Competition and distress in banks in India: An application of panel data

Figures & data

Figure 1. Rising NPA in India.

Source: Date is retrieved from the World Bank’s website “https://data.worldbank.org/indicator/FB.AST.NPER.ZS?end = 2019&locations = IN&start = 2008&view = chart”

Table 1. List of variables

SN	Variable	Code	Definition	Citations
1	ZS (Original)	ZS_O	It is Altman’s Z-score originally developed by Altman (1968). This indicates the position of a firm’s financial distress.	Altman (1968), Pradhan (2014)
2	ZS (without sales)	ZS_WS	It is an extension of original Z-score for the firms dealing in service as its product.	Altman (2013), Pradhan (2014)
3	ZS (for emerging economies)	ZS_E	It is another variant of Altman Z-score especially used in measuring the financial distress of the firms operating in emerging economies.	Max and Altman (2011)Pradhan (2014)
4	Learner’ Index	LI	Lerner’s index is the measure of market power or competition.	Agaroki et al.(2011), Lerner(1934)
5	Capital adequacy ratio	CAR	It is the bank’s capital to risk weighted assets ratio. It represents the bank regulation.	Moyer (1990), Bateni et al. (2014)
6	Asset	ASS	It is the value of bank’s total assets representing bank size.	Nikolova et al. (2016), Narula and Singla (2014)
7	Net Interest margin	NIM	It is the representative of a bank’s profitability. It is the bank’s profit out of its total revenue.	Nguyen (2012), Hamadi and Awdeh (2012)
8	Market Capitalization	MC	It represents a bank’s value, and measured by multiplying the number of a bank’s shares by current market price of the share.	Kumar and Shah (2009) Narayan et al. (2011)

Note: ZS_O, ZS_WS, and ZS_E are three variants of Altman’s Z-score used as dependent variables.LI is measure of competition used as explanatory variable. CAR, ASS, NIM, and MC are used as control variables.

Sources: Authors’ own compilation.

Table 2. Descriptive statistics and correlation matrix

Correlation Matrix						Descriptive
	LI	CAR	LASS	NIM	MC	Mean	SD
ZS_O						−0. 505	0.509
ZS_WS						−0. 922	1.397
ZS_E						−4. 172	1.397
LI	1					0.247	4.54
CAR	−0. 086	1				13.218	2.531
LASS	0.238	0.018	1			361759	547082.5
NIM	−0. 036	0.495*	−0. 119	1		2.611	0.719
LMC	0.173	0.338*	0.738*	0.285*	1	139338.3	218302.7

Note: Values in correlation matrix are coefficients. * shows significance at 0.05. Mean values of LASS and LMC (log values of asset and market cap) are not taken as log values.

Table 3. Static panel data model (fixed effect)

Model 1: Dependent Variable: ZS_O (Z-Score Original)
	Co-efficient	Robust Standard Error	t-value	p-value
constant	−7.4436	8.3375	−0.89	.3780
LI	.1237*	.0068	18.11	.0000
CAR	.0714	.0734	.9700	.3370
L_ASS	.3264	.7254	.45	.6560
NIM	−.9775*	.3693	−2.65	.0120
L_MC	.08992	.1134	.7900	.4330
F-test (Model)	7.36* (.0000)
R-Square (Adjusted)	0.2758
F-test Fixed Effect	3.78 (.0000)
Breush-Pagan Test	18.04* (.0000)
Hausman Test	22.43* (.0002)
σvit	.8575
No of observations	136
Degree of freedom	97
Wooldridge Autocorrelation Test	26.794* (.0000)
Wald test for Heteroscedasticity	1.10 × 10^6 *(.0000)

Note: Authors’ own analysis. L_ASS is natural log of the assets. L_MC is natural log of market capitalization. LI is learners’ Index. Capital Adequacy Ratio. NIM is net interest margin. Wooldridge test of autocorrelation in panel has the null of no autocorrelation (with 1 lag). Wald test of heteroscedasticity has the null of no heteroscedasticity. σ_vit is the standard error of the remainder error term. (value) presents p-value. * significant at 5% level of significance.

Source: Authors’ own analysis

Table 4. Robustness of the results

Model 2: Dependent Variable: ZS (Z-Score without Sales)
	Co-efficient	Robust SE		t-value	p-value
constant	−2.4242	2.7720		−.87	.388
LI	.0221*	.0011		19.40	.0000
CAR	.01469	.017878		.82	.417
L_ASS	.1283	.2401		.53	.5960
NIM	−.2863*	.1112		−2.57	.015
L_MC	.0902	.03936		2.29	.0280
F-test (Model)	118.89* (.0000)
R-Square (Adjusted)	.2540
σvit	.2402
Model 3: Dependent Variable: ZS_E (Z-Score without Sales for emerging economies)
	Co-efficient		Robust SE	t-value	p-value
Constant	−4.1936		8.3375	−.50	.618
LI	.1237*		.0068	18.11	.0000
CAR	.07149		.0734	.9700	.337
L_ASS	.3264		.7254	.45	.656
NIM	−.97754*		.36929	−2.65	.012
L_MC	.089925		.1133	.79	.433
F-test	101.18* (.0000)
R-Square (Adjusted)	.2758
σvit	.8859

Note: L_ASS is natural log of the assets. L_MC is natural log of market capitalization. LI is learners’ Index. Capital Adequacy Ratio. NIM is net interest margin. Wooldridge test of autocorrelation in panel has the null of no autocorrelation (with 1 lag). Wald test of heteroscedasticity has the null of no heteroscedasticity. σ_vit is the standard error of the remainder error term. No of observations and degree of freedom are same as of the main model (136 and 97) respectively. In the robustness test, we have also estimated fixed effect and robust estimate of standard error for the consistency. Due to the similar reasons, autocorrelation and heteroscedasticity tests are not repeated. (value) presents p-value. * significant at 5% level of significance.

Source: Authors’ own analysis

Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.

 Web of Science ®Google Scholar

Pradhan, R. (2014). Z score estimation for Indian banking sector. International Journal of Trade, Economics and Finance, 5(6), 516. https://doi.org/10.7763/IJTEF.2014.V5.425

 Google Scholar

Altman, E.I. (2013). Predicting financial distress of companies: revisiting the Z-score and ZETA® models. In Handbook of research methods and applications in empirical finance. Edward Elgar Publishing. available at https://www.elgaronline.com/view/edcoll/9780857936080/97 https://www.elgaronline.com/view/edcoll/9780857936080/9780857936080.00027.xml accessed during July 2021

 Google Scholar

Max, L. H., & Altman. (2011). PREDICTING FINANCIAL DISTRESS OF COMPANIES: REVISITING THE Z-SCORE AND ZETA® MODELS. New York University.

 Google Scholar

Agoraki, M. E. K., Delis, M. D., & Pasiouras, F. (2011). Regulations, competition and bank risk-taking in transition countries. Journal of Financial Stability, 7(1), 38–48. https://doi.org/10.1016/j.jfs.2009.08.002

 Web of Science ®Google Scholar

Lerner, A. P. (1934). The concept of monopoly and the measurement of monopoly power. The Review of Economic Studies, 1(3), 157–175. https://doi.org/10.2307/2967480

 Google Scholar

Moyer, S. E. (1990). Capital adequacy ratio regulations and accounting choices in commercial banks. Journal of Accounting and Economics, 13(2), 123–154.

 Web of Science ®Google Scholar

Bateni, L., Vakilifard, H., & Asghari, F. (2014). The influential factors on capital adequacy ratio in Iranian banks. International Journal of Economics and Finance, 6(11), 108–116. https://doi.org/10.5539/ijef.v6n11p108

 Google Scholar

Nikolova, L. V., Rodionov, D. G., & Mottaeva, A. B. (2016). Securitization of bank assets as a source of financing the innovation activity. International Journal of Economics and Financial Issues, 6(S3), 1–7 https://dergipark.org.tr/en/pub/ijefi/issue/31980/352636.

 Google Scholar

Narula, S., & Singla, M. (2014). Empirical study on non-performing assets of bank. International Journal of Advance Research in Computer Science and Management Studies, 2(1), 194–199 https://d1wqtxts1xzle7.cloudfront.net/35504237/v2i1-0032-with-cover-page-v2.pdf?Expires=1662644463&Signature=eOSHpt~VkkeKdFU-33IfgQR-uDdaGT56~2zibz4db~IhjEWvO~MH5GRRIsdzEohPAOggj2xiza1cdWIF8CRuofud52qoviki6yZhwilbW3aVQY-Xu-1UiMGyhxNuca8yHfuLZxvmXPNA1W12DkpPJYMGTF81wzpTDa~N9itgLcXaoLUH~TuxkWKgkeRcF5qwNtYDpAgy~NHozLjCZmpzMWU1gx8VC~5apl0joRnns9TV610UUhmH-cfDX11~QjDwSynjzKsdW61SNJBHFazF2NODVkKmwQfXQOSFUXVEIfAT9Sd-bi-FuBtbN6dVxCTBfoFJ6-6D92yZY6qh9dBB0Q__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA.

 Google Scholar

Nguyen, J. (2012). The relationship between net interest margin and noninterest income using a system estimation approach. Journal of Banking & Finance, 36(9), 2429–2437. https://doi.org/10.1016/j.jbankfin.2012.04.017

 Web of Science ®Google Scholar

Hamadi, H., & Awdeh, A. (2012). The determinants of bank net interest margin: Evidence from the Lebanese banking sector. Journal of Money, Investment and Banking, 23(3), 85–98 https://www.researchgate.net/profile/Ali-Awdeh/publication/325997470_The_Determinants_of_Bank_Net_Interest_Margin_Evidence_from_the_Lebanese_Banking_Sector/links/5b326c580f7e9b0df5ccac1b/The-Determinants-of-Bank-Net-Interest-Margin-Evidence-from-the-Lebanese-Banking-Sector.pdf.

 Google Scholar

Kumar, V., & Shah, D. (2009). Expanding the role of marketing: From customer equity to market capitalization. Journal of Marketing, 73(6), 119–136.

 Web of Science ®Google Scholar

Narayan, P. K., Mishra, S., & Narayan, S. (2011). Do market capitalization and stocks traded converge? New global evidence. Journal of Banking & Finance, 35(10), 2771–2781. https://doi.org/10.1016/j.jbankfin.2011.03.010

 Web of Science ®Google Scholar