Big 4 can be found in the estimates of entry and exit probabilities, which are presented below. Such evidence may be interpreted as a sign of competitive tension amongst the largest banks. The method for estimating probabilities is described by Wooldridge (2005).
We estimated entry and exit probabilities for four representative postcodes as follows. P1 has no other institutions of any type present, P2 has 1 other Big 4 bank and 2 non‐Big 4 institutions present, P3 has 2 other Big 4 banks and 3 non‐Big 4 institutions present, and P4 has 3 other Big 4 banks and 4 non‐Big 4 institutions present. These representative postcodes represent market structures that can be ranked from lowest amount of competition (P1) to highest (P4). We computed entry and exit probabilities for three years 2005‐06, 2009‐10 and 2012‐13.
In order to compute entry and exit probabilities we need to choose representative values for taxable income, total taxable and non‐taxable individuals and net personal income or loss reported from businesses. These are based on the mean values for the relevant year. The values used are $43,000, 4,600 and $6.4 million in 2006, $56,000, 5,000 and $8.8 million in 2010 and $50,000, 4,900 and $10 million in 2013. Table 12 shows the estimated entry and exit probabilities for branches.
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Table 9: Estimated Transition Probabilities Branches
2005‐06 2009‐10 2012‐13 P1 P2 P3 P4 P1 P2 P3 P4 P1 P2 P3 P4 ANZ Entry 0.035 0.051 0.074 0.104 0.015 0.023 0.034 0.050 0.005 0.007 0.012 0.019 Exit 0.022 0.013 0.007 0.003 0.061 0.039 0.024 0.014 0.160 0.114 0.077 0.049 CBA Entry 0.111 0.146 0.215 0.302 0.088 0.117 0.175 0.253 0.071 0.095 0.145 0.214 Exit 0.233 0.190 0.138 0.100 0.275 0.226 0.164 0.119 0.316 0.261 0.191 0.138 NAB Entry 0.089 0.158 0.252 0.372 0.009 0.022 0.046 0.086 0.018 0.040 0.078 0.137 Exit 0.111 0.051 0.021 0.007 0.488 0.337 0.211 0.116 0.368 0.231 0.130 0.063 Westpac Entry 0.197 0.173 0.180 0.187 0.073 0.062 0.065 0.068 0.049 0.042 0.044 0.046 Exit 0.006 0.008 0.007 0.007 0.035 0.043 0.040 0.038 0.058 0.072 0.068 0.064
Table 9 suggests that, with the exception of Westpac, the behaviour of Australia’s largest banks, while quantitatively different, is qualitatively similar. The behaviour of Westpac was most likely impacted by its acquisition of St George in 2008‐09.
In particular, the estimated probability of entry increases monotonically as the market becomes more competitive and the probability of exit decreases monotonically with competition. That is, with the exception of Westpac, these two stylised facts taken together suggest that the Big 4 are more likely to be located in more competitive markets.
The estimated probabilities can also be seen as circumstantial evidence that the concern that banks may not be competing in remote areas may not be related to the competitiveness of the banking sector but may rather be driven by fundamentals such as low economic activity. The estimated probabilities suggest that Australia’s banks do not shy away from competition.
6. Concluding Comments
The trade‐off between bank competition and financial stability remains an important policy issue in the wake of the GFC. In Australia, this trade‐off is encapsulated by the Four Pillars Policy, which effectively prevents mergers between Australia’s four largest banks.
The impact of this policy on the competitiveness of the bank sector is not well‐understood. While at face value, it may prevent further concentration, the mechanism through which this policy can aid competition is not clear. For example, there are no obvious theoretical results in industrial organisation that suggest that a sector with three large players is more or less competitive than a sector with four large players.
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Existing empirical studies provide little guidance on the impact of the Four Pillars policy beyond suggesting that the Australian banking sector is characterised by oligopolistic behaviour, as measured by a positive mark up over costs, as with any other banking sector in the world.
The inability of measures of concentration to capture the dynamics of competition in the banking sector is the motivation for our study that focuses on trends in, and determinants of, the decisions made by the four largest Australian banks about whether to establish or maintain branch‐ and ATM‐ level presence in a local market (defined by a postcode) between 2002 and 2013.
Our analysis suggests that past presence is the most important factor for explaining current presence in a particular postcode. Moreover, we present evidence that the four largest banks co‐ locate branches. The impact of the location of other banks on the location of the four largest banks is less clear; there is some limited evidence that this impact is negative for two of the four largest banks. Our results also suggest that the four largest banks responded differently to the GFC in terms of their branch location decisions. Some of these responses may have implications for competition, particularly in regional areas, but there does not appear to have been an overall reduction in pro‐ competitive behaviour between the Big 4. Our analysis of ATM location decisions reveals a divergence of behaviour among the Big 4, with different banks following different strategies.
This study aimed to shed light on one aspect of competition in the Australian banking sector (branch and ATM location) between 2002 and 2013. At an aggregate level, this period was one of relative stability, in contrast to the rapid changes of the 1980s and 1990s. Branches and ATMs per head of population were both stable, and there is little evidence of systematic changes in the distribution of these points of presence across the country.
Regression analysis based on the framework of Wooldridge (2005) suggests that the Big 4 banks do not shy away from competition with each other, or to a lesser extent with other institutions. There is also some evidence of a diversity of strategic approaches to branch and ATM placement based on demographic characteristics. There is some evidence of changed behaviour post‐GFC, but only evidence of a reduction in pro‐competitive behaviour in limited cases. CBA and Westpac, which both acquired smaller institutions at the beginning of the GFC, were less likely to co‐locate with other smaller institutions (possibly in regional areas). This cannot be explained as an immediate effect of the acquisitions, but may reflect post‐acquisition consolidation without clear implications for competition. Overall, we do not find convincing evidence to support concerns about the willingness of the Big 4 to compete in branch or ATM placement, either after the GFC or in the 2002‐2013 period as a whole. Concerns about service levels in regional areas may reflect fundamentals rather than an insufficiently competitive banking system.
References 1. Allen, F. and D. Gale (2014), ‘Competition and Financial Stability’, Journal of Money, Credit, and Banking, Vol. 36 (3), pp. 453‐480. 2. Amel, D.F., Kennickell, A.B. & Moore, K.B. (2008), Banking Market Definition: Evidence from the Survey of Consumer Finances, Federal Reserve Board, Washington D.C.
4. Australian Prudential Regulation Authority (2008), Reporting Form ARF 396.0: Points of Presence: Instruction Guide, APRA,
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22. Mundlak, Y. (1978), ‘On the Pooling of Time Series and Cross Sectional Data’, Econometrica, Vol. 56, pp. 69‐86. 23. Panzar, J. C. & Rosse, J. N. (1987), ‘Testing for ‘monopoly’ equilibrium’, Journal of Industrial Economics, Vol. 35, pp. 443‐456. 24. Reserve Bank of Australia (1996), Bank Branch Trends in Australia and Overseas, Reserve Bank of Australia Bulletin,
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