Information sharing in bank diversification channels of bank stability in Ghana

Figures & data

Table 1. Review of recent empirical literature.

Authors	Purpose	Methods	Results	Gaps
(Wu, et al., 2023)	Provided a theory and empirical examination on how climate change affects the systemic risk of sixteen listed banks in China.	Mediation effect regression model using five empirical models.	Climate change increases the systemic risk of banks, especially privately owned banks. They also found that volatility of banks’ assets can mitigate the climate change-systemic risk nexus. Finally, the chain of relationship is that climate change leads to assets volatility which leads to credit risks which in turns lead to systemic risk.	The study is limited to the Chinese banking system. Also, the authors failed to account for reverse causality. Finally, the role of information system in the banks’ diversification and systemic risk nexus was not investigated.
(Abbas & Ali, 2022)	Examined the role of capital in the relationship between loan growth and credit risk for Islamic banks after the financial crisis.	Annual data was mobilized from 217 Islamic banks from thirty-eight countries between the period 2010–2019. The study used the two-step system GMM for the empirical estimation.	The rise in loan growth increases loan loss provision, loan loss reserves and non-performing which indicates that loan growth increases systemic risks. However, the authors found that bank capital can attenuate the harmful effect of loan growth on systemic risks since it can boost the liquidity position of the banks. The impact of capital was more profound for larger Islamic banks.	The study concentrated on only Islamic banks. The authors investigated how diversification of the loan portfolio can affect systemic risk of the banks. The role of information sharing system was not investigated.
(Yan, Jeon, & Wu, 2023)	The study analysed the contribution of banks across income groups to systemic risk during the COVID-19 pandemic period.	The authors estimated the conditional value at risk, marginal expected shortfall. Thereafter, the used the difference-in-differences method to examine the impact of the COVID-19 disease on systemic risk.	They found that the COVID-19 had a strong positive effect on systemic risk globally. However, the impact of the pandemic was much more severe in advanced economies than developing countries. The also found than large banks were affected by the pandemic notwithstanding their larger capital base.	The authors investigated how diversification of the loan. portfolio can affect systemic risk of the banks. The role of information sharing system was not investigated.
(Nguyen, Le, & Tran, 2023)	The paper analysed the role of income diversification in the relationship between Intellectual Capital and bank performance in Vietnamese commercial banks period 2007–2020.	The authors used the system generalised method of moments (GMM).	The study found that income diversification can dampen the strong positive effect of intellectual capital on performance of banks.	The study did not consider the systemic risk of banks. The study did not consider the contingent role of credit information sharing in the bank diversification and systemic risk nexus.
(Abbas et al., 2021)	Examined the effects of bank capitalization on the risk-taking behaviour of banks over the period from 2002 to 2019.	Two-step GMM panel methods.	Capitalisation of banks boost their risk-taking behaviour except for well-capitalised and low liquidity banks.	The study did not consider the systemic risk of banks. The study did not examine the contingent role of credit information sharing in the bank diversification and systemic risk nexus.

Figure 1. The distribution of the variables from 2010 to 2019. Note: inzscore is log of zscore; income is income diversification; asset is asset diversification; info 1 (information sharing) measured as natural log of Credit Reference Bureau enquiries made by banks; info 2 (information sharing) measured as cost of enquiries divided by total loans offered.

Table 2. Cross-sectional averaged distribution of the main variables.

Banks	Income	Asset	Info one	Info two	Z-score	Ranking Z-score
ABSA	0.6602	0.4932	9.6068	0.0001	22.6971	3rd
ACESS	0.7392	0.4229	7.4520	0.0004	18.3803	5th
ADB	0.6703	0.4578	8.5863	0.0001	6.5562	13th
BOA	0.6635	0.4674	8.1607	0.0002	5.7195	14th
CAL	0.5868	0.4143	7.7950	0.0001	11.1096	10th
ECO	0.7439	0.4422	7.5047	0.0000	21.9074	4th
FDL	0.6725	0.4571	8.1046	0.0001	11.8293	9th
GCB	0.4073	0.4449	7.6593	0.0001	9.3189	11th
GTB	0.8427	0.4464	6.6152	0.0001	13.4240	8th
RB HFC	0.5454	0.4042	8.7136	0.0001	8.3441	12th
PBL	0.6900	0.3361	6.9774	0.0001	15.2540	6th
SG	0.6907	0.3507	9.6248	0.0001	48.3590	1st
SCB	0.6349	0.4923	8.3614	0.0052	14.2535	7th
ZBL	0.7177	0.4210	6.4527	0.0083	25.9243	2nd

Note: Z-score is a measure of bank stability (higher values are preferable); income is income diversification; asset is asset diversification; info 1 (information sharing) measured as natural log of Credit Reference Bureau enquiries made by banks; info 2 (information sharing) measured as cost of enquiries divided by total loans offered.

Table 3. Definition of the variables.

Variable	Definition	A priori sign
SRisk	Measures systemic risk
${\mathit{\text{SRisk}}}_{-1}$	Measures the lag of systemic risk	+
ID	Measures income diversification	-+
AD	Measures asset diversification	-+
FCRB	Measures the number of times banks patronize the services of CRB	–
WCRB	Measures the willingness of banks to use the services of CRBs	–
ID*FCRB	Measures the interaction of ID and FCRB	–
ID*WCRB	Measures the interaction of ID and WCRB	–
AD*FCRB	Measures the interaction of AS and FCRB	–
AD*WCRB	Measures the interaction of AD and WCRB	–
LAR	Measures loan to asset ratio	+
BS	Measures bank size	-+
EAR	Measures equity to asset ratio	-+
CIR	Measures cost to income ratio	+

Table 4. Descriptive statistics.

Variable	Mean	Std. Dev	Min	Max
Z-SCORE	16.648	11.127	0.642	58.692
ID	0.662	0.176	0	1.421
AD	0.432	0.077	0.137	0.5
FCRB	7.972	1.584	4.143	11.622
WCRB	0.001	0.008	0	0.082
LAR	0.397	0.125	0.036	0.682
CIR	0.573	0.159	0.308	1.005
EAR	0.15	0.051	0.009	0.44
BS	21.492	0.891	19.098	23.303
Observation	140

Table 5. The correlation matrix of the variables.

Variables	SRisk	ID	AD	FCRB	WCRB	LAR	CIR	EAR	BS
SRisk	1
ID	0.152	1
AD	−0.205	−0.041	1
FCRB	0.135	−0.092	0.034	1
WCRB	0.093	−0.072	−0.123	−0.151	1
LAR	−0.052	0.101	−0.312	0.171	−0.198	1
CIR	−0.433	−0.122	−0.099	−0.035	−0.136	0.291	1
EAR	0.42	0.069	−0.072	0.065	0.061	−0.169	−0.269	1
BS	0.148	−0.286	0.169	0.349	0.106	−0.354	−0.284	−0.207	1

Table 6. The effect of income diversification, asset diversification and information sharing on systemic risk.

Variables	ID and SRisk	AD and SRisk	FCRB and SRisk	WCRB and SRisk
First Lag	0.200**	0.276**	0.272***	0.221**
	(0.089)	(0.115)	(0.099)	(0.097)
LAR	0.512***	0.536***	0.360***	0.244
	(0.170)	(0.160)	(0.107)	(0.167)
CIR	−0.053	−0.108	−0.147***	−0.118
	(0.117)	(0.109)	(0.043)	(0.098)
EAR	5.486***	5.327***	5.373***	4.900***
	(0.780)	(0.250)	(0.622)	(0.713)
BS	0.026	0.061***	0.052**	0.028
	(0.024)	(0.023)	(0.023)	(0.018)
ID	−0.239**
	(0.122)
AD		0.619
		(0.388)
FCRB			−0.023***
			(0.006)
WCRB				−0.009
				(0.008)
Constant	0.792**	−0.664	0.125	0.714
	(0.389)	(0.553)	(0.401)	(0.511)
Observations	126	126	126	126
Number of banks	14	14	14	14

Note.

*** means significance at 1% level.

** means significance at 5% level; *means significant at 10% level.

Values in parentheses are the standard errors.

Table 7. Interaction effect of bank diversification and information sharing on systemic risk.

Variables	Interaction of ID and FCRB	Interaction of ID and WCRB	Interaction of AD and FCRB	Interaction of AD and WCRB
FIRST LAG	0.217**	0.263	0.214	0.297**
	(0.097)	(0.212)	(0.133)	(0.133)
LAR	0.471***	0.114	0.593***	0.403
	(0.154)	(0.388)	(0.115)	(0.272)
CIR	−0.084	−0.031	−0.079	−0.051
	(0.064)	(0.203)	(0.094)	(0.134)
EAR	5.769***	5.051***	5.755***	5.726****
	(0.624)	(0.895)	(0.402)	(0.412)
BS	0.042*	−0.039	0.047	−0.020
	(0.023)	(0.039)	(0.035)	(0.051)
ID	0.689	−0.093
	(0.731)	(0.374)
AD			−1.463**	0.412
			(0.656)	(0.509)
FCRB	0.044		−0.023
	(0.058)		(0.027)
WCRB		−0.013		−0.004
		(0.084)		(0.071)
ID*FCRB	−0.098
	(0.085)
ID*WCRB		−0.014
		(0.152)
AD*FCRB			−0.309*
			(0.181)
AD*WCRB				−0.070
				(0.073)
Constant	−0.138	1.215**	−0.304	0.414
	(0.554)	(0.485)	(0.752)	(0.656)
Observations	126	126	126	126
Number of banks	14	14	14	14

Note.

*** means significance at 1% level.

** means significance at 5% level.

* means significant at 10% level.

Values in parentheses are the standard errors.

Table 8. The diagnostic tests.

Model	Objective	Test	Chi-Square	P-Value
ID	One	Sargan	8.408	0.423
		AR (1)	−0.97	0.332
		AR (2)	0.259	0.796
AD	Two	Sargan	11.119	0.492
		AR (1)	−1.384	0.167
		AR (2)	1.166	0.244
FCRB	Three	Sargan	10.902	0.459
		AR (1)	−1.228	0.22
		AR (2)	0.526	0.599
WCRB	Three	Sargan	8.398	0.401
		AR (1)	−0.929	0.353
		AR (2)	0.479	0.632
Interaction of ID and FCRB	Four	Sargan	6.193	0.612
		AR (1)	−1.077	0.282
		AR (2)	0.574	0.566
Interaction of ID and WCRB	Four	Sargan	8.114	0.624
		AR (1)	−1.118	0.264
		AR (2)	0.789	0.43
Interaction of AD and FCRB	Four	Sargan	7.412	0.681
		AR (1)	−1.305	0.192
		AR (2)	0.107	0.915
Interaction of AD and WCRB	Four	Sargan	8.628	0.692
		AR (1)	−1.342	0.18
		AR (2)	1.181	0.238

Wu, X., Bai, X., Qi, H., Lu, L., Yang, M., & Taghizadeh-Hesary, F. (2023). The impact of climate change on banking systemic risk. Economic Analysis and Policy, 78, 419–437. https://doi.org/10.1016/jeap.2023.03.012

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