Post Financial Deregulations Era and Efficiency of Pakistan Banking Sector
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ijcrb.webs.com DECEMBER 2011
INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS VOL 3, NO 8
Post Financial Deregulations Era and Efficiency of
Pakistan Banking Sector
Rafaqet Ali
PhD Student
Department of Economics
Gomal University, D. I. Khan
Muhammad Afzal
Professor
Department of Management Sciences,
COMSAT Institute of Information Technology, Islamabad
Abstract
This study examines technical, pure technical and scale efficiency of Pakistani banks during post financial reforms
period by using data from 2004 to 2009. Non parametric Data Envelopment Analysis is applied for this purpose.
The results suggest that technical efficiency decreases during middle period but increases in recent years. Small
banks are found more efficient as compare to medium and large banks. Decomposition analysis explains
dominance of scale inefficiency over pure technical efficiency for technical efficiency of banks. This study further
explores potential determinants of calculated efficiencies. Diversification of income, market share in respect of
deposits and issuance of loans are positively associated whereas bad cost management of banks and current
depressing economic situation of this country exerts negative impact on banking efficiency.
Keywords: Pakistani banks; deregulations; efficiency; determinants
1. Introduction
Financial system is a vital part of any modern economy and a well developed financial system is pre-requisite for
optimal utilization of financial resources. Role of financial institutions in this system is of paramount importance
and underdeveloped financial institutions can be hindrance for financial markets to channelize surplus saving
efficiently into productive investments. Dominance of banking sector in overall financial system is one of the
major characteristics of developing countries (Limi, 2004; Staub, et al., 2010). Like other developing countries,
banking industry is also a vital entity in Pakistan financial system (Hussain, 1999; Zaidi, 2005). Apart from
theoretical development on finance and growth nexus, ample empirical evidences are available that financial
development through financial intermediary role of banks enhances economic growth (Ataullah & Le, 2006).
Considering active role of this sector in any economy, improvement in performance of this sector has become
crucial question. Most of countries started financial reforms in their banking systems from 1980s with the aim of
enhancing performance and efficiency of this sector (Hardy and Patti, 2001). Banking reforms have been
implemented in Pakistan since 1990s in order to improve performance, competitiveness and services quality of
this sector. Since the years 1991 to date, a number of developments have occurred in banking industry of this
country such as: domestic private banks are now enjoying hefty share, some big nationalized banks have been
privatized, automation services of banks are flourishing etci.
Efficiency analysis is one of the ways to examine the performance of banking sector. Moreover, this analysis is
helpful to understand which banks are most efficient compared to their counterpart’s at point in time. Various
studies examined efficiency of banking sector all over the world with the intention to assess the performance of
this sectorii (see Berger & Mester, 1997; Isik, & Hassan, 2002; Das & Ghosh (2006). Apart from that, numerous
empirical studies have also been carried out to finding out prudent determinants of banking efficiency (e.g. Miller
& Noulas 1996; Hao et al., 2001; Dacanay III 2007).
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More recently global financial crisis started from United State of America at the end of 2007 with the emergence
of sub-prime crisis and through contagion affects this crisis spread all over the world in 2008. The major sources
of its infectious spread are globalization and technology and this is probably the largest crisis after the great
depression of 1930s (Llanto & Badiola, 2009). There is a possibility that banking sector of developing countries
might get contagion effect of this crisis.
This paper has three objectives. First objective is to empirically address the efficiency of Pakistan banking
industry in post reform period. Second objective deals with examining the important determinants of this sector
using more recent data. The last objective is to see whether banking sector of Pakistan was affected by the
contagious effect of the recent global financial crisis. The rest of this study consists of the following parts. Section
2 provides brief historical summary of Pakistan banking sector. Section 3 is meant for methodology and data
issues. Results discussions are given in section 4 whereas section 5 presents concluding remarks.
2. History of Pakistan banking industry
At the time of independence in 1947, Pakistan inherited a weak banking structure. All the Indian banks moved
their head offices to India and closed most of their branches. There was only one bank which had its head office in
Pakistan in August 1947 and this was the only bank which moved its head office from India to Pakistan. In
addition to that there were a few foreign banks which were merely attached with the financial business of
international trade. There was no separate central bank of this country at that time. State Bank of Pakistan (SBP)
was established as a central bank in 1948 and was entrusted the goal to strengthen banking sector of Pakistan.
Later on, National Bank of Pakistan (NBP) was established and this bank was marked as agency bank of SBP to
handle the banking business smoothly (Zaidi 2005). In earlier period, informal financial intermediary business
was prevailed but over the period of time, formal banking system replaced informal sector gradually (Hussain
1999).
Expansion in branches of commercial banks had been observed during 1950s. Another important development in
Pakistan’s banking system was expansion in credit provision by Pakistani banks. The share of overall credit,
issued by Pakistani banks increased from 38 percent in 1952 to 59 percent in 1955. During 1960 to 1965, banking
sector of Pakistan flourished and branch offices of scheduled banks increased from 430 in 1960 to 1591 in 1965.
Several new banks were also established. Exponential rising trend in expansion of banking sector of Pakistan
occurred during 1960s and total number of branches reached up to 3133 in June 1970 (Zaidi, 2005; Meenai,
2010).
A report of State Bank of Pakistan in 1970 revealed that only eighty-eight account holders in banks had access to
almost 25 percent of the total credit and most of these account holders were the directors of banks themselves. In
the wake of this and other contemporary issues, banking reforms were introduced in 1972 (Zaidi, 2005). In
January 1974, banks were nationalized by the government. Pakistani banks operating before nationalization were
merged into five banks namely; National Bank of Pakistan, Habib Bank Ltd., United Bank Ltd., Muslim
Commercial Bank Ltd. and Allied Bank of Pakistan (Meenai, 2010). This nationalization policy drastically altered
financial system of this country. Besides the other social objectives of nationalization, nationwide branches
expansions and allocation of credit to public and agricultural sectors were notable. Nationalization witnessed
inefficient role of banking sector due to overstaffing, over-branching and political influence for credit allocations
etc. These problems signaled negative repercussions for financial system of Pakistan (Limi, 2004). Moreover,
during this era real interest rate was negative in most of the years (Hussain, 1999). In sum, prior to 1990
administrative control on interest rates, government control on the banking system, credit allocation to the priority
sectors instead of borrowing firms’ profitability were the major characteristics of financial sector of Pakistan
which impeded efficiency of this sector.
The public sector’s ownership of commercial banks created lot of problems e.g. political intervention into credit
allocation, loan recovery problems and deterioration in services quality etc. Nationalized commercial banks were
not operating on commercial principles and consequently the efficiency, market responsiveness and financial
strength of the banks were badly affected; therefore, reforms in the banking sector were introduced during 1990s
(Khan, 1996). The notable features of Pakistan’s banking sector reforms are: (a) determination of interest as per
market signals; (b) abolition of subsidized credit allocation; (c) restructuring and re-capitalizing the state-owned
commercial banks through autonomy and privatization of nationalized banks; (d) removal of restriction on
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opening new private banks and; (e) improvement in the regulation and supervision criteria of the financial
institutions and many more (Khan and Khan, 2007).
As mentioned earlier, to encourage the private sector, initiatives for opening-up of new banks were also taken.
Ten new commercial banks were permitted to start their operations in 1991. In the later years eleven more banks
were also allowed to be operational. Moreover, two provincial banks; Bank of Punjab and Bank of Khyber were
also announced as scheduled banks in 1994iii. Considerable autonomous power was also given to State Bank of
Pakistan to act more neutrally. Among the other development during the financial reform period, privatization of
four state-owned banks was made in order to improve the efficiency and operations of these banks (Zaidi 2005).
Even during the recent past some reforms were introduced in the banking sector of Pakistan. These include (i)
liberalization of bank branches in order to enhance their share in the market, (ii) merger and acquisition of banks,
(iii) notable measures were taken to reduce non-performing loans, (iv) enhancement of automation services of
banks i.e. ATM, on-line banking (Khan and Khan, 2007).
All these measures strengthened the confidence of private sector on financial sector of Pakistan. Now, Pakistan’s
banking sector consists of 25 private local banks along with 6 foreign banks. Besides that, there are four public
sector commercial banks and three specialized scheduled banks. It is pertinent to note that in the year 1990, prior
to financial reforms, there was no private bank in Pakistan but now the situation has completely changed and over
the period of time private sector got dominance in the banking sector. The year 2009 witnessed that share of
private local bank assets in the total banking sector is more than 80 percent (GOP 2008-09).
3. Methodology and Data
Two types of efficiency estimation methodologies have been applied in literature in order to assess banking
efficiency. First, econometric based parametric techniques and second, linear programming based non parametric
techniques. Data Envelopment Analysis (DEA) is non-parametric technique which has been extensively used in
financial literature. Instead of pre-specifying production frontier, DEA technique formulates production frontier
according to actual outputs and inputs data used in the analysis (Miller and Noulas, 1996; Coelli, 1996). This
approach is more feasible for small sample as compared to parametric technique (Damar, 2006). This study uses
small cross section sample for each year, therefore, we apply this approach.
Farell (1957) is the pioneered the efficiency analysis concept. However, Charnes et al., (1978) introduced non
parametric DEA method to assess efficiency of the firms at micro level. For efficiency analysis through DEA
technique, consider each firm, out of total set of firmsiv (T), uses K numbers of outputs marked as zi and M
numbers of inputs known as yi. Further, K x T is output matrix: Z and M x T is input matrix: Y, shows the data of
all T numbers of firms. In DEA framework, efficiency is examined through ratio form, therefore; for single firm
(bank), ratio of all outputs to all inputs is measured. Usually multiple outputs and inputs are used in financial
literature. Considering this, weights are needed to be assigned to all outputs and inputs, hence K x 1 vector of
output weights: u and M x 1 vector of input weights: v are applied. In order to have optimal weights, the following
problem is defined.
maxu,v (uzi/vyi) (1
Subject to
uzj/vyj < 1, j = 1, 2……T (2
u, v ≥ 0 (3
This problem mentioned in equations 1 to 3 shows that maximize the efficiency of ith firm subject to the constraint
that efficiency of all firms ( from 1 to T) is less than or equal to unity and values of output as well as input weights
are non negative. Nevertheless, infinite solution occurs with this problem, therefore, the constraint vyi=1 is to be
applied to overcome infinite solution. By applying this constraint, the following new formulation occurs.
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maxu,v (uzi). (4
Subject to
vyi= 1,
uzj/vyj < 1, j = 1, 2……T (5
u, v ≥ 0 (6
Duality in linear programming, which is input oriented problem, is presenting in the following form:
minθ, λ θ (7
Subject to.
– zi + Zλ ≥ 0 (8
θyi – Yλ ≥ 0 (9
λ≥0 (10
In the above problem θ is a scalar refers to efficiency of ith firm ranging from 0 to 1 and; λ represents vector of
constant (n x 1);. This equation has to be solved for each bank up to T numbers of banks.
Efficiency can be measured in output oriented and/or input oriented approaches. With given level of inputs,
production of maximum outputs is known output oriented technique whereas its opposite is output oriented
technique which postulates that considering output is fixed, minimum level of inputs utilization is input oriented
approach. Following Das and Ghosh (2006); Sufian (2009), we apply input oriented approach.
The above mentioned DEA based efficiency analysis is based on constant return to scale (CRS) assumption and is
generally marked as technical efficiency (TE) which means; for given level of outputs, minimum use of inputs by
the banks. Banker et al. (1984) introduced return to scale (RTS) by relaxing CRS, therefore, with equations 7 to
10, convexity constraint N1’λ = 1 has to be applied in order to have pure technical efficiency (PTE) which refers
to managerial efficiency and shows minimum level of inputs used –avoiding wastage of inputs by management.
Once, TE and PTE results are given, scale efficiency (SE) can be calculated as TE / PTE. Sale efficiency shows
whether banks operates on the right scale or not but its results do not help find out which bank is operating at
decreasing or increasing return to scale (Coelli 2005). For this purpose, NIRSv constraint: (N1’λ = 1) has to be
included with equations 7 to 10.
3.1 Input and Output Variables
There are a few approaches which guide to select input and output variables for banking efficiency analysis and it
is upheaval task that according to which approach, these variables should be selected. Among them, production
and intermediation approaches are most commonly used. According to production approach, banks are service
providers to their users whereas as per intermediation approach, banks dominantly play financial intermediary
role. Chen et al. (2005) explicate that the former approach is more appropriate for branch appraisal whereas for
overall efficiency analysis of financial institutions, it is pertinent to apply latter approach. Efficiency analysis of
overall banking sector of Pakistan is a subject matter of this study, therefore, following Miller and Noulas, 1996;
Isik and Hassan, 2003; Das and Ghosh, 2006; Sufian, 2009, we opt intermediation approach for selection of inputs
and outputs.
Number of variables to be used for analysis is also equally important question. Utilization of more input and
output variables may loss the power of DEA to discriminate between efficient and inefficient banks. Mostafa
(2009) argues that the number of banks must be greater than three times to the number of selected variables.
Considering these points, we have selected two outputs and three inputs which constitute five variables. Following
Mostafa (2009) five variables based study should take more than 15 firms / banks. This study uses the data of 26
schedule banks operating in Pakistan, hence fulfill this condition. Another debatable point is the selection of
deposits variable. Some researchers used this variable as input, considering this as interest earnings source where
some selected this as output with mark as final product (Miller & Noulas, 1996). Following Miller & Noulas
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(1996); Hsiao et al. (2010), this study selected deposits as input variable. Finally, following intermediation
approach, numbers of employees, operating fixed assets and deposits plus other accounts are selected as inputs
whereas investment and loans are used as output variables. Table 1 depicts statistical summary of these variables.
Table 1: Input and Output Variables
( Million Pak Rs.)
Standard
Variables Mean Deviation Minimum Maximum Median
Inputs
Labor* 4,205 5,020 55 18,625 1,682
Fixed assets 3,995.91 5,663.76 34.90 25,922.98 1,354.56
Deposits 114,520.59 161,930.57 232.66 726,464.83 37,259.90
Outputs
Investment 34,072.91 48,999.81 15.37 217,642.82 11,019.00
Advances 80,802.60 109,044.26 57.62 475,243.43 31,569.96
Note: * Labor is defined as numbers of employees
3.2 Potential Determinants of Efficiency
After ascertaining efficiency of banking sector, at second stage, we examine the determinants of efficiency
through multivariate panel data analysis. In this case, dependent variable is efficiency having values 0 to 1. There
is a general view in banking efficiency literature that Tobit model is more appropriate technique to manage the
characteristics of distribution of efficiency and gives better results for policy prescriptions (Das and Ghosh, 2006).
Following Das and Ghosh, 2006; Ariff and Can, 2008; Sufian, 2009), this study uses Tobit model for the
following model:
φjt = βo + β1Depjt + β2lnAsstjt + β3NIEjt + β4Loansjt + β5NIIjt + Β6Debtjt
+ β7GDP-Grjt + β8Crisisjt + β9Largebanksjt + β10Smallbanksjt + εjt (11
Where
φ = Following, Damar (2006), this study uses TE, PTE & SE as dependent variables separately.
These efficiency measures have been discussed earlier in details.
Dep = Loan-net/ Deposits and accounts for market share of the banks.
lnAsst = Assets of banks in real formvi.
NIE = Ratio of non-interest expenses to total assets, representing cost management of banks
Loans = Ratios of net loans to total assets, accounts for liquidity position
NII = Ratio of non-interest income to total assets is an indicator of diversifications of income
Debt = Ratio of provision and bed debt written off directly to total assets and accounts asset quality
GDP-Gr = Real GDP growth rate of Pakistan account for the impact of prevailing macroeconomic
conditions on banking efficiency
Crisis = Dummy = 1 for the year 2008 otherwise 0, representing recent global financial crisis 2008.
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Largebanks = Dummy =1 for the banks, possess assets more than Rs.250,000 (millions)
Smallbanks = Dummy =1 for the banks, possess assets less than Rs.50,000 (millions)
We expect positive signs for Dep, NII and Loans whereas signs of LnAsst, are priori undecided. Moreover, sign
of NIE is expected to be negative. GDP-Gr variable is included to find out the impact of prevailing
macroeconomic conditions on banking efficiency. The effect of this variable may vary from country to country;
therefore, we are uncertain about its expected sign. Recent global financial crisis sparked after mid of 2007, got
worst momentum in 2008 and affected all over the world, therefore, this study included Crisis variable to find its
impact on banking sector of Pakistan. We can’t predict its expected sign priori. For deep understanding about
efficiency trend in banking sector of Pakistan, we further decomposed all banks into large, medium and small
banks groups according to asset accumulations of banks. In order to avoid dummy variable trap, we only included
two categories of banks; large and small banks and sign of these dummy variables are also priori undecided.
This study used annual data of 26 schedule banks operating in Pakistan during the period from 2004 to 2009. The
sample of 26 schedule banks consists of 16 private commercial, 3 public sector commercial, 3 specialized and 4
foreign banks. Banks are categorized into large, medium and small banks as per their assets accumulations. The
banks possessed assets more than Pak. Rs.250,000 (in millions), between Pak Rs. 50,000 to 249,000 (in millions)
and less than Pak Rs. 49,000 (in millions) are marked as large, medium and small banks respectively. Numbers of
large, medium and small banks varied year by year throughout the analysis period. Data on Input and output
variables are collected from Banking Statistics of Pakistan 2009, published by State Bank of Pakistan (SBP). For
equation 11, data on banks related variables are also taken from Banking Statistics of Pakistan 2009 whereas data
on real GDP growth rate is taken from annual reports of SBP.
4. Empirical Results
4.1 Efficiency
The results of technical efficiency are presented in Table 2. Further decompositions of this efficiency – pure
technical and sale efficiency have also been exercised and their results are also given in this table. The first part of
this table provides annual average efficiency score of all banks. This table reveals that average technical
efficiency enhances in the initial period, however, deteriorated in the middle period. Once again in last two years
of the selected period this efficiency continuously increases. The results of pure technical and scale efficiencies
illuminate that scale inefficiency is the major cause of deterioration in technical efficiency during the year 2006
and 2007 whereas pure technical efficiency which is also generally called managerial efficiency almost stagnated
during the analysis period.
For deep understanding about efficiency in banking sector of Pakistan, efficiency trends of large, medium and
small banks groups according to asset accumulations of banks are also presented in the same table. It is evident
from this table that large banks are least technical efficient as compare to their other counterparts; however,
technical efficiency of large bank groups only decreases in 2006 whereas in the next three years this efficiency
increases continuously. This group is the most pure technical efficient whereas, also least scale efficient among
the three groups of banks. The role of scale (in)efficiency dominated over technical efficiency of large banks.
Efficiency results of medium and small banks explain that technical efficiency decreases during 2006 and 2007 as
it is evident from the efficiency results of all banks but after that this efficiency constantly increase in these
groups. Pure technical efficiency of medium banks continuously decreases throughout the analysis period whereas
this efficiency of small banks increases during first two years, decreases in middle and again increases during the
last period as is the case of all banks analysis. Average scale efficiency of these groups contains almost same
trends.
The results of return to scale (RTS), both in numbers and percentage, are presented in Table 3. This table depicts
most of the banks operate at decreasing return to scale (DRS) during the years 2006 and 2007. These results are
consistent with efficiency analysis results which also explain that efficiency of the banking sector decreased in
these two years. It is also unveiled from this table that no any large banks could operate at increasing return to
scale (IRS) throughout the analysis period and even not any single enjoyed constant return to scale(CRS) from
2004 to 2008. This shows that all large banks are in trap of DRS throughout the period except in the year 2009
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where only one bank could attain CRS mark. Most of medium banks face diseconomies of scale and there is no
any medium banks which can touch IRS hallmarks from 2004 to 2008. Only two banks operated at IRS while 60
percent banks were in the trap of DRS in 2009. Most of the small banks reap the benefits of economies of scale
and even during the turmoil year 2006, as is evident from efficiency analysis, less than 50 percent banks operated
at DRS. In 2009, two third small banks operated at IRS and remaining one third small banks were at CRS. All this
analysis illuminate that small banks are most scale efficient whereas large banks are the most sale inefficient
banks in Pakistan banking sector.
Table 2: Average Efficiency Results
Nos. of TE1 PTE1 SE1
Banks Mean S.D.2 Mean S.D. Mean S.D.
All Banks
2004 26 0.808 0.185 0.908 0.177 0.896 0.129
2005 26 0.828 0.176 0.910 0.134 0.911 0.134
2006 26 0.697 0.191 0.903 0.145 0.774 0.164
2007 26 0.692 0.192 0.885 0.157 0.788 0.171
2008 26 0.804 0.178 0.900 0.138 0.899 0.156
2009 26 0.851 0.165 0.904 0.132 0.944 0.121
Large Banks
2004 4 0.654 0.023 0.982 0.037 0.667 0.014
2005 5 0.741 0.055 0.965 0.076 0.769 0.043
2006 6 0.583 0.044 0.920 0.094 0.636 0.046
2007 6 0.625 0.059 0.965 0.053 0.647 0.045
2008 6 0.843 0.077 0.972 0.053 0.867 0.057
2009 7 0.847 0.106 0.919 0.108 0.922 0.046
Medium Banks
2004 7 0.872 0.135 0.987 0.035 0.884 0.133
2005 7 0.867 0.144 0.912 0.096 0.947 0.074
2006 7 0.729 0.144 0.925 0.118 0.787 0.107
2007 9 0.681 0.155 0.878 0.123 0.777 0.133
2008 10 0.802 0.143 0.855 0.146 0.943 0.093
2009 10 0.880 0.171 0.887 0.169 0.991 0.012
Small Banks
2004 15 0.819 0.211 0.851 0.218 0.963 0.048
2005 14 0.839 0.212 0.888 0.164 0.944 0.148
2006 13 0.733 0.237 0.884 0.180 0.831 0.190
2007 11 0.738 0.257 0.847 0.206 0.875 0.195
2008 10 0.782 0.250 0.902 0.156 0.873 0.231
2009 9 0.823 0.204 0.910 0.111 0.908 0.198
1
Note: TE, PTE & SE represent technical, pure technical efficiency.
2
SD= standard deviation
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Table 3: Return to Scale Analysis
All Banks Large Banks Medium Banks Small Banks
Years
IRS CRS DRS IRS CRS DRS IRS CRS DRS IRS CRS DRS
2004 Nos. 5 8 13 0 0 4 0 3 4 5 5 5
% 19.2 30.8 50.0 0.0 0.0 100.0 0.0 42.9 57.1 33.3 19.2 19.2
2005 Nos. 5 10 11 0 0 5 0 3 4 5 7 2
% 19.2 38.5 42.3 0.0 0.0 100.0 0.0 42.9 57.1 35.7 50.0 14.3
2006 Nos. 3 5 18 0 0 6 0 1 6 3 4 6
% 11.5 19.2 69.2 0.0 0.0 100.0 0.0 14.3 85.7 23.1 30.8 46.2
2007 Nos. 3 5 18 0 0 6 0 1 8 3 4 4
% 11.5 19.2 69.2 0.0 0.0 100.0 0.0 11.1 88.9 27.3 36.4 36.4
2008 Nos. 6 6 14 0 0 6 2 2 6 4 4 2
% 23.1 23.1 53.8 0.0 0.0 100.0 20.0 20.0 60.0 40.0 40.0 20.0
2009 Nos. 10 9 7 0 1 6 4 5 1 6 3 0
% 38.5 34.6 26.9 0.0 14.3 85.7 40.0 50.0 10.0 66.7 33.3 0.0
Table 4: TOBIT Model Results Regarding Determinants of Efficiency
Variable TE PTE SE
Constant 0.782774 0.279091 1.491152*
Dep 0.001683** -0.001519** 0.003210*
LnAST -0.002548 0.042084** -0.044104***
NIE -0.021343* -0.017769* -0.005367
Loan 0.000138** -1.98E-05 0.000164*
NII 0.042224* 0.024793* 0.019576**
Debt -0.010685 -0.006922 -0.004371
GDP-Gr -0.015702** -0.000974 -0.016124*
Crisis -0.021522 -0.001993 -0.02287
Largebanks -0.085225 0.009402 -0.094512**
Smallbanks 0.036917 0.042077 -0.002884
Note: *, **, & *** represent significant at 1%,5% and 10% level respectively.
4.2 Determinants of efficiency
The findings of factors which have potential to influence efficiency of banks are vital for policy guidelines in
order to enhance the performance of banking sector. Considering this, we also examine determinants of banking
efficiency and the results of this analysis are presented at Table 4. This table explains that market share with
respect to deposits of banks positively contribute to technical efficiency. Dacanay III (2007) also found same
results in his analysis. Bad cost management make adverse impact on technical efficiency as the NIE – proxy of
the cost management, contain negative sign and also significant at 1 percent level. Issuance of loans, which is one
the key source of income for banks, enhances technical and scale efficiency of banks. This finding is consistent
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with the results of Sufian (2009). Non-interest income variable is used to assess the impact of income
diversifications on the performance of banking sector. It reveals that income diversifications significantly enhance
technical, pure technical and scale efficiency. Significant positive impact of this variable on all three efficiency
variables illuminates the importance of income diversification in banking sector and dictate that instead of sole
reliance on interest income, diversification in income would be better strategy for banks because this
diversification not only enhances efficiency of this sector but also beneficial for financial intermediary role of
banks. Prevailing economic condition is also crucial for efficiency of banking sector of any country. In recent
years, macroeconomic condition of this country was not remarkable. Therefore, to envisage the impact of current
economic situation on the performance of banking sector, we include GDP growth rate as an explanatory variable
in the model and the results illumine that recent deteriorated economic growth exerted hampering impact on
technical as well scale efficiencies. The coefficient of this variable contains negative signs for both these two
efficiency measures and also significant at 5 percent and 1 percent significant levels respectively. It means current
unhealthy economic progress postulate negative impact on banking efficiency, therefore, to improve the
efficiency, economic stability should also be taken into consideration. A qualitative variable for global financial
crisis 2008 is included in the model. The results explain that Pakistan banking sector safely escaped from its
negative impact. Foreign banks in host countries may be one of the important causes for transmitting negative
contagious affect of developed countries’ financial crisis to developing countries through capital in or out flow.
Foreign banks have tiny share in Pakistan banking sector because branches of these banks have merely 1 percent
share in total bank branches networkvii. Moreover, major banking business of foreign banks is to provide financial
facilitation for foreign trade. Therefore, due to these reasons efficiency of Pakistan banking sector could safely
escaped from the negative signals of recent global financial crisis. This very fact can also be confirmed from
efficiency analysis where banking efficiency improved from 2007 to 2008 and this positive pace remain continued
in 2009. Large banks are negatively associated with scale efficiency because its coefficient is significant at 5
percent level and contains negative sign. This negative impact indirectly transmits hampering impact on technical
efficiency. This finding is also consistent with the finding of scale efficiency analysis of large banks group where
this group is observed as most scale inefficient as compare to medium and small banks groups.
5. Conclusions
This study analyzes efficiency of Pakistan banking sector in post-banking reforms era by taking data of individual
banks from 2004 to 2009. It has been found that technical efficiency decreases in middle, however, increasing
trend prevails in last period of data. Small banks are the most technical and sale efficient. Efficiency, return to
scale and efficiency determinants analyses illuminates that large banks are least efficient with respect to scale
operations. Return to scale analyses elucidate that small banks enjoy economies of scale as compare to medium
and large banks. Efficiency determinants analysis explains that market share with respect to deposits, income
diversifications, and issuances of loans positively enhance efficiency. On the other hand current unhealthy
economic conditions of Pakistan and bad costs management of banking sector exert negative impact on efficiency
of this sector.
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Endnote
i
For details see Khan & Khan (2007).
ii
Both Parametric and non-parametric techniques have been used in finance literature in order to assess efficiency
of banking sector.
iii
Both are public sector banks.
iv
In efficiency analysis literature these are called decision making units (DMUs). In our case, firms represent
banks.
v
Non increasing return to scale
vi
GDP Deflator with base year 1999-00 is used to make nominal variable into real form.
vii
In 2008, out of more than 7700 branches, numbers of foreign bank branches was only 64. Almost same
situation prevailed throughout the history of Pakistan banking industry.
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