Upload
duongthien
View
223
Download
1
Embed Size (px)
Citation preview
1
Determinants of non-performing assets of banking
sector in India: A quantitative study
T.K. Jayaraman1
Keshmeer Makun2
Ajeshni Sharma3
Working Paper 2017/5
Fiji National University
CBHTS
Nasinu campus
Fiji Islands
1 Research Professor, International Collaborative Programme, University of Tunku Abdul
Rahman, Kampar campus, Perak State, Malaysia 2 Lecturer in Economics, School of Economics, Banking and Finance, Fiji National University,
Nasinu campus, Fiji. 3 Lecturer in Banking, School of Economics, Banking and Finance, Fiji National University,
Nasinu campus, Fiji.
2
Determinants of non-performing assets of banking
sector in India
A quantitative study
T. K. Jayaraman
Keshmeer Makun
Ajeshni Sharma
Abstract
India’s commercial banks are under stress. Their gross non-performing assets
(NPAs) have been growing since 2010. As a percent of gross advances, NPAs
reached 9.3 percent in Fiscal Year (FY) 2015/16 from 5 percent a year earlier.
For the banks in the public sector alone, which dominate the Indian banking
scene, the ratio rose to 7.2 percent in FY 2015/16 from 3.4 percent in the
previous year. Increased provisioning for bad loans and fall in banks’ net
interest incomes of the banking system in recent years have resulted in gradual
reduction in annual credit flows. The official Economic Survey 2016-17,
released early this year, hinted at the likely adverse impact of falling bank
credit to private sector on economic growth. There are growing concerns all
around about the impact of NPAs on the declining efficiency in the banking
system. This paper presents results of an empirical study focusing on
determinants of NPAs, covering a 56 quarterly observation (2000-2015). The
results indicate that real GDP, gross advances, total operating expenditures and
inflation are indeed important determinants of NPAs. In the long run,
economic output represented by real GDP and total operating expenditure are
found to be inversely related with NPAs, while gross advances and inflation
are found to be positively influencing the NPAs.
I. INTRODUCTION
India’s financial system has been recognized recognised to be more sophisticated in the
developing world with numerous institutions. Among these institutions, the commercial banks
under the fractional reserve system play a very significant role by providing credit to private
3
sector by mobilizing savings of the country and providing credit to aspiring investors and
households. Thus, the role of the banking system as an intermediary, between savers and
investors has been growing in importance over the years.
Bank credit as a proportion of total domestic credit to economic agents has increased from 27.8
percent of gross domestic product (GDP) in 2000 to 52.6 percent in 2015 (WDI, 2017). As a
result, private sector investment rose, as a ratio of GDP rose from 24.3 percent of GDP in 2000
to 33.3 percent in 2015 (WDI, 2017). Increase in credit growth has a negative side too. If banks
were inefficient and the loan recovery process was poor, their failure would be imminent
imposing heavy costs on the economy. Bank failures in India are not uncommon4.
The commercial banks’ non-performing assets (NPAs)5 as percent of total assets, which have
been growing for past three years, reached 9.3 percent in Fiscal Year (FY) 2015-16 from 5
percent a year earlier (IMF 2017a). The public sector banks (PSB)6, which dominate the banking
scene to the extent of 70 percent in terms of share of business, have more than 80 percent of
NPA of the entire banking system. The NPAs of all commercial banks according to the
Economic Survey for 2016-17 (Government of India, 2017) stood at a record level of 12 percent
as of January 20177. This is officially acknowledged to be higher than in any emerging market
and with the sole exception of Russia in the developed world8
4 Referring to 477 institutions which went into liquidation or amalgamation in between 1951-1969 since India’s
independence in 1947, an IMF study (Mohan and Ray 2017) mentions liquidation is common in the private sector, whereas the public sector banks are often rescued by re-capitalization. 5 The term Non-performing assets refer to non-performing loans. Both terms are interchangeable. See Mohan and
Ray ( 2017 )
6 The term public sector bank (PSB) covers (i) fully owned State Banks of India from the days of British India
before 1947 and State Banks in the princely states until fully brought in the political fold by 1950 when India
became a single entity as the Republic of India ; and (ii) the nationalized banks in 1969, some of which are being
partly privatized with majority still retained by the government ; (iii) regional rural banks (RRBs) set up later on to
serve the agricultural and microenterprise interests, as commercial banks were unable to meet the credit needs
(Mohan and Ray 2017) 7 An Asset quality review (AQR) carried out by the banks in response to Reserve Bank of India’s directive in late
2014-15, according to the stressed assets (a sum of gross NPA, re-structured assets and written off accounts) was
estimated to be in the range of 17.7 percent of gross advances in 2016 (Mohan and Ray 2017, Mundra 2016a, 2016b). The most recent figures, released in May 2017, show that it is still close to 17 percent (Panagaria 2017).
8 The latest data released by Reserve Bank of India through the right-to-information request shows banks’ total
stressed loans - including non-performing and restructured or rolled over loans - rose 4.5 percent in the six months
to end-June. In the previous six months they had risen 5.8 percent. They were on October 11, 2017 Rs 9.5 lakh
crores (Rs 9.5 trillion or US$145.6 billion).
4
In this context, measurement of efficiency of banks ever since the banking reforms which were
introduced in the early 1990s and implemented well into the late 1990s becomes important not
only for the bank managements but also for the central bank, the Reserve Bank of India (RBI),
which is the entrusted with the responsibility of maintaining financial stability. The objective of
this paper is to identify potential determinants and investigate their effect on NPAs in India over
the period of 2000- 2015.
The paper is organized along the following lines. The next section gives an overview of the
India’s financial sector and commercial banks in general with specific focus on trends in the
operations of commercial banks during 2000-2015 since the introduction and gradual
implementation of economic reforms since the late 1990s. Section three presents the literature
survey on NPAs and its determinants. Section four outlines the model, data source, methodology
and results, and finally section five provides a summary and conclusions.
II. INDIA’S FINANCIAL SECTOR AND COMMERCIAL BANKS
Ranked as the sixth largest economy in the world in terms of nominal GDP by IMF (2016),
India’s financial sector (Figure 1) has been playing a major role since liberalization of the
economy with the introduction of reforms in the late 1990s, which have facilitated a market
driven economy. The Indian financial system has seen drastic changes and transformation since
then. The financial institutions, as of 2017, comprise 93 scheduled banks9 of which 27 and 21 are
in the public and private sectors; and the rest owned by foreign interests10
. The other institutions
cover besides development finance institutions; 95,487 cooperative institutions, 56 regional rural
banks, post office banks, 53 insurance companies and stock markets (Mohan and Ray 2017).
Commercial banks, empowered to create money under the fractional reserve system by lending,
naturally dominate the system; and this paper, therefore, looks at them more closely. In
comparison, the non-banking financial institutions (NBFI) sector operates under three
institutionalized categories of All-India Financial Institutions (AIFIs), Non-Banking Financial
Companies (NBFC) and Stand-Alone primary dealers (PDs) are regulated and supervised by
Reserve Bank of India (RBI). The NBFIs address the gap in credit financing not met by the
scheduled commercial banks such as physical asset financing, infrastructure loans and
government securities market makers in primary and secondary market (RBI, 2017).
9 The term ―scheduled banks‖, inherited term from the British India days, refers to banks originally included in the
Second Schedule to the Reserve Bank of India (RBI) Act before Independence in 1947; and those others
subsequently added by ―virtue of having paid up capital and reserves being more than Rs 500, 000 in the aggregate‖
(RBI Act amended in 2008). 10 In this paper, the terms commercial banks and scheduled banks are used interchangeably.
5
India’s insurance sector is considered as one of the largest in the world in terms of population
demographics. The establishment of Insurance Regulatory and Development Authority Act
(1999) ensured the insurance market became competitive and remove the sole monopoly enjoyed
by Life Insurance Corporation of India (LICI). Currently, LICI dominates more than seventy
percent of the insurance market (Indian Brand Equity Foundation, 2017), followed by 24 life
insurance companies, 28 general insurance companies and one re-insurer in the private sector.
The growth impact of the commercial banks during the 15-year period of study is well reflected
in rapid growth of deposits and credit disbursed (Table 2). Their deposits increased from 41.3
percent in 1999-2000 to 69.5 percent of GDP in 2015-16; whereas the advances have grown
from 20.5 to 54.4 percent of GDP during the corresponding period.
Table 1: India's Financial Sector Institutions: Number
Institutiuons Av. (2000-2005) Av. (2002-2010) 2010 2011 2012 2013 2014 2015 2016
Commercial Banks
Public Sector Banks 27 28 28 28 28 26 27 27 27
Private Sector Banks 29 24 20 20 20 20 20 20 21
Foreign Banks 0 29 32 34 41 43 43 44 45
Insurance Companies
Life 11 18 23 23 24 24 24 24 24
Non-Life 12 17 25 25 27 27 28 28 29
Re-Insurers 1 1 1 1 1 1 1 1 1
Pension Fund 1 0 1 1 1 1 1 1 1
India's Financial Sector Institutions: Number
Source: RBI (2017)
Figure 1: Financial Institutions in India: 2017
Source: Mohan and Ray (2017)
Financial Institutions
Banking Sector
Non-Banking Sector
Commercial Banks
Co-operative Banks
Pension Funds
Mutual Funds
Non-Banking Financial
Institutions
Insurance Companies
6
As of 2015, PSBs’ aggregate deposits stood at Rs. 65,025.01 billion (47.9 percent of GDP)
dominating 72.9 percent of market share. The PSBs hold Rs. 49,283.11 billion of total credit
(36.3 percent of GDP) with 71.6 percent of market share. The private sector banks’ aggregate
deposits amounted to Rs. 17,573.15 billion (12.9 percent of GDP) controlling 19.7 percent of
market share and holding Rs. 14,334.22 billion of total credit (10.6 percent of GDP) with 20.8
percent of market share, while; foreign banks’ deposits were Rs. 2,678.91 billion (2.9 percent of
GDP) with a narrow market share of 4.4 percent and total credit outstanding at Rs. 3,355.09
billion (2.5 percent of GDP), which amounted to 4.9 percent of market share. On the other hand,
regional rural banks, also in the public sector, held a limited market share of 3 percent of deposit
market constituting of Rs. 2,678.91 billion (2.0 percent of GDP) and with a modest credit share
of 2.6 percent or Rs. 1,812.31 billion (1.3 percent of GDP).
Table 2: Growth of Commercial Banks in India: 2000-2015
Year
Deposits
(Billions) Rs.
GDP (Billions)
Rs.
Deposits As
Percentage (%)
of GDP
Loans &
Advances
(Billions) Rs.
Loans as
Percentage (%)
of GDP
1999-00 9003.07 21774.13 41.35 4434.69 20.37
2000-01 10552.33 23558.45 44.79 5256.83 22.31
2001-02 12026.99 25363.27 47.42 6457.43 25.46
2002-03 13556.23 28415.03 47.71 7392.33 26.02
2003-04 15755.3 32422.10 48.59 8636.32 26.64
2004-05 18375.59 36933.69 49.75 11508.36 31.16
2005-06 21646.79 42947.06 50.40 15168.1 35.32
2006-07 26969.34 49870.90 54.08 19812.35 39.73
2007-08 33200.61 56300.62 58.97 24769.36 43.99
2008-09 40632.01 64778.27 62.72 29999.24 46.31
2009-10 47524.56 77841.15 61.05 34970.54 44.93
2010-11 56158.74 87360.39 64.28 42974.88 49.19
2011-12 64535.49 99513.44 64.85 50735.59 50.98
2012-13 74296.77 112727.64 65.91 58797.73 52.16
2013-14 85331.73 124882.05 68.33 67352.13 53.93
2014-15 94351.01 135760.86 69.50 73881.79 54.42
Source: RBI (2017) and Author’s Calculations
Assets of commercial banks have grown from 50.9 percent in 2000 to 88.6 percent of GDP in
2015. The deposits growth reflected the increase in number of branches across the country. A
total number of 125,672 branches were in operation in 2015, compared to 65,919 in 2000
increasing by 91 percent which indicates spread of banking operations across rural, semi-urban,
7
urban and metropolitan centers in the country (Figure 2). The PSBs had 89,711 branches (68.8
percent); the private sector banks had 20,434 (15.7 percent); foreign banks had 332 (0.3 percent)
and regional rural banks had 20,005 (15.3 percent) number of offices operation in 2015.
However, the total number of banks (including regional rural banks) has decreased to 149 in
2015 compared to 296 in 2000 as failures led to liquidation and amalgamation of banks (RBI,
2016a)
Figure 2: Commercial Banks: Deposits and Advances (Rs. Billions): 2000-2015
As the quality of assets was seen to be weakening with the emergence of rising ratio of gross
NPA to gross advances since 2013-14, RBI applied rigorous assessment standards. The newly
introduced Asset Quality Review (AQR) in mid 2015 revealed that the system wide gross NPA
ratio went up from 5.1 percent in September 2015 to 7.6 percent in March 2016 (IMF, 2017a).
The rising NPA signals the deteriorating asset quality and stressed assets for commercial banks
as a whole. The stressed assets which is derived from non-performing assets plus restructured
loans plus written-off assets quiet alarming has reached 17.08 percent in 2016 for PSBs,
compared to 2.83 percent for private sector banks and 4.20 percent for foreign banks.
Consequently, PSBs account for 70.84 percent of stressed assets for the entire banking system
(Figure 3).
8
Figure 3: Gross Non-Performing Assets of Commercial Banks (Percentage): 2000-2016
Source: Handbook of Statistics on Indian Economy 2015-16
As the PSBs, whose share of business in the banking system being around of 70 percent, have
been found to have 80 percent of NPAs of the system, the deteriorating quality of assets has been
causing concerns regarding stability of the financial system. Aside from long term stability
concerns, the immediate concerns are about steady fall in credit to private sector, which is
affecting private sector investment. A major proportion of non-recoverable loans are the public
sector banks, notably State Bank of India and its associates; most of them are due from large
conglomerates, in steel and infrastructure11
. The banks are now required to make higher
provisions to account for more defaulters being pushed into bankruptcy.12
11 According to latest data released by RBI under RTI Act on October 11, the bad loans as a percentage of total
loans reached 12.6 percent at end-June, the highest level in 15 years (Tripathy and Choudhary 2017)
12 Under the strict provisioning regime introduced in August 2017, Reserve Bank of India requires the banks to
provide for at least 50 percent of the secured loans to companies taken to bankruptcy proceedings, and 100 percent
for the unsecured part. It is reported by Reuters (2017). A dozen of the biggest such cases account for nearly 1.78
trillion rupees, or a quarter of total non-performing assets and more than 20 other sizeable companies are at risk of
being taken to bankruptcy court.
9
Since banks have been the main source of financing private sector credit needs, the growing bad
loans problem has reduced bank profits; and fear of new debts has further discouraged new
lending, especially to smaller firms. Growth rate of new loans during Jan-March Qr. of 2017
credit is 5 percent the lowest growth rate in more than six decades, and the decline is also steady,
which is of great concern at a time when India’s growth rate, though positive, has been falling
for the last few quarters13
Reforms which were suggested include re-capitalization of PSBs14
and setting an up ―bad bank‖
purchasing the NPAs15
,
III Literature Survey on NPL & Determinants
The literature on NPAs has started to build up only very recently. That was more because of the
interest in finding out the reasons behind various banking and financial crises such as those in the
United States of America (USA) during 1975-80, in Latin America (the early 1980s) and in East
Asia (the mid1990s), in the Sub-Saharan Africa (later 1990s). Aside from the impact of banking
crises on depositors (households and firms), these crises especially of the 1990s became serious
tests of vulnerability of financial sectors. This is because of the growing globalization the impact
of a banking crisis in one country, spreading across the borders at a lightning speed. Once the
crises are found linked to the impaired assets cf the banking system, empirical studies began to
mushroom since the late 1980s.
The theoretical base for the link between impaired assets and financial crisis triggered by
banking crisis, as pointed out by Ekanayake and Azeez (2015), lies in the delegated monitoring
authority of financial intermediation (Diamond,1984). Savers all over the world have been
finding the reliable and credit worthy borrowers difficult to lend their investible funds and to
13 Economic growth rates for the past 4 quarters are: 2.4% for 2016 Jan –March; 1.5% for July_Sept 1.5%; Oct-
Dec: 1.6%’; 2017 Jan-march 1.5%; April-June: 1.3%; and July-Sep 1.4%.
14 Fitch Ratings estimates Indian banks will need $65 billion of additional capital by March 2019 to meet Basel III
global banking rules. Moody’s expects the top 11 state lenders alone will need nearly $15 billion. The government
has just $3 billion left in its budget for bank recapitalization (Tripathy and Choudhary 2017)
15 Many commentators on the Indian banking scene, based on the past experiences, are pessimistic of any set of new
efforts, which are proposed from time to time to solve the mounting NPA problem. For example, Chakravarty
(2017) dubs the latest initiative empowering Reserve Bank of India as one of ―an alphabet-soup of initiatives‖. They
carry abbreviations, which include AQR (Asset Quality Review), ARC (Asset Reconstruction Companies), SDR
(Strategic Debt Restructuring (SDR), and the S4A (Scheme for Sustainable Structuring of Stressed Assets). Citing the Economic Survey for fiscal year: 2016-17 by Government of India (2017), Chakravarty (2017) he argues that
such past initiatives have not been successful since (i) banks are unwilling to recognize losses; (ii) there is no
coordination between consortium lenders; (iii) bankers want to avoid any investigations and inquiries, resulting in
writing down losses; and (iv) bankers fear that banks’ capital position, already strained, will be further eroded if
large write-offs are required.
10
monitor the use of borrowed funds. As a result they look upon banks as ideal intermediaries
because of their specialization in the areas of appraisal of loan applications, supervision and
monitoring the use of borrowed funds, to minimize occurrences of adverse selection and moral
hazards (Mishkin, 2013).
Under this theory, depositors prefer to commercial banks’ for handling their funds, who also
assure them of attractive interest rates as well as other services, which are not in terms of cash
but incentives to depositors. As long as the role of financial intermediation is carried out in full
faith and the funds are held in trust by banks, the system is not expected to fail. It is only when
greed takes over precedence over safety of depositors’ funds and banks are tempted to give out
risky loans. If the delegated authority is abused by banks, as Diamond (1984) noted in his study,
more adverse selection ensues; and when banks become slack in monitoring the utilization of
borrowed funds, loan defaults become the order of the day16
.
Following the financial and banking crises in the 1980s in the US and in the 1990s in Latin
America and East Asia, empirical studies categorized the causal factors into two broad
categories: macroeconomic; and bank specific. Macroeconomic determinants include a wide
range of variables impacting cash flows of businesses and households, who are the savers as well
spenders. They include economic growth, inflation, real exchange rate and investment climate
affected by expectations of various economic agents. All of them are beyond the control of banks
and hence they are called exogenous. The bank specific factors, by definition being specific, are
within the control of individual banks. They include rapid growth in lending by a bank which
may be due to relaxation of credit standards with a desire to capture a greater share of market.
Consequently, this leads to rise in loan defaults if borrowers tend to fulfill debt servicing in in
time. Besides, during a credit boom often associated with the expansionary phase of the
economy, bank managers tend to take risks. Though maybe an aberration once in a while,
consistent risk taking falls under the description of bad management. Higher credit growth
16 The worst possibility occurs, when the public sector dominates the banking system with the majority of the banks
are fully owned or more than 51 percent of shares are held by the government, as in India. The socialistic policies of
the leftist oriented governments since 1947, (which led to nationalization of banks) and until the late 1990s, the
banks were required by government to lend to ―priority sectors‖; and under this borrowers were given generous
loans without any due regard to their capability in the use of funds or ability to generate any cash flows to return the
loans. Nearly five decades, the Indian banking system ―suffered from the imperatives of societal concerns and thus
were torn between the dilemma of equity versus efficiency‖ (Mohan and Ray 2017). The adverse effects are still
lingering and have not yet disappeared, as India clings on to the idea of public ownership of banks. The latest
Economic Survey: 2016-17 (Government of India 2017) reports the gross NPA ratio to gross advances rose to the
highest level (9.1 percent) in 2017 amongst all emerging economies (with the exception of Russia at 9.2 percent).
This is a clear example of the adverse outcome of diluting the autonomy of the banks and interference by the
governments, resulting in the failure of banks in their role under the delegated monitoring (Diamond 1984).
11
which is due to a combination of all these bank specific factors has been centre of many
empirical studies
Macroeconomic Determinants
The earliest study (Keeton and Morris 1987) examining more than 2000 failed commercial banks
in the United States put the blame on general economic conditions. This was followed by a study
by Sinkey and Greenwalt (1991) which was more specific. As they focused on different regions,
mainly agrarian and industrialized, in the mainland USA, the two authors brought out the subtle
differences in regional economic conditions which affect debt financing abilities of borrowers.
Other researchers in their studies covering other countries and undertaken in different countries
mostly as panel studies or single country study confirmed the negative relationship between
growth in real GDP (RGDP) and NPA (Brownbridge 1998; Salas and Suarina 2002, Rajan and
Dahal 2003, Fofack 2005, Jimnez and Saurina 2006, Das and Ghosh 2007; Al-Samadi and
Ahmad 2009; Kemraj and Pasha 2009, Ekanayake and Aziz 2015; Warue (2013).
The next variable, which has a high degree of influence on NPA, is inflation. Fofack (2005) in
his study on NPA in Sub-Saharan African countries found the existence of a positive association
of NPA with inflation. His argument was inflation was responsible for erosion of commercial
banks’ equity over time and consequently higher credit risk, a view which was also the inference
drawn from the study by Al-Samadi and Ahmad (2013) and Warue (2013). Thus, a higher
inflation leads to higher level of NPA. There is also an opposite view : in shortage –ridden
economy, with high import restrictions , inflation would boost incomes of and profits of business
enterprises, given wages and costs of raw materials in the short run do not rise immediately’ and
consequently, the windfall rise in earnings would push up loan repayment ability faster and
hence NPA would decrease. The possibility of a negative relationship renders an a priori
conclusion. Thus, the relationship becomes ambiguous and is subject to testing by empirical
investigation and proven either way beyond doubt.
Bank Specific factors
The bank specific factors which have been identified in various studies on NPA are credit
growth, poor bank management, and aggressive credit policies with eagerness to increase the
market share. Keeton (2003) in his study confirmed a positive relationship between growth in
credit and NPA and attributed the rise in credit growth to lowering of credit standards. The risk
taking behavior is linked to poor management when bank managers failed to enforce high levels
of bank efficiency resulting in the increasing ratios of NPA to gross advances (Berger and Young
1997, Kwan and Eisenbis 1997).
12
While Misra and Dal (2010) noted a positive connection between NPA and credit growth, Das
and Ghosh (2007) stressed the positive impact on rising NPA. Hu et. al. (2006) banks with a
larger credit-deposit ratio had higher NPA. Salas and Saurina (2002) put the problem in a
broader perspective linking all the key variables and observed that rapid credit growth, bank size,
capital and market power explained variation in NPA
IV. Model, Data, Methodology and Results
The model proposed for the empirical investigation into the causes behind India’s NPA is
primarily conditioned by the data availability in regard to the two broad categories of likely
determinants discussed in Section III.
Among the macroeconomic factors, the economists consider annual economic performance,
represented by changes in gross domestic product in constant prices or real GDP (RGDP), as the
most important amongst all. Increase in annual RGDP indicates growth in the incomes of
businesses and households, which enables improving their ability to borrow more and enhancing
their capacity to undertake additional debt financing obligations. The banks in turn would have
less addition to NPA stock; and eventually the existing NPA stock would get decreased. In
regard to the effect of inflation on NPA, as noted earlier, there is some ambiguity. It may be
argued inflation would hurt reduce purchasing power of households with fixed incomes, thereby
decreasing their ability to service debts, paying interest and installments in time. Inflation would
thus contribute to rise in NPA stock. It can also be argued that inflation would boost incomes of
and profits of business enterprises, given wages and costs of raw materials in the short run do not
go up in the short run, and hence NPA would decrease.
An important variable which is more system oriented than bank specific is gross advances (GA)
reflecting the entire banking system’s role in an expansionary phase or a recessionary phase of
the economy. Banks are more optimistic with high expectations during expansion and become
more aggressive than ever before, letting their normal credit standards relaxed and watered
down. On the other hand, during recession banks become more pessimistic than borrowers as
their expectations makes them reluctant to lend since they are uncertain of the borrowers’ ability
to return the loans, let alone making interest payments. So, it is likely NPA may rise when GA
goes up, as credit standards generally get loosened to facilitate more borrowings.
The following hypotheses are formulated on the foregoing relationship between NPA, and the
likely determinants:
13
(i.) NPA and RGDP are indirectly related: higher the growth of the economy, lower
would be NPA.
(ii) NPA and GA are positively related. A rise in bank credit under various
circumstances, either due to aggressiveness of bank managers to improve their market
share of business or the relaxed credit standards would increase the chances of bad loans
rising; .
(iii) NPA and inflation (rise in consumer price index) is ambiguous: higher inflation
may either lead to reduction in NPA or would contribute to rise in NPA. The
ambiguous relationship has to be empirically tested
(iv) NPA and total expenditure (operating expenditures and the provision for bad loans)
are inversely related. Higher the total expenditure devoted to recovery of loans, the less
would be NPA.
Model and Data
The model for testing the above hypotheses is formulated as follows:
NPA = f (RGDP, GA, TE, CPI) (1)
Where:
NPA = Non-performing assets
RGDP = Real gross domestic product
GA = Gross advances
TE = Total operating expenditure
CPI = consumer price index
Since all the data series of all the variables are in terms of annual observations ( 2000-15) they
are split by resorting to cubic spline procedure into quarterly observations. Thus we have 56
observations. The data sources are ADB (2016), World Bank (2017) and RBI (2016).
Methodology
First, in regression analysis, knowing the time series properties of data series is crucial.
Generally, they contain a unit root and tend to be non-stationary. Gujarati and Porter (2009)
argued that to avoid any inconsistencies in coefficient estimation, series are required to be
stationary. Therefore, it is important to check the stationarity properties. The Augmented
Dickey-Fuller (ADF) unit-root test is used for potential non-stationary concerns. Second, we
use Autoregressive Distributed-lag (ARDL) procedure to empirically estimate the model. There
are several advantages of the ARDL method. One, it is possible to test the cointegrating
association between the variables regardless of different orders of integration (Pesaran et al.,
2001). The other improvement to the ARDL technique is that it is appropriate to test long run
14
associations among the series if the sample period is small and it can also correct for probable
endogeneity (Pesaran et al., 2001).
Cointegration Analysis
The bound F-test for cointegration is within the ARDL methodology. The ARDL method is a
two-step technique. To examine the presence of long run cointegration, equation (1) is re-
arranged as an unrestricted error correction model (UECM) in the ARDL framework as
equation (2):
t
n
i
t
n
i
tt
n
i
t
n
i
t
n
i
tttttt
CPITEGARGDPNPL
CPITEGARGDPNPLcNPL
1
110
1
191
1
81
1
71
1
6
15141312110
)(ln)(ln)(ln)(ln)(ln
)(ln)(ln)(ln)(ln)(lnln
(2)
where delta ( ) is the difference operator and represents short term dynamics. The parameters
attached along with one period lagged variables measure long term relationships. The null of no
long run cointegration ( oHo
54321
: ) is disputed in opposition to the
alternative hypothesis which states the presence of a long run association
( oH 543211
: ). If the null proposition of zero cointegration is discarded,
the existence of the long term cointegration relationship is established.
The Bounds F-statistic is compared against the lower and upper bound critical values calculated
by Pesaran et al. (2001). There could be three probable outcomes: (1) when the estimated F-
statistic surpasses the upper bound critical value, then the null proposition can be discarded in
favor of the alternative hypothesis, (2) If the expected F-statistic is less than the lower bound
critical value, then the null proposition cannot be discarded and (3) when the estimated F-statistic
is in between the lower and upper bound critical values, then the outcome is inconclusive.
Narayan (2004) argued that a critical value of Pesaran et al. (2001) is for large sample studies.
Narayan (2004) calculated a new set of critical values based on small samples. Therefore, we
used Narayan’s (2004) critical values.
The succeeding step examined the ARDL model to obtain long run estimates. Finally, the error
correction short run model was estimated. The short run error correction model is used to
identify short run dynamics and to verify the robustness of the estimated coefficient of long run
with respect to equation (2). It is specified as shown in equation (3):
15
tttt
n
i
t
n
i
t
n
i
t
n
i
t
ECMCPITE
GARGDPNPLcNPL
11101
1
9
1
1
81
1
71
1
60
)()(ln)(ln
)(ln)(ln)(lnln
(3)
Here: ECM represents the error correction item. The ECM was computed from the long term
estimated parameters in equation (2). The error correction term is expected to be significant and
negatively associated with the dependent variable.
Results
The likely non-stationary concern was addressed using the Augmented Dickey Fuller (ADF) test.
Even though the Autoregressive Distributed-lag (ARDL) technique does not necessitate prior
checking of the unit root issue, in the empirical analysis it is essential to undertake this test to
ensure that variables are stationary so that results obtained are robust. Table 3 reports the ADF
unit-root test result.
Table 3: Unit root test result Augmented Dickey Fuller Test
In Level In First Difference
Variables Constant Constant with trend Constant Constant with trend Conclusion
lnNPLt -1.920 -2.573 -6.649* -6.756* I(1)
lnRGDPt -3.952** -4.039** -8.438* -8.247* I(1)
lnGAt 2.386 -0.984 -7.479* -8.592* I(1)
lnTEt -0.142 -3.147 -6.608* -5.870* I(1)
lnCPIt 0.695 -2.593 -6.713* -7.039* I(1)
t -5.473* -5.353* -5.448* -5.315* I(1)
Note: * and ** represent significance levels at one and five percent, respectively. The critical value
of the constant is -3.65 at one percent. A critical value for the constant with trend is -4.26 at one
percent. The lag length based on Schwarz information criterion is 2. t
is the residual from the
unrestricted regression
The ADF unit-root test was applied on two sets, that is, constant and constant with time trend.
The results indicated that the variables in the levels were non-stationary except for RGDP which
16
were stationary in levels under constant and trend, andt
. However, in the first difference they
were all stationary.
The result of the estimated bounds F-test is provided in Table 4. For equation (2) when NPA is a
dependent variable, the F-statistic of 5.92 was higher than the upper band critical value of 4.156
at the five percent significance level. Hence, the null hypothesis of zero cointegration was
discarded, implying that there was a single cointegration, long term, economic relation between
the variables when normalized on non-performing loans.
Table 4: Bounds F- test Significance Level Critical Value Calculated F statistic
Lower Band Upper Band
1 percent 4.400 5.664
5 percent 3.152 4.156 5.92
10 percent 2.622 3.506
Note: Critical values for bounds test are from Narayan (2004), Case D: restricted intercept and no trend.
ARDL Estimates
In the previous section, we examined cointegration relationship and found that the series were
cointegrated in the long term. The following step examined the ARDL model and the associated
long term relationship between the real gross domestic product, gross advance, total operating
expenditure, inflation and non-performing loans. Table 5 provides the estimates of the ARDL
model.
17
Table 5: ARDL model
Panel (A)
Variable coefficient T-Ratio P-Value
lnNPL (-1) 1.827 4.124 0.000
lnRGDP(-1) -0.349 -2.308 0.026
lnGA(-1) 4.307 7.769 0.000
lnTE(-1) 0.061 1.975 0.054
lnCPI(-1) 1.831 1.841 0.072
Constant 0.014 0.117 0.907
Panel (B) Diagnostic check
R-squared – 0.92
DW statistic – 1.66
Serial correlation – 4.036 (0.104)
Functional form – 0.764 (0.382)
Normality – 1.85 (0.553)
Heteroscedasticity – 3.269 (0.071)
Note: In panel (B), figures in the parentheses are the p-values.
Panel (A) of Table 5 presents the estimates for the ARDL model and panel (B) shows the results
of a number of diagnostics checks conducted to assess the overall reliability of the estimated
model. The outcome of the diagnostic checks indicated that the model did not suffer from severe
econometric problems. The LM test indicated that the null hypothesis of no serial correlation
cannot be rejected. The Ramsey and Jarque-Bera check for model specification and normality
showed that the specification was correct and the errors were normally distributed. Furthermore,
the autoregressive conditional heteroscedasticity (ARCH) test indicated that the regressors were
independent and errors were homoskedastic. Thus, the autoregressive distributed-lag model was
found to be reliable.
18
Next, the long term parameter of the independent variables (real gross domestic product, gross
advance, total operating expenditure and inflation) was estimated. The long run parameter is
provided in Table 6.
Table 6: Long run coefficients Dependent variable is lnGDP
Variable coefficient T-Ratio P-Value
lnRGDPt -1.910 -3.243 0.002*
lnGAt 0.539 2.631 0.012**
lnTEt -0.338 -1.723 0.092***
lnCPIt 4.513 14.253 0.000*
Constant 0.081 0.118 0.906
Note: *, ** and *** represent significance at one, five and ten percent levels.
Discussion on the Long run results
The results for the long run relation between NPA and its determinants (real gross domestic
product, gross advance, total operating expenditure and inflation) in India are illustrated in Table
6. The results show strong evidence for a negative relation between NPA and RGDP in India.
The estimated coefficient of -1.91 indicates that higher economic growth lowers the non-
performing loans in India. This is in line with our initial hypothesis, that is, an increase in real
GDP raises the incomes of businesses and households, which enhances their ability to borrow
and their eligibility to undertake additional debt financing obligations. This in turn reduces the
NPA stock of banks.
The effect of gross advance is found to be positively related to NPA. The estimated coefficient
suggests that a one percent increase in gross advances raises that non-performing loan on average
by 0.5 percent. This reflects the risk taking behavior and aggressive credit policies to increase the
market share of the Indian Banks, resulting in higher non-performing loans. The finding is
similar to that of Keeton (2003).
Further, the effect of Indian banks operating expenditure is found to be mixed. While in the short
run it is positively related, in the long run the total operating expenditure is inversely related with
non-performing loans. The result indicates that higher total operating expenditure devoted to
recovery of loans reduces the non-performing loans in the long run.
19
Finally, the results also indicate strong positive relation between inflation and NPA.. This was
very contentious as the literature on inflation and non-performing loans is ambiguous. However,
in this case the estimated coefficient is found to be positive and statistically significant. The
result is consistent both in short run and long run. The positive relationship indicates that
increase in inflation reduces purchasing power of households and businesses and thereby
decreases their ability to meet the debt obligation, pay interest and installments in time. This
finding is similar to the Fofack (2005).
Short Run Error Correction Model
The error correction model was examined to evaluate the short run dynamic relationship between
NPA and its determinants (RGDP, GA. TE, and CPI), and to confirm the reliability of the long
term coefficient. It was estimated by normalizing the long run estimates. Table 7 shows the error
correction model results.
Table 7 Short run error correction model Panel (A)
Variable Coefficient T-Ratio P-Value
D(lnNPL(-1)) 1.010 9.836 0.000
D(lnRGDP) -0.349 -2.308 0.025
D(lnGA) 2.063 7.077 0.000
D(lnTE) 0.061 1.975 0.054
D(lnCPI) 1.831 1.841 0.072
ECM (-1) -0.182 -6.747 0.000*
Constant 0.014 0.117 0.907
Panel (B) Diagnostic Check
R-squared – 0.93
DW statistic – 1.66
Serial correlation – 1.752 (0.099)
Heteroscedasticity- 3.712 (0.142)
Note: * represents significance at one percent level. In panel (B), figures in the parentheses are the p-
values.
20
In Panel (A) the results indicate that the ECM coefficient carries an inverse sign and is
statistically significant at the one percent level which is preferable. Thus, the short run model
was consistent. The estimated ECM coefficient (-0.182) also determines the speed (0.182) of the
correction towards an equilibrium relationship. Further, the ECM also indicates that any
divergence from the long run relation in the current period should be adjusted by around 18
percent in the following period—implying that adjustment is rather slow. The model shows that
in the short run operating expenditure have positive influences on non-performing loans. Further,
the inflation was also found to be positive and significantly associated with non-performing
loans in the short run. The reliability check in panel (B) validated that the calculated ECM
equation did not have serious estimation issues.
.
V SUMMARY AND CONCLUSIONS
This paper undertook a quantitative study on the determinants of non-performing assets of
banking sector in India. The paper used quarterly data from 2000 to 2015. The quantitative
analysis was carried out using Autoregressive Distributed Lag (ARDL) procedure.
The findings indicated that the determinants such as real GDP, gross advances, operating
expenditure and inflation certainly matter for long term non-performing assets of Indian banks.
Real GDP was found to have significant negative relation with non-performing assets, implying
that rise in real GDP would increase the incomes of households as well as the business
enterprises thereby raising their ability to meet the debt obligations and hence, reduction in NPA
of banks. Total gross advances have positive impact on NPA. The risk taking attitude and
readiness to increase market share combined with poor bank management leads to higher bad
loans. The findings also show that total operating expenditure in relation to recovery of loans
reduces the non-performing assets of banks. Finally, inflation as another determinant of NPAs; is
found to be positively related, indicating that rise in CPI would reduce the incomes and the
ability to service debt.
Based on these findings, the policy implications are clear. Although one may argue that the two
macroeconomic conditions, namely economic growth and inflation in an open economy, are
affected by external conditions and beyond the full control by the government, there are certain
policy tools that policy makers can use appropriately to manage and maintaining these two
conditions. Secondly, banking sector itself on their own should implement policies and
procedures towards minimising risks, by stricter control over processing loan applications and
by efforts for restoring and maintaining high level of confidence in their operations.
21
References
Asian Development Bank (2016). Key Indicators for Asia and Pacific. [Online]
https://www.adb.org/publications/key-indicators-asia-and-pacific-2016 [Accessed: April,
2017]
Berger, A.N. and DeYoung, R, (1997). Problem loans and cost efficiency in commercial banks.
Journal of Banking and Finance, 21(6): 849-870.
Brownbridge, M., (1998). The causes of financial stress in local banks in Africa and implications
for prudential policy. UNCTAD Discussion Papers, United Nations Conference on Trade
and Development. pp: 132.
Chakravarty, M. (2017). Questions Raised by the NPA Ordinance,
http://www.livemint.com/Opinion/JAAfNEfzWda4xZfhm4eq3H/Questions-raised-by-the-
NPA-ordinance.html, accessed on May 10, 2017
Das, A. and Ghosh, S. (2007). Determinants of credit risk in Indian State-Owned banks: An
empirical investigation. Economic Issues-Stoke on Trent, 12(2): 27-46.
Diamond, D.W., (1984). Financial intermediation and delegated monitoring. Review of Economic
Studies, 51(3): 393-414.
Ekanayake, E.M.N.N., & Azeez, A.A. (2014). Determinants of Non-Performing Loans in
Licensed Commercial Banks: Evidence from Sri Lanka, Asian Economic and Financial
Review, 5(6): 868-882.
Fofack, H., (2005). Non-performing loans in Sub-Saharan Africa: Casual analysis and
macroeconomic implications. World Bank Policy Research Working Paper No. 3769.
Government of India (2017). The Economic Survey 2016-17. [Online]
http://indiabudget.gov.in/survey.asp [Accessed: April, 2017]
Government of India. Reserve Bank of India Act (2008), Amended: RBI
Gujarati, D. N., & Porter, D. C. (2009). Basic Econometrics. New York, NY: McGraw-
Hill/Irwin.
Hu, J.L., Yang, L., Yung-Ho and Chiu. (2006). Ownership and non-performing loans: Evidence
from Taiwan’s banks. The Developing Economics, 42(3): 405-420.
International Monetary Fund (IMF) (2017a): Article IV Consultation Staff Report, IMF Country
Report No.17/54, Washington, D.C.: IMF
International Monetary Fund (IMF) (2017a): India: Selected Issues, IMF Country Report
No.17/55, Washington, D.C.: IMF
Jimenez, G., Salas, V and Saurina, J. (2006). Credit cycles, credit risk, and prudential regulation.
Banco de Espana, January.
Keeton, W.R., (2003). Does faster loan growth lead to higher loan losses. Economic Review,
84(2): 57-75.
Kwan, S and Eisenbis, R. (1997). Bank risk, capitalization and operating efficiency. Journal of
Financial Services Research, 12(2): 117-131.
22
Misra, B.M. and Dhal, S. (2010). Pro-cyclical management of banks’ non-performing loans by
the Indian public sector banks. A research publication of Bank of International
Settlement: BIS.
Mohan, R. and Ray, P. (2017). Indian Financial Sector: Structure, Trends and Turns.
International Monetary Fund (IMF) Working Paper 17/7: IMF
Narayan, K. P. (2004). Reformulating critical values for the bounds F statistics approach to
cointegration: An application to the tourism demand model for Fiji. Department of
Economics, Monash University, Discussion Paper No. 02/04.
Panagaria, A. (2017). Bad bank is not needed,
http://www.livemint.com/Industry/SaoIXO98QAzwPC2bdvT5aJ/Bad-bank-not-needed-
to-solve-India-loan-mess-says-Arvind-Pa.html, accessed on May 10, 2017
Pasha, S. and Khemraj, T. (2009). The determinants of non-performing loans: An econometric
case study of Guyana. Presented at the Caribbean Centre for Banking and Finance Bi-
Annual Conference on Banking and Finance, St. Augustine, Trininad.
Pesaran, M.H., Shin, Y., Smith, R.J., (2001). ―Bounds testing approaches to the analysis of level
Relationships‖, Journal of Applied Economics, 16, 289–326.
Rajan, R. and Dhal, S.C. (2003). Non-performing loans and terms of credit of public sector
banks in India: An empirical assessment. Department of Economic Analysis and Policy:
Reserve Bank of India, Occasional Papers, 24(3): 81-121.
Reserve Bank of India, Basic Statistical Returns of Scheduled Commercial Banks in India -
Volume 44, March 2015 [Online]
Reserve Bank of India, Handbook of Statistics on Indian Economy. [Online]
https://rbi.org.in/scripts/AnnualPublications.aspx?head=Handbook%20of%20Statistics%
20on%20Indian%20Economy [Accessed: April, 2017]
Reserve Bank of India. Statistical Tables of Trends. [Online]
https://dbie.rbi.org.in/DBIE/dbie.rbi?site=home [Accessed: April, 2017]
Reserve Bank of India, Report on Trends and Progress of Banking in India 2015-16, Reserve
Bank of India Bulletin, March 2016.
Salas, V. and Saurina, J. (2002). Credit risk in two institutional regimes: Spanish commercial and
savings banks. Journal of Financial Services Research, 22(3): 203-224.
Sinkey, F. and Greenwalt, M.B. (1991). Loan-loss experience and risk-taking behaviour at large
commercial banks. Journal of Financial Services Research, 5(1): 43-59.
Strategic Debt Restructuring Scheme, Framework for Revitalising Distressed Assets in the
Economy – Guidelines on Joint Lenders’ Forum (JLF) and Corrective Action Plan
(CAP), Reserve Bank of India. [Online]
https://rbi.org.in/Scripts/NotificationUser.aspx?Id=9767 [Accessed: April, 2017]
Tripathy, D. and Choudhry, S. (2017).‖ No respite for Indian banks as bad loans hit record $146
billion‖, Reuters, Oct 11, 2017
23
Warue, B.N., (2013). The effects of bank specific and macroeconomic factors on non-performing
loans in commercial banks in Kenya: A comparative panel data analysis. Advances in
Management and Applied Economics, 6(3): 135-164.
World Bank (2016). World Development Indicators, Washington, D.C. World Bank. [Online]
http://data.worldbank.org/indicator/FS.AST.DOMS.GD.ZS [Accessed: April, 2017]
24
Appendix 1: List of Scheduled Commercial Banks in India: (2015-2016)
Number Public Sector Banks Private Sector Banks Foreign Banks
1 Allahabad Bank Axis Bank AB Bank Limited
2 Andhra Bank Bandhan Bank Abu Dhabi Commercial Bank
3 Bank of Baroda Catholic Syrian Bank Ltd. American Express Corp
4 Bank of India City Union Bank Ltd. ANZ Banking Group
5 Bank of Maharashtra DCB Bank Ltd. Bank of America
6 Bhartiya Mahila bank Dhanlaxmi Bank Bank OF Bahrain & Kuwait
7 Canara Bank Federal Bank Bank of Ceylon
8 Central Bank of India HDFC Bank Bank of Nova Scotia
9 Corporation Bank ICICI Bank Bank of Tokyo-Mitsubishi Ltd
10 Dena Bak IDFC Bank Barclays Bank PLC
11 IDBI Bank Indusind Bank BNP Paribas
12 Indian Bank Jammu &Kashmir Bank Ltd. Citibank N.A.
13 Indian Overseas Bank Karnataka Bank Ltd. Commonwealth Bank of Australia
14 Oriental Bank of Commerce Karur Vysya Bank Cooperative Rabobank U.A.
15 Punjab & Sind Bank Kotak Mahindra Bank Ltd Credit Agricole
16 Punjab National Bank Lakshmi Vilas Bank Credit Suisse AG
17 SB of Hyderbad Nainital Bank CTBC Bank
18 SB of India RBL DBS Bank Ltd
19 SB of Mysore South Indian Bank Deutsche Bank AG
20 SB of Patiala Tamilnad Mercentile Bank Ltd. Doha Bank QSC
21 SB of Travancore Yes Bank Ltd. Firstrand Bank Ltd
22 State Bank of Bikaner HSBC
23 Syndicate Bank Industrial &Comm Bank of China
24 Union Bank of India Industrial Bank of Korea
25 United Bank of India JP Morgan Chase Bank
26 United Comm Bank JSC VTB Bank
27 Vijaya Bank of India KBC Bank NV
28 Keb Hana
29 Krung Thai Bank Public Co. Ltd
30 Mashreq Bank PSC
31 Mizuho Bank Ltd
32 National Australia Bank
33 National Bank of Abu Dhabi
34 PT Bank Maybank Indonesia
35 Royal Bank of Scotland
36 Sberbank
37 SBM Bank (Mauritius) Ltd
38 Shinhan Bank
39 Societe Generale
40 Sonali Bank
41 Standard Chartered Bank
42 Sumitomo Mitsui Banking Corp
43 United Overseas Bank
44 Westpac Banking Corporation
45 Woori Bank