Awareness about Minimum Support Price and Its Impact on Diversification Decision of Farmers in India: MSP Awareness and Crop Diversification

Abstract

In this article, we have analysed farmers' awareness about Minimum Support Price (MSP) and its impact on diversification of crops grown in India. We used nationally representative data collected by National Sample Survey Office, 70th round data. The data revealed that only 23.72 and 20.04 per cent of farmers in the rural agricultural households in India are aware of MSP of crops grown by them in kharif and rabi season, respectively. From the results of probit model, it is inferred that MSP needs to be backed up by effective procurement coupled with awareness creation by extension system to enable more number of farmers to take benefit of this safety net. We have also explored the relationship between farmers' awareness about MSP and decision to go for crop specialization using Heckman selection model. The study shows that farmers' knowledge of MSP had not lead to specialization.

Full text

Original Article
Awareness about Minimum Support Price and Its Impact on
Diversification Decision of Farmers in India
K.S. Aditya, S.P. Subash, K.V. Praveen, M.L. Nithyashree, N. Bhuvana and
Akriti Sharma*
Abstract
In this article, we have analysed farmers’
awareness about Minimum Support Price
(MSP) and its impact on diversification of crops
grown in India. We used nationally representa-
tive data collected by National Sample Survey
Office, 70th round data. The data revealed that
only 23.72 and 20.04 per cent of farmers in the
rural agricultural households in India are
aware of MSP of crops grown by them in kharif
and rabi season, respectively. From the results
of probit model, it is inferred that MSP needs
to be backed up by effective procurement
coupled with awareness creation by extension
system to enable more number of farmers to
take benefitofthissafetynet.Wehavealsoex-
plored the relationship between farmers’
awareness about MSP and decision to go for
crop specialization using Heckman selection
model. The study shows that farmers’knowl-
edge of MSP had not lead to specialization.
Key words: agricultural policy, crop
diversification, Minimum Support Price, crop
specialization
1. Introduction
Minimum Support Price (MSP) is an integral
component of Agriculture Price Policy of
India. It targets to ensure support price to
farmers and affordable price to consumers
through public distribution system (PDS)
(Parikh & Singh 2007). The price support sys-
tem was conceptualized during pre-green revo-
lution period as an institutional mechanism for
incentivizing farmers to adapt new technolo-
gies (Planning Commission 2005; Deshpande
2008). Later, Agriculture Price Commission
was established in the year 1965, based on
Jha committee recommendations to suggest
support prices for crops after considering the
cost of cultivation to account (Kadasiddappa
et al. 2013). Broad objectives of the commis-
sionaretoensureremunerativepricesto
farmers and reasonable prices to consumers
and promote sustainable use of resources
towards socially desirable crop mix (Parikh &
Singh 2007).
Price incentives in the form of support prices
helped India to increase food production dur-
ing green revolution period. MSP also aims at
procuring food grains from food surplus states
for distribution through PDS and maintaining
buffer stockand thus bridge the demand supply
gap (Jha & Srinivasan 2006; Chand 2008).
Price incentives in form of MSP are credited
for the increase in area under rice and wheat
in the green revolution states like Punjab and
Haryana.
Agricultural situation in India has under-
gone sea change after the green revolution
* Aditya, Praveen and Nithyashree: Division of Ag-
ricultural Economics, Indian Agricultural Research
Institute (IARI), New Delhi, India; Subash: National
Institute of Agricultural Economics and Policy
Research (NIAP), New Delhi 110012, India;
Bhuvana: Agricultural Extension, PJTSAU, Hydera-
bad; Sharma: Zonal Technology Management and
Business Planning and Development Unit, IARI.
Corresponding Author: Praveen, email <veenkv@
gmail.com>.
Received: 5 May 2017 | Revised: 2 August 2 017 | Accepted: 7 August 2017
Asia & the Pacific Policy Studies, vol. ••,no.••,pp.••–••
doi: 10.1002/app5.197
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period, but the agriculture price policy has
more or less remained same (Chand 2003).
Food surplus is available in many states and
not just Haryana, Punjab and Andhra Pradesh;
however, the procurement has largely confined
to these regions (Planning Commission 2005).
MSP is viewed as a safety net to ensure price
security for a long-term investment decision
to farmers.
There have been many concerns off late
regarding operation and effectiveness of
MSP. Many studies have pointed out that
MSP is leading to regional imparity in incomes
as it is effective only in few states where it is
backed by procurement (Ali et al. 2012;
Tripathi 2013; Schiff et al. 1992). MSP is also
said to have favoured crop specialization in
with rice and wheat at the cost of pulses and
oil seeds (Chand 2003; Jha & Srinivasan
2006; Jha 2009; Mittal & Hariharan 2016).
With demand–supply situation undergoing
sea change over the last couple of decades or
so, the agriculture price policy needs a relook.
Treating MSP as a safety net, in this study,
we explore the farmers’awareness of MSP of
crops grown by them, across crops and states,
with the hypothesis that awareness is the bare
minimum requirement for policy interventions
to have any impact. We also explore the
reasons for the apathy of farmers to sell their
produce to procurement agencies. The corre-
lates of awareness about MSP have also been
examined. The study also tries to reconnoitre
the possible relationship between knowledge
of MSP and farmers’choice of crop
specialization/diversification. The key objec-
tives of the study are to understand the status
of farmers’awareness of MSP of crops grown
by them and its correlates and to explore the
nature of the relationship between farmers’
awareness of MSP and decision to diversify
the crops.
2. Theoretical Framework
When viewed from a safety net perspective,
MSP helps farmers by setting floor price if
procurement agency purchases the product at
MSP when the open market price falls below
the floor price. In absence of procurement, a
farmer can refuse to settle for a price below
MSP if he is aware of the support price for
the crops. If he is not even aware of MSP of
crops, traders and middlemen can turn exploit-
ative and offer price less than MSP (Economic
Survey 2016). So, in this study, awareness
about MSP is considered as a proxy for the
impact of support prices.
The underlying theoretical framework for
the relationship between crop diversification
and MSP awareness is drawn from livelihood
diversification theory. Livelihood diversifica-
tion is defined as the process by which rural
families construct a diverse portfolio of activi-
ties and social support capabilities in their
struggle for survival in order to improve their
standards of living (Ellis 1998). The theory
states that farmers respond to policy, environ-
mental and resource constraints with measures
like changing crop choices. This study is based
on the assumption that if there is no MSP, the
farmers would rationally diversify his cropping
pattern. In India, a significant amount of area
under coarse cereal crop has been replaced by
fine cereals like rice and wheat, which clearly
suggest the biasness of MSP towards the later
mentioned crops. Farmers who are aware of
MSP will be inclined to allocate major area
share to that crop as there is little price risk
involved. This assumption is valid in our case
as most of the farmers who were aware of
MSP of crops are either rice or wheat growers.
Our aim here is to see whether knowledge of
MSP makes them to allocate more area to that
crop leading to crop specialization (or less or
no diversification) or not.
3. Data and Methods
We have used the data from ‘Situational
Assessment Survey of Farmers’(National
Sample Survey Office 70th round). The sample
consists of data regarding 35,200 rural agricul-
tural households spread across 4,529 villages
of India collected using stratified multistage
random sampling (NSSO 2015). The data were
collected for kharif and rabi (the two major
agricultural seasons of India) in two separate
visits and pertain to the year 2012–13. Only
the households that grow at least one crop for
2 Asia & the PacificPolicyStudies •• 2017
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published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University

which MSPs are announced were considered
for analysis as MSP is announced only for 24
crops at present (Table A1). A household,
which is aware of MSP of at least one crop, is
treated as being ‘aware’about MSP and vice
versa.
We have also used agro-climatic region
dummies at the district level to account for
the difference in agro-climatic parameters that
affects the crop diversification. Theoretically,
amongst the climatic variables, rainfall plays
an important role in farmers’choice of crop
diversification. Hence, district-wise rainfall
data for the corresponding year was collected
from Indian Meteorological Department and
used explicitly in the regression equation.
3.1. Simpson Diversity Index for Measuring
Crop Specialization
There are different methods to measure
crop diversity. Diversity indices are a common
measure for assessing plant diversity
(Magurran 2004). Simpson index is more
responsive to dominant species (Aguilar et al.
2015). Crop diversification was quantified
using Simpson index in this study. The index
is based on the share of the ith crop in gross
cropped area for the season (Mittal &
Hariharan 2016). The value of the index
depends on a number of crops grown and
their share in gross cropped area. Assuming
all the crops grown have the same share of
gross cropped area, as the number of crops
grown approaches infinity, the value of index
approach unity. Theoretically, Simpson index
ranges between zero and one, zero being the
case of complete specialization.
SI ¼1∑
n
i¼1
P2
i
where P
i
represents the share of area under
each crop to gross cropped area.
3.2. Heckman Model
Heckman selection model could be used to
access whether there is an underlying regres-
sion relationship between awareness about
MSP (regression equation) and cropping
diversity (selection equation). The underlying
assumption is that awareness about MSP is
non-random contingent upon a set of ob-
served and unobserved characteristics. Gen-
erally, estimated impact of MSP awareness
may be biased and inconsistent because of
‘selection biases’. Source of this bias is
endogeneity of ‘awareness’variable (corre-
lated with the error term consequential upon
non-randomness). We need to account for
unobservable factors affecting both probabil-
ity of being aware of MSP and choice of
crop diversification.
The first step in our model is the selection
equation that captures the factors that affect
probability of farmer being aware of MSP
of crops that he grows. Then we estimate
‘inverse lambda’to account for selection bias
and include it as an explanatory variable in
the outcome equation that establishes the rela-
tionship between crop diversification and
MSP awareness (following Briggs 2004). As
suggested by Heckman, this approach will
account for the unobserved selection bias and
produce efficient estimates. And also, the first
equation helps us to identify the drivers of
awareness about MSP awareness.
Regression equation used in the study
is expressed mathematically as outcome
equation:
Yj¼Xjβþu1j(1)
where Y
j
is the Simpson index of diversifica-
tion and X
j
is the vector of independent
variables.
The corresponding selection equation is
M¼zjγþμ2jand observed only if zjγ
þμ2j>0(2)
where Mis equal to awareness about MSP of
crops
μ1
eN0;σðÞ
μ2
eN0;σðÞ
corr u1;u2
ðÞ¼ρ
3Aditya et al.: MSP Awareness and Crop Diversification
© 2017 The Authors. Asia and the Pacific Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University

When ρ≠0, standard regression techniques
applied to the first equation yield biased results.
Heckman provides consistent, asymptotically
efficient estimates for all the parameters in such
models. The variables used in the model are
given in Table 1.
3.3. Limitations
Farmers’choice of crop diversification also
depends on the scope for diversification in
the region and socio-economic criteria. We
have tried to account for most of these
factors within the constraints imposed by the
availability of data and methods. We admit that
research question can be better addressed with
panel data.
4. Results and Discussion
Minimum Support Prices are an important
component of agriculture price policy in
India. The scheme provides the floor price for
farm produceand also makes food grains avail-
able for buffer stock and PDS. It provides secu-
rity for long-term investment decisions of the
farmers. Another important objective of MSP
is to incentivize the farmer to allocate resources
in socially desired cropping patterns. MSP is
expected to provide a sense of price security
to the farmer and motivate them to diversity
the crops. MSP as an incentive for diversifica-
tion is superior to other incentives (Planning
Commision 2005).
Policy decisions by the Government in
terms of MSP for the past 14 years are
Table 1 Variables Used in the Analysis and Their Description
Name Type
Outcome equation: Dependent variable is Simpson index of diversification
Aware about MSP of crops Dummy = 1 if aware and 0 otherwise
Inverse mills Variable to account for selection bias
Annual rain Mean annual rainfall of the district (for 2012–13)
Annual drought Dummy = 1 if actual rainfall is deficient by more than 20%
SC Dummy = 1 if belongs to scheduled caste and 0 otherwise
ST Dummy = 1 if belongs to scheduled tribe and 0 otherwise
OBC Dummy = 1 if belongs to other backward caste and 0 otherwise
Age Age of the head of the household
Age
2
Squared term of age of head of household
Household size Number of members is family
Literate Dummy = 1 if literate and 0 otherwise
Received training in agriculture Dummy = 1 if received training in agriculture and 0 otherwise
Public extension contact Dummy = 1 if accessed public extension service and 0 otherwise
Progressive farmer contact Dummy = 1 if accessed progressive farmer as source of information and 0 otherwise
Mass media contact Dummy = 1 if accessed progressive farmer as source of information and 0 otherwise
Agriculture as primary income
source
Dummy = 1 if agriculture is the main source of income and 0 otherwise
Marginal farmer Dummy = 1 if size of operational holding is less than 1 ha and 0 otherwise
Small farmer Dummy = 1 if size of operational holding is between 1 and 2 ha and 0 otherwise
Semi-medium farmer Dummy = 1 if size of operational holding is between 2 and 5 ha and 0 otherwise
Medium farmer Dummy = 1 if size of operational holding is between 5 and 10 ha and 0 otherwise
BPL Dummy = 1 if farmer belongs to below poverty line category
AEZ_dummy Dummies for 14 different ACZ, =1 if the farmer belongs to that particular ACZ.
Selection equation: Dependent variable n= awareness about MSP—Dummy = 1 if farmer is aware
Male headed Farm household where head of the family is male
Effective states Dummy = 1 if the farmer belongs to states where there is the active procurement of
food grains, that is, Haryana or Punjab or Chhattisgarh or Andhra Pradesh or Karnataka
and 0 otherwise (classification based on literature survey)
Notes: Squared term of age is used to improve the fit of the model as the reviewed literature suggests thatthe variable follows
quadratic form. ACZ, agro-climatic zones; MSP, Minimum Support Price.
4 Asia & the PacificPolicyStudies •• 2017
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presented in Figure 1. MSP for all crops is
continuously rising and conscious effort by
the policy to favour pluses, oilseeds and minor
cereals can be seen particularly after 2007–08.
During this period, MSP for major pulses was
raised by Rs. 526 (Tur), 955 (each for Moong
and Urad), 624 (Groundnut) and 156 (Gram)
over the previous years when compared with
Rs. 240, 367 and 240 hikes in MSP for Jowar,
Ragi and Bajra respectively in real terms.
Further, in 2011–12, MSP for these was hiked
by Rs. 615 (Tur), 860 (Moong), 961 (Urad),
967 (Groundnut), 588 (Gram), 506 (Jowar),
436 (Ragi) and 184 (Bajra) over the previous
years in real terms. But is this a sufficient
incentive for the farmer to diversify the crops
with the inclusion of pulses and other minor
crops? Does the announcement of MSP instil
a sense of price security in a farmer?
For MSP to function as safety net, there
must be a system of procurement, which
should buy the produce at MSP whenever
market prices fall below support price for the
crop, and farmer must be aware of the MSP
for the crops grown by him so that he can
refuse to sell his produce at price below MSP.
Table 2 presents the percentage of farmers
who are aware of MSP of crops grown by
them. At crop level (each farmer may be grow-
ing more than one crop), the awareness stands
at around 17 per cent for both kharif and rabi.
But MSP is announced only for selected crops
in each season and calculating the share to a
total number of crops may be erroneous. We
have considered subsample of farmers who
grow at least one crop for which MSP is
announced, and data of such farmers are used
in the analysis (except summary statistics).
Any household having knowledge of MSP
for at least one crop is considered as aware,
and the share of such households is 28.30 and
23.13 per cent in rabi and kharif, respectively
(Table 2). We can draw inference for India as
Figure 1 Trends in Minimum Support Price for Major Pulses, Oilseeds and Cereal Crops (in Rs. at 2011–12 Prices)
Table 2 Share of Farmers’Aware of MSP of Crops
Grown by Them (%)
Awareness of MSP Rabi Kharif
Awareness at crop level 17.51 17.16
Awareness at household level 28.30 23.13
Awareness at household level
with sampling weight
23.72 20.04
Note: MSP, Minimum Support Price.
Source: NSSO data.
5Aditya et al.: MSP Awareness and Crop Diversification
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published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University

a whole only after accounting for sampling
weights. So we can say that only 23.72 and
20.04 per cent of Indian households are aware
of MSP of crops grown by them (Figure 2).
Even after more than 40 years after its
implementation, less than 25 per cent of
farmers knows the MSP of crops they grow.
Although MSP is announced for the whole of
India, the operation is limited only to few states
where the designated government agencies
procure the produce from farmers. Statewise
figures on farmers’knowledge of MSP of
crops support our proposition. In states where
procurement of food grains through designated
agencies is more active, like Punjab, Haryana,
Chhattisgarh, Uttar Pradesh and Telangana,
the awareness of MSP is also high (Table 3).
Further, 27.83 and 30.48 per cent of farmers
are reported of being unaware about the agency
that procures the food grains at MSP (Table 4).
There is a need for creating a good network of
procurement agencies and also for awareness
amongst farmers about the operation of MSP.
Except for crops like rice and wheat, quantity
procured is very limited leading low level of
awareness. Even for rice and wheat, procure-
ment takes place only in few states, and more
farmers are aware of MSP in those states
(Figure 3).
Minimum Support Price of rice and wheat is
known to the majority of the farmers in
Figure 2 Statewise Awareness of Farmers about Minimum Support Price (MSP) of Crops in Major
Crop Seasons (%)
Table 3 StatewiseAwareness of Farmers about MSP of
Crops (%)
States Rabi Kharif
Punjab 52.94 48.93
Chhattisgarh 37.09 47.20
Delhi 64.29 41.18
Odisha 9.85 36.23
Haryana 32.10 27.80
Uttar Pradesh 22.43 27.59
Bihar 22.84 27.49
West Bengal 19.29 26.23
Telangana 30.82 25.32
Kerala 19.47 22.09
Rajasthan 20.90 15.06
Andhra Pradesh 14.35 14.60
Karnataka 14.61 13.97
Jharkhand 4.96 13.25
Himachal 10.24 13.24
Madhya Pradesh 30.47 12.19
Gujarat 9.97 12.02
Uttaranchal 9.14 9.81
Jammu and K 6.03 8.21
Maharashtra 8.00 8.19
Tamil Nadu 15.12 7.71
Arunachal 7.39 6.49
Tripura 21.50 5.9 9
Mizoram 0.30 4.24
Assam 3.88 4.09
Nagaland 1.96 3.87
Chandigarh 6.67 3.13
Meghalaya 12.52 1.33
Manipur 0.14 0.48
Sikkim 0.00 0.00
Note: MSP, Minimum Support Price.
Source: NSSO data.
6 Asia & the PacificPolicyStudies •• 2017
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Haryana, Punjab and Chhattisgarh; major
states from where food grains are procured
for buffer stock or PDS. Knowledge of MSP
of rice and wheat is less in other growing
regions like Andhra Pradesh, Telangana,
Karnataka, Uttar Pradesh and Bihar. For
important pulse crops like Tur and Gram, the
share of farmers knowing MSP of crops is very
less across all states. Less than 10 per cent
awareness for most the pulses is another cause
of concern (Tables 5, 6). With India trying to
increase the pulse production, support prices
can act as an incentive if the government starts
procurement through a good network of chan-
nels. Then only the benefit of support prices
will reach farmers and be able to provide price
security that it intends to.
Out of meagre proportion of farmers who
were aware of MSP, 75.09 and 75.58 per cent
of farmers (in rabi and kharif, respectively)
have not sold the produce to procurement
agencies. The reasons for not selling to pro-
curement agency are given in Table 7. The role
of MSP is to set the floor price, and if farmers
have received a better price than MSP, it is fine.
Only 7.97 and 8.77 per cent of farmers reported
that they did not sell to procurement agency as
they received a better price in the market. But
nearly 25 per cent of farmers reported that there
is no procurement agency/local purchaser
available to procure the produce at MSP.
Ideally speaking, the benefitofMSPshould
reach all farmers across all states and for which
a good network of procurement agencies with
required infrastructure is also must. But from
a practical point of view, this may not be
feasible. Government agencies are already
facing difficulty in storage and maintenance
Table 4 Farmers Awareness of Procurement Agencies
(%)
Procurement agency Rabi Kharif
Food Corporation of I ndia 18.8 0 19.02
Jute Corporation of India 1.02 1.35
Cotton Corporation of India 0.62 2.13
National Agricultural Cooperative
Marketing Federation of India Ltd
5.24 3.30
State Food Corporation 17.74 13.23
State Civil Supplies 7.73 9.13
Others 21.03 21.36
Did not know 27.83 30.48
Total 100 100
Source: NSSO data.
Figure 3 Cropwise and Statewise Awareness of Farmers about Minimum Support Price of Crops
Table 5 Farmers Awareness about MSP by Crop
Groups (%)
Crop group Rabi Kharif
Cereals 29.02 22.04
Pulses 10.1 8.67
Oilseeds 17.67 15.88
Sugarcane, cotton and jute 24.24 21.04
Total 17.51 17.16
Note: MSP, Minimum Support Price.
Source: NSSO data.
7Aditya et al.: MSP Awareness and Crop Diversification
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published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University

of food grains procured. The infrastructure
available may not allow us to expand procure-
ment to the whole country. Instead, MSP can
be backed by the method of ‘deficiency pay-
ments’as suggested by many earlier studies
and reports. If the market prices fall below
MSP, 50 per cent of the difference between
price and MSP will be paid to a farmer under
this system. MSP needs to be backed by either
effective procurement or system of deficiency
payment if it were to help the farmer by setting
floor prices.
Another thing to note is 66.68 and 63.22 per
cent of farmers in rabi and kharif reported that
they have not sold to procurement agency
because of other reasons. The other reason
might include a delay in payments of money
by procurement agencies. A study by
(Deshpande 2008) reported lag of nearly
1 month between procurement and payment.
The method of payment using checks rather
than cash might also add to farmers’indifferent
attitude to procurement agencies.
We have used a probit model to identify the
correlates of farmers’knowledge of MSP of
crops grown by them. The same model is used
as selection equation (first step) in Heckman
two-step selection model. Household’s aware-
ness of MSP is the dependent variable for the
regression. State dummies were used to nullify
the effect of unobserved heterogeneity across
the states. Standard errors were clustered at
the region to enhance the precision. The results
indicate that literacy and extension contact
(both public extension and progressive farmer
contact) are positively associated with aware-
ness about MSP. Marginal farmers have less
probability of knowing MSP of crops com-
pared with their counterparts. Households
below the poverty line
1
were also found to
have lesser awareness about MSP (Table 8).
If farm household belongs to states where
MSP is coupled with active procurement
1. In this study, the poverty line is measured using posses-
sion of below povertyline cards. These cards are distributed
amongst rural households that make them eligible under
PDS.
Tabl e 6 Fa r me r s’Knowledge of MSP by Crops
Crop
Rabi Kharif
Aware Not aware Aware Not aware
Number Percentage Number Percentage Number Percentage Number Percentage
Paddy 1,183 28.15 3,020 71.85 4,987 27.43 13,192 72.57
Jowar 46 8.65 486 91.35 128 7.8 1,514 92.2
Bajra 7 5.22 127 94.78 320 16.15 1,661 83.85
Maize 200 15.55 1,086 84.45 466 11.29 3,663 88.71
Wheat 3,621 33.2 7,285 66.8 2 11.11 16 88.89
Gram 256 11.67 1,937 88.33 7 5.38 123 94.62
Tur 38 7.72 454 92.28 97 7.87 1,135 92.13
Urad 48 13.83 299 86.17 121 10.07 1,080 89.93
Sugarcane 458 55.18 372 44.82 702 46.34 813 53.66
Groundnut 37 9.44 355 90.56 67 7.88 783 92.12
Cotton 111 24.24 347 75.76 491 21.04 1,843 78.96
Note: MSP, Minimum Support Price.
Source: NSSO data.
Table 7 Reasons quoted by farmers for not selling to
procurement agency
Rabi Kharif
Percentage of farmers not selling to procurement agencies
75.09 75. 58
Reason for not selling to procurement agencies
Procurement agency not available 16.53 17.53
No local purchaser 6.63 6.94
Poor quality of crop 1.25 2.31
Crop pre-pledged 0.75 1.20
Received better prices 7.97 8.77
Other 66.88 63.26
Total 100.00 100.00
Source: NSSO data.
8 Asia & the PacificPolicyStudies •• 2017
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published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University

(variable-effective states), the probability of
such household knowing MSP of the crop is
high. But, probit coefficients cannot be
compared based on magnitude. So, we calcu-
lated marginal effect of each variable that sig-
nifies magnitude of increase in the probability
Table 8 Correlates of MSP Awareness: Results of Probit Analysis (First Stage of Heckman Model = Selection
Equation)
Variables
Kharif Rabi
Coefficient P>|z|Coefficient P>|z|
Male headed 0.144 0.004 0.158 0.246
Literate 0.266 0.000 0.261 0.000
Received training in agriculture 0.153 0.030 0.085 0.553
Household size 0.007 0.079 0.015 0.042
ST 0.157 0.059 0.128 0.274
SC 0.118 0.023 0.014 0.863
OBC 0.068 0.061 0.009 0.917
Age 0.011 0.010 0.008 0.572
Age
2
0.000 0.116 0.000 0.726
Marginal farmer 0.566 0.000 0.637 0.002
Small farmer 0.280 0.011 0.286 0.149
Semi-medium farmer 0.146 0.163 0.236 0.194
Medium farmer 0.026 0.802 0.255 0 .109
BPL 0.152 0.000 0.145 0.059
Land leased in 0.035 0.099 0.039 0.224
Agriculture as primary income source 0.040 0.304 0.068 0.162
Public extension contact 0.347 0.000 0.336 0.000
Progressive farmer contact 0.386 0.000 0.412 0.000
Mass media contact 0.089 0.137 0.090 0.202
Effective states 1.169 0.000 1.156 0.001
Constant 1.763 0.000 1.918 0.000
Notes: Dependent variable: Aware about MSP. Log pseudolikelihood = 30,483,404, number of observations = 27,715,
pseudo-R
2
= 0.1699, standard error adjusted for 84 clusters in region and state dummies were also used (for kharif). Log
pseudolikelihood = 26,819,397, number of observations = 20,816, pseudo-R
2
= 0.1453,standard error adjusted for 85 clus-
ters in region and state dummies were also used (for rabi). BPL, below poverty line; MSP, Minimum Support Price; OBC,
other backward caste; SC, scheduled caste; ST, scheduled tribe.
Figure 4 Marginal Effects of Variables on Probability of Farmer Being Aware about Minimum Support Price. BPL,
Below Poverty Line; OBC, Other Backward Caste; SC, Scheduled Caste; ST, Scheduled Tribe
9Aditya et al.: MSP Awareness and Crop Diversification
© 2017 The Authors. Asia and the Pacific Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University

of being aware of MSP for a unit increase
in a respective variable. The result is depicted
in Figure 4. Most important variables that
increase the awareness about MSP are active
procurement, awareness creation (public ex-
tension contact and progressive farmer contact)
and literacy. This reinstates that announcing of
MSP does not work in isolation unless coupled
with procurement. Recent hikes in MSP of
pulses and oilseeds to increase in production
may not work unless it is backed by procure-
ment or deficiency payment.
In the next step, using Heckman selection
model, we tried to establish the relationship
between farmers’decision to diversify (or to
specialize) and MSP awareness (Table 9). For
kharif season, variable for awareness was
statistically insignificant, and for rabi, it was
positive as well significant. So we do not
accept the null hypothesis that awareness about
Table 9 Relationship between Knowledge of MSP and Crop Diversification: Result of Heckman Outcome Equation
Simpson index
Kharif Rabi
Coefficient P>|t| Coefficient P>|t|
Aware about MSP of crops 0.007 0.379 0.023 0.009
Inverse mills 0.055 0.305 0.045 0.603
Annual rain 0.000 0.047 0.000 0.944
Annual drought 0.010 0.515 0.000 0.976
SC 0.006 0.676 0.022 0.085
ST 0.059 0.001 0.014 0.356
OBC 0.004 0.699 0.006 0.457
Age 0.001 0.490 0.002 0.048
Age
2
0.000 0.575 0.000 0.077
Household size 0.003 0.000 0.003 0.000
literate 0.023 0.166 0.015 0.458
Received training in Agriculture 0.014 0.274 0.008 0.559
Public extension contact 0.017 0.190 0.019 0.427
Progressive farmer contact 0.008 0.322 0.012 0.274
Mass media contact 0.019 0.014 0.020 0.034
Agriculture as primary income source 0.070 0.125 0.074 0.122
Marginal farmer 0.029 0.437 0.017 0.608
Small farmer 0.004 0.912 0.005 0.845
Semi-medium farmer 0.019 0.442 0.003 0.873
Medium farmer 0.008 0.611 0.008 0.749
BPL 0.003 0.784 0.000 0.974
AEZ15_dummy 0.060 0.187 0.113 0.024
AEZ2_dummy 0.051 0.172 0.015 0.822
AEZ3_dummy 0.016 0.743 0.101 0.402
AEZ4_dummy 0.009 0.922 0.005 0.970
AEZ5_dummy 0.058 0.542 0.017 0.891
AEZ6_dummy 0.059 0.151 0.000
AEZ7_dummy 0.135 0.001 0.052 0.665
AEZ8_dummy 0.025 0.761 0.059 0.606
AEZ9_dummy 0.051 0.522 0.036 0.750
AEZ10_dummy 0.061 0.270 0.097 0.314
AEZ11_dummy 0.121 0.000 0.055 0.607
AEZ12_dummy 0.011 0.865 0.044 0.694
AEZ13_dummy 0.014 0.813 0.003 0.950
AEZ14_dummy 0.036 0.706 0.053 0.653
Constant 0.444 0.000 0.207 0.298
Notes: Number of observations = 26,330, R
2
= 0.1700, standard error adjusted for 84 clusters in the region and state dummies
were also used in kharif season. Number of observations = 19,901, R
2
= 0.1350, standard error adjustedfor 83 clusters in the
region and state dummies were alsoused in rabi season. BPL, below poverty line;MSP, Minimum Support Price; OBC, other
backward caste; SC, scheduled caste; ST, scheduled tribe.
10 Asia & the PacificPolicyStudies •• 2017
© 2017 The Authors. Asia and the Pacific Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University

MSP leads to crop specialization. Although the
sign of awareness variable is positive, the mag-
nitude of the coefficient is very small indicat-
ing the weak relationship it has with crop
diversification. Most of the variability is
absorbed by state dummies, indicating a deci-
sion to diversify mostly depends on the poten-
tial of the region more than the other factors
(Figure 5).
5. Concluding Remarks
Minimum Support Prices are considered as an
important pillar of Indian Agricultural price
policy rolled out with an intention of providing
price security to farmers. Theoretically, the
support prices are to benefit farmers of most
of the crops in the entire nation. In this article,
we tried to analyse the level of awareness of
farmers about MSP of crops they grow and
its correlating factors using a comprehensive
dataset of National Sample Survey Office,
70th round. We found that more than 75 per
cent of Indian households is not aware of
MSP of crops grown by them. Awareness
was high only in case of rice and wheat that
too only in few states like Punjab, Haryana
and Chhattisgarh, from where food grains are
heavily procured by designated agencies for
maintaining buffer stock or PDS. Awareness
of MSP of pulse crops was even less (<10
per cent for most of the crops), which is a cause
of concern. If the farmers are awareof the MSP
of crops, they can bargain price and refuse to
settle for less. Their ignorance would make it
easy for middlemen and other traders to exploit
the farmers by quoting less price.
Out of few who were aware of MSP, nearly
25 per cent of farmers reported not selling the
produce to procurement agencies. Unavailabil-
ity of procurement agencies and local pur-
chasers were reported as the major reason.
From probit regression, we conclude that to
make more farmers aware about MSP of crops
and to enable them to take benefit of it, better
network of procurement agencies should be
developed. Decentralized procurement agen-
cies with local presence coupled with increased
storagecapacityorsystemofdeficiency pay-
ments to bypass the need for procurement can
extend the benefits of support prices to a larger
segment of the farming community. Public
extension machinery will also play a vital role.
We found no empirical evidence to prove that
awareness of MSP leads to crop specialization
as procurement is biased towards rice and
wheat.
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Appendix A
Table A1 List of Crops for Which MSP Is Declared
Serial number. Crop Common/other name Type/variety
Kharif crops
1Paddy Common/grade‘A’
2 Sorgum Jowar Hybrid/maldandi
3 Pearl millet Bajra
4MaizeCorn
5 Finger millet Ragi
6 Pigeon pea Arhar (Tur)
7 Green gram Moong
8BlackgramUrad
9 Cotton Medium staple/long staple
10 Groundnut In shell
11 Sunflower Seed
12 Soyabeen Yellow and black
13 Sesamum –
14 Niger Seed
Rabi crops
15 Wheat
16 Barley
17 Gram
18 Lentil Masur
19 Rapeseed/mustard
20 Safflower
Other crops
21 Copra Milling/ball
22 De-husked coconut
23 Raw jute
24 Sugarcane
Notes: MSP of the crops are declared by the government based on recommendations of CACP. CACP is under Ministry of
Agriculture and Farmers Welfare, Government of India. They provide price policy reports to government for crops grown in
kharif and rabi and for other cops such as sugarcane, raw jute and copra. Although cost of production is a key factor in de-
termination of MSP, other factors such as demand and supply, price trends (domestic and international), intercrop price par-
ity, terms of trade between agriculture and non-agriculture and implication of MSP on consumers are considered. For more
details on MSP, see CACP website http://cacp.dacnet.nic.in/. CACP, Commission for Agricultural Costs and Prices; MSP,
Minimum Support Price.
13Aditya et al.: MSP Awareness and Crop Diversification
© 2017 The Authors. Asia and the Pacific Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University