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Agricultural Economics Research Review

Vol. 23 July-December 2010 pp 253-264

Institutional Credit to Agriculture Sector in India: Status,

Performance and Determinants

Anjani Kumara*, K. M. Singhb and Shradhajali Sinhac

a
National Centre for Agricultural Economics and Policy Research, New Delhi – 110 012
b
ICAR Research Complex for Eastern Region, Patna – 800 014, Bihar
c
Prist University Vallam, Thanjavur – 613 403, Tamil Nadu

Abstract
The institutional credit has been conceived to play a pivotal role in the agricultural development of India.
A large number of institutional agencies are involved in the disbursement of credit to agriculture. However,
the persistence of money lenders in the rural credit market is still a major concern. In this backdrop, the
present study has examined the performance of agricultural credit flow and has identified the determinants
of increased use of institutional credit at the farm household level in India. The study based on the
secondary data compiled from several sources, has revealed that the institutional credit to agriculture in
real terms has increased tremendously during the past four decades. The structure of credit outlets has
witnessed a significant change and commercial banks have emerged as the major source of institutional
credit in recent years. But, the declining share of investment credit in the total credit may constrain the
sustainable agricultural growth. The quantum of institutional credit availed by the farming households is
affected by a number of socio-demographic factors which include education, farm size, family size, caste,
gender, occupation of household, etc. The study has suggested simplification of the procedure for a better
access to agricultural credit of smallholders and less-educated/illiterate farmers.

Introduction long-term needs of the farmers. Several initiatives have

been taken to strengthen the institutional mechanism
Credit is one of the critical inputs for agricultural
of rural credit system. The main objective of these
development. It capitalizes farmers to undertake new
initiatives was to improve farmers’ access to institutional
investments and/or adopt new technologies. The
credit. The major milestones in improving the rural credit
importance of agricultural credit is further reinforced
are acceptance of Rural Credit Survey Committee
by the unique role of Indian agriculture in the
Report (1954), nationalization of major commercial
macroeconomic framework along with its significant
banks (1969 & 1980), establishment of RRBs (1975),
role in poverty alleviation. Realizing the importance of
establishment of National Bank for Agriculture and
agricultural credit in fostering agricultural growth and Rural Development (NABARD) (1982) and the
development, the emphasis on the institutional financial sector reforms (1991 onwards), Special
framework for agricultural credit is being emphasized Agricultural Credit Plan (1994-95), launching of Kisan
since the beginning of planned development era in India. Credit Cards (KCCs) (1998-99), Doubling Agricultural
A large number of formal institutional agencies like Credit Plan within three years (2004), and Agricultural
Co-operatives, Regional Rural Banks (RRBs), Debt Waiver and Debt Relief Scheme (2008). These
Scheduled Commercial Banks (SCBs), Non– Banking initiatives had a positive impact on the flow of
Financial Institutions (NBFIs), and Self-help Groups agricultural credit. However, the inadequacy of credit
(SHGs), etc. are involved in meeting the short- and to agriculture is often a hotly debated topic in India.
The persistence of money lenders in the rural credit
* Author for correspondence, Email: anjani@ncap.res.in market is still a major concern. But, most of the
254 Agricultural Economics Research Review Vol. 23 July-December 2010

discussions on the issue of agricultural credit are, by The growth rates were calculated separately for
and large, swayed by emotions and the empirical four sub-periods to capture the implication of different
validation of the issues is often lacking. In this backdrop, policy initiatives taken during different periods. The
this study was undertaken to (i) examine the regional disparities in the flow of institutional agricultural
performance of agricultural credit flow including the credit were assessed by estimating the coefficient of
the issues of inequity in the disbursement of institutional variation (CV).
agricultural credit flow, and (ii) identify the factors that
are responsible for increasing the use of institutional Determinants of Farmers’ Access to Institutional
credit at the household level. Credit

Data and Methodology The flow of agricultural credit depends on the

availability of funds with financial institutions, rate of
Data interest, and the government policies. A number of
socio-economic variables affect the amount of
The study is based on the secondary data compiled agricultural credit to be borrowed by the households.
from diverse sources. The data on gross cropped area To capture different factors responsible for the use of
(GCA) and agricultural gross domestic product agricultural credit by farming households, Tobit model
(AgGDP) were compiled from the Agricultural was used. Tobit model is preferred when the dependent
Statistics at a Glance (2008), published by the variable is censored so as to avoid the loss of
Department of Agriculture and Co-operation, Ministry
information. Tobit model used in the study was of the
of Agriculture, Government of India (GoI). The data
form:
on credit were collected from the Reports on Currency
and Finance, published by Reserve Bank of India Yi* = Xi β +εi ...(1)
(RBI). The data on wholesale price index (WPI) and
where, Yi* is the share of institutional credit in total
disbursement of Kisan Credit Cards (KCCs) were
borrowings of the farming households. Thus, the value
collated from the Economic Survey, published by
of the dependent variable ranges between 0 and 1.
Ministry of Finance, GoI. Data pertaining to investment
The vector Xi represents explanatory variables used in
credit and state-wise distribution of KCCs were
the regression analysis. The explanatory variables
compiled from the website of NABARD. Besides, the
included in the model were: X1 = Age of household-
study also used the unit level data of debt and investment
survey carried out by National Sample Survey head (years), X2 = Gender of household-head (male =
Organisation (NSSO) during 1992 (48th round) and 1, otherwise = 0), X3 = Household size (number), X4 =
2003 (59th round). The debt and investment survey is Operated land-size (hectares), X5 = Social group (ST=1,
generally carried out once in 10 years by NSSO and it otherwise = 0), X6 = Social group (SC=1, otherwise =
provides useful information on different dimensions of 0), X7 = Social group (OBC=1, otherwise = 0), X8 =
rural finance. Educational level (Primary = 1, otherwise = 0), X9 =
Educational level (Secondary = 1, otherwise = 0), X10
Methodology = Higher secondary or certificate / diploma (Course =
1, otherwise = 0), X11 = Graduate and above = 1,
Performance of Agricultural Credit otherwise = 0, X12 = Household type (Agricultural
labour = 1, otherwise = 0), X13 = Household type (Other
The performance of agricultural credit system has
labour = 1, otherwise = 0), X14 = Household type (Self-
been assessed in terms of different indicators. The
employed in agriculture = 1, otherwise = 0) and X15=
share of agricultural credit in agricultural GDP (AgGDP)
Household type (Other occupation, otherwise = 0); and
and overall GDP and the credit per unit of GCA was
examined to assess the overall performance of εi = Error-term.
institutional agricultural credit flow. Temporal changes
Performance of Agricultural Credit
in the composition of agricultural credit flow were
assessed to examine the structural changes in the
Institutional Credit Outlets and their Shares
sources of agricultural credit. The growth of agricultural
credit in real terms was estimated to measure the real Agricultural credit started depicting a growth after
growth in the institutional agricultural credit flow. bank nationalization and it has been growing
 Kumar et al. : Institutional Credit to Agriculture Sector in India 255

Table 1. Institutional credit flow to agricultural sector

(Rs in crore)
Year (TE) Co-operative banks Region rural banks Scheduled commercial banks Total

TE 1972-73 824 0 18 952

(86.5%) (0.0%) (1.9%)
TE 1981-82 2109 168 1245 3553
(59.4%) (4.7%) (35.0%)
TE 1991-92 4763 526 4988 10277
(46.3%) (5.1%) (48.5%)
TE 2001-02 20923 4082 28709 53713
(39.0%) (7.6%) (53.4%)
TE 2008-09 42162 23866 174775 240803
(17.5%) ( 9.9%) (72.6%)
Note: During TE 1972-73, remaining 11.6 per cent of total loan was issued by the state government
Sources:(a) Economic Survey and NABARD Databank (various issues)
(b) Website of Reserve Bank of India (RBI)

continuously since then (Table 1). This has resulted in 70

a significant increase in the access of rural cultivators 60
to institutional credit and the contribution of informal
Share (%)

50
agencies as credit sources has declined. The share of 40
institutional agencies in the total agricultural credit supply 30
was 7 per cent in 1951, which rose to 66.3 per cent in 20
1991. The next decade witnessed a slight decline in its
10
share and it fell to 64.3 per cent in 2002-03 (Figure 1).
0
The government has made renewed efforts to enhance 1951 1961 1971 1981 1991 2002
the credit supply and the agricultural credit through Figure 1. Share of institutional credit in total borrowing
institutional sources has more than quadrupled in the of farm households in India
past seven years in nominal terms (Table 1). The efforts Source: Mohan (2004); NSSO 59th Round (2003)
like nationalization of banks, establishment of RRBs,
strengthening of credit institutions etc. have been quite 1972-73 to 73 per cent in TE 2008-09. Prior to
effective in reducing the role of informal agencies in nationalization, the commercial banks were virtually not
rural credit market. However, still non-institutional lending credit to the agricultural sector. The share of
agencies continue to play a significant role in the rural RRBs in institutional credit disbursement increased from
credit market. Inspite of all these developments, the about 5 per cent during TE 1981-82 to 10 per cent
age-old problems of rural credit still persist. These during TE 2008-09. The co-operative banks which were
include reliance of borrowers on moneylenders and the primary source of institutional credit to agriculture
other informal sources despite their usury and have witnessed a sharp decline in their share in
exploitation. Kumar et al. (2007) have reported that agricultural credit, which has consistently declined from
the interest rate being charged by the informal sources 86.5 per cent in TE 1972-73 to 18 per cent in TE 2008-
was to the tune of 36 per cent to 120 per cent per 09.
annum.
Trends in Agricultural Credit Performance
The share of different institutional agencies in the
Indicators
agricultural credit flow is also depicted in Table 1. A
perusal of Table 1 reveals that the institutional sources In spite of impressive increase in the flow of
of agricultural credit flow have undergone a structural agricultural credit, the recent years have again been
change. The share of scheduled commercial banks characterized by a concern over the falling share of
(SCBs) has increased from a mere 1.9 per cent in TE agricultural credit in total credit. It is mainly attributed
256 Agricultural Economics Research Review Vol. 23 July-December 2010

to the high growth witnessed by the non-agricultural These indicators suggest that the agricultural credit
sector in recent years. The share of agriculture in system is geared to the agricultural growth and the
national income has also declined. The correct yardstick availability of credit to the rural cultivators has
to look at the progress of agricultural credit is evaluation increased substantially.
of agriculture as a proportion of AgGDP and trends in
real agricultural credit in terms of per unit gross cropped Compound Annual Growth Rates of Agricultural
area. The performance of agricultural credit in terms Credit
of these indicators seems to be noteworthy. Interestingly, The compound annual growth rates of institutional
the share of agricultural credit as a proportion of agricultural credit in real terms have been estimated
AgGDP has been rising continuously since 1970. It was and are presented in Table 3. The institutional
only about 5 per cent in TE 1972-73, which rose to about agricultural credit in real terms has registered a
8 per cent in TE 1981-82 and made a quantum jump in significant positive growth (7%) and this positive growth
recent years and rose to 31 per cent in TE 2008-09. rate has been registered by all the agencies involved in
The agricultural credit as a proportion of total GDP the disbursement of agricultural credit. During the past
increased during the 1980s, but declined during 1990s. four decades, the average agricultural credit flow from
Later on, it increased again and accounted for about 6 SCBs has registered an annual growth rate of 13 per
per cent of GDP in TE 2008-09. The agricultural credit cent. The credit flow from RRBs has grown at an
per hectare of gross cropped area has shown an annual growth rate of 14 per cent during the period
increasing trend with a tremendous rise in recent years. 1970-71 to 2008-09. The lowest growth has been
It has increased from Rs 375 in TE 1972-73 to Rs registered by the co-operative banks (4%).
5651 in TE 2008-09. About fifteen-fold increment has The sub-period-wise results are more enlightening.
been registered in agricultural credit in real terms during The agricultural credit disbursement from the SCBs
the period 1970-2008. grew at the rate of 52 per cent per annum during the

Table 2. Flow of agricultural credit

Years (TE) Agricultural credit /AgGDP Agricultural credit /Total GDP Agricultural credit /GCA
(%) (%) (Rs /ha)

TE 1972-73 4.99 2.06 375.13

TE 1981-82 7.71 2.67 565.48
TE 1991-92 6.76 1.99 753.55
TE 2001-02 11.65 2.77 1849.55
TE 2008-09 30.88 5.54 5651.36
Sources:(a) Economic Survey and NABARD Databank various issues
(b) Agricultural Statistics at a Glance (2008)
Table 3. Compound annual growth rates of institutional agricultural credit in real terms
(Per cent)
Period Co-operative Regional rural Scheduled commercial Total Credit / ha of GCA
banks banks banks (Rs)

1970-71 to 1979-80 1.79 - 52.36 6.29 5.71

1980-81 to 1989-90 3.81 9.51 10.82 6.42 6.03
1990-91 to 1999-00 7.88 15.93 12.41 10.08 9.66
2000-01 to 2008-09 7.99 27.05 25.28 21.15 18.20
1970-71 to 2008-09 3.89 14.41 13.27 7.47 7.00
Sources:(a) Economic Survey (2008) and NABARD Databank (various issues)
(b) Agricultural Statistics at a Glance (2008)
 Kumar et al. : Institutional Credit to Agriculture Sector in India 257

Table 4. Share of investment credit in total agricultural credit

(Per cent)
Years (TE) Co-operative banks Regional rural banks Scheduled commercial banks All

TE 1985-86 11.2 39.5 22.6 14.4

TE1991-92 16.3 62.1 18.3 17.6
TE 1995-96 15.8 44.7 15.9 16.1
TE 2001-02 17.7 28.4 7.5 11.7
TE 2005-06 11.8 14.1 3.2 6.0
Source: NABARD Databank (Various issues)

Table 5. Compound annual growth rate of agricultural investment credit in real terms
(Per cent)
Period Co-operative banks Regional rural banks Scheduled commercial banks All

1983-84 to 1990-91 3.04 8.99 1.05 4.12

1991-92 to 2000-01 8.33 6.45 -0.73 4.28
2001-02 to 2005-06 -9.30 0.19 22.90 2.95
1983-84 to 2005-06 5.89 5.65 -0.39 4.02
Source: NABARD Databank (various issues)

1970s. It was attributed to the nationalization of growth. But, the share of investment credit in total
commercial banks in 1969 and mandatory lending for agricultural credit has been declining continuously and
the priority sector of agriculture. Later on, agricultural it is pervasive across all institutional sources of
credit by commercial banks grew at the rate of 11 per agricultural credit. The share of investment credit has
cent per annum during 1980s and 12 per cent during decelerated from 18 per cent in TE 1991-92 to 12 per
the 1990s. From 2000 onwards, it has registered a cent in TE 2001-02 and further to 6 per cent in TE
growth rate of 25 per cent per annum. Similarly, the 2005-06 (Table 4). It is not favourable to accelerating
growth rate of agricultural credit by co-operative banks agricultural growth. A balance between short-term
has increased in each decade. It was 2 per cent per operational credit and long-term investment credit has
annum during the 1970s and 4 per cent per annum to be maintained to ensure sustainable agricultural
during the 1980s. It stepped up to 8 per cent during growth. The declining share of investment credit
1990s and has been continuing at 8 per cent during indicates that farmers seem to borrow more short-term
2000 onwards. A similar trend has been observed in credit in order to meet their input needs to maintain
lending by RRBs. The growth rates per annum were continuity in agricultural operation and do not pay
10 per cent during the 1980s and 16 per cent during the adequate attention to the long-term capital formation
1990s. The RRBs have registered a higher annual for agriculture. From the supply side, short-term credit
growth rate of 27 per cent during 2000 onwards. The entails a lower credit risk, lower supervision and
agricultural credit per hectare of GCA has also monitoring cost and a better asset-liability management.
witnessed a significant growth in real terms. On average, These factors probably could induce a faster expansion
it has registered 7 per cent growth per annum during of the short-term agricultural credit from financial
the past four decades. The highest growth was achieved instititions.
during the period 2000-01 to 2008-09 (18%). The compound annual growth rates of institutional
investment credit to agriculture in real terms have been
Investment Credit estimated and are presented in Table 5. The investment
Investment credit is meant for building productive credit in real terms has registered a modest positive
assets to enhance agricultural production. It plays a growth of 4 per cent / annum. The agricultural
significant role in ensuring a sustainable agricultural institutional credit flow from co-operative banks has
258 Agricultural Economics Research Review Vol. 23 July-December 2010

registered an annual growth rate of 6 per cent during 7.5 per cent in TE 2005-06. The share of farm
the period 1983-84 to 2005-06. In fact, SCBs registered mechanization was about 15 per cent in TE 1990-91, it
a slight decline. The sub-period-wise results are more went up to 31 per cent in TE 2000-01, but dropped to
revealing. The growth rate of co-operative banks was 14 per cent in TE 2005-06. Similarly, the share of
accelerated during the decades of 1980s and 1990s. It government sponsored programs has declined from 32
was 3 per cent per annum during 1980s and 8 per cent per cent in TE 1990-91 to 5 per cent in TE 2005-06.
per annum during 1990s. But, during the next decade, The shares of land development, animal husbandry and
it registered a sharp fall and was -9.3 per cent per non-farm sector have witnessed substantial growths
annum. The investment credit disbursement from the during this period. The most notable change has been
SCBs has depicted wide fluctuations. It grew at the observed in the case of non-farm sector. Its share of
rate of only 1 per cent per annum in 1980s, but dropped about 3 per cent in TE 1990-91 has increased to 29 per
to - 0.73 per cent per annum in 1990s. However, from cent in TE 2005-06.
2000 onwards, it has grown again at an impressive rate
of 23 per cent per annum. The investment credit flow Equity in Institutional Credit to Agriculture
from RRBs has registered an annual growth rate of 9 The avowed objectives of agricultural policy in India
per cent from 1983-84 to 1990-91. It has shown a growth are to make credit easily accessible to the all regions
rate of 9 per cent per annum in 1980s and of 6 per cent and classes of farmers. However in reality, a skewed
per annum in 1990s, but it has decelerated steeply during distribution of institutional credit across regions has been
2000s. found to persist. In view of glaring disparities in the
distribution of agricultural credit across regions, it is
Sectoral Distribution of Agricultural Investment
argued that the benefits of institutional credit have
Credit
largely accrued to the relatively prosperous regions and
The sectoral distribution of agricultural investment richer sections within each region.
credit, depicted in Table 6, has undergone a significant
The extent of variations in the distribution of
change during the past two decades. The shares of
institutional credit can be gauged from the fact that the
minor irrigation and government sponsored programs
institutional credit per hectare in 2007-08 in Assam (Rs
in total investment credit have witnessed a significant
1979) was about one-eighth of the national average
decline. The shares of fisheries and plantation &
(Rs 15936) and about 3 per cent of Kerala (Rs 56890).
horticulture have also declined.
There seems to be a direct relationship between
The share of minor irrigation in total investment institutional credit flow and the level of agricultural
credit, which was about 28 per cent in TE 1990-91, development (Table 7). For instance, per unit
declined to 11 per cent in TE 2000-01 and further to disbursement of institutional credit has been significantly

Table 6. Sectoral distribution of agricultural credit

(Per cent)
Sector TE 1990-91 TE 1995-96 TE 2000-01 TE 2005-06

Animal husbandry 7.89 10.23 15.35 11.92

Farm mechanisation 14.75 22.87 31.14 14.49
Fisheries 1.26 2.98 0.57 0.35
Govt. sponsored programs 31.88 19.77 14.37 4.98
Land development 0.94 0.73 1.52 4.49
Minor irrigation 28.32 20.33 11.25 7.55
Non-farm sector 2.95 13.61 15.33 28.99
Plantation & horticulture 5.26 4.40 3.92 3.27
Self-help group 0.00 0.00 1.54 11.05
Others occupational 6.77 5.08 5.00 12.89
Source: NABARD Databank (various issues)
 Kumar et al. : Institutional Credit to Agriculture Sector in India 259

Table 7. Distribution of institutional agricultural credit credit flow in isolation may mask the real issues.
across major states of India Therefore, efforts should be made to enhance the
(Rs / ha) resource base by making investment in capital formation
States 1990-91 2000-01 2007-08 which, in turn, will be helpful to bridge the flow of
institutional credit between advanced and backward
Andhra Pradesh 1120 4604 23441 states. The distribution of institutional credit across
Assam 54 311 1979 farm-size categories is also skewed (Table 8). Though,
Bihar 233 1075 8880 the majority of farmers (82%) in India possess less
Gujarat 501 2809 12626 than two hectares of land, they together account for
Haryana 482 2964 34012 only 50 per cent of the institutional credit; while18 per
Himachal Pradesh 207 2555 19490 cent of the farmers having more than two hectares of
Jammu & Kashmir 191 764 7893 land, account for 49 per cent of the institutional credit.
Karnataka 546 3432 15448 The skewed distribution of institutional credit in
Kerala 2766 7666 56890
agriculture seems to emanate from the skewed
Madhya Pradesh 320 698 9627
distribution of land. It may be mentioned that 18 per
Maharashtra 387 1352 12138
cent of these farmers operate about 53 per cent of the
Orissa 319 479 6730
total cultivable land in the country. The share of farmers
Punjab 856 5352 46593
Rajasthan 168 667 6673
having up to 2.5 acres of land, in total institutional credit
Tamil Nadu 2857 9403 52427 has declined from 27 per cent in TE 1982-83 to 25 per
Uttar Pradesh 376 1529 29065 cent in TE 2005-06. However, the share of farmers
West Bengal 329 1708 14025 operating 2.5-5 acres of land has increased from 19
All India 549 2169 15936 per cent in TE 1982-83 to 25 per cent in TE 2005-06.
Coefficient of variation 121.88 94.15 80.71 The share of large farmers, operating greater than 5
acres of land, in institutional credit has witnessed a
Source: Report of Advisory Committee on Flow of Credit modest decline from 53 per cent in TE 1982-83 to 49
to Agriculture and Related Activities from Banking
per cent in TE 2005-06.
System, RBI, Mumbai, 2004
Progress in Kisan Credit Card Scheme
higher in states like Haryana (Rs 34012/ha), Kerala
The Kisan Credit Card (KCC) Scheme was
(Rs 56890/ha), Punjab (Rs 46593/ha), Tamil Nadu (Rs
introduced in 1998-99 to facilitate farmers’ access to
52427/ha), and low in states like Assam (Rs 1979/ha),
short-term credit from the formal financial institutions.
Bihar (Rs 8880/ha), Madhya Pradesh (Rs 9627/ha),
The credit under this scheme is sanctioned in proportion
Orissa (Rs 6370/ha), Rajasthan (Rs 6673/ha), etc.
to the size of owned land, but there is some flexibility
However, regional disparities in the distribution of
for the farmers cultivating leased-in land, in addition to
institutional credit seem to have declined over time.
their owned holding. The KCC scheme has made a
The coefficient of variation in the distribution of
rapid progress and till 31 March, 2009, about 80.8 million
institutional credit across states was 122 per cent in
KCCs have been issued by the co-operative banks,
1990-91 which declined to 94 in 2000-01 and further to
commercial banks and RRBs (Table 9). The share of
81per cent in 2007-08. But, 81 per cent is quite a
co-operative banks and commercial banks in distribution
significant level which reveals that the regional
of KCCs was 44 per cent and 43 per cent, respectively;
disparities in institutional credit flow do exist and are
the remaining 14 per cent was issued by RRBs. The
still a part of rural credit system.
growth in distribution of KCCs has been phenomenal.
The agricultural growth and corresponding support The distribution of KCCs grew at the rate of 44 per
for institutional credit are the functions of technological cent per annum; the highest growth rate (75%) was
change. Much of inter-state or inter-regional disparities witnessed by RRBs. The distribution of KCCs by co-
in the institutional credit flow may emanate from the operative banks grew at the rate of 46 per cent per
differences in resource endowments or lack of annum during this period. The KCCs issued by the
appropriate technology for different regions/ states. commercial banks witnessed an annual growth rate of
Under such circumstances, comparing agricultural 42 per cent during this period.
260 Agricultural Economics Research Review Vol. 23 July-December 2010

Table 8. Distribution of institutional agricultural credit by SCBs to farmers according to size of landholdings
(per cent)
Up to 2.5 acres Above 2.5 acres to 5 acres Above 5 acres
Period Number of Credit Number of Credit Number of Credit
accounts amount accounts amount accounts amount

TE 1982-83 51.12 27.31 23.99 19.35 24.91 53.29

TE 1991-93 46.96 29.40 30.71 24.79 22.33 45.81
TE 2001-02 39.79 25.50 30.44 25.61 29.77 48.90
TE 2005-06 42.29 25.41 30.57 25.34 27.13 49.25
Source: RBI, Report on Currency and Finance (various issues)

Table 9. Progress in the distribution of Kisan Credit Cards (Agency-wise)

(million)
Year Co-operative banks Regional rural banks Scheduled commercial banks Total

1998-99 0.16 0.01 0.62 0.78

1999-00 3.75 0.18 1.99 5.91
2000-01 9.36 0.83 5.38 14.56
2001-02 14.8 1.66 8.45 23.9
2002-03 19.38 2.62 11.15 32.14
2003-04 24.26 3.89 14.24 41.39
2004-05 27.82 5.62 18.64 51.07
2005-06 30.42 6.87 22.8 59.08
2006-07 32.72 8.28 27.61 67.59
2007-08 34.81 10.05 32.21 76.05
2008-09 35.87 11.26 33.67 80.80
Share in Total (%) 44.39 13.94 41.67 100
CAGR (%) 45.87 75.27 42.12 44.25
Note: Term loan financing under KCC was introduced in August 2004
Source: NABARD and Economic survey

The higher growth rate witnessed in the distribution & Kashmir in distribution of KCCs has been dismal.
of KCCs is reflected in higher density of KCCs. On an For instance, only 5 per cent of the farming households
average, two-thirds of the farming households possess in Jammu & Kashmir and 13 per cent in Assam have
KCCs in India. However, the distribution of KCCs has obtained KCCs. In Bihar and Himachal Pradesh, only
depicted a significant variation across states. The about one-fourth of the farming households have
distribution of KCCs and its intensity in terms of per
received KCCs. The density of KCCs in terms of
unit farming household and per unit farm size is
operational area varied from 0.07 / ha in Jammu &
presented in Table 10.
Kashmir to 1.76 / ha in Kerala.
The highest intensity in distribution of KCCs was
observed in Punjab (2.02). The distribution of KCCs Determinants of Farmers’ Access to Institutional
was more than two-times the number of operating Credit
households in Punjab. Some other states which have
distributed more number of KCCs than the number of Tobit model was applied to identify the factors that
farming households are: Haryana (1.44), Andhra determine the quantum of credit borrowed from the
Pradesh (1.06) and Orissa (1.04). The performance of institutional sources. The variables included in the model
states like Assam, Bihar, Himachal Pradesh and Jammu and related hypotheses are defined below.
 Kumar et al. : Institutional Credit to Agriculture Sector in India 261

Table 10. State-wise distribution of Kisan Credit Cards: because with age, people mature and hence have better
2008 appreciation for the source of credit. The effect of
States No. of KCCs Intensity of KCCs gender was positive, which implied that the households
issued No./ No./ headed by males were able to get higher amounts of
(million) household hectare loan from the institutional agencies. It may be
mentioned here that only 11 per cent of the rural
Andhra Pradesh 12.17 1.06 0.85 households in the study area were estimated to be
Assam 0.34 0.13 0.11 headed by females. The bigger household-size and
Bihar 3.06 0.26 0.45 larger farm-size increased the probability of taking
(includes Jharkhand) credit from the institutional sources. The bigger size of
Gujarat 2.46 0.58 0.25 household could spare a family member to pursue the
Haryana 2.19 1.44 0.62 loan disbursement procedures from the institutional
Himachal Pradesh 0.25 0.28 0.26 sources. The credit requirement of larger farm-size
Jammu & Kashmir 0.07 0.05 0.07 was more because of its higher requirement of inputs
Karnataka 4.21 0.59 0.34 and services. The large-farm size also enhanced the
repayment capacity and thus facilitated credit
Kerala 2.76 0.42 1.76
disbursement from the institutional source. The results
Madhya Pradesh 5.90 0.56 0.27
have reconfirmed the vulnerability of weaker sections
(includes Chhattisgarh)
in getting credit from the institutional sources. It was
Maharashtra 7.19 0.59 0.36
observed that the households belonging to scheduled
Orissa 4.22 1.04 0.83 castes, scheduled tribes and other backward castes
Punjab 2.02 2.02 0.50 could get less credit from the institutional source than
Rajasthan 4.37 0.75 0.21 the general caste households.
TamilNadu 4.87 0.62 0.70
The effect of education on the use of credit outlet
Uttar Pradesh 14.62 0.65 0.78
was interesting. The higher the level of education, the
(includes Uttarakhand)
higher was the probability of having bigger loans from
West Bengal 2.69 0.40 0.49
the institutional sources. The education makes the
All India 80.80 0.67 0.51 borrower wiser not to take credit from non-institutional
Source: NABARD and Agricultural Census Division, sources at the higher rates of interest. Higher education
Ministry of Agriculture, New Delhi also helps the farmers to have better access to credit;
they may appear to lenders to present less of a credit
It was hypothesized that the age of decision-maker risk; they are more likely to be aware of financial
may influence the amount of credit as it will act as a opportunities and it may be easier for them to visit
proxy of experience. Female-headed households were financial institutions, do the required paper work for
hypothesized to have less access to formal credit than loan applications and interact with officials in the
male- headed households. The education level was financing institutions. This suggests the need for
hypothesized to influence the amount of formal credit simplification of credit disbursement procedure by the
positively, i.e. higher the level of education, higher is institutional sources so that even the illiterates could
the probability of accessing the formal credit sources have increased access to institutional credit in the rural
for loan. Different households’ types were supposed areas.
to influence the credit decision differently. Irrigated The effect of major occupation of a household on
environments were hypothesized to influence the the use of institutional credit was mixed. The households
quantum of formal credit positively. with self-employment in agriculture depicted higher
The variables used in the model with descriptive probability of availing higher amounts of institutional
statistics are summarized in Annexure I. The final credit; labour households obviously had fewer
estimation results of Tobit model are presented in Table propensities to avail institutional credit. This seems to
11. The effect of age on borrowing from institutional be rational as the households whose major occupation
sources was significant and positive. It was expected is agriculture, obviously need higher amounts of credit.
262 Agricultural Economics Research Review Vol. 23 July-December 2010

Table 11. Estimates of Tobit regression

Explanatory variables Coefficient t-value

Age of household-head 0.09868* 7.15

Gender of household-head, (male=1, otherwise=0) 1.89837* 2.57
Household size 0.16033* 2.49
Operated land-size (ha) 1.18063* 14.36
Social group
ST=1, otherwise=0 -6.96248* -9.62
SC=1, otherwise=0 3.49309* -5.93
OBC=1, otherwise=0 -1.82026* -4.23
Education level
Primary=1, otherwise=0 2.61911* 5.67
Secondary=1, otherwise=0 3.27122* 6.58
Higher secondary or certificate / diploma Course=1, otherwise=0 3.59646* 3.79
Graduate and above=1, otherwise=0 4.24205* 3.97
Household type
Agricultural labour=1, otherwise=0 -1.8462* -2.37
Other labour=1, otherwise=0 0.48568 0.53
Self-employed in agriculture = 1, otherwise=0 1.55796* 2.46
Other occupation=1, otherwise=0 1.43836* 1.49
Constant -33.209*** 26.96
Number of observations 54254
F- value 84.77
σ 16.43
*indicates at 1 per cent level of significance.

Conclusions and Policy Implications some evidence of convergence. Inequity in the

distribution of institutional credit across different
The agricultural performance engrosses many
categories of farmers also persists. The choice of a
production factors; agricultural credit is one of them.
credit outlet and the quantum of institutional credit
The performance of institutional credit to agriculture availed by farming households have been found to be
and the determinants of institutional agricultural credit affected by a number of socio-demographic factors.
use at households’ level have been analyzed. The study The effect of education has indicated the need for
has shown that the institutional credit flow to the capacity building of borrowing farmers. Imparting
agriculture has been increasing for the past four training to borrowers regarding procedural formalities
decades. However, different patterns in the growth of of financial institutions could be helpful in increasing
agricultural credit have been observed during different their access to institutional credit. Further, procedure
sub-periods. The structure of the sources of credit has for loan disbursement could be made simple so that it
witnessed a clear shift and commercial banks have may not be difficult for the less-educated and illiterate
emerged as the major source of institutional credit to households to access institutional financing agencies
agriculture in the recent years. Further, the portfolio of for credit. The weaker sections of the society like SCs,
institutional credit to agriculture has also changed and STs and OBCs and smallholders are more exposed to
the share of investment credit in total credit has declined non-institutional sources for their borrowings and thus
over time. The declining share of investment credit may end up paying higher rates of interest, which have a
constrain the agricultural sector to realize its full potential. negative bearing on their economic situation. This needs
Regional disparity in disbursement of agricultural credit to be ameliorated by strengthening the on-going special
has been glaring, though in recent years it has shown schemes for these groups.
 Kumar et al. : Institutional Credit to Agriculture Sector in India 263

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264 Agricultural Economics Research Review Vol. 23 July-December 2010

Annexure I
Mean and standard deviation of explanatory variables used in Tobit model
Explanatory variables Mean Standard deviation

Age of household head 45.435 13.377

Gender of household head (Male=1, otherwise=0) 0.910 0.286
Household size (No.) 5.191 2.492
Operated land size (ha) 0.872 1.908
Social group
ST=1, otherwise=0 0.058 0.234
SC=1, otherwise=0 0.220 0.414
OBC=1, otherwise=0 0.459 0.498
Education level
Primary=1, otherwise=0 0.912 0.454
Secondary=1, otherwise=0 0.201 0.401
Higher secondary or certificate / diploma Course=1, otherwise=0 0.034 0.182
Graduate and above=1, otherwise=0 0.027 0.162
Household type
Agricultural labour=1, otherwise=0 0.257 0.437
Other labour=1, otherwise=0 0.116 0.320
Self-employed in agriculture=1, otherwise=0 0.401 0.490
Other occupation=1, otherwise=0 0.085 0.278