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Market Integration, Transaction Costs and the Indian Wheat Market: A Systematic Study

This paper examines whether the wheat market is integrated across states in India, and concludes that the market is integrated in the long run. This long run integration, however, does not come from the free flow of goods across states in the country, but from the sharing of similar production technologies by farmers across states. The paper also shows that the market for wheat is not integrated in the short run. This implies that at a given time period there exist two prices for the same commodity, since transaction costs are the main barriers to market integration. The paper also estimates such transaction costs using transport and communication infrastructure indices across states, and concludes that there exist large variations resulting in high transaction costs.

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Market Integration, Transaction Costs and the Indian Wheat Market: A Systematic Study

Megha Mukim, Karan Singh, A Kanakaraj

This paper examines whether the wheat market is integrated across states in India, and concludes that the market is integrated in the long run. This long run integration, however, does not come from the free flow of goods across states in the country, but from the sharing of similar production technologies by farmers across states. The paper also shows that the market for wheat is not integrated in the short run. This implies that at a given time period there exist two prices for the same commodity, since transaction costs are the main barriers to market integration. The paper also estimates such transaction costs using transport and communication infrastructure indices across states, and concludes that there exist large variations resulting in high transaction costs.

The authors would like to thank T N Srinivasan for valuable comments and suggestions. This paper was written while Megha Mukim and A Kanakaraj were based as Fox International fellows at Yale University and Karan Singh was a consultant with the Indian Council for Research on International Economic Relations.

Megha Mukim (m.mukim@lse.ac.uk) is reading for her PhD at the London School of Economics. Karan Singh is a visiting fellow at the German Development Institute, Bonn. A Kanakaraj is a research fellow at the Jawaharlal Nehru University, New Delhi.

S
harp increases in foodgrain prices have a number of implications – in India, for instance, they tend to mostly affect the welfare of the poor. Also, within the agricultural m arket, prices are considered the most important indicator of the performance of a region or of a country.

From a macroeconomic perspective, the main factors underlying elevated foodgrain prices can be policy-related (such as the existence of inappropriate price policies, lack of a regional growth-monitoring policy, ineffective farmer or welfare policies, stagnant land reform policy and export-import policy) or supply side-related (such as poor technologies affecting productivity, low public investment, etc; and unexpected factors such as fluctuations in weather). In order to have stable prices, a representative government needs to focus on both, short- and long-term policies. In the short term, it is important to have policies that deal with efficient demand management across regions within a country, reduce excessive speculative and arbitrage opportunities and aid in the implementation of effective import-export systems. In the long term, governments may need to analyse the roles of land and diminishing returns as they influence grain supply and pay more attention to investment in human capital and rural infrastructure.

In this study, we will focus our attention on microeconomic aspects, in particular on the notion of market integration and its existence within the Indian agricultural market. In India, the free flow of goods and commodities is discouraged under the Essential Commodity Act of 1955. This has had a major influence on the process of price formation within the country. A number of scholars have also tried to test the existence of the law of one price among spatially separated markets for agricultural commodities – the primary objective being to check for market integration. However, testing the hypothesis of the existence of one price, given the data and theoretical constraints, makes it difficult to conclude if a market is integrated or not. Enke (1951) and Samuelson (1952) examined the concept of spatially separated markets and emerging equilibrium prices. Their analysis illustrated the process of price formation through trade in spatially separated markets of a homogeneous product, with transportation as the sole constraint. In this paper, in order to check for market integration within the Indian agricultural market, we try and analyse spatial price movements for selected agricultural commodities. We consider both the wholesale and the retail price i ndex in India to test for the presence of co-integration, using the Johansen method of co-integration testing and the common trend Stock and Watson method.1

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Trend in Agricultural Prices in India

Rising foodgrain prices have been one of the most major concerns in recent years in India. Prices have been rising consistently, particularly since 2004 (Figure 1). While it is widely accepted that a minimum rise in prices could be owing to systematic and unavoidable fluctuations in production and policy, the present state of high prices is mainly attributed to a fall in the supply of foodgrains and rising demand in relation to supply. One of the ways to curtail excessive price rises would be to import foodgrains from abroad. However, even world markets are experiencing short-term fluctuations in foodgrains2 and thus, relying on international trade to deal with domestic supply problems to limit price rises may not be an easy solution, especially if the domestic market is not well-integrated to diversify the risk. The ability to diversify risk through domestic market integration remains an important factor that maximise the benefits derived from trade liberalisation.

In India, with the marked exception of rice, almost every commodity in the foodgrains category shows high rises and fluctuations in prices (Figure 2). One of the ways to stabilise prices would be to allow goods to flow freely across different states in India, which in turn, would lead to a better balance in the demand and supply of the agricultural commodity in question, perhaps leading to price stability. This study will consider market integration as one of the key factors that leads to better economic performance. It will study the level and fluctuations in foodgrain prices for selected agricultural commodities India, with a view to measure the level of market integration systematically across regions in the country, especially since such integration could be the key to price stability.

Wholesale and Retail Wheat Prices

This section will discuss the trend in wholesale3 (Figure 3) and retail (Figure 4, p 151) prices in the Indian wheat market during recent years. The wholesale price index captures information and constraints relating to production that could lead to variations across regions. The retail price in

dex, on the other hand, captures the additional costs of the commodities making 50 it to the final consumer in the market. F igures 3 and 4 provide a fair idea of the

40

pattern of price variation in different domestic markets in India. The two figures 30 show that price variations move in a sim

20

ilar pattern in the wholesale market, but do so in a lesser degree in the retail market. 10 The price data is further analysed in order to provide more scientific evidence of 0

Figure 1: Inflation in Foodgrains in India (January 2001-January 2007, in %)

14 12 10 8 6 4 2 0

1/01 5/01 9/01 1/02 5/02 9/02 1/03 5/03 9/03 1/04 5/04 9/04 1/05 5/05 9/05 1/06 5/06 9/06 1/07 -2 -4

Source: Reserve Bank of India.

Figure 2: Average Retail Price of Important Foodgrains (January 2006-January 2007, inflation (%)) 60

50

40

30

20

10

0

Urad Moong Gram Wheat Jowar Maize Bajra Barley Ragi Arhar Masur Rice Source: Central Statistical Organisation, India.

theoretical studies on spatially separated prices of a homogeneous good. Enke discusses equilibrium prices across spatially separated markets using a simple electric circuit. He first formalises three spatially separated markets to examine the net equilibrium prices in each region and the expected quantity of exports and imports between the regions under certain simple assumptions.

Figure 3: Year on Year Price Variations – Wholesale Wheat Market

Bihar Patna Haryana Ambala Karnal Rohtak Gujarat Dohad Amritsar Jalandhar Ludhiana Kota Jaipur Jodhpur Hardoi Jhansi Saharanpur Delhi

market integration with regards to wheat

in India.

-10

Apr, 1997Jul, 1997Oct, 1997Jan, 1998Apr, 1998Jul, 1998Oct, 1998Jan, 1999Apr, 1999Jul, 1999Oct, 1999Jan, 2000Apr, 2000Jul, 2000Oct, 2000Jan, 2001Apr, 2001Jul, 2001Oct, 2001Jan, 2002Apr, 2002Jul, 2002Oct, 2002Jan, 2003Apr, 2003

Gujarat Patan

Rajkot

Bangalore Maharashtra Akola

-20 Aurangabad
Literature and Theory Allahabad
The most important concepts in studying -30 Agra Hapur
market integration are spatial arbitrage, the law of one price and spatial market -40 Kanpur Sultanpur Dara: Delhi
efficiency. The papers by Enke (1951) and -50
Samuelson (1952) are the most important Source: Central Statistical Organisation, India
150 may 30, 2009 vol xliv no 22 EPW Economic & Political Weekly

Figure 4: Year on Year Price Variations – Retail Wheat Market

60

50

40

30

20

10

0 07/05 08/05 09/05 10/05 11/05 12/05 01/06 02/06 03/06 04/06 05/06 06/06 07/06 08/06 09/06 10/06 11/06 12/06 01/07

-10

-20

-30

Source: Central Statistical Organisation, India.

He then generalises the results obtained in the three market case. He assumes that the regions are separated by the transportation cost, which is taken to be independent of the volume of goods traded. He constructs a simple trading function, Ei = β(Pj – Ai), where Ei is the exports by region ‘i’, Ai is the price at which local use equals local production, where Pj is the several local price. He explains the entire structure when A1<A2<A3 and there is a positive transportation ‘T12’ (transportation from region 1 to region 2). That is, export will take place from region 1 when (Pi – A1) > T1i similarly import will take place when A1 – Pi > Ti1. The equilibrium condition is that the sum of the exports will be equal to zero. The price of the good in the importing region will be the sum of the price given according to the excess supply function between the two regions and the transportation cost. This paper, thus, provides a valuable understanding of emerging equilibrium prices and their behaviour across regions.

The Samuelson paper is an immediate extension of that of Enke, wherein he systematically studies several comparative static changes. We try to illustrate the simple two-region model in detail, as it is shown in the Samuelson paper (see Appendix I, p 155). The model is elegant in explaining the systematic changes in prices across regions. Furthermore, the theoretical model is coherent in showing that when there are spatially differentiated markets with transportation costs, under certain simple assumptions, the free flow of goods across regions maximises total social welfare. Additionally, it also shows that the prices of homogeneous goods across regions will behave according to aggregate demand and supply and in a systematic and expected pattern, subject to the transportation costs. The theoretical literature outlined above applies to the case of purely competitive markets and deal with a strong form of market integration within a static picture. However, the reality is more dynamic and there are a number of other factors that can influence the price of agricultural commodities.

In the recent literature, one can find theoretical models dealing with price determination and empirical models to understand price behaviour across spatially separated markets. Scholars now

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use more dynamic models such as vector au
toregressive and co-integration models to
understand and predict the patterns of mar
ket integration across regions. For instance,
Lele (1967) studies the case of sorghum
prices in a number of primary and terminal
Delhi markets in India and concludes that the ob-
Lucknow served differences are not caused because of
Ahmedabad
Bhopal the activities of traders, but mainly owing to
Jaipur product differences and transport costs. She
West Zone: Mumbai Bhubaneswar Patna hypothesises that wholesale prices in sorghum are not very different across the coun
02/07 Bangalore Hyderabad try, and that the existing price discrepancies are a result of mainly transport costs. She esti
Madras Thiruvananthapuram mates the correlation coefficients of weekly sorghum prices across different markets and
uses this correlation coefficient as an indi
cation of the extent to which the markets
are integrated. Shiue (2002) asserts that
18th century China saw relatively high lev

els of intra-regional trade, but that this did not necessarily lead to fast economic growth. In examining price correlations, she also takes into account storage, which indicates inter-temporal market integration. She takes data on local weather conditions into account to study common supply relationships. She finds that regions which did not trade much with one another were more likely to use storage to smooth the impact of supply shocks, indicating the important role played by inter-temporal trade in market development. Barrett (2004) attempts to clarify the difference between flow-based indicators (production and consumption) of tradability which best reflect market integration, and price-based notions of market equilibrium which best reflect efficiency. He also laments that since price analysis methods rely so heavily on simplifying assumptions, tests of the hypothesis of market efficiency are sometimes difficult to distinguish from tests of the underlying strength of the assumptions that underpin the model. He thus suggests that to study efficiency in trade, price data must be supplemented with data on transactions, costs and trade volumes. Rapsomanikis et al (2004) indicate that although price transmission can be formally tested in the long run, the extent to which price signals are transmitted from one market to a nother is ambiguous. They provide an overview of the price transmission-testing framework, and then apply it to a few s elected developing country cash crop markets.

Methodology and Results

In order to test the presence of wheat market integration in India we use co-integration test and common trend method. According to Engle and Granger (1987), if a series Pwith no deterministic

t

components can be represented by a stationary and invertible ARMA (autoregressive moving average) process after differencing d times, the series is integrated of order d, that is P~ I(d).

t

A demonstration of a co-integration price process for two regions can be shown as follows: P1t = βP2t + Є1t …(a)

…(b)

P2t = P2, t-1 + Є2t

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where, P1t and P2t are the year on year price variation in wheat in greater than ‘r’. The trace values in every alternative hypothesis India over the monthly price series of the previous year (January exceed the critical values such that every null hypothesis is 2001 to January 2007 retail price series; April 1997 to June 2003 rejected. Alternatively the results suggest that there is strong

whole sale price series). With Є1t and Є2t co-integration between the wholesale

Table 1: Summary Statistics of Regional Wheat Market – being uncorrelated white noise process, Wholesale Price Variation of Monthly Series (April 1997 to June 2003) price series across the regions under

Regional No of Mean Standard Min Max

ΔP2t= P2t– P2, t-1 = Є2t. Now let us con- Observation Deviation consideration.

sider the difference of equation (a),

ΔP1t = βΔP2t + ΔЄ1t = βЄ2t + Є1t – Є1,t-1 …(c)

From equation (c) we can infer that and P2t are integrated of order 1. Also

P1t if linear combination of P1t – βP2t is stationary then series P1t and P2t are cointegrated of order 1. In short, the attributes and elements of Pt are co-

Patna 75 2.5 12.2 -20.6 30.0

Ambala 75 3.3 11.5 -27.0 32.7

Karnal 75 3.8 11.1 -20.0 34.7

Jalandhar 75 3.6 12.0 -22.0 44.6

Amritsar 75 3.6 13.1 -24.5 38.2

Jaipur 75 3.9 13.6 -31.5 33.3

Jodhpur 75 5.0 12.6 -22.5 39.3

Ludhiana 75 3.9 10.2 -20.9 32.3

Delhi 75 5.0 12.6 -26.5 27.8

Bangalore 75 1.0 16.2 -27.9 50.0

Jhansi 75 2.0 12.2 -29.1 28.0

Next, we tested the retail price variations and their integration across different regions using the same techniques. The retail price series are recently constructed and are for January 2001 to January 2007, taken from the Ministry of Consumer Affairs’ (Food and Public Distribution) monthly series. Given that data is not available for many regions and there is incomplete data on transportation costs, we expect low or no cointegration. Additionally, we did not make any zonal restrictions while testing for co-integration in retail price variations across the regional markets, and there exist co-integration for only integrated if there is a linear combination of them which is integrated of order zero. Given the notion of cointegration, we use a multivariate approach developed by Johansen (1988, 1991) to analyse the prices across regions

Source: CSO, India.

Table 2: Co-integration Test of Wholesale Price Variation – 11 Regions’ Wheat Market

Null Hypothesis Eigenvalue Trace 5% Critical Value

r=0 0.7 482.5 256.2

r<=1 0.7 382.1 214.1

r<=2 0.6 297.6 175.5

in the Indian wheat market. Johansen r<=3 0.5 225.5 140.7 few regions such as Hyderabad, Madras,

method allows for more than one co-r<=4 0.5 167.5 109.9 Jaipur, Delhi and Ahemdabad.

integration relation among the variables (wheat prices) considered in the study.

As an implication of co-integration we furthermore tested the existence of common trend in the variations of wholesale and retail price series of wheat among several regions of the country. Elements of ΔP, will have a

t

common trend if there exist a linear combination of them which is an innovation with respect to all observed information prior to time t (Engle and Vahid 1993). For this purpose we used the Stock and Watson (1988) common trends testing method. The null hypothesis is that k-dimensional time series Pt has m<=k common stochastic trends, and the alternative is that it has s<m common trends. We will use first order serial correlation matrix of Pt to test the hypothesis.

r<=5 0.4 118.3 82.6

r<=6 0.3 80.3 59.2

r<=7 0.3 56.3 39.7

r<=8 0.2 34.4 24.1

r<=9 0.1 17.8 12.2

r<=10 0.1 5.9 4.1

Table 3: Summary Statistics of Regional Wheat Retail Price Variation (Year on Year) of Monthly Series (January 2001-07)

Regions No of Mean Standard Min Max Observation Deviation

Hyderabad 74 4.2 9.5 -10.0 30.0

Madras 74 6.7 15.4 -13.0 54.5

Ahmedabad 74 6.3 8.7 -7.1 36.8

Jaipur 74 7.8 13.6 -22.2 50.0

Delhi 74 6.8 8.7 -12.5 25.0

Source: CSO, India.

Table 4: Co-integration Test of Retail Price Variation – Five Regions’ Wheat Market

Co-integration Rank Test Using Trace Null Hypothesis Eigenvalue Trace 5% Critical Value

r<=0 0.45 98.1 59.24

r<=1 0.25 54.3 39.71

r<=2 0.20 32.49 24.08

r<=3 0.16 15.6 12.21

r<=4 0.03 2.9 4.14

Table 3 briefly summarises the means and standard deviations in price variations across regions. Table 4 provides the results of the Johansen co-integrating test and shows that every null hypothesis is rejected.

Next, we analysed the co-features or common trend for the selected regions with regards to wholesale and retail prices. Our objective is to discover whether there is a common trend in the regional market, and we show that there exists more than one common trend and in fact, there exist several common trends in regional markets with regard to wheat in India.4 Intuitively, if there is more than one common trend then the markets will not converge.5 Thus, one has to be very careful with dealing with concepts such as the law of one price, co-integration and convergence hypothesis. Looking

Wheat Wholesale and Retail Price Movements, Co-integration and Common Trend in Price Variations

Table 1 provides the standard deviation in wholesale price variations and average price variations across the different regions. The wholesale price series were obtained from the Ministry of Agriculture, government of India and are taken for the regions under consideration from April 1997 to June 2003. Table 2 summarises the results of the co-integration analysis using trace statistics. The trace statistics test the null hypothesis that there are at most ‘r’ co-integrating vectors against the alternative that the rank is at the precise relationship between these concepts is beyond the scope of this paper. It is also very difficult to prove the law of one price and convergence in reality. With regard to retail price variation for the regional markets under selection (Table 5, p 153), the study shows that there are three common trends (filter value -32.0 against the critical value -24.7) at 5% critical value. In the wholesale price variation data we divided the regional markets into two groups to avoid the estimation constraint with higher dimen sions. There are no other restrictions for grouping the r egional markets. The first group of regions (Table 6, p 153) such

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as Ambala, Karnal, Jalandhar, Amritsar and Jaipur shows that low prices in the primary markets and to high prices in the termithere are four common trends (filter value -12.9 against the criti-nal markets. In addition, transportation costs can be a high procal value -12.4) at the 5% level. In the second group of regions portion of agricultural commodity prices as opposed to those for (Table 7) such as Jodhpur, Ludhiana, high-value technology goods and man-

Table 5: Common Trend Test – Regions

Delhi, Bangalore and Jhansi, there are (Hyderabad, Madras, Ahmedabad, Jaipur and Delhi) ufactures. In fact, if transfer costs are

three common trends (filter value -34.5 against the critical value -24.7) at the 5% level.

Therefore, this study clearly shows that wholesale prices are co-integrated in the long run. The co-integration in wholesale prices can be interpreted as the regions under study using similar technologies in production and dealing

Null Hypothesis Alternative Hypothesis Eigenvalue Filter 5% Critical Value

5 0 0.91 -6.8** -1.64

  • 1 0.91 -6.8 -7.2
  • 2 0.81 -14.7** -14.5
  • 3 0.57 -32.0** -24.7
  • 4 0.57 -32.0 -41.9
  • Table 6: Common Trend Test – Regions

    (Patna, Ambala, Karnal, Jalandhar Amritsar and Jaipur)

    Null Hypothesis Alternative Hypothesis Eigenvalue Filter 5% Critical Value

    6 0 0.82 -13.6** -1.4

    high enough, price transmission signals may be hindered since any sort of arbitrage would be prohibitive, completely insulating one market from another.

    Price differentials could also occur as a result of poor dissemination of knowledge regarding market conditions, for instance, the trader may not be aware of opportunities to make profits by ex

    with similar input costs, which in turn 1 0.82 -13.6** -6.4 ploiting price differences through the
    would imply similar price variation pat 2 0.76 -17.9** -12.4 movement of goods. Imperfect know
    terns. In short, it is the similarities 3 0.57 -32.3** -20.4 ledge could thus lead to inadequate flow
    within supply-side factors that explain 4 0.44 -42.0** -31.5 of goods, leading to differences in prices
    the co-integration in wholesale wheat 5 0.44 -42.0 -49.8 greater than that of the costs of ship
    prices across the selected regions within the country. Table 7: Common Trend Test – Regions (Jodhpur, Ludhiana, Delhi, Bangalore and Jhansi) ment. In this study, we have constructed a simple index of transportation and
    In order to ensure that demand and Null Hypothsis Alternative Hypothesis Eigenvalue Filter 5% Critical Value communication infrastructure so as to
    supply are integrated across regions, we further tested the retail market, which also shows co-integration but for 5 0 1 2 3 0.87 0.75 0.75 0.54 -9.9** -18.6** -18.6** -34.5** -1.6 -7.2 -14.5 -24.7 analyse how this may have an effect on market integration (Appendix III, p 155). Using the principal component
    fewer regions. This leads to the ques 4 0.54 -34.5 -41.9 analysis, the index has been con

    tion of the constraints or impediments which could be accountable for the lower degree of co-integration within regional retail prices in the wheat market. We try and address this question within the next section by analysing the transaction cost factor, in general, and in particular, the levels transportation and communication between the regions. We thus, look at the infrastructure pattern in India systematically, and observations from the same allow us to address the reasons for fewer regions with co-integration in retail prices in the wheat market.

    Transaction Costs Influencing Market Integration in India

    In developing countries, the existence of high transaction costs can serve as a big barrier to market integration, mainly because it interferes with the assumption of perfect mobility in the goods market. High transaction costs in developing countries arise primarily due to poor transport and communications infrastructure, inadequate contract enforcement mechanisms and unstable political environment. Given the difficulties in measuring transaction costs, it is difficult to try and capture their effect on market integration.

    Transportation and communications are basic and necessary infrastructure for market integration. The markets in India have continued to face problems with regards to such basic infrastructure. Transaction costs can be high owing to an inefficient transport system or because of long distances between markets and the perishable nature of commodities. Low correlation in prices across different regions can also be a result of transport bottlenecks which obstruct the flow of goods from surplus to deficit regions6 resulting in excessively depressed or high prices. Thus, an excessive accumulation of goods can lead to artificially

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    structed for 15 major states in India, stretching from 1993-94 to 2004-05. We found huge variations in the transportation and communication endowments across states in the country (Figure 5, p 154). We concluded that variation in the levels of infrastructure was one of the most important factors affecting the degree of market integration.

    The variables used in the construction of the infrastructure index are: State-wise density of rail routes; State-wise density of road routes; and State-wise tele-density.

    Another important factor that could account for price variations in the agricultural market is storage. Storage, or inter-temporal trade, could be a substitute to inter-regional trade, since good storage facilities could smooth the impact of supply shocks (Shiue

    Table 8: Transportation and Communication Infrastructure Index (1993-2004)

    State 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
    Kerala 163 167 171 176 180 184 187 195 205 206 211 244
    Punjab 133 136 140 145 148 151 155 159 168 165 167 222
    Tamil Nadu 123 126 128 131 134 139 142 149 147 147 149 174
    Gujarat 111 113 116 118 121 123 125 134 138 137 137 164
    Haryana 117 118 120 123 126 128 130 133 137 138 141 161
    Maharashtra 111 114 124 127 130 133 137 143 137 137 139 160
    Karnataka 107 109 112 114 116 118 121 125 128 128 129 156
    Andhra Pradesh 105 108 109 112 113 115 117 122 126 124 126 146
    West Bengal 123 126 129 129 131 132 133 137 132 134 136 141
    Uttar Pradesh 116 117 120 122 125 126 127 126 131 132 132 140
    Assam 116 117 118 119 119 124 129 132 133 134 135 139
    Orissa 113 114 115 122 122 124 124 122 124 125 127 133
    Bihar 117 118 118 119 119 119 120 119 124 123 124 128
    Rajasthan 100 102 103 104 105 107 108 110 112 112 113 124
    Madhya Pradesh 103 104 104 105 105 106 107 106 111 109 110 122
    Source: Authors’ calculations, using Economic Intelligence Unit data, CMIE, Mumbai.
    153

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    2002). This implies that even markets that are not integrated government and private storage is not encouraged, in India, need not be susceptible to price variations as a result of shocks, buffer stocks of wheat have not played a significant role in if their buffer stocks from previous harvests are large enough price stabilisation. to absorb these shocks. However, one must keep in mind that storage could also have adverse impacts, as inventory-holding Conclusions

    Market integration is a necessary condition for stabilising prices

    Figure 5: Index of Transport and Communication Infrastructure (2004-05)

    across regions, both within domestic and in global markets. In re

    260

    cent years, the Indian economy has seen several policy changes leading to higher growth in gross domestic product. However, growth within agriculture has lagged behind and a number of bot

    240

    220

    tlenecks exist which have led to relatively high fluctuations within the prices of agricultural commodities. We have analysed the level

    200

    of market integration in the context of wheat in India during recent years by looking at variations in both, the retail (consumer side)

    180

    and wholesale (supplier side) price data. We used a sample of regional markets across the country for the purposes of our study.

    160

    Our study also shows that although there is long-run market integration with regard to wholesale prices, this does not hold true for retail prices for the selected regions. One of the most im

    140

    120

    portant factors that could explain this disparity could be differences in infrastructure endowments across regions. Our in

    100

    frastructure has tried to capture these variations and shows that

    Source: Authors’ calculations, using Economic Intelligence Unit data, CMIE, Mumbai.

    there does indeed exist disparities in endowments across regions, behaviour relating to price expectations could also lead to and that this could be a crucial factor in explaining why there is asymmetries in the movements of two prices. In India, the accu-low or no market integration in retail prices of wheat across remulation of wheat stocks depends primarily on price policies gions in the country. This implies that infrastructure development implemented by the government, such as the policy of minimum could help to diversify the risk through improvements in market support prices (MSP). To maintain these prices, the government integration. A failure to improve integration would lead to sellers procures a certain percentage of wheat production each year – in the market being worse off since they would be forced to sell the average figure for wheat procurement by the government their produce at lower prices, and consumers being worse off between 1996-97 and 2006-07 was 20%. In the past, these since they would be forced to pay higher prices. Thus, to sum up, wheat stocks have not always been utilised for the purpose of to achieve integration in the wheat market characteristic of the buffering, but have been sold at less than the MSP to foreign law of one price, a single common trends and convergence, infranational traders. Since the wheat stock limit is fixed by the structure development remains a necessary condition.

    Kerala 244 Punjab 222 Gujarat 164 Haryana 161 Karnataka 156 Maharashtra 160 Andhra Pradesh 146 Tamil Nadu 174 Assam 139 Bihar 128 Orissa 133 West Bengal Uttar Pradesh 140 Rajasthan 124 Madhya Pradesh 122 ♦♦♦141 ♦♦♦♦♦♦♦♦♦♦♦♦

    Notes

    1 According to Stock and Watson (1988) cointegrated multiple time series share at least one common trend.

    2 In the case of wheat, a sharp decline in world production in 2006-07 (estimated to be 590 million tonnes compared to 618 million tonnes in 2005-06) resulted in an increase in international prices from an average of $152.4 (for US hard red wheat) during January-December 2005 to $212.1 in October 2006 (Economic Survey 2006-07).

    3 While there is no change in the operational content of the concept of wholesale prices, it was clarified that the price quotations pertained to bulk transactions, generally at an early stage of trading. It was also found that “wholesale price” data are frequently collected, where unavoidable, from a variety of sources such as ex-farm, ex-factory gate, ex-mine level and could be either with transportation cost or without. They are sometimes akin to producer prices, but by no means always or even generally so. In fact, it is felt that in future a good part of manufactured products prices would have to be collected from traded data rather than the more commonly used company cost/price data. In several cases, retail prices are used or those for which one-off deals take place.

    4 A filter value statistic greater than the critical

    value indicates the existence of common trend or co-feature. 5 For more discussion on this, see Bernard and D urlauf 1991.

    6 We refer to surplus and deficit regions in terms of the excess supply curve described in Samuelson (1952).

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    may 30, 2009 vol xliv no 22

    SPECIAL ARTICLE

    The graph shows the process of market integration in a spatially separated market (Samuelson 1952). Where, T1 and T2 are the cost of transportation and taxes assumed to be constant per unit. The equilibrium price in the integrated market (PI) is assumed to be the sum of P3, T1 and T2.

    Market A Market B

    S

    A1

    S

    A

    +E2 PI

    S

    B2

    PI-E2

    1

    D

    A

    D

    B

    P5

    T1 + T2

    Market A Market B

    P1 PI1 -E1 SA DA P4 P2 T1 + T2 +E1 SB DB

    Market A Market B

    S

    A

    S

    B

    S

    +E3 B1

    PI

    1

    PI

    3

    -E3 D

    A

    D

    B

    P6

    T1 + T2

    Appendix II:

    Stationary test of wholesale price variation of monthly series (April 1997 to June 2003) – India (Dickey-Fuller Test) of order (1)

    Variable Type Rho Pr < Rho

    Patna Zero Mean -10.6 0.02

    Single Mean -11.0 0.09

    Trend -10.9 0.35

    Ambala Zero Mean -11.3 0.02

    Single Mean -11.9 0.07

    Trend -11.8 0.29

    Karnal Zero Mean -14.7 0.01

    Single Mean -16.2 0.02

    Trend -16.0 0.12

    Jalandhar Zero Mean -17.8 0.00

    Single Mean -19.8 0.01

    Trend -19.9 0.05

    Appendix I: Equilibrium in a Spatially Separated Market – 2 Region Model

    Amritsar Zero Mean -16.7 0.00
    Single Mean -18.1 0.01
    Trend -17.7 0.09
    Jaipur Zero Mean -13.5 0.01
    Single Mean -14.2 0.04
    Trend -14.8 0.16
    Jodhpur Zero Mean -14.5 0.01
    Single Mean -17.5 0.02
    Trend -17.5 0.09
    Ludhiana Zero Mean -27.2 0.00
    Single Mean -31.7 0.00
    Trend -31.6 0.00
    Delhi Zero Mean -11.7 0.01
    Single Mean -13.4 0.05
    Trend -13.9 0.20
    Bangalore Zero Mean -24.1 0.00
    Single Mean -24.0 0.00
    Trend -24.9 0.02
    Jhansi Zero Mean -14.7 0.01
    Single Mean -14.8 0.03
    Trend -14.9 0.16

    Stationary test of retail price variation of monthly series (January 2001 to January 2007) – India (Dickey-Fuller Test) of order (1)

    Region Type Rho Pr < Rho
    Hyderabad Zero Mean -8.9 0.04
    Single Mean -11.6 0.08
    Trend -11.2 0.33
    Madras Zero Mean -10.0 0.03
    Single Mean -12.8 0.06
    Trend -18.8 0.07
    Ahmedabad Zero Mean -6.4 0.08
    Single Mean -12.3 0.07
    Trend -16.0 0.13
    Jaipur Zero Mean -23.3 0.00
    Single Mean -35.1 0.00
    Trend -60.0 0.00
    Delhi Zero Mean -6.3 0.08
    Single Mean -13.8 0.04
    Trend -24.6 0.02
    Appendix III

    Wholesale prices in the regional market and its corresponding states

    Regional States Regional States
    Patna Bihar Jaipur Rajasthan
    Ambala Haryana Jodhpur Rajasthan
    Karnal Haryana Delhi New Delhi
    Jalandhar Punjab Bangalore Karnataka
    Amritsar Punjab Jhansi Uttar Pradesh
    Ludhiana Punjab

    Retail prices in the regional market and it corresponding states

    Regional States
    Hyderabad Andhra Pradesh
    Madras Tamil Nadu
    Ahmedabad Gujarat
    Jaipur Rajasthan
    Delhi New Delhi

    Economic & Political Weekly

    EPW
    may 30, 2009 vol xliv no 22

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