Corporate Debt Vulnerability and Non-performing Assets in India

Abstract

This article examines the increasing corporate debt vulnerability and its impact on the asset quality of the Indian public sector banks (PSBs) in the post-global financial crisis (post-GFC) of 2008. The study shows that the stress in both corporate and bank balance sheets has increased in the post-GFC. As a result, there has been a steep increase the proportion of firms with negative profitability. The article finds that the declining profitability has severely affected the debt serviceability of the firms. Consequently, the debt at risk has risen significantly, which in turn has contributed to increase in non-performing assets (NPAs) of the banking sector, particularly, the PSBs. Using the panel regression technique, the study finds that the corporate debt vulnerability is an important determinant of the growth of NPAs along with other factors such as debt concentration, corporate sales growth, lending to sensitive sectors, bank profitability, bank size and the efficiency of banks.

Keywords

Corporate debt vulnerability non-performing assets (NPAs)public sector banks (PSBs)

Introduction

Excess corporate debt and debt vulnerability pose serious risks to the banking sector, leading to the twin balance sheet problem of corporate fragility and rising NPAs in the banking sector. Twin balance sheet problem is considered to be disturbing for economic growth as it creates a vicious cycle where weak corporate balance sheets and stressed banking sector feed off each other, resulting in a severe slump in economic growth (IMF, 2016a). In India, the scenario of twin balance sheet crisis has been building up in the post-global financial crisis (GFC). The GDP growth, after registering a notable growth rate of 9.3 per cent in 2010–2011, has witnessed a significant decline subsequently. This is accompanied by an increase in corporate distress and a decline in investment. Consequently, the proportion of corporate debt and the debt at risk has increased substantially in the post-GFC. In a bank-dominated financial system, corporate distress will have a direct impact on the asset quality of the banking sector. This is evident from the fact that the Indian public sector banks (PSBs) have accumulated unprecedented amount of bad loans in recent years. The accumulation of NPAs is severely affecting the financial health of PSBs as the return on assets (ROA) dipped to an alarming −1.87 per cent in 2018–2019. Rising bad loans also seriously impair the ability of the banking sector to lend to even healthy borrowers, which is evident from the fact that the credit growth of the PSBs has witnessed a severe downfall. Declining credit adversely affects both demand and supply sides of the economy. Hence, corporate distress and the debt vulnerability, the accumulation of NPAs and the resulting slowdown in credit flow are considered to be the important factors underlying the recent slowdown in the Indian economy. It is with this backdrop the current study attempts to understand the corporate indebtedness and its impact on the growth of NPAs in the Indian PSBs.

The following is the layout of the article. The second section includes a review of literature. The third section provides the rationale and objectives of the study. The fourth section describes the methodology. The fifth section deals with the analysis and discussion and the sixth section contains the conclusion and policy implications.

Related Literature

Rising corporate fragility and the consequent debt vulnerability are considered to be one of the important causes for the accumulation of NPAs (IMF, 2016a). Several other key reports also underline the increasing twin balance sheet problem in emerging markets in the post-GFC. For instance, the International Monetary Fund (IMF) (2014) highlights the deterioration in the financial performance of the Indian corporate sector in the post-recession period. It notes that the corporate sector’s vulnerability to severe systematic shocks has increased to an unprecedented level. RBI (2014a) also stresses the problem of increasing leverage and declining profitability in the corporate sector and the contraction in investment growth. Most recently, IMF (2017a) observed that the corporate fragility and banking system weaknesses are the key challenges faced by the emerging markets. Particularly, it warns that India, China and South Africa would experience the greatest deterioration in corporate balance sheets. It further points out that external threats emanating from tighter global financial conditions and the increased trade protectionism could further affect corporate distress in some emerging economies, resulting in increased stress in the banking sector. IMF (2017b) also cautions that unhedged foreign currency borrowing of the Indian corporate sector would put further pressure on the balance sheet, which would amplify the amount of bad loans in the banking sector. Consistent with the observations of these reports, the Indian PSBs have recorded an unprecedent rise in NPAs in recent years.

Rising NPAs are attributed to the set of macroeconomic and bank-specific factors (Ahmed, 2009; Berger & DeYoung, 1997; Corsetti et al., 1999; Dhananjaya, 2019; Kadanda & Raj, 2018; Krugman, 1998; Patel, 2017; Rajan, 2018; Ramu, 2009; RBI, 2012, 2015; Sahoo, 2015). Berger and DeYoung (1997) explain these factors using four hypotheses; Bad luck Hypothesis, Bad Management Hypothesis, Skimping hypothesis and Moral Hazard Hypothesis. Bad luck hypothesis attributes external causes such as slower economic growth and the consequent decline in corporate profit and business failure for the growth of NPAs. Rajan (2018) argues that the overall economic slowdown is one of the important reasons for the accumulation of NPAs. He maintains that over-optimism during strong economic growth leads to aggressive lending by the banks, resulting in the banks taking excessive leverage in big projects. However, as growth slows down in the subsequent years, the lending becomes NPAs. Sahoo (2015) also argues that the factors like recession and lack of a legal system for recoveries are some of the elements in business environment that would increase NPAs. Empirically, Dhananjaya (2019) finds a negative relationship between corporate profitability and growth of NPA in India, which indicates that stress in a corporate sector results in higher bad loans in the banking sector. Bad Management Hypothesis postulates that growth of NPAs is largely due to banks’ internal factors such as poor credit appraisal skills, lending to projects with low or negative net present value, lack of expertise to appraise the value of collateral pledged against the loans, poor monitoring ability after the loans are issued, lack of effective NPA management and corruption (Rajan, 2018; Sahoo, 2015). Particularly, in the case of the Indian PSBs, bad management is considered to be one of the important causes for the accumulation of bad loans (RBI, 2014).

The Skimping Hypothesis argues that in the pursuit of long-run profit, banks may deliberately choose to incur lower costs in the short run by saving on spending devoted to screening loan customers, appraising collateral and monitoring and controlling borrowers after loans are issued. This makes the banks look to be cost-efficient in the short run. However, due to poor due diligence in lending, NPAs may eventually increase (Berger & DeYoung, 1997).

Lastly, the Moral Hazard hypothesis suggests that banks with relatively lower capital have a moral hazard incentive by taking excessive risk in their loan portfolio. Krugman (1998) observes that the institutions whose liabilities are perceived as having implicit government guarantees are subject to severe moral hazard problems. Patel (2017) also argues that regulatory labelling of systematically important financial institutions/banks may convey the implicit government guarantees in the event of insolvency, resulting in these institutions taking additional risks to earn additional return on capital leading to laxity in three important areas of lending: credit appraisal, customer relationship management and monitoring mechanism. Since the PSBs in India enjoy the implicit (rather explicit) state guarantee, the growth of NPAs may partly be ascribed to the moral hazard problem.

Improper choice of projects, cessation of projects, resource crunch, wilful default, loan frauds and corruption (Rajan, 2018; Sahoo, 2015), rising interest rates, exposure to sensitive sectors (Dhar & Bakshi, 2015; Digal & Kanungo, 2015; RBI, 2015), debt concentration (RBI, 2017) and bank size (Ali & Puah, 2018; Bhardhan & Mukherjee, 2016) are the other probable causes for the growth of NPAs.

Empirically, studies have attempted to understand the extent (PwC, 2014; Ramanadh & Rajesham, 2013), the determinants (Bhardhan & Mukherjee, 2016; Dhananjaya, 2019; Kadanda & Raj, 2018; Singh, 2010) and the impact of NPAs (Bhatia et al., 2012; Dhar & Bakshi, 2015; Kaur, 2012; Sharma, 2005; Verma & Bodla, 2006;) in India. However, Rajan (2018) points out that a comprehensive study of the determinants of NPAs in India is lacking. Particularly, studies examining the impact of corporate debt vulnerability on the growth of NPAs is very sparse. Given this, the article attempts to understand the major determinants of NPAs in the PSBs with emphasis on the role of corporate debt vulnerability. Alongside, it examines the four hypotheses of Berger and DeYoung (1997) in the case of the Indian PSBs.

Rationale and Objectives

Rising NPAs have motivated the researchers to understand the determinants of it. Most of the studies focus on bank-specific and macro-economic factors. A systematic analysis of the role of corporate debt vulnerability has not been explored. Therefore, the objectives of this article are threefold. First, it aims at understanding the increasing corporate debt vulnerability in India in the recent years. Second, it studies the growth of NPAs in the Indian PSBs, and last, it examines the relationship between corporate debt vulnerability and NPAs in the Indian PSBs.

The study is confined to the Indian PSBs since they account for about 85 per cent of the total NPAs of the scheduled commercial banks in India. The analysis uses the most recent data spanning from 2009 to 2019. The article contributes to the extant, but inadequate (Rajan, 2018), literature on the determinants of NPAs in the PSBs.

Methodology: Variables, Source of Data and the Model

To examine corporate distress, the study employs three key financial ratios: the interest coverage ratio (ICR), the return on assets (ROA), and the debt equity ratio (DER). The sample for the analysis includes non-financial service and manufacturing public limited companies. Data on the financial ratios have been obtained from the CMIE-Prowess database. The analyses span from 2009–2010 to 2017–2018.

The second objective aims at understanding the extent of NPAs in Indian PSBs. Specifically, it deals with the bank group-wise movement of NPAs since 2009–2010 and interbank disparity in asset management among the PSBs. The study uses three variables to address this objective. First, gross non-performing assets (GNPA) is used to assess the bank group-wise movement of NPAs and to understand the inter-bank disparity in asset quality in PSBs. Second, provision coverage ratio (PCR) is used to examine the extent of provision for bad debts. Lastly, the ROA of the PSBs is used to understand the profitability of the PSBs. The growth of NPA and inter-bank disparity in NPA management are analysed, covering 21 PSBs in the last two financial years (2017–2018 and 2018–2019). The data on GNPA, PCR and ROA have been collected from the annual financial reports of the the PSBs and RBI (2019). The last objective deals with the empirical examination of the relationship between corporate debt vulnerability and NPAs in the PSBs. The variables used for the study and the sources of data are described in Table 1.

Table 1.

Description of Variables

Variables	Indicator	Definition	Source of Data
NNPA¹	The extent of non-performing assets	The ratio of net NPA to advances	RBI (2019) and published annual reports of the banks
DAR	Debt at risk	Percentage of debt held by firms with ICR < 1	Computed from CMIE Prowess Database
DC	Concentration of debt	Percentage of debt held by the top 20 firms	Computed from CMIE Database
SG	Sales growth	Sales growth of non-financial public companies	Computed from CMIE Prowess
LSE	Lending to sensitive sectors	The ratio of sensitive sector advance to total advances	CMIE Prowess Database
CG	Credit growth	Growth of credit	Computed from RBI (2019)
ER	Efficiency of banks	The ratio of non-interest expenditure to net total income	Computed from RBI (2019)
SIZE	Bank size	The logarithm of fixed assets	RBI (2019)
CAR	Capital adequacy ratio	Capital to risk-weighted assets ratio	Published annual reports of the banks
ROA	Profitability	Return on advance	Published annual reports of the banks

Source: The author.

Econometric Framework

To examine the relationship between corporate debt vulnerability and NPAs, the study employs a fixed effect panel regression model. Panel data regression has two important advantages over the pure cross-section or time series regression. First, since panel data pools cross-section (N) and time dimension (T), it gives large (NT) number of observations, resulting in a greater degree of freedom, greater variation and less multicollinearity. Second, the panel data also overcomes the problem of omitted variable bias by controlling the unobserved individual-specific effects (heterogeneity) and thus, it can reduce the problem of aggregation bias.

The panel data model is specified as

Y_{i t} = α + β X_{i t} + u_{i t}

(1)

where Y is the dependent variable, X is the vector of independent variables, µ_i is the observation-specific error term, (α, β) is the vector of the parameters to be estimated in the model and i and t denote cross-section and time dimension of the panel structure. Unobserved effects are included as the component of error term as follows:

u_{i t} = µ_{i} + λ_{t} + v_{i t}

(2)

where, $µ_{i}$ is the unobservable individual effect, $λ_{t}$ represents the unobservable time effect. $v_{i t}$ is the random error term. The estimation of a panel data model depends on the assumption about the $µ_{i} and λ_{t}$ . If $µ_{i} and λ_{t}$ are correlated with the regressors, the fixed effect estimation is appropriate. On the other hand, if $µ_{i} and λ_{t}$ are not correlated with the regressors, random effect model is used for estimation. This article uses following fixed effect panel regression model:

\begin{array}{l} {NNPA}_{it} = α + β_{1} {DAR}_{t - 1} + β_{2} {DC}_{it - 1} + β_{3} {SG}_{it - 1} + β_{4} {LSE}_{it - 1} + β_{5} {ER}_{it - 1} + β_{6} {SIZE}_{it - 1} + β_{7} {CAR}_{it - 1} + β_{8} ROA \\ + β_{9} {CG}_{it - 1} + ε_{it} \end{array}

(3)

Analysis and Discussion

Corporate Indebtedness in India: Some Stylized Facts

The corporate debt structure is broadly classified into institution-based borrowing and market-based borrowing. Institution-based borrowing comes from banks and other financial institutions and market-based borrowing is mobilized by issuing debt instruments such as corporate bonds and debentures. A balanced debt structure is crucial for diffusing the excessive pressure on one component of the financial system by diversifying credit risk across the economy (RBI, 2015). In this regard, this section discusses the observed shift in the composition of corporate debt structure in India.

Tables 2 –4 present the sector-wise composition of corporate borrowing in the manufacturing, the service and the construction sectors, respectively. A few important observations emerge from the corporate debt structure. First, bank borrowing is the major source of debt finance for public limited companies. Bank borrowing stood at around 50 per cent of the total borrowing in the manufacturing and the construction sectors in 2017–2018. Similarly, around 28.5 per cent of the total borrowing in the service sector flows from the banking sector. This shows that the corporate sector depends heavily on the banking sector for debt financing. Alternatively, this also signifies the limited role of market-based debt financing, since the capital raised through bonds and debentures accounted for only about 11.6 per cent during 2017–2018 in the manufacturing sector. In the service sector, the share of debentures and bonds significantly declined from 35.69 in 1990–1995 to just 13.6 per cent in 2017–2018. A similar trend is also observed in the construction sector, though it increased to 16 per cent in 2017–2018. This demonstrates that the corporate bond market in India is playing a limited role in corporate financing.^{2, 3} Clearly, there is excessive pressure on the banking sector as far as corporate debt financing is concerned. RBI (2013) also observes that the Indian corporate bond market lacks depth and liquidity, leading to excessive dependence on bank financing. As a result, there is a disproportionate risk concentrated in one part of the financial system. The concentration of credit and the consequent risk is a serious threat to the financial soundness of the banks, with the potential to create systemic risk, particularly, during the downturn of the business cycle. An unprecedented increase in bad loans in the Indian PSBs in the past few years is a clear indication of the problem of excessive risk emanating from the concentration of bank credit. However, it is notable that the proportion of debt raised through commercial papers significantly increased in both the manufacturing and the service sectors during 2015–2018, though it is a short-term debt instrument.

Table 2.

Trends in the Composition of Corporate Debt in the Manufacturing Sector (%)

	1990–1994	1995–1999	2000–2004	2005–2009	2010–2014	2015–2017	2017–2018
Borrowing from banks	29.94	33.74	40.83	55.82	54.72	54.71	53.5
Borrowing from financial institutions	25.67	22.57	12.78	3.17	1.65	1.78	1.7
Borrowings from govts.	3.55	2.65	2.88	2.35	1.12	0.96	0.7
Borrowings syndicated across banks and institutions	0.00	0.14	0.51	0.05	0.01	0.00	0.0
Debentures and bonds	16.31	16.55	17.87	7.71	9.84	10.99	11.6
Foreign currency borrowings	9.40	9.62	9.59	20.45	22.42	16.51	14.7
Loans from promoters, directors and shareholders	0.02	0.21	0.33	0.31	0.22	0.29	0.3
Inter-corporate loans	2.39	1.83	1.99	2.92	2.50	2.35	2.4
Deferred credit	1.72	1.09	2.03	1.81	2.84	2.82	2.1
Interest accrued and due	2.03	1.58	3.41	1.32	0.62	1.82	1.8
Hire purchase loans	0.05	0.12	0.08	0.05	0.19	1.07	1.0
Fixed deposits	5.36	4.37	2.42	0.77	0.65	0.19	0.2
Commercial papers	0.74	0.86	0.94	0.54	1.46	4.79	5.9
Other borrowings	2.81	4.66	4.35	2.74	1.75	1.72	4.1
Source: Author’s calculations based on the CMIE Prowess database.

Source: Author’s calculations based on CMIE Prowess database.

Table 3.

Composition of Corporate Debt in the Service Sector (%)

	1990–1994	1995–1999	2000–2004	2005–2009	2010–2014	2015–2017	2017–2018
Borrowing from banks	19.21	29.80	42.27	49.91	40.80	30.72	28.5
Borrowing from financial institutions	12.20	14.10	11.20	3.77	2.23	2.17	2.9
Borrowings from govts.	13.32	3.76	1.77	0.40	0.73	0.27	0.0
Borrowings syndicated across banks and institutions	0.00	0.07	0.14	0.00	0.00	0.00	0.0
Debentures and bonds	35.69	27.76	12.30	7.09	10.22	13.10	13.6
Foreign currency borrowings	5.78	7.65	9.57	22.74	26.80	17.22	16.4
Loans from promoters, directors and shareholders	0.03	0.39	0.47	0.47	0.25	0.27	0.4
Inter-corporate loans	2.67	2.55	3.83	3.74	4.54	2.97	6.5
Deferred credit	4.55	2.56	0.53	0.49	4.40	23.26	17.7
Interest accrued and due	1.99	2.39	3.47	0.86	0.54	1.18	1.3
Hire purchase loans	0.11	2.58	6.43	5.50	4.29	2.42	1.3
Fixed deposits	2.34	3.11	1.68	0.31	0.36	0.03	0.0
Commercial papers	0.18	0.14	0.40	0.62	0.66	2.22	3.3
Other borrowings	1.91	3.13	5.94	4.10	4.18	4.19	8.0

Source: Author’s calculations based on CMIE Prowess database.

Table 4.

Composition of Corporate Debt in the Construction Sector (%)

	1990–1994	1995–1999	2000–2004	2005–2009	2010–2014	2015–2017	2017–2018
Borrowing from banks	39.63	37.85	44.08	60.35	58.33	55.93	47.2
Borrowing from financial institutions	17.67	18.85	10.47	4.04	6.83	7.16	9.0
Borrowings from govt	2.17	3.59	0.84	0.02	0.00	0.00	0.0
Borrowings syndicated across banks and institutions	0.00	0.04	0.28	0.00	1.68	4.84	0.0
Debentures and bonds	17.44	15.81	13.90	8.97	9.18	9.08	16.1
Foreign currency borrowings	0.01	0.30	1.87	6.36	8.30	6.20	4.8
Loans from promoters, directors and shareholders	1.23	2.12	2.08	0.72	0.24	0.32	0.4
Inter-corporate loans	3.46	2.82	4.53	9.79	5.49	5.97	7.3
Deferred credit	1.35	1.15	1.28	1.72	1.64	0.46	0.5
Interest accrued and due	2.33	3.64	3.02	0.49	0.67	2.96	2.5
Hire purchase loans	0.51	0.85	1.16	0.40	0.08	0.02	2.4
Fixed deposits	5.27	3.36	2.20	0.51	1.13	0.30	0.1
Commercial papers	0.05	2.45	3.04	2.43	0.91	0.56	1.2
Other borrowings	8.86	7.18	11.26	4.20	5.51	6.20	8.7

Source: Author’s calculations based on CMIE Prowess database.

Another important development in corporate debt structure is the withering away of the development financial institutions (DFIs) which were the major source of long-term debt finance for capital expenditure. The average annual share of DFIs in total corporate debt of the manufacturing sector was 25.67 per cent during 1990–1995, which significantly declined to 1.7 per cent during 2017–2018. A similar trend can also be noted in the service, construction and mining sectors. Ideally, the role of DFIs should have been taken over by the corporate bond market. However, in India, due to the underdeveloped corporate bond market, the burden of financial institutions has shifted to commercial banks. This has resulted in excessive pressure on the banking sector.

The increase in foreign currency borrowing in recent years is another disturbing feature of the corporate debt structure. There has been a significant rise in foreign currency borrowing in all three sectors since post-GFC, though it slightly came down in 2017–2018. Rise in external corporate debt is problematic since it creates potential vulnerabilities for the corporate sector. For instance, increased foreign currency borrowing is stressful for firms in the event of large currency depreciation. Patnaik et al. (2016) point out that unhedged foreign currency borrowing is a concern in emerging markets where the exchange rate is not fully floating. They also observe that in an emerging market with a managed floating exchange rate regime, firms may choose to have unhedged foreign currency borrowing since the firms expect central banks to intervene when faced with large depreciations. RBI (2013) also expresses concern over the increasing unhedged foreign exposure. It cautions that unhedged foreign debt would pose a serious risk to corporate as well as to the whole financial system. IMF (2017) also notes that external financing vulnerability is one of the key challenges faced by emerging market economies. This observation comes in the backdrop of the fact that India witnessed a surge in external commercial borrowing (ECB) in recent years. Total ECB increased from ₹2492.43 billion in 2008 to ₹11990. 43 billion in 2017 with a whopping 381 per cent growth. As a result, the net commercial borrowing position deteriorated from a surplus of ₹912.12 billion to a deficit of ₹509 billion over 2008–2017. As observed by IMF (2015), the increase in ECB took place in an environment of unprecedented monetary expansion in advanced economies in the post-GFC. It further warns that monetary policy reversals in advanced countries like the USA, in conjunction with corporate underperformance, could trigger a series of corporate failures in emerging markets.

The Concentration of Debt

The concentration of debt is another important feature of corporate indebtedness in India. This is also arguably one of the important reasons for the growth of NPAs in the PSBs. RBI (2017) shows that the large borrowers account for 56 per cent of gross advances and 86.5 per cent of GNPAs of scheduled commercial banks in India. This is also evident from the fact that the PSBs had a high NPAs concentration ratio⁴ and a credit concentration ratio⁵ of 18.70 per cent and 13.05 per cent, respectively, in 2016–2017. State Bank of India, the largest bank in India, had an NPA concentration ratio⁶ of 27.36 per cent in 2015–2016. Similarly, other large banks such as Canara Bank, IDBI Bank, and Syndicate Bank had high NPA concentration ratios in 2015–2016.

To ascertain the extent of concentration of debt, the sector-wise percentage share of the top 20 firms in total debt is examined. Table 5 shows that the top 20 highly indebted firms in the manufacturing sector hold 56.66 per cent of the total debt of the sector. In the case of the service and the construction sectors, this number is dismal as top 20 firms account for about 72.45 per cent and 68.35 per cent of the total debt, respectively. In the manufacturing sector, Reliance Industries Ltd. held 12.3 per cent of the total debt as of March 2018 (Table A1). However, Reliance Industries Ltd. has a healthy ICR. In the case of other top firms such as Tata Steel BSL Ltd., Vedanta Ltd., Alok Industries Ltd. and Videocon Industries Ltd., the situation is alarming since they do not have enough earning to repay the debt. In the service sector, Bharti Airtel Ltd. accounts for a whopping 20.18 per cent of the total debt (Table A2). This represents the debt at risk as the firm does not have enough earning to make the interest payments. Other highly indebted firms such as Reliance Communications Ltd., Mahanagar Telephone Nigam Ltd., Tata Teleservices (Maharashtra) Ltd., Adani Power Ltd. and Adani Transmission Ltd. are also in distress as they recorded negative or very low profitability and ICR. The situation is not different in the case of the firms in the construction sector (Table A3). Hence, it is clear from Tables A1 –A3 that about 40 per cent of the top 20 indebted firms in all three sectors are in distress. Clearly, this would put further pressure on already strained asset quality of the PSBs.

Table 5.

The Share of Top 20 Indebted Firms (as of March 2018)

Sector	Percentage of Total Debt
Manufacturing	56.66
Service	72.45
Construction	68.35

Source: Author’s calculations based on the CMIE Prowess database.

Corporate Debt Vulnerability in India

The corporate debt-to-GDP ratio in India increased from 46.2 per cent to 52.2 per cent over 2007–2015. Though India’s corporate debt position appears to be comfortable as compared to the emerging market debt position,⁷ the ability of firms to pay back debt has severely deteriorated in the recent past. One of the key indicators of corporate debt vulnerability is deteriorating ICR. ICR, which is the ratio of profit before interest and tax (PBIT) to interest expenses, reflects the debt serviceability of the firm. An ICR of less than 1 indicates a firm’s inability to meet its interest expenses, and hence, they are classified as weak firms (IMF, 2017). Besides, IMF (2015) observes that an ICR lower than 2 often indicates that a firm is in distress. Therefore, all firms with ICR less than 2 are categorized as ‘challenged’ firms by IMF (2017).

Table 6 shows the percentage distribution of firms in terms of ICR. The debt serviceability has deteriorated since 2010–2011 as the proportion of weak firms increased from about 17 per cent in 2010–2011 to 28.67 per cent in 2015–2016. In addition, about 23 per cent of the firms were vulnerable in 2017–2018 since their ICR ranged from 1 to less than 2. Table 7 presents the size of the challenged firms in terms of the percentage of total liabilities held by them. It is clear that there is a significant increase in vulnerability in the post-GFC period. The total percentage of liabilities held by challenged firms increased from 31.3 percent in 2006-2007 to 54.8 percent in 2014-2015. Particularly, the size of weak firms increased considerably from 16.4 percent to 29.7 percent over 2006-2015. However, there is a significant decline in the number and the size of weak firms in 2017–2018. This may indicate that the corporate sector is on a recovery trajectory after a prolonged distress.

Table 6.

Distribution of Firms in Terms of Interest Coverage Ratio

	2006-–207	2009–2010	2010–2011	2011–2012	2012–2013	2013-–204	2014–2015	2015–2016	2016–2017	2017–2018
ICR < 1	448 (16.4)	651 (23.1)	476 (16.9)	682 (24.0)	715 (25.0)	805 (27.8)	846 (29.7)	810 (28.6)	704 (26.7)	247 (9.3)
1 ≤ ICR < 2	409 (14.9)	639 (22.7)	652 (23.1)	733 (25.8)	759 (26.6)	730 (25.2)	714 (25.1)	651 (23.0)	535 (20.3)	622 (23.4)
2 ≤ ICR≤ 5	833 (30.4)	724 (25.7)	794 (28.2)	687 (24.1)	668 (23.4)	649 (22.4)	561 (19.7)	578 (20.4)	587 (22.3)	703 (26.4)
ICR > 5	1,048 (38.3)	803 (28.5)	898 (31.8)	743 (26.1)	716 (25.1)	709 (24.5)	729 (25.6)	795 (28.10)	812 (30.8)	1,087 (40.9)
Median ICR	3.45	2.84	2.67	2	1.91	1.81	1.74	1.89	2.2	3.5
No. of firms	2,738	2,817	2,820	2,845	2,858	2,893	2,850	2,834	2,638	2,659

Source: Author’s calculations based on the CMIE Prowess database.

Note: Percentage in the bracket.

The decline in profitability is another important measure of corporate fragility. The ROA, which is the ratio of profit after tax (PAT) to total assets, is used to understand the position of firms in terms of profitability. As evident from Table 8, the ROA has been deteriorating since 2009–2010. About 37 per cent of the firms recorded negative ROA in 2014–2015 as against 26.44 per cent in 2006–2007. Similarly, about 14 per cent of firms recorded weak ROA ranging between more than 0 and less than or equal to 1. Though the proportion of firms with negative profitability has been declining since 2014–2015, it still remains high. The median ROA witnessed a sharp decline over 2007–2015. Clearly, the corporate distress has been building up since the post-GFC.

Table 7.

Percentage of Total Liabilities Held by ‘Challenged Firms’

ICR	2006–2007	2009–2010	2010–2011	2011–2012	2012–2013	2013–2014	2014–2015	2015–2016	2016–2017
ICR < 1	5.528	7.048	17.972	12.825	15.010	19.801	18.968	27.931	23.300
1 ≤ ICR< 2	5.588	12.570	21.550	21.550	24.697	17.478	17.149	12.913	10.614
Total	11.11	19.619	39.522	34.375	39.707	37.279	36.117	40.844	33.914

Source: Author’s calculations based on the CMIE Prowess database.

Table 8.

Distribution of Firms in Terms of ROA

ROA	2006–2007	2009–2010	2010–2011	2011–2012	2012–2013	2013–2014	2014–2015	2015–2016	2016–2017	2017–2018
ROA < 0	914 (26.44)	1,141 (29.16)	1,095 (27.71)	1,347 (33.73)	1,427 (34.93)	1,469 (36.10)	1,469 (36.89)	1,409 (35.96)	1,137 (32.72)	1,337 (31.20)
0< ROA ≤1	429 (12.41)	423 (10.81)	439 (11.11)	542 (13.57)	561 (13.73)	640 (15.73)	560 (14.06)	574 (14.65)	471 (13.55)	579 (13.51)
1< ROA ≤5	850 (24.59)	1106 (28.26)	1189 (30.09)	1099 (27.52)	1146 (28.05)	1035 (25.44)	1057 (26.54)	1014 (25.88)	924 (26.59)	1180 (27.54)
5 < ROA ≤10	598 (17.30)	676 (17.28)	685 (17.34)	570 (14.27)	571 (13.98)	560 (13.76)	547 (13.74)	548 (13.99)	556 (16.00)	698 (16.29)
ROA > 10	666 (19.27)	567 (14.49)	543 (13.74)	436 (10.92)	380 (9.30)	365 (8.97)	349 (8.76)	373 (9.52)	387 (11.14)	491 (11.46)
Median ROA	2.78	2.1	2.2	1.28	1.14	0.87	0.98	1.04	1.44	1.42
No. of firms	3,757	3,913	3,951	3,994	4,085	4,069	3,982	3,918	3,475	4,285

Source: Author’s calculations based on the CMIE Prowess database.

Note: Percentage in the bracket.

Table 9.

Distribution of Firms in Terms of Debt Equity Ratio (DER)

	2009–2010	2010–2011	2011–2012	2012–2013	2013–2014	2014–2015	2015–2016	2016–2017	2017–2018
0 < DER ≤ 1	1812 (57.78)	1519 (60.49)	1516 (60.06)	1505 (68.28)	1490 (59.50)	1474 (61.19)	1518 (64.16)	1493 (68.20)	2090 (72.4)
1 < DER ≤ 5	1126 (35.91)	868 (34.57)	875 (34.67)	561 (25.45)	860 (34.35)	789 (32.75)	716 (30.26)	596 (27.23)	719 (24.9)
5 < DER ≤ 10	105 (3.35)	71 (2.83)	77 (3.05)	80 (3.63)	84 (3.35)	88 (3.65)	68 (2.87)	55 (2.51)	67 (2.3)
DER > 10	93 (2.97)	53 (2.11)	56 (2.22)	58 (2.63)	70 (2.80)	58 (2.41)	64 (2.70)	45 (2.06)	10 (0.3)
Mean⁸ DER	1.52	1.41	1.52	1.50	1.62	1.45	1.55	1.30	0.5
No. of firms	3136	2511	2524	2204	2504	2409	2366	2189	2886

Source: Author’s calculations based on the CMIE Prowess database.

Note: Percentage in the bracket.

To examine the leverage position of the firms, the level of DER is examined. As depicted in Table 9, 72.4 per cent of the firms were sound in terms of their leverage with 1 or less than 1 DER in 2017–2018. Further, about 25 per cent of the firms had DER between more than 1 and less than 5. In addition, about 3 per cent of the firms are highly leveraged with a DER of more than 5 in 2017–2018. Though a higher DER indicates a build-up of corporate indebtedness, it may not be construed as risky as long as firms generate enough earning to meet their debt obligations. Hence, the quality of debt is the major concern than the extent, which is examined in the next section.

Corporate Debt at Risk

Although the increase in corporate debt can be used for investment, thereby enhancing growth, the rising indebtedness naturally raises concerns when it is accompanied by a decrease in corporate health resulting in rising debt at risk. According to IMF (2016), the debt at risk refers to the debt held by firms that do not earn enough profit to cover interest payments. In other words, this is the amount of debt held by firms with ICR of less than 1. Table 10 shows that the debt at risk increased by about 18 percentage over 2010–2017. In 2016–2017, a staggering 37.25 per cent of corporate debt was at risk. Similarly, around 35.25 per cent of the total debt was held by the firms that reported negative profitability in 2016–2017. This highlights that a significant portion of the corporate debt is at risk. Table 10 also presents the sector-wise debt at risk. The debt at risk witnessed a substantial rise in all the three sectors over 2010–2017. The service sector had an alarming 60 per cent of debt at risk in 2016–2017. The construction sector has also recorded a very high debt at risk in recent years. Around 50 per cent of the total debt of the sector was risky in 2015–2016, though it declined slightly in 2016–2017. As the proportion of debt at risk increased, the problem of the NPA has also aggravated. Particularly, a significant rise in the debt at risk in the recent years is accompanied by a substantial accumulation of bad loans in the PSBs. This indicates that the debt at risk increases the probability of default, resulting in the deterioration of asset quality in the banking sector. However, it is noteworthy that there was a significant decline in the debt at risk in 2017-2018.

Table 10.

Sector-wise Debt at Risk

	All Sectors	Manufacturing	Service	Construction
Percentage of total borrowing
2010/2011	19.55	8.90	42.28	5.26
2011/2012	27.25	16.71	48.26	11.67
2012/2013	29.61	22.21	54.14	17.19
2013/2014	34.86	29.72	48.64	31.41
2014/2015	36.78	35.11	38.92	34.77
2015/2016	36.17	49.23	41.2	50.17
2016/2017	37.25	38.43	60.33	44.87
2017/2018	22.27	18.6	32.82	24.82

Source: Author’s calculations based on the CMIE Prowess database.

Corporate Debt Vulnerability and Non-performing Assets in Public Sector Banks

The Indian PSBs are the most important component of the financial system in India. Bank borrowing accounts for about 50 per cent of corporate debt financing. Hence, the stress in the corporate balance sheet will have a direct impact on the asset quality of the PSBs. This is evident from the fact that the banking sector accumulated unprecedented NPAs to the tune of ₹10,387.89 billion as of March 2018, and PSBs account for 86.43 per cent of the total NPAs in the banking sector as depicted in Table 11. This is alarming since the PSBs account for about 65 per cent of the total banking sector credit. This clearly shows that the vital part of India’s financial system is in deep trouble, which poses a serious threat to the stability of the financial system.

Table 12 reveals that the GNPA has been gradually increasing since 2009–2010. However, the increase has been significant after 2014–2015. This is largely due to the Asset Quality Review (AQR) by RBI in 2015. Further, Table 12 also shows that in both the PSBs and the private sector banks (PVBs), the addition of NPAs is far above the reduction. This indicates the ineffectiveness of various debt recovery mechanisms as pointed out by Kadanda and Raj (2018). However, the PVBs have managed their assets far better than the PSBs with just 4.16 per cent of Gross NPAs in 2017–2018 as against 14.58 per cent in the PSBs during the same period. This raises serious questions about the operations risk management and the governance in the PSBs. Several reports highlight the poor corporate governance and the political interference in the operations of the PSBs. For example, the Nayak Committee report clearly points out the prevalence of weak corporate governance in most PSBs resulting in poor business strategy and risk management (RBI, 2014). This suggests that unless the governance issues in PSBs are addressed, the problem of NPA is bound to recur. Rajan (2018) also argues that there is a lack of professionalization of the bank board due to the inevitable politicization of board appointments. He clearly points out the need for creating strong bank boards with autonomy in decision-making with accountability to reduce bad loans.

Table 11.

Commercial Banks at Glance (end of March 2018)

	Deposits (%)	Advances (%)	Assets (%)	Share in NPAs (%)
Public sector banks	70.06	65.14	65.79	86.43
Private sector banks	25.55	30.45	28.18	12.13
Foreign banks	4.20	4.01	5.69	1.33
Small finance banks	0.20	0.40	0.34	0.11

Source: Author’s calculations based on RBI (2019).

Table 12.

Movement of GNPAs (₹ billion)

	PSBs					PVBs
Year	Addn.	Redn.	Write-off	Closing GNPA	GNPA (%)	Addn.	Redn.	Write-off	Closing GNPA	GNPA (%)
2009–2010	448.19	197.68	100.81	599.27	2.22	148.17	58.56	82.10	176.40	2.79
2010–2011	582.27	258.85	171.60	746.16	2.26	86.86	47.68	30.18	182.41	2.29
2011–2012	928.09	353.21	148.90	1172.62	3.02	98.74	56.78	36.67	187.68	1.94
2012-2013	1196.13	455.41	268.73	1644.61	3.68	142.43	70.01	49.39	210.71	1.84
2013–2014	1635.46	679.15	328.28	2272.64	4.46	193.80	93.67	65.41	245.42	1.83
2014–2015	1778.62	756.78	509.79	2784.68	5.09	266.80	98.87	72.29	341.06	2.15
2015–2016	3859.62	650.29	594.45	5399.56	9.65	482.68	135.16	119.27	561.86	2.90
2016–2017	3274.79	1000.47	826.57	6847.32	13.27	813.66	236.53	206.91	932.09	3.71
2017–2018	4881.8	822.8	1295.0	8956.0	14.58	1076.8	408.0	307.5	1293.4	4.62

Source: Prepared by the author based on RBI (2019).

Interbank Disparity in Asset Quality of Public Sector Banks

As discussed above, the Indian PSBs have accumulated an unprecedented level of bad loans since 2014–2015. Therefore, in this section, the inter-bank disparity in asset quality among the PSBs is examined in terms of GNPA and PCR. Table 13 presents the extent of the GNPA and the PCR among the PSBs during the financial year 2017–2018 and 2018–2019. Except for Indian Bank and Vijaya Bank, all other PSBs had a very high GNPA in 2017–2018 with an average GNPA of 16.91 per cent. The GNPA remained high even in 2018–2019, though it declined marginally. However, banks such as Bank of Baroda, Canara Bank, Oriental Bank of Commerce, State Bank of India and United Bank of India managed to reduce their NPAs significantly in 2018–2019. Bank of Baroda had the lowest GNPA (3.3%) in 2018–2019 followed by Indian Bank, State Bank of India and Canara Bank. All other PSBs reported GNPA of more than 10 per cent during the same period. The problem of bad loans is severe in IDBI Bank Ltd.(27.47%) followed by UCO Bank (25%), Indian Overseas Bank (21.97%), Central Bank of India (19.29%), Allahabad bank (17.55%) and United Bank of India (16.48%).

Table 13.

Interbank Disparity in Asset Quality: PSBs

Bank	2017–2018		2018–2019		2017–2018		2018–2019
Bank	GNPA (%)	Rank	GNPA (%)	Rank	PCR (%)	Rank	PCR (%)	Rank
Allahabad Bank	15.96	9	17.55	15	62.91	9	79.85	4
Andhra Bank	17.09	11	16.21	12	60.9	10	73.84	12
Bank of Baroda	12.26	7	3.33	1	67.21	1	78.68	6
Bank of India	16.58	10	15.84	11	65.85	3	76.95	7
Bank of Maharashtra	19.48	15	16.40	13	58.71	16	81.49	3
Canara Bank	11.84	6	8.83	4	58.06	18	68.13	16
Central Bank of India	21.48	16	19.29	16	63.31	8	76.6	8
Corporation Bank	17.35	12	15.35	9	63.65	6	83.3	1
Dena Bank	22.04	17	—	—	60.2	12	—	—
IDBI Bank Ltd.	27.95	21	27.47	19	63.4	7	82.88	2
Indian Bank	7.37	2	7.11	2	64.27	4	65.72	19
Indian Overseas Bank	25.28	20	21.97	17	59.45	14	71.39	14
Oriental Bank of Commerce	17.63	13	12.66	7	64.07	5	75.84	9
Punjab & Sind Bank	11.19	4	11.83	6	53.22	21	71.354	15
Punjab National Bank	18.38	14	15.50	10	58.42	17	74.5	11
State Bank of India	10.91	3	7.53	3	66.17	2	78.73	5
Syndicate Bank	11.53	5	11.37	5	60.71	11	66.43	17
UCO Bank	24.64	19	25.00	18	60.05	13	74.93	10
Union Bank of India	15.73	8	15.01	8	57.16	19	66.24	18
United Bank of India	24.1	18	16.48	14	53.48	20	72.94	13
Vijaya Bank	6.34	1	—	—	59.39	15	—	—
Average	16.91	—	14.99	—	60.98	—	74.73	—

Source: Prepared by the author based on the published financial results of banks.

Further, in terms of the PCR, there was a significant increase of about 14 per cent from 60.98 per cent in 2017–2018 to 60.98 per cent in 2018–2019. This shows that the increase in bad loans has forced the banks to earmark higher provision. Though it signifies the increased ability of the banks to manage bad loans, it adversely affects their profitability. This is clearly reflected in the deteriorating profitability of the PSBs in 2018–2019. The ROA has dipped further from −1.08 per cent in 2017–2018 to −1.87 in 2018–2019. All PSBs, except five, have continued to report negative returns on assets. Corporation Bank (−13.02) has experienced a severe dip in profitability followed by IDBI Bank (−4.68), Allahabad Bank (−3.48) and Bank of Maharashtra (−3.01). Deterioration in ROA across almost all the PSBs indicates that the growth of the NPA is adversely affecting the profitability of the banks, which is confirmed by the presence of a strong negative correlation (−0.90) between GNPA and ROA over 2010–2018. However, it is noteworthy that the growth of NPA declined significantly from 30.9 per cent in 2017–2018 to −7.7 per cent in 2018–2019 as shown in Table 14.

Table 14.

Growth of NPA and Return on Assets (ROA) (%)

Bank	Growth of NPA (2017–2018)	Growth of NPA (2018–2019)	ROA(2017–2018)	ROA(2018–2019)
Allahabad Bank	28.40	8.06	1.96	3.48
Andhra Bank	59.16	3.02	1.46	1.09
Bank of Baroda	24.36	14.60	0.34	0.06
Bank of India	19.76	2.68	0.91	0.84
Bank of Maharashtra	7.24	16.86	0.73	3.01
Canara Bank	38.79	17.37	0.75	0.06
Central Bank of India	39.93	15.14	1.61	1.7
Corporation Bank	30.32	6.71	1.67	13.02
Dena Bank	29.65	—	1.59	—
IDBI Bank Ltd.	24.21	10.00	2.37	4.68
Indian Bank	21.54	11.37	0.53	0.12
Indian Overseas Bank	8.78	12.52	2.33	1.35
Oriental Bank of Commerce	14.32	16.90	2.31	0.02
Punjab & Sind Bank	23.88	10.31	0.69	0.47
Punjab National Bank	56.44	9.41	1.6	1.25
State Bank of India	25.62	22.68	0.19	0.02
Syndicate Bank	46.28	4.19	1.05	0.87
UCO Bank	35.53	2.17	1.88	1.84
Union Bank of India	46.45	1.30	1.07	0.59
United Bank of India	51.13	27.18	0.75	1.6
Vijaya Bank	17.93	—	0.44	—
Average	30.93	7.73	1.08	1.87

Source: Prepared by the author based on the published financial results of banks.

Determinants of the Non-performing Assets

To understand the implications of the corporate distress for the asset quality of the PSBs, the study empirically examines the impact of corporate debt vulnerability on the NPAs of the PSBs in India along with the other determinants of NPAs. Particularly, it examines the four hypotheses of Berger and DeYoung (1997) in the case of the Indian PSBs. As discussed in the second section, the Bad Luck Hypothesis attributes external causes such as slower economic growth and corporate profit, business failure, etc., to the growth of NPAs. Corporate sales growth, debt at risk, lending to sensitive sectors and concentration of credit are used to test the bad luck hypothesis. The efficiency ratio of the banks and credit growth are used to understand the Bad Management Hypothesis and the Skimping Hypothesis. Finally, bank size and capital adequacy ratio are included to test the Moral Hazard hypothesis.

Table 15 presents the results of the F test and the LM test. The F test for fixed effect is significant, which indicates that there is a fixed effect in the data. Similarly, the significant LM test directs that there is a random effect in the data. The Hausman test result suggests that the fixed effect model is appropriate for the analysis. Table 16 provides the results of the fixed effect estimation. Tables 17 and 18 present the results of the tests for normality⁹ and constant variance, respectively.¹⁰

Table 15.

Results of F, LM test for Fixed and Random Effects

Tests	Test Values
F test for fixed effect	3.24 (0.00)
LM test random effect	8.13 (0.002)
Hausman test	23.60 (0.05)

Source: The author.

Note: p-Value in brackets.

Table 16.

Results of Fixed Effect Panel Regression

Dependent Variable: NNPA	Coef.
DAR	0.121* (5.75)
DCON	0.268** (3.32)
SG	0.114* (5.09)
LSE	0.10** (2.24)
ER	0.05*** (1.84)
CG	0.003*** (1.88)
SIZE	1.53** (2.48)
CAR	0.13 (0.71)
ROA	0.736* (3.40)
F test	17.24* (0.00)
R ²	0.72

Source: The author.

Notes: *, ** and *** Significant at 1%, 5% and 10% level, respectively. t-Values in parenthesis. Robust standard error has been used.

Table 17.

Skewness/Kurtosis Tests for Normality

Pr (Skewness)	Pr (Kurtosis)	Joint
		Adj. χ²	Prob > χ²
0.000	0.0151	18.88	0.0001

Source: The author.

Table 18.

Modified Wald Test of Heteroskedasticity

χ² (21) =	149.86
Prob > χ² =	(0.00)

Source: The author.

As pointed out earlier, the main focus of this study is to understand the impact of corporate fragility on NPAs. Four variables are used for this purpose: debt at risk, debt concentration, corporate sales growth and lending to sensitive sectors.

The results show that there is a highly significant positive relationship between the debt at risk and NPAs, which clearly shows that the corporate debt vulnerability is a major factor contributing to the growth of NPAs in the PSBs. As discussed earlier, the debt at risk has increased phenomenally in the post-GFC, which is clearly affecting banks’ asset quality. The result clearly points out the twin balance sheet problem currently prevailing in India.

Similarly, the concentration of debt is positively related to the growth of NPAs. The result indicates that the high credit concentration is one of the contributing factors for the rise in NPAs of the PSBs. As discussed above, the concentration of debt remains significantly higher. Hence, any repayment problem in the top largest borrowers results in huge bad assets for the banking sector. The result is consistent with the observation of RBI (2017) that large borrowers contribute 86.5 per cent of Gross NPAs of the scheduled commercial banks in India.

Corporate sales growth has a significant negative relationship with NPA, which indicates that during higher sales growth, firms’ profit improves and loan recovery rate increases, resulting in a reduction in NPAs. However, the reverse happens when corporate sales growth falls, suggesting that the problem of NPA is cyclical in nature. Lending to sensitive sector is another factor that is contributing to the growth of NPAs. This suggests that the stress in high-risk sectors such as construction, real estate, aviation, iron and steel, and infrastructure is adversely affecting the asset quality of the PSBs The finding corroborates the observation of RBI (2015) that the banks’ exposure to sensitive sectors is contributing to the growth of NPAs.

The above findings support the Bad Luck Hypothesis of Berger and Young (1997). Further, it also shows that the corporate-bank nexus is very strong in India since bank borrowing is the major source of corporate debt financing. Hence, it may be inferred that the bank recapitalization alone will not resolve the problem of NPAs. There is also a great need for stimulating demand and investment in key sectors, which have fallen substantially in the recent period.

The efficiency ratio of the banks is another significant factor that contributes to NPA in the PSBs. The significant positive relationship between the efficiency ratio and NPA shows that the inefficient banks accumulate higher bad loans in the subsequent years, which is consistent with the prediction of the Bad Management Hypothesis. The prevalence of bad management in the PSBs is also highlighted by the Nayak Committee Report (RBI, 2014). Further, the result suggests that the repeated recapitalization without much-needed governance reforms in the PSBs will not be useful.

The positive relationship between credit growth and NPAs is another noteworthy finding. This clearly indicates that aggressive lending by the PSBs in the past is another source of NPAs in the future. As pointed out by Rajan (2018), aggressive lending compelled by credit targets often leads to laxity in appropriate due diligence, leading to higher NPAs in the future. In this regard, he cautions the government against setting ambitious credit targets.

The positive relationship between the credit growth and NPA also supports the bad management hypothesis of Berger and DeYoung (1997) since, in the pursuit of aggressive lending during upturn, banks may fail to carry out proper credit risk appraisal and monitoring of the projects either due to the lack of skill or deliberate oversight on the part of the banks, resulting in higher bad loans in the subsequent years.

The positive relationship between bank size and NPA suggests that larger banks produce more NPAs. This finding is contrary to the argument that the large banks are efficient in managing their assets by diversifying their loan portfolio. First, this indicates the moral hazard problem since large banks, particularly, large PSBs enjoy implicit state government due to their too big to fail status. As argued by Patel (2017), systematically important financial institutions/banks may enjoy the implicit state guarantees in the event of insolvency which results in moral hazard problems. This is truer in the case of the Indian PSBs as they enjoy the privilege of repeated recapitalization by the government in times of crisis. Hence, this finding supports the moral hazard hypothesis of Berger and DeYoung (1997).

Further, the negative relationship between ROA and NPAs suggests that the profitable banks are relatively efficient in managing their asset quality. Two possible explanations may be advanced for this finding. First, profitable banks are in a better position to handle the recovery process or to write off bad loans if recovery is difficult. Second, the profitable banks are also able to earmark higher provisions for bad loans.

In succinct, it is clear that the PSBs, the pillars of the Indian banking system, are in deep crisis in terms of rising NPA, which has severely affected their profitability. Corporate fragility is an important contributor to the deteriorating asset quality of the PSBs, which suggests that the corporate bank nexus is very strong in India. The rising problem of bad loans in the PSBs is also caused by other factors such as debt concentration, bad management, lending to sensitive sectors, aggressive lending and moral hazard problem.

Conclusion and Policy Implications

This article has attempted to understand the increasing corporate distress in Indian non-financial public limited companies and its impact on the growth of NPAs in the Indian PSBs. The analysis shows that the bank-based borrowing dominates the corporate debt structure. The role of the market-based debt financing is limited, which suggests that the corporate bond market is not attractive to the corporate sector. The composition of the corporate debt structure also shows that there has been a significant rise in foreign currency borrowing in the post-GFC. The analysis also shows that the corporate debt at risk has increased significantly in recent years along with an increase in the concentration of debt. The study finds that the corporate debt vulnerability is an important determinant of the growth of NPAs in the Indian PSBs along with other factors such as the efficiency of the bank, profitability, bank size and credit growth. In sum, the results support bad luck hypothesis, bad management hypothesis and moral hazard hypothesis. The results clearly indicate that there is an immediate need to revive the demand and investment in key sectors to resolve the problem of bad loans. Once the current NPA crisis is brought under control, the government needs to seriously consider distancing itself from the PSBs and make them truly professional corporations. There is also an immediate need to reduce the excessive pressure on the banking sector by encouraging the firms to raise more market-based debt finance from the corporate bond market. In a nutshell, a robust financial system is going to be critical in the post-COVID-19 era to stimulate investment and demand.

Appendix A

Table A1.

Top 20 Indebted Firms in Manufacturing Sector (as of March 2018)

Company Name	Return on Total Assets	Interest Cover (times)	Debt to Equity Ratio (times)	Debt(₹ billion)	% of the Total Debt
Reliance Industries Ltd.	5.77	7.35	0.37	1369270	12.31
Tata Steel BSL Ltd.	3.13	0.05	—	824649.3	7.41
Indian Oil Corpn. Ltd.	6.17	10.17	0.64	605346.6	5.44
Vedanta Ltd.	0.89	1.89	0.51	452860	4.07
JSW Steel Ltd.	5.47	3.12	1.33	399500	3.59
Alok Industries Ltd.	47.47	—		343971.4	3.09
Jindal Steel & Power Ltd.	0.07	0.87	1.02	2503918	2.25
Videocon Industries Ltd.	12.76	—	5.87	245063.8	2.20
Bharat Petroleum Corpn. Ltd.	7.6	8.53	0.69	238501.1	2.14
Hindustan Petroleum Corpn. Ltd.	7.42	15.91	0.88	223888	2.01
Tata Motors Ltd.	1.74	0.51	0.92	206719	1.86
Monnet Ispat & Energy Ltd.	17.37	—	—	204566.6	1.84
Hindalco Industries Ltd.	1.74	2.23	0.47	203026.2	1.83
Ultratech Cement Ltd.	4.24	3.63	0.67	172727.1	1.55
Steel Authority of India Ltd.	1.15	0.53	1.27	122443.2	1.10
Mangalore Refinery & Petrochemicals Ltd.	6.62	10.79	0.72	113230.9	1.02
Jayaswal Neco Inds. Ltd.	6.63	0.1	4.42	86522.3	0.78
Rajesh Exports Ltd.	2.15	2.17	2.07	85945.4	0.77
Piramal Enterprises Ltd.	1.58	1.74	1.29	79791.7	0.72
Bajaj Hindusthan Sugar Ltd.	2.78	0.37	1.95	75633.3	0.68

Source: Prepared by the author based on CMIE Prowess database.

Table A2.

Top 20 Indebted Firms in Service Sector (as on March 2018)

Company Name	ROA	ICR (times)	DER (times)	Debt(₹ million)	% of the Total Debt
Bharti Airtel Ltd.	0.06	0.94	0.64	682955	20.28
Reliance Communications Ltd.	0.09	5.47	3.89	441690	13.11
Adani Ports & Special Economic Zone Ltd.	6.12	4.31	1.13	205987.1	6.12
Mahanagar Telephone Nigam Ltd.	17.51	—	—	173641	5.16
Tata Teleservices (Maharashtra) Ltd.	23.93	—	—	135860.4	4.03
Adani Power Ltd.	4.69	0.18	1.02	98108.5	2.91
Adani Transmission Ltd.	0.21	0.96	2.02	83716.9	2.49
CESC Ltd.	3.16	2.81	0.63	69459.9	2.06
Wipro Ltd.	12.58	59.47	0.14	68855	2.04
Adani Enterprises Ltd.	1.34	1.53	1.71	67970.9	2.02
Shipping Corpn. Of India Ltd.	0.92	0.5	0.79	65897.6	1.96
Chambal Fertilisers & Chemicals Ltd.	5.44	3.64	1.95	56093	1.67
Great Eastern Shipping Co. Ltd.	1.38	1.47	0.81	47119.6	1.40
Essar Shipping Ltd.	3.19	0.28	2.03	46273.7	1.37
Hotel Leelaventure Ltd.	0.06	1.03	9.84	43705.2	1.30
Gujarat State Petronet Ltd.	8.91	23.38	0.6	43561.7	1.29
GAIL (India) Ltd.	7.29	22.78	0.05	31849.2	0.95
Interglobe Aviation Ltd.	10.47	10.2	0.35	26640.7	0.79
Coffee Day Enterprises Ltd.	2.11	0.5	0.55	25594.8	0.76
Ushdev International Ltd.	76.49	—	—	24985.4	0.74

Source: Prepared by the author based on CMIE Prowess database.

Table A3.

Top 20 Indebted Firms in Construction Sector (as of March 2018)

Company Name	ROA	ICR (times)	DER (times)	Debt(₹ million)	% of the Total Debt
Reliance Infrastructure Ltd.	1.2	1.21	0.75	206820.6	10.40
IL & FS Transportation Networks Ltd.	1.96	0.82	4.46	161777.6	8.13
DLF Ltd.	0.25	1.3	0.92	160229.6	8.05
Larsen & Toubro Ltd.	4.23	5.59	0.22	114972.7	5.78
Punj Lloyd Ltd.	1.31	—	44.4	87532.1	4.40
IVRCL Ltd.	16.61	—	—	77577.3	3.90
GMR Infrastructure Ltd.	4.36	0.3	1.48	75957	3.82
IRB Infrastructure Developers Ltd.	4.47	3.29	2.17	60848.8	3.06
Parsvnath Developers Ltd.	2.03	0.63	0.95	46137.6	2.32
Hindustan Construction Co. Ltd.	0.52	1.18	1.34	41458.4	2.08
Dilip Buildcon Ltd.	7.79	2.46	1.2	37523.6	1.89
Godrej Properties Ltd.	3.73	2.25	1.67	37230	1.87
Simplex Infrastructures Ltd.	0.15	1.09	2.2	36213.8	1.82
Pratibha Industries Ltd.	46.77	—	—	32432.9	1.63
Ircon International Ltd.	2.38	9.41	0.85	32000	1.61
IL & FS Engg. & Construction Co. Ltd.	0.08	0.98	19.12	30795.6	1.55
Gayatri Projects Ltd.	3.34	1.68	1.75	30357.9	1.53
Puravankara Ltd.	0.87	1.16	0.92	30333.7	1.52
Supreme Infrastructure India Ltd.	13.76	—	21.37	30174.3	1.52
Prestige Estates Projects Ltd.	1.75	1.64	0.82	29293	1.47

Source: Prepared by the author based on CMIE Prowess database.

Footnotes

Acknowledgements

The author is grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

ORCID iD

K. Dhananjaya

Notes

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