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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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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]

mailto:[email protected]

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.


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.


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.


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”.


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.

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).


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.


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/


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.


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


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.


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/


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.


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.


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).


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.


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<0.01, ** p<0.05, * p<0.1. Other controls include controls for district-year specific output and district-month specific rainfall. Prices and procurement are at the district-month level.

Discussion of Results

From table 6, it is clear that in case of paddy, relative to districts with no procurement, the average market price is at least 4% higher than MSP in districts where there is procurement. This estimate is robust to different specifications including unobservable district specific time trends and controls for district specific monthly rainfall and annual output. Therefore, farmers are worse off in districts where they do not have access to government procurement. The result also points to the possibility that in the absence of government procurement, the bargaining power of the farmers against intermediaries is likely to be attenuated.

We should be careful in that a positive coefficient on procurement implies that the market price is higher and not necessarily above the MSP. Let’s first take the case when the market price is greater than MSP before and after procurement. This might seem like a contradiction at first, because if the market price is greater than MSP then no farmer has the incentive to sell to the government. Hence, procurement should be zero. However, recall that we are averaging prices over time (for every month) and over geography (over all mandis in a district). Therefore, if there is procurement in a district


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<0.01, ** p<0.05, * p<0.1. Other controls include controls for district-year specific output and district-month specific rainfall. Prices and procurement are at the district-month level.

One possible rationale for this counter-intuitive result could be that only bad quality wheat is sold in mandis and most good quality wheat is procured directly by the government. However, this seems unlikely and in any case without supporting data, we leave this as an open question at this stage. A potential concern could be that all procurement happens whenever market price is below MSP and almost nothing when price is above MSP. In this case, we would expect the coefficients to be downward biased – and in line with the results for wheat. However, in that case the results for paddy would strengthen even further.

The two biggest concerns are that: (a) the act and timing of procurement in districts may be correlated with time varying unobservables and (b) that there may be anticipatory responses by the market players based on their expectations. By controlling for as many district characteristics as possible and putting in flexible fixed effects we have tried to mitigate the former concern to some extent. In the absence of high frequency and more disaggregate procurement data however, addressing the latter does not seem possible.


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).


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)𝑚𝑟

𝑟=5,10,15

+ ∑ 𝛽2𝑐

𝑐∈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

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.


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.

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.

References

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Banerji, A., & Meenakshi, J. V. (2004). Buyer collusion and efficiency of government intervention in wheat markets in northern India: An asymmetric structural auctions analysis. American journal of agricultural economics, 86(1): 236-253.

Banerji, A., & Meenakshi, J. V. (2008). Millers, commission agents and collusion in grain markets: evidence from basmati auctions in North India. The BE Journal of Economic Analysis & Policy, 8(1).

Bardhan, P. K. (1980). Interlocking factor markets and agrarian development: A review of issues. Oxford Economic Papers, 32(1): 82-98.

Bell, C., & Srinivasan, T. N. (1989). Interlinked transactions in rural markets: An empirical study of Andhra Pradesh, Bihar and Punjab. Oxford Bulletin of Economics and Statistics, 51(1): 73-83.

Chand, R. (2012). Development Policies and Agricultural Markets. Economic & Political Weekly, 47 (52): 53-63.

Chatterjee, S. (2016). Essays in Trade and Development. (Princeton University PhD Dissertation in progress).

Donaldson, D. Forthcoming. “Railroads of the Raj: Estimating the impact of transportation infrastructure.” American Economic Review.

Goyal, A. (2010). Information, direct access to farmers, and rural market performance in central India. American Economic Journal: Applied Economics, 2(3): 22-45.

Singh, S., & Bhogal, S. (2015). Commission Agent System. Economic & Political Weekly, 50(45): 57.

Jensen, R. (2007). The digital provide: Information (technology), market performance, and welfare in the South Indian fisheries sector. The quarterly journal of economics, 879-924.

Krishnamurthy, M. (2012). States of Wheat. The Changing Dynamics of Public Procurement in Madhya Pradesh. Economic & Political Weekly, 47(52): 72-83.

Krishnamurthy, M. (2015). The Political Economy of Agricultural Markets: Insights from Within and Across Regions. IDFC Foundation (ed) India Rural Development Report 2013-2014, Orient Blackswan.

Visaria, S., Mitra, S., Mookherjee, D. & Torero, M. (2015). Asymmetric Information and Middleman Margins: An Experiment with Indian Potato Farmers. HKUST IEMS Working Paper No. 2015-29. Available at SSRN: http://ssrn.com/abstract=2639972 or http://dx.doi.org/10.2139/ssrn.2639972.

http://dx.doi.org/10.2139/ssrn.2639972


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.

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