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172
1,3,5,6Scientist (shiva644@gmail.com, Anuja.AR@icar.gov.in,
Rajesh.T@icar.gov.in, Harishkumar.HV@icar.gov.in), 4Principal
Scientist and Head (kn.singh@icar.gov.in), Division of Forecasting
and Agricultural Systems Modeling, ICAR-IASRI, New Delhi;
2PhD Scholar (raghavkj@gmail.com), Division of Agricultural
Economics, ICAR-IARI, New Delhi.
Indian Journal of Agricultural Sciences 90 (2): 412–7, February 2020/Article
Impact of institutional credit on agricultural productivity in India:
A time series analysis
SHIVASWAMY G P1, RAGHAVENDRA K J2, ANUJA A R3, K N SINGH 4, RAJESH T5 and
HARISH KUMAR H V6
ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
Received: 03 April 2019; Accepted: 19 July 2019
ABSTRACT
The institutional credit has always been perceived as a critical factor for agricultural development in India through
complementing working capital, easing liquidity and investment constraints. The present study has examined the trends
and regional variations in institutional credit ow to agriculture in India for the period 1991–92 to 2016–17 using
compound annual growth rate. Further, impact of institutional credit on agricultural productivity was also assessed
using panel data regression. The study is based on the secondary data collected from various published sources. Results
indicated that institutional credit to agriculture in real terms has registered a signicant positive growth during the past
four decades and the highest annual growth was observed during 2001–02 to 2010–11. Scheduled commercial banks
have emerged as the dominant source of agricultural credit. However, cooperative banks are still the major sources
of production credit. Regional analysis showed that southern states had access to highest production and investment
credit per hectare, while eastern and northeastern states had the least credit outreach per hectare. Panel data regression
model testied that institutional credit has a signicant and positive impact on agricultural productivity. Therefore,
the study has suggested for better access to credit of smallholders especially in eastern, western and north eastern
states through simplication of procedures.
Key words: Agricultural productivity, Credit outreach, Institutional credit, Panel data model, Regional
variations
Agriculture is characterized by high initial xed
capital investment and a lag between expenditure and
income. Credit is one of the basic inputs in agriculture.
This necessitates timely availability of credit at affordable
rates as a precondition for improving rural livelihood and
fast-tracking rural development (Kumar et al. 2015). The
rural credit system is of great importance given that majority
of the Indian farmers possess marginal and small land
holdings with poor nancial savings. Role of institutional
credit in rural poverty alleviation is also well documented
(Khandkar and Faruquee 2003, Awotide et al. 2015, Kumar
et al. 2017). In the last three decades, institutional credit
not only facilitated survival of small and marginal farmers
but also aided large farmers in enhancing their income
(Das et al. 2009).
Agricultural credit growth in India mainly followed the
path of supply led approach. Over the years, concentrated
efforts of government such as nationalisation of banks
(1969 and 1980), establishment of Regional Rural Banks
(RRBs) (1975) and National Bank for Agriculture and Rural
Development (NABARD) (1982), nancial sector reforms
(1991), introducing Kisan credit cards (1998) and doubling
agricultural credit plan (2004) helped increase the share of
institutional credit in total agricultural credit. Consequently,
the share of informal credit in total agricultural credit has
declined from 93% in 1951 to 36% in 2013 (Mohan 2006,
Kumar et al. 2017).
Credit constraints have signicant adverse impact on
farm efciency, productivity and protability (Feder 1990,
Chavas and Aliber 1993, Sabasi and Kompaniyets 2015,
Guirkinger and Boucher 2008). There exists a signicant
positive relationship between variable inputs usage and
disbursement of production credit (Sidhu et al. 2008, Kumar
et al. 2013, Karlan et al. 2014). According to Narayanan
(2016), 10% increase in the credit ow in nominal terms
leads to 1.7% increase in fertilizers consumption, 5.1%
increase in pesticides consumption and 10.8% increase in
tractor purchases. Prior literature reports enhancement in
farm performance and acreage due to removal of credit
constraints (Blancard et al. 2006, Dong et al. 2010).
Role of institutional credit in the economic wellbeing of
farm households is well documented (Das et al. 2009,
413February 2020]
173
INSTITUTIONAL CREDIT FOR AGRICULTURAL PRODUCTIVITY
Narayanan 2016, Kumar et al. 2017). Lack of access to
institutional credit can adversely affect the adoption of
modern technology and capital formation.
The current policy regime emphasises increasing
agricultural productivity for enhancing farmers’ welfare
(Chand 2017) and the institutional credit has always been
perceived as a critical factor for agricultural development
in India. Therefore, it is pertinent to establish causality of
credit with productivity. Given this background, the present
study has been undertaken to assess: (i) the trends and
regional bias in the institutional credit ow to agriculture,
and, (ii) the impact of institutional credit on agricultural
productivity in India.
MATERIALS AND METHODS
The present study is based on the secondary data
collected from various sources for the period 1991–92
to 2016–17. Data on the state-wise value of output from
agriculture was collected from statistical publications of
the Ministry of Statistics and Program Implementation
(MOSPI), Government of India. State-wise institutional
credit disbursement data was collected from NABARD.
The data was then deated using GDP deator at 2011–12
prices. To study the trends in region-wise lending, as
per Reserve Bank of India (RBI) guidelines, states were
grouped into different regions, viz. Northern (Haryana,
Himachal Pradesh, Jammu and Kashmir, Punjab, Rajasthan,
Chandigarh, Delhi), North-eastern (Arunachal Pradesh,
Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura),
Eastern (Bihar, Jharkhand, Odisha, Sikkim, West Bengal,
Andaman & Nicobar Islands), Central (Chhattisgarh,
Madhya Pradesh, Uttar Pradesh, Uttarakhand), Western
(Goa, Gujarat, Maharashtra, Dadra & Nagar Haveli, Daman
& Diu) and Southern (Andhra Pradesh, Karnataka, Kerala,
Tamil Nadu, Lakshadweep, Puducherry, Telangana). Data
on gross cropped area and the gross irrigated area were
collected from the Land use statistics reports of Directorate
of Economics and Statistics (DES), Ministry of Agriculture
and Farmers welfare, Government of India (GoI). Data on
state-wise fertilizer consumption was collected from the
Handbook of statistics on Indian states published by RBI.
Compound Annual Growth Rates (CGRs): The trend
in region-wise disbursement of institutional credit over the
years was estimated using CGR. CGR can be written as:
Yt – abt eut (1)
whereYt, institutional credit outlay at time‘t’; a, intercept;
b, regression coefcient; t, time variable; ut, an error term
corresponding to tth observation.
The equation (1) is estimated after transforming it to
logarithmic form as follows:
In Yt = In a + tln b + ut (2)
The CGR (r) is computed using the relationship:
r = {antilog(In b) – 1} × 100 (3)
Panel data regression: Panel data analysis has
advantages over ordinary least square (OLS) regression
models in terms of increased precision in estimation and
capturing unobserved individual heterogeneity that may
be correlated with regressors (Bruderl and Ludwig 2015).
State wise value of output from agriculture (crop sector) per
hectare was used as an indicator of agricultural productivity.
A balanced panel was constructed for 13 major Indian states
for the period 1991–92 to 2015–16. The major agricultural
states included in the study were Andhra Pradesh, Gujarat,
Bihar, Haryana, Karnataka, Kerala, Madhya Pradesh,
Maharashtra, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh,
and West Bengal. These states together contributed 87% of
total value of output in agriculture and allied sector during
TE 2015 (MoSPI 2015).
To estimate the impact of institutional credit on
productivity, xed effect model (FEM) and random effect
model (REM) were used. Hausman specication test was
used to identify the best model between FEM and REM.
The FEM has constant slopes but intercepts differ according
to the cross-sectional (states) unit. For i classes, i–1 dummy
variables are used for designating a particular state.
Yit – ai + b1 X1 it + b2 X2 it + b3 X3 it + eit (4)
eit ~ IID(0, se2
In the REM, the intercept is assumed to be a random
outcome variable, whereas the random outcome is a function
of a mean value plus a random error
Yit – a + b1 X1 it + b2 X2 it + b3 X3 it + wit (5)
wit = Œi + eit
Where Yit, Value of output from agriculture (crop sector)
expressed as `/ha in the ith state (i=1 to 13) and tth year
(t=1 to 21); X1, Institutional credit (`/ha); X2, Irrigation
coverage (share of gross irrigated area in the gross cropped
area expressed in % age); X2, Fertilizer consumption (kg/ha);
wit, composite error term including Œi which is a cross
section error component and eit, which is a combined time
series and a cross-section error component; ai, bi, b2, b3,
parameters to be estimated.
RESULTS AND DISCUSSION
Trend in institutional credit disbursement to agriculture:
The overall institutional credit disbursement for agriculture
and allied activities in real terms has increased tremendously
from ₹ 107742 crores during 1991–92 to ₹ 836937 crores in
2016–17 (Fig 1). A signicant increase in institutional credit
disbursement is evident during the early 2000s. Contribution
of SCBs in the overall institutional credit disbursement
surpassed co-operative banks from 2004 onwards. Enhanced
institutional credit disbursement has resulted in reduced
role of informal agencies as credit sources (Kumar et al.
2010, Pradhan 2013).
During 1991–92 to 2016–17, total institutional credit
ow to agriculture has witnessed a signicant positive
growth rate of 10.37% (Table 1). The agricultural credit ow
from SCBs has registered an annual growth rate of 12.45%.
RRB’s lending to agriculture has grown at an annual rate
414 [Indian Journal of Agricultural Sciences 90 (2)
174
SHIVASWAMY ET AL.
of 13.52%. Whereas, cooperative banks have witnessed
lowest growth (4.88%). The sub-period wise analysis of ow
of institutional credit to agriculture shows that the SCBs
and RRBs had registered the highest growth rates during
2001–02 to 2010–11. It was mainly due to the government
policy of doubling agricultural credit in 2004 for the next
three years in order to boost the agricultural production.
Over the years, the share of cooperatives in total
institutional credit to agriculture has declined from about
41% during the period 1991–95 to 17% during the period
2012–16. On the other hand, the share of SCBs has increased
significantly from 52% to
70% during the same period.
Concentrated support extended
from the central government
to SCBs and RRBs through
recapitalisation to cleanse their
balance sheet aided this process
(Jumrani and Agarwal 2012).
Since the nancial restructuring
of co-operatives were under the
purview of state governments,
they were not provided with
such a nancial support (Satish
2007). However, it is interesting
to note that cooperatives lent
over half of the total production
credit during Triennium ending
2016–17. Whereas, SCBs
disbursed about 80% of total
investment credit during the
same period.
Region-wise distribution of institutional credit to
agriculture: Flow of institutional credit to agriculture is not
homogeneous across the different regions of the country
as shown by varying share in total institutional credit and
credit ow per ha of gross cropped area (Table 2). Southern
states had received the highest amount (`135036/ha) of
institutional credit per ha followed by northern states (`
51772/ha) during 2016–17. Whereas, eastern (` 31123/ha),
western (` 33181/ha) and north-eastern (` 33807/ha) states
received a lower amount. The share of institutional credit
to agriculture was also strikingly low in these regions for
the study period.
During 2016–17, production and investment credit
disbursed per ha of gross cropped area were highest in
southern states of Andhra Pradesh, Telangana, Tamil
Nadu, and Kerala. Agriculturally advanced states of Punjab
and Haryana and hill state of Uttarakhand also had high
production credit per ha (Fig 2). However, North eastern
states, and Jammu and Kashmir had received the lowest
production and investment credit per ha. These inter-
state and inter regional disparities in institutional credit
0
100000
200000
300000
400000
500000
600000
700000
800000
Institutional credit (`crore)
Co-operatives SCBs RRBs
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Fig 1 Trend in ow of institutional credit to agriculture and allied activities in real terms. Source:
NABARD
Table 2 Region-wise ow of institutional credit by SCBs and RRBs in India
Region Share in total institutional credit (%) Amount outstanding per ha1 (₹)
1991–92 2001–02 2011–12 2016–17 1991–92 2001–02 2011–12 2016–17
Northern 16.89 19.08 21.25 20.27 4400 5744 27773 51772
North-eastern 2.33 1.34 1.40 1.81 3773 3600 15151 33807
Eastern 12.59 10.43 10.16 9.95 2703 4217 20628 31123
Central 20.10 20.42 17.83 19.75 3133 4008 22536 37929
Western 12.54 12.82 11.30 12.51 5098 6740 21947 33181
Southern 35.55 35.92 38.06 35.70 8637 14400 76118 135036
All 100 100 100 100 4624 6452 30692 53808
Note: 1 in real terms
Source: Authors’ calculation using data from RBI and DES, GoI
Table 1 Compound annual growth rates of institutional credit
ow to agriculture (%)
Period Co-operatives SCBs RRBs Total
1991–92 to 2000–01 6.43 1.61 7.35 4.20
2001–02 to 2010–11 -4.10 20.42 17.24 12.08
2011–12 to 2016–17 15.81 6.50 12.42 8.79
1991-92 to 2016–17 4.88 12.45 13.52 10.37
Source: Authors’ calculation using data from NABARD
415February 2020]
175
INSTITUTIONAL CREDIT FOR AGRICULTURAL PRODUCTIVITY
outreach may be due to varying resource endowments and
technology adoptions. Therefore, there is a need for increase
in investment in capital formation to improve the resource
base in backward regions.
Kisan Credit Card scheme: The Kisan Credit Card
(KCC) scheme was a milestone in the rural credit history
of India. The KCC scheme was instituted in 1998–99
as a agship program to disburse short term agricultural
credit. The scheme was meant to expand credit outreach
and simplify the credit delivery process. Later in 2004,
investment credit was brought under KCC scheme making
it a single window for availing rural credit. Based on the
number of cards issued, the KCC scheme was branded as
a major success in the rural credit delivery system. On an
average, two-thirds of the farming households possess KCCs
in India (Kumar et al. 2010).
Figure 3 slows the state-wise distribution density of
KCC during 2016-17. Odisha had the highest density of
KCC (one card per 1.09 ha) followed by Kerala (one card
per 2.06 ha) and Andhra Pradesh (one card per 2.07 ha).
Gujarat (one card per 4.61 ha) and Rajasthan (one card per
4.03 ha) had the lowest density of KCC.
Impact of institutional credit on agricultural
productivity-Panel data model: The panel was constructed
for 13 major states for the period 1991-92 to 2015-16.
The model specication was done using the Hausman
specication test. This test revealed that xed effect and
random effect models were indifferent enough to accept
the null hypothesis. Therefore, random effect
model was applied for the estimation.
The results of the random effect model are
presented in Table 3. The results indicated that
institutional credit has a signicant positive
impact on the value of output from agriculture
which is a proxy for crop productivity. The
other variables such as irrigation coverage and
fertilizer consumption also had a signicant
and positive impact on productivity. Earlier
literatures also support the positive impact of
institutional credit on agricultural productivity
(Hazarika and Alwang 2003, Foltz 2004, Das
et al. 2009, Diagne and Zeller 2001, Kannan
2011, Awotide et al. 2015, Kumar et al. 2017).
This study sought to investigate the
trends and impact of institutional credit on
Fig 2 State-wise production and investment credit per ha during 2016–17.
Source: Authors’ calculation using data from NABARD and DES, GoI
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Area (ha) served per KCC
Odisha
Kerala
Andhra Pradesh
Uttar Pradesh
Bihar
Himachal
Tamil Nadu
Karnataka
West Bengal
Haryana
Madhya
Maharashtra
Jammu & Kashmir
Punjab
Rajasthan
Gujarat
Pradesh
Pradesh
Fig 3 State wise distribution of KCC in India during 2016–17.Source: Authors’
calculation using data from NABARD and DES, GoI
416 [Indian Journal of Agricultural Sciences 90 (2)
176
SHIVASWAMY ET AL.
agricultural productivity in India. The institutional credit
ow to agriculture in India has been increasing over the years
with the rise being more pronounced during 2001–10. There
was a structural shift in the institutional ow of agricultural
credit with a rising share of SCBs and RRBs. Cooperatives
were the major sources of production credit whereas, SCBs
were the major lenders of investment credit. We found a
persisting disparity in institutional credit outreach to different
regions. Per ha disbursement of institutional credit was
highest in southern states, whereas eastern states received
the lowest amount during the study period. Panel data model
revealed a signicant and positive impact of institutional
credit on agricultural productivity. These results highlight the
need for expansion of credit outreach through simplication
of procedure for loan disbursement. There is also a need
to ensure equitable credit distribution across regions with
specic focus on credit hungry eastern, western and north
eastern states.
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Table 3 Panel data regression results by using random effect
model
Variable Value of output from agriculturea
(crop sector) (`/ha)
Institutional credit (₹/ha) 0.21***
(0.01)
Irrigation coverage (%) 585.27***
(95.43)
Fertilizer (Kg/ha) 72.94***
(18.78)
Constant 13382.06***
(3343.06)
R Squared 0.90
Wald chi-square 3028.12
Prob>chi square 0.00
Observations 325
Number of years 25
Note: ain real terms; Standard errors in parentheses; ***represents
1% level of signicance
417February 2020]
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INSTITUTIONAL CREDIT FOR AGRICULTURAL PRODUCTIVITY
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