The chicken or the egg? The trade-off between bank fee income and net interest margins

Abstract

This study considers the time series relationship between bank fee income and bank net interest margins in Australia, applying panel vector autoregressions to a unique, hand-collected dataset. Increases in bank fee income are being used to supplement decreases in net interest margins. The increase in magnitude of fee income associated with reductions in margin income is smaller than the decrease in net interest margins, resulting in a net wealth transfer favouring users of bank services; although not all users of bank services gained and/or gained equally. The overall increase in fee income is marginally greater that the reduction in margin income. It is argued that banks have responded to falling margin revenue by increasing their range of fee-based services, especially insurance. Increases in fee income are found to pre-date declines in margin income, thus Australian banks were pro-active in the process of disintermediation.

JEL Classifications: G21, G11, C33

Keywords

Bank interest margins non-interest income panel data vector autoregression

1. Introduction

Changes in the nature of financial intermediation have been accompanied by a change in the nature of bank income (Allen and Santomero, 2001). In particular, the revenue of banks has seen a shift in emphasis from traditional income sourced from the provision of intermediary services (margin income) to the less traditional fee income.¹ Such a shift has a number of important implications from the perspectives of bank management and regulatory policy. A conventional view of this process is that banks have offset the impact of reduced traditional income sourced from margin income via increases in fee income. Reductions in margin income can be attributed to the process of disintermediation and increased competition.

It is also often assumed that the reductions in net interest margins pre-date the increases in fee income and that increases in fee income are a reaction to falling revenue. It has been concluded that observed increases in fee income are acting to supplement declines in margin income rather than replacing margin income.² However, it is possible that fee income increases pre-date falls in margin income and this would indicate that any observed trade-off in fact represents a fundamental shift in the nature of bank revenue reflecting the overall impact of disintermediation.

Recent studies found that increases in fee income are accompanied by increased variability in profits and worsening in bank risk–return trade-off (DeYoung and Rice, 2004; Laeven and Levine, 2007; Lepetit et al., 2008b). Further, the level of exposure of U.S. banks to fee income has resulted in worsening of bank’s risk–return trade-off.³ Other recent European studies such as Lepetit et al. (2008a, 2008b) and Schmid and Walter (2009) found that bank income diversification is value reducing. The global study by Laeven and Levine (2007) also found increased levels of financial conglomeration generated a diversification discount and increased bank agency problems. This paper addresses the issue of changing bank revenue from a perspective that has not been applied previously in this literature. This paper will also offer the benefit of employing a unique hand-collected database sourced from the annual reports of Australian banks (domestic and foreign) over the period 1988 to 2010.

The research question ‘Is there a stable time series relationship between fee income and margin income in Australia?’ will be considered. If a stable long-term relationship is found, this will tend to support the argument that increases in fee income have been used to offset declines in margin income. If no stable long-term relationship is found, it will support the argument that the factors causing the observed declines in margin income and the accompanying increase in fee income are in fact derived from different sources (such as providing market-based services or ventures into new avenues of financial service provision, rather than intermediation services), suggesting a change in the nature of financial intermediation, in particular an increased move toward the use of market-based financial solutions (Allen and Santomero, 2001). It would be expected that if the substitution argument presented above is true, then there will be a negative time series relationship between fee income and margin income. A further question that this paper will also address is whether the nature of this relationship differs between the three types of banks considered in this study.

These results are of interest as this is the first time that the issue of margins and fee income has been considered from this perspective that these authors are aware of. These results will contribute to the body of knowledge considering the changing nature of intermediation, and so aid in our understanding of whether the observed changes in bank revenue composition are transitory or permanent. The Reserve Bank of Australia (2006) found that bank fees from domestic banking activity had grown over the previous years, but that as a percent of total assets, domestic fee income had declined. The Reserve Bank survey confines its focus to fee income earned in the process of taking deposits and writing loans, and so excludes income from funds management, wholesale banking, insurance and trading activities. As the broader type of fee income has been a growing area of banking operations in Australia and globally (Allen and Santomero, 2001; Laeven and Levine, 2007), a wider perspective of bank fee income is also relevant, as will be adopted in this paper. This paper offers the second advantage of addressing this question by analysing a unique hand-collected database that includes both domestic and foreign banks in the sample, thus offering wider scope for analysis than previous Australian studies.

In general this paper finds that there exists a stable relationship between bank fee income and margin income, indicating that increases in fee income are being used to supplement declines in margins. It is also found that increases in fee income pre-date declines in margins, suggesting that the Australian banking system has been pro-active in dealing with the process of disintermediation by increasing fee income prior to declines in margins becoming statistically significant. This paper argues that the observed increase in bank fees has two components. The first component is the increase in fees associated with the decline in margin revenue; this is found to be smaller than the decrease in margins. As the magnitude of increases in fees is smaller than the observed reduction in bank margins, this suggests that bank consumers have experienced positive wealth transfers overall as a result of this relationship. The second component of increased bank fees is argued to be due to banks offering a wider range of financial services (particularly insurance) than previously. The sum of these two increases in bank fees is marginally greater than the observed decline in margin revenue in absolute terms, and this difference is extremely small when the growth of bank assets is considered. In net, Australian banks have increased their fee revenue to compensate for the decline in revenue due to falling margins, but the overall impact has represented two distinctly different strategies.

These results do raise some policy concerns, as overexposure to fee income results in a worsening of bank risk–return trade-offs (De Jonghe et al., 2007; Stiroh and Rumble, 2006) as well as increased agency conflicts (Laeven and Levine, 2007) and worsening loan quality (Lepetit et al., 2008a). These potentially negative outcomes are of concern to bank shareholders, bank management, prudential regulators and borrowers from banks as they all face agency concerns resulting from bank diversification (Froot and Stein, 1998; Laeven and Levine, 2007; Stiroh and Rumble, 2006), and particularly from potential bank failure.

This paper is structured as follows: the next section will provide an overview of the relevant literature. The third section will discuss the sample to be used and presents the relevant descriptive statistics. The fourth section will discuss the method used to address the research question posed. The fifth section will discuss the results, while the final section will provide some concluding comments and suggest directions for further research endeavour.

2. Literature review

As pointed out by Allen and Santomero (2001), the nature of the financial system has changed dramatically over the last decade, with banks becoming increasingly active in the provision of non-traditional services such as insurance products, funds management and securitisation.⁴ This change is sourced from the increased competition posed to traditional intermediaries from non-traditional sources including the evolution of more sophisticated market-based products that directly compete with banks. Slager (2006, Ch 4) illustrates this trend, showing that across a number of developed nations margins have declined while fee revenue has increased. This trend is viewed by Slager (2006) to represent the impact of disintermediation.

The evidence considering this issue to date has a focus upon the case of the United States, mainly driven by the research question posed by the impact upon bank risk resulting from the removal of the Glass–Steagall separation of commercial and investment banking. DeYoung and Roland (2001) find that the resulting change in income mix emphasising fees is accompanied by increased earnings volatility that represents both the volatility of fees as well as volatility due to its leverage effects. DeYoung and Rice (2004) find that banks that are less reliant upon fees generally exhibit higher management quality, and that customer focus and technology use are associated with higher levels of fee income. Further, increases in fees are associated with a worsening of the bank’s risk–return trade-off and increased profit variability.

Stiroh and Rumble (2006) find that increased reliance upon fees as a revenue source generated a positive portfolio diversification effect and a negative impact via the higher volatility of fees. As this level of exposure increases so the volatility effect outweighed the diversification effect resulting in a worsening of bank risk–return trade-off. Stiroh (2006b) find that increased fees were not accompanied by higher share market returns, but were accompanied by increased market risk (beta, total volatility and idiosyncratic volatility). Thus large U.S. banks may have become overexposed to fee-based revenue (Stiroh, 2006a). Further, Stiroh (2004) finds that the correlation between margins and fees has increased over time, so reducing any portfolio diversification benefits from combining the two income sources. Overall the evidence drawn from the United States indicates that increased fee-based income is risk increasing rather than risk reducing.

In the European Union Smith et al. (2003) find that fees are of increased importance to banks, but that fees display a higher volatility than margins. De Jonghe et al. (2007) find that European banks with higher levels of fees have higher expected returns as measured by Tobin’s Q, but also have higher beta risk. They also conclude that overexposure to fees increases bank risk. Laeven and Levine (2007) studied this issue from a different perspective across 43 nations and concluded that financial conglomerates had a lower market value than focussed financial institutions and thus there exists a diversification discount in multiple activity financial firms, due to the negative effect of agency problems. European studies by Lepetit et al. (2008a), Lepetit et al. (2008b) and Schmid and Walter (2009) all confirm that any diversification benefits from increased fees are more than outweighed by increased bank risk. Lepetit et al. (2008a) document that European banks accept lower loan portfolio returns and higher loan risk to obtain higher commission and fee income.

Three reasons have been presented to explain why fees are more volatile than margin income (DeYoung and Roland, 2001). First, as bank lending has a substantial relationship component, the costs of switching loan providers are higher than when changing providers of fee-based transactions, which have a lower relationship component. Second, fee revenue is substantially reliant upon staff costs to provide the required services, which generates a high fixed-cost component, as opposed to margins which are more reliant upon interest expenditure as a variable cost input. Thus, fees have a higher level of operating leverage. Third, fees have higher financial leverage due to lower levels of required fixed assets, and so have higher financial risk. Overall, this literature has emphasised the risk–return characteristics of bank fees, and therefore provides scope for considering the time series relationship between margins and fees to determine if this relationship is stable through time.

The exposure of banks to increased fee revenue is of concern to a number of stakeholders in the banking system. The conventional view of bank shareholders is that they can diversify away bank-specific risk by their holding of a well-diversified portfolio. However, bank diversification increases agency costs and income volatility (Laeven and Levine, 2007; Stiroh and Rumble, 2006). As discussed by Froot et al. (1993) and Froot and Stein (1998), increased income volatility has a nonlinear impact on bank cost of funds and makes risk management by the bank on behalf of the shareholders worthwhile. From the perspective of bank management, their holding of a poorly diversified wealth portfolio means that that they are concerned about bank total risk (Stulz, 1984). From a borrower’s perspective, the implicit value of the bank–client relationship means that borrowers face switching costs in the event of bank failure.⁵ Bank regulators, with a focus on maintaining the viability of the financial system, are concerned about bank total risk due to the potential risk of contagion and systemic failure resulting from the failure of a single (large) bank. Thus, increased bank exposure to non-interest income has the potential (given the current evidence) to increase the likelihood of systemic failure due to higher income volatility and agency conflicts as well as worsening loan quality.

It is possible that management of large U.S. banks have ‘. . . gotten the diversification idea wrong . . .’ (Stiroh and Rumble, 2006: 2158). This infers that bank management are more concerned with increasing the level of returns rather than managing risk–return trade-offs. If this is true, this would represent an agency conflict between regulators who are concerned with financial system stability and bank management and bank shareholders who are concerned with profits. Stiroh and Rumble (2006) suggest that the negative aspects of ‘too big to fail’ have encouraged this agency problem in that bank management and shareholders profit from higher returns while regulators bear any costs of bank failure due to higher risk, thus creating an asymmetry in the risk–return trade-off that explains the U.S. evidence of overexposure to fee income.

Several reasons have been advanced to explain the increased move of banks into less traditional activities. As discussed above, it could be that bank management are focussed on the level of returns rather than risk and return. It is also possible that managerial non–profit-maximising activities are to blame. Aggarwal and Samwick (2003) propose that managers choose to diversify their firms to increase personal utility rather shareholder wealth. Mergers in the banking industry, resulting in increased exposure to non-traditional activity, may also be motivated by managerial utility maximisation rather than shareholder wealth maximisation (Berger et al., 1999; Bliss and Rosen, 2001; Milbourn et al., 1999). It is also possible that increased exposure to fees reflects the changing nature of the financial process in which markets are increasingly taking the place of traditional intermediation (Allen and Santomero, 2001). In such a changing environment, banks are seeking new revenue lines to take the place of declining interest margins, with a resulting change in bank risk. It is also possible that some of the negative effects of increased exposure to fees are due to start up and initial learning costs. However, given that the negative effects of increased bank exposure to fees have been documented in several different national settings as well as from a number of empirical perspectives, it must be concluded that the negative impact of increased bank fees are systematic and enduring. Exploring this issue from a dynamic perspective, as this study does, will add a further dimension to this literature.

In the Australian context the main discussion of this issue has been provided by the annual series of discussions by the Reserve Bank of Australia. These discussions do not provide any statistical testing of the relationship between bank net interest margins and fees and do not include in their ambit any fees drawn from activities such as underwriting, funds management and insurance. Instead these discussions focus only upon fee income resulting from the processes of taking deposits and writing loans. With these restrictions in mind, the Reserve Bank of Australia (2005) concludes that increases in bank fees have not offset declines in bank margin income from traditional banking activity. Given that Allen and Santomero (2001) find that there has been a switch in bank activity from traditional intermediation (taking deposits and writing loans) toward fees in the United States, a wider perspective is important given the policy issues raised by bank income volatility. Thus, in this study a wider view of bank fee income will be taken, along with a longer time series than the most recent survey by the Reserve Bank of Australia encompasses. When exploring this time series relationship it would be expected that if a trade-off between margins and fees is occurring, then a negative relationship between the two revenue sources would be expected over time, with margins declining and fees increasing.

A study by Williams (2007) applied the Ho and Saunders (1981) model of bank net interest margins to Australian data and found that margins have fallen over the study period 1989 to 2001, with larger banks showing higher margins, but some evidence of decreasing returns to scale.⁶ Evidence was also found of banks buying market share and some mispricing of risk. It was also found that foreign banks in Australia experienced significantly lower margins. The issue of a trade-off between margins and fees was not explored by Williams (2007). It was concluded that the area of fee income is still under-researched relative to their importance to bank revenue. Recently, Williams and Prather (2009) considered the issue of bank revenue risk and return in Australia and concluded that bank fees are riskier than margin income. However, it was also concluded that, at the relatively low levels of fee income in Australia, portfolio diversification benefits were still present.⁷ Overall, however, the time series relationship between bank margins and fees has not been investigated previously that these authors are aware of and thus a dynamic approach to this question will add to the growing literature considering bank revenue composition.

3. Data

The main data sources for this study are the individual bank annual reports. The sample covers 1988 to 2010, with a total of fifty-six banks in the sample. The banks are categorised into three groups. The first group is the Big Four, which represent the four major banks in Australia, who together account for over sixty-five to seventy-five percent of Australian banking assets over the study period. The second group is the Other Domestic banks, mainly regional and state-owned banks with a focus on retail finance, with the state-owned banks largely leaving the sample in the mid-1990s as these institutions were privatised. This section of the Australian banking system has experienced declining market share due to acquisitions by the Big Four banks. The final group is the Foreign banks. These are generally smaller, more wholesale-oriented banks. A bank is categorised as foreign if it has more than fifty percent foreign ownership.⁸ With only a few exceptions the foreign banks are largely fully foreign owned operating as subsidiary banks. While foreign banks can operate in Australia using a branch structure as an alternative to subsidiary operations, data regarding foreign bank branches were not available for this study.⁹ There is a total of 24 Other Domestic and 22 Foreign banks in the sample. Details of the sample are in Table 1 with descriptive statistics in Table 2. For our regressions, margins will be measured by (interest received – interest expense)/total assets as a percentage, while fees will be measured by non-interest income/total assets as a percentage.¹⁰ Figure 1 shows the time series properties of both margins and fees as percent of total revenue over the study period for all banks, the Big Four banks, the Other Domestic banks and the Foreign banks.

Table 1.

Sample composition.

	All banks	Big four	Other domestic	Foreign
All Years	458	89	205	164
1988	32	4	13	15
1989	33	4	14	15
1990	31	4	14	13
1991	29	4	12	13
1992	28	4	12	12
1993	28	4	13	11
1994	25	4	10	11
1995	24	4	11	9
1996	23	4	11	8
1997	19	4	8	7
1998	19	4	9	6
1999	18	4	7	7
2000	20	4	8	8
2001	19	4	8	7
2002	16	4	7	5
2003	14	4	7	3
2004	14	4	7	3
2005	12	4	7	1
2006	12	4	7	1
2007	13	4	7	2
2008	13	4	6	3
2009	12	4	5	3
2010	4	1	2	1

56 Banks in sample: 29 Other Domestic; 23 Foreign; 4 Big Four.

Table 2.

Descriptive statistics.

All banks
	Mean	Standard deviation	Minimum	Maximum	Number of observations
Net interest margin/total assets (%)	2.1779	1.2542	−0.16543	18.6761	458
Non-interest income to total assets (%)	2.0182	2.2143	−0.76173	12.2089	453
Total assets $A, 000	50,431,196	109,367,830	29,278	656,799,000	492
Interest income $A, 000	3,254,157	6,269,223	14,576	38,385,000	459
Interest income as a share of revenue (%)	80.97	16.49	23.08	114.55	446
1988
Net interest margin/total assets (%)	2.3142	1.0379	0.6166	4.9634	32
Non-interest income to total assets (%)	1.6654	1.1696	0.2387	5.7217	30
Total assets $A, 000	10,868,099	21,926,917	450,862.0	84,578,600.0	34
Interest income $A, 000	1,132,799.4	2,149,390.3	44,682.0	8,174,200.0	32
Interest income as a share of revenue (%)	87.27	8.21	62.10	98.22	30
1995
Net interest margin/total assets (%)	2.6187	0.9624	0.2422	3.9612	24
Non-interest income to total assets (%)	1.8023	2.0082	0.2242	8.1340	24
Total assets $A, 000	20,576,530	39,836,301	170,000	147,077,000.0	29
Interest income $A, 000	1,757,974.4	3,013,785.95	66,824.0	10,461,000.0	24
Interest income as a share of revenue (%)	83.46	13.89	46.89	97.42	23
2004
Net interest margin/total assets (%)	2.0101	0.6793	0.6168	3.6806	14
Non-interest income to total assets (%)	2.2711	2.3940	0.0827	8.5632	14
Total assets $A, 000	102,740,559	139,200,716	1,010,681	411,309,000.0	14
Interest income $A, 000	5,005,890	6,601,627	84,716.0	18,650,000.0	14
Interest income as a share of revenue (%)	73.59	20.71	30.78	98.56	14
2009
Net interest margin/total assets (%)	1.766	0.4954	0.7400	2.4679	12
Non-interest income to total assets (%)	1.5281	2.4277	0.0806	8.9952	12
Total assets $A, 000	225,678,348	271,170,255	1,212,624	654,120,000	12
Interest income $A, 000	11,630,827	13,621,492	89,890	31,519,000	12
Interest income as a share of revenue (%)	83.44	16.62	38.21	98.55	12

Big four
	Mean	Standard deviation	Minimum	Maximum	Number of observations

Net interest margin/total assets (%)	2.4492	0.6161	1.6217	3.8212	89
Non-interest income to total assets (%)	1.7185	0.5547	0.5760	3.9504	89
Total assets $A, 000	227,864,478	159,989,244	48,636,800	656,799,000	89
Interest Income $A, 000	13,644,383	7,919,530	4,902,800	39,385,000	89
Interest income as a share of revenue (%)	79.32	8.64	55.80	114.55	89
1988
Net interest margin/total assets (%)	3.3052	0.4586	2.7567	3.6925	4
Non-interest income to total assets (%)	1.6226	0.3294	1.2881	2.0693	4
Total assets $A, 000	67,665,450	15,290,310	48,636,800	84,578,600	4
Interest income $A, 000	6,522,000	1,337,377	48,636,800	84,578,600	4
Interest income as a share of revenue (%)	85.62	3.16	81.19	88.67	4
1995
Net interest margin/total assets (%)	2.9591	0.2148	2.7366	3.1771	4
Non-interest income to total assets (%)	1.5224	0.1864	1.3143	1.7542	4
Total assets $A, 000	116,273,500	21,209,908	99,595,000	147,077,000	4
Interest income $A, 000	8,155,525	1,681,985	6,678,100	10,461,000	4
Interest income as a share of revenue (%)	82.17	1.2201	80.80	83.76	4
2004
Net interest margin/total assets (%)	1.8706	0.1347	1.7483	2.0259	4
Non-interest income to total assets (%)	1.7733	0.7414	1.2516	2.8531	4
Total assets $A, 000	305,432,000	75,225,776	245,079,000	411,309,000	4
Interest income $A, 000	14,748,250	2,647,689	12,939,000	18,650,000	4
Interest income as a share of revenue (%)	73.73	9.12	61.38	81.31	4
2009
Net interest margin/total assets (%)	1.8842	0.1727	1.6605	2.0562	4
Non-interest income to total assets (%)	0.9568	0.1427	0.7971	1.1404	4
Total assets $A, 000	585,266,500	76,846,855	476,987,000	654,120,000	4
Interest income $A, 000	29,818,250	2,448,332	26,206,000	31,519,000	4
Interest income as a share of revenue (%)	84.23	2.46	81.87	87.33	4

Other domestic
	Mean	Standard deviation	Minimum	Maximum	Number of observations

Net interest margin/total assets (%)	2.3830	1.0665	0.1079	5.2613	205
Non-interest income to total assets (%)	2.1391	2.6477	0.0627	12.2090	202
Total assets $A, 000	17,375,175	28,488,395	229,778	152,294,000	217
Interest income $A, 000	1,111,596	1,526,753	46,362	10,710,000	205
Interest income as a share of revenue (%)	80.91	19.05	23.08	99.44	202
1988
Net interest margin/total assets (%)	2.5681	1.1754	0.6166	4.9634	13
Non-interest income to total assets (%)	1.8721	1.5047	0.2387	5.7217	12
Total assets $A, 000	4,536,563	5,768,639	450,862	20,055,882	15
Interest income $A, 000	505,656	584,627	51,913	1,993,514	13
Interest income as a share of revenue (%)	86.03	9.78	62.10	98.22	12
1995
Net interest margin/total assets (%)	2.9070	0.7739	1.2516	3.9612	11
Non-interest income to total assets (%)	1.3618	1.8676	0.3169	6.8699	11
Total assets $A, 000	6,971,665	5,609,032	1,675,7269	17,738,900	13
Interest income $A, 000	609,141	604,909	73,000	1,784,473	11
Interest income as a share of revenue (%)	83.90	18.03	43.71	95.55	11
2004
Net interest margin/total assets (%)	2.1311	0.8965	0.6168	3.6806	7
Non-interest income to total assets (%)	2.8588	3.2294	0.1964	8.5632	7
Total assets $A, 000	27,249,088	25,248,506	2,082,693	69,960,000	7
Interest income $A, 000	1,375,258	1,378,317	162,169	4,116,000	7
Interest income as a share of revenue (%)	71.04	25.93	30.78	97.54	7
2009
Net interest margin/total assets (%)	1.4825	0.5105	0.7400	2.0600	5
Non-interest income to total assets (%)	2.5491	3.7091	0.1841	8.9952	5
Total assets $A, 000	62,672,611	50,647,978	4,368,854	130,405,000	5
Interest income $A, 000	3,445,781	2,424,333	354,006	6,267,000	5
Interest income as a share of revenue (%)	77.23	24.96	38.21	97.78	5

Foreign
	Mean	Standard deviation	Minimum	Maximum	Number of observations

Net interest margin/total assets (%)	1.7744	1.5891	−0.1654	18.6761	164
Non-interest income to total assets (%)	2.0416	2.1769	0.0486	12.0982	162
Total assets $A, 000	4,094,252	6,112,540	29,278	48,010,527	186
Interest income $A, 000	311,700	415,107	14,576	3,389,139	165
Interest income as a share of revenue (%)	82.02	16.32	28.95	99.32	155
1988
Net interest margin/total assets (%)	1.8299	0.7511	0.9146	3.7731	15
Non-interest income to total assets (%)	1.5007	1.0206	0.2688	3.9495	14
Total assets $A, 000	2,053,674	1,421,896	523,920	5,073,748	15
Interest income $A, 000	239,204	186,010	44,682	665,078	15
Interest income as a share of revenue (%)	88.79	7.89	65.28	96.82	14
1995
Net interest margin/total assets (%)	2.1151	1.9986	0.2412	3.7278	9
Non-interest income to total assets (%)	2.4651	2.6284	0.2242	8.1340	9
Total assets $A, 000	3,416,144	3,507,933	170,000	10,075,000	12
Interest income $A, 000	302,132.2	293,043.4	66,824.0	819,700.0	9
Interest income as a share of revenue (%)	78.36	18.80	46.89	97.42	8
2004
Net interest margin/total assets (%)	1.9164	0.6765	1.3193	2.6512	3
Non-interest income to total assets (%)	0.8806	1.0167	0.0827	2.0254	3
Total assets $A, 000	8,632,070	12,247,384	1,010,681	22,759,461	3
Interest income $A, 000	487,551	692,489	84,716	128,7161	3
Interest income as a share of revenue (%)	86.42	17.55	66.30	98.56	3
2009
Net interest margin/total assets (%)	2.0822	0.6364	1.3477	2.4680	3
Non-interest income to total assets (%)	0.5882	0.5360	0.0806	1.1487	3
Total assets $A, 000	19,903,707	25,616,009	1,212,624	47,397,197	3
Interest income $A, 000	1,022,672	1,378,384	89,890	2,605,925	3
Interest income as a share of revenue (%)	92.74	6.10	86.40	98.55	3

Figure 1.

Graph of the time series properties.

4. Method

4.1. Panel VAR

In this section, we econometrically investigate the linkages between margins (MARGIN) and fees (FEE) using panel data for 56 banks and 24 years, 1988 to 2010.

Unit root tests, described in Appendix 1, suggest that our data are stationary. Hence, we estimate a panel vector autoregressive (VAR) model to analyse the relationships between MARGIN and FEE. The multivariate VAR(q) model with fixed effects takes the form

(\begin{matrix} M A R G I N_{i, t} \\ F E E S_{i, t} \end{matrix}) = \sum_{j = 1}^{q} (\begin{matrix} β_{11}^{(j)} & β_{12}^{(j)} \\ β_{21}^{(j)} & β_{22}^{(j)} \end{matrix}) (\begin{matrix} M A R G I N_{i, t - j} \\ F E E S_{i, t - j} \end{matrix}) + (\begin{matrix} η_{1, i} \\ η_{2, i} \end{matrix}) + (\begin{matrix} η_{1, B I G 4} \\ η_{2, B I G 4} \end{matrix}) + (\begin{matrix} η_{1, F o r e i g n} \\ η_{2, F o r e i g n} \end{matrix}) + (\begin{matrix} ε_{1, i, t} \\ ε_{2, i, t} \end{matrix}),

(1)

where MARGIN and FEE are, respectively, Net Interest Margin/Total Assets (%) and Non-interest Income to Total Assets (%) for banks i (= 1, . . ., N) at time t (= 1, . . ., T). η_i is a bank-specific fixed effect and ϵ_i,t is a multivariate normally distributed random disturbance. Fixed effects for the Big Four and Foreign banks are captured through η_BIG4 and η_Foregin respectively. We estimate a fixed effects model with bank-specific dummies, rather than a random effects model, as the η_i’s are likely to represent omitted bank-specific characteristics which are correlated with other explanatory variables.¹¹

The system of equations described by equation (1) assumes that the random error terms are orthogonal to the bank-specific fixed effects, as well as the lagged values of the endogenous variables. Further, the errors are assumed to have positive variance and to be uncorrelated across cross-sectional units and time. However, due to the likely correlation between the lagged endogenous variables and the fixed effects in equation (1), the least squares dummy variable estimator produces biased parameter estimates (Nickell, 1981). Accordingly, we remove the fixed effects by differencing, i.e., equation (1) is rewritten as

(\begin{matrix} Δ M A R G I N_{i, t} \\ Δ F E E S_{i, t} \end{matrix}) = \sum_{j = 1}^{q} (\begin{matrix} β_{11}^{(j)} & β_{12}^{(j)} \\ β_{21}^{(j)} & β_{22}^{(j)} \end{matrix}) (\begin{matrix} Δ M A R G I N_{i, t - j} \\ Δ F E E S_{i, t - j} \end{matrix}) + (\begin{matrix} Δ ε_{1, i, t} \\ Δ ε_{2, i, t} \end{matrix}),

(2)

where Δ is the first difference operator, e.g., ΔX_i,t = X_i,t – X_{i,t-
1}.

Since the transformed lagged endogenous variables and the transformed error terms in equation (2) may be correlated in panels with a limited time dimension (see Kiviet, 1995; Nickell, 1981), we estimate the coefficients in equation (2) by the generalised methods of moments (GMM) technique proposed by Arellano and Bond (1991). This technique uses the pre-determined lags of the system variables as instruments to exploit a potentially large set of over-identifying restrictions and provides consistent coefficient estimates (see Bond, 2002).

The errors in equation (2) satisfy the following orthogonality conditions:

E (M A R G I N_{i, s} Δ ε_{i, t}) = E (F E E S_{i, s} Δ ε_{i, t}) = 0, \forall s < t - 1 .

(3)

Assuming serially uncorrelated errors, the orthogonality conditions imply that the vector of instruments available to identify the parameters of equation (2) has the form

Z_{i, t} = [M A R G I N_{i, t - 2}, \dots, M A R G I N_{i, 1}; F E E S_{i, t - 2}, \dots, F E E S_{i, 1}] .

(4)

Letting Z_i* be a block diagonal matrix whose tth block is given by equation (4), for t = 1,. . . ., T – 2, the matrix of instruments for each equation of the VAR is Z = (Z₁*,. . . Z_N*)′.

The one-step GMM estimator for the k × 1 coefficient vector for each equation of the VAR in equation (2) is given by¹²

\hat{β} = {({\tilde{X}}^{*}' Z A_{N} Z^{'} {\tilde{X}}^{*})}^{- 1} {\tilde{X}}^{*}' Z A_{N} Z^{'} Y,

(5)

where Y is an N(T – q – 1) × 1 vector of stacked dependent variables, $A_{N} = {(\frac{1}{N} \sum_{i = 1}^{N} Z_{i}^{*}' H Z_{i}^{*})}^{- 1}$ , with H a T – 2 square matrix with 2’s on the main diagonal, −1’s on the first sub-diagonal and 0’s elsewhere, and ${\tilde{X}}^{*}$ is an N(T – q – 1) × k design matrix stacked by cross-sectional units with typical row

{\tilde{X}}_{i, t}^{*} = [Δ M A R G I N_{i, t - 1}, \dots, Δ M A R G I N_{i, t - q}, Δ F E E S_{i, t - 1}, \dots, Δ F E E S_{i, t - q}] .

(6)

Finally, the asymptotic variance-covariance matrix of the GMM coefficient vector is given by

a var (\hat{β}) = N {({\tilde{X}}^{*}' Z A_{N} Z^{'} {\tilde{X}}^{*})}^{- 1} {\tilde{X}}^{*}' Z A_{N} V_{N} A_{N} Z^{'} {\tilde{X}}^{*} {({\tilde{X}}^{*}' Z A_{N} Z^{'} {\tilde{X}}^{*})}^{- 1},

(7)

where $V_{N} = N^{- 1} \sum_{i}^{N} Z_{i}' Δ ε_{i} Δ ε_{i}' Z_{i}$ and the Δϵ_i’s are the GMM residuals.

Despite the popularity of GMM estimators in dynamic panel regression studies, the error process of the panel VAR does not necessarily follow a multivariate normal distribution especially in small samples. The standard errors and the corresponding hypothesis testing could be misleading under the normality assumptions. To overcome this problem, the empirical distribution is constructed by drawing a bootstrap sample of 5000. An important advantage of the bootstrapping technique is that the error process of the estimated panel VAR does not necessarily follow a multivariate normal distribution and the critical values are obtained from appropriate percentile values of the empirical distribution.

5. Estimation and results

In this exercise, the model described by (2) is estimated for four different groups: (i) Pool of all 56 banks, (ii) Big 4, (iii) Foreign Banks and (iv) all Other Domestic banks. Within each group the model is estimated for five different sample periods, namely (a) the full sample period from 1988 to 2010, (b) sub-sample from 1988 to 1992, (c) sub-sample from 1993 to 2010, (d) sub-sample 1988 to 2002, and (e) sub-sample 2003 to 2010.

The first two sub-periods were chosen because, as discussed in Avkiran (1999), the early 1990s saw a recession in the Australian economy, resulting in increased bad loans and a change in bank strategy following this experience. The second two sub-periods were selected due to a change in manner in which banks disclosed income from insurance activity. Prior to 2002 those Australian banks with insurance operations disclosed this non-interest revenue on a net basis. However, after 2002 as the materiality of this revenue source increased, insurance income was disclosed on a gross basis. Thus, the last two sub-periods will be employed to determine if any significant structural breaks are apparent due to this change in reporting.¹³

The optimal lag length q is determined by nested likelihood ratio tests.¹⁴ In all cases, we find that q = 1 is optimal.¹⁵ The GMM estimates for the panel VAR(1) are reported in Tables 3 –6. For T > 3 the model is over-identified and the validity of the assumptions used to estimate equation (2) can be tested using the standard GMM test of over-identifying restrictions or a Sargan test. From the Sargan test statistics and the p-values reported in Tables 3 –6, the null hypothesis that the moment conditions are valid (i.e., equation (3)) is unable to be rejected. In this context, the key identifying assumption of no serial correlation in the ϵ_it disturbances can be examined by testing for no second-order serial correlation in the first-differenced residuals (Arellano and Bond, 1991).¹⁶ The results generally show the absence of serial correlation and that the estimated models satisfy the standard assumptions.¹⁷

Table 3.

All banks.

	Full sample		Sub-sample 1 1988 to 1992		Sub-sample 2 1993 to 2010
	ΔMARGIN_t	ΔFEE_t	ΔMARGIN_t	ΔFEE_t	ΔMARGIN_t	ΔFEE_t
ΔMARGIN_t−1	0.587*** (0.17)	−0.31** (0.14)	0.084 (0.22)	0.157 (0.56)	0.62*** (0.14)	−0.54** (0.26)
ΔFEE_t−1	−0.09** (0.04)	0.65*** (0.12)	−0.108*** (0.02)	0.055 (0.14)	−0.07 (0.07)	0.39*** (0.09)
Constant	−0.33* (0.18)	−0.05 (0.17)	−0.09** (0.04)	0.022 (0.08)	0.18 (0.16)	−0.06 (0.11)
Diagnostics
Sargan statistic	13.23	23.89	26.0	28.6	13.79	15.83
Sargan p-value	1.00	1.00	0.11	0.16	100	1.00
AR(1)	−1.82*	−1.73*	−1.64*	−1.67*	−2.04**	−1.66*
AR(2)	−1.43	0.47	−0.78	−0.10	−0.97	1.55

Notes: (a) Standard errors are in the parentheses. ***, ** and * denote rejection of null of zero restriction at 1%, 5% and 10% levels of significance (based on bootstrapping), respectively.

(b) The Sargan statistic tests over-identifying restrictions (based on bootstrap samples). AR(1) and AR(2) are tests for first-order and second-order serial correlation (based on bootstrap samples).

Table 4.

Big4 banks

	Full sample		Sub-sample 1 1988 to 1992		Sub-sample 2 1993 to 2010
	ΔMARGIN_t	ΔFEE_t	ΔMARGIN_t	ΔFEE_t	ΔMARGIN_t	ΔFEE_t
ΔMARGIN_t−1	0.73*** (0.06)	−0.57** (0.22)	−0.047 (0.09)	−0.303*** (0.08)	0.56*** (0.06)	−0.67* (0.38)
ΔFEE_t−1	−0.06** (0.03)	0.33*** (0.11)	−0.121** (0.06)	0.028 (0.165)	−0.04* (0.02)	0.29** (0.12)
Constant	−0.41*** (0.11)	0.12 (0.16)	−0.065* (0.03)	0.051*** (0.01)	−0.12*** (0.04)	−0.17** (0.07)
Diagnostics
Sargan statistic	54.07	60.01	12.7	10.31	38.55	43.41
Sargan p-value	1.00	1.00	0.81	0.92	1.00	1.00
AR(1)	−1.88*	−1.89*	−1.67*	−1.77*	−1.83*	−1.83**
AR(2)	−0.84	1.45	−0.39	−0.73	−0.81	−0.37

Notes: (a) Standard errors are in the parentheses. ***, ** and * denote rejection of null of zero restriction at 1%, 5% and 10% levels of significance (based on bootstrapping), respectively.

(b) The Sargan statistic tests over-identifying restrictions (based on bootstrap samples). AR(1) and AR(2) are tests for first-order and second-order serial correlation (based on bootstrap samples).

Table 5.

Foreign banks.

	Full sample		Sub-sample 1 1988 to 1992		Sub-sample 2 1993 to 2010
	ΔMARGIN_t	ΔFEE_t	ΔMARGIN_t	ΔFEE_t	ΔMARGIN_t	ΔFEE_t
ΔMARGIN_t−1	0.15* (0.07)	−0.32 (0.69)	−0.091 (0.06)	0.486** (0.21)	0.44** (0.21)	−0.15 (0.38)
ΔFEE_t−1	−0.24** (0.07)	0.36 (1.14)	−0.035 (0.03)	−0.131 (0.10)	−0.22 (0.23)	0.35*** (0.11)
Constant	−0.24 (0.22)	−0.16 (0.37)	−0.172** (0.07)	0.166 (0.15)	0.21 (0.24)	−0.03 (0.37)
Diagnostics
Sargan statistic	130.3	64.72	24.97	39.77	57.5	49.6
Sargan p-value	1.00	1.00	0.13	0.21	1.00	1.00
AR(1)	−1.94*	−2.49**	−1.71*	−1.66*	−1.73*	−1.92*
AR(2)	−0.65	0.26	−1.42	−0.67	−1.03	−0.29

Notes: (a) Standard errors are in the parentheses. ***, ** and * denote rejection of null of zero restriction at 1%, 5% and 10% levels of significance (based on bootstrapping), respectively;

(b) The Sargan statistic tests over-identifying restrictions (based on bootstrap samples). AR(1) and AR(2) are tests for first-order and second-order serial correlation (based on bootstrap samples).

Table 6.

Other domestic banks.

	Full sample		Sub-sample 1 1988 to 1992		Sub-sample 2 1993 to 2010
	ΔMARGIN_t	ΔFEE_t	ΔMARGIN_t	ΔFEE_t	ΔMARGIN_t	ΔFEE_t
ΔMARGIN_t−1	0.489*** (0.10)	−0.49** (0.19)	0.098 (0.21)	0.653 (0.52)	0.52*** (0.09)	−0.81** (0.36)
ΔFEE_t−1	−0.08** (0.03)	0.38*** (0.13)	−0.214*** (0.07)	0.402** (0.17)	−0.08** (0.04)	0.39*** (0.11)
Constant	−0.49* (0.29)	0.01 (0.43)	−0.061 (0.07)	−0.106 (0.07)	0.30 (0.26)	−0.25 (0.18)
Diagnostics
Sargan statistic	71.13	141.4	20.79	42.27	72.96	88.5
Sargan p-value	1.00	1.00	0.29	0.30	1.00	1.00
AR(1)	−2.66***	−1.99**	−1.93*	−1.64*	−2.18**	−1.69*
AR(2)	0.92	0.90	−0.01	−1.25	−1.19	−1.02

Notes: (a) Standard errors are in the parentheses. ***, ** and * denote rejection of null of zero restriction at 1%, 5% and 10% levels of significance (based on bootstrapping), respectively.

(b) The Sargan statistic tests over-identifying restrictions (based on bootstrap samples). AR(1) and AR(2) are tests for first-order and second-order serial correlation (based on bootstrap samples).

Sensitivity analysis: To see how sensitive our results are to pooling the banks by group (i.e., Big Four, Foreign, and Other Domestic banks), we first delete each bank one at a time and compare the resulting model with the results reported in Tables 4 –6. Overall, this procedure did not affect any of the signs of the coefficients reported in the tables. The experiment is conducted for each group for the full sample period. However, the sensitivity analysis is not conducted for the sub-sample periods within each group, as the deletion of banks will result in a lack of degrees of freedom.

Unsurprisingly, however, due to the smaller sample sizes, the regression results are somewhat sensitive to exclusion of few banks in the Foreign and Other Domestic bank groups. Depending on the bank, the coefficients of some variables became statistically significant, while others became statistically insignificant. It is important to note that there were no sign reversals. These results are summarised in Appendix 1 Table 11.

To illustrate how to interpret Appendix 1 Table 11, consider deleting the Colonial Bank (COL) from the sample. Doing so makes the coefficient for FEE become statistically insignificant in the MARGIN equation. In addition, the coefficient for FEE becomes statistically significant in the FEE equation. Overall, what is immediately obvious from Appendix 1 Table 11 is that our most robust results are for those reported in Tables 3 –6 and 7 –10. That is, the conclusions that were drawn about the relationships between MARGIN and FEE are the most defensible.

Table 7.

All banks.

	Full sample		Sub-sample 1 1988 to 2002		Sub-sample 2 2003 to 2010
	ΔMARGIN_t	ΔFEE_t	ΔMARGIN_t	ΔFEE_t	ΔMARGIN_t	ΔFEE_t
ΔMARGIN_t−1	0.59*** (0.17)	−0.31** (0.14)	0.59** (0.23)	0.18* (0.10)	0.59** (0.11)	−1.25** (0.52)
ΔFEE_t−1	−0.09** (0.04)	0.65*** (0.12)	−0.20** (0.08)	−0.08 (0.17)	0.09 (0.06)	0.13*** (0.01)
Constant	−0.33* (0.18)	−0.05 (0.17)	−0.43** (0.20)	−0.06 (0.16)	0.01 (0.06)	0.39 (0.27)
Diagnostics
Sargan statistic	13.23	23.89	45.51	205.1	47.73	37.8
Sargan p-value	1.00	1.00	1.00	0.54	1.00	0.58
AR(1)	−1.82*	−1.73*	−2.17**	−1.87*	−2.34**	−1.64*
AR(2)	−1.43	0.47	−1.14	−1.03	−1.34	1.34

Notes: (a) Standard errors are in the parentheses. ***, ** and * denote rejection of null of zero restriction at 1%, 5% and 10% levels of significance (based on bootstrapping), respectively.

(b) The Sargan statistic tests over-identifying restrictions (based on bootstrap samples). AR(1) and AR(2) are tests for first-order and second-order serial correlation (based on bootstrap samples).

Table 8.

Big4 banks.

	Full sample		Sub-sample 1 1988 to 2002		Sub-sample 2 2003 to 2010
	ΔMARGIN_t	ΔFEE_t	ΔMARGIN_t	ΔFEE_t	ΔMARGIN_t	ΔFEE_t
ΔMARGIN_t−1	0.73*** (0.06)	−0.57** (0.22)	0.63*** (0.03)	−0.35* (0.19)	0.01** (0.006)	−4.63* (2.56)
ΔFEE_t−1	−0.06** (0.03)	0.33*** (0.11)	−0.30*** (0.03)	0.23 (0.32)	−0.04 (0.05)	0.08 (0.23)
Constant	−0.41*** (0.11)	0.12 (0.16)	−0.41*** (0.08)	0.05 (0.12)	0.04 (0.04)	−0.15 (0.16)
Diagnostics
Sargan statistic	54.07	60.01	48.51	47.42	13.85	12.22
Sargan p-value	1.00	1.00	1.00	1.00	1.00	1.00
AR(1)	−1.88*	−1.89*	−1.84*	−1.89*	−1.87*	−1.91*
AR(2)	−0.84	1.45	−1.12	1.01	−1.61	0.72

Notes: (a) Standard errors are in the parentheses. ***, ** and * denote rejection of null of zero restriction at 1%, 5% and 10% levels of significance (based on bootstrapping), respectively;

(b) The Sargan statistic tests over-identifying restrictions (based on bootstrap samples). AR(1) and AR(2) are tests for first-order and second-order serial correlation (based on bootstrap samples).

Table 9.

Foreign banks^a.

	Full sample		Sub-sample 1 1988 to 2002
	ΔMARGIN_t	ΔFEE_t	ΔMARGIN_t	ΔFEE_t
ΔMARGIN_t−1	0.15* (0.07)	−0.32 (0.69)	0.21** (0.09)	−0.11 (0.22)
ΔFEE_t−1	−0.24** (0.07)	0.36 (1.14)	−0.28*** (0.05)	0.13 (0.16)
Constant	−0.24 (0.22)	−0.16 (0.37)	−0.34 (0.24)	−0.05 (0.23)
Diagnostics
Sargan statistic	130.3	64.72	124.5	84.89
Sargan p-value	1.00	1.00	1.00	1.00
AR(1)	−1.94*	−2.49**	−2.04**	−1.73*
AR(2)	−0.65	0.26	−1.41	−0.27

Notes: (a) Standard errors are in the parentheses. ***, ** and * denote rejection of null of zero restriction at 1%, 5% and 10% levels of significance (based on bootstrapping), respectively.

(b) The Sargan statistic tests over-identifying restrictions (based on bootstrap samples). AR(1) and AR(2) are tests for first-order and second-order serial correlation (based on bootstrap samples).

The reduction in sample size for the foreign bank meant that robust estimates of the standard errors of the estimates were not possible for the 2003 to 2010 sub-period and so these results were not shown.

Table 10.

Other Domestic Banks.

	Full sample		Sub-sample 1 1988 to 2002		Sub-sample 2 2003 to 2010
	ΔMARGIN_t	ΔFEE_t	ΔMARGIN_t	ΔFEE_t	ΔMARGIN_t	ΔFEE_t
ΔMARGIN_t−1	0.489*** (0.10)	−0.49** (0.19)	0.32** (0.13)	−0.04 (0.20)	0.51*** (0.12)	−1.16* (0.65)
ΔFEE_t−1	−0.08** (0.03)	0.38*** (0.13)	−0.10** (0.04)	0.22* (0.14)	0.003 (0.03)	0.13 (0.13)
Constant	−0.49* (0.29)	0.01 (0.43)	−0.48* (0.27)	−0.09 (0.36)	−0.07 (0.08)	0.56 (0.42)
Diagnostics
Sargan statistic	71.13	141.4	86.74	150.6	26.79	25.57
Sargan p-value	1.00	1.00	1.00	0.99	0.95	0.96
AR(1)	−2.66***	−1.99**	−2.59**	−1.95*	−1.85*	−1.99**
AR(2)	0.92	0.90	−0.38	−1.01	−1.58	−1.33

Notes: (a) Standard errors are in the parentheses. ***, ** and * denote rejection of null of zero restriction at 1%, 5% and 10% levels of significance (based on bootstrapping), respectively.

(b) The Sargan statistic tests over-identifying restrictions (based on bootstrap samples). AR(1) and AR(2) are tests for first-order and second-order serial correlation (based on bootstrap samples).

Overall, it is found that changes in margins are offset by changes by in fees with each showing offsetting time series properties.¹⁸ Consistent with increased emphasis on fee income based revenue sources (Allen and Santomero, 2001; Lepetit et al., 2008b; Stiroh, 2004), this relationship is strongest in the second sub-period, post 1992. Thus banks in Australia reacted to reductions in margins by increasing fees in the following period, and this relationship is fairly robust over time.¹⁹ However, the coefficient of the increase in fees was generally smaller than the reduction in margins.

The economic significance of these changes will be considered from the perspective of the coefficients estimated using the full sample. As the model is estimated using changes, the coefficient drawn from the regression of ΔMARGIN _t on ΔMARGIN _t−1 is the estimated elasticity. Using the average value of MARGIN as well as the average value of total assets implies an annual average reduction in margin revenue for each bank of approximately AU$644 million per annum. The corresponding increase in fee revenue can be decomposed into two sources. First, there is the increase in fees that is a reaction to reduction in margin revenue; this is represented by the results of the regression of ΔFEE _t on ΔMARGIN _t−1, which produces an estimated elasticity of 0.33453, implying an increase in bank fee revenue per annum of approximately AU$368 million per annum per bank. Second, there is the time series pattern of increases in fees, which has an annual value of approximately AU$661 million. This increase in fee income is marginally larger than the overall decline in margin income, meaning that overall the banking system has slightly increased its revenue base as a result of changing revenue composition. However, this increase should be borne in mind as it occurred against a background of increasing bank assets. In net terms the increase in bank revenue is approximately AU$17 million per annum; using the average assets figure for the whole period in Table 1, this implies an increase in return on assets of 0.0000003%. If the 2009 average asset figure is used the increase in return on assets is far smaller. Further, the increase in bank fees that this study can directly attribute to falling bank margins is slightly more than half (fifty-seven percent) of the decline in margin revenue.

The other part of the observed increase in bank fees is due to banks increasing their range of product offerings into new financial services. As will be argued below, when the results of the second sub-period analysis are considered, it is most likely that this portion of increased fee income is largely attributable to increased insurance revenue occurring after 2002. Thus, this paper will argue that the changing pattern of bank revenue has generated a net welfare transfer in favour of consumers of bank products, but with not all consumers benefitting or benefitting equally from this transfer. In response to this observed decline in revenue, banks have responded by diversifying their portfolio of product offerings to new areas of financial services (particularly insurance), which has marginally offset (in total) the decline in margin income, but the impact of these changes in terms of return on assets is extremely small.

An interesting result is that the first set of sub-period analysis finds the coefficient on fees is negative and significant in the first sub-period, while the margin coefficient is insignificant. This indicates that Australian banks were being pro-active in the process of disintermediation before the move away from traditional (margin) income was resulting in a statistically significant decline in margin revenue. Thus, it seems that the Australian banks were pre-positioning their revenue streams to deal with the negative impact of reduced margin income by become active participants in the market-based processes that resulted in increased fees. These processes can be considered market based in that (i) banks became active in the process of using market products to replace traditional intermediation, and (ii) fees and charges on bank products became increasingly focussed upon earning a market-based rate of return.

As discussed above, the bank revenue and revenue reporting saw a distinct change after 2003, with this change being most apparent in the area of insurance revenue. Accordingly we conducted a second set of sub-period analysis to determine what impact (if any) this change had on our estimated model. These results are in Tables 7 –10. What is immediately noticeable is that the coefficients of ΔFEE are far larger in the 2003 to 2010 sub-period that in any of the proceeding analysis,²⁰ with the change particularly noticeable for the Big Four banks, but also quite apparent for the Other Domestic banks. Based on this evidence this paper will argue that the Australian banks responded to reductions in revenue due to falling margins by increasing their offerings of financial services, particularly insurance. As discussed above, it is this increased revenue from offering a wider range of financial products that has meant Australian banks have been able to offset the impact of falling margin income from providing traditional banking products.

The results for the Big Four banks were largely the same as for the All Banks sample, with the exception that the trade-off between margins and fees seemed to begin a little earlier in the first sub-sample period, 1988 to 1992. For the Big Four banks the evidence that the larger banks were pro-active in increasing fees prior to a statistically significant reduction in margin income is also present, again indicating that the large Australian banks were pro-active in this change in revenue composition.

For the Foreign banks, the lagged relationship over time between fees and margins income is weaker, no doubt due to the foreign bank’s higher level of emphasis on fee income rather than margin income. In the case of the foreign banks, no evidence was found of a systematic lagged relationship over time between fees and margins. Instead the time series process of the two income streams seem to be entirely separate. This lack of relationship reflects the foreign banks’ lower emphasis upon the provision of intermediation services as part of their strategic mix.²¹ Within the context of this study, the foreign banks can be considered as a control sample to provide a contrast to the trends exhibited by the other two bank types which are more active in traditional bank transactions.²²

In the case of the Other Domestic banks, fees are again found to move in the opposite direction to margins. However, evidence of a time trend in margins is weaker. In both sub-periods the evidence supports the argument that fee income is moving in the opposite direction to margins. Again there is little evidence of a time trend in net interest margins. There is, however, evidence of a time trend in fees in the first sub-period. This evidence supports the argument that increases in fees tended to pre-date reduction in net interest income, and that Australian banks were pro-active in responding to the threat of disintermediation by pre-positioning their revenue streams to compensate for potential reduction in traditional (margin) income.

As a further sensitivity analysis, the impact of removing each bank in turn from the sub-samples was analysed. These results are shown in Appendix 1 Table 11. In the case of the Big Four banks, only the constant was found to be sensitive to outlier banks. In three of four cases, the removal of one of the Big Four banks resulted in the constant becoming statistically insignificant. In the case of the Foreign banks, three banks were found to impact upon the coefficients for the margin equation, with the removal of each bank resulting in the coefficient becoming statistically insignificant. All three banks had experienced a significant restructure during the sample period. In the case of the FEE equation, the removal of three banks resulted in the estimated coefficient becoming statistically significant. Again each of these banks experienced a significant restructure during the sample period. It is noteworthy that Standard Chartered Bank acted as an outlier bank for both the margin and fee equations. In the case of the Other Domestic banks, again three banks acted as outlier banks. For the FEE equation removal of these outliers resulted in the estimated coefficient becoming insignificant.

6. Conclusions and directions for further research

Overall, this study finds that there is a systematic relationship between decreases in Australian bank interest margins and increases in fee income. Further, increases in fees pre-date reductions in margin income. This suggests that Australian banks pre-positioned their revenue streams for the impact of falling margin income by increasing their fee income. It is also found that the increases in fees associated with falling bank margins are of a smaller magnitude than the decreases in margins. This indicates that consumers of traditional banking products as a whole have been made better off by this process, thus producing a wealth transfer in favour of bank customers overall. However, increases in bank revenue resulting from increased fees are of marginally larger magnitude than declining margin income.

This paper argues that the increases in fees observed in this study reflect a strategic effort by Australian banks to counteract the impact of falling interest margins by diversifying their income sources away from traditional intermediation toward other revenue sources such as funds management and, in particular, insurance. The evidence of this study is that this process began before falls in margin income were statistically significant; suggesting that banks actively re-focussed their revenue streams toward fees prior to falling margin income becoming statistically significant. This would reflect the changing nature of the financial system as discussed by Allen and Santomero (2001). This is exemplified by the growing trend toward larger banks providing one-stop financial services.

It is possible (but less likely) that the increases in fees observed in this study are directly due to increases in fees and charges being levied as part of intermediation services. This is the trend observed by the Reserve Bank of Australia (2006), although with the caveat that the Reserve Bank of Australia (2006) used a narrower definition of fees than applied in this study and so the two studies may not be reflective of the same effects. If this is the case, then Australian banks are pre-empting decreases in margin income by increasing the fees charged to users of bank products and approximately (but not exactly) the same consumer groups are paying for decreases in margins via higher fees and changes. However, depending on their patterns of bank service usage there may remain some consumer groups that are worse off at the margins from this process, particularly those bank consumers making frequent use of deposit services but do not have loan accounts which benefit from the effects of lower interest margins. However, the most likely interpretation of the results in this study is that banks have increased fee income via increased insurance revenue and as a result consumers of bank products are better off than previously. In order to determine in more detail where the wealth transfers have occurred as a result of the processes observed in this study, further research will be necessary, employing more detailed data than available for the sample employed in this study.

This trend of increasing bank fees raises different agency concerns to the different stakeholders in the banking system. It is conventionally assumed that bank shareholders are able to diversify away the impact of idiosyncratic risk. However, as discussed by Froot and Stein (1998), increased income volatility resulting from increased exposure to fees can have a nonlinear impact on bank cost of funds and result in increased reliance upon internal bank hedging on behalf of shareholders. Furthermore, the recent global financial crisis has demonstrated the importance of systemic risk in banking for the economy as a whole.

Borrowers of banks bear increased agency costs in times of bank failure resulting in the loss of the implicit value of their bank–client relationship (Stiroh and Rumble, 2006). As discussed by Stulz (1984), bank management hold poorly diversified wealth portfolios and so are concerned about bank total risk. Further, increased bank fees results in increased returns but worsening risk–return trade-offs, as well as (in Europe) worsening loan quality. Prudential regulators are concerned about financial system stability, and increased fee income results in increased bank systematic risk (De Jonghe et al., 2007; Stiroh, 2006b). With bank management potentially more concerned about the level of returns rather than risk and return, the negative aspects of ‘too big to fail’ increase the risk to regulators of bank failure due to increased income volatility resulting from higher levels of fees. The outcome for regulators is an increased exposure to systemic risk. Further, as discussed by Laeven and Levine (2007), the increased firm complexity resulting from higher levels of fee income also increases the scope for agency problems. The evidence presented in this study finds that banks in Australia are increasing their level of fees while at the same time margin income is declining. Thus the agency costs resulting from this trend are more likely to increase rather than decrease and all bank stakeholders should be aware of the resulting changes in bank risk characteristics. These changes in bank portfolio composition and bank risk also offer a number of potentially fruitful avenues for future bank research.

Footnotes

Appendix 1 Acknowledgements

The authors are grateful for comments from Tom Smith, Andy Mullineux, Katrina Ellis and Bruce Arnold as well as seminar participants at the AFAANZ conference, Gold Coast 2007; the Australasian Finance and Banking Conference, 2007; the Swiss Society for Financial Market Research, Zurich, 2008; Midwest Finance Association Meeting, San Antonio, Texas, 2008; Asian Finance Association Annual Conference, Brisbane, June 2009; Finance and Corporate Governance Conference, Melbourne, April 2011, KOF (Swiss Economic Institute) at ETH (Swiss Federal Institute of Technology), Zurich and the Australian Prudential Regulation Authority. Excellent research assistance has been provided by Phillippa Wright. All errors remaining are the responsibility of the authors.

Funding

The authors are grateful for financial support from the Bond University Vice Chancellors research fund.

Date of acceptance of final transcript: 1 February 2012.

Accepted by Associate Editor Garry Twite (Finance).

Notes

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