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Understanding Price Variation
in Agricultural Commodities in India: MSP, Government
Procurement, and Agriculture Markets
Shoumitro Chatterjee Princeton University
Devesh Kapur University of Pennsylvania
India Policy Forum July 12–13, 2016
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The findings, interpretations, and conclusions expressed are
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the Governing Body or Management of NCAER.
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Understanding Price Variation in Agricultural Commodities in
India: MSP, Government
Procurement, and Agriculture Markets*
Shoumitro Chatterjee Princeton University
Devesh Kapur University of Pennsylvania
India Policy Forum July 12–13, 2016
*Preliminary draft. Please do not circulate beyond the
discussion at NCAER India Policy Forum 2016, for which this paper
has been prepared. Chatterjee: [email protected] Kapur:
[email protected] . The authors would like to thank Amarsingh
Gawande and Beeban Rai for excellent research assistance.
Abstract Spatial variations in real prices of agricultural
commodities in India are large. The paper first describes the
evolution of agricultural commodity markets in India and provides
some descriptive statistics. Next it documents the spatial
variation in wholesale prices of the principal cereal crops (rice
and wheat) in all APMC mandis across India and within each state.
It further shows persistence in this variation over time. Using a
Shapley-Shorrocks decomposition, the paper analyzes the relative
contributions of different factors in explaining this price
variation. It then examines the effects of two key government
interventions in agriculture markets, the Minimum Support Price
(MSP) program and procurement by government agencies, and the
effects of the monopsony power of mandis on price formation in
agriculture output markets. The paper concludes with some thoughts
on future research directions. JEL Classification: D43, D45, O1,
Q11, Q12, Q13, Q18 Keywords: Agriculture, Market Imperfection,
Economic Development
mailto:[email protected]:[email protected]
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Shoumitro Chatterjee and Devesh Kapur 1
Understanding Price Variation in Agricultural Commodities in
India: MSP, Government
Procurement, and Agriculture Markets Shoumitro Chatterjee and
Devesh Kapur
1. Introduction
A quarter century after India’s historic shift to a more market
oriented economy, with industrial delicensing, trade
liberalization, and (more limited) reforms in factor markets, one
sector continues to be plagued by a curious combination of severely
intrusive government regulations in both factor and product
markets, an arbitrary policy and regulatory environment and low
public investments where needed. Unfortunately, that sector –
agriculture – not only accounts for the livelihoods of the majority
of India’s population, but is also critical to multiple long-term
challenges facing the country from food security to natural
resource sustainability, especially soil and water.
The challenges facing Indian agriculture and its tens of
millions of farmers have been well recognized, whether the media
attention and hand wringing on farmer suicides, the reports of the
National Commission on Farmers (led by M. S. Swaminathan) or
official government documents, such as the Economic Survey, 2016.
While there has been much attention to subsidies in factor markets
in agriculture (especially water, electricity and fertilizers)
because of their high fiscal costs, with the exception of the
public distribution system, there has been relatively less
attention on how government actions shape product markets in Indian
agriculture.
In this paper we focus on how (a) government interventions in
support prices and procurement and (b) regulation and physical
location of wholesale agriculture commodity markets affects price
variation across space. We focus on rice and wheat which together
account for about three-fourths of foodgrain output in India
(coarse grains and pulses account for the remainder). We find large
variances in prices of agricultural commodities across the country.
Real wholesale prices across wholesale markets have an average
standard deviation of 0.18, much higher than the US and also many
developing countries like Philippines. Moreover, it has been high
each year of the last decade. This is especially puzzling in light
of the huge increases in cellphone penetration and a massive
expansion of the rural road network during this period.1
Information frictions can impede trade in a manner distinct from
trade costs (Jensen 2007) and greater connectivity should (in
principle) reduce spatial price differences as was the case between
regions connected by railroads following railroad construction in
colonial India (Donaldson 2015).
The large variance in prices is important to understand because
it implies not only that consumers pay different prices at
different locations for the same product (unless subsidized by
schemes such as the PDS) but producers get different prices
1 Cellphone penetration in India increased from 78 million in
2005 to more than 900 million in 2014. Between 2005-06 and 2013-14
under the Pradhan Mantri Gram Sadak Yojana (PMGSY) the government
released nearly Rs, 100,000 crores for rural roads construction. In
this period 332,835 km of rural roads connecting around 80,000
rural habitations were constructed. Source: Ministry of Rural
Development Annual Report 2013-14: 49-50.
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depending on where they are physically situated. This issue has
been largely neglected in the contentious debates on agriculture
policy which have largely focused on subsidies in agriculture input
markets and price support for agriculture outputs (with some
notable exceptions). There have also been public discussions on
large price wedges between farm-gate and retail prices (a
discussion we get to later).
These discussions have largely ignored agriculture output
markets, which as we demonstrate is evident in the severe
underinvestment in the physical infrastructure of mandis, the
regulatory framework and their internal governance. A rural road
does only so much for a farmer if there is no well-functioning
market in reasonable proximity. Despite attempts at regulatory
reform there is a great degree of hysteresis and path dependence in
how agriculture markets function in India today. For instance,
agriculture market liberalization in Bihar and Andhra Pradesh has
not lead to much private investment in output markets. In order to
gauge the potential impact of reforms we must first understand the
underlying mechanisms. This paper is a modest beginning and focuses
on two aspects: (a) Government interventions in the output market,
namely procurement and support prices. Given that the government
does not uniformly procure across space and commodities what are
the implications for output prices of the principal cereals, rice
and wheat and (b) what is the source and implications on market
prices of the market power enjoyed by the agricultural mandis? A
key strength of this paper is its all-India scope (spanning the 16
largest states) which to our knowledge is the first such
attempt.
The remainder of the paper is organized as follows. Section 2
describes the evolution of agricultural output markets and their
regulation in India. Section 3, discusses the data. The subsequent
analytical section of the paper begins with some descriptive
statistics of agricultural market infrastructure in India (section
4(i)) followed by an analysis of price variation in the principal
cereal crops, rice and wheat (section 4(ii)). Section 4(iii),
examines the effects of two key government interventions in
agriculture markets, the Minimum Support Price (MSP) program and
procurement by government agencies. Section 4(iv) has a discussion
of price formation in agriculture output markets where mandis enjoy
market power locally and section 5 concludes with some thoughts on
future research directions.
2. History of Agriculture Markets in India
The roots of the regulatory regime in agriculture markets in
India go back to the Royal Commission on Agriculture (1928) which
recommended enactment of market legislation to create common
standards to measure the quality of produce and curb rampant
malpractices by private market operators (especially on weights and
measures) and help farmers realize better returns.
As with other aspects of economic life in post-independent
India, agriculture markets were also subject to a more onerous
regulatory regime. These regulations, many of which derive from the
Essential Commodities Act 1955, include controls on private
storage, transport, processing, exports, imports, credit access,
and market infrastructure development. The rationale for these
regulations was ensuring a reasonable income for farmers and access
to food commodities by consumers at affordable prices.
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Shoumitro Chatterjee and Devesh Kapur 3
Since agriculture is a state subject, the regulation of
wholesale agriculture markets has been governed by various state
specific Agricultural Produce Marketing Acts which date back to the
1960s. These Acts empowered state governments to notify the
commodities and designate market areas where the regulated trade
could take place. The Acts also provided for the formation of
agricultural produce market committees (APMC) tasked with operating
these markets. Prices are discovered through what in principle is
an open auction. Critically, once an area was declared a market
area and falls under the jurisdiction of a Market Committee, no
person or agency was allowed freely to carry on wholesale marketing
activities elsewhere. Not only did the government issue licenses to
trade in these markets but also the licenses were state and mandi
specific. As the GOI’s own website puts it: "Once a particular area
is declared as a market area and falls under the jurisdiction of a
Market Committee, no person or agency is allowed to freely carry on
wholesale marketing activities. APMC Acts provide that first sale
in the notified agricultural commodities produced in the region
such as cereals, pulses, edible oilseed, fruits and vegetables and
even chicken, goat, sheep, sugar, fish etc., can be conducted only
under the aegis of the APMC, through its licensed commission
agents, and subject to payment of various taxes and fee. The
producers of agricultural products are thus forced to do their
first sale in these markets."2
The APMC Acts were just one among a plethora of laws promulgated
by the Centre and State governments, all aimed at regulating the
conduct of market functionaries and processing units.3 The result
was to put up multiple barriers restricting competition among
agriculture commodity buyers as well as increase the transaction
cost for marketing operations. The Task force on Employment
Opportunities of the Planning Commission in its report in 2001 had
observed, “The Essential Commodities Act is a central legislation
which provides an umbrella under which the States are enabled to
impose all kinds of restrictions on the storage; transport and
processing of agricultural produce. These controls were
traditionally justified on the ground that they were necessary to
control hoarding and other type of speculative activity, but the
fact is that they do not work in times of genuine scarcity and they
are not needed in normal times. Besides, they are typically misused
by lower level of administration and become an instrument of
harassment and corruption”.
The APMC Acts were co-joined with another intervention, namely
the Minimum Support Price (MSP) for foodgrains. These are a sub-set
of numerous price support schemes (PSS) for multiple agriculture
commodities (for 23 crops in 2015) and in principle function as
options for farmers.4 The floor prices that MSPs are supposed to
set have little impact unless the state backs it by being prepared
to purchase substantial
2http://www.arthapedia.in/index.php?title=Agricultural_Produce_Market_Committee_(APMC).
3These include the Prevention of Food Adulteration Act, 1954,
Essential Commodities Act, 1955, Standards of Weights &
Measurement Act, 1976, Prevention of Black Marketing &
Maintenance of Supply of Essential Commodities Act, 1980, Consumer
Protection Act, 1986, Bureau of Indian Standards Act, 1986,
Agriculture Produce (Grading & Marketing) Act, 1986. 4 Each
year before the harvest (rarely before planting), the GOI announces
the minimum support prices (MSP) for procurement on the basis of
the recommendation of the Commission of Agricultural Costs and
Prices (CACP), which is supposed to take into consideration the
cost of various agricultural inputs and then add a reasonable
margin for the farmers to come up with a MSP. In practice the final
figure is also shaped by political and fiscal considerations.
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amounts at the MSP.5 To facilitate procurement of food grains,
the FCI and various state governments’ agencies have established a
large number of purchase centers at various mandis and key points,
whose numbers and locations are decided by the State Governments.
The establishment of a large network of markets has contributed to
a doubling of the marketed surplus to output ratio since
independence (from one-third to two-thirds). For instance, during
2015-16 more than 20,000 procurement centers were operated for
wheat procurement and 44,000 for paddy across India. However, there
is substantial geographic variation in procurement of produce,
which has implication on market prices. The reason for this
variation in procurement is unclear to us at present and remains a
puzzle. However, we are trying to interview officials at the FCI to
understand the reason and is part of the research question.
Yet despite the seemingly large number of rural markets,
post-harvest distress sales, absence of grading and packaging at
the farm level and inter-locking credit and commodity markets
continue to be common place. The severe underinvestment in market
infrastructure has been well recognized (Chand 2012). A study on
paddy sales by the Karnataka State Agriculture Prices Commission in
2002 found that only 29% of the sample farmers sold their produce
through the regulated markets. The vast majority (71%) did not
because of distance (31%), no knowledge of regulated market (8%),
payment delays (8 %), no provision for paddy sale (5 %), harassment
by hamals/coolies (3 %), good price at the local market (18 %),
small quantity (13%), and advance taken (9 %). The latter indicates
that while interlocked credit and commodity markets might lead
farmers to sell at lower prices to money lenders, it is not the
dominant factor.6 However, another study in Punjab (Singh and
Bhogal 2015) finds widespread presence of commission agents in the
state’s agricultural markets and interlinked credit, input and
output markets which take place due to the credit linkage these
agents provide to farmers.
We analyzed data from the NSS-SAS (2012) and found that the
lion's share of sales at mandis is made by large farmers while
small farmers sell mostly to local intermediaries (Table 1). This
is likely both because of higher fixed transport costs for small
farmers as well as less bargaining power within the mandi
setting.
5 Public procurement of grains occurs mainly by state government
agencies (well over 90 percent) with the Food Corporation of India
(FCI) a minor player. The PSS for procurement of oilseeds and
pulses, is carried out by the National Agricultural Cooperative
Marketing Federation of India Ltd. (NAFED), the Small Farmer’s
Agri-business Consortium (SFAC), Central Warehousing Corporation
(CWC) and the National Consumer Cooperative Federation (NCCF).
Recently the FCI has been added to this list. NAFED is the central
nodal agency for procurement of cotton. 6 For certain crops (like
cotton and tobacco), systems of private banker's credit operate in
the country-side with the objective of guaranteeing supplies.
Hariss-White, 1999:204.
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Shoumitro Chatterjee and Devesh Kapur 5
Table 1: Percentage of cereal output by farm size sold to
various actors
Paddy Farm Size Local Private Mandi Government Input Dealers
Processors
0-2ha 55.44 20.19 11.17 8.72 1.62
2-5ha 41.89 28.92 5.54 19.44 2.44
5-10ha 29.58 34.77 6.52 27.46 0.51
>10ha 14.15 50.43 3.76 15.38 0.65
Wheat Farm Size Local Private Mandi Government Input Dealers
Processors
0-2ha 41.40 38.71 11.01 8.1 0.14
2-5ha 25.23 49.97 5.02 19.42 0.24
5-10ha 16.68 45.68 7.36 29.8 0.3
>10ha 6.07 40.45 1.67 51.77 0.08
Source: NSS Situation Assessment Survey of Agricultural
Households (2012).
While the intention of the APMC Acts was to ensure that farmers
were offered fair prices in a transparent manner, it has led to the
creation of local monopsonies by restricting free entry in market
creation, discouraged investments by the private sector and
generally discouraged free trade and competition. The result has
been local restrictive monoposonies with broad scope, multiple and
often non-transparent levies and charges. Mandi functionaries often
do not allow new entrants in the market further reducing
competition. Their combined effects have ensured fragmented and
inefficient markets. (See Chand, 2012) for a very insightful and
detailed discussion).
Therefore, despite (or perhaps because of) the intensely
regulated markets which were intended to cut the role of
intermediaries, there are multiple intermediaries between the
farmer and the consumer, and as a result consumers pay high prices
for agricultural commodities while farmers get meager returns.
These regulatory problems have been amplified by severe
governance challenges within mandis and according to one estimate
four of five of the APMCs have been superseded.7 In principle the
mandi is like a public utility, but when utilities are poorly
governed consumers suffer, as do Indian farmers. The mandis suffer
from major operational weaknesses ranging from poor transparency in
auctions to high and multiple market charges (often unauthorized),
from rigged weighing and inefficient operations to poor treatment
meted to farmers by mandi employees at the market yards. Few mandis
have the infrastructural facilities mandated by regulation.
In 2003, recognizing that the role of the APMCs and the State
Agriculture Marketing Boards needed to change from market
regulation to market development, which required removing trade
barriers and creating a common market, the central government
formulated a model APMC Act for adoption by the states. While in
principle the model APMC Act provides greater freedom to the
farmers to sell their produce directly to markets set up by private
entities, the latter are still required to pay the market fee to
the notified APMCs, even if they provide no services, in addition
to the fees charged for providing trading platform and other
services, like loading, unloading,
7 This para draws from findings of the National Commission on
Farmers, Second Report, “Serving Farmers and Saving Farming -
Crises to Confidence”.
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grading, weighing and so on. The different provisions of the Act
have been adopted by states to different degrees (see Appendix
Table 1).
The reality of the reforms carried out by the different states
paint a different picture. Maharashtra for example, went back on
the reforms soon after they were announced.8 U.P. has not yet
adopted any of the main features of model APMC act excepting giving
permission to some big players for direct procurement of food
grains (primarily wheat), on condition that total procurement in a
season should exceed 50,000 tons. Crucially this notification is
issued year to year and no changes have been made in the
legislation, thereby ensuring little private investment (and
possibly annual rents). In other states, by putting in large
up-front license fees to set up new markets or insisting that
traders outside the market still pay the market fees, the reforms
have been effectively stymied.
3. Data
Our analysis of agriculture trade and commodity price formation
in India is based on a dataset put together specifically for this
project form several sources. We obtained price and quantity data
of commodities sold in AMPC mandis from the Agmarknet project of
Government of India (http://agmarknet.dac.gov.in). From our
discussions with officials in the Ministry of Agriculture, we
learnt that the Agmarknet project achieved near full coverage since
2005. Hence, we chose 2005-2014 as the period of our analysis. For
each mandi, Agmarknet records the total quantity sold and the modal
price of each commodity traded in any week. We have aggregated the
data up to the month for our analysis. We also restrict our
analysis to the 16 big states – Andhra Pradesh, Bihar,
Chhattisgarh, Gujarat, Haryana, Jharkhand, Karnataka, Kerala,
Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu,
Uttar Pradesh, and West Bengal. Since, Telangana was formed in June
2014 our analysis covers undivided Andhra Pradesh.
The Agmarknet portal also provides us with the village, district
and state of each mandi. We used Google Maps API to geocode these
villages, hence our mandi locations are the geographic centroids of
the villages where the mandis are located. We have excluded fruit
and vegetable mandis from this analysis unless we found at least
one instance of grain trade in these mandis in the 10-year
period.
Geospatial data on district and state boundaries were obtained
from the Geospatial Information Systems library at Princeton
University. We obtained gridded monthly rainfall estimates from
Willmott and Matsuura (2012) dataset at the Center for Climatic
Research, University of Delaware. To estimate district level
average precipitation in any given month we average precipitation
over all latitude-longitude coordinates that fall within a district
boundary.
Monthly data (2005-2014) on district level government
procurement of rice and wheat was provided to us by the Food
Corporation of India9. Data on minimum support prices (henceforth
MSP), area under crops, district and state-level production and
yields
8 http://tinyurl.com/maharashtra-apmc-reform. 9 At present we
have data from the following states – Andhra Pradesh, Bihar (2009
onwards), Chhattisgarh (2008 onwards), Gujarat, Haryana, Karnataka
(2006 onwards), Madhya Pradesh, Maharashtra (2008, 2012-2014),
Odisha, Punjab, Uttar Pradesh, and West Bengal (2008 – 2014). We
hope to update the analysis with the complete data before the final
submission.
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Shoumitro Chatterjee and Devesh Kapur 7 estimates were obtained
from Ministry of Agriculture and Farmer Welfare, Government of
India.
We have also used the National Sample Survey Organization’s
survey on Situation Assessment of Agricultural Households (NSS-SAS
henceforth) 2012-13 to compute district level estimates of
awareness about minimum support prices amongst farmers, price
received by farmers, land under irrigation and other farmer
characteristics.
4. Analysis
4.1 Market Infrastructure
We begin with descriptive statistics on the physical presence of
wholesale agriculture markets across India. In figure 1, we plot a
simple graph of the stock of total number of mandis each year
starting from 1950.
Figure 1: Fraction of total mandis constructed by year
Source: Agmarknet.
It is clear that the number of mandis grew commensurately as the
Green revolution took off but investments in market infrastructure
slackened in more recent decades even as output continue to grow.
As a result, the number of mandis per million tons of cereal output
has declined (Figure 2).
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Figure 2: Number of Mandis per unit output
Source: Agmarknet and Ministry of Agriculture and Farmer
Welfare.
As is the case of most infrastructure in India, there is large
variation in agriculture market infrastructure across Indian
states. In the absence of data on capacity of each mandi it is hard
to make a definitive claim. However, suggestive evidence can be
seen in Table 2 which shows the number of villages served per mandi
across states in 2015 and number of mandis per million tons of
cereal output across states.
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Shoumitro Chatterjee and Devesh Kapur 9
Table 2: State-Wise Spatial Distribution of Mandis
State
No. of
Villages Served per
Mandi (2015)
No. of Mandis Mean no. of mandis within
r km of each mandi
per million ton cereal
production (2012)
per million hectare
NCA (2012) r = 10 r = 20 r = 30
Andhra Pradesh* 86 18.20 30.46 0.38
[0.72] 1.61
[1.58] 3.78
[2.72]
Bihar N/A 3.75 11.11 0.43
[0.85] 0.73
[1.07] 1.57
[1.35]
Chhattisgarh 208 24.10 39.39 1.42
[2.83] 3.54
[3.77] 7.14
[5.36]
Gujarat 90 37.50 25.72 0.91
[1.65] 2.63
[2.38] 5.77
[3.96]
Haryana 67 9.0 39.85 0.61
[0.88] 2.87
[1.67] 6.94
[2.96]
Jharkhand 668 6.0 19.20 0.22
[0.64] 0.22
[0.64] 0.37
[0.69]
Karnataka 198 17.50 19.40 0.53
[1.19] 1.17
[1.46] 2.84
[2.31]
Kerala 10 N/A 56.64 1.07
[1.11] 3.95
[2.25] 8.28
[3.63]
Madhya Pradesh 239 10.25 15.83 0.29
[0.87] 0.80
[1.24] 1.92
[1.80]
Maharashtra 138 32.35 20.47 0.27
[0.58] 1.24
[1.20] 3.17
[1.79]
Odisha 360 13.60 24.85 0.40
[0.75] 1.06
[1.22] 2.15
[1.70]
Punjab 89 8.0 47.95 0.97
[1.20] 4.65
[2.43] 10.78 [4.30]
Rajasthan 212 9.0 9.15 0.59
[1.22] 0.86
[1.27] 2.01
[2.05]
Tamil Nadu 101 36.0 45.13 0.67
[1.46] 2.33
[2.33] 5.22
[3.55]
Uttar Pradesh 229 5.35 16.42 0.42
[0.72] 1.24
[1.09] 3.00
[1.81]
West Bengal 52 5.0 15.56 0.42
[0.79] 1.19
[1.53] 3.21
[2.82]
Notes: NCA: Net Cropped Area and Cereal Production for 2012-13
from Ministry of Agriculture & Farmer Welfare. Mandi Data from
http://agmarknet.dac.gov.in/. Fruit & Vegetable mandis
excluded. Standard Deviation in brackets. *Andhra Pradesh includes
Telangana.
http://agmarknet.dac.gov.in/
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Overall this basic data on agriculture market infrastructure
shows that while considerable investments were made in the heyday
of the green revolution but fell (sharply) from the 1990s
onwards.10 This market infrastructure varies considerably across
states, both by volume of production and proximity to production
sites (villages).
The second important observation is the geographical variation
in the location of markets. First, there is large variation in the
number of markets farmers have access to across states (see Table
2). Mandi density is considerably higher in states like Punjab and
Haryana as compared to other like Rajasthan and Madhya Pradesh.
Second, even within states the spatial distribution of mandis is
far from uniform. This can be observed in the last three columns
reporting number of mandis near each mandi and their standard
deviations in Table 2 and in maps of Uttar Pradesh (figure 3),
Madhya Pradesh (figure 4) and Maharashtra (figure 5). There is of
course the question of cause and effect – does more output create a
larger demand for – and supply of – mandis? The steady decline in
the number of mandis per unit output over the past quarter century
does not appear to support this argument.
Figures 3: Mandi Locations in Uttar Pradesh
Source: Agmarknet.
10 The underinvestment in market infrastructure continues.
Rashtriya Krishi Vikas Yojana (RKVY) was launched in 2007-2008 to
incentivize States to increase public investment in agriculture and
allied sectors. Of the score odd schemes under RKVY, just 2 percent
of the more than twenty thousand crores annual expenditures in
2013-14 and 2014-15 were on markets and post-harvest
management.
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Shoumitro Chatterjee and Devesh Kapur 11
Figure 4: Mandi Locations in Madhya Pradesh
Source: Agmarknet.
Figure 5: Mandi Locations in Maharashtra
Source: Agmarknet.
4.2 Price Variation
We now focus on average monthly price data for wheat and rice
sold in any mandi in India between 2005–2014. It should be noted
that the prices we analyze are wholesale prices observed at APMC
mandis. It is very likely that these are not prices received by
farmers, especially since it is large farmers who sell in mandis
and small farmer are more likely to sell to local village
intermediaries (Table 1). However, our price data has several
advantages. They are actual prices recorded at a high frequency
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12 India Policy Forum 2016
and at a crucial stage in the supply chain – mandis are key
points of aggregation. Other sources of price data are usually
recalled estimates of unit values, geographically aggregated and
very low frequency.
We use log real prices (with the CPIAL (Food) as the deflator)
so that the variance of log prices is unit independent and makes it
compatible for across country comparisons. The average standard
deviation of log (real) prices across mandis in a given month is
0.18. For comparative purposes this is higher than Philippines:
where for rice and corn (the main food commodities grown there),
Allen (2014) found the standard deviation to be 0.15 in
Philippines, which a country formed by group of islands and has
high transport and information costs.
Our variance estimate is robust to including all cereals. The
variance in prices has been high since 2005 and hence the results
are not capturing the effect of an outlier year. Moreover, we do
not observe any trend in time-series of the standard deviation
which implies that an increase in cellphone penetration during this
period does not appear to have had a causal effect on price
variation in grains across India (see Figure 6).
Figure 6: Average Variation in Log real price across APMC
markets for Paddy and Wheat
Source: Agmarknet.
The average standard deviation of log (real) prices across
mandis within states is also high. To the extent that high average
standard deviation of log (real) prices across mandis in the
country might be due to different varieties of wheat and rice grown
in different agro-ecological zones prevailing in different states,
this finding attenuates this concern. High within-state variation
suggests that the variation is not entirely due to quality. We
present the results for 2014 in Table 3. The results for previous
years are similar.
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Shoumitro Chatterjee and Devesh Kapur 13
Table 3: Variation in Real Prices within States
State Standard Deviation Andhra Pradesh 0.15 Chhattisgarh 0.13
Gujarat 0.14 Haryana 0.13 Jharkhand 0.14 Karnataka 0.18 Kerala 0.17
Madhya Pradesh 0.21 Maharashtra 0.16 Odisha 0.70 Punjab 0.26
Rajasthan 0.14 Tamil Nadu 0.21 Uttar Pradesh 0.11 West Bengal
0.07
Source: Price data from Agmarknet
http://agmarknet.dac.gov.in/.
What is the relative weight of different factors in the
variation in prices? To get at this we performed a
Shapley-Shorrocks decomposition. This procedure considers the
various factors which together determine an indicator (such as the
overall variation in prices), and assigns to each factor the
average marginal contribution of each factor. The technique ensures
that the decomposition is always exact and that the factors are
treated symmetrically. The results from the Shapley-Shorrocks
decomposition found that 37% of the variation in log (real) prices
is due to time-invariant district fixed-effects (which in this case
could be soil quality), 20% is due to location-invariant aggregate
time shocks (like global demand), 4% is due to differences in
monthly rainfall across districts, and 39% remain unexplained.
One important time invariant location fixed factor that we’ll
explicitly consider in this paper is the spatial location of
mandis. As already discussed there has been insignificant mandi
construction in our period of study. We look at how this might
affect prices later in the paper. The unexplained variation could
be due to location and time varying factors like rural road
construction, or procurement of grains by state agencies. We
analyze the latter’s role as well.
4.3 Government Interventions in agriculture markets: MSP and
Procurement
Two key government interventions that affect agriculture markets
and commodity prices in India are the MSP and procurement by
government agencies. The rationale of the MSP is to ensure that
farmers are not compelled to sell their produce below support price
either due to exploitation by large market players or due to a
bumper harvest. The MSP is effective mainly for four crops: wheat,
paddy, cotton (modestly) and sugarcane (for which mills are legally
obligated to buy cane from farmers at prices fixed by
government).
http://agmarknet.dac.gov.in/
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14 India Policy Forum 2016
Even for these crops, there is large variation among states in
the degree to which efforts are made by public agencies to procure
and within states the efforts are restricted to a subset of
farmers. We first consider an indirect measure that illustrates
this issue: farmers’ awareness about MSP. The reason to choose this
measure over actual procurement is that the quantum of procurement
is a choice of the farmer. If market prices are good, then even in
the presence of efforts by public agencies farmers may choose not
to sell to them since the MSP acts like an option. However, the
farmer’s awareness about MSP is more likely to reflect the presence
of government agencies in his neighborhood.
Figure 7 shows that most farmers are not even aware of the
existence of MSPs and there is considerable variation in this
across states. Whereas most farmers in Punjab and Haryana are aware
of the minimum support price program, very few are aware about it
in other states like Gujarat, Maharashtra, Jharkhand or West
Bengal. This is indicative of the absence of government procurement
agencies in many parts of the country.
Figure 7: Farmer Awareness about minimum support prices 2012
Source: NSS-SAS.
It follows, therefore, that there are large disparities across
states in actual procurement. In Tables 4 and 5, not surprisingly
one observes that the states where awareness of MSP is high are
also the states where there is heavy procurement of grains – both
in absolute terms and relative to total production. Therefore,
awareness is highly correlated to the intensity of procurement in a
state (Figure 8). Notice also that as paddy is more intensely
procured than wheat (as a % of total production), the overall level
of awareness is higher for paddy than for wheat.
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Shoumitro Chatterjee and Devesh Kapur 15
Table 4: Production and Procurement of Rice
State Production (in million tonnes)
Procurement by FCI and State Agencies (in million tonnes)
% of all India
procurement
Procurement as a % of total
production 2013-14 2014-15 2013-14 2014-15
A.P. 6.97 7.23 3.737 3.596 11.65 51.63
Bihar 5.51 6.36 0.942 1.614 4.06 21.55
Chhattisgarh 6.72 6.32 4.29 3.423 12.26 59.16
Gujarat 1.64 1.83 0 0 0.00 0.00
Haryana 4.00 4.01 2.406 2.015 7.03 55.23
Jharkhand 2.81 3.36 0 0.006 0.01 0.10
Karnataka 3.57 3.54 0 0.088 0.14 1.24
Kerala 0.51 0.56 0.359 0.374 1.16 68.42
M.P. 2.84 3.63 1.045 0.807 2.94 28.62
Maharashtra 3.12 2.95 0.161 0.1988 0.57 5.93
Odisha 7.61 8.30 2.801 3.357 9.79 38.70
Punjab 11.27 11.11 8.106 7.786 25.26 71.03
Rajasthan 0.31 0.37 0 0 0.00 0.00
Tamil Nadu 5.35 5.73 0.684 1.051 2.76 15.66
Telangana 5.75 4.44 4.353 3.504 12.49 77.06
U.P. 14.64 12.17 1.127 1.698 4.49 10.54
West Bengal 15.37 14.68 1.359 2.032 5.39 11.29
Source: Ministry of Agriculture and Farmer Welfare, Government
of India.
Table 5: Production and Procurement of Wheat
State Production (in lakh tonnes)
Procurement by FCI and State Agencies
(in lakh tonnes)
% of All India
Procurement
Procurement as a percentage of
Total Production 2013-14 2014-15 2013-14 2014-15
A.P 0.04 0 0 0 0.00 0.00
Bihar 47.38 39.87 0 0 0.00 0.00
Chhattisgarh 1.34 1.35 0 0 0.00 0.00
Gujarat 46.94 30.59 0 0 0.00 0.00
Haryana 118.00 103.54 58.73 6.50 23.29 55.8
Jharkhand 3.70 3.30 0 0 0.00 0.00
Karnataka 2.10 2.61 0 0 0.00 0.00
M.P 129.37 171.04 63.55 70.94 25.33 44.8
Maharashtra 16.02 13.08 0 0 0.00 0.00
Odisha 0.01 0.006 0 0 0.00 0.00
Punjab 176.20 150.50 108.97 116.41 42.45 69.0
Rajasthan 86.63 98.24 12.70 21.59 6.46 18.5
Telangana 0 0.07 0 0 0.00 0.00
Uttar Pradesh 298.91 224.17 6.82 6.28 2.47 2.5
Source: Ministry of Agriculture and Farmer Welfare, Government
of India.
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16 India Policy Forum 2016
Figure 8: MSP awareness vs Procurement
Source: NSS-SAS and Food Corporation of India.
While there has been considerable discussion on procurement of
foodgrains by public agencies for the PDS, the key point that is
often missed is that government procurement is a luxury for most
farmers in the country. There are large differences not only across
states but within states as well. And this variation in procurement
has substantial consequences on the price farmers receive and the
crops they choose to produce.
The disparity in procurement within states can be seen in the
maps in figures 9 and 10. For illustrative purposes we present the
results for paddy. Whereas all districts in Punjab see uniformly
high procurement, this is not the case in UP or Maharashtra.
Conditional on production, some districts in Maharashtra and UP
have very low or zero procurement (see map 10 which plots
procurement as a fraction of production).
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Shoumitro Chatterjee and Devesh Kapur 17
Figure 9: Average Annual Paddy Procurement 2005-2014
Source: Food Corporation of India.
Figure 10: Average Fraction of Paddy Production procured
2005-2014
Source: Food Corporation of India and Ministry of Agriculture
and Farmer Welfare.
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18 India Policy Forum 2016
Intervention in any market by the government is bound to have
consequences for equilibrium prices. One would expect that the
presence of MSP would at least provide a soft floor on the actual
prices observed in markets. The data however paints a different
picture. Figure 11 shows the cumulative distributions of the
relative difference of average monthly market prices at the
district level from the prevailing MSP for a 10-year period
(2005-2014). A well-functioning procurement system would have
ensured that prices received by farmers would have been at or above
the MSP and the graph would have started at 0. However, it is
glaring that about half of the market prices are below the minimum
support prices – both in paddy and wheat.
Figure 11: Distribution of Average market prices is a district
relative to MSP
Source: Agmarknet. Note: Upper Panel is for Paddy and Lower
Panel is for Wheat.
This raises several interesting questions, which we can only
partially address in this section owing to data limitations. As
described earlier, there is disparity in procurement of grains
across districts and over time. These variations allow us to
implement a difference-in-difference identification strategy to
compare districts where there is procurement in certain months to
districts where there is no procurement to identify the impact on
the market prices.
Our dependent variable is the relative difference of monthly
average prices at the district level from the prevailing MSP. Its
distribution is plotted in figure 11. The key regressor of interest
will be an indicator which will take a value 1 if there was any
procurement in any district in any month and 0 otherwise. We chose
this variable as the regressor as opposed to the actual quantity
procured since conditional on access to a procurement center, the
quantity sold to the government is a choice exercised by the farmer
and hence endogenous. Whether or not there is any procurement in a
district is more likely to be outside the farmer’s choice set when
the market prices are falling below MSP.
-
Shoumitro Chatterjee and Devesh Kapur 19 Our basic econometric
specification will thus take the following form:
(price
𝑑𝑡− MSP𝑡
MSP𝑡) = 𝛼 + 𝛽𝟏{𝑝𝑟𝑜𝑐𝑢𝑟𝑒𝑚𝑒𝑛𝑡𝑑𝑡 > 0} + 𝛾𝑑 + 𝛾𝑡 + 𝜖𝑑𝑡
Here, d denotes a district and t denotes a month-year. 𝛾𝑑 and 𝛾𝑡
are district and time specific fixed effects. The coefficient of
interest 𝛽, captures the differential effect of government
procurement on market prices relative to MSP in districts where
there is procurement to districts where there is none. For
inference, we are going to cluster standard errors at the district
level.
The identification assumption here is that the procurement
indicator should not be correlated with pre-existing district
specific trends. Since procurement is likely to be correlated with
district specific production, for robustness we will further
introduce district specific year trends and control for district
specific rainfall shocks and total output.
Table 6: Regression Results for Paddy
(1) (2) (3) (4) Relative Price 1{Procure>0} 0.04 0.04 0.06
0.05 (0.01)*** (0.01)*** (0.01)*** (0.01)*** Observations 18508
18508 12815 12815 District FE
Time FE
District specific Linear Trend
Other Controls
Notes: Robust standard errors, clustered at the district level
in parentheses. *** p
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20 India Policy Forum 2016
it might raise average prices at most mandis in that district.
Given trade costs, some farmers might still prefer to sell to the
government. In general equilibrium however, farmers selling to
mandis get a better deal than in the counterfactual. Moreover,
there are likely dynamic effects, i.e. procurement occurs in some
locations first, triggering a rise in market prices in
geographically adjacent areas leading to subsequent sale in mandis.
This would also be consistent with our results.
These forces are at play even when the market price is always
below MSP or the knife-edge case of procurement pushing the price
above MSP. However, to better analyze these cases we need access to
high-frequency geocoded data on procurement which unfortunately is
not available.
The results for wheat however, are somewhat puzzling. Relative
to districts with no procurement, the average market price of wheat
is 2% lower than MSP in districts where there is procurement (Table
7). We need to do more work to understand them. One caveat is that
at present we do not have data on wheat procurement in two big
wheat producing states – Maharashtra and Rajasthan – and hence
those states are not in our sample.
Table 7: Regression Results for Wheat
(1) (2) (3) (4) Relative Price 1{Procure>0} -0.02 -0.02 -0.02
-0.02 (0.004)*** (0.004)*** (0.004)*** (0.004)*** Observations
24253 24253 17426 17426 District FE
Time FE
District specific Linear Trend
Other Controls
Notes: Robust standard errors, clustered at the district level
in parentheses. *** p
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Shoumitro Chatterjee and Devesh Kapur 21
The minimum support price program is also likely to generate
externalities with effects on crop choice, on the environment and
for long term sustainable development that we do not discuss here
but is a part of our future research agenda. In this paper we focus
on the direct impacts of the minimum support price program.
4.4 Agricultural Markets as Local Monopsonies11
We started with a puzzle about price variation between Indian
agricultural markets and then we moved to discussing how selective
intervention by the government in procurement might be leading to
differential prices across regions. In this section, we bring
together a more complete story by providing evidence for a possible
general theory of price formation in Indian agricultural markets
which will help us understand how local market power effects
equilibrium prices. For reasons of generality the results we
provide here include all major food grains produced in India but
the results are robust to just including paddy and wheat. Here, we
describe the main intuition and results in brief.
The key idea is that by limiting freedom in creation of new
agricultural markets, over the years state governments have created
virtual monopsonies. Having access to greater number of market
places increases the bargaining power of farmers vis-à-vis
intermediaries helping them get a better price for their produce.
The bargaining power of post-harvest liquidity strapped small
farmer is going to be very limited.
It is possible, of course, that there is competition amongst
intermediaries within a market but the limited evidence we have on
this points towards collusion within mandis (Banerji and Meenakshi
(2004, 2008)). In absence of data on number of traders in each
mandi this feature cannot be tested and hence we choose to focus on
between market competition. Further, there is some evidence of
ex-post bargaining between farmers and intermediaries (Visaria et
al. 2015). In the Madhya Pradesh soyabean market, entry by private
players have increased prices that farmers received in mandis
because now the mandis faced competition from private players
(Goyal 2010).
Consider a simple model where farmers live in space. They choose
which mandi to go and sell their produce where they Nash-bargain
with the intermediaries. The outside option of farmers at a
particular location endogenously depends on how many other markets
the farmer has access to in his neighborhood. To illustrate this
point, suppose a farmer is being exploited at a mandi then he can
choose to go to a different mandi in search of a better price.
However, he can do so only if there is a mandi in the vicinity. If
there is none then he would be forced to sell at whatever price the
intermediary offers. The model assumes that it is easier to sustain
collusion within mandis as traders can observe each other but is
difficult to collude between mandis separated in space.. This model
is also general in that it does not matter whether the farmer or a
village intermediary comes to sell at the mandi because what
matters is the price observed at mandis.
In general equilibrium this model yields the prediction that
regions which are dense in number of markets will have higher
prices as compared to regions which are sparse in number of
markets. Even when there are inter-linkages in other markets (like
credit) between farmers and intermediaries, the forces described
above are likely to
11 This section draws heavily from Chatterjee (2016).
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22 India Policy Forum 2016
determine the bargaining power of the farmers. However, it is
very hard to credibly isolate and identify this relationship since
density of markets in a region would be highly correlated with
other local characteristics like production.
To investigate the presence of local market power and its
relation with equilibrium prices, Chatterjee (2016) presents two
strategies. The first exploits variation in mandi density in space
within a state and the second across the state border.
In the first strategy the, the paper examines the relationship
between price in a mandi and the number of markets in the
neighborhood controlling for as many observables like local crop
production, local demand, local rainfall shocks as possible and
flexible fixed effects to account for unobserved heterogeneity. The
preferred non-parametric specification is the following, where
standard errors are clustered at district and crop-season level for
inference.
ln(price)𝑐𝑚𝑑𝑡 = ∑ 𝛽1𝑟(#mandi)𝑚𝑟𝑟=5,10,15
+ ∑ 𝛽2𝑐𝑐∈𝐶
Rain𝑐𝑑𝑡 + 𝑿′𝜷𝟑 + 𝜖𝑐𝑚𝑑𝑡
Here, c is crop, m is market, d is district and t is time.
Therefore, we regress price of crop c, in market m, in district d
at time t on the number of mandis in the neighborhood. We break the
neighborhood into three bins – 0 to 5 km, 5 to 10 km and 10 to 15
km – and count the number of other mandis in each bin. For example,
(#mandi)𝑚𝑟 for r=10 would mean number of other mandis at a distance
of more than 5kms but less than 10km from mandi m. All distances
are geodetic distances.
Crop-district specific rainfall shocks are denoted by Rain𝑐𝑚𝑑𝑡.
We always include crop and state specific effects because we want
to focus on within state and within crop variation. For robustness,
we also include controls for local demand (measured by population
in the neighboring tehsil) and state specific time trends.
The second strategy exploits the restriction of the APMC acts,
that crops grown in a particular state cannot be sold in a mandi
another state. This means that if we look at two mandis close to
each other but on either side of a state border then they must be
similar in all respects but the competition they face. One would
expect that soil type, crop choice, rainfall, demand etc. are
continuous and hence similar very close to the state border.
However, since crops can only be sold in the state they are grown
in, means that any mandi faces competition only from mandis in its
own state. Hence competition is discontinuous as one crosses the
state boundary. This lends itself very naturally to a matching
identification framework, where the relation between price
difference for the same crop in the same month in two
geographically close mandis on either side of the state border and
the difference in their local competition can be interpreted to be
causal. Here, while counting the number of mandis in the
neighborhood of any mandi m, we weight each mandi by the inverse of
the distance from mandi m. Therefore, competition at each mandi m
is:
comp𝑚
= ∑ 𝑑𝑖𝑠𝑡𝑚𝑗−1
𝑗𝜖𝑀(𝑚,𝑟)
where, 𝑑𝑖𝑠𝑡𝑚𝑗 is distance from mandi m to mandi j, and 𝑀(𝑚, 𝑟)
is the set of all
mandis in the r km neighborhood of mandi m. Then we can look at
the relation between
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Shoumitro Chatterjee and Devesh Kapur 23 price differences and
competition differences between all mandi pairs, that are 20 km or
30 km apart from each other but on either side of a state
border:
ln(price)𝑐𝑚𝑡 − ln(price)𝑐𝑚′𝑡 = 𝛽(𝑐𝑜𝑚𝑝𝑚 − 𝑐𝑜𝑚𝑝𝑚′) + 𝜖𝑐𝑚𝑡
For inference, we follow a simple rule that if two mandi pairs
share at least one district in common then they belong to the same
cluster.
Both designs estimate the impact of one additional mandi in the
neighborhood to be an increase between 1% and 6% in price. There is
some variation across crop types and states and regression models.
The preferred border regression estimates are on the higher end of
the spectrum. In our data, the minimum number of mandis in a 10 km
radius neighborhood is 0 and the maximum number is 12. So if we
compare a neighborhood which does not have any mandi close to it
versus one which has 5 mandis then the price variation between two
such neighborhoods is likely to be between 5 and 30%.
5. Conclusion
Analyzing trade in agricultural markets in India is a complex
and daunting task, especially in absence of data on trade flows. It
is important nevertheless for multiple reasons. The literature has
mostly taken a micro approach understanding forces and mechanics in
select mandis, crops and regions. In this paper we have approached
the problem from an all-India perspective. Based on a large, unique
dataset we find large overall variation in prices among mandis.
About 37% of this variation is because of time invariant location
specific factors and another 39% is because of time and location
varying factors.
In trying to understand the mechanisms that might explain these
results we focus on key government interventions in agriculture
output markets: geographically selective intervention by the
government in procurement of grains; and the market power that the
mandis enjoy because of restrictions in the APMC acts. We find that
selective intervention by the government creates a 2-4% variation
in prices depending on crop. We find that for paddy, government
intervention improves terms of trade in favor of the farmers as one
would expect but in the case of wheat it goes the other way round.
This result is puzzling and we will address it in future work. One
possible reason could be that procurement results in lower-grade
varieties (or distinct varieties) being sold in mandis and thus
government intervention might depress the market price.
We also find that farmers sell their produce at up to 5% lower
prices in geographically isolated mandis which enjoy market power
because they face little competition, compared to areas where
mandis enjoy little market power.
Future work
This paper is an initial attempt in understanding the
complexities of agriculture output markets in India. Future
research questions include modeling what might happen if the APMC
restrictions are done away with so that there are no fees for
private players to enter agricultural markets and farmers can
freely trade across borders etc.
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24 India Policy Forum 2016
Chatterjee (2016) has been developing a structural model that
tackles this general equilibrium problem.
Alongside price variation, analyzing the sources of wedges
between farmgate to mandi prices, mandi to retail prices and
farmgate to retail prices is of crucial importance as they tell us
about costs and inefficiencies involved at each step in the supply
chain. Currently credible estimates12 of these wedges do not exist
because it is very hard to compare the varieties of commodities
found in retail markets to varieties of those commodities in mandis
or at the farm. For example, wheat consumed in urban homes is a
mixture of different varieties grown at different places. Source
and destination data on trade flows of agricultural commodities is
not available for India or any developing country13. In such a
scenario, large scale primary data collection following supply
chains is the only option, an approach adopted by Visaria et. al.
(2015) who follow the potato supply chain in West Bengal.
Not only is access to domestic markets important for farm
incomes but also international markets. The Economic Survey, 201614
discusses India’s highly volatile agricultural export policies.
Within a matter of days cotton farmers are abruptly denied access
to international markets. Quantification of the impact of such
policies is a very important policy research question.
Another area that needs better understanding is the political
economy of agriculture commodity markets. We have little systematic
knowledge of the internal governance of mandis, mandi elections and
their relationships with local and state politics. Traders are a
powerful lobby and often have partisan political preferences.
States such as Madhya Pradesh undertook reforms as early as the
1980s without any major protest and Bihar did so in the mid-2000s
(Krishnamurthy 2014). Variegated reforms in APMC acts, emerging new
rural institutions (such as farmer producer companies and primary
agricultural credit societies), commodity future markers and NAM,
are all likely to alter the political economy of agriculture
commodity markets. But exactly how and with what effects? A related
aspect that we have little knowledge on but is important are the
effects of use of muscle power to prevent farmers from getting
access to mandis and restricting entry in the intermediation and
transportation sector.
The long-run consequences of MSP and procurement is another
fruitful area for research. While well intentioned, minimum support
price policies could be counter-productive. As discussed in the
Economic Survey, 2016, the MSP has incentivized farmers to
over-produce certain crops, especially wheat and paddy, crowding
out other less-water intensive crops like pulses. In absence of
proper storage facilities not only is there large wastage but these
crops, along with sugarcane, are relatively water-intensive, with
severe consequences for water tables in a water scarce country like
India. Furthermore, the incentive effects of MSP appear to favor
specific varsities of paddy and wheat, which might result in
permanent loss of local varieties of these grains (Krishnamurthy
2012).
The direction of future research should carefully examine
general equilibrium responses because as India changes its pattern
of production, international prices and
12 Some estimates can be found in Chapter 4 on Agriculture in
the Economic Survey of India, 2016. 13 The only exception is Allen
(2014) for Philippines. 14 See Chapter 1, pp33 – Volatile Trade
Policy.
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Shoumitro Chatterjee and Devesh Kapur 25 terms of trade will
change since India is a large country and this will further effect
international production patterns. This is an important
consideration for food security. But careful analysis is
handicapped if the government does not make public the location
details of procurement centers each year and high frequency data on
the quantum of procurement of grains at each of its procurement
centers.
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26 India Policy Forum 2016
Appendix Table 1: State-wise progress of reforms as on
11/02/2016
Area of marketing reforms States adopted the suggested area of
marketing reforms
1 Establishment of private market yards/ private markets managed
by a person other than a market committee.
AP, Arunachal Pradesh, Assam, Chhattisgarh, Gujarat, Goa, HP,
Jharkhand, Karnataka, Maharashtra, Mizoram, Nagaland, Orissa
(excluding paddy / rice), Rajasthan, Sikkim, Telangana ,Tripura,
Punjab, Uttarakhand, West Bengal & Chandigarh.
2 Establishment of farmer/consumer market by a person other than
Market Committee (Direct sale in retail by the farmers to the
consumers).
Arunachal Pradesh, Assam, Chhattisgarh, Gujarat, Goa, HP,
Jharkhand, Karnataka, Maharashtra, Mizoram, Nagaland, Rajasthan,
Sikkim, Tripura, Uttarakhand & West Bengal.
3 Direct wholesale purchase of agricultural produce by
processors/exporters/ bulk buyers, etc… at the farm gate.
Andhra Pradesh, Arunachal Pradesh, Assam, Chhattisgarh, Gujarat,
Goa, Haryana (with collection centres for specified crops), HP,
Jharkhand, Karnataka, MP, Maharashtra, Mizoram, Nagaland, Punjab,
Rajasthan, Sikkim, Telangana, Tripura, Uttarakhand, West Bengal
& Chandigarh.
4 Provision for Contract Farming. AP, Arunachal Pradesh, Assam,
Chhattisgarh, Goa, Gujarat , Haryana, Himachal Pradesh, Jharkhand,
Karnataka, Maharashtra, MP, Mizoram, Nagaland, Orissa, Punjab
(separate Act) , Rajasthan, Sikkim, Telangana, Tripura &
Uttarakhand.
5 Unified single license/registration for trade transaction in
more than one market.
AP, Goa, Gujarat, Haryana, HP, Karnataka, Rajasthan,
Chhattisgarh, MP, Maharashtra, Mizoram, Nagaland, Sikkim &
Telangana.
6 Provision for e-trading (provided in varied ways).
AP, Chhattisgarh, Gujarat, Jharkhand, Haryana, HP, Karnataka,
Rajasthan, Sikkim, Goa, Madhya Pradesh, Maharashtra (license to
Commodity Exchanges registered under FMC), Mizoram, Telangana and
Uttarakhand.
7 Single point levy of market fee across the State.
AP, Chhattisgarh, Gujarat, Goa, HP, Karnataka, Madhya Pradesh,
Mizoram, Nagaland, Punjab, Rajasthan, Sikkim, Telangana,
Uttarakhand, Uttar Pradesh, Jharkhand & Chandigarh.
Source: Ministry of Agriculture and Framer’s Welfare. Annual
Report 2015-16, p. 105.