analysis on rbi growth

ANALYSIS ON RBI STUNNING GROWTH AFTER 1991

1.Post-Reform
Period: A State Level Analysis
Biswa Swarup Misra
This paper examines whether allocative efficiency of the Indian Banking system
has improved after the introduction of financial sector reforms in the early 1990s.
Allocative efficiency has been studied for twenty three States of India. To get a
comparative perspective, allocative efficiency has been estimated for two periods
1981-1992 and 1993- 2001; broadly corresponding to the pre financial sector
reforms and the post reforms periods, respectively. The analysis carried under
panel cointegration framework reveals that overall allocative efficiency of the
banking system has almost doubled in the post reform period. This goes to suggest
the success of reforms in improving allocative efficiency of the banking system in
India. Allocative efficiency at the State and sectoral level has
also been estimated to get a deeper insight. While allocative efficiency of Banks'
funds deployed in the services sector has improved that in the agriculture and
industry has deteriorated in the post reform period for the majority of the States.
The study finds improvement in the overall allocative efficiency in the post reform
period for the majority of the States. Further, the improved allocative efficiency is
more marked for the services sector than for industry across the States.

1.1 Introduction

TY.B.F.M Page 1


Enduring growth, in the context of a developing economy like India invariably
requires that the economy be put to a trajectory of higher savings and ensuring,
further, that the realised savings are chanelised into productive investment. In this
scheme of growth, the banking system has a dual role to play. The banking system
acts both as a mobiliser of savings as well as an allocator of credit for production
and investment. Effectiveness of the banking sector ’s contribution to the economic
growth and development is broadly determined by its efficiency in the allocation of
the mobilised savings amongst competing projects. Financial sector reforms were
initiated in India in 1992-93 to promote a diversified, efficient and competitive
financial system with the prime objective of improving the allocative efficiency of
available resources. Banking sector being the dominant segment in India's
financial system, a number of measures specific to the banking system were
initiated to improve its allocative efficiency. Freedom to price their products along
commercial considerations, relaxation in various balance sheet restrictions in the
form of statutory pre-emptions, exposing the banking sector to an increased
competition by allowing entry of new private sector banks and the introduction of
prudential norms relating to income recognition, asset classification and capital
adequacy were some of the ingredients of the banking sector reforms. Improved
allocative efficiency was sought to be achieved through operational flexibility,
improved financial viability and institutional strengthening. The early initiatives in
the banking reforms were geared towards removing the functional and operational
constraints impinging upon bank operations, and subsequently, providing them
with greater operational autonomy to take decision based on commercial
considerations. With gradual relaxation of administered controls, banks and
financial institutions were expected to evolve as truly commercial entities. More
importantly, the operation of banks under free interplay of market forces in a
deregulated atmosphere was expected to lead to increased allocative efficiency of
scarce resources among competing sources of demand. Banking sector reforms
have been in vogue for more than a decade in India. In this context, it would be
appropriate to study whether the various reform measures have helped in
improving the allocative efficiency of the banking system.This study seeks to
TY.B.F.M Page 2


enquire whether the financial sector reforms in general, and banking sector
reforms in particular had any beneficial impact on the allocative efficiency of the
banking system. To get a comparative perspective, the allocative efficiency of the
banking system in the post banking sector reforms period has been compared and
contrasted with that of the pre-reform period. Allocative efficiency is measured for
the twenty-three States of India, individually and as well for all the States taken
together. In addition to the scenario at the aggregate level, the allocative efficiency
in the sectoral context has also been studied to get a deeper insight. Therest of the
study is schematised as follows. Section I discusses the manner in which allocative
efficiency has been construed in this study. Section II reviews the literature on
allocative efficiency. Some of the stylized facts regarding the credit deployment
pattern are discussed in Section III. The data and the empirical framework have
been discussed in Section IV. The econometric findings are discussed in Section V.
Finally, Section VI presents some concluding observations.
Section I
Interpreting Allocative Efficiency
Efficiency of a financial system is generally described through four broad
nomenclatures i.e., information arbitrage efficiency, fundamental valuation
efficiency, full insurance efficiency and functional efficiency. The ensuing
discussion in this paper would centre around the concepts of functional or
allocative efficiency. Allocative efficiency can be judged either directly by
monitoring some proxy of allocative efficiency or indirectly by estimating the
contribution of a financial variable to economic growth. As far as direct measures
are concerned, the interest rate structure, cost of intermediation and net interest
margin (RBI, 2002a) as measures of bank efficiency are the oftenly-used criterions
to evaluate the allocative efficiency of the banking system. Allocative efficiency,
however, can also be inferred indirectly by studying whether a bank's resources are
allocated to most productive uses or not. Most productive use, in turn, can be
defined in terms of the economic rate of return (ERR) of a project financed by the
banking system. Allocative efficiency would mean that projects with very high ERR
are being financed by the banks. It would imply that the funds of the banking
system are so deployed as to maximise the rate of return (ERR) of the projects
financed by them. The ERR o f individual bank financed projects, however, is
difficult to quantify in practice. Akin to the interpretation of allocative efficiency of
a bank's resources in terms of the ERR for individual projects, one can

TY.B.F.M Page 3


conceptualise the allocative efficiency of the entire banking system. In an
aggregated sense, allocative efficiency would imply that maximum output is
obtained from the deployment of banking system's resources. The concept of
'maximum output', however, is rather vague. As such, studying changes in
allocative efficiency reflected in changes in output from a given pool of financial
resources under two different time periods or circumstances is more
comprehendible than the concept of allocative efficiency per se. Allocative efficiency
of an individual bank involves some sort of constrained optimisation. When studied
in the cross section dimension, efficiency measurement generally involves use of
nonparametric frontier methodology (English, Grosskopfet al., 1993). In the panel
context, however, the frontier approach does not capture the panel nature of the
data and treats each observation as a separate unit. So it is like a pooled
regression, unlike random/ fixed effects models. There are recent developments to
overcomethis problem, but it is still in a nascent stage. Consequently in a panel
context, following RBI (2002a) allocative efficiency has been approximated by the
elasticity of output with respect to credit in this study

Section II
Review of Literature
There has been a revival of finance and economic development linkage by the
endogenous growth theory over the past decade. In the endogenous growth theory
framework, bank finance has a scope to influence economic growth by either
increasing the productivity of capital, lowering the intermediation cost, or
augmenting the savings rate. The role of financial institutions is to collect and
analyse information so as to channel investible funds into investment activities
that yield the highest returns [Greenwood and Jovanovic (1990)]. Though in a pure
neo-classical framework, the financial system is irrelevant to economic growth, in
practice, an efficient financial system can simultaneously lower the cost of external
borrowing, raise the return to savers, and ensure that savings are allocated in
priority to projects that promise the highest returns ; all of which have the
potential for improving growth rates (RBI, 2001a). Commercial banks are the main
conduit for resource allocation in a bank dominated financial system like India.
Commercial banks generally provide the working capital needs of business. There
is no strict boundary of division, however, in the us age of the funds;once
disbursed by financial institutions. Once allocated, a part ofthe bank funds may
very well be put towards building up fixedcapital. This is because, a business
enterprise would be encouraged to undertake fixed capital formation, once it is
assured of working capital needs. Though in India there have been institutions
created specifically to meet the long term investment needs of business enterprise,
the pervasive character of the scheduled commercialbanks had a greater role to
play in reaching to a wider mass of people through its vast branch-banking
network. Pattrick (1966) provides a reference framework to study financial
TY.B.F.M Page 4


development by enunciating the 'demand-following approach' and the 'supply-
leading approach' to financial development. Demand following is defined as a
situation where financial development is an offshoot of the developments in the
real sector. In the case of supply leading, financial development precedes and
stimulates the process of economic growth; the supply of financial services and
instruments create the demand for them. Patrick suggested that in the early stages
of economic development, a supply-leading relation is more likely since a direct
stimulus is needed to mobilise savings to finance investment for growth. At a later
stage, when the financial sector is more developed, the demand-following relation
will be more prevalent. Empirical studies such as Gupta (1984), Jung (1986) and
St. Hill (1992) are broadly suggestive of the pattern of financial development
envisaged by Patrick (1966). However, such a theoretical dichotomy between
'demand following' and 'supply leading' is difficult to defend in the context of
continuous interaction between the real and the financial sectors in practice.
Regarding the impact of bank finance on growth, a number of empirical studies
drive home the positive impact of bank credit on output. Employing GMM panel
estimators on a panel data set of 74 countries and a cross sectional instrumental
variable estimator for 71 countries, Levine et al(2000) find that the exogenous
component of financial intermediary development is positively associated with
economic growth. Further, empirical studies by King and Levine (1993), Gregorio
and Guidotti (1995) strongly borne out the positive effect of financial development
on the long run growth of real per capita GDP. In the tradition of disentangling the
impact of bank credit on growth, Reserve Bank of India (2002a) explored the
relative impact of finance in inducing output growth using panel regression
techniques. Estimates of elasticity of output with respect to credit improved from
0.30 during the period 1981-1991 to 0.35 during 1992- 2001 indicating as
improvement in the allocative efficiency of the banking system at the all India level
(RBI 2002a). Sector-wise credit elasticities of output also indicate as improvement
in the allocative efficiency for most of the sectors in the post reform period
compared to the 1980s. However, no attempt has been made to study allocative
efficiency at the State level and across the sectors. The present study seeks to fill
this gap.

Section III
Credit and Output in the Spatial Dimension: Some Stylised Facts
The relative growth rates in credit and output in the pre and post- reforms periods
can act as pointers to allocative efficiency. Aggregate credit has grown at a similar
pace both in the pre reform and the post
Table 1: Growth of Output and Credit
(Per cent)

TY.B.F.M Page 5


1981-1992 1993-2001 1981-2001
VARIABLE

Output Credit Output Credit Output Credit
NSDP* 2.7 12.9 4.1 12.9 3.1 13.2

Agriculture 1.6 11.1 0.7 9.6 1.5 9.1
Industries 3.6 15.1 5.6 11.5 4.2 14.2
Services 4.0 11.2 6.0 15.3 4.6 13.3

* Net State Domestic Product
Source : Central Statistical Organisation and Reserve Bank of India

reform period, aggregate output, however, grew at a distinctly higher rate in the
post reform phase. This indicates that at the aggregate level, there could be some
improvement in the allocative efficiency. However, one finds a mixed picture at the
sectoral level. While both output and credit growth has decelerated for the
agricultural sector, that for services sector has accelerated in the post reform
phase as compared to the pre reform phase. For industry, however, higher
growth in output is witnessed in spite of deceleration in credit growth in the reform
period. Focusing only on growth rates of output and credit to comment on the
allocative efficiency may be quite misleading, if the share of different sectors in
aggregate credit and output has not remained the same. In fact, the share in credit
and output has increased for both industry and services sector and has declined
for the agriculture sector in the post reform period (Table 2). Thus, a much deeper
Table 2: Share in Output and Credit
(Per cent)
Average Share in the pre- Average Share in the post
Sector banking sector reform banking sector reform
period period

Output Credit Output Credit

Agriculture 37 15.7 29 10
Industry 23 43.5 25.5 48
Services 40 40.8 45.5 42

Source : Central Statistical Organisation and Reserve Bank of India.

analysis is required to comment on the allocative efficiency in different sectors in
the post reform phase. At the State level, all the States under study can be broadly
classified into four categories based on their shares in aggregatecredit and output.
TY.B.F.M Page 6


States with increased share in output and credit in the post reform phase as
compared to the pre reform period are the 'Group A' States. States with increased
share in output but reduced share in credit are the 'Group B' States. States ith
increased share in credit and reduced share in output are 'Group C' status, and
States with decline in their share in output and credit belong to the 'Group D'
category. As can be seen from Table 3, the majority of the States (Thirteen) belong
to Group D, which have suffered a decline in their share in aggregate output and
credit. In total, share of credit in the aggregate credit has gone down for 16 States
and has improved for 7 States in the post reform phase. Considerable inequality is
thus , seen among the States in terms of their share in overall credit. In such a
scenario, it becomes interesting to enquire, whether, States receiving an
increasing share of the credit resource have been able to make the most of it. In
other words, whether, rising credit shares are also accompanied with improved
allocative efficiency. Further, if allocative efficiency of credit has improved even
Table 3 : Changing Share of Different States in Output and
Credit: A Comparison of Pre-Reform and Post-Reform Period
States with States with States with States with decline

increased share in increased share in increased share in in their share in
output and credit output but reduced credit and reduced output and credit
share in credit share in output
(Group A) (Group B) (Group C) (Group D)

Andhra Pradesh, Arunachal Pradesh, Kerala Assam, Bihar,

Delhi, Tamil Nadu, Rajasthan and Himachal Pradesh,

Maharastra, West Bengal Jammu & Kashmir,
Karnataka Pondicherry,
and Gujarat Manipur,
MadhyaPradesh,
Punjab, Orissa,
Uttar Pradesh,
Tripura, Meghalaya
and Haryana

TY.B.F.M Page 7

for States that have undergone a decline in their share of credit, it would have well
served the purpose of reforms in the banking sector. Hence, it would be useful to
decipher,if any pattern is emerging at the State level, when allocative efficiency of
the banking system is seen in conjunction with their credit shares. Apart from
differences in their shares in output and credit, States have also exhibited a varied
pattern in their growth of output and credit in the post reform period. Based on
their growth in aggregate credit and output, there can be four categories of States.
States with increased share in output and credit in the post reform phase as
compared to the pre reform period are the 'Group E' States. States with higher
growth in output but lower growth in credit belong to 'Group F'. 'Group G' States
are those with higher growth in credit and lower growth in output and States with
reduced growth both in output and credit belong to the 'Group H' category. The
differential growth pattern in credit and output can act as a guide to comment on allocative efficiency
across States. Group F States that have shown an increased growth in output along with low credit
growth in the post reform period are likely to exhibit higher allocative efficiency. On the other hand,
Group G States with lower output and higher credit growth are clear candidates where allocative
efficiency would be deteriorating. However, it is tricky to judge about the allocative efficiency for
States belonging to the Group E and group H, that have experienced either increased or

Table 4: Growth in Output and Credit of Different States:
A Comparison of Pre – Reform and Post - Reform Period
States with higher States with States with higher States with

growth in output higher growth growth in credit and lower growth in

and credit output but lower lower growth in output and credit
growth in credit output
(Group E) (Group F) (Group G) (Group H)

Delhi, Karnataka, Andhra Pradesh, Punjab and Haryana Arunachal Pradesh,
Kerala Maharastra, Gujarat, Assam, Bihar, Orissa

and Rajasthan Himachal Pradesh, and Uttar Pradesh
Jammu & Kashmir,
MadhyaPradesh,
Manipur,Meghalaya
Pondicherry,
Tamil Nadu, Tripura
and West Bengal


reduced growth both in credit and output. For Group E States, that have witnessed

higher growth both in credit and output, allocative efficiency would be guided by
the relative growth of output vis-a-visthat of credit. Similarly, for Group H States
that have experienced a lower growth of both credit and output in the post reform
phase, allocative efficiency would depend on the relative decline in onevis-a-vis the
other. The indications for allocative efficiency obtained from the above informal
analysis, however, need to be corroborated with more rigorous analysis to arrive at
robust inferences. The empirical framework to estimate the allocative efficiency is
discussed in the next section.

Section IV
Data and Empirical Methodology

The study examines the allocative efficiency of the banking system for 23 States of
India. Allocative efficiency has been estimated separately for the two periods
1981-1992 (first period) and 1993-2001(second period). The periods have been so
chosen as torepresent the pre banking sector reforms and the post banking sector
reforms scenario s, respectively. The credit output dynamics has been studied for
three broad sectors of each State viz, agriculture, industry and services. While
measuring output; the following classification has been used. Agriculture includes
agriculture, forestry and fishing and logging. Industry includes mining, quarrying
and manufacturing (registered and non-registered) and services include electricity,
gas and water supply, transport, storage and communication, trade, hotels and
restaurants, banking and insurance, real estate, ownership of dwellings and
business services, public administration and other services. Income originating
from the States rather than income accruing to State concept has been used to
measure output. The data on output has been taken from the information supplied
by the various States to the Central Statistical Organisation. SDP data at the
1993-94 base has been used in the study. The data on credit refers to the
outstanding credit to different sectors from all scheduled commercial banks in a
region. The data for credit has been taken from the 'Basic Statistical Returns'
published by the Reserve Bank of India. The output variable is represented by log
of per capita net State Domestic Product (LPNSDP) and the credit variable by the
log of per capita credit for the State (LPTCAS). Though certain new regions have
been carved out from the existing ones in the year 2000, for analytical purposes,
necessary adjustments have been made to make the output and credit figures for
the year 2001 comparable to that for the previous years. The choice of the regions
and the time period have been completely motivated by the availability and
consistency of the data. However, with inclusion of regions having share of less
than one percent and as well having more than ten percent in the combined NSDP
for all the 25 regions, heterogeneity that prevails across the regions in India has
been captured considerably.

Empirical Methodology
To estimate the credit elasticities of output, we have twelve data points for the pre
reform and nine data points in the post reform period. Use of time series
estimation techniques, however, isprecluded given the small number of
observations for estimation.However, taking advantage of the panel nature of the
data, one canuse panel data techniques. With panel data techniques, information
from the time-series dimension is combined with that obtained from the cross-
sectional dimension, in the hope that inference about the existence of unit roots
and cointegration can be made more straightforward and precise. To ascertain the
appropriate estimation technique , the variables have been first examined for
stationarity in a panel context. If the variables are found to contain a unit root, the
variables are then examined for possible cointegration. In the event cointegration
between the variables, Fully Modified OLS (FMOLS) estimation technique is used
to obtain coefficient estimates. Specifically, the panel unit root tests developed by
Levin, Lin and Chu and Im, Pesaran and Shin have been employed. Pedroni's
method is used to test for panel cointegration. Fully modified OLS estimation
technique given by Pedroni is used to derive the elasticities. The details of the
empirical methodology are given in the Annex 6.

Section V
Empirical Results

The results of the panel unit root tests for each of our variables are shown in
Annex 3. In no case, can we reject the null hypothesis that every country has a
unit root for the series in log levels. Once ascertained that both the variables are I
(1), we turn to the question of possible cointegration between log of per capita SDP
and log of per capita credit. In the absence of cointegration, we can first
Differentiate the data and then work with these transformed variables.However, in
the presence of cointegration, the first differences do not capture the long run
relationships in the data and the cointegration relationship must be taken into
account. Annex 4 depicts the evidence on the cointegration property between per-
capita SDP

and per-capita credit for the Indian States. The panel cointegration tests suggested
by Pedroni (1999) have been applied. In general, the Pedroni (1999) tests turn out
to be in favour of a cointegrating relation between the variables that are non
stationary. The agriculture sector has not been studied for cointegration as the
output variable for agriculture is stationary and the credit variable is non
stationary. 2 Efficient FMOLS estimation technique is used to obtain the estimate
of elasticity of output with respect to credit for each sub-period. The results are
given in Annex 5. The changing allocative efficiency over time and across States
can be seen from Chart 1. The results broadly indicate an improvement in the
allocative efficiency for the majority of the States.3 For instance, for fifteen States,
there was an improvement in allocative efficiency with respect to the State
Domestic Product. It may be noted that eight out of these fifteen States had
undergone a decline in their share in aggregate credit in the post reform period. As
indicated by the analysis of growth in terms of credit and output, the allocative
efficiency of banks' funds has improved for all States that had higher output and
lower credit growth in the post reform phase.For all States taken together,
allocative efficiency has improved from 0.18 to 0.34 as indicated by the pooled
estimates. An overview of the results in terms of States and sectors that have
witnessed an improvement in allocative efficiency of bank funds is given in Table 5.
At the sectoral level, an improvement in allocative efficiency of bank funds in the
services sector is witnessed for 18 States and in the industrial sector for 12
States (Table 5).

Table 5: Allocative Efficiency Across Sectors and States
RESERVE BANK OF INDIA in the Post reform period
OCCASIONAL PAPERS Sectors
State
Industry Services Overall5
ANDHRAPRADESH Ö Ö Ö

ARUNACHAL PRADESH Ö

ASSAM Ö Ö

BIHAR Ö Ö

DELHI

GUJARAT Ö Ö

HARYANA Ö

HIMACHAL PRADESH Ö Ö Ö

JAMMU & KASHMIR Ö Ö

KARNATAKA Ö Ö Ö

KERALA Ö Ö Ö

MADHYAPRADESH Ö Ö

MAHARASHTRA Ö Ö Ö

MANIPUR

MEGHALAYA Ö Ö

ORISSA Ö

PONDICHERRY Ö Ö Ö

PUNJAB Ö Ö

RAJASTHAN Ö

TAMIL NADU Ö Ö Ö

TRIPURA Ö Ö

UTTARPRADESH Ö
WEST BENGAL Ö Ö Ö

Note :Ö indicates improvement in allocative efficiency in the post reform phase as compared
to the pre reform period. Blank cells indicate deterioration in allocative efficiency in
the post reform period.

Section VI
Conclusion

One of the main aims of financial sector reforms in the post 1990s was to improve
the allocative efficiency of the financial system. The efficiency improvement of the
banking system has a bearing on the overall efficiency of the Indian financial
system as the banking sector has a dominant role to play in the entire financial
edifice. This study attempted to enquire into the allocative efficiency of the Indian
banking system on a wider canvass encompassing twenty three States and across
the agriculture, industry and services sectors. Th e finding of the study broadly
corroborates that there hasbeen an improvement in allocative efficiency for all
States taketogether as far as elasticity of total output to total credit is concerned.
At the sectoral level, however, the picture is mixed. For the services sector there
has been a distinct improvement in allocative efficiency of credit in the post reform
period. The agriculture and industry sector, however, have witnessed a decline in
the allocative efficiency of credit in the same period. At theState level, majority of
the States witnessed an improvement in the overall allocative efficiency in the post
reform period. The improved allocative efficiency is more marked for the services
sector than for industry across the States.

Notes

1 Given that credit – output relations involve relatively short time
series dimen-
sions, and the well known low power of conventional unit root tests
when applied
to a single time series, there may be considerable potential for tests
that can be
employed in an environment where the time series may be of limited
length, but
very similar data may be available across a cross–section of
countries, regions,
firms, or industries.
2 Both fixed and random effects estimation of elasticity of output
with respect to
credit shows deterioration in allocative efficiency in the post reform
period for
the agriculture sector.
3 Allocative efficiency as defined by elasticity of SDP with respect to
total credit.
The individual and pooled FMOLS estimates are given in Annex-5.
4 Manipur is an exception

5 Overall refers to the State Domestic Product

State Agriculture Industry Services NSDP

1981 1993 1981 1981 1993 1981 1981 1993 1981 1981 1993 1981
-1992 -2001 -2001 -1992 -2001 -2001 -1992 -2001 -2001 -1992 -2001 -2001
ANDHRA 0.1 1.5 0.7 6.1 6.2 6.3 6.0 5.8 5.4 3.6 4.5 3.8
PRADESH

ARUNACHAL 5.1 -3.5 2.4 5.1 0.9 5.3 6.0 6.8 6.6 5.4 1.0 4.4
PRADESH
ASSAM 0.1 -0.3 -0.1 1.4 2.0 0.5 2.4 1.4 2.3 1.2 0.8 1.0
BIHAR 0.2 -0.4 -1.3 4.3 3.8 2.1 3.2 3.6 2.7 2.2 2.1 0.9
DELHI -0.3 -10.8 -6.8 4.1 -0.3 2.7 3.4 5.9 4.5 3.5 4.1 3.8

GUJARAT -2.8 -3.1 -0.2 4.8 4.3 5.9 5.0 6.8 5.5 2.4 3.7 4.0
HARYANA 2.1 -0.3 1.3 6.4 4.1 4.3 5.4 7.2 5.1 4.0 3.5 3.3
HIMACHAL 0.3 -1.8 -0.2 5.4 7.2 6.5 5.0 5.1 4.1 3.0 3.6 3.1
PRADESH
JAMMU & -2.6 1.2 -0.8 2.4 -2.9 0.2 1.1 3.7 2.2 -0.3 1.8 0.7
KASHMIR
KARNATAKA 0.7 3.0 1.9 4.9 5.8 4.8 5.5 9.0 6.4 3.4 6.1 4.3
KERALA 1.2 0.4 1.8 1.9 4.1 4.3 2.8 6.8 4.8 2.0 4.3 3.7

MADHYA -0.4 -1.8 0.3 2.7 7.4 6.8 4.1 4.0 3.5 1.6 2.1 2.1
PRADESH
MAHARA- 0.7 -0.9 1.7 3.9 4.4 4.3 5.0 5.9 6.2 3.6 4.2 4.6
SHTRA
MANIPUR -0.4 1.9 0.2 4.0 8.1 3.0 4.1 5.3 4.2 2.2 4.9 2.7
MEGHALAYA -1.6 2.7 -1.1 2.6 6.7 4.0 4.9 2.8 3.6 2.3 3.4 2.2

ORISSA -0.8 -0.9 -1.4 5.1 -1.9 4.1 4.3 5.9 4.4 2.0 1.6 1.4
PONDI- -1.8 -2.7 -2.6 1.0 21.6 3.2 2.2 10.0 5.2 0.9 12.3 2.8
CHERRY
PUNJAB 3.1 0.2 1.9 5.1 4.9 5.0 2.5 4.9 2.8 3.3 2.8 2.9

RAJASTHAN 1.9 0.0 1.7 4.3 7.0 5.6 6.2 5.8 5.4 3.7 4.1 3.8
TAMILNADU 2.6 0.8 2.7 3.2 4.4 4.1 5.1 8.2 6.2 3.9 5.3 4.7
TRIPURA -0.1 0.4 -0.6 -1.2 12.3 4.2 6.2 5.0 5.9 2.6 4.4 3.1

UTTAR 0.5 0.0 0.3 5.2 2.5 3.3 3.9 2.9 3.0 2.5 1.7 1.9
PRADESH
WEST 3.2 2.1 2.9 1.3 4.4 2.6 2.7 8.3 4.6 2.4 5.5 3.5
BENGAL

1 Compound annual growth rates.

Annex 2: Growth of Sector-wise Credit2
(Per cent)
State Agriculture Industry Services TotalCredit

1981 1993 1981 1981 1993 1981 1981 1993 1981 1981 1993 1981
-1992 -2001 -2001 -1992 -2001 -2001 -1992 -2001 -2001 -1992 -2001 -2001
ANDHRA 14.0 11.1 11.0 17.1 12.3 14.9 19.7 17.2 17.4 17.0 14.1 14.8
PRADESH
ARUNACHAL 37.3 7.7 19.6 36.4 -7.2 11.1 23.8 20.3 18.5 32.3 5.7 15.2
PRADESH

ASSAM 15.3 -1.9 7.2 19.4 1.7 8.9 17.8 13.7 13.2 18.0 6.8 10.6
BIHAR 14.8 0.3 10.0 11.0 1.6 8.7 20.2 8.4 14.8 15.1 4.9 11.5
DELHI -5.9 19.4 9.1 14.1 10.3 16.2 4.3 15.1 11.0 7.9 12.3 13.2

GUJARAT 14.3 6.7 11.1 15.1 15.4 14.0 15.3 16.0 15.6 15.0 14.5 14.0
HARYANA 11.4 8.5 7.6 12.8 15.8 12.4 13.2 13.3 12.1 12.4 13.5 11.0
HIMACHAL 13.4 7.1 7.6 18.0 12.2 12.4 16.8 12.2 13.3 16.5 11.6 12.1
PRADESH
JAMMU &
KASHMIR 13.0 8.6 7.3 16.6 4.8 8.9 16.1 17.9 14.8 15.9 14.2 12.6
KARNATAKA 16.1 12.2 12.1 14.8 15.1 14.0 17.2 19.5 16.0 15.9 16.3 14.3
KERALA 13.6 12.3 11.1 11.8 11.1 11.0 14.9 17.6 15.3 13.5 14.9 13.2

MADHYA 17.1 10.2 12.1 18.7 14.6 14.6 19.2 10.7 15.0 18.5 12.1 14.1
PRADESH
MAHARA 12.0 12.8 10.6 14.1 16.6 15.5 13.1 17.6 15.4 13.4 16.9 15.1
-SHTRA
MANIPUR 23.3 7.9 13.0 38.8 1.3 19.9 21.2 12.8 14.1 25.3 8.6 15.3
MEGHALAYA 27.2 -3.7 10.1 36.0 5.7 16.0 17.1 9.5 14.3 23.3 6.3 13.7
ORISSA 14.0 8.1 9.2 19.8 7.9 12.2 20.1 14.1 14.9 18.5 11.0 12.7
PONDI 7.8 7.5 6.9 15.4 7.1 12.2 16.2 15.1 15.8 14.0 10.6 12.5
-CHERRY
PUNJAB 7.9 11.0 7.0 15.9 14.2 13.4 10.1 14.7 12.7 11.3 13.8 11.3

RAJASTHAN 14.2 12.3 11.1 12.9 12.7 13.0 14.6 16.1 14.1 13.8 13.9 12.9
TAMILNADU 16.1 8.4 12.2 16.0 16.1 15.5 17.9 17.6 17.8 16.6 15.8 15.9
TRIPURA 20.4 1.7 10.1 26.9 -2.3 10.9 21.8 4.6 12.5 22.5 2.8 11.6
UTTAR 13.6 9.0 10.8 13.8 8.5 11.3 16.7 11.3 13.2 14.8 9.8 11.9
PRADESH
WEST 14.4 3.9 8.1 11.8 8.7 10.9 16.7 13.1 14.4 13.4 10.0 11.8
BENGAL

2 Compound annual growth rates.

Annex 3 : Panel Unit Root Tests 1981-1992 1993-2001

Variable Levin- Levin- Levin- IPS Levin- Levin- Levin- IPS
Lin rho Lin t-rho Lin ADF Lin rho Lin t-rho Lin ADF

-stat -stat ADF-stat -stat -stat -stat ADF-stat -stat

LPAGRI -7.80 -4.52 -2.58 -6.13 -6.67 -4.56 -3.73 -6.31

LPINDS 1.15 2.27 2.37 2.45 0.47 0.73 0.73 -0.42
LPSERV 2.45 3.36 3.53 4.54 2.49 3.46 3.25 2.85
LPNSDP 1.75 2.91 3.58 3.99 1.58 2.18 2.51 2.29
LPACS 0.82 0.68 1.33 1.46 1.67 2.82 2.63 2.36
LPICS 2.09 2.40 1.98 0.74 1.49 2.57 1.87 0.17
LPSCS 1.08 1.20 2.81 5.31 2.36 3.49 3.22 3.88
LPTCAS 1.64 1.73 2.58 2.20 2.47 3.53 3.33 2.54

Notes : a. The critical values are from Levin and Lin (1992).
b. IPS indicates the Im et al. (1997) test. The critical values are taken from Table 4.
c. Unit root tests include a constant and heterogeneous time trend in the data.

Annex 4 : Panel Cointegration Tests
1981-1992 1993-2001

Statistics LPINDS LPSERV LPNSDP LPINDS LPSERV LPNSDP

and and and and and and

LPICS LPSCS LPTCAS LPICS LPSCS LPTCAS
Panel v-statistics 4.52 2.49 2.97 1.02 2.80 1.79

Panel rho-statistics -1.96 -1.71 -1.51 -0.39 -0.84 -0.80

Panel pp-statistics -3.57 -2.96 -2.96 -3.83 -2.89 -3.65

Panel adf-statistics -4.45 -3.47 -1.99 -2.03 -3.32 -2.48

Group rho-statistics -0.34 0.21 0.0006 1.01 1.35 0.47

Group pp-statistics -4.31 -3.02 -3.20 -6.66 -3.56 -6.44

Group adf-statistics -5.75 -5.09 -3.75 -23.83 -15.36 -22.65

Notes : The critical values for the panel cointegration tests are base on Pedroni (2001a).
LPAGRI = Log of per capita agricultural output
LPINDS = Log of per capita industrial output
LPSERV = Log of per capita services sector output
LPNSDP = Log of per capita net State domestic product

LPACS = Log of per capita agricultural credit
LPICS = Log of per capita industrial credit
LPSCS = Log of per capita services sector credit
LPTCAS = Log of per capita total credit outstanding for all sectors of the State

Annex 5 : Individual and Pooled FMOLS Results
States 1981-1992 1993-2001 1981-1992 1993-2001 1981-1992 1993-2001
LPNSDP LPNSDP LPINDS LPINDS LPSERV LPSERV

ANDHRAPRADESH 0.22 0.31 0.41 0.44 0.32 0.35

(-12.95) (-33.96) (-10.60) (-27.86) (-13.61) (-45.14)
ARUNACHAL PRADESH 0.17 0.06 0.15 0.1 0.34 0.38

(-42.90) (-26.11) (-31.56) (-6.07) (-19.96) (-8.08)
ASSAM 0.05 0.11 -0.03 0.25 0.14 0.09

(-78.06) (-48.25) (-86.56) (-11.31) (-37.71) (-52.65)
BIHAR 0.14 0.19 0.34 0.05 0.17 0.37
(-26.38) (-8.86) (-12.21) (-6.08) (-153.24) (-8.82)
DELHI 0.42 0.33 0.32 -0.09 0.55 0.36

(-10.74) (-11.09) (-32.89) (-16.46) (-2.82) (-9.69)
GUJARAT 0.15 0.21 0.28 0.27 0.34 0.47

(-29.75) (-13.17) (-15.23) (-24.29) (-27.64) (-14.50)
HARYANA 0.37 0.26 0.52 0.25 0.43 0.52
(-11.96) (-85.73) (-9.53) (-235.33) (-8.25) (-31.67)
HIMACHAL PRADESH 0.22 0.29 0.03 0.47 0.34 0.46

(-12.84) (-41.42) (-14.24) (-7.34) (-11.42) (-18.74)
JAMMU & KASHMIR -0.02 0.1 -0.19 -0.24 0.08 0.2
(-38.75) (-61.07) (-13.13) (-13.86) (-67.00) (-51.85)

KARNATAKA 0.21 0.39 0.02 0.4 0.34 0.47

(-25.53) (-13.58) (-43.88) (-12.76) (-24.92) (-15.15)
KERALA 0.15 0.28 0.09 0.3 0.2 0.4
(-15.67) (-49.23) (-13.33) (-36.07) (-31.35) (-25.86)

MAHARASHTRA 0.08 0.15 -0.05 0.29 0.23 0.38

(-33.23) (-36.83) (-47.65) (-27.18) (-47.62) (-18.57)
MANIPUR 0.31 0.24 0.03 0.25 0.4 0.35
(-14.19) (-74.81) (-9.61) (-55.47) (-5.83) (-24.06)

MEGHALAYA 0.09 0.48 -0.01 0.02 0.2 0.44

(-97.31) (-2.92) (-129.84) (-1.38) (-47.02) (-7.77)
MADHYAPRADESH 0.08 0.2 -0.06 0.14 0.29 0.24
(-22.14) (-6.10) (-75.61) (-5.11) (-10.05) (-9.58)

ORISSA 0.14 0.11 0 -0.59 0.25 0.43

(-55.82) (-58.34) (-16.08) (-9.70) (-76.56) (-60.82)
PONDICHERRY 0.06 1.09 -0.12 2.19 0.14 0.66
(-57.65) -0.48 (-13.49) -1.18 (-133.73) (-8.45)

PUNJAB 0.29 0.22 0.16 0.34 0.27 0.37

(-11.00) (-86.15) (-7.50) (-17.70) (-18.51) (-16.08)
RAJASTHAN 0.32 0.27 0.14 0.53 0.46 0.37

(-12.24) (-11.18) (-6.93) (-13.45) (-8.75) (-16.27)
TAMILNADU 0.25 0.33 0.16 0.24 0.32 0.5
(52.30) (-63.08) (-23.21) (-28.70) (-65.09) (-15.10)
TRIPURA 0.11 1.46 0 -2.31 0.3 0.97

(-22.23) -1.91 (-39.83) (-3.05) (-19.08) (-0.64)
UTTARPRADESH 0.19 0.17 0.05 0.29 0.27 0.28

(-63.23) (-38.75) (-51.47) (-11.36) (-30.85) (-64.79)
WESTBENGAL 0.21 0.5 0.21 0.49 0.17 0.63
(-30.22) (-29.80) (-16.57) (-29.83) (-70.59) (-9.82)
POOLED 0.18 0.34 0.03 0.18 0.28 0.42

(-162.03) (-166.41) (-156.24) (-124.94) (-194.26) (-111.37)

Note : Figures are estimated elasticities of output with respect to credit of the respective sectors.
Figuresinparenthesisindicatet-value

Annex 6
Panel Unit Root, Panel Cointegration and Fully Modified OLS Estimation
Panel unit root Tests
There are several techniques, which can be used to test for a unit root in
panel data. Specifically, we are interested to test for non- stationarity against the
alternative that the variable is trend stationary. Levin, Lin and Chu (LLC) Test
One of the first unit root tests to be developed for panel data is that of Levin and
Lin, as originally circulated in working paper form in 1992 and 1993. Their work
was finally published, with Chu as a coauthor, in 2002. Their test is based on
analysis of the equation:∆yy na l y t na tt na l yi tiii i t,, 1i , 1,2,.. ,N t
1,2,... .1 , 2 , ,This model allows for two-way fixed effects (a and q) and unit-
specific time trends. The unit-specific fixed effects are an important source of
heterogeneity, since the coefficient of the lagged dependent variable is restricted to
be homogeneous across all units of the panel. The test involves the null hypothesis
H0: ri= 0 for all I against the alternative
HA: ri =r< 0 for all I with auxiliary assumptions under the null also being required
about the coefficients relating to the deterministic components. Like most of the
unit root tests in the literature, LLC assume that the individual processes are
cross- sectionally independent. Given this assumption, they derive conditions and
correction factors under which the pooled OLS estimate will have a standard
normal distribution under the null hypothesis. Their work focuses on the
asymptotic distributions of this pooled panel estimate of r under different
assumptions on the existence of fixed effects and homogeneous time trends. The
LLC test may be viewed as a pooled Dickey-Fuller (or ADF) test, potentially with
differing lag lengths across the units of the panel.
The Im-Pesaran-Shin Test
The Im-Pesaran-Shin (IPS, 1997) test extends the LLC framework to allow for
heterogeneity in the value of riunder the alternative hypothesis. Given the same
equation:∆q ua t i o n: yi ti t iitt tt yi i 1,2,.. ,N t 1,2,... .1 , 2 , ,The null and
alternative hypotheses are defined as:H0: : t e ∀i0 I and H AA:ii N0,i
, 1,2,...,1;; ii , 0,i , N11 1, N11 , 2,...N Thus under the null hypothesis, all series

in the panel are nonstationary processes; under the alternative, a fraction of the
series in the panel are assumed to be stationary. This is in contrast to the LLC
test, which presumes that all series are stationary under the alternative
hypothesis. The errors are assumed to be serially autocorrelated, with different
serial correlation properties and differing variances across units. IPS propose the
use of a group- mean Lagrange multiplier statistic to test the null hypothesis. The
ADF regressions are computed for each unit, and a standardized statistic
computed as the average of the LM tests for each equation. Adjustment factors
(available in their paper) are used to derive a test statistic that is distributed
standard Normal under the null hypothesis. IPS also propose the use of a group-
mean t-bar statistic, where the t statistics from each ADF test are averaged across
the panel; again, adjustment factors are needed to translate the distribution of t-
bar into a standard Normal variate under the null hypothesis. IPS demonstrates
that their test has better finite sample performance than that of LLC. The test is
based on the average of the augmented Dickey-Fuller (ADF) test statistics
calculated independently for each member of the panel, with appropriate lags to
adjust for auto- correlation. The adjusted test statistics, [adjusted using the tables
in Im, Pesaran, and Shin (1995)] are distributed as N(0,1) under the null of a unit
root and large negative values lead to the rejection of a unit root in favor of
stationarity.

Panel Cointegration Tests and Efficient Estimation

Cointegration analysis is carried out using a panel econometric approach.
Since the time series dimension is enhanced by the cross section, the analysis
relies on a broader information set. Hence, panel tests have greater power than
individual tests, and more reliable findings can be obtained. We use Pedroni's
(1995, 1997) panel cointegration technique, which allows for heterogeneous
cointegrating vectors. The panel cointegration tests suggested by Pedroni (1999)
extend the residual based Engle and Granger (1987) cointegration strategy. First,
the cointegration equation is estimated separately for each panel member. Second,
the residuals are examined with respect to the unit root feature. If the null of no-
cointegration is rejected, the long run equilibrium exists, but the cointegration
vector may be different for each cross section. Also, deterministic components are
allowed to be individual specific. To test for cointegration, the residuals are pooled
either along the within or the between dimension of the panel, giving rise to the
panel and group mean statistics (Pedroni, 1999). In the former, the statistics are
constructed by summing both numerator and denominator terms over the
individuals separately; while in the latter, the numerator is divided by the
denominator prior to the summation. Consequently, in the case of the panel
statistics the autoregressive parameter is restricted to be the same for all cross
sections. If the null is rejected, the variables in question are cointegrated for all

panel members. In the group statistics, the autoregressive parameter is allowed to
vary over the cross section,as the statistics amounts to the average of individual
statistics. If the null is rejected, cointegration holds at least for one individual.
Therefore, group tests offer an additional source of heterogeneity among the panel
members. Both panel and group statistics are based on augmented Dickey Fuller
(ADF) and Phillips- Perron (PP) method. Pedroni (1999) suggests 4 panel and 3
group s tatistics. Under appropriate standardization, each statistic is distributed
as standard normal, when both the cross section and the time series dimension
become large. The asymptotic distributions can be stated in the form Z Z*
−e c N(1)v where Z* is the panel or group statistic, respectively, N the cross
section dimension m and n and arise from of the moments of the underlying
Brownian motion functionals. They depend on the number of regressors and
whether or not constants or trends are included in the co-integration regressions.
Estimates for m and n are based on stochastic simulations and are reported in
Pedroni (1999). Thus, to test the null of no co-integration, one simply computes the
value of the statistic so that it is in the form of (1) above and compares these to the
appropriate tails of the normal distribution. Under the alternative hypothesis, the
panel variance statistic diverges to positive infinity, and consequently the right tail
of the normal distribution is used to reject the null hypothesis. Consequently, for
the panel variance statistic, large positive values imply that the null of no co-
integration is rejected. For each of the other six test statistics, these diverge to
negative infinity under the alternative hypothesis, and consequently the left tail of
the normal distribution is used to reject the null hypothesis. Thus, for any of these
latter tests, large negative values imply that the null of no cointegration is
rejected. The intuition behind the test is that using the average of the overall test
statistic allows more ease in interpretation: rejection of the null hypothesis means
that enough of the individual cross sections have statistics 'far away' from the
means predicted by theory were they to be generated under the null.

Panel FMOLS
In the event the variables are co-integrated, to get appropriate estimates of the co-
integration relationship, efficient estimation techniques are employed. The
appropriate estimation method is so designed that the problems arising from the
endogeneity of the regressors and serial correlation in the error term are avoided.
Due to the corrections, the estimators are asymptotically unbiased. Especially,
fully modified OLS (FMOLS) is applied. In the model
yitxy i t x y iiitxx i uiti t xx ,, t (uu )(2) (2)itit −1ititit,itthe asymptotic distribution of the
OLS estimator depends on the long run covariance matrix of the residual process
w. This matrix is given by Ω. lim1l i T∑E ϖ T∑i mϖϖi m1 hi s ′mϖϖ,ϖu,, i (3)
(3)iT→∞) TtT 1itti 1itiiϖi u iϖu u ,ifor the i-th panel member,
where1TT he r e 1 22 h∑r e ∑ϖϖϖϖ′∑r u iilimTEititT ,, E2,2 T →∞→t1u iu ,ii (4)1 T
−1T∑4 ∑1 t ,u iu i iiT→∞TkT k t1E wwitit k′t k ui,, i ,i(4)denote the matrices of

contemporaneous correlation coefficients and theauto-covariance, respectively,
where the latter are weighted according to the Newey and West (1994) proposal.
For convenience, the matrix F o r c o ,, ui,, i r ∑o nv e n ii∞∑r E wwE'(5)(5)ii ,, ( ,ij
0ii 0 is defined. The endogeneity correction is achieved by the transformation
** −0 ϖˆuiˆyityit,ϖ, , −,1i∆xit(6) and the fully modified estimator is ˆ( 6 *
6 ) −1*ˆ1 *i'iiX yii−i TT u)('(7) (7)ˆ( where,*wuu hˆ ˆ u − e r ϖϖϖˆ ˆˆ −i1ˆ1 ,provides
the autocorelation correction, The estimates needed for the
transformations are based on OLS residuals obtained in a preliminary step. The
panel FMOLS estimator is just the average of the individuals parameters.

Narasimham Committee Report - Some Further Ramifications and
Suggestions
Jayanth R. Varma, V. Raghunathan, A.Korwar and M.C. Bhatt
Working Paper No. 1009
February 1992
Indian Institute of Management, Ahmedabad
2. Narasimham Committee Report
Some Further Ramifications and Suggestions
Abstract
This paper while agreeing with the general thrust of the Narasimham Committee
Report, calls attention to some logical corollaries of the Report and analyses some
possible fallout from implementing the Report. We agree with the view that control
of banking system should be under an autonomous body supervised by the RBI.

However at the level of individual banks, closer scrutiny of lending procedures
may be called for than is envisaged in the Report. In a freely functioning capital
market the potential of government bonds is enormous, but this necessitates
restructuring of the government bond market. The government bonds may then
also be used as suitable hedging mechanisms by introducing options and futures
trading. We recommend freeing up the operation of pension and provident fund to
enable at least partial investment of such funds in risky securities. In the
corporate sector, we believe that the current 2:1 debt equity norm is too high and
not sustainable in the long term. We envisage that high debt levels and higher
interest rates, combined with higher business risk may result in greater incidence
of corporate sickness. This may call for various schemes for retrenched workers
and amendment to land laws for easy exit of companies. On account of
interdependencies across different policies, any sequencing of their implementation
may be highly problematic. We therefore suggest a near simultaneity in the
implementation of various reforms in order to build up a momentum which would
be irreversible if people are to have confidence that the reforms will endure, and if
we are to retain our credibility with international financial institutions.
Narasimham Committee Report
Some Further Ramifications and Suggestions
The Narasimham Committee Report is without doubt a major path- breaking piece
of work and deserves the support of all who yearn for a more rational and effective
banking system in this country. We strongly agree with the general thrust of the
report and enthusiastically endorse its major recommendations. In particular, we
welcome its proposals to delink the entire issue of concessional credit from the
issue of banking operations, to reduce the SLR limits, to strengthen the capital
base of banks, and to bring about a general freeing of interest rates. We also
strongly endorse the call for greater transparency in banking reports as well as the
proposal to strengthen the regulatory role of SEBI while abolishing the office of the
CCI. The concept of ARF for bad debts and the idea of having special tribunals to
expedite recovery of dues are also very practical and eminently implementable. The
intent of this note is not to comment paragraph by paragraph on the Committee
Report or to attempt to pick holes in what is a welcome as well as a comprehensive
set of recommendations to reform the banking system. Instead, what we shall
attempt to do here is to call attention to some natural corollaries of the Report, and
to speculate about some possible fall-out from implementing the Report which the
Government and the financial system in general may want to look out for. The note
is structured in five parts: in the first, we shall examine the implications of the
Report for the government bond markets. This will be followed by a look at the
implications for the corporate sector. After this section, a brief look at the
implications for the rural sector will be followed by some speculations regarding
the financial auditing and consulting sector. Finally, a look at the interlinkages
between the financial sector and the real economy, and we conclude with a word

about the pace of reform.

I. Restructuring the Government Bond Market
Today, the government bond market is exclusively the province of banks and
banking institutions. From the point of view of the banks, the chief function of
government bonds is to satisfy the SLR requirements. One likely consequence of
the proposed reduction in SLR limits from 38.5% to 25% is that government bonds
will increasingly be subject to some of the market pressures other bonds
experience in financial markets. The government bond market is likely to be
increasingly integrated into the mainstream capital market with investors
comparing the yields on government bonds with yields available on comparable
financial instruments elsewhere. A considerable widening and deepening of the
government bond market will be necessary to handle these changes. Currently,
while government bonds are listed on the stock exchanges, they are not actively
traded. Trading is essentially restricted to the interbank market. The potential role
of government bonds in a freely functioning capital market is enormous - one has
only to observe that the U.S. treasury bill and bond market is the largest in the
world, to recognize this fact. Because of the virtual absence of default risk on
government debts, government bonds have the potential to offer investors a
riskless investment with which to manage overall portfolio risk. Private corporate
funds, both large and small, would be attracted to such an investment as a place
to park cash without undue risk. Mutual funds could use the government bond
markets to manage the risk of their overall portfolios on a day to day basis -
switching in and out of government bonds depending on their perception of the
likely course of the stock markets. Government bonds are also an excellent vehicle
to manage inflation risk - in a freely functioning bond market, yields on
government bonds would have high correlations with expected inflation rates.
Forecasting of inflation rates would also become possible as the government bond
market develops and matures. Various organizations including corporations, trade
associations and trade unions could use such forecasts in pricing and bargaining.
Individuals would be able to use government bonds as part of their investment
strategy, especially for trusts and legacies for their children. To cater to such
demands, a number of bond trading firms would probably arise, specializing in
dealing in government bonds. Operating on thin, almost invisible margins, such
firms would help keep the government bond markets efficient in the informational
efficiency sense, rather like Salomon Brothers, for instance, in the U.S. Public
sector enterprises and government agencies may well find that an active, efficient
bond market which attracts private capital could be a major source of much-
needed funds.
SLR
It is clear that the SLR limits are intended mainly to ensure that banks maintain
adequate liquidity to discharge their obligations. It is difficult to see how long-term
bonds - government or otherwise - could qualify as liquid assets. At the same time,
there are a number of other financial assets which could qualify - short-term
corporate debt instruments like commercial paper of the highest quality, for

instance. There is a need to rethink the meaning of liquidity, keeping foremost the
basic intent of the SLR. This would be in line with the spirit of the Narasimham
Committee Report - to return to sound banking practices. It would, in any case, be
necessitated by the expected integration of the government bond market with the
rest of the financial markets.
Trust Securities
Bringing government bonds into the mainstream of financial markets would also
mean that they should compete openly with other high-grade securities for
inclusion in the portfolios of provident funds and pension funds. These, and
similar bodies, are currently required to invest only in approved Trust securities
which are essentially government bonds. We believe that non-government
securities of comparable risk should be permitted as investment vehicles. In a
further move to free up the operation of pension and provident funds, employees -
the ultimate investors - should be permitted the option of choosing to have their
funds deployed at least partly in equity securities. We believe such liberalisation of
the investment activities of pension and provident funds will fuel an unprecedented
boom in such funds. Strong funds of this kind can help mobilize savings just as
mutual funds have in the past few years. Strong pension funds can serve two
purposes - they can act as major sources of funding, both loans and equity, for
companies in both the private and public sector. This would help alleviate some of
the financing crunch so many companies are facing today. Secondly, well-managed
pension funds can provide the banking system some healthy competition, which
would force them to strive for greater efficiency and productivity.
Interest Rate Hedging
With interest rates deregulated, there will be a need to develop suitable hedging
mechanisms in the form of futures and options. In the long run, these mechanisms
may well be needed for all securities. However, since government bonds would be
influenced by a relatively small number of factors such as inflation and the term
structure of interest rates, they would provide an ideal vehicle to experiment and
learn how to operate options and futures markets in the Indian context. We believe
government bonds should be the first choice of securities exchange boards
contemplating introducing options and futures trading.
II. The Corporate Sector
If we compare corporate debt levels in India with those elsewhere, we would find
that Indian companies operate with an astoundingly high degree of borrowing.
Debt levels of 2:1 and 3:1 are commonplace in India - whereas they would be
unthinkable in most other financial markets of the world. There are many aspects
to this issue - a high debt level permits control of the company with a very small
equity investment. The results of such 'control without commitment' are not always
healthy for the company, to say the least. When major shareholders strip a
company of its productive earning power and leave a shell behind, at least part of
the blame must be ascribed to a system which allows such extraordinary levels of

debt financing. In economic downturns and recessions - inevitable in any economy
- high levels of debt will often cause a company to fall when it should only stumble.
Why have such high debt levels been permitted? There are probably mean reasons,
rooted in the history of the growth pains of a developing economy. One such reason
would be that government controlled financial institutions have often seen it as
their duty to provide funds to an 'approved' company - namely, any company
which has been able to secure a license. Even companies implementing the riskiest
of projects have been able to find debt financing, often at concessional rates, once
they have been able to get a license for the project. With the reform of the financial
system proposed by the Narasimham Committee, financial institutions will begin to
move away from such concerns with developmental or societal objectives. One
result will be that corporations will be forced to reduce their reliance on debt
financing. There are at least three other reasons why the historical high debt levels
of corporations cannot be sustained in the future. One is that, as the interest rates
are deregulated, they are likely to rise, at least in the short term. This is especially
the case because so much of corporate debt has been obtained in the past at
concessional rates from financial institutions. The increase in interest rates will
increase the debt service burden sharply at current levels of borrowings. As the
equity markets grow, equity financing will appear more and more attractive in
comparison. Further, with the greater reliance upon borrowing from the capital
markets rather than from Development Finance Institutions, there will be less
flexibility in terms of rescheduling of payments, since it is hardly practicable to
convene a meeting of
debenture-holders at every turn. Finally, since high debt levels increase the overall
risk of the corporation, companies will have to seek ways to control their financial
risk as they struggle to cope with the increased business risks they will face in
openly competitive product markets. With the risk of mistakes and stumbles
greatly increased, companies will find their equity values depressed if they burden
themselves with debt and thereby invite financial disasters. This is one of the likely
but thus far unheralded consequences of the liberalization of industrial policy by
the present government, which has left few protected markets for companies to
keep harvesting as they have in the past.
Corporate Sickness
Until such time as the corporate debt levels are brought down to more manageable
levels, the corporate sector will probably see a greater incidence of sickness on
account of its inability to absorb the higher debt service charges. This is especially
true of the older, more established companies which will, at the same time, find
their hitherto profitable
and protected markets invaded by new and more aggressive competitors. The
erosion of profitability and the increase in debt service burden will be a vise many
such companies will find themselves inexorably squeezed in. Needless to say, this
brings up issues such as exit policy, which we address in the section on
Interlinkages. At this stage, however, we suggest that the debt equity norm should
be reduced in a time-bound manner, say over a period of two years, from 2:1 to

1:1, in order to give the corporate sector some time to adjust their long-term
financing mix. Eventually, of course, the debt equity norm will have to be
determined purely on business considerations, and will vary in a complex manner
from industry to industry if not from company to company. However, a phased
move in this direction must be implemented as soon as the Narasimham
Committee report itself is implemented in its final form.
III. Rural Sector Banks
With the implementation of the Narasimham Committee Report, commercial banks
will no longer be cross-subsidizing loans to the rural sector with earnings from the
urban sector. While this will certainly put an end to the strategic schizophrenia
banks have been afflicted with in the past, it does mean that commercial banks,
including their rural subsidiaries, will find it increasingly difficult to compete with
specialized rural banks. We anticipate that the need and the demand for credit in
the rural sector will only grow as the economy grows. To meet this demand, a
number of such specialized banks are likely to arise, probably floated by
entrepreneurs with strong rural roots. Because such entrepreneurs are likely to
perform much better than the rural subsidiaries of the existing commercial banks
at the critical tasks of credit appraisal and understanding the real needs of rural
people, we expect these new financial institutions to serve rural markets better.
However, they will always suffer from two major problems: they will always be
localized and therefore not adequately diversified, which will make them prone to
failure with every local disaster; secondly, they will be short of capital in the short
run. We expect that government will have to find ways to provide capital to such
new banks, preferably in the form of venture capital in the form of equity. It is hard
to see what can be done to solve the problem of inadequate geographical
diversification without jeopardizing the strong local expertise which will be the
main competitive advantage for these new banks.
IV. Financial Auditing and Consulting
We believe that the scheme proposed by the Committee for supervision of banks
will be found to be inadequate, in as much as it relies strongly on self-regulation
by banks with a small supervisory board. The main aim of bank supervision
should be to protect the interests of depositors and to prevent any run on the
banking system which may be follow any significant bank failures. We propose that
the best way to ensure this would be a strong system of bank examiners, coupled
with a system of insurance of bank deposits. Bank examiners would be charged
with the task of auditing the portfolios of individual banks, at a detailed level, and
to assess the overall portfolio of the individual bank. Examiners should be able to
provide an early warning system to the bank itself as well as to the RBI if the bank
has excessive exposure to particular risks, for instance. Such examiners would
need to be independent of the both the bank and the RBI. Ideally, they would be
professionals, trained in financial and investment management. We suggest that
such the RBI hire such professional services on a contract basis. A number of

other financial services would need to be developed. For instance, we have
proposed in the section on government bonds that pension and provident funds be
allowed to invest in 'high grade' debt securities other than government bonds.
Naturally, then, there will need to be a number of independent agencies
specializing in the appraisal of debt securities.
V. Interlinkages with the Real Economy
Strong interrelationships obviously exist between the banking system and the rest
of the economy.
Exit Policy
Opening up the entries but keeping the exit clogged is clearly not a viable
procedure. The need for a workable exit policy to go along with the liberal entry
policies introduced by the current government, is a rather obvious one. The point
to be made here is that this need for a workable exit policy will be greatly increased
by some of the fallouts from the proposed reform of the banking sector. Quite apart
from the fact that some banks themselves will become unviable and will have to
start downsizing or adopting a more regional focus, we expect that the incidence of
corporate failures will also increase as the debt burden increases. We have dealt
with this issue at length in a previous section.
Labour Laws
The retrenchment of workers arising from the sickness of firms could be taken care
of by the following options:
a) Rather than force sick units to continue retaining the labour force, which is not
feasible in the long run in any case and results in a downward spiraling of morale
and productivity in the short run, employers could be forced to find alternative
employment for workers elsewhere. In practice, an employer who wishes to lay off
workers may have to pay a new employer to take them on. Some form of insurance
could be obtained by the old employer to help defray such costs in the event of
sickness. We expect an active market in this area if this option is resorted to.
b) An employment retrenchment insurance scheme wherein the employer pays an
insurance premium to an insurance company to cover retrenchment payments to
employees (not covering retrenchment on disciplinary grounds etc.) The insurance
company could pay the retrenched worker directly to provide him or her some
cushion or to pay finance any retraining which would be needed for him or her to
find a new job. Various combinations of the above schemes could also be worked
out. In any case, as sickness and layoffs become more common, workers also need
to have a variety of insurance and pension schemes which would not be dependent
on any one employer. We anticipate a growing demand for independent insurance
and pension fund companies as the proposed reforms are implemented.
Land Laws
Certain restrictions on the sale of certain kinds of land properties have acted as
major impediments in the way of sick companies which could otherwise have sold
the land to raise funds to finance rehabilitation efforts. With the increased

incidence of corporate sickness we predict as a consequence of both the liberalized
industrial policy and the reforms proposed in the Narasimhan Committee report,
some major amendments to land laws appear to be urgently called for.
VI. Pace of Reform
Major economic reforms are being contemplated today. One issue which naturally
arises is that of sequencing these reforms. At first blush, it may appear that it
would be logical to implement reforms in some logical order of priority, based
perhaps on some sense of relative urgency. However, a closer examination reveals
that there is some sort of circular sequencing requirement here, where each reform
appears to be a precondition for another. For example, it would make little sense to
reform the banking system first, since the real urgency driving this set of reforms
comes from the need to rationalize the entire economic system. On the other hand,
how feasible would it be to implement the reform of the industrial system first, if
there is not a strong banking system to finance the new entrants into newly
deregulated industries? Again, how feasible would it be to implement an easy entry
policy without an easy exit policy and how would an exit policy work without a
system of insurance for retrenched workers, which would require a reformed
financial system as a precondition? Indeed, reforms in industrial policy are hardly
likely to win the enthusiastic support of industry if industry leaders did not have
reason to believe that reforms in the financial system are imminent if not
concurrent. We believe the simplest way out of such a dilemma is to aim for a near
simultaneity in these reforms. This will necessarily mean a rapid pace of reform in
which time is measured in days, not years. Days as units connote a sense of
urgency not communicated by months and years. At the same time, there is a need
to build up a momentum which would be irreversible if the people are to have
confidence that the reforms will endure. A slow pace of reform will breed a 'wait
and see' attitude, which would neither bring the benefits of reform nor permit
continued economic growth under the old rules of the game. The greatest danger is
uncertainty - he who hesitates is indeed lost. As we look around us, we see even
more momentous reforms being introduced in the world today, especially in
Europe and the erstwhile Soviet Union. India cannot afford to be slower than these
countries, especially if we are to retain our credibility with international financial
institutions.

Capital Adequacy Ratio
INTRODUCTION

The instructions regarding the components of capital and capital charge required
to be provided for by the banks for credit and market risks. It deals with providing
explicit capital charge for credit and market risk and addresses the issues involved
in computing capital charges for interest rate related instruments in the trading
book, equities in the trading book and foreign exchange risk (including gold and

other precious metals) in both trading and banking books. Trading book for the
purpose of these guidelines includes securities included under the Held for
Trading category, securities included under the Available For Sale category, open
gold position limits, open foreign exchange position limits, trading positions in
derivatives, and derivatives entered into for hedging trading book exposures.

Measurement of capital charge for foreign exchange and gold open positions

Foreign exchange open positions and gold open positions are at present risk
weighted at 100%. Thus, capital charge for foreign exchange and gold open
position is 9% at present. These open positions, limits or actual whichever is
higher, would continue to attract capital charge at 9%. This is in line with the
Basel Committee requirement.

Capital Adequacy for Subsidiaries
1.The Basel Committee on Banking Supervision has proposed that the New Capital
Adequacy Framework should be extended to include, on a consolidated basis,
holding companies that are parents of banking groups. On rudential
considerations, it is necessary to adopt best practices in line with international
standards, while duly reflecting local conditions.
2.Accordingly, banks may voluntarily build-in the risk weighted components of
their subsidiaries into their own balance sheet on notional basis, at par with the
risk weights applicable to the bank's own assets. Banks should earmark additional
capital in their books over a period of time so as to obviate the possibility of
impairment to their net worth when switchover to unified balance sheet for the
group as a whole is adopted after sometime. Thus banks were asked to provide
additional capital in their books in phases, beginning from the year ended March
2001.
3.A consolidated bank defined as a group of entities which include a licensed bank
should maintain a minimum Capital to Risk-weighted Assets Ratio (CRAR)as
applicable to the parent bank on an ongoing basis. While computing capital funds,
parent bank may consider the following points :i.Banks are required to maintain
a inimum capital to risk weighted assets ratio of 9%. Non-bank subsidiaries are
required to maintain the capital adequacy ratio prescribed by their respective
regulators. In case of any shortfall in the capital adequacy ratio of any of the
subsidiaries, the parent should maintain capital in addition to its own
regulatory requirements to cover the shortfall. ii.Risks inherent in deconsolidated
entities (i.e., entities which are not consolidated in the Consolidated Prudential

Reports) in the group need to be assessed and any shortfall in the regulatory
capital in the econsolidated entities should be deducted (in equal proportion from
Tier I and Tier II capital) from the consolidated bank's capital in the proportion
of its equity stake in the entity.
Procedure for computation of CRAR
1. While calculating the aggregate of funded and non-funded exposure of a
borrower for the purpose of assignment of risk weight, banks may ‘net-off’ against
the total outstanding exposure of the borrower -(a) advances collateralised by cash
margins or deposits,(b) credit balances in current or other accounts which are not
earmarked for specific purposes and free from any lien,(c) in respect of any assets
where provisions for depreciation or for bad debts have been made (d) claims
received from DICGC/ ECGC and kept in a separate account pending adjustment,
and (e) subsidies received against dvances in respect of Government sponsored
schemes and kept in a separate account.
2.After applying the conversion factor as indicated in Annex 10, the adjusted off
Balance Sheet value shall again be multiplied by the risk weight attributable to the
relevant counter-party as specified.
3. Computation of CRAR for Foreign Exchange Contracts and Gold: Foreign
exchange contracts include- Cross currency interest rate swaps, Forward foreign
exchange contracts, Currency futures, Currency options purchased, and other
contracts of a similar nature Foreign exchange contracts with an original maturity
of 14 calendar days or less, irrespective of the counterparty, may be assigned "zero"
risk weight as perinternational practice. As in the case of other off-Balance Sheet
items, a two stage calculation prescribed below shall be applied:
(a) Step 1- The notional principal amount of each instrument is multiplied by the
conversion factor given below:
Residual Maturity Conversion Factor
One year or less 2%
Over one year to five years 10%
Over five years 15%

(b) Step 2 - The adjusted value thus obtained shall be multiplied by the risk weight
age allotted to the relevant counter-party as given in Step 2 in section D of Annex
10.
4. Computation of CRAR for Interest Rate related Contracts::

Interest rate contracts include the Single currency interest rate swaps, Basis
swaps, Forward rate agreements, Interest rate futures, Interest rate options
purchased and other contracts of a similar nature. As in the case of other off-
Balance Sheet items, a two stage calculation prescribed below shall be applied:
(a)Step 1 - The notional principal amount of each instrument is multiplied by the
percentages given below :
Residual Maturity Conversion Factor
One year or less 0.5%
1.0%
Over one year to five years

Over five years 3.0%

(b) Step 2 -The adjusted value thus obtained shall be multiplied by the risk
weightage allotted to the relevant counter-party as given in Step 2 in Section I.D.
of Annex
The Committee on Banking Regulations and Supervisory Practices (Basel
Committee) had released the guidelines on capital measures and capital
standards in July 1988 which were been accepted by Central Banks in various
countries including RBI. In India it has been implemented by RBI w.e.f. 1.4.92

Objectives of CAR : The fundamental objective behind the norms is to
strengthen the soundness and stability of the banking system.

Capital Adequacy Ratio or CAR or CRAR : It is ratio of capital fund to risk
weighted assets expressed in percentage terms i.e.

Minimum requirements of capital fund in India:
* Existing Banks 09 %
* New Private Sector Banks 10 %
* Banks undertaking Insurance business 10 %
* Local Area Banks 15%
Tier I Capital should at no point of time be less than 50% of the total capital.
This implies that Tier II cannot be more than 50% of the total capital.

Capital fund
Capital Fund has two tiers - Tier I capital include
*paid-up capital
*statutory reserves
*other disclosed free reserves

*capital reserves representing surplus arising out of sale proceeds of assets.
Minus
*equity investments in subsidiaries,
*intangible assets, and
*losses in the current period and those brought forward from previous periods
to work out the Tier I capital.
Tier II capital consists of:
*Un-disclosed reserves and cumulative perpetual preference shares:
*Revaluation Reserves (at a discount of 55 percent while determining their value
for inclusion in Tier II capital)
*General Provisions and Loss Reserves upto a maximum of 1.25% of weighted
risk assets:
*Investment fluctuation reserve not subject to 1.25% restriction
*Hybrid debt capital Instruments (say bonds):
*Subordinated debt (long term unsecured loans:

Risk weighted assets - Fund Based : Risk weighted assets mean fund based
assets such as cash, loans, investments and other assets. Degrees of credit risk
expressed as percentage weights have been assigned by RBI to each such assets.

Non-funded (Off-Balance sheet) Items : The credit risk exposure attached to
off-balance sheet items has to be first calculated by multiplying the face amount
of each of the off-balance sheet items by the credit conversion factor. This will
then have to be again multiplied by the relevant weightage.

Reporting requirements :
Banks are also required to disclose in their balance sheet the quantum of Tier I
and Tier II capital fund, under disclosure norms.
An annual return has to be submitted by each bank indicating capital funds,
conversion of off-balance sheet/non-funded exposures, calculation of risk
-weighted assets, and calculations of capital to risk assets ratio,
Asset - Liability Management System in banks - Guidelines
Over the last few years the Indian financial markets have witnessed wide ranging
changes at fast pace. Intense competition for business involving both the assets
and liabilities, together with increasing volatility in the domestic interest rates as
well as foreign exchange rates, has brought pressure on the management of banks
to maintain a good balance among spreads, profitability and long-term viability.
These pressures call for structured and comprehensive measures and not just ad
hoc action. The Management of banks has to base their business decisions on a
dynamic and
integrated risk management system and process, driven by corporate strategy.

Banks are exposed to several major risks in the course of their business - credit
risk, interest rate risk, foreign exchange risk, equity / commodity price risk,
liquidity risk and operational risks.
2. This note lays down broad guidelines in respect of interest rate and liquidity
risks management systems in banks which form part of the Asset-Liability
Management (ALM) function. The initial focus of the ALM function would be to
enforce the risk management discipline viz. managing business after assessing the
risks involved. The objective of good risk management programmes should be that
these programmes will evolve into a strategic tool for
bank management.
3. The ALM process rests on three pillars:
ALM information systems
=> Management Information System
=> Information availability, accuracy, adequacy and expediency
= ALM organisation
=> Structure and responsibilities
=> Level of top management involvement
= ALM process
=> Risk parameters
=> Risk identification
=> Risk measurement
=> Risk management
=> Risk policies and tolerance levels.
TRENDS IN DOMESTIC RATES AND YILED CURVE

The major focus of prudential regulation in developing countries has traditionally
been on credit risk. While banks and their supervisors have grappled with non-
performing loans for several decades, interest rate risk is a relatively new problem.
Administrative restrictions on interest rates in India have been steadily eased since
1993. This has led to increased interest rate volatility. Table I shows the trends in
domestic interest rates in India during the study period. It is clear that the rates
are increasing.

Table I - Trends in Domestic Interest Rates in India (in %)

Effective since reverse repo rate repo rate CRR
Mar 31, 2004 4.50 6.00 4.50
Sep 18, 2004 4.50 6.00 4.75
Oct 2, 2004 4.50 6.00 5.00
Oct 27, 2004 4.75 6.00 5.00
Apr 29, 2005 5.00 6.00 5.00
Oct 26, 2005 5.25 6.00 5.00
Jan 24, 2006 5.50 6.25 5.00
Jun 9, 2006 5.75 6.50 5.00
Jul 25, 2006 6.00 6.75 5.00
Oct 31, 2006 6.00 7.00 5.00
Dec 23, 2006 6.00 7.25 5.25
Jan 6, 2007 6.00 7.25 5.50
Source: RBI Bulletin

The yield curve has shifted upward since March ‘04, with the 10-year yields
moving from 5% to 7% (Fig.I). However,the longer end of the curve has flattened.
The significant drop in turnover in 2004-05 and 2005-06 could be due to a ‘buy
and hold’ tendency of the participants other than commercial
banks (like insurance companies) and also due to the asymmetric response of
investors to the interest rate cycle. Inthe absence of a facility of short selling in
government securities, participants generally refrained from taking positions which
resulted in volumes drying up in a falling market. The Reserve Bank's efforts to
elongate the maturity profile resulted in a smooth and reliable yield curve to act as
a benchmark for the other markets for pricing and valuation
purposes. The weighted average maturity of securitiesincreased from 5.5 years in
1995-96 to 14.6 years during2006-07. The weighted average yield of securities
alsodeclined to 5.7 per cent in 2003-04 and since then, it has increased to 7.3 per
cent in 2005-06 and further to 7.9 percent in 2006-07.The Indian yield curve today
compares with not only emerging market economies but also the developed world.

RBI repo rate - Indian central bank’s interest rate
Charts - historic RBI interest rates
Graph Indian interest rate RBI - interest Graph Indian interest rate RBI - long-
rates last year term graph

The current Indian interest rate RBI (base rate) is 8.500 %

RBI - Reserve Bank of India
The Reserve Bank of India (RBI) is the Indian central bank. The RBI’s most
important goal is to maintain monetary stability - moderate and stable inflation - in
India.. The RBI uses monetary policy to maintain price stability and an adequate
flow of credit. Rates which the Indian central bank uses for this are the bank rate,
repo rate, reverse repo rate and the cash reserve ratio. Reducing inflation has been
one of the most important goals for some time.

Other important tasks of the Reserve Bank of India are:

• to maintain the population’s confidence in the system, to safeguard the
interests of those who have entrusted their money and to supply cost-effective
banking systems to the population;
• to manage foreign currency controls: facilitating exports, imports and
international payment traffic and developing and maintaining the trade in
foreign currencies in India;
• issuing money (the rupee) and adequately ensuring a high quality money
supply;
• providing loans to commercial banks in order to maintain or grow the Gross
National Product (GNP);
• acting as the government’s banker;
• acting as the banks’ banker.

RBI Repo rate or key short term lending rate
When reference is made to the Indian interest rate this often refers to the repo rate,
also called the key short term lending rate. If banks are short of funds they can
borrow rupees from the Reserve Bank of India (RBI) at the repo rate, the interest
rate with a 1 day maturity. If the central bank of India wants to put more money
into circulation, then the RBI will lower the repo rate. The reverse repo rate is the
interest rate that banks receive if they deposit money with the central bank. This
reverse repo rate is always lower than the repo rate. Increases or decreases in the
repo and reverse repo rate have an effect on the interest rate on banking products
such as loans, mortgages and savings.
This page shows the current and historic values of Indian central bank's Repo rate
Base Rate
i.The Base Rate system will replace the BPLR system with effect from July 1, 2010.
Base Rate shall include all those elements of the lending rates that arecommon
across all categories of borrowers. Banks may choose any benchmark to arrive at
the Base Rate for a specific tenor that may be disclosed transparently. An il
ustration for computing the ase Rate is set out in theAnnex. Banks are free to use
any other methodology, as considered appropriate, provided it is consistent and is
made available for supervisory review/scrutiny, as and when required.
ii. Banks may determine their actual lending rates on loans and advances with
reference to the Base Rate and by including such other customer specific charges
as considered appropriate.
iii.In order to give banks some time to stabilize the system of Base Rate calculation,

banks are permitted to change the benchmark and methodology any time during
the initial six month period i.e. end-December 2010.
iv.The actual lending rates charged may be transparent and consistent and be
made available for supervisory review/scrutiny, as and when required.
Applicability of Base Rate
v.All categories of loans should henceforth be priced only with reference to the
Base Rate. However, the fol owing categories of loans could be priced without
reference to the Base Rate: (a) DRI advances (b) loans to banks’ own
employees (c) loans to banks’ depositors against their own deposits.
vi.The Base Rate could also serve as the reference benchmark rate for floating rate
loan products, apart from external market benchmark rates. The floating interest
rate based on external benchmarks should, however, be equal to or above the Base
Rate at the time of sanction or renewal.
vii.Changes in the Base Rate shall be applicable in respect of all existing loans
linked to the Base Rate, in a transparent and non-discriminatory manner.
viii.Since the Base Rate wil be the minimum rate for all loans, banks are not
permitted to resort to any lending below the Base Rate. Accordingly, the current
stipulation of BPLR as the ceiling rate for loans up to Rs. 2 lakh stands withdrawn.
It is expected that the above deregulation of lending rate will increase the credit
flow to small borrowers at reasonable rate and direct bank finance will provide
effective competition to other forms of high cost credit.
ix.Reserve Bank of India will separately announce the stipulation for export
credit.
Review of Base Rate
x.Banks are required to review the Base Rate at least once in a quarter with
theapproval of the Board or the Asset Liability Management Committees (ALCOs)
as per the bank’s practice. Since transparency in the pricing of lending products
has been a key objective, banks are required to exhibit the information on their
Base Rate at all branches and also on their websites. Changes in the Base Rate
should also be conveyed to the general public from time to time through
appropriate channels. Banks are required to provide information on the actual
minimum and maximum lending rates to the Reserve Bank on a quarterly basis, as
hitherto.
Transitional issues
xi.The Base Rate system would be applicable for all new loans and for those old
loans that come up for renewal. Existing loans based on the BPLR system may run
till their maturity. In case existing borrowers want to switch to the new system,
before expiry of the existing contracts, an option may be given to them,on mutually
agreed terms. Banks, however, should not charge any fee for such switch-over.
xii.In line with the above Guidelines, banks may announce their Base Rates after
seeking approval from their respective ALCOs/ Boards.
Effective date
xiii.The above guidelines on the Base Rate system will become effective on July 1,
2010.

analysis on rbi growth

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Andere mochten auch

Andere mochten auch (6)

Ähnlich wie analysis on rbi growth

Ähnlich wie analysis on rbi growth (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

analysis on rbi growth