SOURCES OF PRODUCTIVITY CHANGE IN BANKING IN MACAU*
João Rebelo
Universidade de Trás-os-Montes e Alto Douro
Victor Mendes
Faculdade de Economia do Porto - CEMPRE
R. Dr Roberto Frias
4200 Porto – Portugal
email: [email protected]
JEL classification: D2; G2; O3
Keywords: Banking; Productivity; Malmquist
Abstract
In this paper we evaluate productivity change in the banking industry in Macau during
the nineties, using the Malmquist productivity index. Results show that banks witnessed
an increase in productivity as a result of technological progress. However, banks were
not able to reach the efficient scale and are deviating from the best practice institutions,
either due to poor management or market inefficiencies. In the years following the
deregulating banking act of 1993, banks in Macau changed their operative conditions
and technology, but could not fully rationalize input consumption. In spite of the
increased productivity, both the Malmquist and the catching-up indices show a
downward trend in the last years of the sample, meaning that the banking act of 1993
did slow down productivity increases.
*
The authors thank participants at the 6th Asia-Pacific Finance Association Conference for helpful
comments.
1
SOURCES OF PRODUCTIVITY CHANGE IN BANKING IN MACAU
1. Introduction
Financial market liberalization and deregulation, along with the diffusion of new
information systems and technologies, have had a strong impact on the banking industry
in Macau, allowing banks to offer new products and services and to improve their
organizational and productive systems. As a result, input productivity and production
costs were affected. However, lack of managerial and organizational efficiency and/or
other random external elements did not allow all banks to fully exploit the new
technologies, with a negative impact on productive efficiency and input productivity.
Input productivity growth is related to shifts in both technology and technical
efficiency. Government agencies typically measure productivity growth by the change
in the ratio of an output index (such as value added, assets, deposits, loans or number of
operations) to an input index (labor, capital, branches, etc.)1. These ratios do not
consider the multi-product nature of the banking activity, and do not separate out shifts
of the best practice frontier from changes in inefficiency or deviations from the frontier.
Therefore, it becomes difficult to draw conclusions as to whether technology is
improving or more banks are taking advantage of the existing technology.
The literature on costs, efficiency and the productivity of financial institutions is
voluminous. Earlier papers studied the cost structure of banks by examining the
existence of scale and scope economies. Results of these studies were used to tackle
policy questions such as product and geographic diversification as well as concentration
activities, among others. More recent studies examine efficiency and productivity
changes of financial institutions covering a wide range of countries, from the US
(Berger and Mester [1999], is a recent example), to Europe (Spain - Griffel and Lovell
[1996 and 1997], Norway - Berg et al [1992], Portugal - Rebelo and Mendes [1998],
Germany - Lang and Welzel [1996], Italy – Resti [1997], are examples), to Asia (Japan
1
See Kunze et al (1998) for more information on government measures of productivity change in the
service sector.
2
– Altumbas et al [1999], Thailand - Leightner and Lovell [1998], Korea – Gilbert and
Wilson [1998], are examples).
Bank inefficiency has generally been found to consume a large portion of funds
and to be a far greater source of problems of performance than scale or scope
inefficiencies. The computation of (frontier) efficiency is considered a better indicator
over other performance indicators for it allows the removal of the effects of market
prices and other exogenous factors which have an impact on observed performance
(Bauer et al, 1998).
In this paper we evaluate productivity change in the banking industry in Macau
during the nineties, using the Malmquist productivity index. The computation of the
Malmquist index allows us to decompose productivity changes into changes in
productive efficiency (catching-up with the best practice) and changes in the best
practice. Results of this study can help the decision-taking process of different
economic agents. Regulators should know whether managerial inefficiency exists and,
that being the case, if it will increase the probability of financial institution failure. On
the other hand, regulators should also know if deregulation has helped to increase
efficiency and reduce systemic risk. Bank owners and managers can get some insights
regarding the soundness of the banking firm and its ability to survive in a more
competitive environment.
The Malmquist productivity index and its decomposition have been recently
applied in banking studies, both in the US and some European countries. Two recent
studies on Asian countries (Thailand - Leightner and Lovell [1998] and Korea – Gilbert
and Wilson [1998]) also use this technique. However, the first uses pooled data and
does not decompose the index (one of the main goals of this paper), whilst the second
does it but not on a year to year basis. This is the first study on productivity growth and
efficiency in Macau, a politically and autonomous small enclave in the Pearl River
Delta.
The paper is structured as follows. Section 2 describes the Malmquist productivity
index, and its decomposition. Section 3 gives some insights on banking in Macau.
Section 4 describes the data set and contains the estimation results, and section 4
concludes the study.
3
2. The Malmquist productivity index
There are two basic approaches to the measurement of productivity change: the
econometric estimation of a production, cost or some other function, and the
construction of index numbers. We adopt the latter because it does not require the
imposition of a possibly unwarranted functional form on the structure of production
technology, as required by the econometric approach.
Three different indices are frequently used to evaluate technological changes: the
Fischer (1922), Tornqvist (1936), and Malmquist (1953) indexes. According to Grifell
and Lovell (1996), the Malmquist index has three main advantages relative to the
Fischer and Tornqvist indices. Firstly, it does not require the profit maximization, or the
cost minimization, assumption. Secondly, it does not require information on the input
and output prices. Finally, if the researcher has panel data, it allows the decomposition
of productivity changes into two components (technical efficiency change or catching
up, and technical change or changes in the best practice). Its main disadvantage is the
necessity to compute the distance functions. However, the Data Envelopment Analysis
(DEA) technique can be used to solve this problem.
In this section we present the Malmquist productivity index between periods t and
t+12. Let xt represent the input vector, x t  ( x1t ,, x tm ) , and yt represent the output
vector y t  ( y1t ,, y tn ) , in time period t = 1, 2, …, T. The Malmquist productivity
index between periods t and (t+1) can be defined as3
1
(1)
Mt,t1(y
t1
,x
 D t ( yt , x t )
D t1(y t , x t )  2
, y , x )   t t1 t 1  t 1 t1 t1 
D (y , x ) D (y , x ) 
t1
t
t
where D represents the inverse of the distance function introduced by Caves et al.
(1982). M is the geometric mean of two ratios of input inverse distance functions 4. The
first ratio represents the period t Malmquist index; it gives a measure of productivity
change from period t to period (t+1) using period t technology as a benchmark. The
2
3
For a more detailed explanation see Rebelo and Mendes (1998).
See, for example, Fare, et al. (1994), Pastor (1995), Coelli (1996), and Grifell and Lovell (1996,
1997).
4
second ratio is the period (t+1) Malmquist index and gives a measure of productivity
change from period t to period (t+1) using period (t+1) technology as a benchmark.
M>1 means that period (t+1) productivity is greater than period t productivity, whilst
M<1 means productivity decline and M=1 corresponds to stagnation.
A useful feature of the Malmquist productivity index, first noted by Fare et al.
(1995), is that it can be decomposed into the product of an index of technical efficiency
change and an index of technical change, by rearranging (1) as follows:
(2) Mt,t1(y
t1
,x
1
D t1(yt 1, xt 1 ) Dt 1( yt , x t )  2
, y , x )  t1 t 1 t1   t t1 t1 

D (y , x )  D (y , x )
D t (y t , x t ) 
t1
t
t
t
t
t
D (y ,x )
In (2), the first component is the catching up effect; it is greater than, equal to, or
less than one if the producer is moving closer to, unchanging, or diverging from the best
practice. The square root expression represents technical change; it is greater than, equal
to, or less than one when the best practice is improving, unchanged or deteriorating,
respectively.
M and its two components are local indices. Their values can vary across
producers and between different adjacent time periods. Thus, some producers may
exhibit an increase and others may exhibit a decrease in technical efficiency, and this
can change over time. Similarly, some producers may exhibit technical progress and
others may exhibit technical slippage, and this can also change over time. This feature
allows considerable flexibility in explaining the observed pattern of productivity
change, both across producers and over time.
Calculation and decomposition of the adjacent period version of the Malmquist
index expressed by (2) includes four different functions, Dt(yt, xt), Dt(yt+1, xt+1), Dt+1(yt,
xt), and Dt+1(yt+1, xt+1), which are the reciprocal of the technical efficiency indicators. We
use the Data Envelopment Analysis technique to estimate frontier functions, upon
which we compute radial measures of firm efficiency. Seiford and Thrall (1990), Fare et
al. (1994), and Fare and Grosskopf (1996), among others, offer a good literature review
4
Since the two technologies can be non-neutrally related, or even non-nested, the Malmquist
productivity index computes the geometric mean of the two ratios (Griffell and Lovell, 1996).
5
on this subject. With a sample of H firms producing n outputs using m inputs, and using
period r frontier as a benchmark, the DEA optimization problem for firm h in period s is
h
Min Ers
h  1,
, H; r, s  1,
T
s.t.
H
  h ynr h  yns h
(3)
n  1,
, n ou tputs
h 1
H
  h xmr h  Ersh x ms h
m  1,
, m inp uts
h 1
h  0
Solving the problem for each firm we get E hrs , that is, Farrel’s index of technical
efficiency5 for the constant returns to scale case. For the variable returns to scale case
we need to include in (3) one additional restriction,   h  1. In this paper we follow
the procedure adopted by Pastor (1995), Grifell and Lovel (1996), and Price and
Weyman-Jones (1996), and decompose the global technical efficiency (E) into scale
efficiency (SE) and pure technical efficiency (PTE), with
(4)
Eh 
x hCR S x hVRS x hCR S

 VRS  PTE h  SEh
xh
xh
xh
where x is the observed input consumption, xCRS is the optimal input consumption
under constant returns to scale, and xVRS is the optimal input consumption under
variable returns to scale. If SE is equal to, or less than one, the firm is operating at the
optimal and sub-optimal scale, respectively, and (1-SE) is the potential reduction in
input quantities were the firm able to operate at the constant returns to scale frontier.
5
The E index provides a partial picture of the efficiency status of the firm. To obtain a broader standing
of a firm’s efficiency, we also need to have a measure of the overall productive efficiency (OPE) and
allocative efficiency (AE), with OPE = E*AE. A bank is overall efficient if it is both technically and
allocatively efficient. Mendes and Rebelo (1999) reports the values of OPE for banks in Macau. In
this paper, however, we are not interested in allocative efficiency but rather in technical efficiency and
productivity, and thus do not compute input and output price variables. Bauer et al (1998) gives a
good review of the different techniques, parametric and non parametric, used to compute efficiency in
financial institutions.
6
Finally, the decomposition in (4) allows us to decompose the sources of catching up,
using
(5)
CU(y
t1
,x
t1
t
t
,y ,x ) 
E
t1,t 1
E t,t

PTE
t1,t 1
PTE t,t

SE
t1,t1
SEt,t
where the first and second components represent changes in technical efficiency as a
result of changes in pure technical efficiency and scale efficiency, respectively.
3. Banking in Macau
Macau is a small open economy with very strong connections to the Hong Kong
economy. The territory has been an important trading center between Asia and the rest
of the world, especially Europe. Influenced by economic developments in Hong Kong,
Macau experienced a fast pace of growth since the seventies. In Macau there is not a
capitals market, and households rely on traditional banking business or do business with
Hong Kong banks. The easy access by residents to Hong Kong banks triggers off some
competition in Macau: interest rates are fixed by the Banking Association and aligned
by Hong Kong. At the same time, the banking market in the territory is saturated or
close to saturation, with 22 banks operating (at the end of 1997). The industry is
dominated by one large bank (Bank of China) and is higher concentrated in two other
banks.
The banking industry in Macau has been increasing its relevance in the economy
of the territory, either in terms of employment or in terms of the number of branches
and ATMs available to the public. However, it remains a traditional industry with
highly labor-intensive operations. It has been written elsewhere “the application of
technological innovations in the banking sector [in Macau] is slower than that of Hong
Kong in which it is about two decades behind”6. The use of computer application
6
Han (1997), p.29.
7
systems to process internal routine operations started in the mid-eighties7. Telephone
banking started in 19918. Some banks have followed these technological improvements,
but many of them are not fully automated yet.
At present, the financial system in Macau is governed by the Financial System
Act of 1993 (decree-law no 32/93/M – 5/July/1993). This decree-law sets new
boundaries to the scope of banking activities, the regulation of operations, the capital
adequacy and liquidity rations, among others, bringing a new liberalization wave to the
banking industry in Macau. The promoted changes in the banking environment were
fostered by “the recommendations of the Basle committee on Banking Supervision and
the efforts of the European Community to achieve harmonization in banking legislation
while drawn also on the experiences of countries and territories whose financial systems
are similar to Macau’s” (Han 1997, p.17).
4. Data and results
Data from banks annual balance sheet and income statement for the years 1990 to
1997 is used in this study. The database was provided by the Autoridade Monetaria e
Cambial de Macau9. The sample includes all banks operating in the territory during the
period, although some banks were deleted10. The CEP (Caixa Economica Postal) was
also included in the sample in all years except 1997, for it behaves like a banking
organization.
Variable definition is one of the most difficult tasks in banking studies. There is
consensus concerning the fact that the banking firm is a multi-product organization.
However, there is some disagreement on what banks produce and how to measure bank
7
8
9
10
Nam Tung Bank (today the Bank of China) in 1981 and 1984, Weng Hang Bank in 1985 (Han, 1997,
p.29).
Weng Hang Bank (Han, 1997, p.32).
The usual disclaimers apply.
Banks in their first year of operation as well as offshore banks and other banks behaving as if they
were offshore organizations (ie, with a loan to deposit ratio below 10%) were dropped. On the other
hand, we compute M for adjacent periods and therefore banks in only one of each of the two adjacent
years were also deleted.
8
production11. The final decision depends upon the underlying concept of a bank, the
problem at stake and, last but not the least, the availability of information.
We consider that the banking firm as a multi-product organization produces two
outputs (loans and financial applications) with three different inputs (deposits, labor and
capital). We follow the intermediation approach12. The variables used are defined as
follows:
i) Outputs:
y1 = loans outstanding (loans to clients + bills discounted);
y2 = financial applications (loans to credit institutions + certificates of
deposit + bonds + other financial applications).
ii) Inputs:
x1 = deposits (deposits from clients + deposits from the public sector +
certificates of deposit + deposits from other banks);
x2 = number of employees;
x3 = number of branches.
Table 1 contains some information on the variables used. We should stress the
higher average production of financial applications than loans in five of the eight years,
the decreasing average value of deposits, the large size of the largest bank compared to
the size of the smallest bank, and the increasing average number of employees and
branches.
[Table 1 here]
The annual efficiency indices were computed using (3) and (4). Table 2 includes
the average annual figures for the technical efficiency, pure technical efficiency, and
scale efficiency, as well as the number of banks on the frontier in each year. Our results
suggest that banks in Macau may reduce input consumption by 6.2% on average,
somewhere between 4.1% (1993) and 8.3% (1997), mainly as a result of scale
11
12
Pastor (1995) offers an excellent review of the different input and output definition in banking studies.
This approach views banks as financial intermediaries; outputs are measured in monetary units and
labor, capital and various funding sources are treated as inputs. Gilbert and Wilson (1998) discusses
some variants of the intermediation approach. In the footsteps of Sealey and Lindley (1977),
Altumbas et al (1999) also uses deposits as input only.
9
inefficiency (4.3%)13. Many banks are located on the frontier or very close to it.
Considering PTE, and for the grand total average, 66% of the observations are located
on the best practice production frontier and the potential reduction in input consumption
is 1.9% only, holding constant the production levels.
[Table 2 here]
From a technological standpoint, it seems that the 1993 banking act was bad for
banks in Macau. In fact, technical efficiency estimates show the lowest values in the last
four years of our sample. However, from an economic standpoint, that is not the case:
the best years are 1994-97 (Mendes and Rebelo [1999]). The conclusion seems obvious:
in the years following the deregulating banking act, banks in Macau changed their
operative conditions and technology, but were not able to fully rationalize input
consumption, decreasing their scale efficiency levels. But they were able to rationalize
the costs of those inputs, and thus to increase economic efficiency14. This conclusion is
somewhat surprising, for since 1994 operating costs per unit of assets increased
(reaching the highest values for the sample), and the loan to deposit ratio declined. At
the same time, in 1997 the interest margin per unit of assets more than doubled the 1993
figure (Mendes and Rebelo, 1999, table 2).
Table 3 contains results for the Malmquist index and its components. Figure 1
shows the cumulative indexes for M, CU and TC.
[Table 3; Figure 1 here]
The estimated values show that, on an annual basis, bank productivity has
increased (M>1 in all years). For the entire period, the geometric mean of M suggests a
4.6% annual input productivity growth. 1993 shows the best performance with a 12.8%
productivity growth, and 1997 is the smallest.
13
14
One potential problem with DEA is that this method does not account for randomness (good or bad
luck, or even measurement problems); any random errors are accounted for as efficiency differences
amongst firms. Therefore, it provides an ‘upper bound’ to computed inefficiency scores. Our results,
thus, mean that the above-mentioned figures represent the maximum estimated inefficiency scores,
leading us to conclude that banks in Macau are generally efficient organizations.
“To be technologically efficient, a firm must either minimize its inputs given outputs or maximize its
outputs given inputs. … economic efficiency also involves optimally choosing the levels and mixes of
inputs and/or outputs based on reactions to market prices. To be economically efficient, a firm has to
choose its input and/or output levels and mixes so as to optimize an economic goal, usually cost
minimization or profit maximization” (Bauer et al, 1998, p.90).
10
A decline in productive efficiency occurred in three out of the seven years under
scrutiny. This negative catching-up effect decreased productivity by 1.6% per year over
the whole period. The negative evolution in CU was the result of pure technical
inefficiency (0.4%) as well as scale inefficiency (1.2%). However, the best practice has
improved every other year; technical progress has increased productivity by 6.3% per
annum over the entire period. The highest and lowest values for TC occurred in 1994
(12.4%) and 1992 (4.7%), respectively. In figure 1, M and TC show a similar positive
trend, whilst CU and its components experienced a slightly negative trend.
The picture that emerges from this analysis is that banks in Macau were able to
use the rapidly improving information technologies. However, not all banks were able
to take advantage of the improving technologies, and thus are getting away from the
best practice, following a technical inefficient path. These firms were not able to
efficiently use available resources; market failures, management incapacity and/or
random events could explain that. As regards deregulation, in spite of the increased
productivity, both the Malmquist and the catching-up indices show a downward trend in
the last years of the sample, meaning that the banking act of 1993 did slow down
productivity increases as well as catching-up with the best practice. Once again, the
decomposition of the catching-up index shows that banks in Macau deviating from the
optimal size (SE change <1 after 1993).
These findings are not constant across observations. The sample breakdown for
local banks (table 4), banks reporting a zero capital base (table 5), four largest (table 6)
and four smallest banks (table7) in the sample discloses a very different behavior. On
the one hand, local banks show a lower level of efficiency (table 4), either pure or scale
efficiency (E = 0.903, PTE = 0.967 and SE = 0.933). But on average they show a
higher Malmquist index (M = 1.066) as well as catching-up (slightly) and technological
change (CU = 0.985 and TC = 1.083). Therefore, we may conclude that although less
efficient, local banks have increased their productivity more than the average, mainly as
a result of improved technologies.
[Tables 4, 5 here]
On the other hand, banks reporting a zero capital base are more efficient (E =
0.988, PTE = 0.995 and SE = 0.994), but have lower productivity (M = 1.039). They
have been able to catch up with the best practice (CU = 0.992) but were not able to
11
follow and use the new technologies as quickly as the other banks in Macau (TC =
1.048).
As regards size (measured by net assets - tables 6 and 7), average sized banks are
less efficient than the four largest. The four smallest banks are more efficient than the
four largest even from the standpoint of scale efficiency (E = 0.982, PTE = 0.933 and
SE = 0.989 for the four smallest). But small banks (M = 1.076) have been able to
increase productivity more than the average, and largest banks have been below
average. The frontier change has also been bigger for small banks (CU is 0.996 and
1.000 for the largest and smallest banks, respectively), as well as the catching-up with
the best practice15 (TC = 1.045 for large and TC = 1.076 for small banks).
[Table 6 here]
Deregulation did not have the same impact on all banks. Our results show that the
four largest banks were able to decrease technological inefficiency after 1993, with
increasing pure technical efficiency but decreasing scale efficiency levels. The opposite
is true for the four smallest banks. As regards the Malmquist productivity index trend,
all but the four smallest banks show lower productivity increases after 1993. The four
smallest banks exhibit some instability. The trend behavior of the catching-up effect is
very similar for all types of institutions, meaning that it has been difficult to catch-up
with the best practices. Local banks and the four smallest banks experienced stronger
technological improvements after 1993.
[Table 7 here]
5. Concluding remarks
During the last ten years the banking industry in Macau has undergone profound
structural changes. These changes were fostered by globalization, deregulation, and
improved information and communication systems, and have had a significant impact
on bank productivity. In the years following the deregulating banking act of 1993, banks
in Macau changed their operative conditions and technology, but could not fully
rationalize input consumption. However, they were able to rationalize the costs of those
15
One final word regarding the Caixa Económica Postal. It is a small sized bank-like institution, with
one branch only. Results (not reported) suggest that it was an efficient and highly productive
institution during the nineties.
12
inputs, thus increasing economic efficiency. From a managerial standpoint, it appears
that bank managers reacted to new business conditions with better cost controls, whilst
giving up (at least momentarily) technological efficiency. They were able to use new
and improved technologies and to produce new products and services, but are deviating
from efficient scale. Nevertheless, increasing interest margins and operating costs, along
with decreasing loan to deposit ratio, produced return on asset figures well above 1.5%
during the last 6 years of the sample.
In spite of the increased productivity, both the Malmquist and the catching-up
indices show a downward trend in the last years of the sample, meaning that the
banking act of 1993 did slow down productivity increases as well as catching-up with
the best practice. Such changes are not over yet. The 1997 Asian crises, the recent
merger wave, and the future integration of Macau in the People’s Republic of China
will have some additional impact on the industry. Our results call, therefore, for
renewed attention. Bank regulators and supervisors must keep an eye on bank’s
performance in more recent and future years. It is necessary to monitor the adjustment
process and check whether managers are putting all their efforts on cost control, losing
track of the best practices and efficient size. This could jeopardize the future of the
industry. Bank regulators need to carefully monitor the industry and check whether
deregulation continues to have a negative impact on bank productivity.
Bank customers are also interested in such controls. Poor management practices
could lead to higher prices (and the interest margin has been strongly increasing since
1993). This could be even more problematic insofar as in 1996 and 1997 the
concentration of the banking industry in Macau has increased (CR4 on assets has
reached 68% in those years, well above the 61% of 1993-94) and bank managers could
feel tempted to follow oligopolistic practices.
13
Table 1: Summary information on the output/input variables.
1990
1991
1992
1993
1994
1995
1996
1997
y1 = Loans outstanding*
Max
7 606
8 323
9 559
11 980
11 619
11 260
11 316
12 228
Min
53
131
124
138
150
138
169
78
1 354
1 407
1 477
1 881
1 893
1 819
1 905
2 012
134
136
145
145
139
145
136
141
Max
9 237
13 448
14 271
11 694
13 178
12 492
11 322
9 937
Min
63
52
56
79
50
7
60
46
1 386
1 871
1 979
1 571
1 814
2 000
2 004
1 846
158
165
162
172
167
154
135
131
Max
16 462
19 901
19 611
18 238
17 754
15 354
13 761
12 916
Min
83
141
128
156
115
98
141
89
2 633
2 937
2 808
2 623
2 635
2 487
2 421
2 301
146
153
155
158
154
147
132
132
Max
819
934
1 056
1 163
1 199
1 183
1 159
1 158
Min
7
9
11
11
9
13
13
12
Average
154
165
177
200
208
215
218
229
Coef. Variation (%)
130
135
140
140
138
135
128
124
Max
19
20
20
20
23
24
25
25
Min
1
1
1
1
1
1
1
1
Average
5
6
6
6
7
7
7
8
95
96
94
89
93
98
95
95
Average
Coef. Variation (%)
y2 = Financial Applications*
Average
Coef. Variation (%)
x1 = Deposits*
Average
Coef. Variation (%)
x2 = Employees
x3 = Branches
Coef. Variation (%)
*Deflated 106 MOP, at 1990 prices.
Table 2: Technical efficiency indices (annual averages).
Year
# banks
Technical Efficiency
(E)
Value
# efficient
Pure Technical
Efficiency (PTE)
Value
# efficient
Scale Efficiency
(SE)
Value
# efficient
1990
18
0.939
7
0.980
12
0.958
7
1991
18
0.940
8
0.977
10
0.962
8
1992
17
0.954
8
0.983
12
0.970
8
1993
17
0.959
10
0.982
12
0.977
10
1994
16
0.923
6
0.986
11
0.936
6
1995
17
0.930
7
0.982
11
0.947
7
1996
17
0.944
8
0.980
11
0.963
8
1997
17
0.917
5
0.976
11
0.940
5
Average
17.1
0.938
7.4
0.981
11.3
0.957
7.4
14
Table 3: Productivity change indices
Year
# banks
Malmquist
Catching-up
Technological
Index (M)
(CU)
Change (TC)
CU Decomposition
PTE change
SE change
1991-90
18
1.070
1.000
1.070
0.997
1.004
1992-91
18
1.062
1.014
1.047
1.001
1.013
1993-92
17
1.128
1.005
1.122
0.998
1.007
1994-93
17
1.071
0.953
1.124
1.002
0.950
1995-94
16
1.038
1.009
1.029
0.998
1.011
1996-95
17
1.038
0.988
1.050
0.995
0.993
1997-96
17
1.023
0.968
1.057
0.995
0.972
1.046
0.984
1.063
0.996
0.988
Geometric Average
All indices are geometric averages.
2,0
1,8
1,6
1,4
1,2
1,0
M
0,8
CU
0,6
TC
0,4
0,2
0,0
1990
1991
1992
1993
1994
1995
1996
1997
Figure 1: Cumulative indices of M, CU and TC
15
Table 4: Indices for local banks.
Year
# banks
E*
1990
7
0.916
0.963
0.951
1991
7
0.886
0.959
0.924
1.077
0.977
1.102
1992
7
0.903
0.971
0.930
1.059
1.013
1.045
1993
7
0.939
0.972
0.966
1.125
1.033
1.089
1994
7
0.903
0.946
0.954
1.075
0.914
1.176
1995
7
0.879
0.988
0.889
1.072
1.019
1.052
1996
7
0.916
0.973
0.941
1.007
0.986
1.021
1997
7
0.878
0.966
0.909
1.052
0.955
1.102
0.903
0.967
0.933
1.066
0.985
1.083
Average
* Arithmetic average.
PTE *
SE *
M **
CU **
TC **
** Geometric average, relative to previous year.
Table 5: Indices for banks reporting a zero capital base.
Year
# banks
E*
PTE *
SE *
M **
CU **
TC **
1990
3
1.000
1.000
1.000
1991
3
1.000
1.000
1.000
1.052
1.000
1.052
1992
6
0.994
1.000
0.994
1.022
0.989
1.033
1993
7
0.988
1.000
0.988
1.119
1.016
1.101
1994
7
0.974
0.996
0.981
1.086
0.985
1.103
1995
7
0.969
0.978
0.991
0.997
0.982
1.015
1996
7
0.978
0.982
0.996
1.027
0.991
1.036
1997
6
1.000
1.000
1.000
0.979
0.978
1.001
0.988
0.995
0.994
1.039
0.992
1.048
Average
* Arithmetic average.
** Geometric average, relative to previous year.
Table 6: Indices for 4 largest banks.
Year
E*
PTE *
SE *
M **
CU **
TC **
1990
0.951
0.977
0.973
1991
0.974
0.980
0.994
1.085
1.024
1.059
1992
0.969
0.991
0.978
0.989
0.966
1.024
1993
0.928
0.969
0.958
1.051
0.998
1.053
1994
1.000
1.000
1.000
1.028
0.988
1.040
1995
0.975
0.998
0.977
1.065
1.015
1.049
1996
0.968
0.997
0.971
1.042
0.988
1.055
1997
0.967
0.993
0.974
1.034
0.996
1.038
Average
0.967
0.988
0.978
1.042
0.996
1.045
* Arithmetic average.
** Geometric average, relative to previous year.
16
Table 7: Indices for 4 smallest banks.
Year
E*
PTE *
SE *
M **
CU **
TC **
1990
0.963
1.000
0.963
1991
1.000
1.000
1.000
1.094
1.032
1.060
1992
0.991
0.996
0.995
1.091
1.027
1.062
1993
0.998
0.999
0.999
1.064
1.018
1.045
1994
0.956
0.986
0.969
1.234
0.993
1.243
1995
0.954
0.962
0.992
1.028
0.960
1.071
1996
1.000
1.000
1.000
0.989
0.991
0.998
1997
0.992
1.000
0.992
1.051
0.982
1.070
Average
0.982
0.993
0.989
1.076
1.000
1.076
* Arithmetic average.
** Geometric average, relative to previous year.
17
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