Monopoly Banking Market by evs12523

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									         COMPETITION AND CONTESTABILITY IN NEW
                       ZEALAND’S BANKING SYSTEM


     Paper presented at the 14th Australasian Finance and Banking
                     Conference, Sydney, December 2001


                                       Robert Smith
                                        David Tripe1
                            Centre for Banking Studies
                                   Massey University


Abstract:


This paper explores use of the Rosse-Panzar statistic as a basis for assessment
of competitive conditions in the New Zealand banking market.


From pooled regressions for the whole period analysed, 1996 to 1999, it was
found that total bank revenues appear to have been earned under conditions
of    monopolistic        competition.       Cross-sectional        competitive       conditions
regression analysis suggests that for 1996 total bank revenues were earned
under conditions of monopolistic competition, while, for 1997, the analysis
implies that monopoly or conjectural variation oligopoly conditions were
present. For 1998 and 1999, indications are that the New Zealand banking
market behaved like a natural monopoly in a perfectly contestable market,
perfect competition, or sales-maximising firms subject to a break-even
constraint.




1
 Contact author. Address: Centre for Banking Studies, Massey University, Private Bag 11-222,
Palmerston North, New Zealand. Phone +646 3595799 ext 2337, fax +646 3505651, E-mail
D.W.Tripe@massey.ac.nz
The level of competition in the commercial banking sector has always been a
subject of strong interest. In both New Zealand and Australia, bank customers
are concerned that oligopolistic conditions apply within the banking sector,
causing banks to provide inadequate services and extract excessive profits.
This was part of the background to the Wallis Inquiry in Australia, and there
was debate in both countries as to whether further mergers between banks
should be permitted, and under what conditions. Questions were raised as to
what might be an acceptable level of competition in the New Zealand banking
sector [Tripe (1997), (1999)].


It is also arguable that concerns about potential monopoly have helped to
provide a political constituency for the new bank that is being established by
New Zealand Post, commonly referred to as the Kiwi Bank.2


This paper explores a possible approach to measurement of competitive
conditions in New Zealand banking, using the Rosse-Panzar statistic, figures
for which we develop for the New Zealand market.


Some theoretical perspectives


There are a variety of approaches to measurement of competition such as the
number of competitors and market share of the largest participants, but these
approaches are relatively crude in that they don’t indicate whether these
competitors are able to exercise market power, which is, after all the reason
for trying to measure competitive conditions in the first place. As Shaffer
(1994) has put it:


           “Fewer firms in a market (that is, a concentrated structure) will
           generally lead to less competitive conduct (in terms of higher prices
           and reduced output levels) and less competitive performance (higher

2
    For further discussion of this, refer to Tripe (2001).


                                                        2
         ratios of price to cost and higher profits at the expense of lower
         consumer welfare).”


A more systematic attempt to quantify the potential for firms to exercise
power in a market can be expressed through the Herfindahl-Hirschman Index
(HHI), which takes account of the relative sizes of all market participants.
Values of this index can be expressed in a range from either 0 to 1 or 0 to
10,000, as follows:


         HHI = Σ(Si/S)2                                                    (1)
where Si is the size of the ith firm and S the size of the total market.3 Si/S thus
represents firm i’s market share.


An HHI of zero would thus indicate that there were a large number of firms
in the market, none of which had a market share greater than 0.7% (based on
whole numbers and the 0 to 10,000 scale), while an HHI of 10,000 would
indicate that there was only one participant in the market. The HHI is used as
the basis for merger approvals in a number of environments, most notably the
United States banking market, where a proposed merger will attract the
attention of the competition authorities if the HHI in a particular market
exceeds 1800.4


The HHI has an advantage in terms of being reasonably straightforward to
calculate, with suitable data not particularly difficult to obtain. It is inherently
a static measure, describing the situation at one point of time, and tells us
nothing about the actuality of any collusive behaviour by market
participants.5



3
  If market shares are expressed as fractions, the HHI will be in the range 0 to 1, if as percentages, it
will be in the range zero to 10,000.
4
  For a further, more detailed exposition, see Rhoades (1993).
5
  The HHI thus cannot give any insights into the dynamic framework for monopolistic competition
discussed by Schumpeter (1943).


                                                     3
Orthodox microeconomic analysis would tell us that the real factors
influencing a firm’s ability to exercise monopoly power lie in the slope and
elasticity of the demand curves for its products. What we really want to know
is whether, despite observed measures of competitive conditions, a banking
firm is able to raise its prices without suffering a severe decline in its
revenues. However, because they are often fearful of the potential losses of
market position that might arise from doing so, banks are usually reluctant to
engage in such experimentation to establish the exact shapes of the demand
curves for their products, especially as they are likely to be influenced by
competitor reactions. It is therefore difficult to assess the shape of the relative
demand curves, and even more difficult for outside observers trying to
ascertain the extent of effective competition.


One way in which competitive conditions might be reflected would be in
frequency of price changes: there is a hypothesis that market power would
lead to less frequent and slower adjustments in favour of the consumer, but
more frequent and faster adjustments in the bank’s favour [Shaffer (1994), p
8]. Hannan and Berger (1991) and Neumark and Sharp (1992) have found
some evidence for this in consumer deposit pricing. This area of research
could be worth pursuing for comparing some aspects of bank behaviour in
the New Zealand and Australian markets, but the authors have not as yet
found any record of this having been attempted.


Another approach to the conundrum of competition in banking markets and
its effect is through the theory of contestable markets. This argues that the
actual number of competitors and their respective market shares are not
important as long as new entrants are able to enter the market freely. The
threat of new entrants undercutting price and displacing an incumbent’s
market share induces optimal pricing across likely market structures. The free
movement of resources in and out of markets thus provides a check on
uncompetitive behaviour [Oliver (1996)].



                                         4
The Rosse-Panzar Statistic


In response to previous researchers’ failure to find significant connection
between concentration and monopoly power, J N Rosse and J C Panzar
developed a non-structural estimation technique to assess the level of
monopoly power being exercised in a particular market. They applied
Shepherd’s Lemma to a firm’s profit maximising first order conditions to
show that the sum of elasticities of gross revenue with respect to each input
price will be negative in the case of collusive equilibrium, and less than one
for monopolistic competition. A detailed review is provided in Rosse and
Panzar (1987), and it is beyond the scope of this paper to provide a detailed
account of the analysis leading to the relative propositions.


We will, however, note one key assumption. For the positive values of the
Rosse-Panzar statistic to be reliable (and capable of being interpreted in
accordance with the respective models of monopolistic and perfect
competition), the models depend on the firms in question being in long-run
equilibrium, and having stable cost and revenue functions.


A key limitation of the Rosse-Panzar test is that it can give misleading results
under a number of circumstances, such as when the banks in the sample have
not completely adjusted to market conditions. The bias in this instance is
towards the spurious appearance of market power. “This anticompetitive bias
means that, in the absence of a reliable test for market disequilibrium, the
Rosse-Panzar test cannot be used to rule out competitive pricing, as some
studies have claimed” [Shaffer (1994)]. But generally when the test indicates
the market is competitive, we can be relatively sure that monopoly power is
not being exercised.




                                       5
Another weakness of the Rosse-Panzar test is that it cannot distinguish
between competitive pricing and simple cost-plus pricing. Since cost-plus
pricing is not specifically associated with a particular level of market power,
the implications for interpreting the Rosse-Panzar statistic are not clear once
this limitation has been taken into account.


The Rosse-Panzar methodology has been applied to assess competitive
conditions and contestability in banking by a number of researchers. Shaffer
(1982) used the methodology to examine the competitive conditions for banks
in New York. Nathan and Neave (1989) used the same statistic to develop
measures of competition and contestability for the Canadian banking system.
Lloyd-Williams et al (1991) tested for evidence of contestability on a sample of
72 Japanese commercial banks for 1986 to 1988. Shaffer (1993) supported and
extended Nathan and Neave’s findings over a longer period of time using a
different method. Vesala (1995) tested for competition in the Finnish banking
market and the characterisation of banks’ pricing behaviour using the Rosse-
Panzar methodology. Molyneux et al (1994) utilised the Rosse-Panzar statistic
to assess competitive conditions in five major European Community banking
markets. Hondroyannis et al (1999) applied this technique to assess
competitive conditions in the Greek banking system.


We have no indication that the technique has ever been used in the New
Zealand or Australian banking markets.


Methodology


The relevant elasticities which are summed to produce the Rosse-Panzar
statistic are derived as coefficients in the following multiple regression, which
is effectively a log transformation of a production function.6

6
 The specification is the same as that used by Nathan & Neave (1989), which is considered reasonable
given the comparative similarity in banking market structure between Canada and New Zealand (with
both markets dominated by a small number of full service banks). Other researchers have used different


                                                  6
lnTR = β0 + β1 (lnPF) + β2 (lnPKB) + β3 (lnPL) + β 4 (lnAST) + β 5 (lnBR) + β6 D5 (2)
where:
         ln    = natural logarithm
         TR = total revenue
         PF    = ratio of interest expense to total deposits (unit price of funds)
         PKB = ratio of annual expense on premises, furniture, fixtures and
         equipment to the number of branches (unit price of capital)
         PL     = annual salary and benefits per full-time equivalent employee
         (unit price of labour)
         AST = total bank assets
         BR = number of branches to the total number of branches in the
         system
         D5 = 1 for the 5 largest banks, 0 for the others


From this we develop a statistic


         HComp = β1 + β 2 + β3 (3)


We also test for equilibrium, using a statistic HEqm , based on the following
regression equation:


lnROA = α0 + α1 (lnPF) + α2 (lnPKB) + α3 (lnPL) + α 4 (lnAST) + α5 (lnBR) + α 6 D5
(4)
Where ROA = return on assets


Relative to this


         HEqm = α1 + α2 + α3 (5)


specifications. For example, in their study of the Greek market, Hondroyiannis et al (1999) looked at
provisions, but these have not been important (or different between banks) in New Zealand over the
period studied.


                                                   7
We can interpret the values obtained in accordance with the principles
outlined in Table 1, based on a table provided by Hondroyiannis et al (1999).


Table 1: Interpreting the Rosse-Panzar H Statistics

Competitive environment test, HComp                   Equilibrium test, HEqm

H < or = 0   Monopoly or conjectural variation        H<0        Disequilibrium
             short-run oligopoly
0 < H< 1     Monopolistic competition                 H=0        Equilibrium
H=1          Perfect competition or natural
             monopoly in a perfectly contestable
             market or a sales maximising firm
             subject to a break even constraint


Regressions were run first to test for equilibrium and competitive conditions
in the New Zealand banking sector using pooled data over the whole period
1996 to 1999. The pooling procedure improved the parameter estimates by
increasing the sample size, although pooling assumes that parameter values
are constant over time, an assumption which may be unrealistic. A variety of
approaches were tested within SAS, using the MIXED procedure, and, for the
pooled equilibrium test using lnROA as the dependent variable, the best
results were obtained using the UN(1) correlation structure model (where it is
assumed that there is no correlation between years and heterogeneous
residual variance each year).


Regressions were then run for equilibrium and competitive conditions in New
Zealand banking using the cross-sectional data for the individual years 1996,
1997, 1998 and 1999. Even though the individual year sample sizes were
relatively small, the separate samples were retained as they permitted
assessment of differences at different times, which might not be observed
with the pooled data.


                                       8
Data


In our efforts to calculate the Rosse-Panzar statistics, we have used data for
New Zealand banks for four years 1996 to 1999 inclusive. This has allowed us
to extract figures for return on assets, total revenue, interest expense, premises
expense, staff expense, total deposits and total assets. Data for the number of
branches and number of employees were derived from the KPMG Financial
Institutions Performance surveys for New Zealand.


Banks in New Zealand have a variety of different balance dates, with one
bank currently reporting in March, two in June, four in September, and the
rest in December. For part of the period reviewed there was also a bank with
a February balance date. Because there had been significant variations in
general levels of interest rates over the period reviewed, it was thought most
appropriate to standardise to a common December financial year (with the
relevant annual results adjusted using data from the quarterly disclosure
statements required to be provided by all New Zealand banks).7


In some cases, banks have not provided breakdowns of premises-related and
staff expenses (and where banks did provide a breakdown, these were only
available at half-year and year end). We have therefore had to try and
approximate figures based on the breakdown of expenses in other time
periods (for those banks with March and September balance dates), or based
on the breakdown of expenses of other banks whose business was considered
comparable.


Some other assumptions were also necessary to make the data useable. The
New Zealand banks do not report deposits consistently, and we have


7
 It was not considered practical to satisfactorily adjust the data for the bank with the February balance
date, and data for this bank were therefore not adjusted.


                                                    9
therefore used figures for total interest-bearing liabilities (which are required
to be disclosed) in calculating the unit price of funds. Regardless of which
variable was in the denominator, banks in a stronger position, and thus better
able to exercise monopoly power in the acquisition of deposits, are likely to
exhibit significantly lower costs of funds.


A problem with both of the regressions is with calculated values, specifically
lnROA, being undefined as a result of profit being negative. We therefore
follow a common approach used for log transformations in adding a positive
constant to ROA for every observation (the same constant for each one), large
enough to make the smallest sum positive, before taking the natural
logarithm.8 This approach changes the interpretation of the coefficients
somewhat, but does not bias them, so that the test for equilibrium for lnROA
remains valid.


It is not believed that these assumptions will have had significant effects on
the results obtained.


Results and Discussion


The equilibrium test and competitive conditions tests for the pooled data
regressions are reported in Tables 2 and 3.


The estimation of lnROA (to test for equilibrium conditions) yields an R-
squared of 0.237, which suggests that the explanatory cost variables account
for little of the variation in lnROA. The regression coefficients for the unit
prices of fixed assets, labour and funds are all positive, with the first two
significant at the 5% level. A test for co-variance provided no evidence of
multicollinearity among the explanatory variables.



8
    As used, for example, by Berger & Mester (1997).


                                                  10
Table 2: Pooled Equilibrium test for New Zealand Banks, 1996-1999.
                1996-1999         t
Constant           -3.391      -7.990*
ln PF               0.116      3.230*
ln PKB              0.039      2.290*
ln PL               0.061       1.720
ln AST             -0.007      -0.340
ln BR               0.032       1.540
D5                 -0.026      -0.310
H                   0.216      4.390b
R2                  0.237
Adjusted R2         0.223

Notes: Dependent variable is ln ROA, t is the t-statistic for the parameter
estimates, * indicates that coefficient estimates are significant at the 5% level, b
is the t-statistic for the hypothesis H = 0.


Testing of the hypothesis that HEqm = 0 gave a t-statistic of 4.39, allowing us to
reject the null hypothesis at the 1% level. We were therefore unable to confirm
that the data are in long-run equilibrium for the pooled regressions over the
period 1996-1999, and were therefore obliged to treat the results of the
competitive conditions test with caution.


In the lnTR regression equation (the competitive conditions test) the R-
squared was 0.057, indicating that the model explains only a very small
proportion of the variability in lnTR. The regression coefficients for the unit
prices of funds, capital and labour had mixed signs, with the first two
significant at the 5% level. The coefficient of lnAST (which tests for scale
economies) was positive and statistically significant at the 5% level,
suggesting that larger banks may have higher revenues per dollar of assets.
The coefficient of lnBR was positive and significant at the 5% level, which




                                         11
indicates that banks with greater numbers of branches generate higher
revenues.


Table 3: Pooled Competitive Conditions test for New Zealand Banks, 1996-
1999.
                    1996-1999                 t
Constant                2.158              3.020*
ln PF                   0.661              9.060*
ln PKB                  0.173              5.920*
ln PL                  -0.076              -1.260
ln AST                  0.841             23.130*
ln BR                   0.162              5.010*
D5                     -0.081              -0.770
H                       0.758             -2.615a
                                           8.190b
R2                      0.057
Adjusted R2             0.039

                                             -
Notes: Dependent variable is ln TR, t is the t statistic for the parameter
estimates, * indicates that coefficient estimates are significant at the 5% level, a
is the t-statistic for the hypothesis H = 1, b is the t-statistic for the hypothesis
H = 0.


The D5 dummy variable which distinguished New Zealand’s largest five
banks was not significant at the 5% level, indicating no additional explanatory
power. The five largest banks thus do not exhibit any significant revenue
effects beyond those explained by assets and branches.9


The t-statistic for testing the hypothesis HComp = 0 for the pooled competitive
conditions test is 8.19 (p-value < 0.0001), indicating that we can reject the null

9
 Regressions were re-run with the D5 variable omitted, but this had no material effect on the outcome
of the analysis (apart from slightly increasing the significance of the majority of the coefficients).
Results have not therefore been reported.


                                                  12
hypothesis. The t-test for testing the hypothesis HComp = 1 is –2.615 (p-value
0.0132), allowing rejection of that hypothesis. HComp thus lies between 0 and 1,
with an observed value of 0.758, which disproves monopoly or conjectural
variation short-run oligopoly, natural monopoly in a perfectly contestable
market, or a sales maximising firm subject to a break-even constraint. Total
bank revenues thus appear to be earned under conditions of monopolistic
competition.


Results from the cross-sectional equilibrium tests are reported in Tables 4 and
5. For three of the four years studied, the regressions to determine the
existence of equilibrium conditions generated R-squared values between 0.21
and 0.33, while for 1999, the R-squared value was 0.743. the coefficients for
unit prices of funds, fixed assets and capital were not significant (except for
the price of labour in 1999) and had mixed signs.


Table 4: Cross-Sectional Equilibrium Test for New Zealand banks 1996-1999
            1996     t         1997     t         1998     t         1999     t
Constant    -2.258   -3.180*   -7.247   -2.117*   -3.292   -3.367*   -2.896   -5.961*
ln PF       -0.078   -1.053    -0.445   -1.504    -0.061   -0.349    0.085    1.313
ln PKB      0.009    0.303     -0.040   -0.303    0.027    0.591     0.066    1.717
ln PL       -0.003   -0.043    0.230    0.582     -0.031   -0.399    0.106    2.425*
ln AST      -0.038   -1.007    0.061    0.493     0.020    0.373     -0.055   -2.173
ln BR       0.015    0.474     -0.011   -0.072    0.030    0.353     0.110    2.908*
D5          0.040    0.423     0.053    0.120     -0.167   -0.853    -0.177   -1.348
H           -0.071   -0.766b   -0.282   -0.677b   -0.065   -0.254b   0.257    3.248b
R2          0.210              0.247              0.327              0.743
Adjusted    -0.468             -0.204             -0.177             0.550
R2

Notes: Dependent variable is ln ROA, t is the t-statistic for the parameter
estimates, * indicates that coefficient estimates are significant at the 5% level, b
is the t-statistic for the hypothesis H = 0.


                                            13
For 1996, 1997 and 1998, t-values obtained were not high enough to allow us
to reject the null hypothesis that H                               -
                                    Eqm = 0, whereas for 1999, the t value


obtained was 3.248 (equivalent to a p-value of 0.006), which forces us to reject
the null hypothesis. We can thus conclude that the market was in long run
equilibrium for 1996 to 1998, but not for 1999 (although the value of HEqm was
positive). There is no obvious reason as to why the market should have been
in equilibrium in the three earlier years but not in 1999: if anything, one
would have thought that the market was more stable in 1999 than in earlier
years, when we had seen mergers and strenuous cost-cutting. The results
obtained, however, force us to take care in interpreting the competitive
conditions test for 1999.


Table 5: Cross-Sectional Competitive Conditions Test for New Zealand Banks
1996-1999.
             1996     t         1997     t         1998     t         1999     t
Constant     2.126    2.546*    -6.612   -1.657    1.498    0.718     4.413    3.062*
ln PF        0.667    7.655*    -0.218   -0.631    0.965    2.580*    0.893    4.669*
ln PKB       0.184    5.031*    -0.017   -0.111    0.096    0.993     0.154    1.356
ln PL        -0.094   -1.343    0.388    0.953     0.097    0.587     -0.018   -0.142
ln AST       0.843    18.765* 1.042      7.224*    0.876    7.729*    -0.756   10.156*
ln BR        0.159    4.327*    -0.275   1.549     0.187    1.045     0.187    1.673
D5           -0.069   -0.626    -0.914   0.103     -0.248   -0.594    0.124    0.319
H            0.757    -2.229a   0.154    -1.743a   1.157    -0.287a   1.028    0.121a
                      6.929b             0.317b             2.111b             4.388b
R2           0.999              0.980              0.995              0.994
Adjusted     0.998              0.968              0.992              0.990
R2
                                             -statistic for the parameter
Notes: Dependent variable is ln TR, t is the t
estimates, * indicates that coefficient estimates are significant at the 5% level, a
is the t-statistic for the hypothesis H = 1, b is the t-statistic for the hypothesis
H = 0.


                                             14
In the cross-sectional competitive conditions test with lnTR as the dependent
variable, the regression coefficients for the unit prices of funds, fixed assets
and labour are of mixed signs, a result similar to that found by Molyneux et al
(1994). The regression coefficient of lnPF was significant at the 5% level in all
years except 1997, while the coefficient of lnPKB was significant only in 1996
and the coefficient of lnPL was not significant in any of the years reviewed.
The lnAST coefficient was positive and statistically significant at the 5% level
in all years, suggesting that larger banks have higher revenue per dollar of
assets. The coefficient of the variable recording size effects in terms of
branches, lnBR, is statistically significant in only one year, 1996, while the
dummy variable, D5, which distinguished New Zealand’s five largest banks
is not significant at the 5% level in any year.


Table 5 has showed us the calculated values for HComp for the four years


reviewed. Table 6 shows us the interpretation of the t-statistics relating to
these values, in terms of their statistical significance. We can thus say that the
existence of a perfectly competitive banking market has been disproven for
years 1996 and 1997, and that monopoly conditions have been shown not to
exist in 1996, 1998 and 1999. These findings are not inconsistent with the
estimated values obtained for HComp, which has a relatively low value for
1997, raising concerns as to the extent of competition in the banking market in
that year, with an inference of monopoly or conjectural variation oligopoly.


Table 6: Hypothesis testing for HComp for cross-sectional test.
Year                        1996         1997        1998         1999
t-statistic for HComp = 1   -2.229       -1.743      0.287        0.121
p-value                     0.044        0.1         0.778        0.905
t-statistic for HComp = 0   6.929        0.317       2.111        4.388
p-value                     .00001       0.755       0.05         0.0006




                                        15
For 1996, the regression analysis yields a value between 0 and 1 for HComp,
which indicates that total bank revenues were earned under conditions of
monopolistic competition. Values of HComp in this range seem to be relatively
frequent in international studies, and in a personal communication, Sherrill
Shaffer has indicated that monopolistic competition may not be the only
explanation for values in this range. He has suggested that, if the theory were
more completely worked out, one might find these values might be accounted
for by use of limit pricing to deter entry, expense preference behaviour, or
some other explanation.


For the final two years of the study period, 1998 and 1999, HComp is
significantly higher than zero but not significantly different from unity. This
indicates that the New Zealand market appears like a natural monopoly in a
perfectly contestable market, or to be characterised by perfect competition or
sales-maximising firms subject to break-even constraints. For 1999, we have
seen that the sample cannot be shown to be in long-run equilibrium, but as
the value of HEqm was significantly positive, we can be satisfied that we do not
have monopoly or conjectural oligopoly behaviour.


Concluding discussion


We have ruled out monopoly power for the New Zealand banking market
with the exception of the cross-sectional analysis undertaken for 1997. There
are no obvious reasons as to why the year 1997 should have been exceptional.
The model would appear to require stable cost and revenue functions, and
there must be some doubt as to whether these would have existed during any
of the period analysed, having regard to the severe cost-cutting undertaken
by the banks throughout the period.




                                      16
It is therefore interesting to compare the results obtained from use of the
Rosse-Panzar statistic with the trends in the HHI during the period reviewed,
and which are shown for total bank assets in Figure 1.


Figure 1: HHI trend for total assets: New Zealand banks.


 1750



 1700



 1650



 1600



 1550



 1500



 1450



 1400
        30/6/96


                  30/9/96




                                       31/3/97


                                                 30/6/97


                                                           30/9/97




                                                                                31/3/98


                                                                                          30/6/98


                                                                                                    30/9/98




                                                                                                                         31/3/99


                                                                                                                                   30/6/99


                                                                                                                                             30/9/99




                                                                                                                                                                  31/3/00


                                                                                                                                                                            30/6/00


                                                                                                                                                                                      30/9/00
                            31/12/96




                                                                     31/12/97




                                                                                                              31/12/98




                                                                                                                                                       31/12/99




                                                                                                                                                                                                31/12/00
The sharp increase in the September quarter of 1998 reflects the acquisition of
Countrywide Bank by the National Bank of New Zealand, but this change
was not reflected in the trend for the Rosse-Panzar statistic. It is interesting
that the trend of the HHI statistic provides no support for the apparent
finding of uncompetitive conditions in 1997: perhaps this result is a
consequence of statistical noise or some other weakness in the methodology.


The problems may derive from weaknesses in the Rosse-Panzar methodology
itself. The technique has an anti-competitive bias, meaning that when
competitiveness is indicated we can be reasonably sure that monopoly power
is not present. The test cannot, however distinguish between competitive




                                                                                          17
pricing and simple cost-plus pricing, the implications of which weakness have
yet to be ascertained.


There is an identifiable need to carry out more work on the Rosse-Panzar
methodology, expanding on suggestions that values of HComp between zero
and one might be explained by limit pricing to deter entry, expense
preference behaviour, or firms having some objective other than profit.
Reflecting methodological issues identified by Shaffer (1982), it may be worth
omitting figures for newly-established banks in case their figures distort the
data for the rest of the banking sector.


This research could also be extended using further time periods, and by
comparable studies of the Australian market. The approach followed may
well have considerable potential as a tool for monitoring competitive
conditions, particularly in the face of future mergers in the New Zealand and
Australian banking sectors.




                                           18
References:


Berger, A. N. & Mester, L. J. (1997). Inside the black box: what explains the
      differences in the efficiency of financial institutions? Journal of Banking
      and Finance. 21(7). 897-949.

Hannan, T. H. & Berger, A. N. (1991, September). The rigidity of prices:
     evidence from the banking industry. American Economic Review. 81. 938-
     945.

Hondroyiannis, G.; Lolos, S. & Papapetrou, E. (1999). Assessing competitive
     conditions in the Greek banking system. Journal of International
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