Academy of Marketing Studies Journal (Print ISSN: 1095-6298; Online ISSN: 1528-2678)

Review Article: 2022 Vol: 26 Issue: 5S

Performance of Banking Sector- A Case of Select Developed Nations

Deepika Dhawan, Shri Mata Vaishno Devi University

Sushil K. Mehta, Shri Mata Vaishno Devi University

Citation Information: Dhawan, D., & Mehta, S.K. (2022). Performance of banking sector- A case of select developed nations. Academy of Marketing Studies Journal, 26(S5),1-11.


Purpose: The purpose of this paper is to look at productivity and different efficiency aspect of five developed nations in the light of phasing in of Basel III capital adequacy norms. Design/ Methodology/ Approach: The authors used data envelopment analysis technique (DEA) to measure relative efficiencies and Malmquist productivity index (MPI) to measure average total factor productivity (TFP) of 25 banks in 5 countries for the period 2013 to 2019. Findings: D-SIBs performed better in terms of technical, cost, allocation, scale, and managerial efficiency. G-SIBs ranked second while CBs ranked third in terms of relative efficiency. In terms of SE, the banks in Canada performed better while in terms of TE, ME, CE, and AE banks in USA performed better. In terms of productivity, Germany, France, and UK showed positive growth. While, USA remains constant in terms of average productivity, only Canada showed a decline in average TFP. Originality Value: Studies measuring relative efficiency of different countries that is in the same stage of implementing Basel norms are quite rare. This paper will help administrators and central bank supervisors to know how the performance of their regional counterparts is progressing and motivate them to keep at par.


Benchmarking, DEA, Efficiency, Bank Performance, Developed Nations, Malmquist Productivity Index.


A general consensus exists among people that financial sector plays an important part in explaining the concept of sustainable economic growth (Wachtel, 2001). In reality, an efficient banking system plays key role in overall financial development of a country not just by altering the rate of savings but also by allocation of savings. This means which industry or firm is going to use society savings is determined by a financial intermediary. Since commercial banks (CBs) play a vital role as a financial intermediary, it can help to strengthen and contribute to the economy to grow. The global financial crisis (GFC) of 2007-08 already has shown the limitations of banking sector as a whole. Capital inadequacy and improper liquidity management are two major reasons for a failure of bank which is proven by global financial crisis of 2007-08 (Bologna, 2015). In response to the situation faced by banks, Basel III Accord (2010-11) introduced new capital reforms that impose potentially binding constraints on liquidity and quality of capital. Banks are expected to comply with these norms in a phased manner in most countries.

After the phasing in of reforms, the quality of capital has improved. The study focuses on the changes in the efficiency and productivity of commercial banks after the phasing in of new reforms. This study will help the supervisors of central banks to know how their regional peers are progressing in terms of productivity and efficiency. Where are they lacking and in which area can they improve? The study focuses on answering following three key questions. First, how well the selected developed countries fare in terms of technical efficiency (TE) and cost efficiency (CE)? Second, what effect does a definition of bank has on its relative efficiency? Third, how well the sample banks fare in terms of productivity? These three questions will help the banks, regulators of central banks and investors in banks to better utilize resources and improve their performance.

The paper fills up the research gap on the performance of banks in the Basel III regime in developed nations. In the study an in-depth analysis of different efficiencies is done through data envelopment analysis (DEA). To compute the change in productivity, Malmquist productivity index (MPI) is used.

The rest of paper is sorted out in 5 sections. Section 2 reviews related literature. Section 3 explains the approach, data and sources of data. Section 4 presents results and discussion. Section 5 includes concluding remarks.

Review of Literature

There are different studies done over time that focus on comparing the efficiencies of banking sector in different countries. The underlying reason is that policy design is based on the efficient resource allocation by banks. Some studies use ratios to measure performance of financial institutions. But these ratios sometimes do not reveal true picture because these ratios do not divulge production process. For example, skilled personnel are reflected in high operating cost that can generate high quality loans. So, to measure performance, production process has to be considered. Farrell (1957) gave an original method to measure technical efficiency based on production frontier. Based on Farrell concept, Charnes et al. (1978) developed DEA (CCR Model). Later, this model got extended by to assimilate VRS for production technology also known as BCC model. Previously, CCR model focus on constant returns to scale (CRS). With the help of these models, different studies are conducted to evaluate efficiency in different sectors. Mainly, these studies focus on developed nations as they have the technology and means to implement the changes faster. Also, the method and techniques are both limited to stochastic frontier analysis (parametric approach) and DEA (non-parametric approach). Due to its non- parametric nature, DEA is widely used and preferred method for the banking sector.

Sherman and Gold (1985) first used DEA method to analyze branch efficiency and found it complementary to other methods for measuring efficiency. Several studies are done to investigate the same nexus. For example, Coughlan et al.(2010) for UK; Azizi and Ajirlu (2010) for Iran; Cook and Bala (2007) for Canada; Camanho and Dyson (2005) for Portugal; Das et al. (2009) for India; Dekker and Post (2001) for Netherlands; Deville (2009) for France; Hartman et al. (2001) for Sweden; Oral et al. (1990) for Turkey. Afterwards a lot of scholar applied the DEA for performance evaluation of banks. For example, Bhattacharya et al (1997),Sathye (2003), Goswami et al. (2019) for India; Ebrahimnejad et al. (2014) for East Virginia, Novickyt? and Dro?dz (2018) for Lithuania, Partovi and Matouskek (2019) for Turkey; Pasiouras et al. (2008) for Greece; Wong and Deng (2016) for ASEAN;Ullah (2020) for Pakistan; Drake et al. (2006) for Hong Kong; Casu and Molyneux (2003) for Europe.

The authors found so many studies for different regions but failed to find comparative study for different countries’ bank efficiency in the same stages of Basel III implementation. Considering the fact above, this study will be valuable input in the existing stock of knowledge.

Research Methodology

In the study, the researchers follow the term “efficiency” in the economic sense. It means to measure how well a DMU is utilizing its resources in the form of input to produce the output. The efficiency is analyzed in two ways. First is input-oriented approach which follows the rule of reducing the inputs without changing the output. Second is output-oriented approach which follows the rule of increasing the output without changing the input. With the scarcity of resources, following input-oriented approach is better. As the business thrives in a world of chaos, variable returns to scale (VRS) approach is used to get the results on relative efficiency.

In this study, DEA (CCR, BCC) model is used to evaluate the relative efficiencies of 25 CBs of 5 developed nations (DN). Let’s first explain about the DEA. It is a linear programming method. With the help of input and output, different constraints are presented in the equation form to calculate the relative efficiencies of a DMU. To calculate relative efficiency, the DMUs should have similar inputs and outputs. All DMUs then make up an efficient frontier. Efficient frontier means a linear set of most efficient inputs. Usually, all DMUs either fall on or below efficient frontier. The DMUs that fall on efficient frontier are the most efficient DMUs and are standard to which other DMUs are compared. That is the reason for the saying “efficiency is relative term”.

In this study, to calculate efficiency input-oriented approach is used. To calculate productivity, output-oriented approach is used. MPI is used to evaluate the average TFP for the 5 developed nations. This method can calculate change in average TFP owing to either technology change or efficiency change; which can be further bifurcate into pure change and scale change.
These two methods are used for their wider application in the sense both are non-parametric in approach.

Data and Descriptive Statistics

In the research, the period taken for the study is from the year 2013 to 2019. The sample countries include five developed nations namely, USA, UK, Canada, Germany and France. These five countries are in the same stage of implementing Basel norms (FSB, 2018). The study will help to evaluate the efficiency of banks after the US sub-prime crisis. Due to time and funds constraints, five commercial banks are chosen from each of the sample countries to represent the banking sector. The dataset is balanced. The official websites and annual reports of the banks are the source for data, so only those banks are selected in the sample for which data for all variables are available. The intermediation approach is taken for the selection of variables. The total cost includes sum total of personnel expenses, interest on deposits and other physical capital expenses (land, building, etc.). The input price includes unit price of personnel (personnel expense per employee), unit price of financial capital (expense on interest per dollar of deposits) and unit price of physical capital (expenses on land, building, etc. to physical capital). The total output includes total amount of loans to customers, deposits and investment and securities. For calculation of efficiency, the researchers have taken total output as the output and total cost as the input.

The descriptive statistics for the variables is given in Table I. The representation of data is in USD (in thousands). The average asset size for the banks in the dataset is US $ eight hundred and twenty billion. With one trillion and two hundred and twenty-six billion, French banks are largest average bank asset-wise and with three hundred and one billion, Canadian banks are the smallest average bank asset-wise. Also, input price-wise, French banks scored highest while Canadian banks scored lowest Table 1.

Table 1
Descriptive  Statistics For The Variables
  Mean SD Minimum Value Maximum Value
A. All Sample        
Assets 820062723 909972474 303632 4424909723
Loans 305402531 334798654 734687 1643347689
Deposits 376063952 457876565 745392 2254922975
Investment 331139868 448417455 3606 1766526420
Total Cost 20022967 22784058 68272 92064000
Interest on Deposits 7047936 7541827 22124 32139257
Price of Input 142.57 157.92 37.13 1418.54
B. Germany        
Assets 639163114 610526387 1.57E+08 2221088904
Loans 2059972775 144963095 30250840 519065472
Deposits 256499454 227157262 48601695 727429359
Investment 300342492 361885678 16596170 1348762233
Total Cost 18113937 15317351 4073409 53979325
Interest on Deposits 8479276 6014623 1593886 28992419
Price of Input 132 59 93 457
C. Canada        
Assets 301265183 289107344 17459313 824786240
Loans 162517951 151016449 14888298 449907358
Deposits 203326281 198106372 14804225 556906371
Investment 53516838 48873376 1273075 143851469
Total Cost 8178103 7818757 495594 24560710
Interest on Deposits 3158324 3202479 271232 11851318
Price of Input 86.45 21.8 57.25 140.5
D. France        
Assets 1225717748110 12594625 303632 36249860
Loans 387883961 46174126 40586683 904452801
Deposits 425708897 300614214 38211501 936880682
Investment 593285465 510539682 6961051 1521316646
Total Cost 26263687 18950426 1739689 59039283
Interest on Deposits 11925349 77904029 305907 24143349
Price of Input 246 238 50 815
Assets 622910240 973550876 16453000 2687379000
Loans 247047188 322392628 19750238 984497000
Deposits 367446043 530405885 20876790 1562431000
Investment 188092654 314580912 3606 907684000
Total Cost 19107484 29470504 836299 92064000
Interest on Deposits 3391896 6318427 109408 26795000
Price of Input 141 223 56 1419
F. UK        
Assets 1311257332 1365830795 3628721 4424909723
Loans 523566279 510456826 734687 1643347690
Deposits 627339081 705217613 745392 2254922975
Investment 520461892 571895855 25032 1766526420
Total Cost 28451626 29512534 68272 89794600
Interest on Deposits 8284836 9299457 22124 32139257
Price of Input 107.19 36 37 175


In the subsequent section, the researchers tried to assess the impact of Basel III norms on the productivity and efficiency of the sample banks. The variable returns to scale (VRS) is selected to evaluate the Managerial efficiency (ME), scale efficiency (SE) and Technical efficiency (TE). The constant return to scale (CRS) is selected to evaluate the allocative efficiency (AE) and cost efficiency (CE). From Table 2 it can be seen the overall efficiency for all sample is 0.827 with Germany having the lowest TE with 0.678 while Canada having the highest TE with 0.869.

Table 2
Efficiency Of Banks
 Particulars Year TE SE ME CE AE
All sample 2019 0.816 0.958 0.850 0.584 0.654
2018 0.841 0.948 0.890 0.640 0.724
2017 0.896 0.948 0.945 0.610 0.667
2016 0.901 0.938 0.961 0.728 0.778
2015 0.902 0.942 0.957 0.659 0.707
2014 0.523 0.698 0.766 0.396 0.611
2013 0.911 0.954 0.954 0.662 0.668
  Mean 0.827 0.912 0.903 0.611 0.687
Canada 2019 0.857 0.931 0.921 0.815 0.843
2018 0.848 0.929 0.913 0.809 0.839
2017 0.870 0.919 0.948 0.806 0.823
2016 0.875 0.911 0.960 0.799 0.810
2015 0.867 0.950 0.912 0.772 0.801
2014 0.884 0.955 0.925 0.769 0.808
2013 0.882 0.959 0.919 0.800 0.848
  Mean 0.869 0.936 0.928 0.796 0.825
USA 2019 0.866 0.913 0.952 0.795 0.828
2018 0.875 0.906 0.967 0.921 0.991
2017 0.899 0.930 0.968 0.917 0.971
2016 0.865 0.884 0.978 0.921 0.998
2015 0.901 0.924 0.977 0.881 0.943
2014 0.892 0.936 0.955 0.921 0.946
2013 0.866 0.908 0.958 0.904 0.934
  Mean 0.881 0.914 0.965 0.894 0.944
Germany 2019 0.666 0.762 0.903 0.724 0.793
2018 0.613 0.675 0.927 0.697 0.778
2017 0.778 0.891 0.877 0.695 0.749
2016 0.591 0.707 0.878 0.684 0.737
2015 0.617 0.674 0.933 0.694 0.694
2014 0.737 0.745 0.991 0.735 0.742
  2013 0.741 0.741 1.000 0.656 0.656
  Mean 0.678 0.742 0.930 0.698 0.736
United Kingdom 2019 0.832 0.926 0.882 0.618 0.705
2018 0.742 0.841 0.892 0.701 0.785
2017 0.874 0.982 0.892 0.562 0.607
2016 0.832 0.924 0.904 0.771 0.816
2015 0.823 0.876 0.943 0.686 0.747
2014 0.811 0.872 0.933 0.758 0.791
2013 0.720 0.872 0.832 0.652 0.701
  Mean 0.805 0.899 0.897 0.678 0.736
France 2019 0.687 0.760 0.906 0.599 0.733
2018 0.701 0.773 0.909 0.608 0.741
2017 0.778 0.891 0.877 0.739 0.811
2016 0.814 0.910 0.898 0.747 0.825
2015 0.793 0.885 0.899 0.710 0.792
2014 0.768 0.889 0.866 0.691 0.772
2013 0.775 0.871 0.893 0.680 0.771
  Mean 0.759 0.854 0.893 0.682 0.778

In terms of SE, Canada banks are most efficient (0.936) followed by USA (0.914), UK (0.899), France (0.854), and Germany (0.742). In all years in the sample period, the least value is scored at 0.674 by banks in Germany in the year 2015 and highest value at 0982by banks in UK in the year 2017.

In terms of ME, USA banks are most efficient (0.965) followed by banks in Germany (0.930), Canada (0.928), UK (0.897), and France (0.893). In the year 2013, banks in Germany possess ME 1.000. Even the lowest value scored is 0.832 in the year by the banks in UK. In terms of CE, banks in USA are most efficient (0.894) followed by banks in Canada (0.796), Germany (0.698), France (0.682), and UK (0.678). The highest value scored by the banks in USA in the year 2018 is 0.921 and least value scored by banks in the UK in the year 2017.

In terms of AE, banks in USA are most efficient (0.944) followed by banks in Canada (0.825), France (0.778), Germany and UK (0.736). The highest value scored by banks in USA in the year 2016 is 0.998 whereas; the least value scored bythe banks in UK in the year 2017 is 0.607. Table 2.

Efficiency changes of DN banks according to type

In these categorizations, banks are divided into three parts. First, Global Systemically Important Banks (G-SIBs) followed by Domestic Systemically Important Banks (D-SIBs) and Commercial banks (CB). G-SIBs are banks that are classified important globally after ranking in the top 30 for 12 indicators. D-SIBs are banks that are considered important for the health of financial economy of a country by their respective central banks. CBs are banks that offer services to companies and individuals equally.

Usually, it is very difficult for a bank to be efficient in all the aspects. In all the aspects of efficiency, D-SIBs are most efficient followed by G-SIBs and CBs (Table 3).

Table 3
 Efficiency According To Type
Global Systemically Important Banks (G-SIBs) 2019 0.586 0.745 0.783 0.517 0.622
2018 0.631 0.741 0.854 0.536 0.621
2017 0.819 0.963 0.856 0.676 0.777
2016 0.805 0.953 0.849 0.685 0.759
2015 0.807 0.985 0.819 0.660 0.751
2014 0.853 0.985 0.865 0.658 0.711
2013 0.775 0.941 0.828 0.650 0.729
Mean 0.754 0.902 0.836 0.626 0.710
Domestic-Systemically Important Banks (D-SIBs) 2019 0.663 0.817 0.821 0.686 0.711
2018 0.758 0.894 0.850 0.683 0.699
2017 0.816 0.911 0.896 0.679 0.693
2016 0.889 0.939 0.948 0.811 0.842
2015 0.936 0.968 0.967 0.757 0.772
2014 0.943 0.979 0.964 0.720 0.724
2013 0.943 0.984 0.959 0.705 0.714
Mean 0.850 0.927 0.915 0.720 0.736
Commercial Banks (CBs) 2019 0.650 0.811 0.828 0.657 0.731
2018 0.659 0.799 0.848 0.635 0.704
2017 0.667 0.787 0.868 0.574 0.649
2016 0.671 0.767 0.891 0.623 0.687
2015 0.696 0.778 0.908 0.656 0.821
2014 0.723 0.826 0.886 0.646 0.716
2013 0.691 0.804 0.876 0.529 0.591
Mean 0.680 0.796 0.872 0.617 0.700

Table 4 reveals the average change in TFP country-wise. Overall increase in average TFP is 2%. In terms of average TFP, USA remains constant. Only banks in Canada shows decrease in average TFP by 1.7%. The banks in France show maximum increase by 6.7% followed by banks in Germany by (3.8%) and UK (1.6%) Table 4.

Table 4
 Malmquist Index Summary Of Annual Means
2013-19 Efficiency change Technical change Pure Change Scale change Total factor Productivity
GERMANY 0.981 1.058 1.005 0.976 1.038
FRANCE 0.979 1.091 0.997 0.981 1.067
CANADA 0.995 0.987 1.002 0.993 0.983
USA 1.000 0.999 1.001 0.999 1.000
UK 1.024 0.993 1.029 0.995 1.016
ALL SAMPLE 0.954 1.070 0.970 0.983 1.020


The paper adds to the existing literature on DN banks by presenting an overview on the changes in efficiencies and productivity in the Basel III implementation period. The researchers evaluate efficiency of banks in USA, Canada, UK, France, and Germany. The study focus on DEA approach to calculate different efficiencies of sample banks in the study period (2013-2019). In terms of SE, the banks in Canada performed better while in terms of TE, ME, CE, and AE banks in USA performed better. In terms of definition, D-SIBs performed better in terms of technical, cost, allocation, scale, and managerial efficiency. G-SIBs ranked second while CBs ranked third in terms of relative efficiency.

To evaluate the average TFP in five countries, MPI has been employed to get an insight in the selected period. In terms of productivity, Germany, France, and UK showed positive growth. For the first two, increase in technical change was higher than a decline in efficiency resulting in increased average TFP. For the UK, efficiency change was higher than decline in technical change resulting in increase in average TFP. While, USA remains constant in terms of average productivity, only Canada showed a decline in average TFP. Both efficiency and technical change contributed to it.

Limitation and Future Scope

The banking sector is very vast in developed nations. It will be very time consuming to collect the data on all the banks through annual reports. Therefore, a representative sample is taken to calculate the changes in efficiencies and productivity. For future research, the data on whole banking sector can be taken with the help of paid database Appendix Table 1 & 2.

Appendix Table 1
Description Of Sample Banks
Developed Nations
USA Synovus
Huntington Bancshares
J P Chase
PNC Financial Service Group
Key Corp
FRANCE Credit Agricole
Societe Generale
Crédit Industriel et Commercial
Credit Du Nord
BNP Paribas
CANADA National Bank of Canada
Laurentine Bank of Canada
Bank of Montreal
Canadian Western Bank
Nova Scotia
GERMANY HypoVereins Bank
Deutsche Bank
Commerz Bank
Nord/ LW
UK Lloyds PLC
HSBC Holdings PLC
Virgin money
Appendix Table 2
S.No Description Abbreviation
1 Allocative Efficiency AE
2 Banker, Charnes and Cooper BCC
3 Canada CA
4 Charnes, Cooper and Rhodes CCR
5 Commercial Banks CBs
6 Constant Returns to Scale CRS
7 Cost Efficiency CE
7 Data Envelopment Analysis DEA
8 Decision-making Unit DMU
9 Developed Nations DN
10 Domestic Systemically Important Bank D-Sib
11 Financial Stability Board FSB
12 France FR
13 Germany GR
14 Global financial crisis GFC
15 Global Systemically Important Banks G-SIBs
16 Group of twenty G-20
17 Malmquist Productivity Index MPI
18 Managerial Efficiency ME
19 Scale Efficiency SE
20 United Kingdom UK
21 Stochastic Frontier Analysis SFA
22 Systemically Important Bank SIB
23 Technical Efficiency TE
24 Total Factor Productivity TFP
25 United States of America USA
26 US Dollar USD
27 Variable Returns to Scale VRS


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Received: 06-Jun-2022, Manuscript No. AMSJ-22-12084; Editor assigned: 08-Jun-2022, PreQC No. AMSJ-22-12084(PQ); Reviewed: 20-Jun-2022, QC No. AMSJ-22-12084; Revised: 22-Jun-2022, Manuscript No. AMSJ-22-12084(R); Published: 24-Jun-2022

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