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).

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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

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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,

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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).

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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.

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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

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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