Chapter 5 Concluding Remarks and Future Researches
2. Future Researches
2.2 Research Issue
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According to Fried et al.’s proposal, DEA is applied to inputs and outputs to get initial measure of DMUs performance in the first stage. In the second stage, SFA31 is used to regressd efficiency measures from the first stage against a set of exogenous environment variables. This gives a kind of decomposition in the variation of performance into a part attributable to environmental effects, a part attributable to managerial inefficiency and a part attributable to statistical noise. In the final stage (that is, the third stage), either outputs or inputs (depending on the input-oriented or output-oriented mode of the first stage DEA) are adjusted to account for the influence of the environmental effects and the statistical noise (good and bad luck) explored in the second stage and DEA is used to re-evaluate producer efficiency.
Three-stage DEA model will be used to measure the China banking industry’s operating efficiency and to verify whether the three-stage DEA method is more effective than the traditional method, which can more accurately measure the efficiency of the management of the bank to provide effective management decisions.
2.1.2 Sensitivity Analysis in DEA
In a DEA model each Decision Making Unit (DMU) is classified either as efficient or inefficient. The efficiency measure may be changed through modification in the number of inputs and (or) outputs in DMUs. Therefore, any DMUs can alter its classification, i.e. an efficient DMU can become inefficient and vice versa. The goal of this study is to assess changes in inputs and outputs and whether they alter banks’ efficiency statuses. The study estimates the dynamic efficiency of China banks over the period 2005-2011 with 2 inputs and 2 outputs using DSBM. This study is expected to increase labor (total number of employees) and non-interest income as input and output variables. We also use sensitivity analysis to compare efficiency by choosing different input and output combinations. This can be considered as a kind of sensitivity analysis in DEA. Sensitivity analysis of DEA models which is based on the linear programming is both theoretically and practically important.
2.2 Research Issue
In response to the global financial crisis of 2007-2009, the focus on financial stability concerns has understandably been dominant in the discussion of international financial reform, posing a challenge to policymakers facing the trade-off between financial stability and efficiency of financial intermediation. Therefore, we will further study the trade-off relationship between stability (bank risk) and efficiency.
31 Because the error term is asymmetric, only when the variance of management inefficiency (σun2 =0(γ =0)) is not rejected is the Tobit regression model suitable for use. Otherwise, we should use SFA in the second stage.
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banks showed a significant difference in static efficiency of bank merger. Results of the tests are shown in Table A2-1. Implementation of the financial restructuring scheme changed static efficiency. These two types of banks had less static efficiency after implementation of financial restructuring than before implementation.Table A2-1 Descriptive statistics and efficiency difference test：The static efficiency of banks with or without the financial restructuring scheme
Descriptive statistics Wilcoxcon sign rank test Item
restructuring 0.8293 0.9065 0.8801
（0.0329）
restructuring 0.8719 0.9135 0.8888
（0.0161）
restructuring 0.7662 0.9000 0.8486
（0.0488）
-3.180*** 0.001
Note：***, **, * represent significance at the 1%, 5%, and 10% levels, respectively.
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In addition to cash flow right, the remaining explanatory variables had a significant difference for banks with participating in financial restructuring. The test results are presented in Table A2-2. The deviation voting right and cash flow right, seat control right, bank size, and merger experience increased after implementation of financial restructuring, and the remaining explanatory variables decreased.
Table A2-2 Descriptive statistics and mean difference test: The explanatory variable of banks with participating in the financial restructuring scheme
Descriptive statistics Mean difference test Item
t value Average variance (standard error）
Before financial
restructuring 0.57 63.34 19.56
（17.17）
restructuring 1.00 126.58 4.13
（19.36）
D_VC
After financial
restructuring 1.00 192.31 23.24
（41.58）
-2.87*** -19.11
（6.65）
Before financial
restructuring 0.93 92.59 12.20
（20.85）
restructuring 0.00 78.00 11.66
（22.72）
restructuring 33.00 100.00 62.93
（21.45）
Seat
After financial
restructuring 42.00 100.00 86.37
（21.37）
-5.208*** -23.44
（4.50）
Before financial
restructuring 18.5656 20.9671 20.11
（0.77）
Size
After financial
restructuring 18.9834 21.2917 20.67
（0.65）
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In addition to cash flow right, director and supervisor pledge ratio, and manager shareholding, the remaining explanatory variables had a significant difference for banks without participating in financial restructuring. The test results are shown in Table A2-3. The deviation between voting right and cash flow right, seat control right, bank size, and merger experience increased after implementation of financial restructuring, and the remaining explanatory variables decreased.
Table A2-3 Descriptive statistics and mean difference test: The explanatory variable of banks without participating financial restructuring scheme
Descriptive statistics Mean difference test Item
t value Average variance (standard error）
Before financial
restructuring 2.18 99.99 20.97
（22.74）
restructuring 0.00 77.05 14.63
（19.49）
restructuring 17.65 100.00 63.17
（24.37）
restructuring 18.31 20.62 19.26
（0.63）
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banks showed a significant difference in dynamic efficiency of bank mergers. The test results are shown in Table A2-4. Implementation of the financial restructuring scheme changed dynamic efficiency. These two types of banks had less dynamic efficiency after implementation of financial restructuring than before implementation.Table A2-4 Descriptive statistics and mean difference test: The dynamic efficiency of banks with or without the financial restructuring scheme
Descriptive statistics Wilcoxon sign rank test Item
restructuring 0.8994 0.9550 0.9346
（0.0205）
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Table A3-1 Correlation matrix
provision coverage ratio
loan-loss provision
ratio
cost to income ratio
loan to deposit
Current ratio
capital adequacy
Tier 1 capital adequacy ratio
Leverage
ratio Bank size Time provision
coverage ratio 1 . . .
.
loan-loss
provision ratio -0.386(**) 1
0.000 cost to income
ratio -0.326(**) -.0103 1 .
0.001 0.294 .
loan to deposit 0.221(*) -0.305(**) 0.265(**) 1
0.023 0.002 0.006
Current ratio 0.134 0.018 -0.250(**) -0.105 1
0.173 0.856 0.010 0.288 .
capital adequacy 0.314(*) -0.311(**) -0.506(**) -0.292(**) 0.135 1 .
0.015 0.001 0.000 0.003 0.168 .
Tier 1 capital
adequacy ratio 0.205(*) -0.241(*) -0.522(**) -0.309(**) 0.025 0.966(**) 1
0.036 .013 0.000 0.001 0.803 0.000
Leverage ratio 0.283(**) -.299(**) -0.456(**) -0.192(*) -0.053 0.914(**) 0.959(**) 1
0.003 0.002 0.000 0.050 0.594 0.000 0.000
Bank size 0.057 0.073 -0.095 -0.141 -0.298(**) -0.065 -0.067 -0.007 1
0.563 0.458 .333 0.152 0.002 0.512 0.500 0.940
Time -0.164 0.059 -0.048 -0.046 -0.281(**) 0.088 0.085 0.143 0.582(**) 1
0.094 0.549 0.625 0.643 0.004 0.374 0.387 0.146 0.000
Note：***, **, * represent significance at the 1%, 5%, and 10% levels, respectively. ※represents significance with bootstrapping method.
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Table A4-1 and A4-2 list a small number of large banks with a high proportions of assets and a larger number of small banks with low proportions of assets, respectively. The proportions of large banks' assets increased after 2006. Similarly, the proportions of small banks' assets decreased after 2006.
Table A4-1 The number of sample banks Number
of bank 2004 2005 2006 2007 2008 2009 2010 2011 total
Large 3 4 4 6 9 9 11 11 57
Small 9 21 36 36 21 22 24 16 185
Total 12 25 40 42 30 31 35 27 242
Table A4-2 The proportions of sample banks’ assets Asset
ratio 2004 2005 2006 2007 2008 2009 2010 2011
Large 88.46 85.80 84.47 88.87 90.46 91.32 91.10 92.68
Small 11.54 14.20 15.53 11.13 9.54 8.68 8.90 7.32
Total 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00
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The following table shows that the large banks demonstrated a higher profit efficiency and lower risk compared to the small banks. In addition to the loan loss provision ratio, the remaining regulation variables all meet the requirement of CBRC. The average loan loss provision ratio stands at 2.40% and 2.07% (lower than 2.5%) for large and small banks.
Table A4-3 Summary statistics for dependent and independent variables Variable Statistics
Bank type
Average Min Max SD Regulated ratio
PE Large banks 0.8161 0.5304 1.0000 0.1535 Small banks 0.6582 0.0966 1.0000 0.2702 ln(Z-score) Large banks 3.8501 1.7446 5.4534 0.7505 Small banks 3.7448 1.3207 7.1241 1.0467
RES_NPL Large banks 182.5477 45.5300 499.6000 99.8267 shall be no lower than 150%
Small banks 166.4506 1.8600 828.8700 142.0697
RES_Loan Large banks 2.3982 1.3200 4.0800 0.6270 shall be no lower than 2.5%
Small banks 2.0727 0.1800 5.9700 1.0001
CIR Large banks 37.6008 29.1730 56.0750 5.4230 less than 45%
Small banks 39.5489 22.4070 74.2710 8.9911
LIQ Large banks 26.5300 11.7120 43.9640 7.7259 shall be no lower than 25%
Small banks 24.9706 3.8830 71.2710 10.3760
LDR Large banks 56.3300 46.4120 66.4620 4.9160 less than 75%
Small banks 57.3850 22.0360 106.0000 10.7803
CAR Large banks 11.8284 9.0700 15.2700 1.4041 shall be no lower than 8%
Small banks 10.9940 -1.4700 30.1400 4.2118
Leverage Large banks 5.2972 2.8902 8.0988 0.9419 leverage rate no lower than 4%, 1% more than the Basel III requirement of at least 3%.
Small banks 5.4153 -0.8369 23.8166 2.6188 Time Large banks 47.2807 12.0000 103.0000 33.7378 Small banks 10.7784 1.0000 23.0000 4.8274
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Table A4-4 The correlation coefficient
PE ln (Z-score) RES_NPL RES__Loan CIR LDR LIQ CAR Leverage Time
PE 1
ln (Z-score) .096 1
RES_NPL .119 .036 1
RES__Loan .162(*) -.014 -.066 1
CIR -.496(**) -.021 -.153(*) -.086 1
LDR -.169(**) .063 -.049 -.304(**) .036 1
LIQ .114 -.086 .450(**) .089 -.065 -.338(**) 1
CAR .264(**) .104 .354(**) -.037 -.300(**) .020 .218(**) 1
Leverage .150(*) .148(*) .203(**) -.248(**) -.174(**) .327(**) .065 .805(**) 1
Time .142(*) -.078 -.029 .149(*) -.076 -.042 -.024 .104 -.002 1
Note：**, * represent significance at the 1% and 5% levels, respectively.