The nexus between Basel capital requirements, risk-taking and profitability: what about emerging economies?

Figures & data

Table 1. Variables description.

Classification	Variables	Description
Profitability (PROF)	ROA	Return on assets
Risk (RISK)	Loan loss reserve ratio (LLRR)	Loan loss reserve ratio
	Ratio of non-performing loans (RNPL)	Non-performing loans to total loans ratio
Capital (CAP)	Regulatory capital (CAP)	Tier 1 Capital ratio (T1R)
		Total Capital ratio (TCR)
Bank Specific Control Variables (BANK)	Net loan to assets ratio (NLA)	Ratio of Net loans to total assets
	Noninterest expenses to revenue ratio (NIER)	Noninterest expenses to total revenue
	SIZE	Measured by natural log of total assets
	Net loans to total deposits ratio (NLTD)	Measured by taking ratio of net loans to total deposits
	Net interest income to total revenue ratio (NIITR)	Net interest income to total revenue ratio
Ownership Variables (OWN)	Ownership Concentration (OC)	% of shares held by top three shareholders who hold greater than or equal to 10% of shares (OC10).
		% of shares held by top five shareholders who hold greater than or equal to 5% of shares (OC5).
	Managerial Ownership (MO)	% of shares held by management and directors
	Foreign Ownership (FO)	% of shares held by foreign entities
Macroeconomic Variables (MACRO)	Inflation (INF)	Measured by Consumer Price Index
	Economic Growth (GDP)	GDP growth rate
	Domestic credit to private sector (DCPS)	Private Sector Domestic credit
	Real interest rate (RIR)	Real rate of interest
	Monetary policy indicator (MPI)	Monetary policy indicator is central bank policy rate between the Banks and other credit institutes
	Interest rate spread (IS)	Interest rate spread measured by the difference of bank lending and deposit rates
Regulatory Pressure Variables (RP)	Bank Market Power (BMP)	Ratio of bank i total assets in country j to total banking system assets in country j

Source: The Authors.

Table 2. Descriptive statistics.

Variable	Obs.	Mean	Std. Dev.	Min	Max
NPLR	2163	.044	.063	−.873	.520
LLRR	2065	.012	.020	−.107	.340
T1R	2163	.144	.282	−1.67	7.99
T2R	2011	.029	.035	−.047	.995
TCR	2163	.172	.281	−1.59	7.99
ROA	2163	.010	.012	−.111	.089
NLA	2163	.510	.171	−1.05	.859
NIER	2163	.618	2.01	−65.2	47.4
NIITR	2163	.957	1.71	−.574	57.2
BMP	2163	.324	.527	0.000	3.92
SIZE	2163	13.3	2.31	3.98	20.8
GDP	2163	6.60	2.53	−1.51	14.2
MPI	2163	9.06	3.27	4.33	16.0
INF	2163	5.43	3.56	−.900	20.3
IS	1540	4.03	1.33	1.45	7.68
DCPS	2163	67.1	43.7	15.4	156.0
RIR	2163	3.27	3.27	−6.77	11.8
MO	675	1.90	4.47	0	25.7
FO	799	18.8	17.4	0	96.7
OC5	971	61.6	22.5	0	100
OC10	954	56.9	24.1	0	100

Source: The Authors.

Table 3. Risk model results.

RISK (LLRR)	Risk model (1)		Risk model (1)		Risk model (1)		Risk model (1)
	Coef.	Prob.	Coef.	Prob.	Coef.	Prob.	Coef.	Prob.
Constant	0.027	0.226	0.025	0.337	0.025	0.300	−0.004	0.924
RISK(t − 1)	0.042	0.533	0.045	0.523	0.043	0.548	0.011	0.872
CAP	0.014***	0.003	0.013***	0.003	0.013***	0.003	0.014**	0.014
PROF	−1.327***	0.000	−1.286***	0.000	−1.291***	0.000	−1.393***	0.000
NLTD	−0.007	0.202	−0.007	0.242	−0.007	0.197	−0.007	0.267
NIITR	0.001***	0.003	0.001***	0.003	0.001***	0.003	0.001*	0.073
SIZE	0.017	0.919	−0.033	0.841	−0.033	0.845	0.110	0.675
GDP	−0.011	0.379	0.004	0.827	−0.000	0.973	0.011	0.665
MPI	−0.033	0.312	−0.004	0.901
DCPS			0.008	0.394	0.008	0.281	0.010	0.300
RIR					−0.012	0.391	0.179
IS								0.202
AR1, AR2, (Prob)	0.029	0.846	0.029	0.839	0.028	0.880	0.042	0.969
Wald chi2 (Prob)	139.74***	0.000	111.84***	0.000	126.81***	0.000	100.18***	0.000
# of banks	155		155		155		117
# of Obs.	1910		1910		1910		1423

Table 3 presents the Risk model 1 results by using Asian emerging markets sample. We use the Arellano and Bover (1995) panel dynamic two-step system GMM methodology and hetro-robust errors are used to account the problem of heterogeneity. The explained variable is bank risk (RISK) measured by loan loss reserve ratio. Profitability (PROF) is measured by return on assets and CAP represents the tier 1 capital ratio. The Arellano and Bond autocorrelation method is employed to check the presence of serial correlation. ***, **, * denotes level of significance at 1, 5 and 10 percentages respectively.

Source: The Authors.

Table 4. Profitability model results.

PROF (ROA)	Profitability model (2)		Profitability model (2)		Profitability model (2)		Profitability model (2)
	Coef.	Prob.	Coef.	Prob.	Coef.	Prob.	Coef.	Prob.
Constant	0.016***	0.005	0.015**	0.006	0.018***	0.005	0.018***	0.003
PROF (t − 1)	0.412***	0.000	0.411***	0.000	0.412***	0.000	0.410***	0.000
CAP	0.003**	0.018	0.003**	0.013	0.003**	0.020	0.003**	0.014
RISK	−0.250***	0.000	−0.252***	0.000	−0.250***	0.000	−0.252***	0.000
NIER	0.0004**	0.052	0.0004**	0.052	0.0005**	0.054	0.0005**	0.054
NLA	−0.018**	0.009	−0.018***	0.005	−0.018**	0.008	−0.018***	0.004
BMP	−0.002	0.212	−0.002	0.335	−0.002	0.209	−0.002	0.308
GDP	0.004	0.583	0.003	0.662	0.004	0.645	0.002	0.765
DCPS	0.003	0.506	0.003	0.432	0.002	0.641	0.002	0.609
INF			0.013	0.199			0.013	0.184
MPI					−0.010	0.572	−0.016	0.342
AR1, AR2, (Prob.)	0.001	0.376	0.001	0.334	0.001	0.372	0.001	0.326
Wald chi2 (Prob)	322.25***	0.0000	353.06***	0.0000	334.08***	0.0000	350.63***	0.0000
# of banks	155		155		155		155
# of Obs.	1910		1910		1910		1910

Table 4 gives the Profitability model results by using Asian emerging markets sample. We use the Arellano and Bover (1995) panel dynamic two-step system GMM methodology and hetro robust errors are used to account for the heterogeneity problem. The explained variable is bank profitability (PROF) and is proxied by return on assets. Risk (RISK) is measured by loan loss reserve ratio (LLRR) and capital (CAP) represents the tier 1 capital ratio. Capital (CAP) is considered as endogenous regressor. The Arellano and Bond autocorrelation method is employed to check the presence of serial correlation. ***, **, * indicates significance level at 1, 5 and 10 percentages respectively.

Source: The Authors.

Table 5. Regulatory capital model.

CAP	Capital model (TCR) (3)		Capital model (TCR) (3)		Capital model (T1R) (3)		Capital model (T1R) (3)
	Coef.	Prob.	Coef.	Prob.	Coef.	Prob	Coef.	Prob.
Constant	0.095	0.155	0.087	0.204	0.078	0.555	0.096	0.398
CAP(t − 1)	0.733***	0.000	0.732***	0.000	0.725***	0.000	0.725***	0.000
PROF	0.082	0.812	0.082	0.818	1.107	0.250	1.186	0.208
RISK	0.163*	0.068	0.165*	0.073	1.003	0.171	1.044	0.140
NLA	−0.122***	0.003	−0.126***	0.004	−0.043	0.206	−0.056*	0.091
SIZE	−0.379	0.533	−0.424	0.498	−0.737	0.575	−0.656	0.578
BMP					0.024	0.233	0.022	0.220
NIER							−0.002	0.224
GDP	0.038	0.464	0.042	0.446	0.064	0.133	0.019	0.717
DCPS	0.066	0.117	0.076	0.100	0.028	0.356	0.026	0.500
INF	0.055	0.189	0.050	0.219			0.067	0.414
MPI			0.115	0.236	0.233**	0.028
AR1, AR2, (prob.)	0.006	0.554	−0.006	0.552	0.035	0.733	0.029	0.530
Wald chi2 (Prob)	72,526.53***	0.0000	74,489.87***	0.0000	135,446***	0.0000	147,235.01***	0.0000
# of banks	162		162		155		155
# of Obs.	2001		2001		1910		1910

Table 5 gives the Regulatory capital model results by using Asian emerging markets sample. We use the Arellano and Bover (1995) panel dynamic two-step system GMM methodology and hetro robust errors are used to account the heterogeneity problem. The explained variable is bank capital (CAP) and is proxied by total capital ratio (TCR) and tier 1 capital ratio (T1R). Risk (RISK) is measured by non-performing loans ratio in first two cases and by loan loss reserve ratio in last two models. PROF represents the return on assets and PROF and RISK both variables are considered as endogenous regressors. The Arellano and Bond autocorrelation method is employed to check the presence of serial correlation. ***, **, * indicate the significance level at 1, 5 and 10 percentages respectively.

Source: The Authors.

Table 6. Ownership structure, regulatory capital and risk-taking.

RISK (NPLR)	Risk model		Risk model		Risk model		Risk model
	Coef.	Prob.	Coef.	Prob.	Coef.	Prob.	Coef.	Prob.
Constant	0.008	0.678	0.010	0.611	0.066	0.133	0.073	0.124
RISK(t − 1)	1.135***	0.000	1.127***	0.000	0.644**	0..036	0.641**	0.033
Risk(t − 2)	−0.238	0.110	−0.231	0.119	−0.092	0.349	−0.086	0.361
Risk(t − 3)	−0.029	0.797	−0.034	0.763
CAP	0.064*	0.065	0.063*	0.065	−0.012	0.821	−0.051	0.578
PROF	−1.125*	0.059	−1.104*	0.067	−0.755**	0.032	−0.784**	0.035
BMP	0.004	0.378	0.005	0.404	0.011	0.125	0.012	0.169
NLTD	−0.021	0.361	−0.023	0.330	0.003	0.749	0.001	0.869
GDP	−0.011	0.780	−0.016	0.718	−0.095	0.272	−0.102	0.312
DCPS	0.014	0.369	0.013	0.212	−0.033	0.335	−0.034	0.323
INF	0.049	0.182	0.051	0.195	0.048	0.473	0.055	0.451
MAN	0.181**	0.009	0.399	0.333
CAP*MAN			−1.416	0.578
FOR					−0.079*	0.083	−0.108	0.148
CAP*FOR							0.204	0.593
AR2, AR3, (prob)	0.416	0.807	0.394	0.853	0.140	0.449	0.121	0.511
Wald chi2 (Prob)	1926.94***	0.0000	2137.24***	0.0000	163.45***	0.0000	194.75***	0.0000
# of banks	81		81		110		110
# of Obs.	661		661		793		793

Table 6 gives the Risk model results by using Asian emerging markets sample. We use the Arellano and Bover (1995) panel dynamic two-step system GMM methodology and hetro robust errors are used to account the heterogeneity problem. The explained variable is bank risk (RISK) and is proxied by non-performing loan ratio (NPLR). Profitability (PROF) is measured by return on assets (ROA) and capital (CAP) represents the tier 1 capital ratio. Capital (CAP) is considered as endogenous regressor. We incorporate the second and third lag of explained variable in order to account autocorrelation problem. The Arellano and Bond autocorrelation method to check the presence of serial correlation. ***, **, * indicate the significance level at 1, 5 and 10 percentages respectively.

Source: The Authors.

Table 7. Ownership structure, regulatory capital and risk-taking.

RISK (NPLR)	Risk model		Risk model		Risk model		Risk model
	Coef.	Prob.	Coef.	Prob.	Coef.	Prob.	Coeff.	Prob.
Constant	0.088	0.104	0.085	0.134	0.098*	0.086	0.098*	0.079
RISK (t − 1)	0.667***	0.005	0.662***	0.004	0.649***	0.002	0.650***	0.002
Risk(t − 2)	−0.010	0.929	−0.010	0.932	0.001	0.992	−0.001	0.989
CAP	−0.005	0.170	−0.073	0.262	−0.006	0.168	−0.049	0.211
PROF	−0.577	0.104	−0.601*	0.097	−0.578	0.104	−0.601*	0.077
NIER	−0.002	0.269	−0.002	0.290	−0.001	0.346	−0.001	0.382
SIZE	−0.006	0.135	−0.005	0.172	−0.006	0.128	−0.006	0.143
BMP	0.013**	0.037	0.014*	0.066	0.013**	0.046	0.013*	0.051
GDP	−0.113*	0.064	−0.115	0.105	−0.130**	0.047	−0.132*	0.061
INF	0.128	0.167	0.138	0.140	0.114	0.207	0.127	0.175
IS	0.518**	0.026	0.578**	0.022	0.511**	0.025	0.520**	0.013
CON5	−0.022	0.123	−0.028	0.100
CAP*CON5			0.068	0.281
CON10					−0.021	0.137	−0.029	0.110
CAP*CON10							0.044	0.234
AR2, AR3, (prob)	0.100	0.423	0.100	0.432	0.072	0.359	0.075	0.392
Wald chi2 (Prob)	976.15***	0.0000	1155.65***	0.0000	987.27***	0.0000	1156.98***	0.0000
# of banks	103		103		101		101
# of Obs.	750		750		733		733

Table 7 gives the Risk model results by using Asian emerging markets sample. We use the Arellano and Bover (1995) panel dynamic system GMM methodology and hetro-robust errors are used to account the heterogeneity problem. The explained variable is bank risk (RISK) and is proxied by non-performing loan ratio (NPLR). Profitability (PROF) is measured by return on assets (ROA) and capital (CAP) represents the tier 1 capital ratio. Capital (CAP) is considered as endogenous regressor. We incorporate the second lag of dependent variable in order to account autocorrelation problem. The Arellano and Bond autocorrelation test is employed to check the presence of serial correlation. ***, **, * indicate significance level at 1, 5 and 10 percentages respectively.

Source: The Authors.

Table 8. Ownership structure, regulatory capital and profitability.

PROF (ROA)	Profitability model		Profitability model		Profitability model		Profitability model
	Coef.	Prob.	Coef.	Prob.	Coef.	Prob.	Coef.	Prob.
Constant	−0.001	0.892	−0.001	0.895	−0.002	0.764	−0.002	0.810
PROF (t − 1)	0.526***	0.000	0.534***	0.000	0.569***	0.000	0.572***	0.000
CAP	0.019	0.101	0.018	0.107	0.017	0.152	0.012	0.508
RISK	−0.024	0.269	−0.022	0.234	−0.028	0.227	−0.030	0.260
NLA	−0.004	0.754	−0.002	0.831	−0.003	0.719	−0.003	0.706
NIITR	−0.002***	0.000	−0.002***	0.000	−0.003***	0.000	−0.003***	0.000
NIER	0.001*	0.072	0.001*	0.083	0.002***	0.001	0.002***	0.000
GDP	0.002	0.863	0.003	0.831	0.030**	0.011	0.033**	0.020
MPI	0.029	0.326	0.027	0.371	0.063	0.214	0.068	0.247
DCPS	0.005	0.455	0.004	0.528	−0.000	0.942	−0.000	0.949
MAN	0.090	0.256	0.045	0.655
CAP*MAN			0.298	0.714
FOR					0.008	0.114	−0.001	0.971
CAP*FOR							0.056	0.790
AR1, AR2, (prob)	0.026	0.160	0.028	0.160	0.052	0.199	0.050	0.194
Wald chi2 (Prob)	541.43***	0.0000	531.28***	0.0000	369.12***	0.0000	403.04***	0.0000
# of banks	81		81		110		110
# of Obs.	673		673		797		797

Table 8 gives the Profitability model results by using Asian emerging markets sample. We use the Arellano and Bover (1995) panel dynamic two-step system GMM methodology and hetro robust errors are used to account the heterogeneity problem. The explained variable is bank profitability (PROF) and is proxied by return on assets. Risk (RISK) is measured by non-performing loan ratio (NPLR) and capital (CAP) represents the total capital ratio. Capital (CAP) is considered as endogenous regressor. The Arellano and Bond autocorrelation method is employed to check the presence of serial correlation. ***, **, * indicate significance level at 1, 5 and 10 percentages respectively.

Source: The Authors.

Table 9. Ownership structure, regulatory capital and profitability.

PROF (ROA)	Profitability model		Profitability model		Profitability model		Profitability model
	Coef.	Prob.	Coef.	Prob.	Coef.	Prob.	Coef.	Prob.
Constant	0.009	0.417	0.005	0.719	0.008	0.490	0.007	0.636
PROF (t − 1)	0.479***	0.000	0.485***	0.000	0.470***	0.000	0.474***	0.000
CAP	−0.001	0.532	0.013	0.496	−0.000	0.641	0..002	0.877
RISK	−0.039	0.132	−0.038	0.150	−0.036	0.185	−0.035	0.194
NLA	−0.012	0.233	−0.011	0.300	−0.012	0.245	−0.012	0.299
NIITR	−0.003***	0.001	−0.003***	0.001	−0.003***	0.001	−0.003***	0.001
NIER	0.002***	0.001	0.002***	0.001	0.002***	0.002	0.002***	0.002
GDP	0.025**	0.023	0.027**	0.011	0.027**	0.038	0.028**	0.030
LI	0.036	0.288	0.043	0.218	0.040	0.267	0.044	0.226
DCPS	0.001	0.774	0.002	0.605	0.002	0.714	0.002	0.637
CON5	−0.000	0.975	0.003	0.725
CAP*CON5			−0.014	0.468
CON10					−0.000	0.880	0.000	0.971
CAP*CON10							−0.003	0.843
AR1, AR2, (prob)	0.041	0.137	0.044	0.135	0.043	0.146	0.048	0.146
Wald chi2 (Prob)	302.20***	0.0000	289.46***	0.0000	254.16***	0.0000	224.69***	0.0000
# of banks	128		128		126		126
# of Obs.	970		970		953		953

Table 9 gives the Profitability model results by using Asian emerging markets sample. We use the Arellano and Bover (1995) panel dynamic two-step system GMM methodology and hetro robust errors are used to account the heterogeneity problem. The explained variable is bank profitability (PROF) and is proxied by return on assets. Risk (RISK) is measured by non-performing loan ratio (NPLR) and capital (CAP) represents the total capital ratio. Capital (CAP) is considered as endogenous regressor. The Arellano and Bond autocorrelation method is employed to check the presence of serial correlation. ***, **, * indicate significance level at 1, 5 and 10 percentages respectively.

Source: The Authors.

Table 10. Ownership structure and regulatory capital.

CAP	Capital model (T1R)		Capital model (T1R)		Capital model (T1R)
	Coef.	Prob.	Coef.	Prob.	Coef.	Prob.
Constant	0.049**	0.039	0.045*	0.072	0.036	0.103
CAP(t − 1)	0.781***	0.000	0.700***	0.000	0.698***	0.000
RISK	0.051	0.277	0.176	0.250	0.163	0.248
PROF	0.623***	0.004	0.823**	0.027	0.794**	0.037
NLA	−0.019	0.499	−0.013	0.676	−0.012	0.682
NIER	−0.002	0.425	−0.001	0.651	−0.001	0.591
GDP	0.017	0.776	−0.028	0.551	−0.058	0.165
MPI	−0.160	0.156	−0.426	0.130	−0.424	0.118
FOR	−0.032	0.174
CON5			0.029	0.428
CON10					0.053**	0.052
AR1, AR2, (prob)	0.000	0.682	0.375	0.355	0.367	0.342
Wald chi2 (Prob)	181.23***	0.0000	207,339***	0.0000	210,340***	0.0000
# of banks	110		128		126
# of Obs.	797		970		953

Table 10 gives the Profitability model results by using Asian emerging markets sample. We use the Arellano and Bover (1995) Two-step dynamic panel system GMM methodology to estimate the model and hetro robust errors are used to account for the heterogeneity problem. The explained variable is bank capital (CAP) and is proxied by tier 1 capital ratio. Risk (RISK) is measured by non-performing loan ratio (NPLR) and profitability (PROF) represents the return on assets. PROF and RISK are considered as endogenous regressor. The Arellano and Bond autocorrelation method is employed to test the presence of serial correlation in error-terms. ***, **, * indicate the significance level at 1, 5 and 10 percentages respectively.

Source: The Authors.

Table 11. Risk model (robustness tests).

RISK (NPLR)	Risk model		Risk model		Risk model
	Coef.	Prob.	Coef.	Prob.	Coef.	Prob.
Constant	0.077	0.102	0.071	0.126	0.084*	0.077
RISK (t − 1)	0.666***	0.000	0.663***	0.000	0.675***	0.000
CAP	−0.010	0.123	−0.009	0.129	−0.011	0.133
PROF	−1.000***	0.000	−0.997***	0.000	−1.000***	0.000
NLA	−0.050***	0.001	−0.049***	0.002	−0.049**	0.005
SIZE	−0.224	0.495	−0.170	0.589	−0.301	0.375
BMP
GDP	−0.071**	0.010	−0.078***	0.001	−0.072**	0.008
INF	0.024	0.542	0.021	0.596	0.011	0.768
MPI	0.088	0.347	0.076	0.430	0.091	0.309
CD	−0.003	0.199	−0.002	0.266
CAP*CD			−0.005	0.620
BTD					0.004	0.113
AR1, AR2, (prob.)	0.016	0.354	0.016	0.360	0.017	0.375
Wald chi2 (Prob)	405.43	0.0000***	546.16	0.0000***	443.73	0.0000***
# of banks	162		162		162
# of Obs.	2001		2001		2001

Table AI4 gives the Profitability model results by using Asian emerging markets sample. We use the Arellano and Bover (1995) panel dynamic two-step system GMM methodology and hetro robust errors are used to account the heterogeneity problem. The explained variable is bank Risk (RISK) and is proxied by non-performing loan ratio (NPLR). Profitability (PROF) is measured by return on assets and CAP represents the tier 1 capital ratio. CD represents the crisis dummy that takes the value of 1 from period 2008–10 and 0 else. BTD represents the Basel III dummy, which takes the value 1 from 2010 onwards and zero else. The Arellano and Bond autocorrelation method is employed to check the presence of serial correlation. ***, **, * indicate significance level at 1, 5 and 10 percentages respectively.

Source: The Authors.

Table 12. Profitability model (robustness tests).

PROF (ROA)	Profitability model		Profitability model		Profitability model
	Coef.	Prob.	Coef.	Prob.	Coef.	Prob.
Constant	0.024*	0.077	0.025*	0.069	0.025	0.116
PROF (t − 1)	0.518***	0.000	0.522***	0.000	0.531***	0.000
CAP	−0.001	0.114	−0.002	0.154	−0.001	0.107
RISK	−0.032*	0.087	−0.033*	0.066	−0.030*	0.097
NLA	−0.005	0.455	−0.006	0.368	−0.004	0.574
SIZE	−0.120	0.227	−0.134	0.125	−0.169	0.147
BMP	−0.003	0.107
GDP	0.009	0.334	0.009	0.364	0.009	0.466
INF	0.008	0.539	0.011	0.451	0.005	0.804
MPI	0.014	0.586	0.023	0.422	0.037	0.100
CD	−0.001**	0.039	−0.001**	0.033
CAP*CD			0.070	0.609
BTD					0.002**	0.028
AR(1), AR(2), (p-value)	0.001	0355	0.001	0.355	0.001	0.369
Wald chi2 (Prob)	174.01	0.0000***	149.78	0.0000***	165.80	0.0000***
# of banks	162		162		162
# of Obs.	2001		2001		2001

Table AI3 gives the Profitability model results by using Asian emerging markets sample. We use the Arellano and Bover (1995) panel dynamic two-step system GMM methodology and hetro robust errors are used to account the heterogeneity problem. The explained variable is bank profitability (PROF) and is proxied by return on assets. Risk (RISK) is measured by non-performing loan ratio (NPLR) and capital (CAP) represents the tier 1 capital ratio. Capital (CAP) is considered as endogenous regressor. CD represents the crisis dummy that takes the value of 1 from period 2008–10 and 0 else. BTD represents the Basel III dummy, which takes the value 1 from 2010 onwards and zero else. The Arellano and Bond autocorrelation method is employed to check the presence of serial correlation. ***, **, * indicate significance level at 1, 5 and 10 percentages respectively.

Source: The Authors.

Table 13. Correlation matrix.

	NPLR	LLRR	T1R	T2R	TCR	ROA	NLA	NIER	NIITR	SIZE	BMP	GDP	IS	LI	DCPS	INF	MAN	FOR	CON5	CON10
NPLR	1
LLRR	0.225*	1
T1r	0.047*	0.095*	1
T2R	−0.286*	0.024	−0.205*	1
TCR	0.045*	0.095*	0.991*	−0.176*	1
ROA	−0.318*	−0.442*	0.077*	0.008	0.086*	1
NLA	−0.052	−0.124*	−0.221*	0.094*	−0.229*	−0.137*	1
NIER	−0.136	−0.044*	−0.056*	0.162*	−0.053*	0.082*	0.028	1
NIITR	−0.069	0.211*	−0.099*	0.194*	−0.099*	−0.217*	0.050*	0.557*	1
SIZE	−0.065	0.022	−0.142*	0.039	−0.141*	0.017	0.375*	0.003	0.029	1			*
BMP	0.010	−.061*	−0.044*	0.073*	−0.038	0.145*	0.155*	−0.010	−0.037	0.403*	1
GDP	−0.214*	−0.067*	−0.057*	−0.001	−0.056*	0.050*	−0.097*	−0.005	0.022	−0.083*	−0.212*	1
IS	0.224*	0.117*	0.102*	−0.050	0.099*	−0.044	−0.071*	0.018	0.031	0.208*	−0.173*	−0.281*	1
LI	0.143*	0.125*	0.098*	−0.043	0.105*	−0.060*	−0.060*	0.020	0.025	0.226*	−0.203*	−0.199*	0.830*	1
DCPS	−0.244	−0.079*	−0.100*	0.051*	−0.104*	0.019	0.086*	−0.033	−0.000	−0.071*	0.105*	0.217*	−0.787*	−0.811*	1
INF	0.127*	0.060*	0.032	−0.020	0.036	−0.046*	−0.038	0.033	0.009	−0.009	−0.183*	−0.156*	0.629*	0.678*	−0.571*	1
MAN	0.016	−0.126*	0.013	0.038	0.009	−0.001	−0.039	0.038	−0.022	−0.169*	−0.061	−0.254*	0.173*	0.116*	−0.166*	0.095*	1
FOR	−0.279	−0.052	−0.067	0.125*	−0.032	0.262*	0.174*	−0.079*	−0.111*	0.260*	0.242*	0.074*	−0.280*	−0.172*	0.120*	−0.158*	0.048	1
CON5	0.137*	0.031	0.132*	−0.126*	0.122*	−0.120*	−0.019	0.079*	0.042	0.099*	0.062	0.023	0.192*	0.183*	−0.251*	0.083*	−0.178*	−0.372*	1
CON10	0.159*	0.065*	0.130*	−0.126*	0.120*	−0.113*	0.030	0.089*	0.051	0.125*	0.095*	−0.000	0.193*	0.186*	−0.251*	0.092*	−0.269*	−0.353*	0.945*	1

Represents the level of significance at 5%.

Source: The Authors.

Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51. https://doi.org/10.1016/0304-4076(94)01642-D

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