document6
TRANSCRIPT
Assets as Liability? :
NPAs in the Commercial Banks of India
A
Research Project Funded By
South Asia Network of Economic Research Institutes
Meenakshi Rajeev Institute for Social and Economic Change
Nagarbhavi, Bangalore-560072, India
Ph: 080-23215468
Fax:080-23217008
Email: [email protected]
January, 2008
Non-Performing Assets in the Indian Banking Sector
(with Special Reference to the Small Industries Sector)
By
Meenakshi Rajeev
Institute for Social and Economic Change
Nagarbhavi, Bangalore-560072, India
Ph: 080-23215468
Fax:080-23217008
Email: [email protected]
186
CHAPTER 1 INTRODUCTION
1.1 Introduction
Financial resource is, no doubt, one of the most important inputs for economic
development. Higher levels of financial development are significantly and robustly
correlated with faster current and future rates of economic growth, physical capital
accumulation and economic efficiency improvements (King and Levine, 1993a). The
relationship between financial development and the economic growth has been
established by various empirical studies ( see Adelman and Morris, 1968 and Goldsmith,
1969). It has been observed historically that banks formed the major part of financial
system and thus played an important role in economic development. In India also
financial system has been synonymous with banking sector. The importance of banking
system in India is noted by the fact that the aggregate deposits stood at 55 percent of
GDP and bank credit to government and commercial sector stood at 26 percent and 33
percent of GDP respectively in 2004-05.
In the earlier stages of development, banking credit was directed towards selected
activities only. For example, in the decade of 1960s, more than 80% of credit was to the
trade and industry sector whereas agriculture and small manufacturing sectors were
completely neglected. In 1969 nationalization of banks took place. At the time of
nationalisation of private sector banks in 1969, the prime concern was to use resources
available with these institutions for supporting the growth of priority sectors, viz.
agriculture, small and village industries, artisans, etc., besides expanding the outreach to
the poor. The assumption then was that credit could ensure faster growth. The planners
adopted a supply side approach, which was possibly the need of the hour. The policy
environment created at that time was primarily to pursue the agenda of social banking.
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The regulatory framework mainly focused on regulation of interest rate, preemption of
resources, restricted investment avenue and expansion of banking network in backward
areas. With multi-agency approach, banking network in the rural areas has made its
formidable presence in providing rural financial services. The loans provided by banks
have contributed substantially for the growth of all the priority sectors. Besides, the
banking facilities were made available in unimaginable remote areas for tapping the
latent savings of the rural masses. Though the volume of loans provided by banks has
increased substantially, the health of these institutions also took a beating with increased
thrust to financing under what is called ‘directed lending’ and by implementing various
government sponsored programs using banking as a channel of credit purveyor. At the
time of carrying out general economic reform in the country a need to initiate financial
sector reform was also been felt.
Since 1991, the Indian commercial banks have undergone the reform process
aimed at putting the Indian banking sector on par with international standards1.
Performance in terms of profitability has become the benchmark for the banking industry
like any business enterprise. In particular, due to the social banking motto of the
Government, the problem of non-performing asset (NPA) was not considered seriously in
India in the post nationalization (of banks) period. However, with the recent financial
sector liberalization drive, this issue has been taken up seriously by introducing various
prudential norms relating to income recognition, asset classification, provisioning for bad
assets and assigning risks to various kinds of assets of a bank. While the Reserve Bank of
India (RBI) as well as the banks have begun to pay considerable attention to the NPA
problem, there are only a limited number of rigorous studies in the Indian context that
look at this issue in some detail. In this project we attempt to look at the determinants of
NPA (using a panel data model with a cross section of over 100 banks) by examining
some of the external and internal factors like extent of competition, total assets of a bank,
size of operations, proportion of rural branches , investment, etc., that can influence
NPA. It is of our interest to examine, between various bank groups (viz, SBI,
1 One can see in this regard Charkavati Committee Report (1985), Narasimham Committee Report (1998), Basel I and II norms.
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Nationalized banks, Private banks and foreign banks), which is the most efficient group
in the context of recovery of loans and what are the factors that determine this efficiency.
For determining efficiency of the different banks in their loan recovery effort, the concept
of technical efficiency will be applied, using a frontier production technique (Bettesse
and Coelli (1995)).
One of the important objectives of the nationalization of commercial banks in
1969 and in 1980 was to provide credit to till then neglected sector (what was later called
the priority sector). Since them lot of effort has been gone in to chanelising credit to
priority sector. One of the major components of the priority sector is the Small Scale
Industrial sector. It has potential to generate substantial employment and also contribute
in terms of production and exports. Unlike agriculture, there is no separate sub-target for
the SSI sector, within the priority sector lending for the Indian public and private sector
banks; and the share of credit to the SSI sector has been falling over the years in the post
reform era2. This is a matter of serious concern as availability of credit has been always
recognized as a constraint to the growth of the SSI sector, be it a women or a rural
enterprise. Government has so far tried to mitigate the problem through various measures.
However, one of the major concerns of banks is the problem of bad loans arising out of
such small and medium enterprises (SME) accounts.
While the problem of non-recovery of agricultural loan is a well-discussed issue
(Bardhan, 1989, Bell and Srinivasan, 1989), not many studies in India have focused on
the non-recovery of loans from the SSI sector. Most authors usually touched upon this
issue in passing amid other problems of the SSI sector. However, the recent figures show
that amongst different sub-sectors within the priority sector, SSI’s contribution is the
highest in total NPA of the priority sector lending (Table3). It is also worth noting in this
context that SSIs share in net bank credit went from 15.89 per cent in 1991 to 11.1 per
cent in 2003, charting a steady decline. The share of SSIs in total priority sector lending
(TPSL) decreased even more dramatically in a shorter span of time: it went down from
36.12 per cent to 26.1 percent. Thus, as expected, channeling the credit away from this
sector appears to be the solution adopted by the banks (EPW Editorial, June 5, 2004).
2 See RBI Bulletin different issues.
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Thus in this research work it is of our interest to look at various aspects of the NPA
problem arising out of this segment for the Indian banking sector.
Given our interest in the commercial banks, to put the issues in perspective, we
first look at the development and the change that have taken place in the Indian banking
sector.
1.2 A Brief Review of Indian Financial System
The financial system of any country consists of specialised and non-specialised
financial institutions, organized and unorganized financial markets, financial instruments
and services, which facilitate transfer of funds. Procedures and practices adopted in the
markets and financial interrelationships are also part of financial system (Bhole, 1999). In
India, the financial system has undergone a significant change over time in terms of size,
diversity, sophistication and innovation. Now India has a well-developed financial system
with a variety of financial institutions, markets and instruments3. The structure of the
India’s financial system is illustrated in Fig.1.1.
Fig. 1.1 Structure of Indian Financial System
3 See Bhole (1999), Sen and Vaidya (1997) for a detailed discussion of India’s financial system.
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∗ Reserve Bank of India (RBI) is the controller and supervisor of India's Financial System. Source: RBI
An important feature of India’s financial system (like any other developing
country) is that, until recently a financial institution has largely been synonymous with
banking4 (Table 1.1). At the time of independence India had a relatively weak economic
base and financial structure. Savings and investment were relatively low and only two
third of the economy was monetised5. And also flow of funds from outside was very
meager and savings from corporate sector were low. At this juncture, savings were
coming mainly from household sector. And banks played very important role in
transforming these savings to investment in industries and other infrastructure
development. The gross domestic saving which was 10% of GDP in 1950-51 increased to
15.7% in 1971-72 and to 25.6% in 1995-96 which further increased to 27.6% in 2001-02
and it was 32% of GDP in 2004-05.
Table 1.1 : Major Balance Sheet Components of Financial Institutions (2004) (Rs. In Crores)
4 Jadhav and Ajit (1996-97) pp 311 5 Lumas.P.S. (1990) pp 390
India’s Financial System*
Financial Markets
Capital Credit Money
Financial Institutions
Other Financial Institutions
Insurance Mutual Funds Development Banks
Commercial Co-operative
Banks
Public Private RRBs
Domestic Foreign
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Banks NBFCs State Co-operative Banks
Central Co-operative Banks
Capital 22348 (66%) 6131 1012 4342
Total Assets 1975020(85%) 130142 71806 133331
Deposits 1575143 (91%) 17946 44316 82098
Loans and Advances 864143 (81%) 91613 37346 73091
Investment 802066(92%) 14298 23289 35830
Figures in bracket represent the percentage of the total. Source: Report on Trends and Progress of Banking in India 2003-04
1.3 Development of Indian Banking sector
Modern commercial banking made its beginning in India with the setting up of the
first Presidency bank, the Bank of Bengal, in Calcutta in 1806. Two other presidency banks
were set up in Bombay and Madras in 1840 and 1843 respectively. They were private
shareholders' banks. These banks were amalgamated into the Imperial Bank of India in
1921, which was nationalised into the State Bank of India (SBI) in 1955. The Reserve Bank
of India (RBI) was established in 1st April 1935 with the passing of the Reserve Bank of
India Act 1934.
Following Sen and Vaidya (1997) the evolution of Indian Banking sector in the post-
independence era can be divided into three distinct periods. (I) 1947-68 saw the
consolidation of Reserve Bank of India (RBI) in its role as the agency in charge of the
supervision and control of banks. In this period Indian banking sector operated in fairly
liberal environment. (II) The second period 1969-1991 marked its beginning with
nationalisation of commercial banks and their dominance in the financial system (III) the
third period starting with liberalisation (1992) of banking and financial sector.
After independence, the major development in the Indian banking sector was the
nationalisation of banks. The first to be nationalised was the Reserve Bank of India (RBI),
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the country's central bank from 1st January 1949. Then came the take over of the (then)
Imperial Bank of India and its conversion into State Bank of India (SBI) in July 1955 and
the conversion of seven major state owned banks into subsidiary banks of the SBI in 1959.
In 1969, 14 major banks were nationalised and in April 1980 six more banks were
nationalised. After nationalisation public sector banks followed two important policies. (a)
Massive expansion of branches especially in rural and semi-urban areas and (b)
Diversification of credit to till then neglected sector (priority sector lending).
In the post nationalisation period there was a rapid expansion of banks in terms of coverage
and also of deposit moblisation. The number of bank offices multiplied rapidly from 8300 in
July 1969 to 59752 in 1990, which further increased to more than 62 thousand in 1995, and
it was 71177 in 2006. This has reduced the population served per bank branch. The number
of people served per bank branch reduced from 65 thousand in 1969 to 14 thousand in 1990
which, however has increased marginally to 16 thousand in 2006 when consolidation has
been in progress. Also the total deposit increased from Rs 4646 crores in 1969 to Rs 323632
crores in 1994 and to Rs 2109049 crores in 2006. Some of the major aggregates of Indian
commercial banks are presented in Table 1.2.
Table 1.2 Major Aggregates of Commercial Banks in India (Real Values)
1969 1990 1995 2000 2005 Number of Banks 89 274 282 298 289 Total Bank Branches 8262 59752 64234 67868 70373 Population Per Branch (thousands) 64 14 15 15 16 Deposit (Rs crore) 33025 235292 324246 536371 1124098 Credit (Rs crore) 25583 142994 177319 285993 727556 Total Investment (Rs crore) 9674 87578 125097 196320 488697 Credit to Priority Sector (Rs crore) 3583 56271 58008 98116 252216 Priority Sector Credit as Percent of Total Credit 14 40.7 33.7 35.4 36.7 Credit-Deposit Ratio 77.5 60.8 54.7 53.3 62.6 Source: Trends and progress of banking in India, RBI
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The post-nationalisation period was also marked by the developmental role of the
banks. Government used banking sector as the instrument to finance its own deficit6. The
fiscal deficit to GDP ratio for the central government increased steadily from an average of
3.56% in the period 1971-76 to 8.29% in the period 1986-91. High Cash Reserve Ratio
(CRR) and Statutory Liquidity Ratio (SLR) are used in order to financed this. The CRR,
which was 3.5% in 1962-63, increased to 15% in 1989-90 and SLR, which was 25% in
1964-65 increased to 38.5% in 1990-91. Along with high CRR and SLR, the operational
freedom of the banks was curtailed with high priority sector lending of as high as 40% of the
total lending in 1989-90. To keep the borrowing cost of the Government low, the interest
rate on bank loan was fixed at lower than market rates. This affected profitability and the
efficiency of banks. Further, owing to the dominance of the public sector banks there was
no competition. Due to the expansionary policy followed by the RBI, the number of loss
making bank branches increased, especially in rural areas, which whittled away resources of
the banking industry. Due to all these factors, towards the end of 1980s banking industry
was badly in need of reforms.
In 1991, Indian economy faced a major balance of payment crisis. The foreign
exchange resources had almost disappeared. The fiscal deficit was high and the inflation rate
reached double digits. To overcome this crisis, Indian Government introduced many
economic reforms, which included amongst others financial sector reforms. As with general
reform private sector grew considerably and growth of the private sector made demands on
financial resources, there was a need to overhaul the financial system. Financial sector
reforms were introduced in 1992.
1.4 Financial Sector Reforms in India
The financial sector reforms in India began as early as 1985 itself with the
implementation of Chakravarti committee report. But the real momentum was given to it in
1992 with the implementation of recommendations of the Committee on Financial System
(CFS) (Narasimham, 1991). The important recommendations of the CFS were; (i)
Reduction in SLR (ii) reduction in CRR, payment of interest on CRR and use of CRR as the
monetary policy instrument (iii) phase out of directed credit (iv) deregulation of interest
6 Sen and Vaidya (1997) pp 15
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rates in a phased manner and bringing interest rate on government borrowing in line with
market-determined rates (v) attainment of Basel norms for capital adequacy within three
years (vi) tightening of prudential norms (vii) entry of private banks and easing of restriction
on foreign banks (iix) sale of bank equity to public (ix) phasing out of development
institutions (x) Increased competition in lending between Development Financial
Institutions (DFI) and a switch from consortium lending to syndicate lending. (xi) easing of
regulation on capital markets combined with entry of foreign institutions.
Almost all of the recommendations of the CFS have been implemented in a phased
manner. In 1998 another committee, the committee on Banking Sector Reforms (BSR)
(Narasimham, 1998) was constituted. The recommendations of the BSR committee have
also been implemented in a phased manner. The important recommendations of the BSR
are:
1. A minimum target of 9% Capital Risk-Adequacy Ratio (CRAR) to be achieved by
the year 2000. The ratio should be raised to 10% for the year 2002.
2. A risk weight of 5% for market risk for government-approved securities should be
attached.
3. An asset to be classified as doubtful if it is in the category of 18 months in the first
instance and eventually for 12 months and loss if it has been so identified but not
written off.
4. Income recognition, asset classification should apply to government advances.
5. The minimum shareholding by government/RBI in the equity of nationalised banks
and SBI should be brought down from 51% to 33%.
Financial sector reforms can be broadly divided into reforms in financial institutions
and reforms in financial markets. Reforms in financial institutions are mostly related to
reforms in banking sector as the banking sector forms a very important part of financial
sector.
1.5 Reforms in Financial Institutions
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Two main objectives of the financial sector reforms are to enhance the stability
and the efficiency of financial institutions7. To achieve these objectives various reform
measures were initiated which can be broadly grouped into three categories.
1. Enabling measures
2. Strengthening measures
3. Institutional measures
Reforms in the commercial banking sector had two distinct phases. The first
phase of reforms introduced subsequent to the release of the report of the CFS (1992)
focused mainly on enabling and strengthening measures. The second phase of reforms
introduced subsequent to the recommendation of the BSR (1998) committee report.
1) The enabling measures: These were designed to create an environment where
financial intermediaries could respond optimally to market signals on the basis of
commercial considerations. Salient among these include reduction in statutory pre-
emption so as to release greater funds for commercial lending, interest rate deregulation
to enable price discovery, greater operational autonomy to banks and liberalisation of the
entry norms for financial intermediaries.
(a) Reduction in statutory pre-emption: This includes reduction in CRR and SLR.
These are mainly used to finance the fiscal deficit of the government and are also
used as tools of credit control. At one stage CRR applicable to incremental deposit
was as high as 25% and SLR was 40% thus pre-empting 65% of incremental deposits.
These ratios were reduced in a series of steps after 1992. The SLR was 25% and CRR
was as low as 5.5% in 2002 and now less than 5% of the total deposit. However,
though the SLR has reduced to a much lower level, banks (especially public sector
banks) hold government securities more than prescribed level.
(b) Interest rate liberalisation: Before 1991, interest rates, both on deposits and loans
were controlled by RBI. With effect from October 1997 interest rates on all time
7 RBI (2001-02)
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deposits have been freed. Only rates on saving deposits remain controlled by the RBI.
Similarly the lending rates were also freed in a series of steps. The RBI now directly
controls only interest rates charged on exports and also there is a ceiling on lending
rate on small loans up to Rs 2 lakhs. The rationale for liberalising interest rate in the
banking system was to allow banks greater flexibility and encourage competition.
(c) Increased autonomy and competition: Banks have been given more autonomy
by reducing government's stake in it. It was recognised that restoration of health of
the banking system was required. Restoration of net worth was achieved through
capital infusion from budget. Competition has been infused by allowing new private
sector banks and more liberal entry of foreign banks (at the end of march 2001, there
were 8 new private sector banks, 23 old private sector banks and 42 foreign banks as
against 23 foreign banks in 1991).
2) The strengthening measures: These (also called prudential norms) were aimed at
reducing the vulnerability of financial institutions in the face of fluctuations in the
economic environment. These include various prudential norms related to capital
adequacy and risk-weighted assets, income recognition, asset classification and
provisioning for bad assets (NPAs). Following the CFS report the capital adequacy ratio
was fixed at 8%. It was increased to 9% following the BSR recommendation. Financial
institutions are asked to assign a risk weight of 100% on those government guaranteed
securities, which are issued by defaulting entities. Further, due regard should be paid to
the record of particular government in housing its guarantees while processing any
further requests for loans to PSUs on the strength of that state governments' guarantees.
3) Institutional measures: These measures are aimed at creating an appropriate
institutional framework conducive to development and functioning of financial markets.
These measures include reforms in legal framework, particularly relating to banks. Banks
are allowed to close down loss making units and merging with other banks. Flexibility is
introduced in resource mobilisation. Financial institutions are not required to seek RBI's
approval for raising resources by way of bond/debentures (by public/private placement).
In order to have a coordinated approach in the recovery of large NPA accounts, as also
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for institutionalising an arrangement for a systematic exchange of information in respect
of large borrowers (including defaulters and NPAs) common to banks and Financial
institutions, a standing committee was constituted in august 1999 under the aegis of
Industrial Development Bank of India (IDBI).
1.6 Non Performing Assets in India
One of the important issues that is drawing attention of policy makers and
researchers is the Non-Performing Assets of Commercial banks. High level of Non-
performing Assets (NPAs) is a concern to everyone involved as credit is very essential
for economic growth and NPAs affect the smooth flow of credit. Broadly, Non
Performing Advance is defined as an advance where payment of interest or repayment of
installment of principal (in case of Term Loans) or both remains unpaid for a certain
period8.
In India though the issue of NPAs was given more importance after the
Narasimham committee report (1991) highlighted its impact on the financial health of the
commercial banks and subsequently various asset classification norms were introduced,
the concept of classifying bank assets based on its quality began during 1985-86 itself
(see Chapter 3). A critical analysis for a comprehensive and uniform credit monitoring
was introduced in 1985-86 by the RBI by way of the Health Code System in banks
which, inter alia, provided information regarding the health of individual advances, the
quality of credit portfolio and the extent of advances causing concern in relation to total
advances. It was considered that such information would be of immense use to bank
managements for control purposes. Reserve Bank of India advised all commercial banks
(excluding foreign banks, most of which had similar coding system in their organisations)
on November 7, 1985, to introduce the Health Code classification indicating the quality
(or health) of individual advances in the categories, with a health code assigned to each
borrowal account. Under the above Health Code System RBI was further classifying
problem loans of each bank in three categories i.e. i) advances classified as bad &
8 This time duration given for an asset to consider it as a NPA varies from country to country and can change over time within a particular country.
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doubtful by the bank (ii) advances where suits were filed/decrees obtained and (iii) those
advances with major undesirable features.
The Narasimham Committee (1991) felt that the classification of assets according
to the health codes is not in accordance with the international standards. It believed that a
policy of income recognition should be objective and based on record of recovery rather
than on any subjective considerations. Also, before the capital adequacy norms are
complied with by Indian banks it is necessary to have their assets revalued on a more
realistic basis on the basis of their realizable value. Thus the Narasimham committee
(1991) believed that a proper system of income recognition and provisioning is
fundamental to the preservation of the strength and stability of the banking system. The
committee suggested that Indian banks should follow the international practice in
defining a NPA. Thus based on the recommendations of Narasimham committee report
the non-performing assets would be defined as an advance where, as on the balance sheet
date:
1. In respect of overdraft and cash credits, accounts remain out of order for a period
of more than 180 days,
2. In respect of bills purchased and discounted, the bill remains overdue9 and unpaid
for a period of more than 180 days,
3. In respect of other accounts, any account to be received remains past due for a
period of more than 180 days.
The stricter regulations on NPA definitely reduced bad loans in the banks. Banks
are now constantly being conscious of such accounts and proper measures are taken when
an account has potential to become NPA10. The Gross NPA of the total banking industry
has increased from Rs 50815 crores in 1998 to 70861 crores in 2002 which however has
declined to Rs 58299 crores in 2005 (Table 1.3). Similarly the Net NPA has increased
from Rs 23761 crores in 1998 to Rs 35554 crores in 2002 which however has declined to
Rs 21441 crores in 2005. The growth rates of both Gross and Net NPAs also have
9 An amount is considered overdue when it remains outstanding 30 days beyond the due date. 10 Revealed during our interviews with the banks.
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declined over time, and after 2003 they have become negative. This shows that the NPA
levels of Indian commercial banks are reducing. This is also confirmed by the fact that
the NPA (both gross and net) as percent of Gross advances as well as total assets is
declining over time. While the Gross NPA as percent of gross advance and total asset has
declined from 14.3% and 6.3% in 1998 to 5.2% and 2.5% in 2005 respectively, the Net
NPA as percent of Gross advance and total asset has declined from 6.7% and 2.9% in
1998 to 1.9% and 0.9% in 2005 respectively.
Table 1.3: Non Performing Assets of Total banking Sector
Non Performing Assets of Total Banking Sector (Rs Crore) 1998 1999 2000 2001 2002 2003 2004 2005Gross NPA 50815 58722 60841 63741 70861 68717 64787 58299Change 7907 2119 2900 7120 -2144 -3930 -6488Percentage growth 15.56 3.61 4.77 11.17 -3.03 -5.72 -10.01As Percent of Gross Advance 14.39 14.71 12.79 11.42 10.42 8.86 7.19 5.27As Percent of Gross Asset 6.36 6.18 5.49 4.91 4.62 4.04 3.27 2.57Net NPAs 23761 28020 30152 32462 35554 32670 24617 21441Change 4259 2132 2310 3092 -2884 -8053 -3176Percentage growth 17.92 7.61 7.66 9.52 -8.11 -24.65 -12.9As Percent of Gross Advance 6.73 7.02 6.34 5.82 5.23 4.21 2.73 1.94As Percent of Gross Asset 2.97 2.95 2.72 2.50 2.32 1.92 1.24 0.95Gross-net 27054 30702 30689 31279 35307 36047 40170 36858Change 3648 -13 590 4028 740 4123 -3312Percentage growth 13.48 -0.04 1.92 12.88 2.1 11.44 -8.24
Source: Report on Trends and Progress of Banks in India, various issues
When we examine the sector-wise scenario we observe that NPAs arising from
the SSI sector is comparatively higher than other sectors that fall even within the priority
sector. From RBI report it is seen that in 2002, NPAs from agriculture loans was 13.8%
and that of SSI was 18.7%. In 2004 NPAs arising from agriculture sector increased to
14.4% but still remained lower to that of SSI sector which was 17.6%. Thus directed
credit to the priority sector in general and loan to SSI sector in particular remained major
concern of the banks as far as NPA issue is concern. It is therefore of interest to look
briefly at the credit to these segments.
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1.7 Directed Credit to Priority Sector 11
After independence it was felt that in order to achieve overall development of the
country it is essential to develop the large rural sector, for which it is necessary to
channelise required financial resources. In 1954 the ‘All India Rural Credit Survey
Committee’ found that not sufficient credit has been directed towards the rural sector of
the economy. Thus the committee recommended for the development of state sponsored
commercial banking system with branches spread in the rural areas. As a result of this a
drive to nationalize commercial banks was launched. Thus one of the main objectives of
nationalization of commercial banks was to provide credit to, what was considered as,
priority sector. As lending to these sectors was not profitable for commercial banks they
were not motivated to lend to these sectors. This was evident from the fact that the
proportion of credit for industry and trade moved up, from 83 per cent to 90 per cent
between 1951 and 1968. This rise was however at the expense of crucial segments of the
economy like agriculture and that small-scale industry. Due to this reason commercial
banks were directed to lend to these sectors by fixing targets. Apart from fixing targets of
minimum credit, banks were also asked to lend to these sectors at a concessional rates.
This was done to ensure that bank advances were confined not only to large-scale
industries and big business houses, but were also directed, in due proportion, to important
sectors such as agriculture, small-scale industries and exports.
To begin with there was no target on the priority-sector lending. It was just
emphasized that commercial banks should increase their involvement in the financing of
priority sectors, viz., agriculture and small scale industries. However, based on the
recommendations of the report submitted by the Informal Study Group on Statistics
relating to advances to the Priority Sectors, the description of the priority sectors was
later formalized in 1972. Later banks were advised to raise the share of the priority
sectors in their aggregate advances to the level of 33 1/3 per cent by March 1979. Further
it was increased to 40 percent at the end of 1985 and also sub-targets were fixed. During
the initial period, only agriculture, small scale Industries, small and marginal farmers and 11 Priority sector comprises agriculture (both direct and indirect), small scale industries, small roads and water transport, small business, retail trade, professional and self-employed persons, state sponsored organizations for Scheduled Castes/Scheduled Tribes, education, housing (both direct and indirect), consumption loans, micro-credit loans to software, and food and agro-processing sector (Repot on Trend and Progress of Banking in India, 2005-06).
201
artisans and exports were included in the priority sector. Later, based on the
recommendations of Narasimham Committee Report (1991), housing, education,
consumption, profession, I.T. Sector, food processing not falling under SSI, etc. were
also included under the priority sector based.
During 1989-90 the target of priority sector lending was fixed at 40 percent for
domestic commercial banks. Within this there were sub-targets which included 18
percent to agriculture and 10 percent to weaker sections. For foreign banks the total target
was 32 percent within which the sub-target was fixed at 10 percent to small scale
industries and 12 percent to export credit. In 1991.
Later Narasimham committee pointed out many problems related to priority
sector lending, the important one being that a large part of NPA comes from priority
sector lending. Thus the committee recommended reduction of priority sector target to
10 percent and expansion of the coverage of priority sector to include more sectors.
However, the target of priority sector was not reduced but the definition of priority sector
was expanded to include more sectors. Also a provision was made such that banks that
cannot meet the priority sector targets can deposit funds in the financial institutions like
National Bank for Agriculture and Rural Development (NABARD) under Rural
Infrastructure Development Fund (RIDF) or some banks can do so in the Small Industries
Bank of India (SIDBI) for lesser interest rates, which in turn will be lent out to the
priority sectors. The distribution of gross non-food bank real credit to various priority
sector is given in Table 1.4.
Table 1.4 Distribution of Commercial Bank Credit to Priority (Rs Crore, Real Values)
Distribution of Commercial Bank Credit to Priority (Rs Crore, Real Values)
Year
Gross Non-food Bank Credit
Total Priority Sector
Percent of 2 to 1 Agriculture
percent of 4 to 1
Small scale Industries
percent of 6 to 1
Other Priority Sector
percent of 7 to 1
1 2 3 4 5 6 7 8 9 1991-92 144564 54121 37.437 21633 14.964 21625 14.959 10863 7.514
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1992-93 153862 54612 35.494 21878 14.219 21947 14.264 10787 7.0111993-94 145950 53880 36.917 21208 14.531 22617 15.496 10055 6.8891994-95 168793 58632 34.736 21916 12.984 25256 14.963 11460 6.7891995-96 186127 61461 33.021 22667 12.178 26724 14.358 12070 6.4851996-97 196111 66214 33.764 24528 12.507 28040 14.298 13646 6.9581997-98 210462 72768 34.575 25499 12.116 31817 15.118 15452 7.3421998-99 220324 77650 35.244 26852 12.188 32848 14.909 17950 8.1471999-00 244511 85926 35.142 28928 11.831 34425 14.079 22573 9.2322000-01 268250 96517 35.980 32454 12.098 35004 13.049 29059 10.8332001-02 291729 105910 36.304 36718 12.586 34566 11.849 34626 11.8692002-03 366226 124983 34.127 43422 11.857 35671 9.740 45890 12.5312003-04 411538 149059 36.220 51153 12.430 37206 9.041 60700 14.7502004-05 540426 206203 38.156 67703 12.528 40318 7.460 98182 18.1682005-06 720588 261492 36.289 88355 12.262 46276 6.422 126861 17.605
Source: Handbook of Statistics on Indian Economy
The total priority sector credit of commercial banks was around Rs 54121 crores
during 1991-92, which increased to Rs 96517 crores during 1999-2000 and it was Rs
261492 crores during 2005-06. It is observed that the priority sector credit has registered
higher growth rate during the recent years. While it was around 6 percent for the period
1991-92 to 1999-2000, it increased to around 21 percent during the period 1999-2000 to
2005-06. This could be because the growth rate of the total credit itself has increased
from around 7 percent during the period 1991-92 to 1999-2000 to around 20 percent
during the period 1999-2000 to 2005-06. Though the growth rate of the total priority
sector has been increasing over the years, similar trend is not observed in the case of the
percent of priority sector credit in the total non-food credit. It was around 37 percent of
total non-food credit in 1991-92, which declined to around 33 percent during 1994-95.
This however has improved in the following years and reached around 38 percent during
2004-05, but again declined marginally to 36 percent during 2005-06. The increase in the
percent of total non-priority sector credit in the total non-food credit is not substantial. It
was around 35.14 percent during the period 1991-92 to 1999-2000 which increased
marginally to around 36.17 percent during the period 1999-2000 to 2005-06.
Looking at the growth rates of the sub-sectors of the priority sector; the growth
rate of credit to agriculture and other priority sector are similar to that of the total priority
sector credit, whereas the growth rate of credit to Small Scale Industries (SSI) shows a
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varying trend. The growth rate of the credit to agriculture sector was around 3.76 percent
during 1991-92 to 1999-2000, which increased to around 20.7 percent during 1999-2000
to 2005-06. The growth rate of credit to SSI was around 6.06 percent during the period
1991-92 to 1999-2000 which has declined marginally to around 5.17 percent during the
period 1999-2000 to 2005-06. However, the growth rate of credit to other priority sector
has registered substantial growth over time. It was around 10 percent during the period
1991-92 to 1999-2000 which has increased to around 34 percent during the period 1999-
2000 to 2005-06.
Looking at the percentage share of credit to the sub-groups of the priority sector,
it is observed that the credit to agriculture sector as well credit to SSI sector has declined
over time where the credit to other priority sector credit has increased over time. The
decline in the credit to SSI is sharper than the decline in the credit to agriculture sector.
The share of credit to agriculture sector in the total non-food credit declined from around
15 percent in 1991-92 to around 12 percent during 2005-06, whereas the credit to SSI
declined from around 15 percent in 1991-92 to around 6.4 percent during 2005-06. This
decline is sharper in the last few years. On the other hand the credit to other priority
sector has increased from around 7.5 percent during 1991-92 to around 18 percent during
2005-06. The increase in the share of priority sector credit could because of the
substantial increase in the housing credit, as housing credit also forms a part priority
sector credit.
1.8 Credit to SSI
Small Scale Industrial sector is one of the important sectors in India for a number
of reasons, prominent amongst them being the employment generation capability.
Recognizing its potential in terms of the employment generation and the production, it
has been give the priority sector status. During 1991-92 the total number of SSI units was
around 68 lakhs which increased to around 119 lakhs during 2004-05. The total
investment also increased from Rs 93555 crore during 1991-92 to Rs 178699 crores
during 2004-05. Production, measured at constant price (1993-94 base) which stood at Rs
84728 crores during 1991-92 increased to Rs 251511 crores during 2004-05. Importantly
the employment level which was around 158 lakh during 1991-91 almost doubled and
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reached 283 lakh by 2004-05. Similarly the total export increased from Rs 9664 crores
during 1991-92 increased to Rs 86013 crores during 2002-03.
The definition of Small Scale Industries in India is decided on the basis of the
investment in plant and Machinery which has changed over time. During 1966 an
industry was considered a SSI if its investment in the plant and Machinery was not more
than Rs 7.5 lakh. This limit was increased to Rs 10 lakh in 1975. During 1998 the small
scale industry was defined as an industrial unit having an investment in Plant and
Machinery not exceeding Rs.1 crore for most of the 8000 products produced in the SSI
sector and not exceeding Rs.5 crore in respect of certain selected reserved items. In 2006
with the introduction of Micro Small and Medium Enterprises Development Act, 2006
(MSMED Act), the definition of the SSI was further revised. Now, the small scale
enterprises (engaged in manufacturing) are defined as units with investment in plant and
machinery between Rs. 25 lakh to Rs.5 crore. Within the SSI sector there are a number of
sub sectors including tiny industries sector, ancillary sector, khadi and village industries
sector, women enterprises and so on12.
According to the definition of commercial banks credit to Small Scale Industries
include financing of small, micro and unorganized non-farm sector. As mentioned above,
public and private sector banks have to lend 40 percent of their total credit to priority
sector, and for foreign banks it is 32 percent. Unlike agricultural sector there is no fixed
sub-target in the case of credit to SSI for public and private banks. However, foreign
banks are expected to lend 10 percent of their total credit to SSI sector. If they fail to
reach the target, the remaining amount should be deposited at the Small Industries
Development Bank of India (SIDBI). The distribution of commercial banks credit to SSI
sector is presented in table 1.5.
Table 1.5
Commercial Bank Credit to Small Scale Industries
Total SSI Credit
Growth Rate of 1
Total Non food Credit
Growth Rate of 3
Total Priority Sector Credit
Growth Rate of 5
Percent of 1 to 3
Percent of 1 to 5
1 2 3 4 5 6 7 8 1991-92 21625 144564 54121 14.96 39.96
12There are certain types of industries/activities wherein investment on plant and machinery up to Rs. 5 crores can also be registered under SSI category.
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1992-93 21947 1.49 153862 6.43 54612 0.91 14.26 40.19 1993-94 22617 3.05 145950 -5.14 53880 -1.34 15.50 41.98 1994-95 25256 11.67 168793 15.65 58632 8.82 14.96 43.08 1995-96 26724 5.81 186127 10.27 61461 4.83 14.36 43.48 1996-97 28040 4.92 196111 5.36 66214 7.73 14.30 42.35 1997-98 31817 13.47 210462 7.32 72768 9.90 15.12 43.72 1998-99 32848 3.24 220324 4.69 77650 6.71 14.91 42.30 1999-00 34425 4.80 244511 10.98 85926 10.66 14.08 40.06 2000-01 35004 1.68 268250 9.71 96517 12.33 13.05 36.27 2001-02 34566 -1.25 291729 8.75 105910 9.73 11.85 32.64 2002-03 35671 3.20 366226 25.54 124983 18.01 9.74 28.54 2003-04 37206 4.30 411538 12.37 149059 19.26 9.04 24.96 2004-05 40318 8.36 540426 31.32 206203 38.34 7.46 19.55 2005-06 46276 14.78 720588 33.34 261492 26.81 6.42 17.70
Source: RBI
The total commercial bank credit to SSI sector stood at Rs 21625 crores during
1991-92 which increased to Rs 26724 crores during 1995-95 which further increased to
Rs 34566 crores during 2001-02 and it was Rs 46276 crores during 2005-06. Though
there is an increase in the credit to SSI sector over the years in terms of absolute value,
the annual growth rate shows a varying trend. During 1991-92 it was 1.49 percent which
increased to 11.67 percent during 1994-95, with a sharp decline in following two years it
again increased to 13.47 percent during 1997-98. However it has declined steadily and
reached the lowest level of -1.29 percent during 2001-02. Later it has improved steadily
and it was around 14.78 percent during 2005-06. Unlike the varying trend in the growth
rate, the percentage share of SSI credit in the total non-food bank credit has declined over
time. It was around 15 percent during 1991-92 which declined to 11.85 percent during
2001-02, which further declined to around 6.42 percent during 2005-06. The percentage
share of credit to SSI in the total non-priority sector has marginally increased from 40
percent to 43.7 percent between 1991-92 and 1997-98 which, however, has declined
steadily thereafter and reached around 17.7 percent during 2005-06.
1.9 Conclusion Controlling the occurrence of systemic banking problems is undoubtedly a prime
objective for policy-makers, and understanding the mechanisms that are behind the surge
in banking crises is of utmost importance in this regard. Amongst the problems faced by
the banks of many developing nations, occurrence of non-performing assets (NPA) is a
206
prominent one. While the origin of the problem of high level of NPAs basically lies in
the quality of managing credit risk and the extent of preventive measures adopted,
various factors like real interest rates, directed credit or inflation rate can also effect the
level of NPA. Analysis of factors that cause the ratio of NPAs to total loans to fluctuate,
for selected Asian countries, (viz., Taiwan, Hongkong , Singapore and others) reveals
that a high ratio of corporate loans to individual loans results in lower percentage of
NPA (Wu et al, 2003). In the literature it has also been cited that the reasons why NPAs
are created are sometimes systemic in nature and directly attributable to events such as
real estate bubbles (Thailand and Indonesia) or a high proportion of directed lending
(Krueger et al, 1999). The problem is significant for the Chinese banks as well and in
order to deal with the mounting NPA problem in the Chinese banks, government
constituted four asset management companies (Bonin and Huang, 2001). Thus NPA is a
problem of banking sector of many developing nations which needs to be studied
carefully.
In Indian financial system, an asset is classified as non-performing asset (NPAs) if the
borrower does not pay dues in the form of principal and interest for a period of 180 days.
However, with effect from March 2004, it has been decided that a default status would be
given to a borrower if dues were not paid for 90 days. Further, if any advance or credit
facilities granted by bank to a borrower becomes non-performing, then the bank will have
to treat all the advances/credit facilities granted to that borrower as non-performing
without having any regard to the fact that there may still exist certain advances / credit
facilities having performing status.
Due to the social banking motto of the Government, the problem of NPA had not
received due attention in India in the post nationalization (of banks) period. However,
with the recent financial sector liberalization drive, this issue has been taken up seriously
by introducing various prudential norms relating to income recognition, asset
classification, provisioning for bad assets and assigning risks to various kinds of assets of
a bank. Overtime though NPA as a percentage of total advances have reduced, it still
remains a concern for the Indian banking sector. While the Reserve Bank of India (RBI)
207
as well as the banks have begun to pay considerable attention to the NPA problem, there
are only a limited number of rigorous studies in the Indian context that look at this issue
in some detail (see Ghosh, 2005, Mor and Sharma, 2003, Rajaraman et al, 1999).
Furthermore, while reform regulations attempt to streamline banking operations, norms
of priority sector lending remains intact more or less. In particular, banks need to allocate
40% of their total credit disbursement to agriculture, small-scale industries and other such
designated priority sectors.
However, it is also well known that the small firms, besides generating manufacturing
output and foreign exchange through exports, are also a major source of employment in a
labour surplus economy like India. It is also understood that the lack of access to finance
for working capital and new investment presents a significant constraints on the ability of
small firms to carry out business and to expand (Gang, 1995). Thus it is essential to
examine the problem of NPA arising out of advances made by banks to this sector.
Therefore, at the macro level, there is a need to look at the determinants of NPA in the
Indian banking sector by examining some of the bank specific as well as macro level
indicators. At the micro level on the other hand, one needs to identify the sector specific
factors responsible for non-recovery of loans.
Given this back ground, in the current project, we attempt to look at the determinants of
NPA by examining some of the external and internal factors like, the extent of
competition, total assets of a bank, size of operations, proportion of rural branches ,
investments etc., that can influence NPA. It is of our interest to examine, between
various bank groups (viz, SBI, Nationalized banks, Private banks and Foreign banks),
which is the most efficient group in recovery of loans and what are the factors that
determine this efficiency.
To have a micro perspective of the problem, as a case study, we have taken up a field
survey based exercise concerning the SSI sector to understand the actual workings of the
loan recovery process and the associated problems. In particular, we are interested in
208
examining the factors that have influence on recovery of loans in this segment of the
Indian economy.
Given this background the report is arranged as follows. Credit being our main area of
focus, we concentrate on this aspect in some detail in Chapter 2 , mainly focusing on the
credit to the SSI sector. The issue of non-performing asset in the Indian banking sector is
discussed in general with trends of NPAs in Chapter 3. In Chapter 4 we analyse data
NPA of commercial banks in a panel framework to identify the determinants of NPA.
This is done for the total advances and also for advances to the SSI sector. In Chapter 5
we concentrate on the efficiency issue. In particular we examine the profit efficiency of
the Indian banking sector and in particular check for the significance of NPA as a
determinant of efficiency. Chapter 6 and Chapter 7 are based on our primary data
collection from small firms and banks respectively. A concluding section is presented at
the end.
209
CAHPTER 2
Credit Operations of Indian Banking Sector and Credit to SSI
2.1 Introduction
Lending is the core activity of the banking sector. This is the activity through which a
bank as a firm earns profit.
To make credit available to all sections of the society, at the initial stages of development
a need was felt for a wider diffusion of banking facilities, mainly the credit facilities (see
also Chapter 1). This was due to the fact that at that time, when activities were left
entirely to the banking sector, banking operations concentrated in selected locations and
sectors; for example, credit for industry and trade were as high as 83% to begin with and
moved up further to 90% between 1951 and 1968. Thus credit to some of the core sectors
like agriculture or SSI, that were in dire need of financial assistance were not
forthcoming from the institutional lending agencies like commercial banks. Consequently
certain controlling measures were perceived as necessary at that time. By 1969, 14 major
banks were nationalized and slowly lending norms for the specific , hitherto neglected
sectors were formalized. By 1979 banks were advised to lend to the priority sector (which
210
includes agriculture and SSI sectors) to the tune of 33.33% of total credit. By 1985 this
percentage was raised to 40%. While credit to the SSI sector is treated as a part of priority
sector lending, no specific target was set for this sector for the Indian banks. For the
foreign banks however, 32% of total lending is earmarked for the priority sector of which
10% is needed to go towards the small industries. Any short fall in such lending by
foreign banks has to be deposited with the Small Industries Development Bank of India
(SIDBI).
With liberalization, new changes have been brought into the banking sector (see also
Chapter 1). Efficiency in banking operations was given utmost priority and profit became
a measuring yardstick of performance. Such new emphasis has slowly been changing the
focus of the banking sector. In particular, there was a decline in the percentage of credit
to the small-scale sector and it has been observed that the banks fail to adhere to the
lending norms prescribed for the agriculture sector. Side by side rural branches also
started to decline.
In this background the present chapter looks at the current scenario of bank lending and
in particular lending to the small-scale sector. The next section concentrates on the trends
in bank lending over the years. Section 3 discusses the role of the small-scale sector for
the Indian economy and the problems faced by the sector especially relating to the
financial assistance. Section 4 examines the kind of credit facilities enjoyed by the sector
especially from the commercial banks. A concluding section follows thereafter.
2.2 Credit Operation of Indian Commercial Banks
Lending operations of the Indian commercial banks started much before independence.
According to Beckhart (1967), from the 59 reporting banks in 1939, total advances were
observed to be Rs 151 crores; thereafter there had been some fluctuations with regards to
total lending . These fluctuations were usually due to the overall economic fluctuations at
that time. In the year 1947 total loans and advances increased to Rs 425 crores As the
economic activities gain momentum during plan-period, credit also shows an upward
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trend with credit deposit ratio increasing to above 60% from about 40% to 50% in the
previous years (table 2.1).
Table 2.1 Loans and Advances of Schedule Banks 1939-1952
Year No. of Reporting Banks
Loans and Advances (in crores of rupees)
Credit* Deposit ratio
1939 59 151 59
1942 61 122 23.9
1947 97 425.2 44
1951(first
plan)
92 545.1 67
1952 91 474 60
*loans and advances plus bills purchased and discounted
• Source: Beckhart (1967)
From 1961 onwards (third plan) there was always been an increasing trend of credit
disbursement. Bank credit of scheduled banks increased to about 1320 crores of rupees
and doubled by the year 1966-67. Such trend is due to the growth in economic activities
in general and industrial activities in particular.
By the year 1969, 14 major commercial banks were nationalised and the year 1970-‘71
saw a total lending of 4684 crores of rupees. This figure more than doubled in 1975-‘76
to reach Rs 10877 crores and this trend continued thereafter. In 1980-‘81 , total credit
was Rs 25371 crores and the year 1985 saw an increase of credit disbursement to Rs
56067 crores. By the year 1990-‘91 total credit of commercial banks touched the figure
of Rs 116301 crores.
2.3 Bank Group-wise Total Credit of Commercial Banks
Indian commercial banks are classified into four broader categories, viz., State
Bank and its associates (SB &A) , Nationalised Banks (NB), Private Banks (PB) and
Foreign Banks (FB). The total credit of commercial banks, according to the bank group,
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is presented in table 2.2. It is observed that even in real terms there has been a substantial
increase in credit disbursement. The total bank credit (in real terms) of the banking
sector as a whole was around Rs 169182 crores in 1990 which increased to Rs 175021
crores in 1995. While the extent of rise was not phenomenal during the first 5 years of
reform, the next ten years saw substantial growth of credit. In particular, credit increased
to Rs 277193 crores in 2000 and to Rs 589911 crore in 2005. Between 1991 and 2005 the
total real credit increased by around 3.5 times. Similar trend is observed in the case of
individual bank groups as well. The credit of State Bank of India and Associates (SB&A)
increased from Rs 57004 crores in 1990 to Rs 146014 crores in 2005 (around 2.5 times),
the credit of Nationalised Banks (NB) increased from Rs 98885 crores in 1991 to Rs
292279 crores in 2005 (around 2.9 times), the credit of Private Banks (PB) increased
from Rs 5747 crores in 1990 to Rs 112993 crores in 2005 (around 19.6 times) and the
credit of Foreign banks increased from Rs 7546 crores in 1990 to Rs 38625 crores in
2005 (around 5 times).
Table 2.2 Bank group-wise total commercial bank credit
Real Credit of commercial Banks (Real values, Rs crore) Share of Total Credit SB&A NB PB FB Total SB&A NB PB FB 1990 57004 98885 5747 7546 169182 0.34 0.58 0.03 0.041991 58547 98274 5897 8483 171200 0.34 0.57 0.03 0.051992 59081 98756 7065 10233 175135 0.34 0.56 0.04 0.061993 58941 95957 7984 10641 173524 0.34 0.55 0.05 0.061994 49044 85211 8968 10610 153832 0.32 0.55 0.06 0.071995 53981 95013 13262 12764 175021 0.31 0.54 0.08 0.071996 60945 100955 17453 17573 196926 0.31 0.51 0.09 0.091997 60625 100442 20938 19563 201567 0.30 0.50 0.10 0.101998 66102 109965 23998 19845 219910 0.30 0.50 0.11 0.091999 70672 123143 27841 19233 240889 0.29 0.51 0.12 0.082000 80653 139435 34842 22263 277193 0.29 0.50 0.13 0.082001 90882 159681 41128 25983 317674 0.29 0.50 0.13 0.082002 97212 186694 68768 28724 381397 0.25 0.49 0.18 0.082003 106895 203473 77803 29475 417646 0.26 0.49 0.19 0.072004 119198 222824 92100 32706 466828 0.26 0.48 0.20 0.072005 146014 292279 112993 38625 589911 0.25 0.50 0.19 0.07
SB & A: State Bank and Associates, NB: Nationalized Banks, PB: Private Banks, FB:
Foreign Banks
Source: RBI
213
Another noteworthy feature is that though for obvious reasons nationalized banks
and state bank and associates have shares over 80% of the total bank credit, over the
years the share of private banks are increasing ; foreign banks’ share, however, has
remained more or less the same during the liberalization period. This is partly because
the new foreign banks that entered the market are yet to get stabilized and operate in a
full-fledged manner .
The percent annual growth rates of commercial bank credit, according to bank
group are presented in table 2.3. The average annual growth rate of commercial bank
credit of the total banking sector for the period 1990 to 2005 is 9.06 percent. This,
however, as expected from the above discussion, differs at the bank group level. While
the annual average growth rate of credit of SB&A is around 6.85 per cent, it is around
7.93 percent for NB and around 11.94 percent for FB. Private Banks have registered the
highest annual average growth rate of around 22.83 percent for the period 1990 to 2005.
It is also interesting to note that commercial banks, across all bank groups, have
registered higher growth rate during the period 1995 to 2005 compared to the period
1990-1995.
Table 2.3 Growth rates (percent increment) of credit of commercial banks (in
real terms)
Growth rates of Commercial Bank Real Credit (in per cent) SB&A NB PB FB Total
1991 2.71 -0.62 2.61 12.42 1.19 1992 0.91 0.49 19.81 20.64 2.30 1993 -0.24 -2.83 13.01 3.99 -0.92 1994 -16.79 -11.20 12.32 -0.30 -11.35 1995 10.07 11.50 47.89 20.31 13.77 1996 12.90 6.25 31.60 37.67 12.52 1997 -0.53 -0.51 19.97 11.33 2.36 1998 9.04 9.48 14.61 1.44 9.10 1999 6.91 11.98 16.01 -3.08 9.54 2000 14.12 13.23 25.15 15.75 15.07 2001 12.68 14.52 18.04 16.71 14.60 2002 6.96 16.92 67.20 10.55 20.06 2003 9.96 8.99 13.14 2.62 9.50 2004 11.51 9.51 18.38 10.96 11.78 2005 22.50 31.17 22.69 18.09 26.37
214
Source: RBI
2.4 Total Credit of Commercial Banks According to Occupation
Banks lending activities encompass various sectors of the economy. Naturally
funds are directed to the so-called booming sectors. However, as mentioned above Indian
financial sector is also guided by certain norms prescribed by the Reserve Bank of India
(RBI), which ensures flow of funds to certain core sectors of the economy. Distribution
of total credit according to occupation is presented in table 2.4. Looking at the different
occupation-wise flow of funds one observes that credit to the agriculture sector has
declined in real terms between 1990 and 1995. It was around Rs 22546 crore in 1990
which has declined to Rs 20910 crore in 1995. It should be mentioned in this context that
banks are supposed to direct 18 % of their total lending to the agriculture sector ;
however, in reality many banks often fail to meet this norm. Subsequently , however,
total credit to the agriculture sector has increased and reached around Rs 49356 crores in
2004.
In the case of other sectors the real total credit has been increasing over the years.
Credit to industry increased marginally from Rs 68948 crores in 1990 to Rs 80639 crores
in 1995 which further increased to Rs 171694 crores in 2004. There is a remarkable
increase in the personal loans and also credit to financial institutions. While the personal
loans increased from Rs 9083 crores in 1990 to Rs 15827 crores in 1995, it increased by
around 5 times, from Rs 15827 crores to 91839 crores between 1995 and 2004. Similarly
credit to financial institutions increased from Rs 3030 crores in 1990 to Rs 6664 crores in
1995 which further increased to Rs 20238 crore in 2004 (around 5 times).
Table 2.4 Distribution of Outstanding Credit of Commercial Banks according to
Occupation Distribution of outstanding credit of commercial banks according to occupation
(Rs in Crores, in Real values)
Agriculture Industry Personal Loans
Financial Institutions Others@ Total
1990 22546 68948 9083 3030 37844 141451
215
1991 22129 70406 11436 3344 35900 147981 1992 22179 71467 12260 4385 39527 149818 1993 22060 78964 13530 3959 43954 162467 1994 20902 77390 13862 4166 44414 160734 1995 20910 80639 15827 6664 52758 176799 1996 22474 95374 18433 7031 55373 198684 1997 23134 102609 20623 8259 53333 207958 1998 23891 109144 23545 8291 58670 223541 1999 26652 122505 25805 10307 63998 249268 2000 28526 133624 32277 13672 79477 287576 2001 31261 142877 39848 15987 95407 325380 2002 37806 160431 48738 22216 118261 387452 2003 42901 175044 64374 28613 116169 427101 2004 49356 171694 91839 30238 108314 451442
@ Other include - Transport, personal and professional service, Trade and Miscellaneous
Computation of growth rates of real credit reveals that between 1995 and 2004
growth rates have been much higher compared to the same between 1991 and 1995. The
average annual growth rate of real total deposits between 1991 and 2004 is around 8.61
per cent (Table 2.5). While it is around 5.08 percent between 1991 and 1995, it has
increased to 11.06 percent between 1996 and 2004. Credit to agriculture sector has
registered the lowest annual average growth rate of around 5.50 percent between 1991
and 2004. It was even negative between 1991 and 1995 (around -1.47 per cent per
annum). However, it has increased substantially between 1995 and 2004 (around 10.05
per cent per annum). While on the one hand credit to agricultural sector has registered
low growth rate, on the other hand personal loans and credit to financial institutions has
registered remarkable growth rate. Between 1991 and 2004 personal loan has registered
around 19.77 percent annual average growth rate, while it was around 20.32 percent in
the case of credit to financial institutions. One important observation is that, unlike other
occupations which registered higher growth rate between 1995 and 2004, credit to
financial institutions registered higher growth rate between 1991 and 1995. it was around
22.35 percent during 1991 and 1995, which reduced to around 18.97 percent between
1995 and 2004.
Table 2.5 Occupation-wise growth of outstanding Credit Growth Rates of Outstanding Credit of Commercial Banks According to Occupation
(Rs in Crores, in Real values)
216
Agriculture Industry Personal Loans
Financial Institutions Others Total
1990 -1.44 10.47 38.76 37.09 -0.03 7.24 1991 -1.85 2.11 25.91 10.36 -5.14 4.62 1992 0.22 1.51 7.20 31.16 10.10 1.24 1993 -0.53 10.49 10.36 -9.72 11.20 8.44 1994 -5.25 -1.99 2.46 5.22 1.05 -1.07 1995 0.04 4.20 14.18 59.98 18.79 9.99 1996 7.48 18.27 16.46 5.50 4.96 12.38 1997 2.94 7.59 11.88 17.46 -3.68 4.67 1998 3.27 6.37 14.17 0.39 10.01 7.49 1999 11.56 12.24 9.60 24.33 9.08 11.51 2000 7.03 9.08 25.08 32.64 24.19 15.37 2001 9.59 6.92 23.46 16.94 20.04 13.15 2002 20.94 12.29 22.31 38.96 23.95 19.08 2003 13.48 9.11 32.08 28.80 -1.77 10.23 2004 15.05 -1.91 42.67 5.68 -6.76 5.70
Source: RBI
The difference in the growth rate of the real credit to different occupations has led
to the changing composition of the credit according to the occupation. The percent share
of real credit according to occupation is presented in Fig.2.1. On the one hand the share
of the credit to industry and agriculture sector has declined between 1991 and 2004, on
the other hand, as is clear from the above discussion as well, the share of the personal
loan and credit to financial institutions has increased during the same period. The share of
the credit to industrial and agriculture sector was around 48.74 and 15.94 percent
respectively, which has subsequently declined to around 38.03 and 10.93 percent. And,
the share of personal loan and credit to financial institutions was around 6.42 and 2.14
percent respectively in 1991 which increased to 20.34 and 6.70 percent in 2004.
Fig 2.1 Percentage share of Credit of Commercial Banks According to Occupation
217
0%
20%
40%
60%
80%
100%Pe
r ce
nt
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
Year
Credit of Commercial Bnaks According to Occupation (Per cent Share)
Industry
Agriculture
Personalloans
Financialinstitutions
Others @
@ Other include - Transport, personal and professional service, Trade and Miscellaneous
2.5 Distribution of Total Credit : Rural and Urban
Another important dimension of the commercial banks credit is the credit
disbursement according to location (rural vs urban). Further, since in the post
nationalization period, credit expansion to rural and semi-urban areas was given
considerable importance, it becomes essential to look at the trends of the bank credit to
these areas over time. Distribution of commercial bank real credit according to location is
presented in table 2.6. Similar to the credit to different occupations, in the case of the
credit to different population groups also the increase in the real credit is higher between
1995 and 2004 compared to the increase in credit between 1991 and 1995. The credit to
rural sector increased from Rs 21789 crores in 1991 to Rs 22632 crore in 1996 (around
1.03 time), which further increased to Rs 56397 crores in 2005 (around 2.5 times between
1996 and 2005). Similar trend is observed in the case of credit to other population groups.
Credit Semi-urban areas increased from Rs 24206 crores in 1991 to Rs 25961 crore in
1996 which further increased to Rs 66995 crore in 2005. Similarly, credit to urban and
218
metropolitan areas increased from Rs 31995 crore and Rs 63422 crore respectively in
1991 to Rs 32812 crore and Rs 99088 respectively in 1995 which further increased to Rs
97044 crores and Rs 370571 crores respectively in 2005.
Table 2.6 Population group-wise Distribution of Outstanding Credit
Distribution of Outstanding Credit of Commercial Banks According to Population Group (in Rs Crores, in Real values)
Year Rural Semi Urban Urban Metropolitan Total 1990 21789 24206 31995 63422 141411 1991 22160 24195 33090 68536 147981 1992 22677 23671 32486 70984 149818 1993 22906 23592 33020 82949 162467 1994 22544 22438 32778 82973 160734 1995 21100 23798 32812 99088 176799 1996 22632 25961 35184 114907 198684 1997 23785 27338 36514 120321 207958 1998 25473 28699 39301 130067 223541 1999 27435 31621 44427 145785 249268 2000 30707 35351 49814 173907 289779 2001 33157 37608 57082 200145 327991 2002 38987 43157 63293 238102 383538 2003 42452 48077 71130 269364 431024 2004 45940 54124 81232 294370 475665 2005 56397 66995 97044 370571 591008
On an average percentage increment of credit from period 1991 to 2005 is around
6.34, 7.09, 7.62 and 12.46 for rural, semi-urban, urban and metropolitan areas
respectively. It is important to note that between 1991 and 1995 the rural sector has
registered negative annual growth rate of around -0.503 percent. For the same period the
average growth rate is around 9.484 percent for the metropolitan areas. However from
1996 to 2005 growth rate of credit to all sectors is seen to be positive and increasing (Fig.
2.2).
Fig. 2.2 Population group with percent increment in credit
219
Per cent Growth of Total Credit of Commercial Banks
-10
-5
0
5
10
15
20
25
30
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
Per c
ent G
row
th
RuralSemi UrbanUrbanMetropolitan
Though there is increased growth rate of credit across all population groups, the
difference in the growth rates led to change in the composition of credit to different
population groups. The percent share of credit to different population groups is presented
in Fig.2 3. As can be seen from the chart, the share of credit to rural areas in the total
credit has declined over years, whereas the share of credit to metropolitan areas has
increased. The share of credit to rural areas was around 15.08 percent in 1991 which
declined to around 11.93 percent in 1995 which further declined to around 9.54 percent in
2005. Similarly the credit to semi-urban and urban areas was around 17.11 percent and
22.62 percent respectively in 1991 which declined to 13.46 percent and 18.55 percent
respectively in 1995, which further declined to 11.34 and 16.42 percent respectively in
2005. On the other hand the credit to metropolitan areas has been increasing over time. It
was around 44.8 percent in 1991 which increased to 56 percent in 1995 which further
increased to 62.7 percent in 2005.
Fig 2.3 Share of credit to different population groups
220
0%
20%
40%
60%
80%
100%Pe
r ce
nt
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Year
Distribution of Commercial Bank Credit According to Population (Per cent Share)
Rural
SemiUrban
Urban
Metropolitan
We have seen so far trends of flow of credit to different regions of the economy as per
rural or urban and also to major sectors like agriculture or industry. In this study
however we are particularly interested in credit to the SSI sector. We therefore examine
in some detail the flow of funds to the priority sector and within the priority sector to the
SSI.
2.6 Sector-wise Distribution of Credit
As mentioned above 40% of the total credit needs to be disbursed to the priority sector. It
has been observed that initially banks were unable to meet this prescription. However,
after liberalization a number of new avenues are incorporated within the purview of the
priority sector. Subsequently, banks have been complying with the priority sector norms.
At the sectoral level, credit to priority sector was around Rs 54121 crores during 1991-
221
‘91 which increased to Rs 85926 crores during 1999-2000, and, reached Rs 261492
crores during 2005-‘06. Between 1991-‘92 and 2005-‘06 credit to priority sector has
increased by around 4.5 times. Within the priority sector, credit to agriculture sector has
increased from Rs 21633 crores during 1991-92 to Rs 88355 crores during 2005-06
(around 4 times). Much of the increase in the credit to agriculture is observed during last
few years, especially during 2003-06.
It is noteworthy that the increase in credit to small-scale industries, within the priority sector is much less compared to agriculture sector. It was Rs 21625 crores during 1991-‘92 which increased to Rs 46276 during 2005-06 (around two fold). Credit to industrial sector and wholesale trade increased from Rs 56105 crore and Rs 7332 crore respectively during 1991-92 to Rs 235291 crores and Rs 20362 crore respectively during 2005-06 (Table 2.7).
Table 2.7 Setoral Deployment of Non-food Credit
Sectoral Deployment of Outstanding Non-food Gross Bank Credit (Rs Crore, Real Values)
Year Priority Sector of which Industry
Wholesale Trade
Other Sectors Total
Agriculture Small scale Industries
1991-92 54121 21633 21625 56105 7332 27005 144564 1992-93 54612 21878 21947 64260 7637 27353 153862 1993-94 53880 21208 22617 57865 7330 26875 145950 1994-95 58632 21916 25256 68237 8909 33015 168793 1995-96 61461 22667 26724 77992 10041 36633 186127 1996-97 66214 24528 28040 80041 9626 40229 196111 1997-98 72768 25499 31817 85948 9665 42081 210462
222
1998-99 77650 26852 32848 88426 9461 44787 220324 1999-00 85926 28928 34425 96024 10962 51599 244511 2000-01 96517 32454 35004 101782 11154 58797 268250 2001-02 105910 36718 34566 104137 12364 69318 291729 2002-03 124983 43422 35671 138898 13335 89009 366226 2003-04 149059 51153 37206 139667 14049 108763 411538 2004-05 206203 67703 40318 190435 17594 61640 540426 2005-06 261492 88355 46276 235291 20362 102811 720588
*Medium and large # Other than food procurement
Growth rates of gross commercial bank credit to various sectors are presented in
table 2.8. The average annual growth rate of gross non-food credit of commercial banks
is around 11.3 percent. While the average growth rate of credit to priority sector is around
11 percent per annum, it is around 10 percent and 4.82 percent respectively for the
agriculture and small-scale industries sector during 1991-92 to 2005-06. The average
growth rate of credit to industry and wholesale trade are around 10.23 and 6.8 percent per
annum respectively for the period 1991-92 and 2005-06. It is observed that the growth
rates are higher during the period 1996-97 to 2005-06 compared to the period 1991-92 to
1995-96. The growth rates of credit to priority sector, industry and wholesale trade are
around 1.24, 5.95 and 5.18 percent respectively for the period 1991-92 to 1995-96. This
has increased to 15.95, 12.36 and 7.68 percent for priority sector, industry and wholesale
trade respectively for the period 1995-96 to 2005-06.
Table 2.8 Percentage increment of Sectoral Deployment of Credit
Per cent Growth of Sectoral Deployment of Outstanding Non-food Gross Bank Credit (Rs Crore, Real Values)
Year Priority Sector Agriculture
Small Scale Industries Industry
Wholesale Trade
Other Sectors Total
1991-92 -7.00 -4.76 -7.18 -7.04 -8.51 -1.31 -6.08 1992-93 0.91 1.13 1.49 14.53 4.16 1.29 6.43 1993-94 -1.34 -3.06 3.05 -9.95 -4.03 -1.75 -5.14 1994-95 8.82 3.34 11.67 17.92 21.54 22.85 15.65 1995-96 4.82 3.43 5.81 14.30 12.71 10.96 10.27 1996-97 7.73 8.21 4.92 2.63 -4.13 9.82 5.36 1997-98 9.90 3.96 13.47 7.38 0.41 4.60 7.32 1998-99 6.71 5.31 3.24 2.88 -2.12 6.43 4.69 1999-00 10.66 7.73 4.80 8.59 15.87 15.21 10.98
223
2000-01 12.33 12.19 1.68 6.00 1.75 13.95 9.71 2001-02 9.73 13.14 -1.25 2.31 10.84 17.90 8.75 2002-03 18.01 18.26 3.20 33.38 7.86 28.41 25.54 2003-04 19.26 17.80 4.30 0.55 5.35 22.19 12.37 2004-05 38.34 32.35 8.36 36.35 25.23 -43.33 31.32 2005-06 26.81 30.50 14.78 23.55 15.73 66.79 33.34
The percentage share of gross non-food credit to various sectors, presented in Fig.
2.4 shows varying trend over the period. The share of credit to agriculture sector has
declined from 37 percent during 1991-92 to around 33 percent during 1995-96 which
however has increased to around 38 percent during 2004-05. On the other had credit to
industrial sector has increased from 38.8 percent during 1991-92 to 41.9 percent during
1995-96 which declined to 32.6 percent during 2005-06. The percent share of credit to
wholesale trade in the total gross credit has steadily declined from 5 percent in 1991-92
to 2.8 percent during 2005-06. Looking at the components of the priority sector credit,
the share of the credit to agriculture sector as well small-scale industries has declined
over time. However, the decline is sharp in the case of small-scale industries compared to
agriculture sector. While the share of the agriculture sector in the total priority sector
lending declined from around 40 percent in 1991-92 to around 33.7 percent during 2005-
06, the share of credit to small-scale industries in the total priority sector credit declined
from around 40 percent in 1991-92 to around 17.7 percent during 2005-06.
Fig. 2.5 Share of Gross Non-Food Credit
224
Percent Share of Gross Non-food Credit
0
5
10
15
20
25
30
35
40
45
5019
91-9
2
1992
-93
1993
-94
1994
-95
1995
-96
1996
-97
1997
-98
1998
-99
1999
-00
2000
-01
2001
-02
2002
-03
2003
-04
2004
-05
2005
-06
Year
Per
cent
Priority sector
Industry
Wholesale trade
% of agriculturein total prioritysector
% of SSI in totalpriority sector
Source: RBI
This indeed is a matter of concern as the SSI sector plays a crucial role for the Indian
economy in a number of aspects. By its less capital intensive and high labour absorption
nature, small-scale industries (SSI) sector has made significant contributions to
employment generation and also to rural industrialization, thereby helping in balanced
regional growth. When the performance of this sector is viewed in terms of output as well
as employment growth against other related sectors in the economy, one observes that the
growth performance of the SSI sector is far higher than the large-scale industries sector
and the manufacturing sector.
Given this background we first discuss briefly the importance of the SSI sector in a
labour surplus economy like India and the problems it faces for further growth and
development. In this context credit related norms and issues are highlighted.
2.7. The Role of the Small and Medium Industries Sector
225
Small and medium industries sector is divided into various sub-sectors to come up with
appropriate policy measures for each of them.
Definitional Issues
One of the difficulties in defining small firms is to identify what is ‘small’ (Gang, 1995).
One is often tempted to define small firms in comparative terms, that is, in comparison to
the large firms in the industry. Brock and Evans (1986) suggest that one can consider
those firms that lie on the left hand tail of the size distribution of firms, for example, say,
in the bottom quartile as ‘small’. However, such relativistic definition has certain
shortcomings. A firm that is considered large today may become small due to the entry of
a few larger firms in the market. Thus there is a need to define ‘smallness’ in absolute
terms at least for the purpose of policy formation and implementation. Naturally, the
definition can be on the basis of employment size, turn over, invested capital and so on.
In the Indian context ‘smallness’ is conceptualized on the basis of investment in plant and
machinery.
In India, a firm is considered to belong to the SSI sector, if its investment in plant and
machinery does not exceed Rs 10 million ($ 250,000 approximately)13. Within the SSI
sector there are a number of sub sectors including tiny industries sector, ancillary sector,
khadi and village industries sector, women enterprises and so on14. Since the
government’s policy incentives differ across these sub sectors, there is a need to define
them in precise terms. An ancillary industrial undertaking is a small enterprise as per the
above definition and is engaged in the manufacture of parts, components, sub-assemblies,
tooling or intermediates. On the other hand, a small scale service and business (industry
related) enterprise (SSSBE) face investment limit up to Rs 1 million in fixed assets,
excluding land and building. The tiny and village industries sector receive special
attention of the government given their vulnerability and traditional importance. Hence
the tiny, village & khadi15 and also women enterprises are specifically defined.
13 Source: Government of India website: http://www.smallindustryindia.com/ssiindia/definition.htm 14There are certain types of industries/activities wherein investment on plant and machinery up to Rs. 5 crores can also be registered under SSI category. 15 A kind of traditional handloom.
226
Investment limit in plant and machinery with respect to tiny enterprises is Rs 2.5 million
irrespective of location of the unit. A woman enterprise is the one that is owned or held
more than 51% share by a woman.
According to the Small Industries Development Organisation (SIDO), the term small-
scale industry is used in the context of modern industrial units using a mechanized
process or those engaged in the service sector. i.e, SSSBE. On the other hand, rural
industries refer to village based semi industrial activities including the production of
khadi, silk, coir etc. The coir sector is an agro-based industry relying on coconut fiber.
The nodal agency for coordinating activities related to the SSI sector is SIDO, while for
the Khadi and Village Industries Sector, it is the Khadi and Village Industries
Commission and for the Coir sector, it is the Coir Board. Thus each of these sub sectors
operate under a separate bureaucratic setup.
Considering all enterprises viz., small, tiny and traditional village enterprises both in the
registered and unregistered segments, there were about 6 million enterprises in 1990-’91
which increased to about 10 million in 2004-’05. Output of these enterprises at the 1993-
’94 prices was about rupees of 847 billions which increased to Rs 2515 billion in 2004-
’0516.
Contributions to the Economy
The small-scale industries’ sector plays a vital role in the growth of the country by
contributing almost 40% of the gross industrial value added in the Indian economy.
Some of the major statistics concerning the small scale industries are presented in
table 2.9. The number of total SSI units increased from 68 lakhs in 1990-91 to 83 lakhs
in 1995-96 which further increased 1191 lakhs in 2004-‘05. Along with the number of
units the fixed investment increased over time. It was Rs 93555 crores in 1991-92 which
increased to Rs 125750 crores in 1995-96 which further saw a rise to Rs 178699 crores in 16 These estimates are from the Ministry of Small Scale Industries. (provided by www.indiastat.com).
227
2004-‘05. It is important to note that the fixed investment per unit has also increased over
time. It was around Rs 13.78 lakh during 1991-‘92 which increased to around Rs 15.14
lakh during 1995-96, which however has declined marginally to Rs 15.06 lakh during
2004-‘05. Similarly the production of SSI, measured at constant price has also been
increasing over time. It was around Rs 84728 crores during 1991-92 which increased to
Rs 121175 crores during 1995-96 which further increased to Rs 251511 crores during
2004-05. The production per unit is also increasing over time. It was around 1.24 lakh in
1991-92 which increased to Rs 1.4 lakh during 1995-96 which further increased to Rs
2.12 lakh during 2004-05.
Table 2.9 Major Aggregates of the SSI sector
Production
Year
No. of units
(lakhs)
Fixed
investment (at current prices, Rs.
crore)
At current prices,
(Rs. crore.)
At constant
price (1993-94 base, Rs crore)
Employm
ent (in lakh
persons)
Export
(Rs.crore)
1990-91 68 93555 78802 84728 158 9664 1991-92 71 100351 80615 87355 166 13883 1992-93 74 109623 84413 92246 175 17784 1993-94 76 115795 98796 98796 183 25307 1994-95 80 123790 122154 108774 191 29068 1995-96 83 125750 147712 121175 198 36470 1996-97 86 130560 167805 134892 206 39248 1997-98 90 133242 187217 146263 213 44442 1998-99 93 135482 210454 157525 221 48979 1999-00 97 139982 233760 170379 229 54200 2000-01 101 146845 261297 184401 239 69797 2001-02 105 154349 282270 195613 249 71244 2002-03 109 162317 311952 210636 260 86013 2003-04 114 170219 357733 228730 271 NA 2004-05 119 178699 418263 251511 283 NA
Source : Annual Report 2005-06, Ministry of SSI, Govt. of India
We see from table 2.9 that the total number of SSI units has increased over time.
Also the total fixed investment of the SSI, their production, employment level and
exports have increased over time. However, their growth rates show a different picture.
The growth rate of the number of SSI has remained almost stable around 4 percent per
annum through the years. On the other hand the growth rates of investment in the fixed
228
assets show a varying trend. It was around 7.26 percent during 1991-‘92, which declined
to around 1.68 percent during 1998-‘98, which however has increased thereafter and was
around 4.98 percent during 2004-05. Contrary to this, the growth rate of the production of
the SSI has increased from around 3.10 percent during 1991-‘92 to around 11.32 percent
during 1995-‘96 which has declined to around 6 percent during 2001-‘03 which however
has increased to around 10 percent during 2004-05. Similar to the growth rate of the
number of units the growth rate of the employment level also has remained almost stable
between 4-5 percent per annum over the years. The growth rate of the exports by SSI has
declined steadily over time. It was around 43.66 percent during 1991-92 which declined
steadily and reached the lowest level of around 2 percent during 2001-02 which however
has improved and was around 21 percent during 2002-03.
Table 2.10 Growth rate of certain indicators Growth Rates
Year Units Fixed Investment
Production (Constant price) Employment Export
1991-92 4.07 7.26 3.10 4.83 43.66 1992-93 4.08 9.24 5.60 5.33 28.10 1993-94 4.05 5.63 7.10 4.46 42.30 1994-95 4.07 6.90 10.10 4.80 14.86 1995-96 4.07 1.58 11.40 3.41 25.46 1996-97 4.07 3.83 11.32 4.01 7.62 1997-98 4.06 2.05 8.43 3.55 13.23 1998-99 4.07 1.68 7.70 3.47 10.21 1999-00 4.06 3.32 8.16 3.88 10.66 2000-01 4.07 4.90 8.23 4.20 28.78 2001-02 4.07 5.11 6.08 4.44 2.07 2002-03 4.07 5.16 7.68 4.36 20.73 2003-04 4.07 4.87 8.59 4.31 2004-05 4.07 4.98 9.96 4.11
‘Jobless growth’ is a matter of concern for the Indian policy makers since liberalization.
While large industries sector has not been able to create much employment opportunities,
rather poor performance of the agricultural sector is also forcing the farmers to move out.
Services sector and SSI sector in this regard give the economy some hope. In fact, SSI
sector in India creates largest employment opportunities for the Indian populace, next
only to Agriculture. It has been estimated that 100,000 rupees of investment in fixed
229
assets in the small-scale sector generates employment for four persons. Unlike the
stagnation of employment observed in the large-scale sector, small industries
employment reveals steady rise (Table 2.9 and 2.10). Industry group-wise food products
industry has ranked first in generating employment, providing employment to 0.48
million persons (13.1%). The next two industry groups in terms of employment
generation are non-metallic mineral products with employment of 0.45 million persons
(12.2%) and metal products with 0.37 million persons (10.2%).
Next to employment, forex reserve is India’s another important concern and the role of
SSI sector cannot be undermined in this regard. The role of SSI Sector in forex earning
through exports is well recognized17 (Table 2.11). Direct exports from the SSI Sector
account for nearly 35% of total exports. Besides direct exports, it is estimated that small-
scale industrial units contribute around 15% to exports indirectly. Thus in all, 45%-50%
of the Indian exports is contributed by this Sector. This takes place through merchant
exporters, trading houses and export houses. These may also be in the form of export
orders from large units or the production of parts and components for use for finished
exportable goods.
While Indian small-scale segment is believed to be dominated by traditional goods,
which attracts the foreign consumers, in reality non-traditional products account for more
than 95% of the SSI exports. In particular, export growth has been fuelled mainly by the
performance of garments, leather and gems and jewelry units from this sector. A few
other product groups where the SSI sector dominates in exports are, sports goods, woolen
garments and knitwear, plastic products and processed food. Further, the SSI sector is
reorienting its export strategy towards the new trade regime being ushered in by the
WTO.
The total export of the SSI was Rs 9664 crores in 1991-92 which increased to Rs
36470 crores during 1995-96 which further increased to Rs 86012 during 2002-03. The
per unit exports was around Rs 14 thousand which increased to Rs 44 thousand during
17 Source: www.smallindustriesindia.com
230
1995-95 which further increased to Rs 78 thousand during 2002-03. Share of Ssi in our
total export is also considerable (Table 2.11).
Table 2.11 Share of SSI exports in India
Total Exports
Year (in crores* of
rupees)
Exports from SSI sector (in
crores* of rupees) Percentage Share
1971-72 1608 155 9.6 1976-77 5142 766 14.9 1981-82 7809 2071 26.5 1986-87 12567 3644 29 1991-92 44040 13883 31.5 1992-93 53688 17785 33.1 1993-94 69547 25307 36.4 1994-95 82674 29068 35.1 1995-96 106353 36470 34.2 1996-97 118817 39249 33.4 1997-98 126286 44442.18 35.19 1998-99 141603.53 48979.23 34.59 1999-00 159561 54200.47 33.97 2000-01 202509.7 69796.5 34.47 2001-02 207745.56 71243.99 34.29
*1 crore = 10 million. Source : Small Scale Industries in India, Ministry of SSI, Government of India.
When the performance of this sector is viewed against the growth in the manufacturing
and the industry sector as a whole, it instills confidence in the resilience of the small-
scale sector. Growth performance of the SSI sector is far higher than the large-scale
industries sector and the manufacturing sector, as can be viewed from Table 2.12.
231
Table 2.12 Comparative real growth of overall industrial sector and SSI sector in India (1990-91 to 1999-2000) (in percent) Year Overall Industry Manufacturing
Sector SSI Sector
1990-91 8.2 9 9.1 1991-92 0.6 -0.8 3.1 1992-93 2.3 2.2 5.6 1993-94 6 6.1 7.1 1994-95 8.4 8.5 10.1 1995-96 12.8 13.8 11.4 1996-97 5.6 6.7 11.3 1997-98 6.6 6.7 8.4 1998-99 4 4.4 7.7 1999-00 E 6.4 7 8.1 Abbr.: E : Estimated. Note : Estimated figures of growth for industry and manufacturing sector based on advance estimates released by Central Statistical Organisation. Growth rates from 1994-95 onwards are as per the IIP base 1993-94 = 100 and those for earlier years are as per IIP base : 1980-81 = 100. Estimation for the SSI sector for 1999-2000 made by SIDBI. Source : Report of the Study Group on Development of Small Scale Enterprises, Planning Commission, March 2001, Govt. of India. Year: Period of fiscal year in India is April to March, e.g. year shown as 1990-91 relates to April 1990 to March 1991.
The above indicators reveal that SSIs have made significant progress over the years and
the sector has emerged as a dynamic and vibrant sector in the Indian economy. Industrial
policy of the Government, both at the center as well as at the state level, have
continuously tried to boost this sector in order to impart more vitality and growth-
impetus.
Sickness in Small Scale Industries
The Reserve Bank of India (RBI) was instrumental in appointing a number of
Committees from time to time to look into the issue of sickness affecting this sector. The
232
latest definition of ‘Sickness’ given by the ‘Working Group on Rehabilitation of Sick
Units’ set up by the RBI (also known as Kohli Committee) is given below.
“ A small scale industrial unit is considered sick when
(a) any borrowal accounts of the unit remain substandard for more than six months
or,
(b) there is erosion in the net worth due to accumulated losses to the extent of 50 percent
of its net worth during the previous accounting year, and
(c) the unit has been in commercial production for at least two years.”
In order to measure incipient sickness, the continuous decline in gross output for three
consecutive years was identified as a suitable indicator. Subsequently, the following
criteria were adapted to identify sick/ incipient sick units in the third census: i)
continuous decline in gross output compared to the previous two financial years; ii) delay
by more than 12 months in repayment of loan taken from institutional sources; and iii)
erosion in the net worth to the extent of 50 percent of the net worth during the previous
accounting year.
Magnitude of Sickness/Incipient Sickness18
Sickness identified in the registered SSI sector in terms of delay in repayment of loan or
erosion in the net worth was of the order of 2.5 %, whereas in the unregistered SSI sector,
it was 0.78 %. Out of the units having outstanding loans with institutional sources like
banks and financial institutions, sickness was about 14.08 % in the registered SSI sector
as against 13.47 % in the case of unregistered SSI sector. Incipient sickness identified in
terms of continuous decline in gross output was of the order of 13.01 % in the registered
SSI sector and 7.76 % in the unregistered SSI sector according to 2001-’02 census.
Combining the three yardsticks used to measure sickness, viz; (a) delay in repayment of
institutional loan over one year, (b) decline in net worth by 50 %, and (c) decline in 18 Source: Third All India Census of Small Scale Industries, 2001-’02.
233
output during last three years, about 14.47 % of the units in the registered SSI sector were
identified to be either sick or incipient sick, while this percentage was only 8.25 in the
case of unregistered units.
Reasons for Sickness/ Incipient Sickness
In the census of 2001-’02, the units satisfying one or more of the above criteria were
treated as not being run satisfactorily and the reasons for the same were elicited. Table
2.13 indicates the reasons as given by the units suffering from sickness/ incipient
sickness. ‘Lack of Demand’ and ‘Shortage of Working Capital’ were the main reasons for
sickness, incipient or otherwise, in the SSI sector.
Table 2.13 Reasons for sickness Percentage of Units Reason
Registered Unregistered
Lack of Demand 71.6 84.1
Shortage of working capital 48 47.1
Non availability of raw
materials
15.1 15.2
Power shortage 21.4 14.8
Labour problems 7.4 5.1
Marketing problems 44.5 41.2
Equipment Problems 10.6 12.9
Management problems 5.5 5.1
*The total will exceed 100 %, as some units reported more than one reason. Source: Census of SSIs, 2001-‘02
As is observed, both registered and unregistered units face same problems that lead to
sickness. Working capital related and Marketing problems (equivalently, lack of demand)
are the major hurdles for the registered segment (that receives government support) as
well as for the unregistered firms. The problem of working capital clearly shows that
there exists a problem of credit for the SSI sector. This has been also revealed during our
survey.
234
This led us to look at the credit policy of the SSI sector.
2.8 Credit Policy with Reference to SSI
As mentioned above , at a time when there was no restriction on the lending
activities of the banking sector, lending was directed to only a certain selected activities.
To arrive at a desirable distribution of credit to core and socially important sectors, the
concept of priority sector was developed. Subsequently, as mentioned above, at a
meeting of the Union Finance Minister with the Chief Executive Officers of public sector
banks held in March 1980, a decision was taken that banks should aim at raising the
proportion of their advances to priority sectors to 40 per cent by March 1985. Following
the recommendations of the Working Group on the Modalities of Implementation of
Priority Sector Lending and the Twenty Point Economic Programme by Banks, all
commercial banks were advised to direct 40 per cent of aggregate bank advances to the
priority sector by 1985. In addition, there were sub-targets for lending to agriculture and
to the weaker sections within the priority sector. These norms are undergoing
modifications since then. In the decade of 1990s certain specific reforms have been
brought in. The 40 per cent priority sector lending requirement for net bank credit (NBC)
as applicable to PSBs as well as private sector banks continued, but liberalization of
interest rate has been introduced on loans above Re. 2 lakhs. Few other areas are also
incorporated within the purview of priority sector lending.
Unlike agriculture, there is no separate sub-target for the SSI sector, within the priority
sector lending for the Indian public and private sector banks. It is not mandatory for the
Indian banks to deposit the shortfall in lending to the SSI sector with the Small Industries
Development Bank of India (SIDBI) (as in the case of foreign banks) or any such
organisation. Sub targets however exist for agriculture and loans to the weaker sections
consisting of small and marginal farmers, artisans and others and they are 18% and 10%
respectively. In the case of cooperative bank, 60 per cent of credit comes under priority
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sector lending. In order to fulfill the priority sector lending targets, banks have been
permitted to adopt soft approaches like subscription to the bonds of SFCs, NABARD,
National Housing Bank, Rural Electrification Corporation, Housing & Urban
Development Corporation, etc. instead of undertaking retail lending to the SSI Sector.
Availability of credit was always recognized as a constraint to the growth of the SSI
sector, be it a women or a rural enterprise. Government has so far tried to mitigate the
problem through various measures. Few committees have been formed to understand and
to come up with appropriate measures for this sector. In 1990 a separate financial
intermediary called Small Industries Development Bank of India (SIDBI) was
established. Since then SIDBI has been acting more as a small industries development
organization rather than simply as a bank. Given the special role that the bank has come
to play, some of its activities are considered worth noting.
Role of SIDBI
Promotional Programs: SIDBI’s measures for this sector are modern in approach and
intend to improve competitiveness in the sector. Some of its measures for example
include:
• Creation of awareness on new product / process technologies
• Skill upgradation
• Development of technology related common facilities for the cluster
• Provision of unit-specific modernisation package
• Energy conservation and introduction of environment friendly technologies
• Quality upgradation in terms of systems and products
It took a cluster development approach to take up various developmental measures.
Cluster Development Approach: The first step in its approach to achieve these goals
involves the selection of clusters, which have certain homogeneity in terms of status of
technology, products, production levels, trade practices, and capacity to absorb improved
236
technology. Individual clusters are then assigned to expert consultancy agencies that
assess the technology upgradation needs and prepare unit-specific modernisation
packages including scope for consolidation of technical capabilities of existing units.
Technology Upgradation Program: The competitiveness of the products of SSI units
both in the domestic and international markets is dependent to a large extent on their
productivity levels, price factors and quality characteristics. SIDBI's technology
upgardation and modernisation programme are aimed at improving the technical
capabilities and competitiveness of SSI units located in clusters by introducing
commercially proven technologies which will result in significant improvement in
quality, productivity, cost reduction, saving of energy and raw materials and reduction in
the level of pollution.
Funding: SIDBI provides support and co-ordinates the services of consultants, and backs
up their efforts by arranging financial assistance, through banks or State Financial
Corporations (SFCs), under its refinance assistance schemes. The Bank also provides
direct financial assistance through its Rs. 2 billion Technology Development and
Modernisation Fund (TDMF) scheme. The Bank undertakes regular follow-up and
monitoring of the programmes and the implementing agencies are suitably compensated
by way of professional fee for undertaking the assignment.
Progress: The Bank in more than 25 clusters has launched technology upgradation
program. The clusters identified for intervention range from Sea Food Processing
Industry (Coastal Kerala) to Brass and Bell Metal Industry (Hajo in Assam) and from
scientific instrument industry (Ambala, Haryana) to artisan based Blacksmithy units at
Mylliem, Meghalaya and so on. In addition to this, the Bank is to implement the National
Programme for Rural Industrialisation in 25 clusters of which 12 initiatives are already
underway (www.sidbi.in).
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Even though SIDBI has taken up various measures to aid the SSI sector number of
SIDBI offices are limited and therefore they could reach only a small proportion of the
SSI units. Rest of the sector still depends n various other sources for credit.
Multi-level Financial Institutional Structure
A large number of institutions are engaged in the task of credit dispensation to the small
and other non-farm enterprises. Major national/state-level institutions operating in the
country in addition to SIDBI are:
Commercial Banks
State Financial Corporations (SFCs)
Regional Rural Banks
Cooperative Banks
Credit in Direct/indirect Form by Other Agencies.
In addition to the creation of such specialized financial intermediaries, to improve credit
flows to the SSI sector a few committees have also been constituted and in turn the
Reserve Bank of India (RBI) has taken various measures . Focus and recommendations
of few committees and RBI measures are discussed briefly below.
Nayak Committee
This committee was formed under the Chairmanship of Ex-Deputy Governor, of
RBI Shri R.R. Nayak, to look into the problems of credit, sickness and other relevant
aspects of the SSI sector. The committee submitted its report in September 1992. Based
on the Nayak Committee recommendations, Reserve Bank of India has directed the
commercial banks to modify the definition of sick SSI units and to reduce rate of interest
for rehabilitation. The committee has also suggested various rehabilitation packages.
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Kapur Committee
The Kapur Committee was set up by the RBI to review the working of the credit
delivery system for SSIs with a view to making the system much more effective, simple
and efficient to administer. The committee had also examined the sickness related issues
of the SSI sector. For quick rehabilitation of sick SSI units, the Committee has
recommended the following:
Changing the definition of classifying the SSI unit as sick by reducing the non-
performing period of the SSI account from 2 ½ years to one year.
Converting State Level Inter Institutional Committees (SLIICs) into statutory
bodies under a special statute to enable them to play effective role in
rehabilitation of sick SSIs.
Setting up branches of SLIICs in districts having large concentration of SSIs.
Providing relaxation in income recognition and asset classification amounts to
encourage banks to take up rehabilitation of potentially viable sick SSIs.
Measures taken by Reserve Bank of India
RBI has issued detailed guidelines vide their circular dated 17th April, 1993 and
3rd July, 1993 to banks for rehabilitation of sick SSI units including detection at the
incipient stage and to take remedial measures, including the broad parameters for grant of
relief and concessions such as:
Interest on Working Capital 1.5 % below the prevailing fixed/PLR, wherever
applicable Funded Interest Term Loan Interest free Working Capital Term Loan 1.5% below the prevailing fixed /PLR, wherever
applicable. Term Loan Concession up to 2% ( Not more than 3% in the case of
tiny/decentralised sector units) below the document rate.Contingency/Loan Assistance
Concessional rates for working capital assistance.
2.9 Commercial Banks and Credit to SSI
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One of the important sources of credit to small-scale industries is the ercial bank.
The total credit from commercial banks to SSI sector has increased from Rs 18939 crores
in 1991-92 to Rs 34246 crores during 1995-96, which further increased to Rs 60141
crores during 2000-01 and to 90239 crores during 2005-06. Though at the absolute level,
credit has been increasing over time the growth rate shows a varying trend. The annual
growth rate of commercial banks’ credit to SSI sector increased from around 5.58 percent
in 1991-92 to around 21.67 percent during 1994-95 which almost steadily declined
thereafter and reached the lowest level of -3.58 percent during 2003-04. This however
improved in the subsequent years. The credit to SSI sector as a percent of total net bank
credit has been steadily declining over time. It was around 16.13 percent during 1991-92
which declined to around 15 percent during 1995-96 which further declined to 12.87
percent during 2000-01 and to 6.66 percent during 2005-06 (Table 2.14).
Table 2.14 Flow of Credit from Commercial Banks to SSI (Rs crore)
Net Bank Credit
Annual growth
Credit to SSI
Annual growth
Credit to SSI as percent of Net Bank Credit
1991-92 117443 7.45 18939 5.58 16.13 1992-93 141800 20.74 20975 10.75 14.79 1993-94 152501 7.55 23978 14.32 15.72 1994-95 192424 26.18 29175 21.67 15.16 1995-96 228198 18.59 34246 17.38 15.01 1996-97 245999 7.80 38196 11.53 15.53 1997-98 297265 20.84 45771 19.83 15.40 1998-99 339477 14.20 51679 12.91 15.22 1999-00 398205 17.30 57035 10.36 14.32 2000-01 467206 17.33 60141 5.45 12.87 2001-02 535063 14.52 67107 11.58 12.54 2002-03 668576 24.95 64707 -3.58 9.68 2003-04 763855 14.25 71209 10.05 9.32 2004-05 971809 27.22 83179 16.81 8.56 2005-06 1354603 39.39 90239 8.49 6.66
At the bank group level , the State Bank and Associates group also shows similar trend.
There has been substantial decline of share of SSI credit in total priority sector lending
(Table 2.15). This decline is even more striking for the private sector banks. Share of SSI
credit in the case of Foreign banks however, remained more or less stable due to the
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special norms apply to them regarding priority sector lending. We recall that for the other
bank groups there is no special norm fixed for the SSI sector separately.
Table 2.15
Bank GroupWise Advances to Small Scale Industries 1997 1998 1999 2000 2001 2002 2003 2004
Public Sector Banks Small Scale Industries (Rs Crore) 31542 38109 42674 45788 48445 49743 52988 58278 Per cent to Total Priority sector credit 39.86 41.73 39.81 35.83 33.06 29.06 26.09 23.72 Per cent to total non-food credit 16.63 17.46 17.33 15.63 14.21 12.53 11.09 10.43
Private Banks Small Scale Industries (Rs Crore) 4754 5848 6451 7313 8158 8613 6857 7897 Per cent to Total Priority sector credit 53.83 50.35 45.57 40.58 37.86 33.50 18.68 14.94 Per cent to total non-food credit 22.15 20.60 18.86 15.71 14.40 13.70 8.29 7.08
Foreign Banks Small Scale Industries (Rs Crore) 1836 2084 2460 2871 3716 4561 3809 5438 Per cent to Total Priority sector credit 29.91 30.03 29.75 29.60 31.40 34.00 25.65 29.75 Per cent to total non-food credit 11.29 10.30 11.01 10.36 10.83 11.56 8.70 10.35
Diversion of credit away from the SSI sector may be due to the prevalence of
sickness of the SSI units. If one examines carefully it is observed that the number of sick
SSI units increased from 221472 in 1991 to around 268815 in 1995 (Table 2.16), which
has declined thereafter to 221536 in 1998. After a sharp increase to 306221 in 1999, the
total number of sick units has declined steadily thereafter and it was 138811 in 2004.
However, the value involved in the sick SSI units increased steadily over the years. It was
Rs 2792 crores in 1991, which increased to Rs 3547 crores in 1995 and further climbed
up to Rs 4608 in 2000 and to Rs 5285 in 2004. Since on the one hand the number of sick
SSI units is declining, and on the other hand the amount involved is increasing, the
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amount involved per units has increased steadily over time. It was around Rs 1.26 lakh
per unit in 1991, which increased to Rs 1.74 lakh in 1998 which further increased to Rs
3.80 lakh in 2004.
Though the total sick SSI units show a varying trend, the sick SSI units that are
potentially viable are steadily declining over time. It was 16140 in 1991 that has declined
to 15539 in 1995, which has further gone down to 14373 in 2000 and to 2385 in 2004.
Unlike the case of total sick SSI units, the amount involved in the potentially viable sick
SSI units has been declining over time. It was Rs 693 crores in 1991 which reduced to Rs
597.93 crores in 1995, which further declined to Rs 369 corores in 2006; One however,
observe a marginal increase to Rs 421 crores in 2004. The amount involved per unit of
the potentially viable sick SSI has steadily declined from Rs 4.29 lakh in 1991 to Rs 2
lakh in 1999, however, it has increased sharply there after and it was Rs 17.6 lakh in
2004.
Table 2.16 Sickness in Small Scale industrial Sector
Total sick units Potentially viable Per cent of
Potentially Viable in Total Sick Units
Year No. Amount (Rs.
Crores)
Amount Per unit
(Rs Lakh)
No.* Amount O/s (Rs. Crores)
Amount Per unit
(Rs Lakh)
No. Amount (Rs.
Crores)
1991 221472 2792 1.261 16140 693.12 4.294 7.29 24.83 1992 245575 3101 1.263 19210 728.9 3.794 7.82 23.51 1993 238176 3443 1.446 21649 798.79 3.690 9.09 23.20 1994 256452 3680 1.435 16580 685.93 4.137 6.47 18.64 1995 268815 3547 1.320 15539 597.93 3.848 5.78 16.86 1996 262376 3722 1.419 16424 635.82 3.871 6.26 17.08 1997 235032 3609 1.536 16220 479.31 2.955 6.90 13.28 1998 221536 3857 1.741 18686 455.96 2.440 8.43 11.82 1999 306221 4313 1.409 18692 376.96 2.017 6.10 8.74 2000 304235 4608 1.515 14373 369.45 2.570 4.72 8.02 2001 249630 4506 1.805 13076 399.17 3.053 5.24 8.86 2002 177336 4819 2.717 4493 416.41 9.268 2.53 8.64 2003 167980 5706 3.397 3626 624.71 17.229 2.16 10.95 2004 138811 5285 3.807 2385 421.18 17.660 1.72 7.97
Source: Annual Report 2005-06; Ministry of SSI, Government of India *These units include village industries as well
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The major reason for the non performing assets arising from the SSI sector is their
sickness. While there may be some proportion of willful default, unless one handles the
sickness issue appropriately , reducing the bad loan from the sector becomes difficult.
Banks will then take recourse to diverting their resources to other sectors in the economy.
In the next Chapter therefore we discuss the NPA issue in general and that arising from
the SSI sector in particular.
2.9 Concluding Remarks
The Indian banking sector has come a long way since the last century. Even since the
nationalization of banks took place, the operations of commercial banks spread like never
before. It covered all nook and corner of the nation. Accordingly credit disbursement and
deposit mobilization have increased at a phenomenal rate. Various sectors especially
agriculture sector was given priority in the lending of the commercial banks. Such norms
have helped the sector to come out of the grasp of moneylenders to a large extent if not
completely. Such social banking norms also however, have affected the financial health
of the banks.
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With the liberalization of the Indian economy, various financial sector liberalization
norms have also been introduced. These are aimed at improving the efficiency and
profitability of the public sector banks. A number of reform measures have been
introduced to provide autonomy to the banks (see Chapter 1). While the liberalized
measures improved efficiency of the commercial banks, rural branches, credit share to
some of the priority sectors such as SSI sector declined over time. It has often been
argued that the NPA accounts mainly arise from the rural branches and priority sector
loans. While there is some truth to these allegations, moving away from these sectors
may not be an ideal solution for the economy. Further it is also important to ask whether
these sectors alone need to be blamed for NPA? To examine this we devote our attention
to the nature and extent NPA of the Indian commercial banks in the next chapter.
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CHAPTER 3
Non-Performing Assets of Indian Commercial Banks
3.1 Introduction
High level of Non-performing Assets (NPAs) is a matter of concern for everyone
involved as credit is essential for economic growth and NPAs affect the smooth flow of
credit. Banks raise resources not just from fresh deposits, but they also create credit by
recycling the funds received back from the borrowers. Thus when a loan becomes non-
performing, it affects recycling of credit and in turn credit creation. Apart from the credit
creation, NPAs affect the profitability as well, since higher NPAs require higher
provisioning, which means a large part of the profits needs to be kept aside as provisions
for bad loans. Therefore, the problem of NPAs is not the concern of the lenders alone,
but it a concern of policy makers as well who are involved in putting economic growth on
the fast track. In India the concept of NPA came into existence after the financial sector
reforms were introduced following the recommendations of the Report of the Committee
on the Financial System (Narasimham, 1991).
Broadly, Non Performing Advance is defined as an advance where payment of
interest or repayment of installment of principal (in case of term loans) or both remains
unpaid for a certain period19. In India, the definition of NPAs has changed over time.
According to the Narasimham (1991) committee report, those assets (advances, bills
discounted, overdrafts, cash credit etc) for which the interest remain due for a period of
four quarters (180 days) should be considered as NPAs. Subsequently this period was
reduced, and from March 1995 onwards the assets for which the interest has remained
unpaid for 90 days should be considered as NPAs.
NPA being our prime concern in this study we intend to look at the trends and nature of
NPAs for the Indian economy in some detail in this chapter. However, before coming to
19 This time duration given for an asset to consider it as a NPA varies from country to country and can change over time within a particular country.
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the Indian scenario it is useful to examine the NPA problem for other nations across the
globe to see where India stands vis-à-vis others.
3.2 NPAs at the Global Level
In order to get a global picture it is essential to look at NPA levels of different
countries in the world. Since the concept of NPA is developed in India only in the post-
reform era , it is useful to look at the recent figures rather than taking a historical account.
A closer look at the Non-performing Loans (NPL) as they are called in many nations,
reveals that during 2003 the NPL at the global level was US$ 1300 billion. India ranks
fourth with NPL of around US$ 30 billion ( 2.3 percent of the global NPL), while Japan
has the highest NPL of US$ 330 billion (25.4 percent of the global NPL) Turkey has the
lowest NPL of US$ 8 billion (0.6 percent of global NPL, Table 3.1).
Table 3.1
Global Non-performing Loans: 2003 Countries NPLs (US$ billion) Share in Global (per cent) Japan 330 25.4 China 307 23.6 Taiwan 19.1 1.5 Thailand 18.8 1.5 Philippines 9 0.7 Indonesia 16.9 1.3 India 30 2.3 Korea 15 1.2
Germany 283
21.8 Turkey 8 0.6 Global 1300 100
Source: Global NPL Report 2004, Ernst and Young.
Though one can get an idea about the magnitude of NPA by looking at the
absolute values, it does not reveal the complete picture. This is mainly because absolute
level of NPA depends on total advances. A country with larger population or GDP may
have large advances and in turn NPA as well. Thus apart from the absolute value, it is
also important to look at what proportion of the total loan has become non-performing.
The NPL levels of various countries as per cent of their total loan are presented in table
3.2. It can be seen from the table that the NPA/NPL as a percent of total loans has been
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declining for almost all countries over the years. The average NPL as percent of total
loans across the countries was around 11.89 percent in 2001 which has declined to
around 6.44 percent in 2005. This shows that the quality of bank asset has been
improving across countries over the years. This could be because of the stringent
regulations prescribed by the BASEL norms. Examining the countries in terms of NPA
as percentage of total loans, we observe that for around 16 countries NPA percentage is
below 10 and for around 5 countries it is between 10-20 percent; for another 5 countries
NPA percentage is rather high and above 20 percent. Australia has the lowest NPA to
total loan ratio which is just 0.34 percent whereas Philippines tops the list with 25
percent. India attains the 11th highest position with around 8.6 per cent. One interesting
observation is that, most of the countries which fall under higher ‘NPL/Total Loan’ ratio
category belong to the Asian region. Out of 10 countries which have this ratio above 10
percent 8 countries belong to Asia. The improvement in the quality of the assets across
countries is also shown by the fact that in 2001 there were around 11 countries whose
NPA/Total Loan ratio was above 10 percent, by 2005 this number reduced to 7.
Table 3.2 Bank Non-performing Loans to Total Loans
Countries 2001 2002 2003 2004 2005 Australia 0.6 0.4 0.3 0.2 0.2 Bangladesh 31.5 28 22.1 17.6 15.3 Brazil 5.6 4.8 4.8 3.8 4.4 Canada 1.5 1.6 1.2 0.7 0.5
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Chile 1.6 1.8 1.6 1.2 0.9 China 29.8 25.6 20.1 15.6 10.5 Egypt 16.9 20.2 24.2 16.3 25 France 5 5 4.8 4.2 3.5 Germany 4.6 5 5.3 5.1 4.8 Hong Kong 6.5 5 3.9 2.3 1.5 India 11.4 10.4 8.8 7.2 5.2 Indonesia 31.9 24 19.4 14.2 15.6 Japan 8.4 7.2 5.2 2.9 1.8 Korea 3.4 2.4 2.6 1.9 1.2 Malaysia 17.8 15.8 13.9 11.8 9.9 Mexico 5.1 4.6 3.2 2.5 1.8 Pakistan 23.4 21.8 17 11.6 10.6 Philippines 27.7 26.5 26.1 24.7 20 Russia 6.2 5.6 5 3.8 3.2 Singapore 8 7.7 6.7 5 3.8 Sri Lanka 15.3 15.3 13.7 9.1 9.6 Switzerland 2.3 1.9 1.4 0.9 0.5 Thailand 11.5 16.5 13.5 11.8 11.1 Turkey 29.3 17.6 11.5 6 4.8 United Kingdom 2.6 2.6 2.5 1.9 1 United States 1.3 1.4 1.1 0.8 0.7 Source: Global Financial Stability Report, May 2006, IMF
While comparing the NPA levels of different countries with each other one should
remember that the features relating to the NPA reporting/evaluation practices are not
uniform across countries. In some countries the NPA level may be low because their
losses are written off at an early stage. In some of the developing countries of ‘Asia
Pacific Countries belonging to Economic co-operation (APEC) forum’, a loan is
classified as non-performing only after it has been in arrears for at least six months;
whereas, in India, currently an asset is considered NPA if it is due for 90 days. Also in
India due to the lengthy legal process, it takes considerably long time to recover the loan.
And, due to many safeguards/procedures, even after a NPA is written off banks continue
to hold them in their books, many a time along with the provision made for those loans.
Even the classification of NPA into Gross NPA and Net NPA is not uniform because, in
some countries the provisions made are general provisions whereas, in India NPAs are
considered GNPA for some time even after making provisions. Thus while comparing the
NPA level of India with other countries one should remember that in many respect, asset
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classification norms in India are considerably tighter than the international best practices.
The classification standards adopted in a few countries are given in the Appendix (Table
A3.7).
In addition, countries also do differ in various other respects. In that sense a strict
comparison across countries cannot be arrived at. Nonetheless the global picture do
reflect a comprehensive view of NPAs across the world.
3.3 NPA norms and Non Performing Assets in India
Though the issue of NPA was given more importance after the Narasimham
committee report (1991) highlighted its impact on the financial health of the commercial
banks and subsequently various asset classification norms were introduced, the concept
of classifying bank assets based on its quality began during 1985-86 itself. A critical
analysis for a comprehensive and uniform credit monitoring was introduced in 1985-86
by the RBI by way of the Health Code System in banks which, inter alia, provided
information regarding the health of individual advances, the quality of credit portfolio
and the extent of advances causing concern in relation to total advances. It was
considered that such information would be of immense use to bank managements for
control purposes. Reserve Bank of India advised all commercial banks (excluding foreign
banks, most of which had similar coding system in their organisations) on November 7,
1985, to introduce the Health Code classification system indicating the quality (or health)
of individual advances in the following eight categories, with a health code assigned to
each borrowal account:
1. Satisfactory - conduct is satisfactory; all terms and conditions are complied with;
all accounts are in order; and safety of the advance is not in doubt.
2. Irregular- the safety of the advance is not suspected, though there may be
occasional irregularities which may be considered as a short term phenomenon.
3. Sick, viable - advances to units which are sick but viable - under nursing and units
in respect of which nursing/revival programmes are taken up.
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4. Sick: nonviable/sticky - the irregularities continue to persist and there are no
immediate prospects of regularisation; the accounts could throw up some of the
usual signs of incipient sickness
5. Advances recalled - accounts where the repayment is highly doubtful and nursing
is not considered worth-while; includes where decision has been taken to recall
the advance
6. Suit filed accounts - accounts where legal action or recovery proceedings have
been initiated
7. Decreed debts - where decrees (verdict) have been obtained.
8. Bad and Doubtful debts - where the recoverability of the bank's dues has become
doubtful on account of short-fall in value of security; difficulty in enforcing and
realising the securities; or inability/unwillingness of the borrowers to repay the
bank's dues partly or wholly
Under the above Health Code System RBI was classifying problem loans of each
bank in three categories i.e. i) advances classified as Bad & Doubtful by the bank
(corresponding to Health Code No.8) (ii) advances where suits were filed/decrees
obtained (corresponding to Health Codes Nos.6 and 7) and (iii) those advances with
major undesirable features (broadly corresponding to Health Codes Nos.4 and 5).
The Narasimham Committee (1991) felt that the classification of assets according
to the health codes is not in accordance with the international standards. It believed that a
policy of income recognition should be objective and based on record of recovery rather
than on any subjective considerations. Also, before the capital adequacy norms are
complied with by Indian banks, it is necessary to have their assets revalued on a more
realistic basis on the basis of their realizable value. Thus the Narasimham committee
(1991) believed that a proper system of income recognition and provisioning is
fundamental to the preservation of the strength and stability of the banking system.
The international practice is that an asset is treated as non-performing when
interest is due for at least two quarters. In respect of such non-performing assets interest
is not recognized on accrual basis but is booked as income only when it is actually
received. The committee suggested that a similar practice should be followed by banks
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and financial institutions in India and accordingly recommended that interest on non-
performing assets should be booked as income on accrual basis. The non-performing
assets would be defined as an advance where, as on the balance sheet date:
4. In respect of overdraft and cash credits, accounts remain out of order for a period
of more than 180 days,
5. In respect of bills purchased and discounted, the bill remains overdue20 and
unpaid for a period of more than 180 days,
6. In respect of other accounts, any account to be received remains past due for a
period of more than 180 days.
As mentioned earlier, the grace period was reduced, and from March 1995
onwards the assets for which the interest has remained unpaid for 90 days should be
considered as NPAs. Provisions need to be made for the NPAs and total NPA (gross)
minus the provisions is defined as net NPA.
Along with providing the detailed definition of the Non-performing Asset, the
Narasimham committee (1991) also suggested that for the purpose of provisioning, banks
and financial institutions should classify their assets by compressing the health codes into
the four broad groups; (i) Standard (ii) Sub-standard, (iii) Doubtful and (iv) Loss.
Broadly stated, sub-standard assets would be those which exhibit problems and would
include assets classified as non-performing for a period not exceeding two years.
Doubtful assets are those non-performing assets which remain as such for a period
exceeding two years and would also include loans in respect of which installments are
overdue for a period exceeding two years. Loss assets are accounts where loss has been
identified but amounts have not been written off.
20 An amount is considered overdue when it remains outstanding 30 days beyond the due date.
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One of the related and important aspects of NPAs is the provisioning. According
to the international norms, commercial banks need to keep aside a portion of their income
as provision for the bad loans. The amount of provision depends on the type of the NPAs
and also the time duration. The Narasimham committee (1991) believed that in the Indian
context, given the delays in the legal system, there is bound to be a time lag between an
account becoming doubtful of recovery, its recognition as such, and the realization of the
security. This factor has to be kept in mind in making provisions, besides market value of
the security charged to the banks and institutions. The committee, therefore, recommends
that the basis of provisioning against bad and doubtful debts should be as under:
1. In respect of loss assets either the entire assets should be written off in the books
or if the asset is permitted to remain in the books for certain reasons, 100 percent
of the outstanding should be provided for.
2. In respect of doubtful debts, it should be necessary for the banks to provide 100
percent of the security shortfall, that is, the full extent to which the loans and
advances are not covered by the realizable value of the security. Over and above
this, it will be necessary for banks and institutions to make a further specific
provision to the extent of a certain percentage of even the secured portion. This
percentage could vary from 20 to 50 percent depending on the period for which an
asset remains in the doubtful category.
3. In respect of sub-standard assets, a general provision of 10 percent of the total
outstanding should be created.
3.4 Recovery Mechanism of NPA
It was felt by the Government of India that the usual recovery measures like issue
of notices for enforcement of securities and recovery of dues is a time consuming
process. Thus, in order to speed up the process of recovery of NPAs, the Government of
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India constituted a committee under the chairmanship of late Shri Tiwari in 1981. The
committee examined the ways and means of recovering NPAs and recommended, inter
alia, the setting up of ‘Special Tribunals’ which could expedite the recovery process.
Later the Narasimham Committee (1991) endorsed this recommendation, and also,
suggested setting up of Asset Reconstruction Fund (ARF). The Government of India, if
necessary should establish this fund by special legislation, which would take over NPAs
from banks and financial institutions, at a discount, and follow up on the recovery of the
dues owed to them from the primary borrowers.
Based on the recommendations of the Tiwari committee and also of the
Narasimham Committee, various Debt Recovery Tribunals were established at various
parts of the country. An Asset Reconstruction Company was also established. At present,
various measures taken to reduce NPAs include reschedulement, restructuring at the bank
level, corporate debt restructuring, and recovery through Lok Adalats, Civil Courts, and
Debt Recovery Tribunals and Compromise Settlements. Some of these measures are
discussed briefly here.
3.4.1 Debt Recovery Tribunals
It was felt by the Tiwari committee (1981) that the civil courts are burdened with
diverse types of cases. Recovery of dues due to banks and financial institutions is not
given priority by the civil courts. The banks and financial institutions like any other
litigants have to go through a process of pursuing the cases for recovery through civil
courts for unduly long periods. By 30th Sept 1990, more than 15 lakh cases filed by the
public sector banks and about 304 cases filed by the financial institutions were pending in
various courts. Recovery of debts involved more than Rs 5622 crores in dues of public
sector banks and about Rs 391 crores in dues of the financial institutions. Thus this
committee recommended for establishment of Debt Recovery Tribunals (DRTs) which
was later endorsed by the Narasimham Committee. Subsequently, the “Recovery of
Debts Due to Banks and Financial Institutions Act, 1993” was passed which enabled the
establishment of DRTs. The DRT Act seeks to provide expeditious adjudication and
speedy recovery of dues to banks and financial institutions. Presently, there are 29 DRTs
and 5 DRATs functioning all over the country. The pecuniary jurisdiction of these
253
Tribunals is Rs.10 lakhs and above. Debts Recovery Tribunals also have jurisdiction over
appeals against any of measures taken by the secured creditor or by the authorized
officer.
In order to make the process faster and to correct the legal irregularity, the
Government of India passed an amendment to the DRT Act in 2000. The new statute
gives more power to the DRTs in terms of attaching the property of the defaulter and also
distribute the sale proceedings of the defaulter’s property among secured creditors.
3.4.2 Asset Reconstruction Company (India) Limited
After recognizing the problems of NPAs, various measures were taken to reduce
the NPA levels in the future. However, by then, commercial banks and other financial
institutions had already accumulated enough NPAs. It was necessary to free commercial
banks and other financial institutions from the problems of recovering NPAs so that they
can concentrate on the regular banking business. In this regard, in addition to DRTs,
Government of India also decided to set up an agency which can acquire NPAs from
commercial banks and recover them. This was also suggested by the Narasimham
Committee report where they mentioned about setting up of an Asset Reconstruction
Fund. ARCIL is established under the SARFAESI Act 2002 and is registered with
Reserve Banks of India and commenced business from 2003. The RBI has so far issued
Certificate of Registration to four Securitasation Companies/Reconstruction Companies
(SCs/RCs), of which three have commenced their operations. Assets Reconstruction
Company (India) Limited (ARCIL) set up in 2003 has provided a major boost to the
efforts to recover the NPAs of the banks. During 2005-06, ARCIL acquired 559 cases of
NPAs amounting to Rs 21,126 crores. The portfolio of assets acquired by ARCIL was
diversified across major industry segments, which were generally performing well in the
stock market. About 78 per cent of assets under management were fully/partially
operational. There are certain important legal reforms as well to expedite the process of
loan recovery and SARFAESI act is a significant step in this direction.
3.4.3 SARFAESI Act
254
The legal mechanism for recovery of default loans by usual procedure was too
cumbersome and time consuming. Thus it was felt that banks and financial institutions
should be given power to take over securities and sell in order to recover the dues. In this
regard the Government of India appointed a committee under the chairmanship of Shri T
R Andhyarujina, senior Supreme Court advocates and former Solicitor General of India
in the year 1999 to look into these matters. The Committee submitted four reports. One
among them is related to securitization.
Based on the recommendations of the Andhyarujina committee The Securitisation And
Reconstruction of Financial Assets And Enforcement of Security Interest (SARFAESI)
Act, 2002, was enacted on 17th December 2002. The act provides enforcement of security
interest without taking recourse to civil courts. This act was passed with the aim of
enabling banks and financial institutions to realise long-term assets, manage problem of
liquidity, reduce asset liability mismatches and improve recovery by exercising powers to
take possession of securities, sell them and reduce non-performing assets by adopting
measures for recovery or reconstruction. The ordinance also allows banks and financial
institutions to utilise the services of ARCs/SCs for speedily recovering dues from
defaulters and to reduce their non-performing assets. The ordinance contains provisions
that would make it possible for ARCs/SCs to directly take possession of the secured
assets and/or the management of the defaulting borrower companies without having to
resort to time consuming process of litigation and without allowing borrowers to take
shelter under provisions of SICA/BIFR. In addition to passing SARFAESI Act certain
other legal reforms have also been introduced to speed up the loan recovery process.
3.4.4 Other Legal Reforms
One of the important factors responsible for continuing high level of NPAs in the
Indian banking industry is the weak legal system. According to an international rating
agency called FITCHIBCA “The Indian legal system is sympathetic towards the
borrowers and works against the banks' interest. Despite most of their loans being backed
by security, banks are unable to enforce their claims on the collateral, when the loans turn
non-performing, and therefore, loan recoveries have been insignificant”.
255
However efforts have been made to rectify these problems in terms of judicial
process as well as laws. In 1999 a standing committee under the aegis of Industrial
Development Bank of India (IDBI) was constituted to have a coordinated approach in the
recovery of large NPA accounts, as also for institutionalising an arrangement for a
systematic exchange of information in respect of large borrowers (including defaulters
and NPAs) common to banks and financial institutions. And, as mentioned above, in
2002 Securitisation and Reconstruction of Financial Assets and Enforcement of Security
Interest Act (SARFAESI Act) was passed which empowers the creditors to foreclose
non-performing loans and the underlying collateral without going through a lengthy court
or tribunal process (Basu, 2005). All these efforts have improved the recovery of NPAs
by commercial banks which has in turn helped in reducing the NPA level. The total
NPAs recovered through various channel was around Rs 4039 crores during 2003-04
which has increased by many fold to Rs 20578 crores during 2004-05.
3.4.5 Recovery of NPA
Using the new institutions and legal options banks and financial institutions have
accelerated their recovery of NPAs. The NPAs recovered by scheduled commercial banks
through various channels is presented in table 3.3. Between 2003-04 and 2005-06, the
total cases referred to various institutions are 932377 which related to the total amount of
around Rs 70226 crores out of which around Rs 19075 crore was recovered. In terms of
the number of cases, highest number of cases (553042) were referred to Lok Adalats and
lowest cases (15812) were referred to DRTs. Whereas, in terms of the amount involved
DRTs deal with the highest amount of around Rs 32745 crores and Lok Adalats deal with
lowest amount of around Rs 2965 crores. In terms of the recovery, one-time
settlement/compromise schemes seems to be doing better as around 58 percent of the
amount involved was recovered. DRTs recovered around 29 percent and Lok Adalats
recovered around 16 percent of the amount involved. And, around 22 percent of the
amount involved was recovered under SARFAESI act.
Table 3.3
256
NPAs Recovered by SCBs Through Various Channels
One-time settlement/
compromise Scheme
Lok Adalats DRTs SARFAESI Act
2003-04 No of cases referred 139562 186100 7544 2661 Amount involved 1510 1063 12305 7847 Amount recovered 617 149 2117 1156 2004-05 No of cases referred 132781 185395 4744 39288 Amount involved 1332 801 14317 13224 Amount recovered 880 113 2688 2391 2005-06 No of cases referred 10262 181547 3524 38969 Amount involved 772 1101 6123 9831 Amount recovered 608 223 4710 3423
Source: RBI
Thus we observe that considerable attention has been paid to the NPA issue and various
regulatory as well as institutional mechanisms are put in place. How effective are these
changes? This calls for a closer look at the NPA trends in the recent past.
3.5 Non Performing Assets in India
While efforts are on for NPA classifications, refinement of accounting system
and measures to reduce NPA in the decade of 1990s , proper practice of these norms took
time. Systematic data on NPAs started to become available in a usable form from
1998 only. Though the total GNPA has increased significantly between 1998 and 2002, it
has started to decline after that (Table 3.4, Table A3.121). During 1998 the total Gross
NPA and Net NPA of the total banking sector was Rs. 34428 crore (around 14.4 percent
of gross advance) and Rs. 16098 crores (around 7 per cent of net advance) respectively.
During 2005, the GNPA increased even in real terms to Rs 38558 crores (around 5.2
percent of gross advance) whereas, NNPA has reduced to 14181 crores (around 2 percent
21 Nominal Figures are shown in the Appendix (see Table A3.1- A3.6)
257
of net advance). The growth rate of GNPA was about 11 percent in 1999 to which started
to fall drastically and became negative after 2002. Growth rate becoming negative
implies that there is a substantial decline in the GNPA level of commercial banks
showing some impact of the sensitization and regulatory changes. Similar trend is
observed in the case of Net NPA. The decline in the level of NNPA is sharper than the
GNPA. This is mainly because of the increasing level of provisions, as shown in the last
three rows of Table 3.4.
Table 3.4
Non Performing Assets of Total Banking Sector (Rs Crore, Real Values) 1998 1999 2000 2001 2002 2003 2004 2005
Gross NPA 34428 38275 38320 38828 41430 39012 35007 38558
Change 3848 45 508 2602 -2418 -4006 3551Percentage growth 11.18 0.12 1.33 6.70 -5.84 -10.27 10.14Percent to Gross Advance 14.39 14.71 12.79 11.42 10.42 8.86 7.19 5.27
percent to Gross Assets 6.36 6.18 5.49 4.91 4.62 4.04 3.27 2.57
Net NPA 16098 18264 18991 19774 20787 18548 13302 14181
Change 2165 727 783 1013 -2240 -5246 879Percentage growth 13.45 3.98 4.13 5.12 -10.77 -28.28 6.61Percent to Gross Advance 6.73 7.02 6.34 5.82 5.23 4.21 2.73 1.94
Percent to Gross Assets 2.97 2.95 2.72 2.50 2.32 1.92 1.24 0.95
Gross Advance
239188 260180 299697 340005 397502 440207 486686 731323
Change 20993 39516 40309 57496 42705 46479 244637Percentage growth 8.78 15.19 13.45 16.91 10.74 10.56 50.27
Gross Assets
541311 619827 697384 790257 896895 966570 1070292 1497479
Change 78516 77558 92873 106638 69675 103722 427187
258
Percentage growth 14.50 12.51 13.32 13.49 7.77 10.73 39.91
Gross-net 18329 20012 19329 19054 20643 20465 21705 24377
Change 1682 -683 -275 1589 -178 1241 2672Percentage growth 9.18 -3.41 -1.43 8.34 -0.86 6.06 12.31Source: Computed by author using RBI data.
At the bank group level, public sector banks have the highest non-performing assets with
an average GNPA (for the period 1998-2005) of around Rs 31,000 crore (about 11.54
percent to gross advances) and average NNPA of about Rs 16,000 crores (around 5.72
percent of net advance) (compare Table 3.5 and 3.6). While higher GNPA from the
public sector is expected, as their share in total lending is also much higher in the total
banking sector, GNPA to total advance ratios are also higher for them. In this context we
observe that the old private banks rank second in terms of percentage of NPAs in total
lending (around 9.96 percent of gross advance) and average NNPA of around 6.15
percent of net advance. This is followed by the new private sector banks with an average
GNPA to total advance ration of around 5.51 percent (see table 3.5 and 3.6) and average
NNPA to total net advance ratio of around 3.36 percent of net advance. Similarly, high
absolute level of total NPA is expected of public sector banks as their volume of lending
is also much higher. For the same reason foreign banks rank last both in terms of average
GNPA as well as NNPA. But when we compare the public sector banks against private
banks in terms of percentage of total lending (Table 3.6) we observe that public sector
banks are as good as or as bad as their private counterparts. In some years public sector
banks are indeed doing better that the private sector banks. But when compared with the
foreign banks they do not fare well. This may be partly because foreign banks are long
accustomed to the NPA norms in their parent country. Further, various credit related
welfare programs are carried out through public sector banks. They also have maximum
reach in the rural areas. Whether having maximum rural branches is indeed creating bad
loans is a matter that needs closer scrutiny and will be examined formally in Chapter 4.
One feature however is worth noting here. Growth rate of gross NPAs of the private
sector banks are higher than the public sector banks while growth of advances of public
259
sector banks are at par with the private banks. It thus appears that after reform public
sector banks are able to tackle the NPA problem more effectively than the private banks
(Table 3.6). However, one important observation from the table 3.6 is that, GNPA as
percent of gross advance as well as NNPA as percent of net advance has been declining
over time across all bank groups22.
Table 3.5
Per Cent Growth Rates of Gross and Net NPAs of Scheduled Commercial Banks (Real values)
1998 1999 2000 2001 2002 2003 2004 2005 Public Sector Banks Gross NPAs 30930 33705 33567 33304 33018 30708 27848 31300Growth Rate 8.97 -0.41 -0.78 -0.86 -7.00 -9.31 12.39Net NPAs 14385 15781 16494 17042 16346 14118 10191 11007Growth Rate 9.70 4.52 3.32 -4.09 -13.63 -27.82 8.01Old Private Banks Gross NPAs 1893 2466 2511 2647 2836 2583 2373 2782Growth Rate 30.30 1.79 5.45 7.13 -8.92 -8.13 17.22Net NPAs 1065 1520 1565 1688 1762 1556 1156 1229Growth Rate 42.72 2.93 7.89 4.36 -11.70 -25.67 6.33New Private Banks Gross NPAs 266 568 596 985 3982 4106 3222 3026Growth Rate 113.76 4.95 65.32 304.28 3.10 -21.52 -6.07Net NPAs 197 398.25 400.58 565.91 2141.64 2351.52 1468.10 1515.87Growth Rate 102.00 0.58 41.27 278.44 9.80 -37.57 3.25Foreign Banks Gross NPAs 1339 1536 1647 1892 1594 1615 1564 1450Growth Rate 14.76 7.21 14.88 -15.76 1.34 -3.18 -7.29Net NPAs 451 564 532 478 538 523 486 429Growth Rate 25.10 -5.82 -10.05 12.49 -2.79 -6.99 -11.87
Table 3.6
Percentage Growth Rates of Gross NPA and Gross Advance 1999 2000 2001 2002 2003 2004 2005 Public Sector Banks
22 For nominal figures see table A3.2 and A3.3 in the Appendix. For frequency distribution of NPAs under different classifications see Table A3.6 in the Appendix.
260
Growth of Gross Advance 13.98 17.05 15.82 15.39 13.10 14.83 25.66 Growth of Gross NPA 13.27 3.06 2.59 3.29 -4.22 -4.72 -8.17 Old Private Banks Growth of Gross Advance 12.69 22.12 13.03 10.61 15.93 13.04 21.30 Growth of Gross NPA 35.43 5.34 9.03 11.62 -6.20 -3.47 -4.23 New Private Banks Growth of Gross Advance 25.43 60.33 40.77 141.37 24.34 25.33 6.58 Growth of Gross NPA 122.19 8.61 70.93 321.21 6.18 -17.55 -23.26 Foreign Banks Growth of Gross Advance 0.45 20.46 22.27 10.52 6.33 17.20 24.43 Growth of Gross NPA 19.28 10.95 18.78 -12.23 4.37 1.72 -24.26 Source: Computed by author using RBI data. Going to the disaggregated level of NPAs it has been observed that amongst the bad
loans, maximum is substandard loan and proportion of loss assets is rather low (Table
3.7). At the total banking sector level, on an average (for the period 1998-2005) around
89 percent of the total loan assets fall under the standard asset category. When we
consider the rest, 3.3 percent under sub-standard assets, around 3 percent under doubtful
assets and around 1.3 under loss assets. And, the total NPA is around 10.6 percent of the
total loan asset. Thus, loss assets, even for the public sector banks are rather low.
Looking at the temporal behaviour of the share of various types of assets, on average, as
the share of standard asset is improving over time, naturally, the shares of sub-standard
assets, doubtful assets, loss assets and NPAs in the total loan asset are declining. This
essentially implies that the quality of the loan asset of Indian commercial banks is
improving. What is clear from Table 3.6 about low levels of NPAs of the foreign banks
is also seen from Table 3.7. From a disaggregated analysis at the bank-group level it
appears that the quality of the loan assets of foreign banks is better than the quality of the
loan asset of other bank groups. While the share of the standard asset in the total loan
asset of foreign banks is 94 percent, it is 88.5 and 91 percent in the case of public sector
and Indian private banks. On the other hand while the share of NPA in the total loan asset
of foreign banks is 5.8 percent, it is 11.5 and 8.7 percent in the case of public sector
banks and Indian private banks. However, proportion of loss assets of public sector banks
and foreign banks are quite comparable and they are comparatively lower for the private
banks.
261
Table 3.7 Bank group-wise Classification of Loan Assets of Scheduled Commercial Banks (Rs Crores)
1998 1999 2000 2001 2002 2003 2004 2005 Public Sector Banks Standard Assets 239318 273618 326783 387360 452862 523724 610435 824253 Per cent to total 84.0 84.1 86.0 87.6 88.9 90.6 92.2 94.6 Substandard Assets 14463 16033 16361 14745 15788 14909 16909 10838 Percent to Total 5.1 4.9 4.3 3.3 3.1 2.6 2.6 1.2 Doubtful assets 25819 29252 30535 33485 33658 32340 28756 29988 Per cent to total 9.1 9 8 7.6 6.6 5.6 4.3 3.4 Loss Assets 5371 6425 6398 5644 7061 6840 5876 5771 Per cent to total 1.9 2 1.7 1.5 1.4 1.2 0.9 0.7 Total NPAs 45653 51710 53294 54774 56507 54089 51541 46597 Per cent to total 16 15.9 14 12.4 11.1 9.4 7.8 5.4 Indian Private Banks Standard Assets 33567 38394 53317 65071 109216 131620 167076 216448 Per cent to total 91.3 89.2 91.5 91.5 90.3 90.8 94.2 96.1 Substandard Assets 1766 2657 2137 2585 4738 3703 3127 2213 Percent to Total 4.8 6.2 3.7 3.6 3.9 2.6 1.8 1 Doubtful assets 1077 1591 2355 3069 6539 8512 6391 5578 Per cent to total 2.9 3.7 4 4.3 5.4 5.9 3.6 2.5 Loss Assets 343 407 439 424 390 1118 825 900 Per cent to total 0.9 0.9 0.8 0.6 0.3 0.8 0.5 0.4 Total NPAs 3186 4655 4931 6078 11667 13333 10343 8691 Per cent to total 8.7 10.8 8.5 8.5 9.7 9.2 5.8 3.9 Foreign Banks Standard Assets 28996 28702 34817 42285 47838 50851 59619 72963 Per cent to total 93.6 92.4 93 93.1 94.5 94.6 95.2 97 Substandard Assets 1198 1238 1096 876 856 994 990 714 Percent to Total 3.9 4 2.9 1.9 1.7 1.8 1.6 0.9 Doubtful assets 250 507 798 1202 1004 944 1099 974 Per cent to total 0.8 1.6 2.1 2.6 2 1.8 1.8 1.3 Loss Assets 528 612 721 1033 920 954 924 569 Per cent to total 1.7 2 1.9 2.3 1.8 1.8 1.5 0.8 Total NPAs 1976 2357 2615 3111 2780 2892 3013 2257 Per cent to total 6.4 7.6 7 6.9 5.5 5.4 4.8 3
Source: Report on Trends and Progress of Banks in India, various issues
The above statistics shows the NPA problem at the aggregate level. In order to tackle the
problem a disaggregated analysis is necessary to examine what type of loans lead to more
NPAs. This necessitates an anlysis of sector-wise NPAs.
3. 6 Sector-wise NPA: NPA arising from the SSI sector
262
One of the important issue raised in the case of the NPAs of Indian commercial banks is
that the directed credit policy followed by the RBI under social banking motto of the
Government led to increase in the level of NPAs. To examine this we first look at the
share of NPAs from the priority sector vis-à-vis non priority sector loans. Table 3.8
reveals that the share of the NPA of non-priority sector is indeed higher than the share of
the NPA of priority sector and this trend is continuing over the years.
Table 3.8 Sector Wise Non Performing Assets of Indian Scheduled Commercial Banks
(Rs Crore, Real Values)
Year Item Agriculture Small scale OthersPriority Sector
Public Sector
Non priority Sector
Bank Group: State Bank and Associates 2001 Amount 1839 2317 1283 5439 739 6118 Per cent to total 15.0 18.8 10.4 44.2 6.0 49.72002 Amount 1849 2113 1278 5273 362 5908 Per cent to total 16.0 18.3 11.1 45.7 3.1 51.22003 Amount 1688 1740 1144 4572 299 4757 Per cent to total 17.3 17.8 11.7 46.7 3.1 48.62004 Amount 1351 1232 1272 3856 119 4216 Per cent to total 16.5 15.0 15.5 47.1 1.5 51.52005 Amount 1504 1175 1926 4641 111 5042 Per cent to total 15.4 12.0 19.7 47.4 1.1 51.5
Nationalised Banks 2001 Amount 2654 3982 2640 9276 303 10512 Per cent to total 14.8 22.2 14.7 51.7 1.7 58.62002 Amount 2724 4042 2659 9425 290 11779 Per cent to total 12.7 18.8 12.4 43.9 1.4 54.82003 Amount 2688 4029 2870 9586 897 11726 Per cent to total 12.0 17.9 12.8 42.7 4.0 52.22004 Amount 2561 3871 2922 9026 2379 9669 Per cent to total 13.8 20.8 15.7 48.6 12.8 52.02005 Amount 3294 3972 3569 10834 274 10120 Per cent to total 15.6 18.8 16.9 51.3 1.3 47.9
Private Banks 2001 Amount 194 599 308 1101 75 2676 Per cent to total 5.0 15.6 8.0 28.6 2.0 69.42002 Amount 257 868 364 1489 18 5314 Per cent to total 3.8 12.7 5.3 21.8 0.3 77.92003 Amount 305 716 364 1385 54 5135 Per cent to total 4.5 10.7 5.4 20.6 0.8 76.42004 Amount 248 681 408 1338 40 4204 Per cent to total 4.4 12.2 7.3 24.0 0.7 75.3
263
2005 Amount 307 638 497 1442 28 4331 Per cent to total 5.3 11.0 8.6 24.9 0.5 74.7
Source: Computed by author using RBI data
As can be seen from the table, the average share the NPA of non-priority sector in the
total NPA is around 50.5%, 53.4% and 74.7% for SB&A, NP and PB respectively,
whereas, the average share of NPA of priority sector in the total NPA is around 46.2%,
47.9% and 23.9% for SB&A, NB and PB respectively. One important observation is that
the share of priority sector NPA is less in the case of Private Banks compared to other
bank groups. In the case of sub-category of priority sector, the share of agriculture sector
NPA in the total NPA is only around 4.61 percent for Private Banks whereas it is around
16 percent for SB&A and 13 percent for NB.
While it has been often highlighted in the literature that NPA arising from the priority
sector is less than that of non priority sector related NPAs , a point often missed is that
priority sector constitute about 40% of total lending. Therefore, it is important to
examine NPA figures in proportion to the advances made on that particular sector.
Computation of sector-wise NPAs indeed reveals that NPA arising from SSI sector is
much higher that the other sectors. While NPAs from agriculture sector was about 12.4%
in 2002 (Table 3.9), it was as high as 21.16% for the SSI sector in the same year. This
percentage however declined to 6% for the agriculture sector and to 11% for the SSI
sector. Thus declining trend is prominent uniformly across all sectors (table 3.923).
Table 3.9
Sector wise NPA of Commercial Banks (Real Values, Rs Crore) 2002 2003 2004 2005
Public Sector Banks Agriculture NPA 4573.01 4375.66 3912.20 4796.06 % to credit to agriculture 12.40 10.93 8.57 6.64 SSI NPA 6155.43 5768.80 5102.91 5144.44 % to credit to SSI 21.16 19.30 16.20 11.48 Other Priority Sector NPA 3937.08 4013.53 4194.17 5493.14
23 Nominal figures are presented in table A3.4 in the Appendix.
264
% to credit to other priority sector 12.54 9.93 8.07 6.64 Total Priority Sector NPA 14698.18 14158.16 12881.84 15469.35 % to credit to total priority sector credit 14.69 12.48 9.75 7.62 Non-Priority Sector NPA 18339.32 18093.85 13891.00 15454.60 % to credit to non-priority sector credit 9.28 8.43 6.16 4.10
Private Banks Agriculture NPA 256.76 304.74 248.02 306.80 % to credit to agriculture 5.47 4.52 3.12 2.14 SSI NPA 868.39 716.39 680.87 637.55 % to credit to SSI 17.24 18.40 16.60 11.22 Other Priority Sector NPA 363.62 363.84 408.27 497.16 % to credit other priority sector 6.85 3.64 2.93 1.94 Total Priority Sector NPA 1488.76 1384.97 1338.41 1441.52 % to credit to total priority sector credit 9.90 6.65 5.06 3.12 Non-Priority Sector NPA 5332.73 5334.00 4244.56 4357.16 % to credit to non-priority sector credit 9.58 8.59 6.11 5.15
Source: Computed by author using RBI data.
However, looking at the growth rate of NPAs across sectors we observe a negative trend
for the SSI sector. On the other hand for agriculture and priority sector figures
concerning the year 2005 shows some increment (Table 3.1024).
Table 3.9
Per Cent Growth Rates of Sector Wise Gross Non Performing Assets (Real Value)
Year Agriculture Small scale Others Priority Sector
Public Sector
Non -priority Sector
State Bank and Associates 2002 0.52 -8.79 -0.34 -3.05 -50.98 -3.43 2003 -8.69 -17.66 -10.53 -13.30 -17.57 -19.48 2004 -19.96 -29.18 11.23 -15.67 -60.16 -11.37 2005 11.32 -4.68 51.42 20.36 -6.71 19.59
Nationalised Banks 2002 2.63 1.53 0.70 1.61 -4.34 12.04 2003 -1.34 -0.33 7.94 1.71 208.88 -0.45 2004 -4.71 -3.93 1.82 -5.84 165.37 -17.54 2005 28.60 2.61 22.13 20.02 -88.50 4.66
Private Banks 24 Nominal figures are presented in Table A3.5 in the Appendix.
265
2002 32.18 44.93 18.17 35.20 -75.74 98.57 2003 18.69 -17.50 0.06 -6.97 194.33 -3.38 2004 -18.61 -4.96 12.21 -3.36 -24.89 -18.12 2005 23.74 -6.33 21.81 7.74 -30.51 3.01
Source: Computed by author using RBI data. Thus though SSI sector currently has a higher NPA to total advance ratio there is
an improvement in recovery rates and NPA from this sector shows a declining trend
even in real terms.
3.7 Conclusion The problem of NPA has received considerable attention after financial sector
liberalization in India. Accounting norms have been modified substantially and
mechanisms are put in place for reduction of bad loans. Our survey of banks however
shows that (see chapter 7) mainly due to the awareness of the problem of bad loans at the
bank level, NPAs have indeed come down considerably. It remains true that NPA arising
from the priority sector lending is still higher than the non priority sector. Within priority
sector SSI’s performance is worse than the others. Given this observation a need has been
felt to study this problem in some detail. In the next section therefore, we look at the
determinants of NPA for the Indian banking sector in general and for the SSI sector in
particular.
Appendix to Chapter 3
Table A3.1
Non Performing Assets of Total Banking Sector: Nominal Values (Rs Crore) 1998 1999 2000 2001 2002 2003 2004 2005 Gross NPA 50815 58722 60841 63741 70861 68717 64787 58299 Change 7907 2119 2900 7120 -2144 -3930 -6488 Percentage growth 15.56 3.61 4.77 11.17 -3.03 -5.72 -10.01 Gross Advance 353039 399167 475827 558157 679875 775386 900706 1105760 Change 46128 76660 82330 121718 95511 125320 205054 Percentage growth 13.07 19.20 17.30 21.81 14.05 16.16 22.77
266
Gross Assets 798970 950934 1107234 1297296 1534023 1702529 1980782 2264188 Change 151964 156300 190062 236727 168506 278254 283405 Percentage growth 19.02 16.44 17.17 18.25 10.98 16.34 14.31 Net NPAs 23761 28020 30152 32462 35554 32670 24617 21441 Change 4259 2132 2310 3092 -2884 -8053 -3176 Percentage growth 17.92 7.61 7.66 9.52 -8.11 -24.65 -12.90 Gross-net 27054 30702 30689 31279 35307 36047 40170 36858 Change 3648 -13 590 4028 740 4123 -3312 Percentage growth 13.48 -0.04 1.92 12.88 2.10 11.44 -8.24 Source: RBI
Table A3.2
Per Cent Growth Rates of Gross and Net NPAs of Scheduled Commercial Banks (Nominal Terms) 1998 1999 2000 2001 2002 2003 2004 2005
Public Sector Banks Gross NPAs 45653 51710 53294 54672 56473 54090 51538 47325 Growth Rate 13.27 3.06 2.59 3.29 -4.22 -4.72 -8.17 Net NPAs 21232 24211 26188 27977 27958 24867 18860 16642 Growth Rate 14.03 8.17 6.83 -0.07 -11.06 -24.16 -11.76 Old Private Banks Gross NPAs 2794 3784 3986 4346 4851 4550 4392 4206 Growth Rate 35.43 5.34 9.03 11.62 -6.20 -3.47 -4.23 Net NPAs 1572 2332 2484 2771 3013 2740 2140 1859 Growth Rate 48.35 6.52 11.55 8.73 -9.06 -21.90 -13.13 New Private Banks Gross NPAs 392 871 946 1617 6811 7232 5963 4576 Growth Rate 122.19 8.61 70.93 321.21 6.18 -17.55 -23.26 Net NPAs 291 611 636 929 3663 4142 2717 2292 Growth Rate 109.97 4.09 46.07 294.29 13.08 -34.40 -15.64 Foreign Banks Gross NPAs 1976 2357 2615 3106 2726 2845 2894 2192 Growth Rate 16.16 9.87 15.81 -13.94 4.18 1.69 -32.03 Net NPAs 666 866 844 785 920 921 900 648 Growth Rate 23.09 -2.61 -7.52 14.67 0.11 -2.33 -38.89
Table A3.3 Gross and Net NPAs of Scheduled Commercial Banks (Nominal Values, Rs
Crore) 1998 1999 2000 2001 2002 2003 2004 2005
Public Sector Banks Gross NPAs 45653 51710 53294 54672 56473 54090 51538 47325
Percent to Gross Advance 16 15.9 14 12.4 11.1 9.4 7.8 5.7 Percent to Gross Asset 7 6.7 6 5.3 4.9 4.2 3.5 2.8
Net NPAs 21232 24211 26188 27977 27958 24867 18860 16642 Percent to Net Advance 8.2 8.1 7.4 6.7 5.8 4.5 3 2.1 Percent to Net Assets 3.3 3.1 2.9 2.7 2.4 1.9 1.3 1.0
Old Private Banks Gross NPAs 2794 3784 3986 4346 4851 4550 4392 4206
267
Percent to Gross Advance 10.9 13.1 11.3 10.9 11.0 8.9 7.6 6.0 Percent to Gross Asset 5.1 5.8 5.1 5.1 5.2 4.3 3.6 3.2
Net NPAs 1572 2332 2484 2771 3013 2740 2140 1859 Percent to Net Advance 6.5 9.0 7.3 7.3 7.1 5.5 3.8 2.7 Percent to Net Assets 2.9 3.6 3.2 3.3 3.2 2.6 1.8 1.4
New Private Banks Gross NPAs 392 871 946 1617 6811 7232 5963 4576
Percent to Gross Advance 3.5 6.2 4.2 5.1 8.9 7.6 5.0 3.6 Percent to Gross Asset 1.5 2.3 1.6 2.1 3.9 3.8 2.4 1.6
Net NPAs 291 611 636 929 3663 4142 2717 2292 Percent to Net Advance 2.6 4.5 2.9 3.1 4.9 4.6 2.4 1.9 Percent to Net Assets 1.1 1.6 1.1 1.2 2.1 2.2 1.1 0.8
Foreign Banks Gross NPAs 1976 2357 2615 3106 2726 2845 2894 2192
Percent to Gross Advance 6.4 7.6 7.0 6.8 5.4 5.3 4.6 2.8 Percent to Gross Asset 3.0 3.1 3.2 3.0 2.4 2.4 2.1 1.4
Net NPAs 666 866 844 785 920 921 900 648 Percent to Net Advance 2.2 2.9 2.4 1.8 1.9 1.8 1.5 0.9 Percent to Net Assets 1.0 1.1 1.0 0.8 0.9 0.8 0.7 0.4
Source: Report on Trends and Progress of Banks in India, RBI, various issues
Table A3.4 Sector Wise Non Performing Assets of Indian Scheduled Commercial Banks (Nominal Values)
Year Item Agriculture Small scale Others
Priority Sector
Public Sector
Non priority Sector Total
Bank Group: State Bank and Associates 2001 Amount 3019.44 3803.19 2105.72 8928.35 1212.75 10043.57 20190.70
Per cent to total 14.95 18.84 10.43 44.22 6.01 49.74 2002 Amount 3162.26 3614.21 2186.40 9018.74 619.41 10105.41 19743.57
Per cent to total 16.02 18.31 11.07 45.68 3.14 51.18 2003 Amount 2973.52 3064.80 2014.52 8052.84 525.82 8379.44 17228.10
Per cent to total 17.26 17.79 11.69 46.74 3.05 48.64 2004 Amount 2500.59 2280.54 2354.42 7135.55 220.09 7802.97 15158.61
Per cent to total 16.50 15.04 15.53 47.07 1.45 51.48 2005 Amount 2274.19 1776.01 2912.69 7016.89 167.75 7623.56 14808.32
Per cent to total 15.36 11.99 19.67 47.38 1.13 51.48
268
Nationalised Banks 2001 Amount 4357.21 6536.22 4334.46 15227.89 498.09 17257.44 29464.42
Per cent to total 14.79 22.18 14.71 51.68 1.69 58.57 2002 Amount 4659.29 6913.86 4547.46 16120.60 496.44 20145.74 36762.81
Per cent to total 12.67 18.81 12.37 43.85 1.35 54.80 2003 Amount 4733.83 7096.43 5054.96 16885.52 1579.14 20654.24 39580.99
Per cent to total 11.96 17.93 12.77 42.66 3.99 52.18 2004 Amount 4739.70 7163.38 5407.70 16704.78 4402.97 17894.77 34389.70
Per cent to total 13.78 20.83 15.72 48.57 12.80 52.04 2005 Amount 4979.85 6004.95 5395.68 16380.49 413.54 15300.80 31964.13
Per cent to total 15.58 18.79 16.88 51.25 1.29 47.87 Private Banks
2001 Amount 318.89 983.60 505.15 1807.64 123.37 4393.46 6326.95 Per cent to total 5.04 15.55 7.98 28.61 1.95 69.44
2002 Amount 439.16 1485.27 621.92 2546.34 31.18 9089.47 11667.29 Per cent to total 3.76 12.73 5.33 21.82 0.27 77.91
2003 Amount 536.78 1261.86 640.87 2439.51 94.51 9044.07 11834.88 Per cent to total 4.54 10.66 5.42 20.61 0.80 76.42
2004 Amount 459.01 1260.08 755.58 2476.98 74.58 7780.76 10332.36 Per cent to total 4.44 12.20 7.31 23.97 0.72 75.30
2005 Amount 464.04 964.30 751.96 2180.30 42.34 6547.87 8770.50 Per cent to total 5.29 10.99 8.57 24.86 0.48 74.66
Source: Report on Trends and Progress of Banks in India, various issues
Table A3.5 Per Cent Growth Rates of Sector Wise Gross Non Performing Assets
Year Agriculture
Small Scale
Industries Others Priority Sector
Public Sector
Non priority Sector Total
State Bank and Associates 2002 4.73 -4.97 3.83 1.01 -48.93 0.62 -2.21 2003 -5.97 -15.20 -7.86 -10.71 -15.11 -17.08 -12.74 2004 -15.90 -25.59 16.87 -11.39 -58.14 -6.88 -12.01 2005 -9.05 -22.12 23.71 -1.66 -23.78 -2.30 -2.31
Other Nationalised Banks 2002 6.93 5.78 4.91 5.86 -0.33 16.74 24.77 2003 1.60 2.64 11.16 4.74 218.09 2.52 7.67 2004 0.12 0.94 6.98 -1.07 178.82 -13.36 -13.12 2005 5.07 -16.17 -0.22 -1.94 -90.61 -14.50 -7.05
Private Banks 2002 1306.02 51.00 -93.55 -89.31 6426.62 106.89 84.41 2003 -88.03 -15.04 3.05 -4.20 -98.83 -0.50 1.44 2004 -14.49 -0.14 17.90 1.54 -21.09 -13.97 -12.70 2005 1.10 -23.47 -0.48 -11.98 -43.23 -15.85 -15.12
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Table A3.6 : Frequency distribution of NPAs under various classifications
Bank Group Wise Frequency Distribution of NPA for the period 1997-2005 (Average)
Bank Group/Percent 0-5 5-10 10-15 15-20 20-25 >25 Gross NPA to Total Asset Ratio
SBI&A 41 31 0 0 0 0 NB 94 67 7 3 0 0 PB 176 80 11 0 0 0 FB 191 35 20 10 5 14
Net NPA to Total Asset Ratio SBI&A 71 1 0 0 0 0 NB 163 8 0 0 0 0 PB 244 23 0 0 0 0 FB 237 27 9 2 0 0
Gross NPA to Gross Advance Ratio SBI&A 12 20 27 13 0 0 NB 20 54 46 29 11 11 PB 64 88 71 29 4 11 FB 127 45 24 16 14 49
Net NPA to Net Advance Ratio SBI&A 29 34 9 0 0 0 NB 71 71 22 4 2 1 PB 127 107 22 4 6 1 FB 182 33 23 9 8 20
Gross NPA to Gross Advance Ratio
Year/ Percent 0-5 5-10 10-15 15-20 20-25 >25 1997 33 17 15 16 4 7 1998 26 19 21 15 5 7 1999 17 21 21 18 8 9 2000 18 23 28 12 3 10 2001 22 22 26 5 3 8 2002 21 24 21 9 2 8 2003 19 28 21 7 0 10 2004 26 30 12 3 1 7 2005 41 23 3 2 3 5
Net NPA to Net Advance Ratio
Year / Percent 0-5 5-10 10-15 15-20 20-25 >25 1997 44 34 10 2 2 0 1998 39 32 17 2 2 1 1999 35 32 17 3 5 2 2000 38 37 10 4 2 3 2001 40 33 7 2 1 3 2002 38 31 7 1 2 6 2003 50 25 5 1 1 3
270
2004 60 12 1 2 1 3 2005 65 9 2 0 0 1
Frequency Distribution of NPA for the period 1997-2005 0-5 5-10 10-15 15-20 20-25 >25
Gross NPA to Total Asset Ratio Frequency 502.00 213.00 38.00 13.00 5.00 14.00 Percent 63.95 27.13 4.84 1.66 0.64 1.78
Net NPA to Total Asset Ratio Frequency 715.00 59.00 9.00 2.00 0.00 0.00 Percent 91.08 7.52 1.15 0.25 0.00 0.00
Gross NPA to Gross Advance Ratio Frequency 223.00 207.00 168.00 87.00 29.00 71.00 Percent 28.41 26.37 21.40 11.08 3.69 9.04
Net NPA to Net Advance Ratio Frequency 409.00 245.00 76.00 17.00 16.00 22.00 Percent 52.10 31.21 9.68 2.17 2.04 2.80
Table A3.7
Prudential Norms for Asset Classification Adopted by India and Some Other Countries Country Categories Loans Classification System Provisioning requirements Indonesia Current Installment Credit with no arrears, other
credit in arrears less than 90 days, overdrafts less than 15 days.
0.5 per cent
Sub-standard Generally, loans with payments in arrears between three and six months.
10 per cent
Doubtful Non-performing loans that can be rescued and the value of collateral exceeds 75 per cent of the loan, or loans that cannot be rescued, but are fully collateralised.
50 per cent
Loss Doubtful loans that have not been serviced for 21 months; credit in process of bankruptcy/liquidation.
100 per cent
Loans must be written off 21 months after litigation, indicates the loans will not have to be repaid.
Korea Current Borrower's credit conditions (including collateral) are good and collectibility of interest and principal are certain.
0.5 per cent
Special mention Payments are past due for between three months and six months, but collection is
1 per cent
271
certain. Sub-standard Loans covered by collateral but borrower's
credit conditions are deteriorating and payments are more than six months past due.
20 per cent
Doubtful Unsecured portion of the loans that are more than six months past due and losses are expected.
75 per cent
Estimated loss Unrecoverable amounts due net of collateral.
100 per cent
Loans must be written off within six days of being declared unrecoverable; Write-offs in excess of W300 million require Bank of Korea approval.
Malaysia For loans less than RM 1 million Standard More than a normal risk of loss due to
adverse factors; past due for between 6 and 12 months.
0 per cent
Doubtful Collection in full is improbable and there is high risk of default; past due for between 12 and 24 months
50 per cent of net (of collateral) outstanding value
Bad Uncollectible; past due for more than 24 months.
100 per cent of net outstanding value
Loans must be written off when bankruptcy hearings have finished and/or partial or full repayment is unlikely.
A general provision of at least 1 per cent of total loans net of interest in suspense and specific provisions is also required.
Philippines Unclassified Borrower has the apparent ability to satisfy obligations in full; no loss in collection is anticipated.
0 per cent of net (of collateral) exposure.
Special mention Potentially weak due, for example, to inadequate collateral, credit information, or documentation.
0 per cent
Sub-standard Loans that involve a substantial degree of risk of future loss.
25 per cent
Doubtful Loans on which collection or liquidation in full is highly improbable, substantial losses are probable.
50 per cent
Loss Uncollectible or worthless. 100 per cent Interest is not accrued on past-due
loans, which are loans or other credit not paid at the prescribed maturity date or, in the case of instalment credit, in arrears by more than a prescribed amount depending upon the frequency of instalments.
Argentina Consumer Loans Commercial Loans Liquid G'tee
Preferred G'tee
Without G'tee
Normal Less than 31 days overdue
No doubt exists. 1 per cent 1 per cent 1 per cent
Potential risk 31- 89 days overdue Performing, but 1 per cent 3 per cent 5 per cent
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sensitive to changes or more than 30 days overdue.
Problem 90 - 179 days overdue
Problems meeting obligations; or80-179 days overdue
1 per cent 12 per cent 25 per cent
High risk 180-365 days over- due or subject to judicial proceedings for default
Highly unlikely to meet obligations; or more than 180 days overdue.
1 per cent 25 per cent 50 per cent
Irrecoverable More than 365 days overdue
Obligations cannot be met; more than365 days overdue
1 per cent 50 per cent 100 per cent
Irrecoverable for technical decision
Bankruptcy/ liquidation/ insolvency
Bankruptcy/ liquidation/ insolvency
100 per cent 100 per cent 100 per cent
Chile Consumer Mortgage Commercial Minimum initial provision (for NPAs) (allowance period is 90 days in all the3 types of advances)
A - Current B B C D
Current 1 - 30 days 3 - 59 days 60-119 days >120 days
Current 1- 179 days > 179 days N.A N.A.
Probability of default = 0% Probability of default > 0%,< 5% Probability of default = > 5%, < 40% Probability of default = .> 40%, < 80% Probability of default = >80%, < 100%
on consumer loan and mortgage loan(NPAs) is required to be made @ 60% and 1% respectively whereas the provisioning requirement on commercial loan is subjective
Peru A - Normal Current Current Current with no doubts Minimum initial provision @ 30%, (allowance B - Potential 10-29 days 32-89 daysDemonstrated 1% and 15% is required to be made onperiod is 30, problems deficiencies consumer loan, mortgage loan and 30 and 15 commercial loan (NPAs) respectively.days respectively
C - Sub- standard
30-59 days 90-119 days
60-119 days
for the three types of
D - Doubtful 60-120 days 120 - 365 days
120 - 364 days
advances) E - Loss > 120 days > 365 days> 365 days India Sub-standard Loans that have been non-performing for up to
two years, term loans on which the principal has not been reduced for more than one year, and all rescheduled debts.
10 per cent
Doubtful Loans that have been non-performing for two to three years and term loans on which the principal has not been reduced for more than two years.
100 per cent of unsecured assets; for secured assets; 20 per cent if doubtful for less than one year; 30 per cent if doubtful for one to three years, 50 per cent if doubtful for more than three years.
Loss All other assets deemed irrecoverable, where the loss has been identified by internal or external auditors or by the Reserve Bank of India inspectors, but where the amount has not been written off.
100 per cent
Source: Global NPL Report
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CAHPTER 4
Determinants of Non Performing Assets
4.1 Introduction
Given that the NPA has strong implications on the health of the commercial banks
and also the economy, it is essential to contain the NPA levels of the commercial banks.
This calls for identification of the factors that can cause an asset to become NPA. A study
conducted by the RBI, based on the information related to 33banks shows that the NPA
level in banks are generally related to the performance level of the industrial units to
which the banks have lent loan (Muniappan, 2002). The factors could be management
inefficiency of borrowal units, obsolescence, lack of demand, non- availability of inputs,
environmental factors etc. In a few cases default occurs due to the internal factors of
banks such as weak appraisal or follow-up of loans. While a micro level study will reveal
some of these causes , before going to such analysis it is essential to get a picture of the
determinants of NPA at the macro level. In this regard an attempt is made in the current
chapter to identify the factors influencing the NPA at the macro level.
Given this back ground this present chapter is designed as follows; in the next
section a brief review of studies related to NPAs at the international level, as well as in
India is presented. Section 3 spells out the methodology used in the study to analyse the
determinants of the NPAs. While section 4 will present various sources of data used in
the study, section 5 contains the empirical results and the discussion. Finally, concluding
remarks are presented in section six.
4.2. Review of Literature
Over the last several years the issue of Non-Performing Assets has received
considerable attention from the policy makers and researchers all over the world. High
level of NPA is affecting not only the developing nations in Asia but even some of the
advanced economies like Japan and Germany (see also Chapter 3) are faced with this
problem. The question of NPA was given more emphasis after the BASEL committee
stressed that non-performing assets, from the financial systems across the globe should be
reduced and also prescribed various prudential norms. A number of studies which look at
274
the bank-failures concerning different countries note that the major reason for bank
failure is their Non-Performing Assets, since just before the failure they had accumulated
large amount of NPLs (Demirguc-Kunt 1989, Whalen 1991, and Barr and Siems 1994). It
has been observed that the weak banks, in developed and developing countries alike, do
not report their true no-performing loans (NPL) level, due to which the issues become
complicated when they are hit by a crisis (Tanaka and Hoggarth, 2006).
The problem of Non-Performing Loans is predominant in Asian countries, more
specifically in the East Asian countries (see Chapter 3). In fact, the higher levels of NPL
were one of the important reasons for the East Asian Financial Crisis of 1997. After the
so-called economic bubble burst in Japan in 1990, it went through a decade of economic
depression, which, to some extent, is due of the failure of the Japanese government to
control its NPLs (Asami, 2000). As discussed in Chapter 3, Japan has the highest NPL
levels in the world. In 2004 its NPL was around US$ 330 billion, which accounted to
around 25 percent of the world NPL (Global NPL Report 2004). Japanese banks have lost
72 trillions yen due to bad loans since March 1992 until March 2001. The bad loan per
total loan ratio has risen from 2 percent in 1992 to 6 percent in 1999 (Hanah, 2005).
However, it has been observed that the real problem of Japan’s NPL lies in understanding
the problem of the corporate to which banks are giving loans. Instead of banks continuing
to roll over loans to loss making corporate they should divert the funds to profit making
corporate (Hubbard, 2002). The only East Asian country, which not only avoided the
recent financial crises, but also continued to exhibit strong growth, is China (Huang and
Bonin, 2001). However, like many East Asian countries, China also holds high level of
Non Performing Loans, which might prove dangerous. In 2003 China’s NPL was around
US$ 307 billion, which accounted for around 23 percent of the global NPA (Global NPL
Report 2004). Measured as a percent of the total outstanding loans, the NPL of China
ranged from 18 percent to 40 percent Sprayregen et al (2004). Apart from the problem of
high NPLs, Chinese banking industry suffers from low capital base also. The public
sector dominance of the Chinese banking industry, along with the policy of policy
lending led to the Public Sector Enterprise (Chinese banks are expected to lend around
30-40 percent of their total loans to PSE), increased NPLs of the Chinese banking
industry (Huang and Bonin, 2001).
275
Recognizing the extent of damage NPLs can cause on economic performance,
considerable efforts have gone in understanding the factors that effect NPLs in any
economy and mechanisms to tackle this problem.
Using financial data collected from the public listing companies in China ’s stock
market, Lu, Thangavelu and Hu (2005) empirically examine the relationship between
banks’ lending behaviour and non-performing loans. This study takes the research
strategy to infer banks’ lending behaviour from firms’ debt financing. The lending
behaviour of banks is examined in a borrowing ratio model that captures the relationship
between firms’ borrowing ratios and their default risk, collateral, state-ownership, firm
size, and industrial policy. Their results show that state-owned enterprises (SOEs) got
more loans than other firms, other things being equal, and SOEs with high default risks
were able to borrow more than the low-risk SOEs and non-SOEs. This suggests that
Chinese banks had a systemic lending bias in favour of SOEs, particularly those with
high default risks, during the period under investigation.
In the Indian context, Rajan and Dhal (2003) empirically examine how banks’
non-performing assets are influenced by three major sets of economic and financial
factors, i.e., terms of credit, bank size induced risk preference and macroeconomic
shocks. They estimate a panel regression model, where they take into consideration the
effect of the differential social and geo-political environment confronting banks’
operations. Their results show that, when the bank size is measured in terms of assets, the
bank size has negative impact on NPAs, while the measure of bank size in terms of
capital, gives somewhat opposite result. Further, measure of credit orientation has
significant negative influence on NPAs, implying that borrowers attach considerable
importance to relatively more credit (customer) oriented banks. Also increased economic
activity leads to lower financial distress of borrowers and this lowers NPAs for banks.
The exposure to priority sector has positive impact on the NPA level. Using data on
Indian manufacturing sector for 1993-94, Ghosh (2005) has examined the association
between corporate leverage and banks’ non-performing loan. In this study asset quality of
banks is modeled as a function of corporate leverage and a set of control variables, using
a simultaneous equation framework. The results show that the capital adequacy of banks
276
have a significant and negative effect on asset quality. The findings further indicate that
the lagged leverage is an important determinant of bad loans of banks. In terms of
magnitude, a 10-percentage point rise in corporate leverage is, on an average, associated
with 1.3 percentage point rise in sticky loans, with one period lag.
Rajaraman and Vasishtha (2002) perform a panel regression on the definitionally
uniform data for a five-year period ending 1999-2000, on non-performing loans of
commercial banks. The study notes that a number of exogenous factors contribute to the
general level of bank NPA in India, such as the legal and procedural obstacles the banks
face in the process of liquidation of loss-making enterprises. According to the results of
this study, there is no significant relation between the capital adequacy ratio and NPA;
whereas, operating profit has a significant negative relation with NPA.
Along with identifying the factors causing high NPA levels, an equally important
issue, is how to tackle the problem of NPA. Many measures have been taken in the
direction of reducing NPL. In the case of Japan, it has been argued that a financial safety
net could minimize the spillover effects of failures of banks and other financial
institutions on the financial system as a whole. Bank-safety-net policies could include
lending to banks (a subset of lender of last resort), explicit and implicit insurance of some
or all bank deposits, capital adequacy requirements, bank supervision, closure and
recapitalization rules (see Diamond and Dybvig, 1983; and Allen and Gale, 2000). There
are also arguments that this safety net could lead to moral hazard problem. However, in
the case of Japan it has been observed that the safety net with the government financial
assistance taken from DIC to Japanese banks is not the reason leading to moral hazard of
increasing risk-taking incentive or bad loan, rather it helps to reduce the bad loan,
especially in case of banks with high requirement capital ratio (Hanah, 2005). Similarly,
in China, the government established four Asset Management Companies (AMCs) in
order to deal with the NPLs of each of the four large public sector companies. Drawing
on the experiences of Resolution Trust Corporation in the United States and bank
restructuring in the Central European transition economies, Huang and Bonin (2001)
argue that the original AMC design will not be successful in resolving the existing non-
277
performing loans (NPLs) nor will it prevent the creation of new bad loans due to various
drawbacks in the policy.
4.3. The Model and Estimation Procedure (Methodology)
As discussed in Chapter 1 there are two main concepts of NPA used by the
banking sector, viz., gross and net NPA. Net NPA is the total NPA minus the provisions
made by the bank under new accounting norms. Thus we observe that it is the gross NPA
that gives an indication of total bad loans of a bank. Naturally if total advances are
higher, total NPA level will also be so. Keeping these facts in mind we attempt here to
understand the determinants the share of gross NPA in total advances. We consider a
panel data set of 94 banks over the years 1997- 2005, as only from 1997 bank-wise NPA
data are made available.
Thus, in order to analyse the relation between various bank specific and economy specific variables with the level of NPA of commercial banks, an econometric model is formulated, which is estimated using panel data analysis technique. The model is given as follows:
itiit
ittt
ttitit
IMFBPBNBTBBRBBCRARLRGDP
GDPGDPTAGAGNPA
υµαααααααα
αααα
++++++++++
+++=
11/3
21]/[
10987
654
3210
(4.1)
Where,
GNPA/GA = Ratio of Gross Non Performing Asset to Gross Advance
TA = Total Assets25
GDP1 = Gross Domestic Product of Agriculture Sector
GDP2 = Gross Domestic Product of Industrial Sector
GDP3 = Gross Domestic Product of Service Sector
LR = Lending Rate (SBI Average Advance Rate)
RBB/TBB = Ratio of Rural Bank Branches to Total Bank Branches
NB = dummy for private bank group
PB = dummy for foreign bank group
25 Major aggregates of Total Asset are: Cash and Balance with RBI, Balance with Banks and money at call and short notice, Total Advance, Total Investment and Fixed Assets.
278
FB = other nationalized bank group
IM= interest rate margin (between lending rate and deposit rate)
µi = unobserved bank specific effects which are assumed to be random (like, bank
specific entrepreneurial or managerial skills)
vi = stochastic term which are assumed to be identically and independently
distributed, IID(0, σ2).
i: index for bank, t: time index
Before proceeding further it is essential to discuss the rationale for including each
variable in the model and also the expected signs. Total asset is included in the model as
a proxy for the bank size. The relation between the bank size and NPA share is an
empirical question that needs to be explored. If some kind of economies of scale exists in
recovery mechanism then a bigger bank may have lower NPA share. One of the
important variables that may have strong influence on the NPA level of commercial
banks is the general economic condition. While GDP is usually considered to reflect
general economic condition, in the present study, instead of taking total GDP, the sector
wise GDPs of three important sectors viz., agriculture, industry and services sectors are
included as explanatory variables. The reason for including the sector wise GDP is that
the growth in various sectors is not uniform. While service sector has registered the
growth rate of around 8.5 percent between 1997 and 2005, it is 5.26 and 1.99 for industry
and agricultural sector. In some of the years the growth rate of agricultural sector was
even negative. Thus it is essential to understand the relationship of each sector with the
NPA level of commercial banks. Further, it is argued that the branch expansionary policy
adopted by the government and the resulting expansion of bank branches might have led
to an increase in NPA. In order to see whether the number of Rural Bank Branches
(RBB) has any effect on the level of NPA, the ratio of RBB to Total Bank Branches is
included in the model. One of the reasons for the loan default, as argued in the theory, is
the higher cost of bank loan (Stiglitz et al, 1981). It has been argued that (Stiglitz et al,
1981) if the cost of credit is higher it will attract only the risky borrowers, which would
lead to increase in the NPA level. Thus the Advance rate of SBI is included as a proxy for
the lending rate. Interest rate margin is used to capture the extent of competition. Lower
279
margin may indicate higher level of competition. To examine bank group- wise
differences bank group specific dummies are incorporated (taken State bank of India
group as the base).
The model is estimated using the panel data estimation method. One of the
important decisions in a panel data analysis is whether the model should be estimated as a
fixed effect model or a random effect model. Since the number of firms is more than the
number of time periods, the random effect estimation technique is considered to be
appropriate to estimate the model (as it will have more degrees of freedom) compared to
the fixed effects model. However, if the assumption that the individual invariant effects
)( itu are not correlated with the regressors i.e., 0)/( =itit XuE is not valid, the GLS
estimator of the random effect model becomes biased and inconsistent. In this regard
Hausman (1978) has developed a test which suggests whether a fixed effect model is
more appropriate than a random effect model. In the present study, the Hausman
specification test recommends random effects model (see Section 4.5).
When the model is estimated as a random effect model, it is essential to check for
the presence of random effects. If this is negated, the model reduces to the Classical
Linear Regression (CLR) model, which can be estimated using Ordinary Least Square
(OLS) technique. Using the Breusch-agan test one can check the presence of random
effects, which in our case gives affirmative result (see Section 4.5).
4.4. Data Description For estimating the model given in (4.1), data are collected for 94 banks the period 1997-2005. However as many private and foreign banks are established after 1997 and few are closed during the study period, data are not available consistently for all banks for all years. Thus data used is an unbalanced panel of 94 (27 public sector banks, 33 Indian private banks and 34 foreign banks) banks for 9 years (total observation used are 746).
Data on Gross Non Performing Assets as percent of Gross Advance for each bank are collected from Report on Trends and Progress of Banking in India, published by the Reserve Bank of India. Return on Assets is calculated from the data on net profit and total assets collected from Annual Accounts of Scheduled Commercial Banks published by the Reserve Bank of India. The ratio of Rural Bank Braches to Total Bank Branches is calculated from the data on total number of rural bank branches and the total bank branches collected from the Performance Highlights of Banks published by the Indian Banks’ Association. Data on the sector wise GDP and variables like lending rate are collected from Handbook of Statistics on the Indian Economy published by the Reserve Bank of India. The GDP measured at constant price (1993-94 base) considered for the analysis and all values are converted to real wherever applicable. A summary of the variables included in the analysis is presented in table 4.1.
Table 4.1 Summary of Data Included in the Model (mean of values, at constant 1993-94 prices)
280
Variables 1997 1999 2001 2003 2005 GNPA/TA (%) 5.69 6.54 5.97 5.76 4.16 TA (Rs Crore) 8367.68 10647.93 15006.05 19404.87 28609.90 RRB/TBB (%) 22.47 19.52 19.59 16.81 18.28 GRGDP1 (%) -2.43 0.31 6.28 9.60 3.93 GRGDP2 (%) 3.05 4.05 3.51 6.52 5.90 GRGDP3 (%) 9.87 9.86 6.49 8.87 11.30 LR (%) 14.00 12.00 11.50 10.25 10.25
GRGDP1 = Growth Rate of Gross Domestic Product of Agriculture Sector GRGDP2 = Growth Rate of Gross Domestic Product of Industrial Sector GRGDP3 = Growth Rate of Gross Domestic Product of Service Sector *: Return to asset ratio (measure of profitability)
4.5. Empirical results
As mentioned in the previous section, before estimating the model it is important
to decide whether the model should be estimated as a fixed effect model or a random
effect model. In this regard the Hausman specification test is conducted and the results
are presented in table 4.2. The Hausman specification test statistic fails to reject the null
hypothesis that individual invariant effects )( itu are not correlated with the regressors
i.e., 0)/( =itit XuE . This suggests that the model should be estimated using random
effect estimation technique.
Table 4.2 Hausman Specification Test Results
Chi2 Probability4.33 0.7413
After deciding that the model should be estimated using random effect model, it is
essential to check whether random effects indeed present in the data. If the random
effects are not present in the data then the model can be estimated using Ordinary Least
Squares (OLS) method. As mentioned above, presence of random effect can be checked
by using Breusch Pagan test. The test results are presented in the table 4.3.
Table 4.3 Breusch and Pagan Lagrangian Multiplier Test for Random Effects
Chi2 Probability1034.1 0.0000
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The results presented in table 4.3 shows that the null hypothesis that the random
effects are not present in the data is rejected, validating the use of random effect model
instead of the OLS. Thus after deciding that the model should be estimated as a random
effect model and confirming the presence of random effects, the model is estimated using
Generalised Least Square (GLS) method. The results of the GLS estimation are presented
in table 4.4.
Table 4.426 Determinants of Gross NPA/Gross Advance
Random-effects GLS Regression Dependent Variable: GNPA/GA Coefficient Std. Err. Z P>|z| Constant 9.42181 15.23245 0.62 0.536 TA -0.00066 0.000091 -3.45 0.0000 GDP1 0.0000487 0.000043 1.13 0.257 GDP2 -0.0000171 0.0000912 -0.19 0.851 GDP3 -0.000009 0.0000273 -0.34 0.735 IM 0.422903 0.319696 1.32 0.185 LR -0.812509 0.4937 -1.65 0.11 RBB/TBB 29.65196 7.830 3.790 0.0000 NB -0.71188 5.2374 -0.14 0.892 PB 1.06269 5.0730 -0.21 0.834 FB 13.05654 5.563 2.35 0.019
Wald Chi2(10) 81.24 Number of Observation 746 Prob > Chi2 0.000 Number of Groups 91
R Square Observation Per Group Within 0.078 Minimum 3 Between 0.238 Average 8.2 Overall 0.1736 Maximum 9 Sigma_u = 10.768006 Sigma_e = 7.1756816 Rho = 0.6924851
26 We have also considered various combinations of independent variables such as log of total assets , growth rates of sector-wise GDP values and so on. Qualitatively the results do not change.
282
Since the model is estimated by using GLS method, the overall significance of the
model is tested by using the Wald test statistic, which is essentially a ‘Chi Square’ test.
The Wald test statistic presented in table 4.4 shows that the model as a whole is highly
significant.
Further, coming to the specific relation of each variable with Gross Non Performing Asset to Gross Advance Ratio, we can note that the coefficient of the Total Asset (TA) is significant with negative sign. Since the TA variable is used as a proxy for the size of the bank, the negative sign of the TA shows that bigger banks have lesser GNPA, in relation to their gross advance. Thus there may be some economies of scale involved in loan recovery process especially when loan is given to the borrowers who may be located near to each other. Such possibilities can arise in case of agriculture loan or, of SSI loans given in a cluster. It also could be due to the reason that bigger banks have better network and more information about the customers due to which they manage their asset well, which helps them to keep the GNPA level under check. Further, it is argued in the literature that the massive branch expansion, particularly into rural areas might have led to increased GNPA levels for the commercial banks. Our result seems to support this argument. The positive sign of the RRB/TBB ratio shows that the higher rural bank braches will lead to increased GNPA in the commercial banks. One of the important variables that has strong influence on the level of GNPA of commercial banks is the economic condition. If the economy is doing well agriculturist ,industrial entrepreneurs or other borrowers will be able to repay the borrowed loan. Many studies have used GDP as a proxy for the economic condition. However, as mentioned earlier, the total GDP may not capture the differential growth rates of various sectors. Thus in the present study the GDP growth of three important sectors viz., agriculture, industry and service sector is included in the analysis. Our results justify our approach of including the growth rates of sector wise GDP. Out of the three sector wise GDP related variables all are found to be insignificant.
As mentioned earlier, one of the reasons for the loan default, as argued in the
theory, is the higher cost of bank loan. If the cost of credit is higher it will attract only the
risky borrowers, which would lead to increase in the NPA level. In order to capture the
relation between cost of bank credit and GNPA the Advance rate of SBI was included as
a proxy for the lending rate in the model. However, it has turned out to be insignificant
showing that interest rate on credit does not have any relation with the GNPA of
commercial banks. One reason could be that the lending rate of late has already come
283
down and even though there is some variations in lending rates over the years, they are
not too high to really bring risky borrowers to the lending process. Interest margin is also
not found to be significant. Bank group dummies show that foreign bank dummy is
positively significant. That is, while we take care of other variables like rural branches
etc. there is significant difference between foreign bank and SBI in their abilities to
reduce bad loans27. This result also goes with the finding that larger bank size is better for
reducing bad loans28.
4.6 Sector Specific Determinants of NPA: SSI Sector
We next examine the determinants of NPA at the sectoral level, especially
concentrating on the SSI sector. The model under consideration is similar to the one with
aggregate NPA levels, but now the dependent variable is NPA from the SSI sector as a
percentage of total advances29. Thus the model under consideration is as follows.
itit
itittitit
LRTBBRRBCRARIIPTAGASSINPA
υµαααααα
++++++=
5
43210 /ln]/[ (4.2)
Where,
SSINPA/GA = Ratio of Gross SSI sector Non Performing Asset to Gross Advance
ln TA = log of Total Assets
IIP: Index of Industrial Production
CRAR = Capital to Risk Asset Ratio
RBB/TBB = Ratio of Rural Bank Branches to Total Bank Branches
LR = Lending Rate (SBI Average Advance Rate)
µi = are unobserved bank specific effects which are assumed to be random (like,
bank specific entrepreneurial or managerial skills)
27 This hypothesis will be checked more rigorously when we deal with efficiency issues in Chapter 5. 28 It has been mentioned during the workshop on draft report presentation at Kathmadu by the experts that there may be endogeinity problem in the panel data estimation presented above. Therefore Hausman Wu Durbin endogeneity test has been carried out for different independent variables and the problem is not seen to be present. In the Appendix we present one such results (Table A4.1). 29 However, note that the data considered for the analysis is for the period 2001 –2005, which does not include Foreign Banks.
284
vi = are stochastic term which are assumed to be identically and independently
distributed, IID(0, σ2).
While estimating the panel data model we observe that Hauman specification test
suggests a fixed effect model to be the appropriate one (Table 4.5). In a fixed effect
model bank specific dummies cannot be incorporated. Thus the former model has been
modified accordingly. The results based on this model reveal the following (Table 4.6).
Table 4.5
Hausman Specification Test Results Chi2 Probability18.66 0.0022
After deciding that the model should estimated using fixed model estimation techniques,
it is essential to check whether fixed effects in fact present in the data. In this regard the F
test was conducted, the results of which (see table 4.6) suggest that there are fixed effect
present in the data.
Table 4.6
F-Test for Testing Fixed Effects
F Probability4.72 0.0000
After deciding that the model should be estimated as a fixed effect model and confirming
the presence of fixed effects, the model is estimated using Fixed Effects (Within) method.
The results of the within estimation is presented in table 4.7.
Table 4.7
Determinants of SSI NPA/Gross Advance Fixed-effects (within) regression Dependent Variable: SSINPA/GA Coefficient Std. Err. Z P>|z| Constant 96.9025 25.4395 3.8100 0.0000 ln TA -17.5737 6.6471 -2.6400 0.0090 IIP 0.0011 0.0348 0.0300 0.9760
285
LR -0.1661 0.7573 -0.2200 0.8270 CRAR 0.1096 0.1357 0.8100 0.4200 RBB/TBB -20.4354 10.3834 -1.9700 0.0500
F(5,198) 4.38 Number of Observation 255 Prob > F 0.0008 Number of Groups 52
R Square Observation Per Group Within 0.0996 Minimum 3 Between 0.0011 Average 4.9 Overall 0.0016 Maximum 5 Sigma_u = 13.035762 Sigma_e = 5.0268054 Rho = 0.87054922
The only significant variables are total asset of a bank and the share of rural branches.
Large size banks have lower NPA levels, which is similar to the result we have obtained
above. Importantly, larger number of rural branches appears to have a negative impact on
SSI NPA. Thus rural branches are not appearing to create NPA for the sector. This is a
result of some importance. While we find RBB/TBB to be positively significant in case
of gross NPA , it is negatively significant in the case of SSI NPA. This can happen if the
rural branches provide loans to the SSI sector in cluster and there exists some economies
of scale in monitoring.
The CRAR variable captures the capital to risky asset ratio. This variable is found to be
insignificant in the case of SSI sector NPA. Thus an increase in so called risky assets
does not seem to affect the SSI sector. It is also important to note that if we remove the
CRAR variable and run the estimation , results do not change qualitatively. Only the total
assets and RBB/TBB remain the significant variables, with no change in their signs as
well.
4.7 Conclusion
286
In this chapter we made an attempt to study the relation between GNPA
and some bank specific variables such as bank size, return on assets, the number of rural
branches and also some economy specific variables such as the growth rates of sector-
wise GDP and finally the lending rate. The model was estimated using panel data
technique, specifically through a random effect model. Our results show that bank size
contribute negatively to GNPA. One of the important findings of our analysis is that the
rural bank branches have contributed for higher GNPA in commercial banks. Thus
commercial banks need to focus on the loan recovery process concerning the rural
branches. Such loans usually are small size ones and involve a large number of
borrowers. These borrowers often need other supporting inputs from the banking sector
in addition to credit. Such support may be in the form of providing necessary information
about technology, management techniques, costing and so on for the SSI sector. Similar
supports are necessary to the agriculture sector as well. Finally our result show that the
interest rate charged on the bank credit does not have impact on the GNPA level. This
indicates that current interest rates are not at a significantly high level so as to really bring
risky borrowers to the bank net.
287
Appendix to Chapter 4
A4.1 Hausman specification Test
An important question in panel data analysis is whether to use fixed effect model
or random effect model. The major drawback of the dummy variable model is that it
consumes more degrees of freedom. On the other hand when random component model is
estimated, it is assumed that the individual invariant effects )( itu are not correlated with
the regressors i.e., 0)/( =itit XuE . If this assumption is not valid then the GLS estimator
of the random effect model becomes biased and inconsistent. However, this problem will
not occur under fixed effect model as the within transformation wipes out the )( iu , which
makes the within estimator unbiased and consistent.
In order to choose between fixed effect model and random effect model Hausman
(1978) suggests comparing GLSβ̂ and withinβ̂ under the null hypothesis
that 0)/( =itit XuE . Hausman’s essential result is that the covariance of an efficient
estimator with its difference from an inefficient estimator is zero (Greene 2003). And also
under the null hypothesis the two estimates should not differ systematically. This is tested
by a statistic given by Hausman (1978) which is essentially a chi-squared test, given by,
)(ˆ)( 1/ GLSwithinGLSwithinm ββψββ −−= − ~ χ2 (k)
)()()(ˆ GLSVarwithinVarGLSwithinVar ββββψ −=−= is the difference between the
estimated covariance matrix of the parameter estimates in the LSDV model (within) and
that of the random effects model (GLS). It is notable that an intercept and dummy
variables SHOULD be excluded in computation.
If the test statistic is higher than the critical limits, the null hypothesis that
individual invariant effects are not correlated with regressors is rejected. This indicates
that the GLS estimators are biased which means the fixed effect model is a better choice.
A4.2 Testing Individual Effects in a Fixed Effect Model (F Test)
288
While estimating a fixed effect model one should check whether the fixed effects
are present in the data or not. If the fixed effects are not present the model can be
estimated using the Classical Linear Regression technique. In order to check for the
presence of fixed effect, one has to test the joint significance of the individual specific
dummies. In other words, one her to check the null hypothesis that µ1= µ2…....µn-1 = 0,
by performing an F –test, which can be calculated using the formula given below.
)/(
)1/()(0 KNNTRSS
NRSSRSSF
UR
URR
−−−−
= ~ KTNNF −−− )1(,1
If the calculated F statistic is higher than the critical limit, for the given degrees of
freedom, then the null hypothesis that individual specific dummies are zero is rejected. In
other words, there is presence of individual effect in the data.
A4.3 Breusch - Pagan Test for Random Effects
Similar to the Fixed Effects model estimation, it is essential to check for the
presence of Random Effects under random effect model estimation. If the random effects
are not present, the model can be estimated using the Classical Linear Regression
technique. Checking for the presence of random effects is essentially testing the null
hypothesis that the cross sectional variance components are zero, i.e., 020 == µσH . For
testing this hypothesis Breusch and Pagan (1980) have developed a Lagrange Multiplier
(LM) test, which can be given as (Greene, 2003):
22
1)1(2 ⎥
⎦
⎤⎢⎣
⎡−
′′
−=
eeeeT
TnTLM
In the above formula, e is the n X 1 vector of the group specific means of pooled
regression residuals, and e e' is the Sum of Squared Errors of the pooled OLS regression.
And, the LM is distributed as chi-squared with one degree of freedom.
Table A4.1 Endogeinity Test
289
Endogeinity test for rural bank branches / total bank branches Coefficient Std.Err. Z P>[Z] RRB_Total B.B 11.97679 14.14501 0.85 0.397 RES 25.58089 17.06733 1.50 0.134 GDP_Agr .0000417 .0000432 0.97 0.334 GDP_ Indust -.000026 .0000913 -0.28 0.776 GDP_Ser -8.11 .0000272 -0.30 0.766 IM .3950728 .3200173 1.23 0.217 IR -.8573449 .4942842 -1.73 0.083 NB .4839746 5.288751 0.09 0.927 PB -1.277317 5.299394 -0.24 0.810 FB 7.345348 6.734881 1.09 0.275 Total_Assets -.0000609 .0000194 -3.14 0.002 Constant 19.49389 16.63522 1.17 0.241 F (11) 50.94 Number of Observation 748 Prob>F 0.0000 Number of groups 91 R Square Observation Per Group Within 0.0652 Minimum 3 Between 0.0625 Average 8.2 Overall 0.616 Maximum 9 Sigma_u = 12.081546 Sigma_e = 7.343387 Rho = .73022373
290
CHAPTER 5
Non-Performing Assets and Profit Efficiency
5.1 Introduction
The Non-performing Assets of commercial banks adversely affects commercial
banks in many ways, one of them being the decline in the profit of commercial banks.
Higher NPA has two important impacts on the profit level of commercial banks. First, the
assets that become NPA will not yield any return; second, banks have to make provisions
for the NPA from the profit they earn, which further erodes their profit level. Thus it is
interesting to study the relation between NPA levels and the profit efficiency in a more
rigorous way. Further, may financial sector reforms introduced subsequent to the
Narasimham Committee report (1991), particularly the prudential regulation norms, are
expected to compel the banks to reduce the NPA level, which in turn improve the
efficiency of commercial banks. Thus it of interest to study whether the efficiency of the
commercial banks has improved over time, and importantly whether the reduction in
NPA level has helped commercial banks to improve their efficiency.
Thus in the present chapter an efficiency analysis is conducted using frontier
technique, and an attempt is made to check whether NPA has any relation with
efficiency. The frontier used here is the profit frontier, since profit frontier captures one
of the important objectives of commercial banks namely profit maximization, and also it
is expected that the NPA affects the profitability of commercial banks more than many
other variables concerning the commercial banks.
Given this background, the present chapter is designed as follows; Section 2
presents a brief review of literature related to various studies measuring efficiency of
commercial banks. In Section 3 various methods of measuring efficiency of commercial
banks using frontier method is discussed. Section 4 discusses the detailed methodology
used in the present study. While Section 5 gives various sources of data used in the
present study, in Section 6 the estimated results are presented. Finally, Section 7 presents
the concluding observations.
291
5.2 Review of Literature
There exist a vast literature that concentrates on at the efficiency of commercial
banks in India as well as other countries. However, studies looking at the efficiency of
commercial banks in relation to their Non Performing Assets are very few. The issue no
doubt assume considerable importance, because if there exist a relation between these
two, then the policy prescriptions need to be tailored accordingly.
Studies analyzing the general efficiency of commercial banks look at various
aspects of commercial banks. While some studies look at the efficiency of commercial
banks in producing outputs, whichever manner they are defined, some look at the profit
efficiency of commercial banks and others examine the cost efficiency.
Paster Perez and Quesada (1997), using a non-parametric approach together with
the Malmquist index, analyse the differences in the productivity and efficiency between
different European and US banking system. They further decompose the differences in
productivity of different banking systems into differences in levels of efficiency (catching
up effect) and level of technology (distance from the frontier). The study has found that,
under the assumption of a constant returns to scale production technology, France has the
highest efficiency score of (0.95) and the UK has the lowest efficiency score (0.56).
Vivas (1997) analyse the effects of deregulation on the profit efficiency of
Spanish savings banks over 1986-1991. Profit function is considered to be more
appropriate since it reflects the joint impact of revenue as well as cost effects of
deregulation. The thick frontier approach is used since it selects a relatively large subset
of firms to define frontier unlike the other frontier measures which base the efficiency
estimate on one or a very small subset of firms. The results suggest that the profit
efficiency of Spanish savings banks, which averaged 28 percent, fell by forty percent
between 1986 and 1991. Also there was no significant shift in the profit frontier itself (in
other words, there was no technological change)
Berger and Humphrey (2000) provide a comprehensive survey of 130 studies that
conduct frontier efficiency analysis ( applying different efficiency measurement
techniques) to financial institutions from 21 countries. They note that the impact of
deregulation on the efficiency of banks is mixed. The overall efficiency, in these studies,
is round 77% (median 82%).
292
Berger and Mester (2000) examine the possible sources of differences in
measured efficiency of financial institutions, including differences in efficiency concepts,
measurement methods, the number of banks considered, market, and regulatory
characteristics and others. They estimate the efficiency of almost 6000 US commercial
banks that were in continuous existence over the six-year period from 1990 to 1995. The
study employs three distinct economic efficiency concepts – cost, standard profit, and
alternative profit efficiencies. The paper analyse the effects of a number of measurement
methods, including use of the distribution-free approach versus the stochastic frontier
approach, specification of the Fourier-flexible functional form versus the translog form,
and inclusion of problem loans and financial capital in a number of different ways. Their
results show that the mean cost efficiency from the preferred model is 0.868. It is found
that the mean efficiencies for standard and alternative profit functions are similar to each
other, however, the alternative profit function does not fit the data nearly as well as the
standard profit function. Different functional forms used (translog & Fourier) yield
essentially the same average level and dispersion of measured efficiency, and both rank
the individual banks in almost the same order.
Jonathan and Nguyen (2005) have studied the relationship between commercial
bank performance and bank ownership in South East Asia (Indonesia, Korea, Malaysia,
the Philippines, and Thailand) between 1990 and 2003, where, performance is measured
using three concepts; alternative profit efficiency, technical change and productivity.
They use four types of indicator in their study, viz., changes in governance due to bank
privatization, acquisition by foreign banks, domestic M&A; and bank restructuring. Their
study finds a positive relation between the performance of commercial bank and
deregulation. In terms of state versus private ownership, state-owned banks are found to
under-perform vis-à-vis their private counterparts. Furthermore, the study observes that
bank privatization has raised bank performance to levels in excess of pre-privatisation
era.
Sensarma (2005) measures the cost and profit efficiency of the Indian commercial
banks during the period 1986-2003, using Stochastic Frontier Analysis. The study finds
that while cost efficiency of the banking industry increased during the period, profit
efficiency underwent a decline. Also, in terms of bank groups, domestic banks appear to
293
be more efficient than foreign banks. Further Bigger banks were found to be less efficient
than their smaller counterparts.
Das et al, (2005) empirically estimates and then analyses various efficiency scores
of Indian banks during 1997-2003 using Data Envelopment Analysis (DEA). Instead of
taking a single measure of efficiency, they use multiple measures, i.e. two measures of
technical efficiency, cost efficiency, revenue efficiency and profit efficiency. Their
results show that there is not much difference in the technical efficiency of various banks.
However, for the remaining two measures of efficiency relating to cost and profit banks
appear to be more differentiated; this is particularly true with respect to profit efficiency.
Also, there has been a noticeable improvement in the profit profile of banks over the
years, particularly after 1999-2000. Profit efficiency seems to have a positive relation
with bank size, which shows that bigger banks are more efficient. Further the results also
show that there is a positive association between good management practices and profit
efficiency. Their results, however, does not show any clear association between the
efficiency of commercial banks and their privatization.
Mohan and Ray (2004) compare the performance among public sector banks,
private banks and foreign banks using physical quantities of inputs and outputs, and
comparing the revenue maximization efficiency of banks during 1992-2000. The findings
show that PSBs performed significantly better than private sector banks but no differently
from foreign banks. The study also finds that there is a convergence in performance
between public and private sector banks in the post-reform era.
De (2004) has empirically investigated the interrelation between ownership-
liberalisation-efficiency of the Indian banking industry using a panel data set for the
years 1985-‘86 to 1995-‘96. The study estimates time-invariant and time-variant
technical efficiency levels of the banks in the Indian banking industry using a stochastic
frontier production function by incorporating the Cobb-Douglas technology with four
inputs and two alternative measures of output. The overall finding is that banking
industry is technically inefficient. The average inefficiency levels are 55 percent and 20
percent for the two output measures used in the study. Technical efficiency has increased,
in the post-liberalisation for only 14 banks out of 18 banks, and, for more than two-third
of the banks in our sample technical efficiency is constant over the period.
294
Studies discussed above only concentrate on measuring the performance of
commercial banks through profitability, technical efficiency and/or productivity.
However, in the recent time it is being recognized by many that NPA has strong
implications on the performance of commercial banks. Though the issue of relation
between NPA and the performance of commercial banks has not been explored
sufficiently, there are few attempts in this direction.
Berger and Young (1997) have made one of the early attempts to analyse the
relation between NPA (or problem Loans, as they call it). They employ Granger-causality
techniques to test four hypotheses regarding the relationships among loan quality, cost
efficiency, and bank capital. The four hypotheses they test are; bad luck, bad
management, skimping and moral hazard. The results of their analysis suggest that the
intertemporal relationships between loan quality and cost efficiency run in both
directions. Further provide support for the bad luck hypothesis – increases in
nonperforming loans tend to be followed by decreases in measured cost efficiency,
suggesting that high levels of problem loans cause banks to increase spending on
monitoring, working out, and/or selling off these loans. For the industry as a whole, the
data favor the bad management hypothesis over the skimping hypothesis, however, for a
subset of banks that were consistently efficient across time, the data favor the skimping
hypothesis. Their results also support the moral hazard hypothesis, and suggest that, on
an average, thinly capitalized banks take increased portfolio risk, which results in higher
levels of problem loans in the future.
Dongili and Zago (2005) estimate the technical efficiency of Italian banks by
taking into account problem loans and using directional distance functions. Their results
show that once problem loans are taken into account, the economic efficiency of banks
increase significantly, suggesting that a significant aspect of banking production, credit
quality, needs to be considered when evaluating banks’ performances.
Jordan (1998), examine whether the increase in the problem loans of the banks of
New England is because of the inefficiency of commercial banks or because of the
government policies. In order to determine the reason for the severity of the problem an
analysis of cost and profit efficiencies are conducted using parametric method. In general,
the data suggest that no relationship exists between cost efficiency and problem loans, but
295
they show a positive and statistically significant relationship between profit efficiency
and problem loans. Further, this study finds that higher levels of profit efficiency in the
1984–88 periods are associated with higher levels of problem loans in the 1989–92
periods. These results suggest that managers of these “profit-efficient” banks deliberately
adopted policies designed to generate higher returns, but by taking higher risk.
Matthews, Guo and Zhang (2006) analyse the relation between rational
inefficiency and non-performing loans of Chinese banking industry using boot-strapping
method. They argue that inefficiency relative to 'best practice' is usually blamed on bad
management, ‘rent seeking’ behaviour and poor motivation not just X-inefficiency in the
traditional sense.
Das and Ghosh (2005) examine the interrelationships among credit risk, capital
and productivity change in the Indian context, using the data on state-owned banks
(SOBs) for the period 1995-96 through 2000-01, where credit risk is measured by the
ratio of net non-performing loans to net advances. Their results show that higher
productivity leads to a decrease in credit risk, and also there is a positive relation between
productivity and bank capitalization. This finding supports the fact that poor performers
are more prone to risk taking than better performing banking organizations. Their results
also reveals that efficiency, capital and risk taking tend to be jointly determined,
reinforcing and compensating each other.
5.3 Measuring Efficiency Efficiency has two components: one is purely technical or physical component which refers to the ability to avoid waste by
producing as much output as input usage allows, or by using as little input as production allows. Thus the analysis of technical
efficiency can have an output augmenting orientation or input conserving orientation. Another is the allocative or price component,
which refers to the ability to combine inputs and outputs in optimal proportion in the light of prevailing prices (Lovell, 1993)
Koopmans (1951) was the first to provide a formal definition of technical efficiency. According to his definition a producer
is technically efficient if an increase in any output requires a reduction in at least one other output or an increase in at least one input,
and if a reduction in any input requires an increase in one other input or a reduction in at least one out put. Thus technically inefficient
producer could produce the same outputs with less of at least one input, or could use the same inputs to produce more of at least one
output.
The basic assumption underlying the measurement of technical efficiency is that a gap normally exists between a firm's
actual and potential levels of technical performance. This can be understood from the diagram 1, given in the next page. In the
diagram, the ‘FF’ curve represents the frontier which is the combination of outputs of the best performing firms in the sample.
According to the neo-classical theory, all firms operating on this frontier are technically efficient (Kalirajan and Shand, 1994). A firm
296
operating at point ‘B’, which is on the frontier, will produce Y*1 outputs, which is the maximum possible output for a given set of
inputs X1. Thus the firm operating at point ‘B’ is technically efficient.
.
Diagram 1
Measuring Efficiency
However in practice a firm may not be working at a point on the frontier due to various reasons such as incomplete
knowledge of the best technical practices or other factors that prevent it from operating on its technical frontier. Thus the firm will
operate on an actual or perceived production function, which is below the potential frontier. The firm operating at point ‘A’ is
technically inefficient as it is producing output Y1 which is less than the maximum possible output for the input vector X1. Now, the
technical efficiency of the firm operating at point ‘A’ is measured by the distance between its actual output and the maximum possible
output (which is given by the frontier at ‘B’). Thus the technical efficiency can be measured as the ratio Y1/Y*1.
The most commonly used tool of analysis for measuring technical efficiency is the primal production function. In the neo-
classical theory of production, the primal production function defines the maximum possible output of a firm for combinations of
inputs and technology, i.e., it is frontier production function. The production frontier of the firm, producing a single output with
multiple inputs can be defined as,
);( βii xfy = (1)
Where, yi and xi are output and inputs of the ith firm. Here the firm is operating on the frontier, producing maximum
possible output, thus there is no technical inefficiency. But in reality this may not be the case. A firm, say the ith firm may not be
producing its maximum possible output. Then the production function of ith firm can be written as,
iii TExfy ⋅= );( β (2)
X
Y
A
B
Y1
Y*1
F
F
X10
297
Where, TEi is the technical efficiency of the ith firm which represents the combined effects of various non-price and
organisational factors which constrain the firm from obtaining its maximum possible output. The value taken by TEi depends on the
extent to which the firm is affected by constrains. A measure of technical efficiency of the firm can be defined as.
);( βi
ii xf
yTE = (3)
The above model is a basic model generally used for measuring technical efficiency. Here yi achieves its maximum
possible level only if TEi = 1. In this model, the numerator is observable but the denominator is not. Various methods using different
assumptions have been suggested in the literature to estimate the denominator and there by TEi. Farrell (1957), who gave the definition
of efficiency, also suggested that the production frontier can be can be estimated from sample data using either a non-parametric
piece-wise-linear method or a parametric function, such as the Cobb-Douglas form. Since then the basic model has been extended by
many ways. Various methods of estimating frontier production function, and thus technical efficiency, can be conveniently grouped
under two major groups, namely, programming (deterministic) and statistical (stochastic) methods. The classification of
different frontier production function methodology is given in the chart 1.
Chart 1
Deterministic Approach:
Frontier Methodology
Stochastic
Non Neutral Shift Neutral Shift
Deterministic
Parametric
Programming Statistical
Cross Section Panel Data
Non Parametric
Time Variant Time Invariant
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Farrell pioneered the work on the deterministic approach of measuring technical efficiency in 1957, following the notion of
Debreu (1951) and Koopmans (1951). He assumed constant returns to scale to estimate the frontier production function, and the model
included one out put and two inputs. This model was extended to one output and m-inputs, and a functional form, Cobb-Douglas, was
specified by Aigner and Chu (1968). The principal disadvantage of Farrell model is the assumption of constant returns to scale, which
is quite restrictive. This was extended to increasing returns to scale by Seitz (1970). The Farrell model was generalised in terms of
vector inputs and vector outputs by Carnes,et,al (1978), which is Known as Data Envelopment Analysis (DEA). DEA was further
developed by Varian (1985) who incorporated stochastic characteristics. The aim was to introduce two sided deviations to include
random noise and to calculate the efficiency measures free of such random noise. Land,et.al (1980) provided an alternative approach
to Varian by allowing deterministic frontier to capture the effects of random noise without themselves becoming stochastic. In
literature this method is termed as ‘chance–constrained efficiency analysis’.
Stochastic Frontier Approach:
The programming method does not take into account the statistical errors. This was first pointed out by Timmer (1971)
who provided a simple method to deal with these errors. The full fledged stochastic frontier production function estimation was first
published by Aigner,Lovell and Schmidt and Meeusen and Van den Broeck independently in 1977. Aigner, et.al used a truncated
normal (half normal) distribution for ‘u’, whereas Meeusen and Van den Broeck used exponential for the same. However, this
stochastic frontier production function approach could only provide average technical efficiency measures for the sample
observations. Although these aggregate measures are useful in a way, individual observation-specific technical efficiency measures
are more useful from a policy viewpoint. Jondrow et,at (1982) and Kalirajan and Flinn (1983) independently considered the stochastic
model introduced by Aigner et,al (1977) and Meeusen and Broeck (1977) to predict the combined random variable (ui+ vi). Estimation
of the stochastic frontier production function for a single cross-section requires the explicit specification of distribution of statistical
noise and inefficiency variable term. Several distributions have been considered in the literature, although the most commonly
employed are the positive ‘half normal’ and the ‘exponential’. Much of the criticism surrounding these estimates of the production
frontier have been concerned with the strength of the distributional assumptions. Such strong assumptions are not required when panel
data are available. Schmidt and Sickles (1984) have used panel data to estimate production frontiers. The panel data model can be a
‘fixed effect’ model or ‘random effect’ model; it can have time invariant effect and also time varying effect.
The Stochastic Varying Coefficient Frontier Approach:
Under both approaches discussed above, the firm obtains its frontier potential output by following the best practice
techniques, given the technology. In other words, frontier output is determined by the method of application of inputs, regardless of
levels of inputs. Empirical evidence shows that with the same levels of inputs, different levels of actual output are obtained by
following different methods of application. This implies that the different methods of applying various inputs will produce different
outputs. This means that diversity of individual decision making behaviour leads to variation in production response coefficients,
which include not only the intercept but also slope coefficients, across units and over time for the same unit. This idea was first
appreciated by Nerlove (1965) and was later popularised by Swamy (1970). Kalirajan and Obwana (1994) showed that this method
facilitates the estimation of firm specific and input specific technical efficiency values. Kalirajan and Shand (1994) discuss the
modelling of the frontier production function with cross sectional heterogeneity in slopes and intercepts.
5.4 Methodology
Efficiency analyses in the present study are of two kinds. First, we analyse the
impact of NPA on the efficiency of commercial banks, in particular on the profit
299
efficiency of commercial banks. Second, we analyse the efficiency of commercial banks
in minimising the NPA, where NPA is considered as a bad output. Further we also
analyse the factors that determine the NPA inefficiency.
In order to analyse the impact of Non Performing Assets on the profit efficiency
of commercial banks, we need to first obtain the profit efficiency measures using
stochastic frontier method. Then the impact of NPA on the profit efficiency will be
analysed by regressing the profit efficiency estimates over NPA along with a set of other
control variables. In order to analyse the NPA efficiency of commercial banks, a NPA
frontier is estimated using the available data, and the NPA efficiency is derived as the
ratio of ‘minimum NPA/Actual NPA’, where the minimum NPA is given by the
frontier30. In the present study efficiency is estimated using the stochastic frontier analysis. The basic model of the SFA was
discussed in the previous section. This basic model has been extended by number of ways. Earlier approaches were using a two stage
estimation procedure, where, in the first stage firm-level efficiencies were predicted using stochastic frontiers and in the second stage
they (firm-level efficiencies) were regressed upon some firm-specific variables to identify some of the reasons for differences in
predicted efficiencies between firms. Though this procedure was useful in estimating firm-level inefficiencies, this was considered as
inconsistent in its assumptions regarding the independence of the inefficiency effects in the two estimation stages [Kumbhakar, Ghosh
and McGukin, 1991; Reifschneider and Stevensosn, 1991]. To overcome this problem Battese and Coelli (1993) proposed a model in
which inefficiency effects are expressed as explicit function of a vector of firm specific variables and a random error. Present study
follows the approach used by Bettese and Coelli (1993). The frontier specification used to derive efficiency estimates can be given as
follows;
itit
itititititit
itititititit
ititititit
uvDbWbINbADb
DWbDINbWINbDADbWADbINADb
TbDbWbINbADbbP
−+++++
++++++
+++++=
]lnlnlnln[)2/1(
lnlnlnlnlnlnlnlnlnlnlnln
lnlnlnlnln
244
233
222
211
342423
141312
543210
(4)
Where, P is the Net Profit31
AD is the Advance (output)
IN is the Investment (output)
30 Note that, this method is similar to the method of measuring cost efficiency. However, here the functional form used is a production function, not cost function. It is assumed that commercial banks minimize their NPA level, given the input level. 31 Note that the dependent variable for the profit function is [π + |π|min + 1], where |π|min is the absolute of the minimum value of net profit (π) over all banks. Since the net profit of most banks are negative the constant [|π|min +1] is added to every firm’s net profit so that the natural logarithm is taken of a positive number.
300
W is the wage to capital price ratio (input price)
D is the deposit price to capital price ratio (input price)
T is the time trend
Vit are random variables, which are assumed to be iid. N (0, σv2) and independent of Uit
Uit are non-negative random variables which are assumed to account for technical
inefficiency in production and are assumed to be independently distributed as truncations at zero
of the
N (mit, σu2) distribution;
Where; mit = zitδ,
Where; zit is a 1 x p vector of variables which may influence the inefficiency of a firm
and δ is a p x 1 vector of parameters to be estimated.
The parameterization from Battese and Corra (1977) are used replacing σv2 and σu2 with σ2 = σv2 + σu2 and the parameters
are estimated by ML approach32.
Further, in order to analyse the impact of NPA on the efficiency levels of commercial banks, the estimates of technical
inefficiency (Ui) are regressed over NPA along with a set of control variables. The functional form used to analyse the impact of NPA
on the inefficiency of commercial banks can be given as follows33;
it
it
WFBPBNBGDPIMTBBRBBTAGAGNPAU
+++++++++=
876
543210 //δδδ
δδδδδδ (5)
Where,
U is the Profit Inefficiency
GNPA/GA is the ratio of Gross Non Performing Assets to Gross Advance
TA is Total Assets
RBB/TBB is the ratio of Rural Bank Branches to Total Bank Branches
IM is the Interest Margin (Measured as the difference between lending rate and deposit rate).
GDP – Gross Domestic Product
NB, PB, and FB are bank group specific dummies for Nationalised Banks, Private Banks
and Foreign Banks respectively34
32 The log-likelihood function is given in Battese and Coelli (1993) 33 Note that under Bettese and Coelli approach both frontier and inefficiency equation are estimated simultaneously using ML method. 34 The State Bank of India and Associates (SBI&A) dummy is not considered in order to avoid dummy variable trap. Therefore, the specified dummy variables should be interpreted in comparison to the SBI&A, which serves as the base.
301
As mentioned before, apart from analysing the impact of NPA on the profit efficiency of commercial banks, an attempt is
also made to analyse the efficiency of commercial banks in minimising the NPA level. Here NPAs are considered as output that
commercial banks actually try to minimise. However, this is difficult to estimate in a straightforward manner due to non-availability of
statistical package. In order to measure the NPA efficiency, first a production frontier is estimated considering NPA as the output,
using the standard production frontier approach, under which, the firm that produces the maximum output, (given a set of input and
technology) will be the most efficient one. This firm then can equivalently be interpreted as the one that is least efficient in reducing
NPA with a given level of inputs. The frontier used to derive the NPA efficiency can be specified as follows:
( ) ititititit
itititititit
itititit
uvMaLaKa
MLaMKaLKaTaMaLaKaaGNPA
−++++
+++++++=
]lnlnln[2/1
lnlnlnlnlnlnlnlnlnln
233
222
211
231312
43210
(6)
Where,
Y is Gross Non Performing Assets
K is Capital (fixed assets)
L is Labour (employees)
M is Material (stationery, postage etc)
T is the time trend
Vit are random variables, which are assumed to be iid. N (0, σv2) and independent of Uit
Uit are non-negative random variables which are assumed to account for technical
inefficiency in production and are assumed to be independently distributed as truncations at zero
of the
N (mit, σu2) distribution;
Where; mit = zitδ,
Where; zit is a 1 x p vector of variables which may influence the inefficiency of a firm
and δ is a p x 1 vector of parameters to be estimated.
The parameterization from Battese and Corra (1977) are used replacing σv2 and σu2 with σ2 = σv2 + σu2 and the parameters are
estimated by ML approach35.
Further, the functional form used to analyse the determinants of NPA efficiency can be given as follows;
it
it
WFBPBNBLRGDPIMTBBRBBTAU
+++++++++=
8765
43210 /δδδδ
δδδδδ (7)
35 The log-likelihood function is given in Battese and Coelli (1993)
302
Where,
U is the NPA inefficiency estimates
TA is Total Assets
RBB/TBB is the ratio of Rural Bank Branches to Total Bank Branches
IM is the Interest Margin (Measured as the difference between lending rate and deposit rate).
GDP – Gross Domestic Product
LR – Lending Rate
NB, PB, and FB are bank group specific dummies for Nationalised Banks, Private Banks
and Foreign Banks respectively36
W : random error.
Before proceeding further, it is essential to discuss the rationale for including each variable in the profit inefficiency
equation as well as NPA efficiency equation. In the profit inefficiency equation, the first variable on the right hand side of the equation
is the ratio of Gross Non Performing Assets to Gross Advance. Since higher level of NPA calls for higher level of provisioning, it
affects the profit level of commercial banks. Thus the GNPA/GA is expected to have a negative impact on the profit efficiency. Total
assets represent the size of the bank. It is interesting to know whether size of the commercial banks has any impact on the efficiency
levels of commercial banks. Though the relationship between the bank size and efficiency level is an empirical question, since bigger
banks would have economies of scale, size of bank can be expected to have a positive relation with the efficiency level of commercial
banks. It has been argued that branch expansion policy followed by RBI and the resulting expansion of commercial banks branches in
the rural areas has adversely affected the efficiency of Indian commercial banks. Thus it is important to understand the relationship
between the rural bank branches and the efficiency level. In this regard the ratio of rural bank branches to the total bank branches has
been included in the inefficiency equation. An important development in the post-liberalisation period is the increasing competition. It
has been argued by many that the competition increase the efficiency of commercial banks. Thus in order to capture the impact of
competition on the efficiency of commercial banks, the Interest Margin (IM) is included in the study. The rationale for including IM
as a measure of competition is as follow; when the competition increases, it increases the pressure on the competing banks to minimise
the margin they receive by financial intermediation. Thus when the IM is declining it indicates that the competition is increasing. One
more important variable that has strong influence on the efficiency of commercial banks is the economic condition. Higher economic
growth means higher productivity, which will lead to higher financial transaction, which in turn will lead to higher profit efficiency of
commercial banks. Thus GDP, which is included to capture the economic condition, is expected to have a positive impact on the profit
efficiency of commercial banks. The bank group specific dummies are included to capture the bank group specific characteristics that
can have influence on the profit efficiency of commercial banks.
Coming to the NPA efficiency equation, most of the variables are similar to the ones included in the profit inefficiency
equation. Here also, total asset is included as a size variable. Similar to the case of profit inefficiency equation, here too the ratio of
rural bank branches to total bank braches is included in order to capture the effect of the branch expansion policy of the RBI and
government on the NPA level of commercial banks. Increasing competition would compel commercial banks to reduce their NPA
level, thus Interest Margin (IM), which is included as a proxy for the competition is expected to have a positive relation with NPA
(reduction) efficiency37. Further, an improvement in the economic condition would enable the firms to repay the borrowed loans,
which in turn would reduce the NPA level of commercial banks. Thus GDP is expected to have a positive relation with NPA reduction
36 The State Bank of India and Associates (SBI&A) dummy is not considered in order to avoid dummy variable trap. Therefore, the specified dummy variables should be interpreted in comparison to the SBI&A which serves as the base. 37 By NPA efficiency here we mean efficiency in reducing NPA levels.
303
efficiency. An important variable that is expected to have strong implications on the NPA of commercial banks is the Lending Rate
(LR). In this regard the SBI advance rate is included in the NPA inefficiency equation as a proxy for the LR. Finally the bank group
specific dummies are included to capture the bank group specific characteristics that can have influence on the profit efficiency of
commercial banks.
5.5 Sources of Data Data are collected for the period 1997-2005. However as many private and foreign banks are established after 1997 and
few are closed during the study period, data are not available consistently for all banks for all years. Thus data used is an unbalanced
panel of 94 banks (27 public sector banks, 33 Indian private banks and 34 foreign banks) for 9 years (total observation used are 746).
Banks are grouped into four groups38 (i) State Bank of India and Associates (SB & A) (ii) Nationalised Banks (NB) (iii)
Private Banks (PB) and (iv) Foreign Banks (FB). Net profit is measured as Gross Profit less (-) provisions and contingencies.
Advances are measured as total advances and Investments are measured as total investment. Data on net profit, advances and
investment are measured in value terms and are collected from Annual Accounts of Scheduled Commercial Banks published by
Reserve Bank of India.
Price of labour (employees) is obtained by dividing the total expenditure on employees by total number of employees and
the price of capital is obtained by = (total operating cost – total expenses on labour)/total fixed assets39. Price of deposits is obtained
by dividing the total interest expenditure on deposits by total deposits.
Further, fixed capital (or capital stock, (K)) is the sum of premises, furniture and other fixed assets40. Number of employees
(or Labour (L) is measured as the total number of employees which include officers, sub-ordinates and clerks. Material (M) is
measured as the sum of expenditure on printing & stationeries and postage, telegrams & telephones etc. while data on the fixed capital
and material is collected from the balance sheet of commercial banks, presented in Annual Accounts of Scheduled Commercial Banks,
data on the number of employees are collected from Statistical Tables Related to Banks in India, both documents published by
Reserve Bank of India
5.6 Empirical Results
The focus of the present study is to analyse the relation between the Gross Non-
Performing assets and the efficiency levels of commercial banks. Thus emphasis will be
given on the estimates of the inefficiency equation (equation 5). However, measurement
of efficiency is a pre-requisite to understand its relation with the GNPA of commercial
banks, it would be interesting to have a look at the efficiency levels of commercial banks
before proceeding further. The estimated coefficients of the profit frontier are presented
in the appendix (Table A5.1). It can be seen from the table that majority of the
coefficients are significant. The value of the gamma shows that majority of residual
38This grouping is done following the standard classification of RBI 39 Similar method is used by Kumbhakar and Sarkar (2003) 40 Capital stock is converted into its present value using perpetual inventory method
304
variation is due to the inefficiency effect41. An interesting observation is that the value of
the time trend shows that there is technical progress in the Indian commercial banks. The
profit efficiency estimates obtained using the profit frontier are averaged across bank
group for each year and are presented in table 5.1.
Table 5.1 Profit Efficiency Estimates
Year SB&A NB PB FB TBS 1997 0.9022 0.9217 0.9667 0.7742 0.9578 1998 0.9106 0.9768 0.9711 0.7645 0.9677 1999 0.8706 0.9688 0.9551 0.7008 0.9581 2000 0.8649 0.9618 0.9621 0.6878 0.9548 2001 0.8670 0.9408 0.9517 0.7153 0.9342 2002 0.8582 0.9439 0.9552 0.6879 0.9418 2003 0.8537 0.9466 0.9524 0.6794 0.9502 2004 0.8957 0.9631 0.9611 0.7456 0.9651 2005 0.9152 0.9709 0.9691 0.7817 0.9749
Average 0.8820 0.9549 0.9605 0.7264 0.9561
After estimating the profit frontier, profit efficiency is estimated as the ratio
between actual profit (profit of bank ‘i’) and the maximum possible profit (profit of the
best performing bank in the sample). The average profit efficiency of the total banking
sector, for the total study period is 0.956, which means that Indian commercial banks are
around 96 percent efficient in earning profit in relation to the best performing bank. In
other words, Indian commercial banks are loosing around 4 percent of the net profit due
to technical inefficiency. At the bank group level private banks are the most efficient
bank group with an efficiency estimate of around 96 percent followed by Nationalised
banks with an efficiency estimate of around 95 percent. SBI&A rank third in terms of
profit efficiency with an efficiency estimate of around 88 percent and foreign banks are
the least efficient banks with an efficiency estimate of around 78 percent. The reason for
private banks being most efficient is, their non-interest income is higher than public
sector and foreign banks, which is mainly because they focus more on providing fee
based services than conventional banking activities.
41 A value zero for gamma indicates that the deviations from the frontier are due entirely to noise, whereas a value of one would indicate that all deviations are due to technical inefficiency. Gamma is estimated as; γ=σ2/σs
2; where σ2 is the variance of ui (inefficiency term), and σs2 is total variance (variance of vi plus
variance of ui).
305
Now we move on to the important question of this chapter, i.e., the impact of
GNPA on the profit efficiency of commercial banks. As discussed in section 5.4, the
impact of GNPA on the efficiency of commercial banks is analysed by regressing the
profit efficiency estimates over the GNPA/GA ratio along with some of the control
variables. The results of the regression are presented in table 5.2.
Table 5.2 Determinants of Profit Inefficiency
Coefficient Standard-Error t-ratio
Constant -1.1933 1.8230 -0.6546 GNPA / Gadv 0.0047* 0.0010 4.7355 Total Asset -0.1627* 0.0309 -5.2667 RBB/TBB -0.2555 0.1895 -1.3484 IM 0.0612** 0.0155 3.9455 GDP -0.4408*** 0.2561 -1.7213 NB 0.6291** 0.2320 2.7110 PB -0.1749 0.2993 -0.5844 FB 2.368481* 0.396584 5.972209
*, **, *** :- Significant at 1%, 5% and 10% respectively
Results presented in table 5.2 shows that majority of the coefficients are
significant and the signs are according to expectation42. The coefficient of GNPA/GA
ratio is significant with positive sign. This shows that GNPA/GA ratio has a positive
relation with profit inefficiency, which means that when the GNPA as a ratio of Gross
Advance increases the profit inefficiency of commercial banks will increase. This shows
that Gross Non Performing Assets adversely affect the efficiency of commercial banks.
Further the coefficient of Total Asset is significant with negative sign. This shows that
size of commercial bank has a negative relation with profit inefficiency. In other words,
bigger banks are more efficient compared to smaller banks in the sample in terms of
earning higher profit. This could be because since bigger banks can lend more loans, they
can diversify their risk, due to which handle the loss arising through loan loss. As
42 Note that since the inefficiency equation is estimated simultaneously with frontier, the goodness of fit statistic presented in table A5.1 in appendix also serves as the goodness of fit statistic of the results of inefficiency estimation presented in table 5.2.
306
mentioned earlier, one of the argument in the literature is that the expansionary policy
followed by the RBI in terms of expanding branch network has adversely affected the
efficiency of commercial banks. Our results, however, does not seem to provide any
evidence in favour of such argument. This could be due to the reason that the time period
considered for our analysis falls in the post-liberalization period, where many of the
branch licensing policies has been amended due to which many loss making rural bank
branches have been closed. One of the important developments in the post-liberalisation
period is the increasing competition, which is expected to increase the efficiency of
commercial banks. The coefficient of Interest Margin being significant, with positive
sign, supports this argument. As mentioned earlier, when the competition increases it
increases the pressure on commercial banks to cut down on the interest margin they earn.
Thus a declining IM is an indication of increasing competition. The positive sign of IM
shows that when the IM declines (i.e., competition increases) the inefficiency also
declines (i.e., efficiency increases). Further, the coefficient of the GDP has a negative
sign indicating that an improvement in the economic condition of the country will help
the commercial banks to reduce their inefficiency level.
As mentioned earlier, the NPA efficiency presented in table 5.3, using the
standard production frontier approach. Results of the NPA frontier are presented in the
appendix. As can be seen from the table A5.2, majority of the coefficients are significant.
The value of the gamma shows that majority of residual variation is due to the
inefficiency effect. After estimating the NPA frontier,
Table 5.3
NPA Efficiency Estimates
SB&A NB PB FB Total 1997 0.8978 0.8971 0.6047 0.4234 0.6536 1998 0.9109 0.9019 0.6401 0.5101 0.6864 1999 0.926 0.9128 0.7199 0.5405 0.7191 2000 0.9238 0.9149 0.7448 0.5346 0.7221 2001 0.9248 0.9227 0.7787 0.5624 0.7508 2002 0.9131 0.9309 0.8041 0.5402 0.7502 2003 0.8902 0.9297 0.8073 0.5403 0.7465 2004 0.8562 0.9226 0.7879 0.5462 0.7534
307
2005 0.8406 0.9032 0.7766 0.5281 0.7426 Average 0.8982 0.9151 0.7405 0.5251 0.725
The average efficiency estimate of the total banking sector for the total study period is around 0.72. This shows that Indian commercial banks are around 72% percent efficient in incurring NPA compared to the bank, which is most efficient in incurring NPA (or least efficient in reducing NPA). It was observed in the case of profit efficiency that, Private Bank group was the most efficient bank group followed by the NB and SB&A. This order seems to have changed in the case of NPA efficiency. Here, Foreign Banks are the most efficient bank group in reducing NPA as they have the lowest score on an average; followed by Private Banks (PB). The SB&A and Nationalised Banks are the least efficient banks in reducing NPA with high efficiency scores in incurring NPA (table 3). It is worth noting in this context that NB holds the highest amount of NPA, in absolute terms as well as percent of gross advance, compared to other bank groups.
Looking at the temporal behaviour, the NPA efficiency of the total banking sector
has declined steadily from 1997 till 2001, and remained almost stable there thereafter.
From 1997 till 2002, this trend of NPA efficiency follows the trend of the GNPA of total
banking industry, which has increased steadily from Rs 50815 crores in 1997 to Rs 70861
crores in 2002 (in real terms), and thereafter since 2002 GNPA has declined then reached
Rs 58299 crores in 2005. NPA efficiency scores have remained almost stable in this
period. At the bank group level the temporal behaviour of the NPA efficiency is not
uniform. The SB&A group has shown increase in efficicny in reducing NPA. This is
however, not seen to be true for the nationalized banks . Private banks efficiency scores
also show a fluctuating trend. The temporal behaviour of the NPA efficiency of FB is
distinct from other bank groups. It has remained more or less stable over the period.
We next move on to another interesting question, viz., what are factors that have
influence on the NPA efficiency level. In this regard, the NPA efficiency equation is
estimated, along with the frontier, where the NPA efficiency estimates are regressed over
a set of variables, which are expected to have influence on the NPA efficiency of
commercial banks (Table 5.4).
308
Table 5.4
Determinants of Efficiency in reducing NPA Coefficient Standard-Error t-ratio Constant -6.0298 4.9092 -1.2283Total Asset -1.0925* 0.0847 -12.9054RBB/TBB -4.2486* 0.8343 -5.0922IM 0.0041 0.0235 0.1745GDP 0.7617 0.7340 1.0377LR 0.3159* 0.0656 4.8158NB -0.5196* 0.0979 -5.3054PB 1.0243* 0.2934 3.4905FB 0.9384* 0.3378 2.7776
* - Significant at 1%, ** - Significant at 5%
Total Assets is having a negative relation with NPA reduction efficiency, which
means bigger banks are less efficient in minimizing their Gross Non Performing
Assets. The ratio of rural bank branches to total bank branches also has a negative
relation with NPA reduction efficiency, which means, when the rural bank branches
are increasing, the NPA efficiency declines. This also shows that banks, which have
higher rural bank branches, are less efficient in minimizing their GNPA. Competition
and rate of interest also do not seem to have any impact on the NPA efficiency.
Similarly, GDP also does not seem to have any impact on the NPA efficiency. Bank
309
group dummies show that, NB is less efficient than SB&A whereas PB and FB are
more efficient than SB&A43.
5.7 Conclusion
Non Performing Assets adversely affect the efficiency of
commercial banks. In particular higher NPA has strong implications on
the profitability of commercial banks. On the one hand the assets that
become NPA will not yield any return; on the other hand, banks have to
make provisions for the NPA from the profit they earn, which further
erodes their profit level. Thus higher NPA has strong implication on the
profit efficiency of the commercial banks. In the present chapter an
attempt is made to understand the relation between Gross Non
Performing Asset to Gross Advance ratio and the profit efficiency of
commercial banks and also to analyse the efficiency of commercial
banks in minimizing the Gross Non Performing assets. The relation
between GNPA/GA and profit efficiency is analysed by regressing the
profit efficiency estimates, obtained through frontier analysis, on the
GNPA/GA ration along with some of the control variables. And, the
efficiency of commercial banks in minimizing the GNPA level is
analysed by estimating the NPA efficiency through GNPA frontier,
where GNPA is considered as a bad output and it is assumed that banks
try to minimize it.
Our results suggest that GNPA/GA ratio has significant relation
with profit efficiency of commercial banks. The sign of the GNPA/GA
ratio show that when the GNPA/GA ratio increases the profit efficiency 43 Note that since SB&A dummy is not included in order to avoid a dummy variable trap, rest of the bank group specific dummies should be interpreted with reference to SB&A.
310
of commercial banks also increases. Our results also show that bigger
banks are more efficient in terms of earning profit however, they are
less efficient in minimizing the GNPA level compared to smaller banks.
Further, the analysis suggests that while the higher proportion of rural
bank branches does not have any impact on the profit efficiency of
commercial banks, it does affect the NPA efficiency adversely. Contrary
to the result of rural bank branches, the coefficient of IM shows that
increasing competition lead to increased profit efficiency of commercial
banks, which however, does not seem to have any impact on the NPA
efficiency. Finally, the improvement in the economic condition of the
country will help the commercial banks to improve their profit
efficiency, whereas it does not seem to have any impact on the NPA
efficiency44.
Appendix To Chapter 5
Table A5.1 Profit Frontier Estimates Coefficient Standard-Error t-ratio Constant 4.8865* 0.0877 55.7394 Adv 0.0089 0.0897 0.0989 Inv -0.2289** 0.0882 -2.5941 W 0.2151** 0.0662 3.2471
44 It must be kept in mind that these are preliminary set of results, which will be again re-checked.
311
D 0.7442* 0.0916 8.1262 Adv2 -0.0047 0.0257 -0.1822 Inv2 -0.0153 0.0332 -0.4627 W2 -0.0102 0.0202 -0.5031 D2 0.0643 0.0495 1.2983 Adv*Inv 0.0558 0.0569 0.9809 Adv*W 0.0992** 0.0374 2.6539 Adv*D -0.1210** 0.0545 -2.2193 Inv*W -0.1207** 0.0364 -3.3170 InvD 0.1656** 0.0540 3.0662 W*D -0.1178** 0.0385 -3.0615 T 0.0107* 0.0014 7.9187 sigma-squared 0.1308* 0.0099 13.2378 Gamma 0.9932* 0.0011 895.8645
Log likelihood function = 818.6481 LR test of the one-sided error = 1195.1023
*, **, *** :- Significant at 1%, 5% and 10% respectively
Table A5.2 NPA Frontier Estimates
Dependent Variable - log(GNPA) Coefficient Standard-Error t-ratio Constant 2.3048* 0.2796 8.2431ln PC 0.1093 0.1867 0.5857ln L -0.2354 0.2556 -0.9207ln M -0.6550** 0.2711 -2.4164ln PC2 0.3355* 0.0632 5.3087ln L2 0.0250 0.0589 0.4252ln M2 -0.2350*** 0.1278 -1.8393ln PC*ln L -0.1320*** 0.0764 -1.7269ln PC*ln M -0.5369* 0.1467 -3.6597ln L*ln M 0.6238* 0.1263 4.9393Time 0.0100*** 0.0051 1.9680sigma-squared 0.4588* 0.0457 10.0386gamma 0.9578* 0.0062 154.6655log likelihood function -105.4861LR test of the one-sided error 487.4373*, **,:- Significant at 1%, and 5% respectively
312
CHAPTER 6
Problem of Loan Repayment: Views of the SSI Units
6.1 Introduction Much has been written about the problems of the SSI sector in the Indian context. The
growth of small scale industry in India, to a large extent is induced by the lack of
alternative employment opportunities and promotional policies adopted by government
(Desai, 1983)45. Due to lack of entrepreneurial attitude and proper training a large
number of them have met with untimely closure. Some of the major problems can be
identified as follows.
• Financial Constraints: Though there are a number of efforts to provide finances
to the SSI units, the most needy ones do not get proper information about
different schemes and often depend on the informal credit market for finance.
Coming up with an appropriate proposal also becomes difficult for such small
entrepreneurs.
• Access to raw materials is another problem faced by these units.
• One of the major handicaps of the small-scale sector has been the absence of
improved technology, which alone can ensure quality and higher productivity.
Technology is the most essential factor to remain competitive in a global market.
Lack of information again plays a critical role in the choice of technology.
• Marketing remains the major stumbling block for the growth of SSI sector.
Ignorance of potential markets, in particular, unfamiliarity with export activities
contributes to this problem. Poor designing and finish also often makes the
product not salable in the international market.
45 Desai. V, 1983, Problems and Prospects of Small Scale Industries in India, Himalaya
Publishing House, Bombay.
313
• As far as production methods are concerned there is often faulty planning and
inadequate appraisal of projects (Desai, 1983). Most often no proper viability
studies, technical or economic, are carried out.
Policy makers recognized these shortcomings and created a number of offices to handle
the problems. However, proper implementation has never been accomplished.
Consequently sickness remained a major problem for this sector. To understand the
problems of the sector in general and concerning credit facilities in particular in a
liberalized regime, a survey has been taken up. Though the survey has been conducted in
three states of India viz., Karnataka, Kerala and West Bengal.
6.2 Sampling Technique
It is well recognized that industry sector is not forthcoming in providing information.
This problem has noted down by many authors (see Deshpande et al, 2004)46. In this
background we are forced to adopt snowball sampling technique. Industry Association
gave contacts of the firms and requested them to cooperate with the survey. The firms
that agreed for a discussion were later interviewed using structured questionnaires that
were personally canvassed. The sample firms are from Kerala, Karnataka and West
Bengal and the sample sizes from these locations are respectively 50, 100 and 50.
6.3 Characteristics of the Sampled Firms
One of the important findings of the survey is that 35% of the manufacturing firms in our
sample are not availing loan from the institutional sources. These firms reported to
manage their investments from their own (or borrowing from relatives) resources. Out of
the rest 68% only 2% have availed loan from private banks. Thus dependency on public
sector banks remains prevalent.
46 Deshpande, Lalit et al, 2004, Liberalization and Labour: Labour Flexibility in Indian Manufacturing, Institute of Human Development, New Delhi.
314
While examining the investment in plant and machinery one observes that most of the
firms included in the survey falls under the Government of India definition of SSI. Only
about 2% of the firms have investment above Rs 1 crore. However as far as commercial
banks are concerned, for credit purposes they combine small and medium firms together
table 6.1).
Table – 6.1.Total investment
Investment Percent10000 - 100000 5 100000 - 500000 17.5 500001 - 1000000 37.5 1000001 - 2500000 15 2500001 - 5000000 12.5 5000001 - 1 crore 10 10000001 - Above 2.5 Total 100
Average turnover is between 30 lakhs to 50 lakhs. Thus these are comparatively large
sized firms within the SSI category (table 6.2).
Table 6.2. Total turnovers of the firms.
Turnover in Rs. This year Last year On an
average 300000 - 1000000 8 (20.00) 9 (22.50) 8 (20.00) 1000001 - 3000000 11 (27.50) 12 (30.00) 11 (27.50) 3000001 - 5000000 8 (20.00) 8 (20.00) 10 (25.00) 5000001 - 7000000 5 (12.50) 2 (5.00) 2 (5.00) 7000001 - 1 crore 3 (7.50) 3 (7.50) 3 (7.50) 10000001 - Above 5 (12.50) 5 (12.50) 6 (15.00)
Total 40 (100) 40 (100) 40 (100)
These firms are managed by qualifies entrepreneurs as can be seen from table 6.3. Above
50% of the firms owners are technical degree holders.
315
Table – 6.3 Educational qualifications of the firm manager and Owners.
Qualification Percent 1 7th 3.39 2 SSLC 15.25 3 PUC 5.08 4 BA 1.69 5 Bcom 6.78 6 Bsc 6.78 7 MA(Eco) 1.69 8 deploma(e,p.m.t.tex,) 22.03 9 ITI 1.69
10 BE 27.12 11 Mtech 5.08 12 MBBS 1.69 13 MCA 1.69 14 Total 100
Source: primary data
Source: Field Survey
However, they have the typical characteristic of small firms with lesser number of
workers. Table 6. 4 shows that almost half of firms have less than 10 workers.
Table 6.4 Total Employment positions of the firms.
Employment Total employment
0-2 2.5 3-5 2.5 6-10 42.5 11-15 15 16-20 2.5 21-25 10 26-30 5.00 Total 100
Source: Field Survey
316
These firms are mostly catering to the large firms within the state of Karnataka; as high
as 87% of these firms have subcontracting relation with a firm in Karnataka itself. Only
13% of the firms are marketing their product independently. Out of the 87% of the firms
35% are also catering to firms outside Karnataka and 2.5% are engaged in exports as well
(Table 6.5).
Table 6.5 Marketing of products.
Market
Large firm in the same state
Large firm in the another state
Export
87.5 35 2.5 Source: Filed Survey
6.4 Loan Amount and Rate of Interest: An Inverse Relation
One of the major objectives in this study is to examine the relation between the
borrowings and the rate of interest charged by the banks. It is interesting to note that
about 66% of the small borrowers (below Rs 50,000) pays interest rate of 17% or higher,
whereas the large borrowers pay comparatively lower interest rate. The category of
borrowers that borrows 40 lakhs or more pays interest rate below 12% (Table 6.6). This
may be because banks find the small borrowers comparatively more risky. Banks fixes
the rate of interest based on a number of criteria involving risky ness of a project, amount
of collateral and past records. Small borrowers may have been in a disadvantageous
position if they have started a new business with less collateral. The correlation between
loan amount and rate of interest indeed shows a negative value –0.370, which is
statistically significant at 5% level.
317
Table 6.6 Percentage of firms cross tabulated with respect to Loan amount and rate of
interest.
Bank rate of interest ( in Rs.) Total
Total loan amount ( in Rs ) 9 - 10 11 - 12 13 – 14 15 - 16 17 - 24
33.33 66.67 100
upto - 50000 10 66.67 11.54
50 50 100
50000 - 200000 10 20 7.69
33.33 16.67 33.33 16.67 100
200001 - 500000 20 20 40 33.33 23.08
50 50 100
500001 - 1000000 33.33 20 7.69
50 50 100
1000001 - 2000000 20 40 15.38
20 20 20 40 100
2000001 - 4000000 33.33 10 20 40 19.23
25 75 100
4000001 - above 33.33 30 15.38462
11.54 38.46 19.23 19.23 11.54 100
Total 100 100 100 100 100 100
Source: Field Survey
318
6.5 Problem of Bad Loans : Views of the Karnataka Firms Only about 3% of the firms have admitted to be defaulter in our sample. The rest of the
firms even though did not admit themselves to be defaulter have given possible reasons
for default by the small firms. It is interesting to note that about 80 % of the firms
consider excessive competition as a reason for low profit margin, which in turn affects
repayment capability adversely. But interestingly only 76% felt that opening up of the
market and competition from China is really not hampering small firms in our study area
(Table 6.7). Problem of non repayment of loan arising mainly due to non repayment of
dues by the large firms and large amount of rejection at a point of time. The former has
been well recognized by RBI and now small firms can take actions against large firms in
case of such defaults. However, as far as possible a small firm will not initiate such a
process due to its high dependence on the large firm. The latter may arise due to
increased quality concerns of the large firms. Our discussion with the bank officials also
reveals that diversion of funds is another major reason for default.
Table – 6.7 Reasons are default of the firms.
Reasons for default Yes a) Diversion of funds 53 b) Misunderstanding amongst partners. 42 c) Too much competition in the market 80 d) Huge quantity of finished product rejected 75 e) Competition from China/other country due to opening the market. 24 f) Large firms do not pay in time. 75 g) Too much borrowing. 51 h) Dependence on one or two large units 64 i) Marketing problem 53 Source: primary data Problem of willful default is accepted to be present by all firms, though expectedly no
one admitted to have adhered to such a practice. Diversion of funds has been the major
reason for willful default. This has also been stated during our interactions with the bank
officials. Entrepreneurs sometimes borrow in the name of a project but divert funds later
for purposes that can give more returns than an SSI unit.
319
Secondly, when a firm is running by a manger, the problem of principal –agent and in
turn monitoring exists. This sometime may result in default of loan as collateral belongs
to the firm owner. It has been observed by 7% of the respondents that high influence such
as political influence may result in willful default. This was also found to be true during
our discussions with the bank officials. Borrowers having strong political connections
often hold the view that they cannot be punished in any manner when they default.
Another reason cited by the firm owners is pure negligence. This as well has been found
to be true during our discussions with the bank officials. Even when business is running
well some borrowers do not repay loans as they feel that bank can never take any
stringent action. A few respondents also complained of collusive agreement between
bank officials and borrowers, which lead to such mal practices (Table 6.8).
Table 6.8 Opinions for the willful defaulted of firms.
Opinion Yes a) Misuse of funds 85 b) Technically not confident 8 c) Managerial problem 14 d) Subcontracting firm’s fault 7 e) High influence 7 f) Negligence 5 g) Bankers corruption 10
Source: primary data
It has been felt by the industry circle that taking prompt action and being more vigilant
can control willful default (Table 6.9). Right now it takes on an average about 10 years to
take control of the collateral/security. In the process plant and machinery depreciates
considerably, the defaulter also get sufficient return from the diverted funds and bank
becomes the major loser. SARFEISI act which ensures that a bank need not go through a
regular process of litigations through courts , may help to some extent in this regard. In
certain cases when default may be due to sheer negligence, serious advice from the bank
officials are necessary. To do this effectively bank officials may be given more powers to
take actions.
320
Table 6.9 Opinion from respondents to avoid the bank willful default
Avoid Opinion Yes
Seizing collateral 92.50 Checking 65 Self with athentification 15 Counseling 10
6.6 Problem of the Lending System
There are a number of problems faced by the borrowers from the SSI segment (Table 6.10). All the respondents considered procedures rather complicated, needing too many documentations. Number of times one needs to visit banks initially is as high as 10 to 20 times (Table 6.11).
Table 6.10. Current problems exist with the lending system.
Current Problems
Yes
Collateral problem 65 Excess document/complicated procedure 100 Not enough working capital loan 12.50 High rate of interest 27.50
This needs to be noted in the background of education levels of the respondents. If such highly educated respondents find the process complicated one can easily imagine the plight of the uneducated ones. Above 50% of the respondents who have
321
taken loan needed to visit the respective banks a minimum of 8 to 10 times before getting the loan and about 12% visited between 11 to 30 times. After availing the loan on an average in six months they need to visit 8 to 10 times; another 22% visited the bank between 10 to 30 times. Interestingly, 5% needed to visit the bank about 50 times of more; which amounts to about 10 visits per month. This really adds to the transaction costs to the borrowers.
Table 6.11 Number of times respondents need to visit banks.
Excess requirement of collateral is another major problem. Some banks demand three or four times’ higher value of security, personal guarantee and collaterals vis-a –vis the loan amount. A small entrepreneur does not usually possess assets and needs to refrain from borrowing. Though rate of interest have come down to some extent small borrowers usually pay around 2 to 3% higher than the prime lending rate (PLR). However, what is to note is that, all borrowers are charged considerable amount by the banks for handling their accounts in addition to the rate of interest charged. These comprise of cheque leaf charges, currier charges, charges for returned chaques, charges for bank officers visits and so on. Our estimate from our survey reveals that of such additional charges amounts to an additional rate of interest of 6%. Thus, if for example, the charged rate of interest is 13%, the actual resource goes to the bank at the rate of about 18 to 19%.
The 35 % of the firms that did not take loan from bank have used their own funds or from relatives to finance their business ventures. Maximum investment of these firms is Rs 10 lakhs. As far as reasons for not approaching banks are concerned 84% of them have noted excessive collateral requirement as crucial one. While complicated procedures have been noted by all, 15% also stated that working capital loan to be provided by the bank was so insufficient that they have decided not to approach banks.
Given this scenario naturally, maximum number of respondents voiced for hassle
free lending mechanism (table 6.12). 10% have indicated that a separate cell for SSI
lending may reduce some of the problems. 30% also feels that bank officials do not give
equal treatment to all borrowers. Economically better off or politically linked borrowers
No of times Initially go In six month 8- 10 52.5 30 11-30 12.5 22.5 31-50 0 7.5 51 Above 0 5 No applied 35 35
322
get priority. This can not only increase willful default but also induce good borrowers to
move to informal sector even when faced with high interest rate.
Table 6.12. Suggestions for lending system (banks)
Helpful changes
Percentage of
respondents Easy procedure 77.50 Separate cell 10 No corruption 30
6.7 Problem of Bad Loans: Views of the Firms from West Bengal The firms from West Bengal, maily located in the capital city Kolkata, echoed the same
views as that of their Karnataka counterpart. In our sample 100% of the respondents
have taken loan from the public sector banks. According to them intense competition in
the market is one reason for genuine default. Indeed, they have found the rate of return
to be much lower and comparatively therefore interest rate very high. However, no firm
has considered competition from outside firms such as that from China is responsible for
this. Wrong planning in the form of too much borrowing and , marketing are also
problems of genuine default (Table 6.14).
Table 6.14 Reason for default of the firm
Reason for default Percentage of firms say ‘yes’
a) Diversion of funds 23.8 b) Misunderstanding amongst partners 4.8 c) Too much competition in the market 23.8 d) Huge quantity of finished product rejected
e) Competition from china/other country
323
due to opening the market f) Large firms do not pay in time g) Too much borrowing 23.8 h) Dependence on one or two large units
i) Marketing problem 14.3 j) Any other Wastage problem 4.8 High rate of interest 4.8 Recession 4.8
As far as willful default is concerned respondents from Kolkata has not found bankers’
corruption as a possible reason. Rather respondents feel that misuse of funds and
political influence of the borrowers often lead to such willful default (table 6.15).
Table 6.15 Opinion about willful default
Opinion Yes
Misuse/Diversion of fund 38.1 b) Technical incompetence 14.3 c) Managerial problem 14.3 d) Unavoidable for small firms 9.5 e) High influence 19.0 f) Negligence 0 g) Bankers corruption 0
Source: Survey
Above 90% of the respondent firms voiced that prompt seizing of collateral is the most
effective way to reduce such intentional default. As far as the problem of the current
banking system is concerned one issue that came up again and again in the case of
Kolkata firms is the rate of interest (Table 6.16). This may be due to the fact that in
Kolkata there are large number of SSI firms that operate in lower segment of the market
where competition is intense and price realization is less. Hence the rate of interest that
the bank changes turns out to be high for them. Procedural complications remains a
problem for the borrowers of all regions.
Table 6.16 Currently any problem with the lending system Current problem Yes
324
b) Excess document 57.1 c) Complicated procedure 57.1 d) Discouraging behavior of other staff
4.8
e) Not enough working capital loan
4.8
f) High rate of interest 85.7
Source: Survey
6.8 Problem of Bad Loans: Views of the Firms from Kerala Unlike the other two groups , Kerala firms consider all possible reasons as equally
important in causing genuine default of the SSI firms. More importantly problem caused
by the large firms and competition from inside as well as aboard are highlighted during
our survey (Table 6.17).
Table 6.17 Reason for default Reason for default Respondent Percentage a) Diversion of funds 33.3 b) Misunderstanding amongst partners 22.2 c) Too much competition in the market 44.4 d) Huge quantity of finished product rejected 44.4 e) Competition from china/other country due to opening the market.
33.3
f) Large firms do not pay in time 44.4 g) Too much borrowing 33.3 h) Dependence on one or two large units 44.4 i) Marketing problem 66.7
Source: Survey As far as willful default is concerned, in all regions politically influential borrowers tend
to avoid repayment is a concern of all genuine borrowers. Corruption on the part of the
bank officials has also been highlighted in Kerala as well as in Kranataka (Table 6.18).
Table 6.18 Opinion about willful default Opinion Yes a) Miss use of fund 22.2 b) Managerial problem
55.6
c) High influence
22.2
d) Negligence
66.7
325
e) Bankers corruption
44.4
Source: Survey Prompt seizing of the collateral is the most effective way of reducing such default.
However, 11% of the respondent firms also felt that counseling by bank officials may
help reducing default.
Excessive documentation and complicated procedures appear to be the common problem
felt by all borrowers across regions (Table 6.19). However, high rate of interest is also
another major problem faced by the borrowers. It must be noted in this context that
usually these borrowers do not receive any concessional rate and pay about 2 to 3 percent
higher than the prime lending rate. More importantly a beginner often needs to pay
higher rate of interest and our analysis shows that smaller the loan size is (which often
implies smaller the size of the firm is) higher is the rate of interest.
Table 6.19 Current problems in lending system
Current Problem Percent
a) Collateral problem 22.7
b) Excess document 33.3
c) Complicated
procedure
44.4
d) High rate of
interest
66.7
Source: Survey
Suggestions to the policy makers are many and found to be similar across regions (Table
6.20).
Table 6.20 Suggestions to the policy makers Kind of Help Percent Power rate should be low 33.3 Pay in time
11.1
Provide the subsidy
11.1
326
Reduce the sale tax
44.4
Provide the employ ESI
22.2
Provide the land
33.3
Provide the power supply continuously
66.7
To train the projects guidance of the Bank manager
66.7
Provide the information about bank.
66.7
Source: Source In the infrastructure front power is the major concern. Secondly the firms across regions
want the bank manger to play a more active role rather than being just a fund provider.
As mentioned above most of these firm owners possess technical knowledge but lacks
management oriented knowledge of costing , pricing etc. This is the area where some
help from the banking sector is sought. They are also aware that some capacity building
for the bank officials may be necessary for them to provide effective support. A separate
cell in the bank for the sector may be useful in this regard. Right now the Small Industries
Development Bank is there to cater to this sector in a more involved manner. However,
SIDBI office and few and far between and firms cannot avail their help as and when
required. Thus active role needs to be played by the commercial banking sector.
6.9 Concluding Remarks During our intensive discussions with the bank officials it has been revealed that the
problem of NPA is reducing over time for the SSI sector. On the other hand it is
becoming more prevent in the personal loan segment. From our secondary data analysis
we have also seen that banks’ credit towards the SSI sector is also declining. Our
interviews with the SSI entrepreneurs reveal that non-repayment is often genuine, that
is, due to failure in the business. In the SSI segment competition is much more intense
which results in stiff price competition. While some segments do face competition from
cheap Chinese products, our respondents did not feel that globalization has made the
situation worse. The small firm owners during our survey have suggested several
327
initiatives from the policy makers which may be helpful for the sector. One of the major
problem the sector faces is the quality power supply. Such infrastructure bottlenecks need
to be handled to improve productivity.
Globalization indeed has helped some of the large firms to export and in turn increased
subcontracting business for the small firms; the growing automobile sector is a case in
point here. Exporting firms or multinationals however are quite quality conscious and
not meeting their requirements and resulting large scale rejection of products often put
small firms in the verge of bankruptcy. Non-repayment of dues by the large firms on time
also is a serious concern, which has been well recognized in the literature. These are
some of the genuine reasons for business failure and resulting default. Some of these can
be avoided through proper planning. Bank as a lender can act as a partner of an SSI unit
than as a policeman. For example, many SSI units we interviewed admitted that they
have technological knowledge but lack expertise on management aspects. Thus costing
and pricing strategies are adhoc and faulty. Neither do they have sufficient resources to
engage professionals. The firm owners’ felt that training of bank officials is necessary
for them to impart knowledge and act as a partner. In this regard State Bank of India,
stressed asset and rehabilitation cell have been advising some of the defaulters on these
aspects. More such efforts should come from the banks.
The case of willful default however, needs to be taken rather seriously. Currently, banks
do not identify any defaulter as a willful defaulter. Thus there is no difference in terms of
actions taken by the bank between a genuine and a willful defaulter. This approach
should change. Making a confidential list of willful defaulters may deter these borrowers
to engage in such activities. Right now a defaulter can indeed go to another bank for a
fresh loan and this often goes unnoticed. More vigilance and prompt action is the need of
the hour rather than avoiding the small borrowers.
328
CHAPTER 6
Problem of Loan Repayment: Views of the SSI Units
6.1 Introduction Much has been written about the problems of the SSI sector in the Indian context. The
growth of small scale industry in India, to a large extent is induced by the lack of
alternative employment opportunities and promotional policies adopted by government
(Desai, 1983)47. Due to lack of entrepreneurial attitude and proper training a large
number of them have met with untimely closure. Some of the major problems can be
identified as follows.
• Financial Constraints: Though there are a number of efforts to provide finances
to the SSI units, the most needy ones do not get proper information about
different schemes and often depend on the informal credit market for finance.
Coming up with an appropriate proposal also becomes difficult for such small
entrepreneurs.
• Access to raw materials is another problem faced by these units.
• One of the major handicaps of the small-scale sector has been the absence of
improved technology, which alone can ensure quality and higher productivity.
Technology is the most essential factor to remain competitive in a global market.
Lack of information again plays a critical role in the choice of technology.
• Marketing remains the major stumbling block for the growth of SSI sector.
Ignorance of potential markets, in particular, unfamiliarity with export activities
contributes to this problem. Poor designing and finish also often makes the
product not salable in the international market.
47 Desai. V, 1983, Problems and Prospects of Small Scale Industries in India, Himalaya
Publishing House, Bombay.
329
• As far as production methods are concerned there is often faulty planning and
inadequate appraisal of projects (Desai, 1983). Most often no proper viability
studies, technical or economic, are carried out.
Policy makers recognized these shortcomings and created a number of offices to handle
the problems. However, proper implementation has never been accomplished.
Consequently sickness remained a major problem for this sector. To understand the
problems of the sector in general and concerning credit facilities in particular in a
liberalized regime, a survey has been taken up. Though the survey has been conducted in
three states of India viz., Karnataka, Kerala and West Bengal.
6.2 Sampling Technique
It is well recognized that industry sector is not forthcoming in providing information.
This problem has noted down by many authors (see Deshpande et al, 2004)48. In this
background we are forced to adopt snowball sampling technique. Industry Association
gave contacts of the firms and requested them to cooperate with the survey. The firms
that agreed for a discussion were later interviewed using structured questionnaires that
were personally canvassed. The sample firms are from Kerala, Karnataka and West
Bengal and the sample sizes from these locations are respectively 50, 100 and 50.
6.3 Characteristics of the Sampled Firms
One of the important findings of the survey is that 35% of the manufacturing firms in our
sample are not availing loan from the institutional sources. These firms reported to
manage their investments from their own (or borrowing from relatives) resources. Out of
the rest 68% only 2% have availed loan from private banks. Thus dependency on public
sector banks remains prevalent.
48 Deshpande, Lalit et al, 2004, Liberalization and Labour: Labour Flexibility in Indian Manufacturing, Institute of Human Development, New Delhi.
330
While examining the investment in plant and machinery one observes that most of the
firms included in the survey falls under the Government of India definition of SSI. Only
about 2% of the firms have investment above Rs 1 crore. However as far as commercial
banks are concerned, for credit purposes they combine small and medium firms together
table 6.1).
Table – 6.1.Total investment
Investment Percent10000 - 100000 5 100000 - 500000 17.5 500001 - 1000000 37.5 1000001 - 2500000 15 2500001 - 5000000 12.5 5000001 - 1 crore 10 10000001 - Above 2.5 Total 100
Average turnover is between 30 lakhs to 50 lakhs. Thus these are comparatively large
sized firms within the SSI category (table 6.2).
Table 6.2. Total turnovers of the firms.
Turnover in Rs. This year Last year On an
average 300000 - 1000000 8 (20.00) 9 (22.50) 8 (20.00) 1000001 - 3000000 11 (27.50) 12 (30.00) 11 (27.50) 3000001 - 5000000 8 (20.00) 8 (20.00) 10 (25.00) 5000001 - 7000000 5 (12.50) 2 (5.00) 2 (5.00) 7000001 - 1 crore 3 (7.50) 3 (7.50) 3 (7.50) 10000001 - Above 5 (12.50) 5 (12.50) 6 (15.00)
Total 40 (100) 40 (100) 40 (100)
These firms are managed by qualifies entrepreneurs as can be seen from table 6.3. Above
50% of the firms owners are technical degree holders.
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Table – 6.3 Educational qualifications of the firm manager and Owners.
Qualification Percent 1 7th 3.39 2 SSLC 15.25 3 PUC 5.08 4 BA 1.69 5 Bcom 6.78 6 Bsc 6.78 7 MA(Eco) 1.69 8 deploma(e,p.m.t.tex,) 22.03 9 ITI 1.69
10 BE 27.12 11 Mtech 5.08 12 MBBS 1.69 13 MCA 1.69 14 Total 100
Source: primary data
Source: Field Survey
However, they have the typical characteristic of small firms with lesser number of
workers. Table 6. 4 shows that almost half of firms have less than 10 workers.
Table 6.4 Total Employment positions of the firms.
Employment Total employment
0-2 2.5 3-5 2.5 6-10 42.5 11-15 15 16-20 2.5 21-25 10 26-30 5.00 Total 100
Source: Field Survey
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These firms are mostly catering to the large firms within the state of Karnataka; as high
as 87% of these firms have subcontracting relation with a firm in Karnataka itself. Only
13% of the firms are marketing their product independently. Out of the 87% of the firms
35% are also catering to firms outside Karnataka and 2.5% are engaged in exports as well
(Table 6.5).
Table 6.5 Marketing of products.
Market
Large firm in the same state
Large firm in the another state
Export
87.5 35 2.5 Source: Filed Survey
6.4 Loan Amount and Rate of Interest: An Inverse Relation
One of the major objectives in this study is to examine the relation between the
borrowings and the rate of interest charged by the banks. It is interesting to note that
about 66% of the small borrowers (below Rs 50,000) pays interest rate of 17% or higher,
whereas the large borrowers pay comparatively lower interest rate. The category of
borrowers that borrows 40 lakhs or more pays interest rate below 12% (Table 6.6). This
may be because banks find the small borrowers comparatively more risky. Banks fixes
the rate of interest based on a number of criteria involving risky ness of a project, amount
of collateral and past records. Small borrowers may have been in a disadvantageous
position if they have started a new business with less collateral. The correlation between
loan amount and rate of interest indeed shows a negative value –0.370, which is
statistically significant at 5% level.
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Table 6.6 Percentage of firms cross tabulated with respect to Loan amount and rate of
interest.
Bank rate of interest ( in Rs.) Total
Total loan amount ( in Rs ) 9 - 10 11 - 12 13 – 14 15 - 16 17 - 24
33.33 66.67 100
upto - 50000 10 66.67 11.54
50 50 100
50000 - 200000 10 20 7.69
33.33 16.67 33.33 16.67 100
200001 - 500000 20 20 40 33.33 23.08
50 50 100
500001 - 1000000 33.33 20 7.69
50 50 100
1000001 - 2000000 20 40 15.38
20 20 20 40 100
2000001 - 4000000 33.33 10 20 40 19.23
25 75 100
4000001 - above 33.33 30 15.38462
11.54 38.46 19.23 19.23 11.54 100
Total 100 100 100 100 100 100
Source: Field Survey
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6.5 Problem of Bad Loans : Views of the Karnataka Firms Only about 3% of the firms have admitted to be defaulter in our sample. The rest of the
firms even though did not admit themselves to be defaulter have given possible reasons
for default by the small firms. It is interesting to note that about 80 % of the firms
consider excessive competition as a reason for low profit margin, which in turn affects
repayment capability adversely. But interestingly only 76% felt that opening up of the
market and competition from China is really not hampering small firms in our study area
(Table 6.7). Problem of non repayment of loan arising mainly due to non repayment of
dues by the large firms and large amount of rejection at a point of time. The former has
been well recognized by RBI and now small firms can take actions against large firms in
case of such defaults. However, as far as possible a small firm will not initiate such a
process due to its high dependence on the large firm. The latter may arise due to
increased quality concerns of the large firms. Our discussion with the bank officials also
reveals that diversion of funds is another major reason for default.
Table – 6.7 Reasons are default of the firms.
Reasons for default Yes a) Diversion of funds 53 b) Misunderstanding amongst partners. 42 c) Too much competition in the market 80 d) Huge quantity of finished product rejected 75 e) Competition from China/other country due to opening the market. 24 f) Large firms do not pay in time. 75 g) Too much borrowing. 51 h) Dependence on one or two large units 64 i) Marketing problem 53 Source: primary data Problem of willful default is accepted to be present by all firms, though expectedly no
one admitted to have adhered to such a practice. Diversion of funds has been the major
reason for willful default. This has also been stated during our interactions with the bank
officials. Entrepreneurs sometimes borrow in the name of a project but divert funds later
for purposes that can give more returns than an SSI unit.
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Secondly, when a firm is running by a manger, the problem of principal –agent and in
turn monitoring exists. This sometime may result in default of loan as collateral belongs
to the firm owner. It has been observed by 7% of the respondents that high influence such
as political influence may result in willful default. This was also found to be true during
our discussions with the bank officials. Borrowers having strong political connections
often hold the view that they cannot be punished in any manner when they default.
Another reason cited by the firm owners is pure negligence. This as well has been found
to be true during our discussions with the bank officials. Even when business is running
well some borrowers do not repay loans as they feel that bank can never take any
stringent action. A few respondents also complained of collusive agreement between
bank officials and borrowers, which lead to such mal practices (Table 6.8).
Table 6.8 Opinions for the willful defaulted of firms.
Opinion Yes a) Misuse of funds 85 b) Technically not confident 8 c) Managerial problem 14 d) Subcontracting firm’s fault 7 e) High influence 7 f) Negligence 5 g) Bankers corruption 10
Source: primary data
It has been felt by the industry circle that taking prompt action and being more vigilant
can control willful default (Table 6.9). Right now it takes on an average about 10 years to
take control of the collateral/security. In the process plant and machinery depreciates
considerably, the defaulter also get sufficient return from the diverted funds and bank
becomes the major loser. SARFEISI act which ensures that a bank need not go through a
regular process of litigations through courts , may help to some extent in this regard. In
certain cases when default may be due to sheer negligence, serious advice from the bank
officials are necessary. To do this effectively bank officials may be given more powers to
take actions.
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Table 6.9 Opinion from respondents to avoid the bank willful default
Avoid Opinion Yes
Seizing collateral 92.50 Checking 65 Self with athentification 15 Counseling 10
6.6 Problem of the Lending System
There are a number of problems faced by the borrowers from the SSI segment (Table 6.10). All the respondents considered procedures rather complicated, needing too many documentations. Number of times one needs to visit banks initially is as high as 10 to 20 times (Table 6.11).
Table 6.10. Current problems exist with the lending system.
Current Problems
Yes
Collateral problem 65 Excess document/complicated procedure 100 Not enough working capital loan 12.50 High rate of interest 27.50
This needs to be noted in the background of education levels of the respondents. If such highly educated respondents find the process complicated one can easily imagine the plight of the uneducated ones. Above 50% of the respondents who have
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taken loan needed to visit the respective banks a minimum of 8 to 10 times before getting the loan and about 12% visited between 11 to 30 times. After availing the loan on an average in six months they need to visit 8 to 10 times; another 22% visited the bank between 10 to 30 times. Interestingly, 5% needed to visit the bank about 50 times of more; which amounts to about 10 visits per month. This really adds to the transaction costs to the borrowers.
Table 6.11 Number of times respondents need to visit banks.
Excess requirement of collateral is another major problem. Some banks demand three or four times’ higher value of security, personal guarantee and collaterals vis-a –vis the loan amount. A small entrepreneur does not usually possess assets and needs to refrain from borrowing. Though rate of interest have come down to some extent small borrowers usually pay around 2 to 3% higher than the prime lending rate (PLR). However, what is to note is that, all borrowers are charged considerable amount by the banks for handling their accounts in addition to the rate of interest charged. These comprise of cheque leaf charges, currier charges, charges for returned chaques, charges for bank officers visits and so on. Our estimate from our survey reveals that of such additional charges amounts to an additional rate of interest of 6%. Thus, if for example, the charged rate of interest is 13%, the actual resource goes to the bank at the rate of about 18 to 19%.
The 35 % of the firms that did not take loan from bank have used their own funds or from relatives to finance their business ventures. Maximum investment of these firms is Rs 10 lakhs. As far as reasons for not approaching banks are concerned 84% of them have noted excessive collateral requirement as crucial one. While complicated procedures have been noted by all, 15% also stated that working capital loan to be provided by the bank was so insufficient that they have decided not to approach banks.
Given this scenario naturally, maximum number of respondents voiced for hassle
free lending mechanism (table 6.12). 10% have indicated that a separate cell for SSI
lending may reduce some of the problems. 30% also feels that bank officials do not give
equal treatment to all borrowers. Economically better off or politically linked borrowers
No of times Initially go In six month 8- 10 52.5 30 11-30 12.5 22.5 31-50 0 7.5 51 Above 0 5 No applied 35 35
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get priority. This can not only increase willful default but also induce good borrowers to
move to informal sector even when faced with high interest rate.
Table 6.12. Suggestions for lending system (banks)
Helpful changes
Percentage of
respondents Easy procedure 77.50 Separate cell 10 No corruption 30
6.7 Problem of Bad Loans: Views of the Firms from West Bengal The firms from West Bengal, maily located in the capital city Kolkata, echoed the same
views as that of their Karnataka counterpart. In our sample 100% of the respondents
have taken loan from the public sector banks. According to them intense competition in
the market is one reason for genuine default. Indeed, they have found the rate of return
to be much lower and comparatively therefore interest rate very high. However, no firm
has considered competition from outside firms such as that from China is responsible for
this. Wrong planning in the form of too much borrowing and , marketing are also
problems of genuine default (Table 6.14).
Table 6.14 Reason for default of the firm
Reason for default Percentage of firms say ‘yes’
a) Diversion of funds 23.8 b) Misunderstanding amongst partners 4.8 c) Too much competition in the market 23.8 d) Huge quantity of finished product rejected
e) Competition from china/other country
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due to opening the market f) Large firms do not pay in time g) Too much borrowing 23.8 h) Dependence on one or two large units
i) Marketing problem 14.3 j) Any other Wastage problem 4.8 High rate of interest 4.8 Recession 4.8
As far as willful default is concerned respondents from Kolkata has not found bankers’
corruption as a possible reason. Rather respondents feel that misuse of funds and
political influence of the borrowers often lead to such willful default (table 6.15).
Table 6.15 Opinion about willful default
Opinion Yes
Misuse/Diversion of fund 38.1 b) Technical incompetence 14.3 c) Managerial problem 14.3 d) Unavoidable for small firms 9.5 e) High influence 19.0 f) Negligence 0 g) Bankers corruption 0
Source: Survey
Above 90% of the respondent firms voiced that prompt seizing of collateral is the most
effective way to reduce such intentional default. As far as the problem of the current
banking system is concerned one issue that came up again and again in the case of
Kolkata firms is the rate of interest (Table 6.16). This may be due to the fact that in
Kolkata there are large number of SSI firms that operate in lower segment of the market
where competition is intense and price realization is less. Hence the rate of interest that
the bank changes turns out to be high for them. Procedural complications remains a
problem for the borrowers of all regions.
Table 6.16 Currently any problem with the lending system Current problem Yes
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b) Excess document 57.1 c) Complicated procedure 57.1 d) Discouraging behavior of other staff
4.8
e) Not enough working capital loan
4.8
f) High rate of interest 85.7
Source: Survey
6.8 Problem of Bad Loans: Views of the Firms from Kerala Unlike the other two groups , Kerala firms consider all possible reasons as equally
important in causing genuine default of the SSI firms. More importantly problem caused
by the large firms and competition from inside as well as aboard are highlighted during
our survey (Table 6.17).
Table 6.17 Reason for default Reason for default Respondent Percentage a) Diversion of funds 33.3 b) Misunderstanding amongst partners 22.2 c) Too much competition in the market 44.4 d) Huge quantity of finished product rejected 44.4 e) Competition from china/other country due to opening the market.
33.3
f) Large firms do not pay in time 44.4 g) Too much borrowing 33.3 h) Dependence on one or two large units 44.4 i) Marketing problem 66.7
Source: Survey As far as willful default is concerned, in all regions politically influential borrowers tend
to avoid repayment is a concern of all genuine borrowers. Corruption on the part of the
bank officials has also been highlighted in Kerala as well as in Kranataka (Table 6.18).
Table 6.18 Opinion about willful default Opinion Yes a) Miss use of fund 22.2 b) Managerial problem
55.6
c) High influence
22.2
d) Negligence
66.7
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e) Bankers corruption
44.4
Source: Survey Prompt seizing of the collateral is the most effective way of reducing such default.
However, 11% of the respondent firms also felt that counseling by bank officials may
help reducing default.
Excessive documentation and complicated procedures appear to be the common problem
felt by all borrowers across regions (Table 6.19). However, high rate of interest is also
another major problem faced by the borrowers. It must be noted in this context that
usually these borrowers do not receive any concessional rate and pay about 2 to 3 percent
higher than the prime lending rate. More importantly a beginner often needs to pay
higher rate of interest and our analysis shows that smaller the loan size is (which often
implies smaller the size of the firm is) higher is the rate of interest.
Table 6.19 Current problems in lending system
Current Problem Percent
a) Collateral problem 22.7
b) Excess document 33.3
c) Complicated
procedure
44.4
d) High rate of
interest
66.7
Source: Survey
Suggestions to the policy makers are many and found to be similar across regions (Table
6.20).
Table 6.20 Suggestions to the policy makers Kind of Help Percent Power rate should be low 33.3 Pay in time
11.1
Provide the subsidy
11.1
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Reduce the sale tax
44.4
Provide the employ ESI
22.2
Provide the land
33.3
Provide the power supply continuously
66.7
To train the projects guidance of the Bank manager
66.7
Provide the information about bank.
66.7
Source: Source In the infrastructure front power is the major concern. Secondly the firms across regions
want the bank manger to play a more active role rather than being just a fund provider.
As mentioned above most of these firm owners possess technical knowledge but lacks
management oriented knowledge of costing , pricing etc. This is the area where some
help from the banking sector is sought. They are also aware that some capacity building
for the bank officials may be necessary for them to provide effective support. A separate
cell in the bank for the sector may be useful in this regard. Right now the Small Industries
Development Bank is there to cater to this sector in a more involved manner. However,
SIDBI office and few and far between and firms cannot avail their help as and when
required. Thus active role needs to be played by the commercial banking sector.
6.9 Concluding Remarks During our intensive discussions with the bank officials it has been revealed that the
problem of NPA is reducing over time for the SSI sector. On the other hand it is
becoming more prevent in the personal loan segment. From our secondary data analysis
we have also seen that banks’ credit towards the SSI sector is also declining. Our
interviews with the SSI entrepreneurs reveal that non-repayment is often genuine, that
is, due to failure in the business. In the SSI segment competition is much more intense
which results in stiff price competition. While some segments do face competition from
cheap Chinese products, our respondents did not feel that globalization has made the
situation worse. The small firm owners during our survey have suggested several
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initiatives from the policy makers which may be helpful for the sector. One of the major
problem the sector faces is the quality power supply. Such infrastructure bottlenecks need
to be handled to improve productivity.
Globalization indeed has helped some of the large firms to export and in turn increased
subcontracting business for the small firms; the growing automobile sector is a case in
point here. Exporting firms or multinationals however are quite quality conscious and
not meeting their requirements and resulting large scale rejection of products often put
small firms in the verge of bankruptcy. Non-repayment of dues by the large firms on time
also is a serious concern, which has been well recognized in the literature. These are
some of the genuine reasons for business failure and resulting default. Some of these can
be avoided through proper planning. Bank as a lender can act as a partner of an SSI unit
than as a policeman. For example, many SSI units we interviewed admitted that they
have technological knowledge but lack expertise on management aspects. Thus costing
and pricing strategies are adhoc and faulty. Neither do they have sufficient resources to
engage professionals. The firm owners’ felt that training of bank officials is necessary
for them to impart knowledge and act as a partner. In this regard State Bank of India,
stressed asset and rehabilitation cell have been advising some of the defaulters on these
aspects. More such efforts should come from the banks.
The case of willful default however, needs to be taken rather seriously. Currently, banks
do not identify any defaulter as a willful defaulter. Thus there is no difference in terms of
actions taken by the bank between a genuine and a willful defaulter. This approach
should change. Making a confidential list of willful defaulters may deter these borrowers
to engage in such activities. Right now a defaulter can indeed go to another bank for a
fresh loan and this often goes unnoticed. More vigilance and prompt action is the need of
the hour rather than avoiding the small borrowers.
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CHAPTER 7
Non-performing Asset from the Perspectives of Commercial Banks
7.1 Introduction In the previous chapter we have discussed the views of one set of stakeholders viz.,
the firm owners. The survey of the small firm entrepreneurs has no doubt brought out
important issues concerning non-repayment of loan and willful default. It has also gone to
the root causes of genuine default, which need to be addressed not only by the banks but
also by the policy makers at large. To get a balanced view of any situation looking at both
sides is necessary. In this case the other stakeholder is the ‘commercial bank’ that
advances the resources mobilized by them to the small firm entrepreneurs. Our
discussions with the bank officials reveal that bad loans from the SSI sectors are indeed
in decline. This is mainly due to the pressure on the bank officials to reduce over all NPA
levels.
7.2 Approach to Information In order to understand the views of the banks, we have had discussions with several bank
officials who are in charge of the credit section. In particular, we have covered State
Bank of India, State Bank of Travancore, Syndicate Bank, Canara Bank and others. We
have also collected information about SSI accounts both pertaining to NPA as well as
non-NPA accounts from the banks. For comparison purposes we have collected
information about personal loan accounts as well. Information on individual accounts is a
confidential matter and keeping this in mind banks has not revealed to us the identity of
the borrowers. Data collection is organized as follows. To collect data from a particular
bank, permission from the head office has been sought. After acquiring permissions,
which indeed took considerable time, head offices identified certain branches and we
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have personally visited those branches for data collection49. Information on a sample of
200 accounts has been collected from different banks taken together. Another problem
has been encountered with respect to the manner in which data are preserved by the
banks. Different banks or even branches of a bank do not keep data in a uniform manner.
Further, Companies though provide data on their turnover, profit and other financial
variables to the banks a careful scrutiny revealed that these figures are not reliable. The
reliable figures one can get are on loan amount, rate of interest, value of
security/collateral, type of account (term loan or working capital loan), activity of the
unit.
7.3 An Over all Picture from State Bank of Travancore (SBT)
A disaggregated picture of NPA for small and medium enterprises at the bank level is not
generally available. However, to understand the problem better we spent considerable
time at the head office of SBT to get a sector-wise desegregation of bad loans. SBT has
about 750 branches all over India but its main concentration is in the state of Kerala with
500 branches. Table 7.1 shows the extent of bad loans across different sectors.
Table 7.1 Industry-wise classifications of sick units and NPA
Industry No. Of Units % Of total sick
units
Outstanding (Rs
crores)
% To total
outstanding
Engineering 105 6.46 6.12 8.05
Electrical 72 4.43 3.53 4.64
Textile 75 4.61 5.62 7.39
49 Since these files are confidential they cannot be taken out or photocopied; bank officials also burdened with many responsibilities do no like to entertain such additional work during office hours. This made the data collection a rather slow process. While this process is currently going on, the present chapter makes a preliminary analysis of 200 SSI accounts.
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Paper and paper
products
18 1.11 1 1.31
Rubber and rubber
products
35 2.15 5.12 6.74
Chemical, dyes,
paints
30 1.85 1.75 2.3
Metal and Metal
products
83 5.1 5.27 6.94
Vegetable oils and
vanaspati
32 1.97 0.62 0.82
Food processing
and producing
60 3.69 1.67 2.2
Plastics 3 0.18 0.8 1.05
Bricks 204 12.55 1 1.32
Coir 55 3.38 1.49 1.96
Bamboo 10 0.62 0.53 0.7
Wood 69 4.24 4.01 5.28
Readymade
garments
43 2.64 1.62 2.13
Miscellaneous 669 41.15 33.26 43.77
Total 1626 100 75.99 100
Source: State Bank of Travancore, Head Office
From the above information we observe that the miscellaneous category comprising
various different manufacturing items such as bus seat cover, glass making and so on has
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the highest share in total number of sick units which also gives NPA accounts from SME
segment. Industry-wise brick making industry appears to have large number of sick units
even though amount involved is not comparatively higher. The reason for this industry to
do poorly is the environmental norms imposed on it. Due to digging of soil the wells in
the vicinity of the brick making unit go dry. Due to this reason several restrictions have
been imposed on this segment that effected their normal functioning.
Thus the discussions on SSI segment in the previous Chapter and the record of the banks
indicate that reasons of sickness are many. NPA accounts are mainly due to prevalence of
such sick units.
7.4 Important Observations In the case of SME loan, the problem of genuine default is more common in nature
compared to willful default. A specific example will reveal the situation. In SBT main
branch office, out of a total of 130 SME accounts, 18 are NPA (i.e., 13%). Out of these
18, the concerned officials feel that maximum 5 (28%) may be willful50. The genuine
reasons of default are more or less same as the reasons for sickness of the small units.
According to the assessment of the banks a small entrepreneur does not have the
necessary skill to arrive at proper estimates of costs, prices etc. In order to acquire a loan,
they usually hire an accountant who comes up with figures in the proposal, which are
later found to be unrealistic. Further, once an account becomes somewhat irregular banks
take prompt action due to the pressure on them to reduce NPA levels. They curtail their
funding to the unit concerned, which in turn makes the situation worse for the small firm.
In the case of personal loan however, default is much higher. Personal loan is given
against salary certificates and collateral security is not necessary to avail them. Corporate
employees in the big cities often avail such loans and shift cities for new jobs. It then
becomes difficult for banks to trace them and retrieve loans. We observed several such
50 Due to the lack of concrete evidences, officially no account has been marked as willfully defaulted.
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cases during our survey. Loans to purchase durables also tend to become NPA more
often.
As far as willful default is concerned no bank usually designates a borrower as willful
defaulter. However, bank officials from their field visits can assess the cases. From our
discussions with the bank officials it is observed that one can classify them into 3
categories: (1) People with political influence (2) Negligent borrowers who feel (for some
reason or other) that they need not repay. This usually happens when they get loan under
certain government scheme (during our survey this is observed in the case of SC/ST
borrowers, women getting loan under special schemes)), (3) Borrowers who divert funds
for other purposes. Some borrowers rightly understand that they can earn higher return by
diverting their funds to other businesses such as real estate. By the time bank confiscate
their security, which usually takes considerable time (8 to 10 years), they would earn
much higher return than the lost security.
During our survey one category of willful defaulters have been found which needs to be
dealt with strictly. These borrowers usually get loan under a well-planned government
scheme introduced to help a weaker section such as women or other backward sections.
There is a well organized intermediary network prevails which in collusion with the
potential borrowers (such as poor women) prepare documents to avail loan for certain
income generating purposes as per allowed by the policy. However, rather than investing
it in the stated purpose both parties share the loan amount and in turn mis-utilize it. A
banks has even given loan in a draft form in the name of the party that was supposed to
supply the capital good to the borrower. This intermediary net-work is so effective that it
establishes collusive agreements with the machine suppliers as well.
While only a small proportion of firms are willful defaulter, from a bank’s perspective
this category assumes importance. While reducing the sickness of the SSI sector need not
necessarily fall under the purview of the banking sector, combating the problem of willful
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default is a concern of the banking sector. Observing this RBI has of late came up with a
number of measures to fight this problem.
7.4 Measures to Contain Wilful Default51 In order to disseminate information about wilful default, under the directive of RBI a
scheme was framed under which the banks and notified All India Financial Institutions
were required to submit to RBI the details of the willful defaulters. Wilful default broadly
covered the following:
a) Deliberate non-payment of the dues despite adequate cash flow and good net
worth;
b) Siphoning off of funds to the detriment of the defaulting unit;
c) Assets financed either not been purchased or been sold and proceeds have
misutilised;
d) Misrepresentation / falsification of records;
e) Disposal / removal of securities without bank's knowledge;
f) Fraudulent transactions by the borrower.
The above scheme came into force with effect from 1st April, 1999. Accordingly, banks
and FIs started reporting certain cases of wilful defaults, which occurred or were detected
after 31st March, 1999 on a quarterly basis. It covered all non-performing borrowers
accounts with outstanding aggregating Rs.25 lakhs and above identified as willful default
by a Committee of higher functionaries headed by the Executive Director and consisting
of two GMs/DGMs. Banks/FIs were advised that they should examine all cases of wilful
defaults of Rs 1.00 crore and above for filing of suits and also consider criminal action
51 Master Circular on Willful Defaulter, RBI/2006-07/35 DBOD No.DL.BC.19 /20.16.003/2006-07
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wherever instances of cheating/fraud by the defaulting borrowers were detected. In case
of consortium/multiple lending, banks and FIs were advised that they report wilful
defaults to other participating/financing banks also. Cases of wilful defaults at overseas
branches were required be reported if such disclosure is permitted under the laws of the
host country.
The above scheme was in addition to the Scheme of ‘Disclosure of Information on
Defaulting Borrowers of banks and FIs’ introduced in April 1994; vide RBI Circular
DBOD.No.BC/CIS/47/20.16.002/94 dated 23 April 1994.
Guidelines issued on wilful defaulters (May 30, 2002)
Considering the concerns expressed over the persistence of wilful default in the financial
system in the 8th Report of the Parliament's Standing Committee on Finance on Financial
Institutions, the Reserve Bank of India, in consultation with the Government of India,
constituted in May 2001 a Working Group on Wilful Defaulters (WGWD) under the
Chairmanship of Shri S. S. Kohli, the then Chairman of the Indian Banks' Association,
for examining some of the recommendations of the Committee. The Group submitted its
report in November 2001. An In-House Working Group constituted by the Reserve Bank
further examined the recommendations of the WGWD. Accordingly, the banks/FIs were
advised on May 30, 2002 for implementation, with immediate effect.
Definition of wilful default
As per the definition of RBI a "wilful default" would be deemed to have occurred
if any of the following events is noted: -
(a) The unit has defaulted in meeting its payment / repayment obligations to the
lender even when it has the capacity to honour the said obligations.
(b) The unit has defaulted in meeting its payment / repayment obligations to the
lender and has not utilised the finance from the lender for the specific purposes
for which finance was availed of but has diverted the funds for other purposes.
(c) The unit has defaulted in meeting its payment / repayment obligations to the
lender and has siphoned off the funds so that the funds have not been utilised for
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the specific purpose for which finance was availed of, nor are the funds available
with the unit in the form of other assets."
Diversion and siphoning of funds
The terms “diversion of funds” and “siphoning of funds” should construe to mean the
following:-
Diversion of funds, would be construed to include any one of the undernoted
occurrences:
(a) utilisation of short-term working capital funds for long-term purposes not in
conformity with the terms of sanction;
(b) deploying borrowed funds for purposes / activities or creation of assets other than
those for which the loan was sanctioned;
(c) transferring funds to the subsidiaries / Group companies or other corporates by
whatever modalities;
(d) routing of funds through any bank other than the lender bank or members of
consortium without prior permission of the lender;
(e) investment in other companies by way of acquiring equities / debt instruments without
approval of lenders;
(f) Shortfall in deployment of funds vis-à-vis the amounts disbursed / drawn and the
difference not being accounted for.
Siphoning of funds should be construed to occur if any funds borrowed from banks / FIs
are utilised for purposes un-related to the operations of the borrower, to the detriment of
the financial health of the entity or of the lender. The decision as to whether a particular
instance amounts to siphoning of funds would have to be a judgement of the lenders
based on objective facts and circumstances of the case.
352
The identification of the wilful default should be made keeping in view the track record
of the borrowers and should not be decided on the basis of isolated
transactions/incidents. The default to be categorised as wilful must be intentional,
deliberate and calculated.
End-use of Funds
In cases of project financing, the banks / FIs seek to ensure end-use of funds by, inter
alia, obtaining certification from the Chartered Accountants for the purpose. In case of
short-term corporate / clean loans, such an approach ought to be supplemented by 'due
diligence' on the part of lenders themselves, and to the extent possible, such loans should
be limited to only those borrowers whose integrity and reliability are above board. The
banks and FIs, therefore, should not depend entirely on the certificates issued by the
Chartered Accountants but strengthen their internal controls and the credit risk
management system to enhance the quality of their loan portfolio. Needless to say,
ensuring end-use of funds by the banks and the FIs should form a part of their loan policy
document for which appropriate measures should be put in place. The following are some
of the illustrative measures that could be taken by the lenders for monitoring and ensuring
end-use of funds:
(a) Meaningful scrutiny of quarterly progress reports / operating statements /
balance sheets of the borrowers;
(b) Regular inspection of borrowers’ assets charged to the lenders as security;
(c) Periodical scrutiny of borrowers’ books of accounts and the no-lien accounts
maintained with other banks;
(d) Periodical visits to the assisted units;
(e) System of periodical stock audit, in case of working capital finance;
353
(f) Periodical comprehensive management audit of the ‘Credit’ function of the
lenders, so as to identify the systemic-weaknesses in the credit-
administration.
(It may be kept in mind that this list of measures is only illustrative and by no means
exhaustive.)
Penal measures
In order to prevent the access to the capital markets by the wilful defaulters, a copy of the
list of wilful defaulters (non-suit filed accounts) and (suit-filed accounts) are forwarded
to SEBI by RBI and Credit Information Bureau (India) Ltd. (CIBIL) respectively.
The following measures initiated by the banks and FIs against the wilful defaulters
identified as per the definition indicated at paragraph 2.1 above:
a) No additional facilities should be granted by any bank / FI to the listed wilful
defaulters. In addition, the entrepreneurs / promoters of companies where banks /
FIs have identified siphoning / diversion of funds, misrepresentation, falsification
of accounts and fraudulent transactions should be debarred from institutional
finance from the scheduled commercial banks, Development Financial
Institutions, Government owned NBFCs, investment institutions etc. for floating
new ventures for a period of 5 years from the date the name of the wilful defaulter
is published in the list of wilful defaulters by the RBI.
b) The legal process, wherever warranted, against the borrowers / guarantors and
foreclosure of recovery of dues should be initiated expeditiously. The lenders may
initiate criminal proceedings against wilful defaulters, wherever necessary.
c) Wherever possible, the banks and FIs should adopt a proactive approach for a
change of management of the wilfully defaulting borrower unit.
d) A covenant in the loan agreements, with the companies in which the banks /
notified FIs have significant stake, should be incorporated by the banks / FIs to
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the effect that the borrowing company should not induct a person who is a
promoter or director on the Board of a company which has been identified as a
wilful defaulter as per the definition at paragraph 2.1 above and that in case, such
a person is found to be on the Board of the borrower company, it would take
expeditious and effective steps for removal of the person from its Board.
It would be imperative on the part of the banks and FIs to put in place a transparent
mechanism for the entire process so that the penal provisions are not misused and the
scope of such discretionary powers are kept to the barest minimum. It should also be
ensured that a solitary or isolated instance is not made the basis for imposing the penal
action.
Guarantees furnished by group companies
While dealing with wilful default of a single borrowing company in a Group, the banks /
FIs should consider the track record of the individual company, with reference to its
repayment performance to its lenders. However, in cases where a letter of comfort and /
or the guarantees furnished by the companies within the Group on behalf of the wilfully
defaulting units are not honoured when invoked by the banks / FIs, such Group
companies should also be reckoned as wilful defaulters.
Role of auditors
In case any falsification of accounts on the part of the borrowers is observed by the banks
/ FIs, and if it is observed that the auditors were negligent or deficient in conducting the
audit, they should lodge a formal complaint against the auditors of the borrowers with the
Institute of Chartered Accountants of India (ICAI) to enable the ICAI to examine and fix
accountability of the auditors.
With a view to monitoring the end-use of funds, if the lenders desire a specific
certification from the borrowers’ auditors regarding diversion / siphoning of funds by the
borrower, the lender should award a separate mandate to the auditors for the purpose. To
facilitate such certification by the auditors the banks and FIs will also need to ensure that
355
appropriate covenants in the loan agreements are incorporated to enable award of such a
mandate by the lenders to the borrowers / auditors.
Role of Internal Audit / Inspection.
The aspect of diversion of funds by the borrowers should be adequately looked into
while conducting internal audit/inspection of their offices/branches and periodical
reviews on cases of wilful defaults should be submitted to the Audit Committee of the
bank.
Reporting to RBI / CIBIL
Bank/FIs should submit the list of suit-filed accounts of wilful defaulters of Rs.25 lakh
and above as at end-March, June, September and December every year only to Credit
Information Bureau (India) Ltd. (CIBIL) from the quarter ended on March 31, 2003.
Banks/FIs should, however, submit the quarterly list of wilful defaulters where suits have
not been filed only to RBI.
Grievances Redressal Mechanism
Banks/FIs should take the following measures in identifying and reporting instances of
wilful default:
(i) With a view to imparting more objectivity in identifying cases of wilful default,
decisions to classify the borrower as wilful defaulter should be entrusted to a Committee
of higher functionaries headed by the Executive Director and consisting of two
GMs/DGMs as decided by the Board of the concerned bank/FI.
(ii) The decision taken on classification of wilful defaulters should be well documented
and supported by requisite evidence. The decision should clearly spell out the reasons for
which the borrower has been declared as wilful defaulter vis-à-vis RBI guidelines.
(iii) The borrower should thereafter be suitably advised about the proposal to classify him
as wilful defaulter along with the reasons therefor. The concerned borrower should be
356
provided reasonable time (say 15 days) for making representation against such decision,
if he so desires, to a Committee headed by the Chairman and Managing Director.
(iv) A final declaration as ‘wilful defaulter’ should be made according to the view of the
Committee on the representation and the borrower should be suitably advised.
Criminal Action against Wilful Defaulters: J.P.C. Recommendations
Reserve Bank examined, the issues relating to checking wilful defaults in consultation
with the Standing Technical Advisory Committee on Financial Regulation in the context
of the following recommendations of the JPC and in particular, on the need for initiating
criminal action against concerned borrowers, viz.
a. It is essential that offences of breach of trust or cheating construed to have been
committed in the case of loans should be clearly defined under the existing
statutes governing the banks, providing for criminal action in all cases where the
borrowers divert the funds with malafide intentions.
b. It is essential that banks closely monitor the end-use of funds and obtain
certificates from the borrowers certifying that the funds have been used for the
purpose for which these were obtained.
c. Wrong certification should attract criminal action against the borrower.
Some of these stringent norms if implemented properly can reduce willful default. The
literature on micro finance shows that peer pressure can indeed impact repayment
behavior of a borrower without having to go through legal measures. Given the success
of the micro-finance institutions banks are also now trying to have such innovative
schemes.
7.5 Peer Pressure as a Mechanism to Reduce NPA
357
Banks are now having tie ups with the small scale industries associations and with the
guarantee of the association bank is providing small borrowers loans without collateral.
In case of non-repayment it is the industry association that is going to put the pressure on
the borrower. To avail this facility the borrower has to be a member of the industry
association. In the process both association as well as a bank gain. Such tie ups are going
on between SBT and Kerala SSI association, Canara Bank and Karnataka SSI association
and so on. If these models become successful they can ensure loans to relatively small
borrower with small or no collateral.
Thus we observe that collateral plays an important role in determining the default
tendency, especially the willful default tendency. However, as mentioned above banks
do not usually designate any borrower as willful defaulter. Our survey of banks lending
shows no defaulters , even though in our personal discussions officials raise their
suspicions about some defaulters being willful. Given this problem it is not possible to
empirically test this hypothesis. In this background we tried to formulate a theoretical
model that captures the relation between collateral and willful default.
7.6 Bayesian Game of Willful Default and Collateral From our survey it has been observe that personal loans and loans under various
government schemes face much higher rate of default than the SSI loan provided with
proper collateral. This provide a basis to infer that lack of collateral security do provide
inducement for willful default. To formalize the strategic behaviour of the borrowers we
therefore intend to formulate a theoretical model to examine the relation between the
value of collateral security , time to retrieve the same and the occurrence of willful
default.
The Model
358
The model comprises 3 players viz., ‘nature’ , ‘borrower’ and the ‘lender’ , the last player
in this case is the commercial bank. The important point to note here is that a bank does
not have information about whether a borrower is potentially a willful defaulter or not.
Thus it is appropriate to assume that nature chooses at random from two types of
borrowers, viz., ‘willful defaulters’ (W) and ‘non-defaulters (NWF)’. In a small scale
sector a genuine defaulter is usually the one with lack of sufficient knowledge about
production and marketing management and hence do not follow any strategic move for
default. Therefore in our analysis we do not bring in this group of defaulters separately.
Since default is empirically seen to have link with collateral value we consider two types
of moves followed by the borrowers. A borrower firm may be ready to either provide a
large or a small collateral. Depending on willingness to pay large (L) or small (S)
collateral, a bank may decide either to disburse fund to the borrower or not to disburse.
The game tree can be represented as follows:
Fig. 7.1
Nature
Willful
Large Col
Small col
L
Firm
Bank Disburse loan
Not disburse
D
ND
D
Nature
Willful
Large Col
Small col
L
Firm
Bank Disburse loan
Not disburse
D
ND
D
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Next to derive the pay-offs we make the following assumptions:
Loan Period = τ Time to retrieve collateral in case of default= T Loan amount = X Per period return from investing in the project= π Discount factor = β We further assume that by diverting the loan amount a willful defaulter earns a higher
return π́ , per period.
Pay-off for the firm A firm willfully deciding to default faces the following gains and losses. Through willful default a firm may earn expected higher return π́ for life time. But it loses collateral ‘C’ after T periods where C can be either large or small Total expected discounted pay-off (for say, large C) for the firm could be written as
{π́ / ( 1-β ) }- Clarge βT
On the other hand if the firm decides not to default its expected discounted pay-off is
π / ( 1-β )- X ( 1 + r lending) τ β τ Pay-off for the bank In case of default, the bank loses the principal as well as interest but receives the
collateral after T periods.
-X ( 1 + r lending) τ βτ + Ci βT , i = large, small
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However, in the no default case bank earns the principal with interest after τ period.
X ( 1 + r lending) τ β τ
On the other hand if the bank decides to give no loan it can invest the money in say,
another bank at the market deposit rate . Present value of X will then be X only if we
assume β to be the same as the deposit rate.
Suppose now that the bank can fix a collateral large enough such that pay-off to the
defaulter is negative:
{π́ / ( 1-β ) }- Clarge βT< 0………………
Equilibrium Strategies As mentioned above a bank does not have information about whether a borrower is
potentially a willful defaulter or not. Suppose the bank has prior belief that the
probability of a borrower is a Willful defaulter = q. Suppose it follows the following
strategy , ‘disburse loan only if collateral is large’.
If we assume that a borrower’s strategy is to give a large collateral if he is not a willful
defaulter and small otherwise, will this strategy profile constitute a Bayesian Nash
equilibrium ?
Posterior Probability
Given these beliefs the Posterior Probability as conceived by the bank that the borrower
is a willful defaulter when large collateral can be computed as
Prob ( WF Large) = { Prob ( L WF). Prob (WF) }/ { Prob ( L WF). Prob (WF) + Prob ( L NWF). Prob (NWF)}
361
= 0. q / 0. q + 1. (1-q)=0 Posterior Probability as conceived by the bank that the borrower is a willful defaulter when small collateral is given: Prob ( WF Small) = { Prob ( S WF). Prob (WF) }/ { Prob ( S WF). Prob (WF) + Prob ( S NWF). Prob (NWF)} = 1. q / 1. q + 1. (1-q) = q It can be easily computed that Prob ( NWF Large) = 1 Under condition (*) a willful defaulter will not come forward to take a loan.
Thus given the posterior beliefs, the stated strategies can be sustained as equilibrium
strategies. In the process bank can ensure a separating equilibrium whereby it separates
the willful defaulters from the non willful ones.
However, many genuine borrowers do not possess such high collateral and are forced to
stay away from the formal lending system. Thus, the important question that arises is can
the borrowers providing small collateral be sustained as an equilibrium? More precisely
we consider the following strategy profile:
•Firm: give small collateral •Bank : give loan whether collateral is small or large. Can this strategy profile constitute a Bayesian Nash equilibrium? Posterior Probability as conceived by the bank that the borrower is a willful defaulter when small collateral is given:
Prob ( WF Small) = { Prob ( S WF). Prob (WF) }/ { Prob ( S WF). Prob (WF) + Prob ( S NWF). Prob (NWF)} = 1. q / 1. q + 1. (1-q) = q
362
It is optimal for bank to play this strategy iff q(-X ( 1 + r lending) τ βτ + Csmall βT)+ (1-q)X ( 1 + r lending) τ β τ > X
Let X = X ( 1 + r lending) τ
Then we have condition for giving loan with small collateral, (1-2q) X > X - qCsmall βT………….(**) We next try to represent condition (**) in terms of a simple diagram. Here we measure q in the X-axis and pay-offs in the Y-axis. Line AB represents LHS of
(**) and line XD represents the RHS. The intersection of the two lines and the resulting q
value shows that if probability of willful default is low enough , that is, it lies within OF,
then small collateral may be acceptable for banks; otherwise bank will demand large
collateral. However, if through proper regulatory changes , time to retrieve collateral can
be reduced then the line representing the RHS of (**) shifts down to XE. This in turn
increases the tolerable limit for q for a bank to allow small collateral. Thus if the policy
makers feel that due to the insistence on large collateral many genuine borrowers are
unable to borrow then reducing the time T for collateral retrieval through legal reform is
essential. During our survey we have not seen a single record where a court case has been
decreed and a bank could actually took possession of the collateral.
363
Fig. 7.2
X
P ay-o ffs
q
- X
R H S
L H S
q 1
X
1
X - C S β T
T fa lls
S m aller co lla tera l can b e accep ted
A
B
D
E q F G
O
364
Thus naturally an optimal strategy for the bank to insist on large collateral. The policy
often followed in such circumstances is to compel the banks to lend without or minimal
collateral. Well intended loans under various government schemes fall under this
category. Banks then have to lower the collateral level and without improvement in legal
mechanism to retrieve collateral these lending becomes NPA for the bank. This has
indeed been observed during our survey.
In order to understand the problem of default empirically we have used the data collected
from the selected bank branches (see Section 7.2) in a probit model.
7.6 A Micro Level Analysis of Default Analysis of the determinants of NPA carried out in Chapter 4 is based on aggregative
data from each bank. Having collected micro level data on the SSI and other loan
accounts including personal loans, from selected banks we next tried to look at the
‘determinants’ issue more closely for such small loans in urban areas52. From the banks
we have collected information on both NPA and non-NPA accounts and several other
indicators. However, as mentioned above much of this information is not uniform across
banks and some figures are not reliable. In this background as a first step we estimated a
probit model to look at the factors that determine whether an account would be NPA or
not. Thus NPA is a binary variable and it is our dependent variables. Independent
variables are loan amount, rate of interest and year of establishment of the company.
These are the three variables for which we have consistent information from different
banks. The model under consideration is as follows:
NPAi = α0 +α1 (real_col) i +α2 (rate of interest) i + α3 (year of establishment) i + α4 (loan
under any scheme) i +α5 (loan for SME) i + εi
‘i’ stands for the ith account/ unit.
NPA = 0, if the account is an NPA account
= 1, otherwise.
52 Given the confidentiality of this data we have not presented descriptive statistics etc.
365
Similarly, SME and Scheme are dummy variables, showing whether the loan is under
SME account and given under any Government scheme.
The results of the probit estimates are presented in Table 7.2 Table 7.2 Probit estimates Number of obs = 130 LR chi2(5) = 19.37 Prob > chi2 = 0.0016 Log likelihood = -79.172511 Pseudo R2 = 0.1090 Npa_Non Npa Coefficient Std. Err. Z P>[Z] Real_collateral 5.02 1.89 2.66 0.008 R_o_I (rate of interest)
-.2353688 .0769064 -3.06 0.002
Scheme .248585 .4582996 0.54 0.588 Year .007379 .0165627 0.45 0.656 Sme_non SME -.058827 .3077299 -0.19 0.848 Constant -12.04599 33.37519 -0.36 0.718 F(5) 19.37 Number of Observation 130 Prob>F 0.0016 R Square 0.1090 Probit Estimates Log Liklihood = -79.172511
The probit analysis most importantly shows the role of collateral in case of default. As
the value of collateral increases , the loan has a greater probability of becoming non-npa.
The above results also indicate that in case of the SSI sector interest rate has a positive
relation with NPA. Probability of an account being NPA increases by .07 if interest rate
increases by 1 unit. Thus we get an indication that Stiglitz and Weiss (1981) hypothesis is
holding here. The other variables are turned out to be insignificant. But loan amount and
NPA has negative correlation. An increase in loan size reduces the probability of an
account being NPA in the SSI segment. Other variables are not found to be significant. It
is important to note here that we have experimented with a number of variables such as
loan amount , interaction effects etc. Loan amount and collateral values are found to be
366
correlated and therefore only one of them viz., collateral has been chosen. All othe
variables and interaction effects are not found to be significant.
Out of these default accounts there is no account designated as willful default. However,
the bank officials are sanguine about certain accounts being genuine default due to
business failure or other contingencies while they are doubtful about a few other
accounts. Accordingly, we have categorized the NPA accounts as genuine and not so
genuine and tried to identify the determinants through a probit model (Table 7.3). It must
be noted however, that none of these doubtful accounts are legally proved to be willful.
Within the category of genuine default, given the reason of default, we have categorize
them into two groups: genuine and not so genuine53.
Genuine_noti = α0 +α1 (real_col) i +α2 (rate of interest) i + α3 (loan under any scheme) i
+α4 (loan for SME) i + εi
The variable Genuine_noti = 0, if genuine default
= 1, if not appear to be very genuine
Table 7.3
Genuine_not Coefficient Standard Error
Z P>[Z]
Real collateral value -5.21 3.59 -1.45 0.147 Rate of Interest .1547729 .0777499 1.99 0.047 SME or Non -SME -2.031614 .5602071 -3.63 0.000 Scheme .4600827 .8556752 0.54 0.591 Constant -.4055754 1.108938 -0.37 0.715 F(4) 40.33 Number of Observation 78 Prob>F 0.000 Pseudo R Square 0.3775 Log Likelihood -33.255995
53 This classification is entirely of the researcher.
367
Two interesting results are derived from this part of the analysis. As rate of interest
increases chances of a loan being defaulted under willful category rises. Thus possibly
people do take more risky ventures intentionally with high rate of interest. Secondly, loan
from the SME accounts tend to be more of genuine default type while loan from the
personal account (with low or no collateral) tend to be of willful default category.
People tend to default somewhat intentionally when loan is given under a Government
scheme. However, possibly because such accounts are very few, this variable is not
significant.
7.7 Conclusion
In this chapter we tried to look at the problem of NPA concerning the SSI sector as well
as of the other small-size accounts such as personal loans, from the point of view of a
bank. Our discussions with the bank officials reveal that NPA concerning the SSI sector
is reducing rapidly while from the personal loan segment it is increasing. Banks are now
having tie ups with the industry associations through which they intends to create a peer
pressure on the borrowers and also try to reduce willful default. An analysis of the NPA
accounts shows that interest rate has a positive impact on the probability of an account
being NPA. In addition, the value of collateral plays a significant role in determining
whether an account will be NPA or not and more importantly whether default will be
intentional. The latter can be seen from the fact that personal loans (usually without
collateral) have higher probability of being willful default account.
368
CHAPTER 8
Concluding Remarks
The financial system of any country consists of specialised and non-specialised
financial institutions, organized and unorganized financial markets, financial instruments
and services, which facilitate transfer of funds. Commercial banks form a major part of
financial system in any country in general and in the developing nations in particular.
This is mainly due to the fact that the other financial markets are not usually well
developed. In India, financial system has been synonymous with banking sector. The
importance of banking system in India can be noted by the fact that the aggregate
deposits stood at 55 percent of GDP and bank credit to government and commercial
sector stood at 26 percent and 33 percent of GDP respectively in 2004-05.
Over time however, the Indian financial system has undergone significant
changes in terms of size, diversity, sophistication and innovation. The financial sector
reforms in India began as early as 1985 itself with the implementation of Chakravarti
committee report. But the real momentum was given to it in 1992 with the
implementation of recommendations of the Committee on Financial System (CFS)
(Narasimham, 1991). In the post reform period India has a comparatively well-developed
financial system than before, with a variety of financial institutions, markets and
instruments.
Due to the social banking motto of the Government, the efficiency of operation
and profit earning capabilities are not considered as important criteria for evaluation of
the performance of a bank. Consequently, the problem of non-performing asset (NPA)
was not an issue of serious concern in India in the post nationalization (of banks) period.
However, with the recent financial sector liberalization drive, this issue has been taken up
seriously by introducing various prudential norms relating to income recognition, asset
classification, provisioning for bad assets and assigning risks to various kinds of assets of
a bank. While the Reserve Bank of India (RBI) as well as the commercial banks have
begun to pay considerable attention to the NPA problem, there are only a limited number
369
of rigorous studies in the Indian context that look at this issue in some detail. The current
research project has been taken up in this background.
Given that the NPA has strong implications on the health of the commercial banks and also the economy, it is essential to take measures to reduce the NPA levels in the commercial banks. This calls for identification of the factors that can cause an asset to become NPA. In order to understand this, the study has looked at the data of about 94 banks from 1997-2005 in a panel data framework. We examine whether proportion of rural branches, size of a bank, state of the economy (measured by GDP) , rate of interest and other related variables have impact on NPA levels. It has been found that rural branches indeed contribute to creation of NPA in general. However, in the case of NPA arising from the SSI sector rural branches do not have a negative impact. Rate of interest on the other hand does not seem to have a significant impact on the non-repayment of loans. Some of these results need to be examined carefully once again, in order to arrive at appropriate interpretations.
We have also looked at the efficiency of the commercial banks in generation of
profit. We observe from our analysis that profit efficiency of the public sector banks have
improved over the period (1997-2005) while efficiency of the private and foreign banks
are more or less stagnant. Results show that while rural branches do not contribute to
inefficiency, NPA levels do contribute to profit efficiency (which is something to be
expected).
We have also presented some results from our survey of the small firms and the
banks that deal with such firms. Inadequate loan amount is considered to be a major
reason for default by the firms. Both banks and small firms are aware of the presence of
willful defaulters. Banks consider diversion of funds to more lucrative activities as the
prime cause of willful default. Our discussions with the bank officials reveal that as the
legal process takes a long time (at least 10 years) for confiscating the collateral, it is to
the advantage of the borrower to invest money in other lucrative business such as real
estate and earn higher profit. Our micro level analysis of data collected from the bank on
NPA accounts from the SSI units however shows that interest has positive contribution
towards creation of NPA accounts; more precisely, an increase in the rate of interest
raises the probability of a default.
370
For a comparison purpose we have also looked at the problem of NPA arising out of
personal & other categories of loans such as vehicle or home loan. The problem of NPA
given under personal loan seems a more serious problem than that of the SME sector.
Intentional default is also comparatively more common in the segment mainly due to the
fact that there is no security involved. However, the pressure tactics adopted by the public
sector banks appear to have resulted positive impacts.
186
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