Shelagh heffernan modern banking phần 8 potx

[ 490 ] M ODERN B ANKING could reduce their costs by expanding the size of their existing branches. The exception is Spain, where costs would fall by increasing the number of branches. Based on the results for economies of scope, all banks in France, smaller banks in Spain and larger banks in Germany could reduce costs by increasing their output mix. Branches in Italy and Germany, together with those of large banks in France, should do the same. Branches of smaller French and Spanish banks should do the opposite, i.e. specialise more. Altunbas et al. (2001b) estimate scale economies using the same data set (income and balance sheet data from the Bankscope database for banks from 15 EU countries between 1989 and 1997) as was used for their X-inefficiency scores. They find average scale economies range from between 5% and 7%, suggesting that if outputs were increased by 100%, total costs would rise by 93% to 95%, on average. 19 If the banks are broken down by asset size, it reveals that the significant economies of scale are being enjoyed by the smallest banks in all countries, with assets ranging between 1 and 99 million ECUs. 20 German and Greek banks also enjoyed economies of scale for most asset size categories. Banks in Germany, the UK, Denmark and the Netherlands with assets in excess of 5 billion ECUs (the largest asset category) have significant economies of scale of just under 5%, but the largest banks in Austria, Belgium, Finland, Greece, Ireland and Luxembourg all had diseconomies of scale: so doubling output would lower average cost for the first group by about one-twentieth, but raise it for the second group. However, when equity capital is removed as a factor input, 21 scale economies are found for the largest banks. Thus, the results here are mixed, but it is notable that the scale economies are found for the UK, Germany and the Netherlands – countries with large banks active in global markets. If it is accepted that equity capital is a weak measure of risk taking, the findings for scale economies are strengthened. These findings are more consistent with US studies using post-1990s data. Berger and Mester (1997) review the possible reasons for differences in efficiency estimates. They also used their data to examine scale economies. Recall the database: nearly 6000 US commercial banks over the period 1990–95. They use the Fourier flexible cost model to estimate Scale Efficiency , defined as the ratio of the predicted minimum average costs to average costs, both adjusted to be on the X-efficiency frontier. They find evidence of scale efficiency at every asset size classification, ranging from 0.851 for banks with assets of up to $50 million to 0.782 for banks with assets in excess of $10 billion. From this, scale economies are computed as the bank’s ratio of cost efficient size to its actual size. In column (2) of Table 9.3, the ratio is >1, implying scale economies for all asset sizes. For a given bank’s product mix and input prices, the typical bank needs to be over two times larger to maximise cost scale efficiency. Another way of looking at it is based on column (4), the reciprocal of (3), i.e. the ratio of actual to cost efficient size. Given an average of about 0.4, it indicates that the US system would, on average, reach maximum efficiency by reducing the number of its banks by 60%, with each surviving bank producing, on average, 170% more. 19 Except for Finland and France, when scale economies were not found to be significant in most years. 20 ECU: European currency unit. The term used before the euro was introduced. 21 By including equity capital as a factor input, the authors argue they are controlling for risk in the cost estimation. [ 491 ] C OMPETITIVE I SSUES IN B ANKING Table 9.3 Key Results from Berger and Humphrey (1997) Bank Size (assets) (M/B US$) No. of Banks Cost-Efficient Size/ Actual Size Actual Size/ Cost-Efficient Size 0–$50M 2218 2.2 0.455 $50–$100M 1794 2.363 0.423 $100–$300M 1344 2.523 0.396 $300M–$1B 392 2.815 0.355 $1B–$10B 171 2.986 0.335 >$10B 30 2.673 0.374 Average – 2.723 0.389 Source: Berger and Mester (1997). In the smallest asset category the number of banks should be reduced by 54% and banks in the second largest category, which would benefit by reducing their numbers by 67%. These findings differ from most of the US studies that used 1980s data, where scale economies tended to be found for small banks; larger banks exhibited diseconomies or constant returns to scale. Berger and Mester identify a number of factors which could help to explain the difference: ž The Fourier flexible function was used rather than a translog cost function, but they re-estimated using the translog and found the scale economies to be even larger for the bigger banks. ž Open market interest rates were low in this period, about half what they were in the 1980s. The lower rates would reduce interest rate expenses which are normally proportionately higher for large banks because a greater proportion of their liabilities tend to be market sensitive. For example, they use wholesale funds. ž Regulatory changes tending to favour large banks. In the 1980s, with unit banking, or inter/intrastate branching restrictions, and restrictions on activities, it was costly to become large. For example, branching restrictions meant fewer branches for collecting deposits, contributing to scale diseconomies for large banks. ž New technology has altered the way basic services are delivered, making it possible for banks to expand faster rather than having expensive branch outlets. Drake and Sniper (2002) revisited UK building societies in light of more recent US studies (such as Berger and Mester, 1997) and found more evidence of scale economies. They use a translog cost function but extend it to allow for entry/exit 22 and to estimate two types of technical change. They apply their estimating equation to a sample of UK building societies over the period 1992 to 1997. In their preferred model, the economies of scale estimate is highly significant and indicates that economies of scale exist for all different asset classes. Potential scale economies decline with size. Technical progress is shown to 22 The authors have an unbalanced panel set because the building societies exist through the period. Rather than discarding them, they extend the Dionne et al. specification based on the translog cost function. [ 492 ] M ODERN B ANKING reduce the costs of larger societies relative to smaller ones, suggesting one strategy: mergers to reduce costs. Smaller societies are particularly vulnerable because they have the largest unexploited scale economies. Cavallo and Rossi (2001) uses a translog cost function to estimate X-inefficiencies, scale and scope economies for several European countries (France, Germany, Italy, Netherlands, Spain and the UK) between 1992 and 1997. They have an unbalanced panel set of 442 banks and 2516 observations. The banks include commercial, saving and loans, cooperatives, investment, mortgage, non-banks, some government credit institutions. X-inefficiency is present in all the banking systems, with a mean cost X-inefficiency of 15.64%. The small financial institutions are significantly more efficient, especially those involved in traditional activities, and the coop banks do best among those involved in core banking services. They find evidence of economies of scale of similar magnitude, across the banking systems. They also report evidence for economies of scope, though it is not always significant. The best evidence is for large banks, while medium and small banks did not have significant coefficients. While these results are at odds with most other studies, they are similar to the findings of Berger and Mester (1997), which used 1990s data. All the studies reviewed looked at the question of whether joint production reduces costs because of complementarities in production. Berger et al. (1996) used data from US banks over the period 1978 to 1990, looking for evidence of revenue economies of scope, that is, if complementarities in consumption raise revenues. Based on samples of small banks, large banks, specialists and banks offering a wide variety of products, they find no evidence to support this idea. The authors conclude that banks do not gain (in terms of higher revenues) by offering, for example, deposits and loans. 9.3.3. Technological Change Altunbas et al. (1999) argue a time trend can act as a proxy for technical change. 23 It was found to be significant, and reduced the real annual cost of production by 3%. Also, the bigger the bank, the greater the reduction in costs. In a recent paper, Molyneux (2003) summarises the econometric approach to measuring technical change, which involves using the cost or profit functions summarised in equations (9.3) to (9.5). Using the cost function, estimated with a time trend, technical change is measured by taking the partial derivative of the estimated cost function with respect to a time trend. Following Molyneux (2003, p. 13): ∂ ln TC/∂T = t 1 + t 2 T +  ψ i ln P i +  φ i ln Q i (9.10) where ln TC : natural log of total costs ln Q i : natural log of bank outputs ln P i : natural log of ith input prices (wages, interest rate, price of capital) T : time trend 23 Though they note it must be treated with caution because of problems identified in the literature when using a time trend for this purpose. Also, technical progress rates are not constant. [ 493 ] C OMPETITIVE I SSUES IN B ANKING Equation (9.10) can be broken down into three types of technical progress. 1. Pure technical progress, t 1 + t 2 T: reduces total costs (or raises profits). 2.  ψ i ln P i : non-neutral technical change, reflects changes in the sensitivity of total cost (or profits) to changes in input prices. If ψ i is negative then the share of cost of input 1 towards total cost (profits) is decreasing over time. 3.  φ i ln Q i : scale augmenting technical progress, reflects changes in the sensitivity of total cost (profits) to variations in the quantities of output produced. If φ i is negative, then the scale of production which minimises average cost (or maximises profit) for a given output is rising over time. Molyneux uses balance sheet income data for 4000 European banks for the period 1992–2000, giving a panel of 20 333 observations. His main findings are: ž There was a reduction in costs of 5.62% arising from pure technical change (1.7%), and non-neutral technical change (3.92%). This was offset by a 1.8% increase in annual costs due to augmenting technical change. Overall annual costs fell by 3.8%. ž Classified by asset size, the small banks (with assets ranging from ¤1 million to ¤499.99 million) gained the most from cost reductions due to technical changes. The cooperative and savings banks benefited more than commercial banks, probably because these banks are normally smaller. ž Technical change reduced annual average costs by 2–4% in most EU states. Austria, Denmark, France, Germany, Italy and Spain experienced the largest reduction in costs. The decline in costs was highest in Denmark (6.6%), followed by Germany (4.4%). In the UK, they fell by 2.2%. The effect of technical progress on the profits/profit frontier is estimated in the same way as equation (9.10), but this time the dependent variable is profits – see also equations (9.3) to (9.5). Based on the estimated profit function, it appears that reduced costs due to technical change have not fed into higher profits. ž The average annual reduction in profits as a result of technical change was 0.45% over the period, brought about by a fall in profits of 3.42% from pure technical change (1.9%) and from non-neutral technical change (1.52%), and an increase in profits (2.966) due to scale augmenting technical progress. In the early period, 1992–95, technical change improved profits but since then, it has reduced them by increasing amounts. Molyneux suggests this is due to ‘‘early mover’’ (p. 14) advantage: banks adopting the early technology earned enough revenue to offset the costs of adopting it but by the late 1990s, profits began to decline because all banks were adopting similar technologies, thereby incurring costs but not improving revenues. ž It appears that the banks that benefited most in terms of cost reduction suffered from reduced profits and vice versa. Commercial banks and banks from the top three asset categories experienced an increase in profits, while technical change reduced profitability of the smaller banks including the savings and cooperative banks. Molyneux suggests there [ 494 ] M ODERN B ANKING is a trade-off: banks using technology for large cost cuts (e.g. increasing ATMs and closing branches) ended up with poorer service quality, lower revenues and reduced profits. The commercial banks experience a smaller cost reduction because they use the technology to improve revenues through better services and risk management, etc. – reflected in higher profits. ž Countries that led the way in terms of annual cost reductions as a result of technical progress experienced the biggest declines in profits. For Danish banks, annual profits fell by 2.7% over the period; they also fell for the four other big cost cutters. Austria, Germany, Italy and Spain experienced annual declines in profits, though all except Austria were less than 1%. The other 10 countries experienced a rise in profits as a result of technical change. It is notable that in the UK, which (along with New York) led the way in generating new forms of commercial and investment banking business, 24 technical change led to an annual increase in profits of 0.781, with a small annual cut in costs. Sweden did the best overall, where annual costs fell by 1.8% and profits increased by 1.7%. Luxembourg’s profit increase was about the same as Sweden’s, though costs fell by just 0.41%. Berger (2003) and Berger and Mester (2003) use similar cost and profit equations but look at changes in cost productivity (caused by movements in the best practice frontier and changes in inefficiency) and profit productivity. Berger and Mester looked at US banks from 1991 to 1997 and found annual increases in profit productivity of 13.7% to 16.5%, but cost productivity declined by 12.5%. They argue that these findings are consistent with US banks adopting new technologies that improved a range of services (e.g. mutual funds, derivatives, securitisation) such that the rise in revenues exceeded the increase in costs, hence the rise in profit productivity. Their US results are consistent with Molyneux’s findings for commercial banks and for some European states. 9.4. Empirical Models of Competition in Banking This section reviews different approaches that have been used to assess how competitive the banking sector is and to identify factors influencing competitive structure. The hypotheses most frequently tested are based on the structure–conduct–performance and relative efficiency models. Attempts to measure contestability in banking markets were briefly popular in the late 1980s/early 1990s, and are still mentioned in many papers. Finally, some studies have been trying to obtain more direct measures of competition by looking at bank pricing behaviour. 9.4.1. The Structure–Conduct–Performance Model Since the Second World War, a popular model in industrial economics has been the structure–conduct–performance (SCP) paradigm, which is largely empirical, that is, it 24 It is unclear whether investment banks were included in the sample, but the large European commercial banks also offer investment banking services. Off-balance sheet business is not included in Molyneux’s model, though it would contribute to the profit figures. [ 495 ] C OMPETITIVE I SSUES IN B ANKING relies on empirical data but for the most part, lacks a theoretical base. Applied to the banking sector, SCP says a change in the market structure or concentration of banking firms affects the way banks behave and perform. The more concentrated the market, the more market power banks have, which means they can be inefficient (i.e. avoid minimising costs) without being forced out of the market. This approach assumes a well-defined link between structure, conduct and performance: Structure of the market: determined by the interaction of cost (supply) and demand in a particular industry Conduct: a function of the numbers of sellers and buyers, barriers to entry and the cost structure – a firm’s conduct is reflected chiefly in its pricing decisions Performance: the bank’s conduct (e.g. its pricing behaviour) will affect performance, often measured by profitability How the links between the three might work in practice is: Structure → Conduct (higher prices) → Performance (higher profits) In the actual tests (see below), some authors treat profits as the dependent variable. Others look at the first link and try to explain prices by structure; the argument is that a concentrated market allows firms to set prices (e.g. relatively low deposit rates, high loan rates) which boost profitability. Several theoretical models predict that fewer firms imply higher prices. Cournot oligopoly and Dixit–Stiglitz monopolistic competition models are examples. However, market struc- ture is normally thought of as being endogenous, not exogenous, as assumed in the SCP model. So the SCP framework depends on the assumption that entry is effectively barred. In banking, the SCP model has been used extensively to analyse the state of the banking market in a given country or countries. Given there is no single generally accepted model of the banking firm, and since entry barriers are often high, emphasis on the SCP paradigm 25 is understandable. 9.4.2. The Relative Efficiency Hypothesis 26 This model challenges the SCP approach. Relative efficiency (RE) posits that some firms earn supernormal profits because they are more efficient than others. This firm specific efficiency is exogenous. Greater efficiency may well be reflected in greater output. When the number of firms is small, bigger efficiency differences between them would imply greater concentration. Though RE predicts a similar (positive) profits concentration relationship to the SCP model, its key claim is that firms’ profits should be correlated with this efficiency. Prices and concentration are inversely related, the opposite of SCP. Under the 25 Hannan (1991) developed a theoretical model, from which the SCP relationship is derived. 26 This model is sometimes known as the efficient markets model, but to avoid confusion with the well-known ‘‘efficient markets’’ hypothesis used in finance, this book uses the term ‘‘relative efficiency’’. [ 496 ] M ODERN B ANKING relative efficiency hypothesis, causation runs from greater efficiency, lower prices and higher concentration/market share: Efficiency → Conduct (Higher Output and/or Lower Prices) → Market Share → Performance (Higher Profits) The relative efficiency hypothesis can be linked to the X-efficiency hypothesis: some firms have superior management or production technology, which makes them relatively more cost X-efficient with lower costs. They are able to offer lower prices (if products are differentiated), gain market share (which increases concentration) and earn more profit. Likewise the presence of scale economies would mean these firms produce at low unit cost, lower prices and higher profits per unit of output. The evidence for or against these hypotheses is important because the policy implications are so different. Confirmation of SCP is a case for intervention to reduce monopoly power and concentration. Curbing the exercise of monopoly power may be done by policies to encourage more firms to enter the sector or through a regulator who monitors the prices set by existing firms and/or imposes rules on pricing; e.g. deposit rates may not be more than x% below the central bank official rate. Strong evidence for the relative efficiency hypothesis suggests policy makers should not interfere with deposit and loan rate setting in the banking markets. Mergers should be encouraged if they improve relative efficiency, but discouraged if all they do is increase concentration and market power (SCP). 9.4.3. Empirical Tests: Structure–Conduct–Performance and Relative Efficiency There are a multitude of studies testing the SCP and/or relative efficiency models in banking, especially for the USA. It would be impossible to do justice to them all. This section does not attempt a comprehensive survey of the published work. 27 Instead, it provides a summary of the findings reported in some recent key papers, which will be discussed below. For the SCP model, the general form is: P = f(CONC, MS, D, C, X)(9.11) where P : measure of performance (profits or price) CONC : market structure, with the degree of concentration in the market a proxy for the variable MS : market share, more efficient firms should have a greater market share D : market demand C : variables used to reflect differences in cost X : various control variables 27 For surveys of SCP and relative efficiency, see Gilbert (1984), Molyneux et al. (1996). Brozen (1982), Smirlock (1985), Evanoff and Fortier (1988), Molyneux et al. (1996) provide studies which have tested SCP. Berger (1995) and Goldberg and Rai (1996) review and extend the debate. [ 497 ] C OMPETITIVE I SSUES IN B ANKING The dependent variable, performance, is proxied by either the price of the good or service, or profitability. In the list of performance measures below, the first number in the brackets gives the number of times these measures have been used in 73 SCP studies between 1964 and 1991 using US data, as reported by Molyneux et al. (1996, table 4.1). The second number shows the number of times the performance measure was found to be significantly related to market structure. Measures for price include: ž Loan rates, such as interest rates and fees on personal loans, business loans or residential mortgages (30;14). ž Deposit rates, for example the interest rate paid on a term or savings deposits, money market accounts (25;10). ž Bank service charges, such as a monthly service charge levied on a current account, or service charges on a standard account (22;6). Profitability measures include: ž Return on assets: net income/total assets (24;12). ž Return on capital: net income/capital (14;8). ž Return on equity: used in more recent studies, net income/stockholder’s equity (NA). There is an ongoing debate as to which performance variable should be employed. Profitability, it is argued, addresses the issue of banks supplying multiple products/services. However, it combines a flow variable (profit) with stock variables (assets, capital). The use of interest rates (prices, e.g. deposit or loan rate) has been criticised for the same reason (e.g. loan rates over one year and loans outstanding at the end of the year). Using service charges can be fraught with problems; the way they are computed can vary from bank to bank, and account charges will vary depending on the number of times a service is used, and some customers may be exempt provided they maintain a minimum balance. Some studies employ a price measure as the dependent variable and others used a profit variable. For example, Berger and Hannan (1989) conducted direct tests of the SCP and relative efficiency models using the estimating equation: r ijt = α ij + β j CONC jt +  δ ij x jit + ε ijt (9.12) r ijt : the interest paid at time t on one category of retail deposits by bank i located in the local banking market j CONC jt : a measure of concentration in local market j at time t x jit : vector of control variables that may differ across banks, markets or time periods ε ijt : error term By the SCP hypothesis, β should be less than 0; that is, there is a negative relationship between concentration and deposit rates, the ‘‘price’’ of the banking service. 28 If the relative 28 If loan rates are used as the dependent variable, then β should be positive for SCP, and non-positive under RE. [ 498 ] M ODERN B ANKING efficiency model holds, β ≥ 0. Berger and Hannan (1989) collected quarterly data from 470 banks in 195 local banking markets over a 2.5-year period, from 1983 to 1985, with 3500–4000 observations in six deposit categories. The dependent variables were retail deposit rates paid by commercial banks, as reported in the Federal Reserve’s monthly survey of selected deposits and other accounts. 29 Banks in the sample were assigned to local markets, which were defined as metropolitan statistical areas (MSAs) or non-MSA counties. Banks with less than 75% of their deposits in one local market were deleted from the sample. Berger and Hannan used two concentration ratios to measure the degree of firm concentration in the banking market. The ‘‘three firm’’ concentration ratio, CR 3 is defined as the proportion of output attributed to the top three firms in the industry. More generally, this ratio is written as CR n ,wheren is the output share produced by the top n firms in the industry. The Herfindahl index 30 was also used, defined as H = s 2 i ,wheres i is the market share of the ith firm. These measures were constructed both with and without the inclusion of saving and loans firms. The vector x included a number of additional explanatory variables: ž The growth rate of deposits in the bank’s market, which may reflect local supply and demand conditions, and could have either sign. ž The number of bank branches divided by total bank branches plus savings and loan branches in the local market – it should have a negative coefficient if costs rise with the number of branches. Local per capita income was included to control for factors affecting the supply of funds to banks – in a non-competitive market, it may reflect a greater or lesser elasticity of deposit supply. The local bank wage, reflecting a cost factor, was another explanatory variable. Its sign is not predicted, because bank wages could also reflect local income differences. ž Whether a state in which a given bank operates prohibits (UNIT) or limits (LIM) branch banking. To the extent that such regulations limit entry, and therefore raise costs, one would expect to observe a negative coefficient. The different concentration measures yielded similar results, so only the results using CR 3 were reported. The β coefficient on the concentration variable was found to be negative and significant at the 1% level – that is, the more concentrated the market, the lower the deposit rate, a finding which is consistent with the SCP hypothesis but not the relative efficiency model. For example, ceteris paribus, banks in the most concentrated markets were found to pay money market deposit rates which were 25–100 basis points less than what was paid on the less concentrated markets. Similar findings were obtained for all but some certificate of deposit (CD) rates. For the regressions using the short-term CD rates, there were some large and significantly negative rates; a few of the coefficients were insignificant. But for 29 The six rates were: MMDA – money market deposit account, 10 quarters, September 1983–December 1985; SNOW, super now* account, 10 quarters, September 1983–December 1985; CD rates – certificate of deposit rates for 3, 6, 12 and 30 months, nine quarters from January 1983–December 1985 (CD rates had not been deregulated in September 1983). 30 A more general measure of concentration which does not rely on a single arbitrary cut-off point. [ 499 ] C OMPETITIVE I SSUES IN B ANKING the longer term CD rates (12, 30 months), the CR 3 coefficient was mostly negative but insignificant. This finding is not surprising because the longer the CD’s maturity, the more substitutes will exist and the greater will be the competition from other financial markets. The authors argued that the results were robust with respect to the use of separate OLS cross-section estimates in place of pooled time-series cross-section data, the choice of concentration measure and the inclusion of firm-specific variables such as market share, bank branches or bank size. The treatment of concentration under different state branching laws, modelling the deposit rate as a premium (the difference between the deposit rate and the money market mutual fund rate), and the inclusion of savings and loans in the measures of concentration did not affect the results. Jackson (1992) challenged Berger and Hannan (1989). Jackson reported that a regression conducted for the entire sample period yielded similar results. However, if the sample was divided according to relative degrees of concentration, the findings differed. ž A low concentration group, relatively low market concentration: here the β coefficient was negative, large and significant at the 1% level, which is consistent with the SCP finding. ž A middle concentration group: β was negative but insignificant. ž A high concentration group: β was positive and significant. These results suggest price is non-linear over the relevant range and appears to follow a U-shaped relationship. This finding supports the relative efficiency type model, where high levels of market concentration signal the gaining of market share by the most efficient firms, but low levels of concentration signal entry of efficient new firms. In their reply, Berger and Hannan (1992) questioned some of Jackson’s results, 31 but repeated their earlier work, allowing for the three levels of concentration. They found: ž β<0 and significant for the low concentration group; ž β>0 but insignificant for the middle concentration group; ž β = 0 but insignificant for the high concentration group’s summary equation (though it was significant for seven out of ten individual periods; changing the control variables in the high concentration group reversed the sign, raising the question of how robust the model actually is) Berger and Hannan (1992) concluded that the price–concentration relationship is negative for some ranges of concentration (supporting the SCP model), though it does vary across time periods. It is unclear, they claim, whether, at high concentration levels, it turns positive. More recent studies have used some measure of profitability as the performance variable. Molyneux and Forbes (1996) is typical of the approach taken. They regressed banks’ profits in different markets against a concentration ratio for that market (CR), the bank’s market 31 Jackson (1992) used monthly rather than quarterly observations, but did not correct the standard errors for serial correlation. [...]... ($m) 22 8 15 4 49 4 946 66 33 29 16 144 27 631 113 23 36 12 184 124 87 3 Securities – Europe2 National – intra National – cross Global – intra Global – cross Total Value ($m) 18 7 5 6 36 3 036 .8 46 26 17 8 97 4 975 49 28 25 18 120 16 162 Banking – USA National – intra National – cross Global – intra Global – cross Total Value ($m) 107 4 2 0 113 3 986 356 14 11 0 381 71 417 2 08 34 10 3 255 68 399 Securities... $2.4B $3.7B $ 18. 7B Bliss and Rosen (2001) Cybo-Ottone and Murgia (2000) DeLong (2003) USA 1 986 –95 107 48 19 ‘‘mega’’ mergers 32 126 $105.6B $23.67B US and non-US 1 988 –99 na∗∗∗ na∗∗∗ Beitel et al (2003) EU states plus Switzerland, Norway $ 181 .8B $37B 13 EU states 1 988 –97 plus Switzerland 397 US, 18 non-US 1 985 –2000 98 $11.9B∗∗ ∗ Average size over the period, in $M (millions) or $B (billions) Average assets... ] BANKING Information on Samples from Different Event Studies Countries Cornett and Tehranian (1992) Houston and Ryngaert (1994) Zhang (1995) Pilloff (1996) Siems (1996) Years Total Bank Mergers Bidder Size in Assets∗ Target Size in Assets∗ USA 1 982 87 30 $17.7B $17 698M $6.4B $6399M USA 1 985 –91 153 $100M (minimum) $100M (minimum) USA USA USA 1 980 –90 1 982 –91 1995 $13.9B $13B $60.6B $2.4B $3.7B $ 18. 7B... ‘‘price’’ ž 48 The first change in the regulations appeared in the 1967 Bank Act, the 1 980 Bank Act, the 1 982 revised Quebec Securities Act, ‘‘Big Bang’’ in Ontario in 1 987 , and legislation in 1990 See Heffernan (1994) for a more detailed discussion 49 See Heffernan (1994) 50 The major firm sample consisted of the 12 major banks and trust companies; minor firms operated in local markets [ 516 ] MODERN BANKING. .. months Current, lagged by 1 month Lagged by 3 months Lagged by 3 months 0.626 0.702 0. 184 0. 381 Mortgages (existing) Mortgages (new) Current, lagged by 2 months Lagged by 1, 2 months 0 .84 8 0.714 [ COMPETITIVE ISSUES IN 513 ] BANKING existing borrowers, who are locked in and face high switching costs Compared with the 1 985 89 study, there appears to be little change in this market The very high significant... and estimated the PR statistic to be 0.3 18 He concluded that banks in the sample behave neither as monopolists (their conduct was inconsistent with joint monopoly) nor as perfect competitors in the long run In Nathan and Neave (1 989 ) a similar methodology was applied, using cross-section data (1 982 84 ) from the Canadian banking system, PR values for 1 983 and 1 984 were found to be positive but significantly... in the early 1970s, then again in the late 1 980 s From the late 1 980 s to the new century, M&As in the banking sector enjoyed a prolonged boom in both the USA and Europe To date, there have been few bank mergers in developing/emerging markets, except under duress [ 5 18 ] MODERN BANKING Table 9.6 Number of Mergers and Acquisitions by Country 1990 1995 1999 Banking – Europe1 National – intra National –... that create an environment favourable to M&As They include changes in the structure of the banking sector, such as increased competition from non-bank competitors – as indicated by the decline in the [ 520 ] MODERN BANKING banks’ share of non-financial short-term corporate debt, from about 58% in 1 985 to around 48% a decade later (Bliss and Rosen, 2001) However, as was noted in Chapter 1, banks have expanded... measured by return on assets Note the dependent variable is now a measure of profit rather than price Molyneux and Forbes pool data from a number of European countries32 for 1 986 (756 banks), 1 987 (1217 banks), 1 988 (15 38 banks) and 1 989 (1265 banks) Each European country is treated as a separate local market The SCP hypothesis would predict α1 > 0 = α2 The relative efficiency hypothesis implies α1 = 0... ISSUES IN 519 ] BANKING while the total number of bank mergers in Europe (1267) was under half that of the USA ( 287 1), by 1999, the value of European mergers was much higher: $124.9 billion compared to $ 68. 4 billion in the USA The rise in the number of financial sector M&As with values in excess of $1 billion reflects the general trends: 1990 Number Value ($bn) 1995 19 98 1999 8 26.5 23 113 58 431 46 291 . and Forbes pool data from a number of European countries 32 for 1 986 (756 banks), 1 987 (1217 banks), 1 988 (15 38 banks) and 1 989 (1265 banks). Each European country is treated as a separate local. long run. In Nathan and Neave (1 989 ) a similar methodology was applied, using cross-section data (1 982 84 ) from the Canadian banking system, PR values for 1 983 and 1 984 were found to be positive but. surveys of SCP and relative efficiency, see Gilbert (1 984 ), Molyneux et al. (1996). Brozen (1 982 ), Smirlock (1 985 ), Evanoff and Fortier (1 988 ), Molyneux et al. (1996) provide studies which have