Stanford Center for International Development

Working Paper No. 353

The Integration of the Indian Wheat Sector into the Global

Market

by

Ashish Shenoy1

May 2008

Stanford University

579 Serra Mall @ Galvez, Landau Economics Building, Room 153

Stanford, CA 94305-6015

1   Department of Economics, Stanford University  

The Integration of the Indian WheatSector into the Global Market

May 2008

Ashish Shenoy†

Department of EconomicsStanford UniversityStanford,CA 94305

Abstract

World food prices have risen rapidly in recent months. The trend hasbrought up concerns about how markets in developing nations respond to in-ternational conditions. In this paper, I try to determine whether the price ofwheat in India converges to the world level. Using monthly prices from theUnited States of America, Canada, Australia, Argentina, and India over a pe-riod of thirteen years, I look for evidence of cointegration among the series.Cointegrated series follow a common stochastic process, and thus can be saidto move together. I first test for cointegration without restrictions to identifythe number of cointegrating vectors and common trends, and then impose re-strictions to see how quickly markets adjust to disequilibria. I find evidencethat the world wheat trading centers are integrated, with Australia being themost dominant. The Indian wheat price does not converge with the other four.I next use the Granger Representation Theorem to model the adjustment ofthe markets to shocks. I find that the Indian market adjusts more slowly toa new equilibrium, but the total magnitude of adjustment is greater. Possibleexplanations include poor infrastructure, regional segmentation within India,and high levels of government intervention.

Keywords: India, wheat, food price, cointegrationJEL Codes: F14, Q11

†I would like to thank Professor Hansen for his patient mentoring and guidance, without whichthis project would not have been possible. I am also grateful to Professor Wally Falcon, ProfessorRoz Naylor, Professor Nick Hope, Dr. Charan Singh, Smt. Asha Kannan, Dr. Ramesh Golait,Pankaj Kumar, and Professor Geo!rey Rothwell for their help in various stages of my research. Anyerrors that remain are solely my own.

Contents

1 Introduction 2

2 Literature Review 4

2.1 Price Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.2 World Wheat Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.3 Developing Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3 Indian Wheat 12

3.1 Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.2 Market Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.3 Government Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.4 Recent History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4 Data 18

4.1 Data Selection and Treatment . . . . . . . . . . . . . . . . . . . . . . 184.2 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

5 Methodology 22

5.1 Maximum Likelihood Model . . . . . . . . . . . . . . . . . . . . . . . 235.2 Unrestricted Estimation . . . . . . . . . . . . . . . . . . . . . . . . . 255.3 Restricted Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 265.4 Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

6 Results 29

6.1 Unrestricted Estimate . . . . . . . . . . . . . . . . . . . . . . . . . . 306.2 Restricted Estimate . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316.3 Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

7 Discussion 40

7.1 Interpretation of Results . . . . . . . . . . . . . . . . . . . . . . . . . 407.2 Possible Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

8 Conclusion 44

References 47

CONTENTS 1

1 Introduction

Food prices worldwide are skyrocketing. The repercussions of higher prices can be

felt in every part of the globe. Over the last three years, food prices have risen by an

average of 83%. Cereals seem to be hit the hardest, experiencing an increase of over

20% in the last two months. This recent jump caps o! a three year period in which

cereal prices almost tripled (World Bank, 2008). In the United States, consumers

have responded to the crisis and expectations of further price increases by raising

their foodgrains purchases, driving many stores to strat rationing rice and wheat.

The fallout abroad has been worse, ranging from food riots to political upheaval in

countries from Haiti to Bangladesh, all in response to the the declining availability of

basic staples. O"cials worry about the lasting impacts of the crisis as it is unclear

how soon prices will stop climbing and whether they will return to their previous

levels.

Prices are expected to stay high in the near future. Medium-term forecasts esti-

mate that the cost of food will remain well above its 2004 level until at least 2015

(World Bank, 2008). High prices fall disproportionately hard on low-income families,

who are forced to spend a much larger portion of their income on food in order to

survive (Timmer, 2000). Their e!ective wage will be lower, and many will not be able

to cover the added costs. O"cials worry that without significant policy intervention,

food price increases could cause millions to starve and wipe out decades of poverty

relief (Moon, 2008). Popular resistance to a more harsh economic environment has

already inspired contempt for incumbent governments, a reaction that will continue

to threaten political stability around the world.

Given these concerns, it is important to understand how nations respond to in-

ternational prices on an individual level. In this paper, I focus on the interaction

between the wheat market in India and international wheat markets. Of India’s total

Introduction 2

agricultural land, 50% is devoted to either rice or wheat. The prominence of wheat

in the Indian agricultural sector places the nation among the top three wheat pro-

ducers in the world. Agriculture in India provides employment to almost 60% of the

Indian workforce (Organization for Economic Cooperation and Development, 2007).

The price these people receive for the commodities they produce determines the prof-

itability of farming, which in turn drives the decision to stay in agriculture or move

into a more productive industrial and service sector.

Indian consumers are also extremely sensitive to food prices. On average, Indians

spend 50 to 70% of their income on food. Wheat and rice alone compose 15% of the

average Indian citizen’s total expenditures (Reserve Bank of India, 2006). Because

such a high percentage of earnings goes into buying food, changes in price have

significant impacts on the Indian population. The combined prevalence of wheat as

a source of income and as a consumer staple has placed it at the forefront of political

and economic discourse throghout the nation.

In this paper, I seek to analyze how well the Indian wheat market is integrated

with other world markets. To do so, I will test whether the price of wheat in India

converges to the export price prevalent in the United States of America, Canada,

Australia, and Argentina. In Section 2, I review some of the relevant literature

regarding analysis of price series, the world wheat trade, and the Indian situation.

In Section 3, I undertake a brief overview of the Indian wheat sector. Section 4

describes the data I use in my analysis, and Section 5 covers the methodology used

to analyze the data. In Section 6 I present my results, with a discussion and some

proposed explanations following in Section 7. I close with some concluding remarks

in Section 8.

Introduction 3

2 Literature Review

2.1 Price Integration

Wheat markets, like other commodity markets, display a high degree of volatility. In

the recent past, the standard deviation of prices has ranged from 10% to 50% in the

United States (see, e.g., Crain and Lee, 1996). Among these price movements, it is

possible to isolate related e!ets across markets. Analysis of commodity prices is com-

plicated by the fact that the series tend to be nonstationary. The term nonstationary

describes a subset of autoregressive series, or series in which each observation can be

represented as a function of previous observations with a stochastic component. An

autoregressive process that is stationary has a mean that is either fixed or follows

a deterministic trend over time. In contrast, nonstationary series do not revert to

a predicted mean, which causes the estimated variance to increase with the length

of the series. If a series is nonstationary, but is made stationary after di!erencing d

times, it is said to be integrated of order d, written as I(d). Time series that do not

revert to a predictable mean require special treatment in econometric models (Box,

1970).

Granger and Newbold (1974) find that ordinary least squares(OLS) regression

on nonstationary series is unreliable. They note that regression with time series

often produces estimators with very high R2 values but strong evidence of serial

autocorrelation in the residuals. Such serial autocorrelation indicates that the model

is misspecified and the result does not necessarily hold true. The authors identify the

nonstationary component of many time series as the cause of the unreliable results.

Specifically, OLS regression using nonstationary series produces incorrect estimates of

regression coe"cients and changes the critical values for tests of significance. When

nonstationarity is present in the data, regression is not adequate to draw conclusions

about a model.

Literature Review 4

Several authors have proposed tests to determine the order of integration of time

series (Dickey and Fuller, 1979; Said and Dickey, 1984; Phillips and Perron, 1988;

Kwiatkowski et al., 1992). These tests model a series of zt as an autoregressive

function of the form

zt = !zt!1 + a + bt + "t (1)

where (a + bt) represents a linear deterministic trend and "t is an independent and

identically distributed stochastic process. In (1), ! determines the persistence of past

shocks in the series. A value of ! such that |!| < 1 guarantees that any stochastic

shock to the process will dissipate over time, meaning that the process centers on the

deterministic trend. In contrast, |!| = 1 means that shocks remain persistent over

time, and the process does not necessarily revert to an underlying trajectory. The

presence of a unit root, |!| = 1, in an autoregressive series indicates that the series is

nonstationary.

Even though OLS regression is inappropriate for analyzing nonstationary data, it

is possible to find stable relationships among multiple nonstationary series. Engle and

Granger (1987) identify a condition in which series revert to a long-run equilibrium,

known as cointegration. For a group of time-dependent variables that are I(1), linear

combinations of these variables will tend to be I(1). If there exists one or more

linear combinations of these variables that are I(0), then the variables are said to

be cointegrated. The vector of coe"cients in this linear combination, called the

cointegrating vector, describes the nature of the long-run relationship among the

variables. Cointegration can be generalized to I(d) variables with linear combinations

that are I(b) where b < d. The authors propose a two-step method of identifying

cointegrating vectors. In the first step, they use OLS regression to estimate potential

vectors. They then test the possible cointegrating vectors by plugging them into a

model involving first di!erences.

Literature Review 5

When more than two nonstationary series are present, multiple cointegrating vec-

tors may exist in the data. For a group of p series, the presence of r unique cointegrat-

ing vectors indicates that there are p! r stochastic trends among the p series (Stock

and Watson, 1988). If r = 0, meaning no cointegrating vectors exist, then each series

follows its own random walk and the variables are unrelated. If r = p!1 cointegrating

vectors exist, then a single stochastic trend guides all p series. If 0 < r < p ! 1, the

series are partially integrated, but more than one common trend exists in the data.

The presence of p cointegrating vectors indicates that the model is misspecified and

the series are actually stationary.

The method of finding cointegrating vectors described by Engle and Granger runs

into problems when more than two series are present. For multiple markets, each

pair must be analyzed independently. Furthermore, which variable is chosen to be

the dependent variable a!ects the results and the method may fail to uncover any

cointegrating vectors that are not revealed by regression. Johansen and Juselius

(1990) propose an alternate method to identify cointegrating vectors based on a Vector

Auto-Regressive(VAR) model. In a VAR, each variable is represented as a function of

the lagged values of all variables in the system. I explore this method in more detail

in Section 5.

In this paper, I use cointegration tests to evaluate market integration. The defi-

nition of market integration comes from Ravallion (1986), who notes the distinction

between integration and e"ciency. Integration simply refers to the comovement of

prices; it is still possible that single actors hold monopoly power or are able to ma-

nipulate prices through other means. Cointegration provides appropriate informa-

tion about the comovement of prices by identifying common trends. The number of

trends that appear in a group of prices describes the degree to which the prices move

together. This result makes no determination regarding the e"ciency of resource

allocation within the market.

Literature Review 6

Bernard and Durlauf (1995) add to this definition in a study of whether the Law

of One Price(LOP) holds among OECD countries. Formally, they define the LOP as

limk"#

E(Pi,t+k ! Pj,t+k|It) = 0 (2)

where It is the information available at time t. The definition in (2) implies that for

two price series to converge, they must not only be cointegrated, but must have a

cointegrating vector of [1,!1]. Such a restriction suggests that the two series move

together and respond to shocks by reverting to price equivalence in the long run. The

authors bring up the possibility of other cointegrating vectors of the form [1, #] where

# "= 1 that indicate a weaker type of integration. Bernard and Durlauf posit that

cointegrating vectors of the latter form describe situations where two markets react

to the same shocks, but respond in di!erent magnitudes. I discuss such a possibility

and its implications in more depth in Section 5.

2.2 World Wheat Trade

Researchers employ cointegration tests to describe the price linkages that exist within

world commodity markets. Ardeni (1989) applies the Engle-Granger two-step method

to multiple world commodity markets. The author identifies markets trading a com-

modity and tests every possible pair for cointegration. Among wheat markets, he

finds evidence that the prices in the U.S.A., Canada, and Australia are linked. Es-

timation also reveals cointegrating coe"cients very close to 1, supporting the LOP.

Ardeni finds rejects the cointegration hypothesis for any other commodity, and con-

cludes broadly that trade in commodities does not lead to convergence among world

prices.

Bukenya and Labys (2005) apply a more complicated set of tests to multiple

commodity markets. In the wheat market, they consider prices from the United

Literature Review 7

States, Argentina, Australia, and Canada on an annual basis from 1950 to 1998. They

first look for correlation among the data, breaking the sets into subperiods in order to

overcome problems with stationarity. With this approach, they find varying results

and little evidence for price convergence. The authors then test for cointegration.

Cointegration tests suggest that world wheat prices generally trend toward certain

equilibrium conditions, though the authors do not identify the number of cointegrating

vectors. They use variance decomposition and estimate impulse response functions to

produce a more detailed picture of the world market. They find that the United States

acts as a price leader, accounting for between 18 and 76% of the variance in price in

other markets. With annual prices used to analyze such a volatile market, however,

the relevance of the variance decomposition is questionable. These two studies focus

on commodity markets in general and not specifically on wheat, so the authors do

not explore the market with much depth.

Authors that have looked specifically at the world wheat market have found a

more complex set of relationships. Goodwin (1992) follows up on Ardeni by applying

the maximum likelihood model described by Johansen and Juselius. The author takes

export prices from the U.S., Canada, and Australia and import prices from Japan

and Rotterdam on a monthly bases from January 1978 to December 1989. In his

first estimation, he finds no sign of price convergence. Goodwin adjusts the study by

accounting for freight rates at import markets and reruns the model. In the second

pass, he finds one cointegrating vector, indicating four stochastic trends among the

five markets. He postulates that more cointegrating vectors exist, but cannot be

confirmed due to the low power of the test. Goodwin interprets his findings as an

indication that the LOP holds in world wheat markets.

Mohanty et al. (1996) employ a similar method to analyze the price dynamics

among the five largest export centers of wheat. They analyze monthly prices from

1981 to 1993 in the United States, Canada, Australia, the European Union, and

Literature Review 8

Argentina. Like Goodwin, the authors find one cointegrating vector among world

markets, which they use to define a long-run relationship. They then estimate ad-

justment coe"cients to evaluate the rate of correction in each market. The authors

find that prices in Canada, Australia, and Argentina move more slowly than those

in the U.S. and the E.U. The Canadian price drives all others while only respond-

ing to changes in the Australian price. The American price a!ects prices in every

market except Canada, and adjusts in response to both the Canadian market and

the Australian market. Among the remaining three markets, the authors do not see

interaction except through the United States or Canada. They conclude that no dis-

tinct price leader exists, but prices are determined primarily in Canada, the United

States, and Australia.

Updated versions of the previous studies, using similar methods with newer data,

find stronger evidence of integration among wheat markets. Yavapolkul et al. (2006)

apply maximum likelihood estimation to world wheat and rice markets from April,

1996, to February, 2002. In their analysis of wheat prices, they include the Amer-

ican, Canadian, Australian, Argentine, and Indian markets. They find evidence of

two cointegrating vectors among the five markets, meaning that the markets are con-

nected, but the LOP does not hold perfectly. The authors interpret their findings

as describing one equilibrium relationship among developed countries and another

between developed and developing countries. Analysis of individual responses reveals

that the Indian and Argentine markets react strongly to price changes in the other

markets without much causality going the opposite direction. The Canadian market

appears to be the most exogenous, responding only to the U.S. market. Overall, the

authors suggest that America and Canada feature most prominently in world wheat

price determination.

Although wheat is generally treated as a homogeneous good in the literature, this

may not provide an entirely accurate picture of the market. Wheat is classified by

Literature Review 9

protein content, and di!erent types of wheat have di!erent end uses. Soft wheat has

the lowest protein content and is best suited for cakes and pastries; medium and hard

wheats have higher protein contents that support bread-making; and durum wheat

has the highest protein content, best for pastas and semolina flour. There is evidence

to suggest that the market for wheat ought to be broken down by class and end use

before it can be analyzed (see Wilson, 1989; Larue, 1991).

Goshray and Lloyd (2003) take this level of complexity into account when looking

for wheat price convergence. The authors identify eleven di!erent types of wheat

traded in the United States, Canada, the European Union, Australia, and Argentina

and categorize them into three di!erent classes. Using monthly prices from July, 1980

to December, 1999, they test every possible pair for cointegration. They find evidence

that every price series is cointegrated with every other series except for Australian

prime hard wheat, which appears to act independently. In the second step of analysis,

the authors examine the variance of the cointegrating vectors to identify which are

structural and which are derived form interaction with intermediary products. They

conclude that cointegrating relationships among the same wheat class have the least

variance, meaning they represent the strongest trends. For estimation of the world

market, they divide the system into four hard wheats, four medium wheats, and three

soft wheats. They find evidence of three cointegrating vectors and one common trend

among the hard wheats, one cointegrating vector and three common trends among

the medium wheats, and two cointegrating vector and one common trend among

the soft wheats. They conclude that Canadian prices lead the hard wheat market,

Australian and Argentine prices lead the medium market, and American prices lead

the soft wheat market. All markets are well integrated, especially hard and soft wheat

markets, and prices show signs of responding to common trends.

Literature Review 10

2.3 Developing Markets

Although not many studies have addressed the role of India in the international sector,

it is possible to gain some insight from the literature. Previous research disagrees on

whether and to what extent price signals are transmitted between the developed and

developing world (see Mundlak and Larson, 1992; Quiroz and Soto, 1993; Morisset,

1998). The conflicting evidence has led some to put forth the idea that a di!erent price

for commodities prevails among developing countries than among developed countries.

Yang et al. (2000) address this possibility in the context of soybeans. Looking at

soy prices from the United States, the United Kingdom, Argentina, and Brazil, the

authors find evidence of three cointegrating vectors among the four markets. Their

result clearly shows that the LOP holds among developed and developing nations; two

di!erent world prices do not exist. The estimated adjustment coe"cients indicate that

prices in the developing nations of Argentina and Brazil react quickly to developed

markets, but causality is weak in the other direction. Although this finding suggests

that the Indian market will be similarly integrated, the authors emphasize that their

results can only be generalized to cases with little government intervention.

Reduced government interference in commodity markets does not necessarily fol-

low from broader economic reforms. Ba!es and Gardner (2003) consider the case

of several developing nations that reformed their economies in the 1980’s and ’90’s.

The authors consider cases of economic liberalization that were driven by identifi-

able political decisions followed by implementation to some extent at major trading

centers. They look at 31 di!erent commodity prices spanning eight nations and find

very little evidence that economic reforms increase integration into world markets.

Fewer than half of the cases even provide estimators with the correct sign, and fewer

than half of those are statistically significant. Overall, only Argentina undertook

changes that unambiguously increased commodity market integration, and only two

Literature Review 11

other nations saw a higher degree of integration in any commodities after reform.

The authors hypothesize that even in the context of economic liberalization, political

incentives to protect commodities remain very high. Domestic conditions provide an

alternate explanation for the findings: inertia within a national market may prevent

price transmission even in the absence of restrictive policy. I consider how these two

components factor into the Indian wheat market in the next section.

3 Indian Wheat

Before I begin my analysis, I provide a brief description of the wheat sector in India.

A qualitative understanding of the market helps put the findings into context.

3.1 Production

The Indian wheat market is primarily composed of small farmers holding little land.

The average farmer operated on 1.55 hectares of land in 1995, and more than two

thirds currently farm less than 1 hectare. Fewer than 1% of agricultural landholders

in India own more than 10 hectares (Organization for Economic Cooperation and

Development, 2007). This dispersal of land holdings and productive capacity creates

an agricultural supply sector composed of many actors with little market power.

Among the cereals, almost two thirds of production is consumed within the household,

meaning many in agriculture produce at a near subsistence level (Persaud and Rosen,

2003). Limitations in rural credit, infrastructure, and market information further

hinder farmers’ ability to respond to economic signals. As a result, the price elasticity

of supply in the nation is very low, estimated at below .1 for wheat production (Jha

et al., 2007).

These producers enjoyed large gains in yield from green revolution technology in

the 1960’s and early 70’s. Improvements in irrigation and input usage supported

Indian Wheat 12

Figure 1: Wheat is produced in the north, with high concentration in Punjab and Haryana.Figure from U.S. Department of Agriculture (2000).

rising production in the late ’70’s and ’80’s. During this period, India’s wheat output

grew at an average rate of 5% a year, almost 4% of which came from increases in

yield. Since then, growth in productivity has tapered o!. In recent years, output has

grown at a more modest 2% a year, three fourths of which is increased area planted

(Jha et al., 2007). The current yield in India is slightly below the world average of

2.7 metric tonnes per hectare despite the fact that 87% of the area sown to wheat is

irrigated. For comparison, yield in China is more than double the Indian figure even

though only half of Chinese wheat is irrigated (Government of India, 2004). Some see

this poor performance as an indication of room for improvement with better resource

management and technical knowedge. Others fear that soil degradation and climate

conditions keep the nation’s peak possible yield low.

The wheat grown in India is primarily hard or durum wheat; it has a high protein

content that makes it more suitable for breads than for pastries. The farmers that

grow it employ a wheat/rice crop rotation that keeps the land productive throughout

the year. The wheat season extends from planting in November to harvest in April.

Indian Wheat 13

Production is dominated by the northern states, from where the crop is transported to

mills around the country for processing. Uttar Pradesh leads the country, accounting

for over a third of annual production, followed by Punjab and Haryana at 20% and

10% respectively (U.S. Department of Agriculture, 2000). Figure 1 shows intensity

of wheat production by region.

3.2 Market Structure

To organize the many actors in commodity markets, the Indian government has set up

wholesale market centers known as mandis, usually yards or warehouses inside which

transactions take place. State level agricultural marketing boards are responsible for

organization and administration of mandis. Farmers bring their crop to the local

center to be registered and classified according to type and quality, then negotiate a

sale price and sell the crop to a licensed trader. The trader immediately turns the lot

over to the buyer, taking a small commission from the transaction. Once produce has

been registered at one mandi, farmers are free to take the produce to other mandis to

seek a better price, but they must pay the transportation and carrying costs associated

with moving from market to market. Wholesale trade in agricultural commodities is

generally not allowed outside of the mandi system. In total, more than 750 mandis

exist across the nation, governing markets in 140 di!erent crops (Thomas, 2003).

Given the segmentation of the Indian wheat sector into several trading centers, a

few studies have tried to determine whether the entire national market can be accu-

rately described by a single price. They question whether high transportation costs,

poor infrastructure, and incomplete market information isolate spatially separated

markets, leading to wheat traders in di!erent parts of the country facing di!erent

prices. Ghosh (2003) uses the maximum likelihood method of cointegration to test

price series from 22 markets in 5 states over the 13 year period from 1984 to 1997. He

Indian Wheat 14

finds that within and across states, markets are integrated despite their geographical

separation, meaning that price signals are transmitted from one market to another.

However, the study finds evidence of more than one common stochastic trend among

markets, even among markets within the same state. This result suggests that prices

in di!erent markets may diverge due to local pressures, and one price may not ade-

quately describe all wheat markets in the nation, even after accounting for transaction

costs. The author concludes that, despite the presence of multiple stochastic trends,

markets throughout India respond to related pressures, so it makes some sense to

view the country as a single market rather than several separate ones.

Using a similar method, Srinivasan (2007) analyzes monthly wholesale prices in 11

di!erent markets from April 1997 to June 2003. His results confirm those of the ear-

lier study: There exists evidence of cointegration among markets, but with multiple

common trends. The presence of multiple trends indicates that prices do not neces-

sarily converge, but the cointegration result implies that price signals are transmitted

across markets. The author attributes the lack of perfect integration to a combination

of high transportation and transaction costs, poor methods of communication, poor

contract enforcement, and varying government intervention by state. I discuss the

implications of regional segmentation in Sections 4 and 7.

3.3 Government Policy

Indian government policy is motivated by several di!erent goals. According to the

Ministry of Agriculture, the government seeks to establish environmentally, econom-

ically, and socially sustainable food security through its actions in the agricultural

sector (Roy, 2007). This overarching philosophy generates a patchwork of often con-

tradictory policies with varying objectives. In an e!ort to promote production and

limit reliance on food imports, most state governments o!er water, fertilizer, and

Indian Wheat 15

electricity to farmers at subsidized rates. These subsidies aim to support yields and

keep pace with the growing population. In addition to subsidies, farmers receive price

supports to ensure that their business remains profitable. At the start of each mar-

keting season, the Ministry of Agriculture sets a minimum support price (MSP) for

key agricultural goods such as wheat and rice. The MSP acts as a price floor: If the

market price drops too low, farmers have the option of selling their produce to the

government at the MSP. Using subsidies and price supports, the Indian government

tries to ensure revenues for its agricultural population while maximizing production.

On the demand side, the central government of India actively participates in food

distribution. The Food Corporation of India(FCI), a state-run enterprise, is respon-

sible for procuring foodgrains and distributing them publicly under various schemes.

The largest distribution scheme is the Public Distribution System(PDS), revised to

be more e!ective in 1998, through which grains are sold to low income families at a

discounted rate. The FCI also administers several welfare food-distribution programs,

including the Food for Work program and Midday Meal program. In a more general

role, the FCI is responsible for maintaining national bu!er stocks of wheat and rice.

In the event of a food shortage, it can use these stocks to meet demand and keep

prices low. All wheat procured by the FCI is either bought domestically at the MSP

or imported at the international rate. In total, the Food Corporation of India deals

with 20% of the nation’s wheat product every year (Jha et al., 2007). The Indian

government subsidizes both production and consumption of cereals in an e!ort to

meet its food security and rural revenue targets.

3.4 Recent History

Over the last 13 years, production of wheat in India has ranged from a low of 62

million tonnes in 1995 to a peak of 76 million tonnes in 1999. Most of the variations

Indian Wheat 16

in production can be attributed to annual weather patterns; area sown and yield per

hectare have remained steady over the recent past (Reserve Bank of India, 2006).

The relative stagnation in production combined with rapid economic and population

growth has periodically placed upward pressure on wheat prices.

In the recent past, India has fluctuated between being a net exporter and net

importer of wheat. At the tail end of the reforms of the early 1990s, the Indian gov-

ernment opened the wheat market to allow exports. Wheat traders took advantage

of the opportunity to export large amounts of wheat, leading the government to place

quantitative restrictions on exports in April of 1996. The nation continued to export

in decreasing amounts until 1997, when successive low-yield years led to a shortage

in government stocks and domestic prices started to climb. From 1997 to 1999, India

became a net importer of wheat: In an e!ort to control prices and meet demand,

the Ministry of Commerce banned wheat exports and the State Trading Corpora-

tion(STC), a branch of the FCI, imported large amounts of wheat. At this time, all

imports were canalized through government agencies such as the STC, leaving private

traders unable to act in world markets.

Good weather led to increased yields in 1998 and 1999, which eased the pressure

on the government. By the end of 1999, high yields drove many farmers to sell

their produce to the government, causing the FCI to procure well above the required

amount for public distribution. To avoid excessive carrying costs from storage of extra

wheat, the Indian government decided to export the surplus, subsidizing licensed

exporters to sell wheat on world markets. In July of 2001, the Indian wheat market

was once again opened to export as the government cut back subsidies and lifted

all quantitative restrictions on the sale of wheat abroad. Imports were still canalized

through government agencies in this period, but high stocks left little reason to import

wheat. The nation enjoyed a period of high exports.

India continued to be a significant exporter of wheat until 2005, when depletion

Indian Wheat 17

of bu!er stocks led to concerns that the FCI would not meet its procurement and

distribution targets. As wheat production failed to keep pace with growth in demand,

the STC floated a tender to import wheat in June of the following year. At the same

time, the Indian government lifted restrictions on wheat imports in the private sector,

allowing private traders to freely import wheat for the first time. Prices continued to

rise after the import decision, however, and the government of India banned exports

of wheat in February, 2007. High prices in the domestic market have confounded

government procurement e!orts, so the FCI has turned to further imports to meet

its public distribution and bu!er stock targets.

4 Data

4.1 Data Selection and Treatment

In this paper, I look for evidence of transmission of wheat prices across markets in

India and major world trading centers. The Indian wheat price is obtained from

the O"ce of the Economic Advisor(OEA). The OEA compiles the prices reported at

each mandi into a national figure and publishes weekly, monthly, and annual wholesale

price index(WPI) data for every commodity. These prices reflect the revenue received

by farmers and do not necessarily correlate to the cost of wheat to consumers, al-

though there is reason to believe that the two are fairly well linked (see Persaud and

Rosen, 2003). Consumer price data is not published for individual commodities so the

WPI provides the best approximation possible of the e!ect of international markets

on Indian consumers.

Considering the mixed evidence for a single national price detailed in Section 3,

it is unclear whether my results can be generalized to the entire nation. The method

of data collection used by the OEA is heavily biased toward the large northern wheat

Data 18

producing states of Punjab, Haryana, and Uttar Pradesh, where more mandis trading

in wheat exist. Unfortunately, records at the Indian Ministry of Agriculture are not

complete enough to put together a consistent time series for any individual state

or region, so I am unable to compute a more complex regional analysis. For the

purposes of this study I convert the national monthly WPI price published by the

Indian government to a spot price, using a basket of prices from 33 di!erent markets

in 8 major wheat-trading states for normalization.

I use monthly prices in the selected markets taken from April, 1994 to Septem-

ber, 2007. Although a larger time series may produce more significant results, two

constraints in the Indian data prevent me from extending my analysis further into

the past. First, the Indian method of collecting and recording price data changed in

March, 1994, meaning that including earlier figures may may generate an inconsis-

tent series (Sharma). More importantly, the Indian economy underwent significant

reforms in the period from 1991 to 1993. These reforms began with the government

unpegging the Indian rupee in 1991 to avoid a balance of payments crisis, leading

to full currency convertibility by March, 1994. The new monetary regime brought

with it more openness to market solutions instead of reliance on government licensing

and mandates. By removing protections for most firms and industries, the Indian

government rolled back much of the anti-agricultural bias that had developed in its

policy over the previous forty-five years (Ahluwalia, 2000). These reforms changed

the nature of trade in India at both the macroeconomic and microeconomic levels

and reshaped the attitude of the country with respect to commerce. As a result, it

does not make sense to seek a single, persistent measure of market integration before

and after the reforms.

Exports of wheat in the rest of the world are highly centralized, with five producers

accounting for over 80% of exports. To represent world prices, I take data from the

United States of America, Canada, Australia, and Argentina. These four nations

Data 19

account for about 25%, 15%, 15%, and 10% of annual exports. I use the FOB price of

U.S. No. 2 hard red winter wheat at Gulf ports, Canadian western red spring wheat

at St. Lawrence, Australian soft white wheat set by the Australian Wheat Board,

and No. 2 Trigo Pan Argentine wheat reported by the Secretary of Agriculture (U.S.

Department of Agriculture, 2008). These varieties all represent medium to hard

wheats as Austalian white wheat has a higher protein content than the aveage soft

wheat (Australian Wheat Board, 2006).

I exclude the European Union, the other major exporter, from my study because

of the nature of its product. Exports from the European Union mainly comprise soft

wheat, which is a poor substitute for the medium, hard, and durum wheats favored

in India. Imports of soft wheat have historically come almost exclusively from the

medium-protein soft wheat of Australia (Government of India, 2007). I am also forced

to exclude nations in the Former Soviet Union despite their growing contribution to

the world wheat market due to unavailability of data.

Wheat in international markets is traded in dollars, so I convert the Indian wheat

price to dollars using spot exchange rates. For analysis, I seasonally adjust all series

using the X-12-ARIMA software from the U.S. Census Bureau (see Time Series Sta!,

2007). Seasonal adjustment removes the e!ects of the annual production cycle from

the wheat data, limiting the extent to which data are related simply through time of

year. I take the natural logarithm of each series to eliminate variation in movement

due to level di!erences. Figure 2 plots the five series in logarithmic terms over the

thirteen year span. The American, Canadian, Australian, and Argentine prices ap-

pear to follow the same general trend. The Indian price occasionally displays similar

movement, but with less consistency.

Data 20

Figure 2: Price of wheat in logarithmic terms for the five markets included in the study.

4.2 Descriptive Statistics

Descriptive statistics for the adjusted data are provided in Table 1. Indian prices

show the least variance, and Argentine prices the most variance, over the period of

analysis.

Previous research has found that commodity prices are nonstationary with first

di!erences that are stationary. To confirm that this is the case with my data, I apply

two di!erent unit root tests to the value and first di!erence of each price series. I

first apply the Augmented Dickey-Fuller (ADF) test, which takes the presence of

a unit root as its null hypothesis and tests the alternate hypothesis of no unit root

present (Said and Dickey, 1984). I also apply the Kwiatkowski, Phillips, Schmidt, and

Shin (KPSS) test, which tests the null hypothesis of no unit root present against the

alternate hypothesis that a unit root exists (Kwiatkowski et al., 1992). Because the

Data 21

Descriptive StatisticsU.S.A. Canada Australia Argentina India(PUS) (PCAN ) (PAUS) (PARG) (PIND)

Max 5.80 5.61 5.85 5.57 5.38Min 4.62 4.93 4.88 4.54 4.78Median 5.02 5.21 5.16 4.93 5.00Mean 5.03 5.20 5.19 4.99 5.02Standard Deviation 0.221 0.156 0.179 0.233 0.120

Table 1: Descriptive statistics of the five price series.

two tests take opposite null hypotheses, their agreement provides a strong argument

for or against stationarity.

The ADF test fails to reject the null hypothesis of nonstationarity in level with

even 90% confidence and rejects the null hypothesis of nonstationarity in first di!er-

ences with over 99% confidence for all five of the series. The KPSS test rejects the

null hyothesis of stationarity in level with over 99% confidence for the United States,

Canada, and Australia; and with over 95% confidence for Argentina. While the null

hypothesis of stationarity cannot be rejected for Indian wheat prices at the 95% con-

fidence level, the results of the tests are consistent with a non-stationary price series.

The KPSS testfails to rejet the null hypothesis of stationarity in first di!erence for

all five series. Unit root test results are reported in Table 2. Given these results

and the findings of previous literature regarding wheat prices, I continue under the

assumption that all series are I(1).

5 Methodology

Because I am dealing with nonstationary series, I test for evidence of cointegration.

I choose this method over OLS regressions on the first di!erences because of the

information lost by di!erencing. Cointegration can address simultaneous causality,

accounts for the influence of lags, and provides information regarding both the long-

Methodology 22

Unit Root TestsSeries ADF KPSSUSA

level -1.61 0.262***di!erence -10.21*** 0.125

Canadalevel -1.18 0.271***di!erence -11.42*** 0.077

Australialevel -1.68 0.230***di!erence -10.06*** 0.135

Argentinalevel -2.82 0.182**di!erence -8.27*** 0.075

Indialevel -2.54 0.141*di!erence -9.79*** 0.108

Critical Values:Level:10% -3.14 0.119

5% -3.44 0.1461% -4.02 0.216

Di!erence:10% -2.58 0.3475% -2.88 0.4631% -3.47 0.739

Table 2: Lag length selected using the Akaike Information Criterion (Akaike, 1974).Critical Values derived from MacKinnon (1996). *, **, *** denote rejection of H0 atthe 90%, 95%, and 99% confidence levels.

run conditions and the speed of adjustment. The optimal number of lags is determined

using the AIC (Akaike, 1974). All estimation of cointegration coe"cients is coded by

me using Ox (see Doornik, 2007).

5.1 Maximum Likelihood Model

I use the maximum likelihood method of testing for cointegration to analyze the

selected price series (Johansen and Juselius, 1990). The method starts by modeling

the system in a VAR. If Pt is a vector containing p prices (P1,t, . . . , Pp,t) at time t for

Methodology 23

t = 1, . . . , T , the VAR representation of the system using k lags can be written as

Pt =k

!

i=1

#iPt!i + µ + $t + "t (3)

where µ is a constant, $ describes a linear trend, and " is a stochastic error term.

In (3), each #i is a p # p matrix of coe"cients for its respective vector of lags.

The VAR can be rewritten in its error-correction form (ECM) by di!erencing the

price terms:

$Pt = #Pt!1 +k

!

i=1

%i$Pt!i + µ + $t + "t (4)

In the ECM form, the left-hand side is stationary because it is simply the first di!er-

ence of an I(1) process. All of the $P terms in the right-hand side are I(0) for the

same reason. For the equation to be balanced, #Pt!1 must also be I(0). The matrix

# represents a linear combination of the variables contained in Pt!1, and thus carries

information about the cointegrating relationships.

The number of cointegrating relationships, r, corresponds to the rank of #. For a

value of r where 0 < r < p, # can be broken down into p # r matrices % and & such

that # = %&$. Each column of & represents a cointegrating vector, meaning each

column describes a long-run relationship exhibited in the data. Formally, a column

vector in & of < &1,1, . . . , &p,1 > indicates a long run equilibrium of

&1,1P1,t + · · · + &p,1Pp,t = c (5)

where c is a stationary process. Each column of % represents a vector of adjustment

coe"cients; the values describe the rates at which the variables adjust when the

system is not in the equilibrium described by the corresponding column of &.

Methodology 24

5.2 Unrestricted Estimation

I start by estimating a VAR without any restrictions on % or &. Johansen and Juselius

consider multiple possible specifications for the estimation problem. In my analysis,

I include a constant to account for possible di!erences across markets due to taxes,

transportation, or other transactions costs. I also include a linear trend restricted to

the cointegrating space. Because my prices are reported in nominal values and are not

adjusted for inflation, I must allow for the possibility that the equilibrium relationship

has some drift. Formally, the ECM form under this specification is written as

$Pt = %&$(Pt!1) +k

!

i=1

%i$Pt!i + µ0 + µ1t + "t (6)

where µ1t can can be broken down into the matrix % and the vector ' such that

µ1 = %'$.

The model describe in (6) can be expressed in the form

Yt = #Xt + &Zt + "t (7)

where Xt represents the nonstationary component of the ECM equation with the

linear trend and Zt represents the stationary component and the constant. # can still

be broken down into % and &, where # = %&$. A procedure to estimate coe"cients

in (7) is reported by Hansen (2008).

Given the unrestricted estimation, the rank of # is determined by solving the

eigenvector problem from Johansen and Juselius (1990). For p eigenvalues, ordered

such that (1 > · · · > (p, the authors present a likelihood ratio statistic, or trace

statistic, calculated by

Trace Statistic ((-trace) = !T

p!

i=r+1

log (1 ! (i) (8)

Methodology 25

The trace statistic tests the null hypothesis of at most r cointegrating vectors against

the alternate hypothesis of more than r cointegrating vectors. The statistic is applied

starting with r = 0 and is reapplied using r + 1 cointegrating vectors until it fails

to reject a null hypothesis of at most r cointegrating vectors. & is composed of the

eigenvectors corresponding to the r largest eigenvalues (1, . . . ,(r accepted by the trace

statistic. The unrestricted estimate reveals the number of cointegrating relationships

and common stochastic trends present in world wheat markets.

5.3 Restricted Estimation

To better understand the relationship among wheat prices, I add restrictions to the

VAR estimation. I first limit & so that cointegrating vectors only operate on pairs

of markets. The presence of three or more markets in a single cointegrating vector

would signify that the di!erence in prices between any two of the markets could grow

arbitrarily large, as long as the third market moved in a way to balance the equation

derived from the cointegrating vector. It is more reasonable to test pairs of markets

to see if prices converge. I only need to test p ! 1 unique pairs of markets because

cointegration between any other pair can be expressed as a linear combination of the

existing cointegrating vectors.

As defined by Bernard and Durlauf, price convergence between two markets re-

quires that the di!erence between prices approach zero over time, described in (2).

Although the authors consider the possibility of a cointegrating vector of [1, #] where

# "= 1, such a cointegrating vector would make little sense in the context of wheat

markets. The vector does not attest to a disparity in the magnitude of adjustment

to common trends, but rather to the nature of the long-run equilibrium. It would

indicate that a price shock drives a permanent wedge between the two markets that

increases in size as the markets stray further from their original level. This price wedge

Methodology 26

may never close because nonstationary series are not, by definition, mean-reverting.

Any cointegrating vector other than [1,!1] introduces the possibility that markets

respond to common trends but carry permanent price di!erences. Because this idea

of a permanent, increasing wedge does not seem plausible, I preserve the assumption

that integrated markets follow a [1,!1] relationship.

In the restricted problem, the ECM retains its form in (6), but with a fixed &.

For instance, if r = 4, then the conditions on & are such that

& =

"

#

#

#

#

$

1 1 1 1!1 0 0 00 !1 0 00 0 !1 00 0 0 !1

%

&

&

&

&

'

(9)

The restricted value of & and free parameters in the cointegrating space from ' can

be expressed as vec(&,') = H'+h where ' contains the free parameters, and H and

h are fixed. The estimation procedure for the restricted problem involves a two-step

iterative process based on (7) and is described by Hansen (2002).

5.4 Adjustment

With restrictions placed on &, I can predict how the three markets respond to di!er-

ences in price across trading centers. The estimated values of % describe the rates at

which markets adjust to disequilibrium conditions. Specifically, a [1,!1] cointegrating

vector yields an adjustment in period t of

$Pt =

"

#

$

%i(Pi ! Pj)t!1

%j(Pi ! Pj)t!1

%

&

'

(10)

Methodology 27

Figure 3: The vector ! represents the adjustment made by the two markets i and j, and "%

characterizes their long-run relationship. The projection of ! onto " gives the magnitudeof the correction.

for any pair of markets i and j. The total rate of adjustment is calculated as the

sum of the the two individual vectors projected onto the line perpendicular to the

long-run relationship. Figure 3 provides a graphical interpretation of the vectors %

and &. The values of % corresponding to a cointegrating vector describe the rate at

which a shock disappears and the individual reaction of each independent market.

After estimating the cointegrating relationships and adjustment coe"cients in the

data, I try to predict how a shock to the variables a!ects the entire system. I use the

Granger Representation Theorem from Hansen (2005) that defines a system of I(1)

variables as a function of the residuals. The equation makes use of the concept of an

orthogonal component to a matrix A, written as A%, defined so that A$%A = 0 and

the determinant of the square matrix |(A,A%)| "= 0. With this notation, the long-run

impact on prices predicted by the Granger Representation Theorem is

P = C"t = &%(%$

%%&%)!1%$

%"t (11)

where % = (I !(k

i=1%i) from (6). The matrix C describes the impact of a shock

Methodology 28

in any one price on the final values of all four prices. The high volatility of wheat

markets implies that prices may be set on a new trajectory in response to a new shock

before reaching the equilibrium created by an old shock, so the impacts predicted by

C may never fully materialize.

The Granger Representation Theorem also provides a method for modeling how

prices move toward the new expected equilibrium. For t = 1, . . . , T , the change in

the value of the prices at time t + h due to a shock in period t can be expressed as a

portion of the final value of the shock P &t (Hansen and Lunde, 2006). The size of the

change is given by

)Pt+h

)P &t

= (C + Ch) # '%%(%$

%'%%)!1%$

%%&% (12)

I estimate ' using the average of the residuals, formally ' = 1

T

(Ti=1"i"

$i. Ch is

defined recursively starting with C1 as

$Ct = %&$Ct!1 +k

!

i=1

%i$Ct!i (13)

with the convention that C0 = I ! C and C!1 = · · · = C!k = !C. As t increases,

Ch drops to 0, so the series of (C + Ch) converges at C. Some algebra using (12)

reveals limh"# )Pt+h = )P &t &

$%. This final result can be interpreted as the new

equilibrium condition. In response to a stochastic shock, the series trends toward a

new equilibrium point in the direction of &%, with the new equilibrium described by

C and the adjustments leading to the equilibrium described by the series of Ch.

6 Results

In all models, the first coe"cient corresponds to PUS, the second corresponds to

PCAN , the third to PAUS, the fourth to PARG, and the fifth to PIND. The AIC selects

Results 29

Trace Test ResultsModel: µ0 = 0, µ1 = 0 µ0 = !#$, µ1 = 0 µ0 "= 0, µ1 = 0 µ0 "= 0, µ1 = !#$

Value Concl. Value Concl. Value Concl. Value Concl.

r = 085.25

Reject81.95

Reject78.54

Reject85.25

Reject(59.46) (34.40) (33.46) (37.52)

r $ 156.50

Reject52.98

Reject49.92

Reject56.56

Reject(39.89) (28.14) (27.07) (31.46)

r $ 232.39

Reject28.92

Reject25.88

Reject32.39

Reject(24.31) (22.00) (20.79) (25.54)

r $ 39.23

Accept8.42

Accept6.35

Accept9.29

Accept(12.53) (15.67) (14.07) (18.96)

r $ 41.78

Accept2.70

Accept1.77

Accept2.75

Accept(3.84) (9.24) (3.76) (12.25)

Table 3: Trace Test from (8) to determine the rank of #. Critical values are takenfrom Osterwald-Lenum (1992). All specifications point to the existence of three coin-tegrating vectors and two common trends.

2 lags for estimation in every case.

6.1 Unrestricted Estimate

I start with an unrestricted VAR to determine the number of cointegrating relation-

ships present in the data. The number of cointegrating relationships, r, is computed

as the rank of # in (6). I estimate r using the trace statistic, given by (8). I start

by taking r = 0 as my null hypothesis and testing against the alternate hypothesis

of r > 0. I then update H0 to r $ 1 and test against the alternate of r > 1, and I

continue in this manner until I fail to reject H0. After computing results with an unre-

stricted constant and a restricted trend, I check the cases of an unrestricted constant

and no trend, a restricted constant, and neither a trend nor a constant. I find that

regardless of the specification I use, there is evidence of at least three cointegrating

vectors in the data, but I cannot reject the null hypothesis that r $ 3. Results of the

trace test are presented in Table 3.

I estimate an unrestricted VAR with three cointegrating vectors and present the

Results 30

Unrestricted VAR Estimators

Model ! " µ0 µ1

µ0 "= 0, µ1 = !#$

(log L = 2771.79)

0.007 0.024 -0.039 0.77 0.77 0.89 -0.005 4.5e-50.24 0.079 -0.059 -1 0 0 0.29 1.7e-40.038 0.28 -0.048 0 -1 0 0.13 8.8e-70.13 -0.23 0.20 0 0 -1 0.19 -1.2e-4

-0.014 -0.08 -0.006 0.009 0.19 0.015 -0.05 2.3e-5

µ0 "= 0, µ1 = 0(log L = 2769.88)

0.006 0.013 -0.038 0.76 0.77 0.91 -0.075 –0.23 0.049 -0.051 -1 0 0 0.041 –0.038 0.28 -0.048 0 -1 0 0.13 –0.13 -0.20 0.20 0 0 -1 0.38 –

-0.023 -0.064 0.004 0.18 0.10 -0.39 -0.05 –

Table 4: Unrestricted estimation of the VAR. I first include an unrestricted constantand a restricted trend (top table), then drop the trend and estimate again (bottomtable).

results in the first part of Table 4. Estimation with an unrestricted constant and a

restricted trend yields coe"cients for µ1 that are very close to zero, so I remove the

restricted trend and estimate the VAR again. Likelihood for the estimation decreases

slightly under the new specification, but not enough to indicate a significant loss of

fit. For the rest of this paper, I continue estimation with an unrestricted constant

and no trend. This specification can be written as (6) with the condition that µ1 = 0.

The second part of Table 4 contains the results from the new VAR evaluation.

6.2 Restricted Estimate

Unrestricted estimation of the VAR reveals the optimal specification and the appro-

priate number of cointegrating relations. I next codify the LOP condition from (2)

and try to identify which markets are integrated. I run the model multiple times

with di!erent restrictions on & to find the one with the best fit. Three cointegrating

vectors among the five series implies that wheat prices are guided by two common

stochastic trends. This result most likely describes a situation in which four markets

Results 31

Test of Possible Market StructuresMarket

U.S.A Canada Australia Argentina IndiaExcludedlogL 2758.45 2757.46 2755.61 2757.19 2758.57*

P1 PUS PUS PUS PUS PCAN

P2 PCAN PAUS PARG PIN PAUS

log L 2758.15 2756.49 2756.26 2757.76 2756.19

P1 PCAN PCAN PAUS PAUS PARG

P2 PARG PIN PARG PIN PIN

log L 2757.22 2757.63 2757.42 2755.59 2756.44

Table 5: Likelihood values with di!erent restrictions on &. The top row tests cases inwhich a single market is excluded, and the bottom two rows consider a dual economywith two markets in one relationship and three in the other. * indicates the maximumvalue.

are integrated and one operates independently. It is also possible but unlikely that one

trend guides two markets and the other three markets follow the other trend, meaning

there are actually two international markets for wheat that set prices independently

and do not converge in the long run. Possible reasons for this kind of structure in-

clude di!erentiation by type, traditional trade patterns, or transportation costs that

limit available trade routes. Table 5 presents all potential market conditions with a

four-one or three-two structure.

I find that the highest likelihood value occurs when the Indian market is excluded

from world trade. The result suggests that wheat markets in the United States,

Canada, Australia, and Argentina follow the LOP. It is important to note that the

likelihood value achieved by excluding the Indian market is not very much higher than

that achieved by excluding the American market. Further analysis is sensitive to this

decision of which market to omit, and the evidence far from conclusively selects the

Indian market as the excluded market. However, this finding is consistent with other

studies discussed in Section 2 so I continue with my analysis under the assumption

that the Indian market operates independent of the others.

Results 32

Restricted VAR Estimators

VariableCoe"cients

! " µ0

PUS!0.004 !0.07&&& -0.004

1 1 1!0.005&&&

(0.005) (0.007) (0.003) (0.001)

PCAN0.19&&& !0.18&&& -0.002

-1 0 00.29&&&

(0.005) (0.007) (0.003) (0.001)

PAUS!0.013&&& 0.024&&& -0.001

0 -1 00.13&&&

(0.004) (0.005) (0.003) (0.001)

PARG0.10&&& !0.23&&& 0.19&&&

0 0 -10.1&&&

(0.006) (0.009) (0.004) (0.001)

PIND0.001 0.037&&& 0.002

0 0 0!0.05&&&

(0.002) (0.003) (0.002) (0.0005)

PUS 0-0.07

0 1 1 1-0.01

(0.004) (0.007)

PCAN0.19 -0.18

0 -1 0 00.01

(0.003) (0.004) (0.001)

PAUS-0.013 0.024

0 0 -1 00.004

(0.002) (0.003) (0.0004)

PARG0.10 -0.23 0.19

0 0 -1-0.02

(0.005) (0.007) (0.004) (0.001)

PIND 00.037

0 0 0 00.01

(0.001) (0.0001)

Table 6: Restricted estimation with the Indian market excluded. Estimates of allvariables are presented in the top table and estimates after dropping insignificantvariables are presented in the bottom table. All remaining terms are significant atthe 99% level

The maximum logL value of the VAR with restrictions is 2758.57, a full 11.2

points lower than the likelihood of the unrestricted VAR. (2 % log L) is distributed

as *2 in VAR estimation, giving a *2 statistic of 22.4. My model has six degrees of

freedom after normalization, so the loss of fit from restricted estimation is significant

at the 99% confidence level. Despite this significant decrease in the likelihood of

estimation, I continue with restrictions because the of the prediction made by the

LOP condition. After my initial restricted estimation, I drop all of the insignificant

terms and estimate the system once again. The decrease in log L from dropping

insignificant terms is less than 0.04, signaling an insignificant loss of fit. Full results

Results 33

Figure 4: Price spreads between the PAUS and other prices. PIND shows the least evidenceof mean reversion.

from the restricted estimation with the Indian market excluded are given in Table 6.

Tests of cointegration have low power and rely as much on the total length of a

data series as on the number of observations. It could be the case that I falsely accept

the null hypothesis of r $ 3 due to the fact that I do not cover a long enough time

span in my analysis (Hakkio and Rush, 1991). If this is the case, then r = 4 and all

five markets are integrated. Although I cannot test for this possibility, graphically

analyzing the spreads between markets suggests that it is unlikely. Figure 4 shows

the di!erence between PAUS and prices in other markets. I select the Australian price

because the Australian market is the least likely candidate for exclusion from the

cointegrating relationships. If markets are cointegrated with a [1,-1] relationship, the

spreads should appear stationary. While none of the spreads are very clearly mean-

reverting, the di!erence between the Australian and Indian price varies the most with

Results 34

the least apparent underlying trend. The graph supports the statistical inference that

the Indian market is not integrated with other world trading centers.

6.3 Adjustment

Restricted estimation implies that the price of wheat in the United States, Canada,

Australia, and Argentina converge in the long run. Markets respond to short-run

disequilibria by adjusting at the rates given by the column of % corresponding to the

column of & that describes their long-run conditions. The total rate of adjustment

in two markets is calculated by projecting the sum of the coe"cients in % onto &.

Because the long-run equilibrium lies on the 45' line, the projection problem resolves

cleanly into %2 ! %1.

The first column in % corresponds to the relationship between PUS and PCAN .

The Canadian price responds rapidly to disequilibria, adjusting by almost 20% of the

price disparity every month. In contrast, the United States price does not show any

significant movement at all. The Australian and Argentine prices also respond to

the U.S.-Canada relationship, which indicates tht the Canadian price has a role in

setting prces in those markets. The total speed of adjustment between the U.S. and

Canada is 0.19, accounted for entirely by Canada. The speed of correction means

a shock that pulls the markets out of equilibrium has a halflife of 3.3 months. A

visual representation of the adjustment over three years is given in the first graph in

Figure 5.

The second column in % corresponds to the relationship between PUS and PAUS.

In this relationship, both prices move slowly, with the American price adjusting more

than the Australian price. These adjustment rates indicate that the Australian market

acts as a price leader, especially given the adjustment values corresponding to the

other markets. All wheat prices show evidence of correcting in the direction of the

Results 35

Figure 5: Adjustment rates between pairs of markets out of equilibrium. The graphsonly indicate the speed of adjustment relative to the size of the price di!erence, and themagnitude of the units have no meaning.

Results 36

Australian price when it is not in equilibrium, meaning it has a strong role in setting

the world price. The total speed of adjustment in the two markets is 0.094, which

suggests that shocks to the system last longer, with a halflife of 7.02 months. A

visual representation of the adjustment over three years is given in the second graph

in Figure 5.

The third column in % corresponds to the relationship between PUS and PARG.

Like the Canadian market, the Argentine market reacts quickly to price disparities,

while the American market shows no signs of moving at all. No other markets respond

to this relationship, meaning Argentina is isolated as a price taker. The nation receives

price signals from other trading centers, but domestic fluctuations do not a!ect other

world prices. The total rate of adjustment between the U.S. and Argentine market is

just 0.19, and any shocks that draw markets out of equilibrium have a halflife of 3.3

months. A visual representation of the adjustment over three years is given in the

third graph in Figure 5.

The adjustment coe"cients paint a picture of a world in which the Australian price

leads the market. All markets respond to both PUS and PAUS, but they generally

correct in the direction of Australia. PCAN has an impact on the Argentine market and

a slight impact on the Australian market as well. The Argentine price has no influence

on any other market and does all of the adjusting to meet the world equilibrium. The

Indian market is not integrated with the other four so it does not necessarily revert

to any equilibrium condition, but it is not entirely independent. The coe"cient on

PIND in the second relationship is significant, which means that world prices have an

e!ect on the Indian price. The equilibrium price of wheat in India responds to the

world price but does not necessarily settle at the world level.

Using the adjustment vectors, I can compute the orthogonal complements to cal-

culate the long-run impacts of a stochastic shock. With & fixed, it is easy to define

its orthogonal complement as

Results 37

&% =

"

#

#

#

#

$

1 01 01 01 00 1

%

&

&

&

&

'

(14)

This matrix places the four integrated markets together in one relationship and the

Indian market apart in a separate relationship. The matrix %% is generated using

% from the restricted estimation. With the orthogonal complements, I estimate the

matrix C from (11). The matrix describes the change in the long-run value of the

prices given a shock in the current period. The four integrated prices exist in a

[1,!1] equilibrium, so a shock to any one price will a!ect all four in exactly the same

way. The Indian market operates independently in this system so I cannot model the

impact of a shock in India on the world equilibrium or vice versa.

Because of the low frequency of my data, it makes sense to adjust C to include

correction in between periods. When a shock occurs in time t, it has already trickled

into other markets by time t+ 1. I compute the matrix C to describe projected long-

run responses as the product of C and a matrix of expected errors. The expectation

matrix has 1 along its diagonal and $i,j at every other value, where $i,j = E["i|"j = 1].

This adjusted matrix estimates the change in a long-run relationship caused by a

shock in a variable taking into account the correction that occurs before the next

observation. C assumes no e!ect on "i,t from "j,t over the course of a month and C

assumes that "i,t can be explained entirely by "j,t. The actual long-run impact of a

stochastic shock lies somewhere between C and C.

The two matrices show that shocks in Australia have the largest impact on the

world system, and those in the United States have the second largest impact. This

result is expected given the prominent role of the two markets predicted by %. The

Argentine market has the smallest predicted impact, and the coe"cients from %

Results 38

Long-Run Impacts

E!ect C: Minimum impact on C: Maximum impact onof: PUS PCAN PAUS PARG PIND PUS PCAN PAUS PARG PIND

$US 0.48 0.48 0.48 0.48 – 1.12 1.12 1.12 1.12 –$CAN 0.059 0.059 0.059 0.059 – 0.82 0.82 0.82 0.82 –$AUS 0.93 0.93 0.93 0.93 – 1.50 1.50 1.50 1.50 –$ARG 0 0 0 0 – 0.57 0.57 0.57 0.57 –$IND – – – – 1.27 – – – – 1.28

Table 7: Long-run equilibrium response to shocks in period t. The left table presentslower bounds and the right table presents upper bounds.

suggest that the actual value is at the lower bound of 0. C and C also predict that

shocks in the Indian market have a feedback e!ect that leads to a new domestic

equilibrium farther away from the old one. The a shock in time t is multiplied by

almost 1.3 in the long run. Full results of C and C are presented in Table 7.

When markets face a shock that drives them toward a new equilibrium, they re-

spond at di!erent speeds. The adjustment a market makes in period t+ i in response

to a shock in period t can be given as a portion of the total long-run adjustment as

described by (12). The United States, Canadian, Australian, and Argentine markets

adjust very quickly; they all reach to within 10% of their total adjustment in three

months and to within 5% in eleven months. The American and Argentine markets ac-

tually overshoot the final equilbrium early on and then change direction of movement

to meet the Canadian and Australian markets at the equilibrium point, although the

overshooting is small and may be an artifact of noisy data. In contrast, the Indian

market moves much more slowly. After an initial jump to 55% of the final equilib-

rium, it takes another 18 months for the price of wheat to reach within 10% of the

final value. After three years, the price of wheat in India has still only covered 97%

of its total predicted movement. A graphical representation of the adjustment speeds

of the five markets is given in Figure 6. It is important to note that even though the

Indian market appears on the same graph as the others, it does not follow the same

Results 39

Figure 6: Adjustment rates in response to a new equilibrium. The Indian market respondsmuch more slowly than the other four.

trend. The four export markets all move concurrently toward a common equilibrium,

but the Indian market operates separately.

7 Discussion

I find significant di!erences between the way prices behave in the Indian market and

the way they behave in other world markets. These di!erences might be a result of

Indian government policies or of ine"ciencies in the Indian domestic market.

7.1 Interpretation of Results

My main finding is that the price of wheat in India does not converge to the inter-

national level. There is evidence of two common trends among the markets I study,

and the most likely interpretation is that one trend guides export prices in the United

States, Canada, Australia, and Argentina while the other leads the Indian market.

Among the world export centers, Australia seems to have the most dominant role;

Discussion 40

other prices adjust to match the Australian price when the system is out of equilib-

rium. The American wheat price also drives prices in other markets, though it adjusts

more rapidly than the Australian price to eliminate discrepancies between the two.

Interestingly, the price of wheat in Canada shows evidence of influencing prices in

Argentina and, to a lesser extent, in Australia, but not in America. Argentina acts as

a price taker in this market and corrects in response to price di!erences, but has no

influence on other nations. Estimation reveals a complex system of linkages guiding

the trade of wheat.

Although the Indian market does not show evidence of convergence with other

markets, it does react to changes in the international system. When the the American

and Australian markets, the two leading price setters, are out of equilibrium, prices in

India adjust. Periods of disequilibrium among the large centers correspond to rising

or falling world prices, when world trade places the most pressure on the Indian

market. I find evidence that the Indian wheat market adjusts much more slowly to

shocks than the other four markets, taking almost ten times as long to close 90% of a

gap between current price and expected equilibrium price. Despite the slow pace at

which the Indian price moves, the total adjustment in response to a shock is greater

than the adjustment of the world markets in response to shocks in any individual

market. A short-run fluctuation in the Indian market is inflated 130% in the long

run, more than the lower bound of responses to shocks in any of the other markets

and more than the upper bound in all markets except Australia. Overall, the wheat

price in India does not converge to the international level, but it remains susceptible

to changes in the world market and adjusts significantly more in response to shocks.

Discussion 41

7.2 Possible Explanations

A couple of factors within India may account for the behavior of the wheat price. First,

government policy limits the role of market forces. The philosophy behind government

action motivates its insulating e!ect. To support low-income consumers every year,

the FCI sets a procurement target given requirements for public distribution and

maintenance of stocks, and then enters the market to meet its target. The institution

also acts in defense of producers by creating a price floor for wheat sold at mandis.

Because the state deals in such a significant portion of the annual wheat crop, state

action almost certainly influences the domestic price.

On top of its activities in the market, the Indian government sets import and

export restrictions to stop prices from getting too high or too low. Such limitations

prevent traders from taking advantage of arbitrage opportunities arising from price

di!erences, meaning that price signals may not be e!ectively transmitted through

the market. Quantitative restrictions tend to be immediate responses to political

pressure and not long-term strategies for price control. Once the immediate backlash

to rapid movements lightens up, the Indian government has backed away from trade

regulations. This immediate policy response causes the Indian price to move more

slowly toward new equilibria. The intervention comes at a high cost, as the govern-

ment directly spends more than $4 for every dollar of grain distributed to consumers

on top of the additional burden of taxation and distortion of incentives (Persaud

and Rosen, 2003). In the future, such intervention will become increasingly di"cult

as higher prices raise the opportunity cost of not exporting and make state imports

more expensive. Thus far, however, the government has demonstrated its willing-

ness to take on the costs of protecting the domestic market. In the autumn of 2007,

for instance, the STC signed a deal to import wheat at almost 20% more than the

prevailing domestic price in order to prop up domestic supply. As long government

Discussion 42

o"cials continue to intervene in this manner, the Indian wheat market may remain

separated from the rest of the world.

The behavior of the government in response to price changes helps explain the slow

adjustment shown by the Indian market to new equilibrium conditions. The state

manipulates bu!er stocks and foreign trade to ease the pace of price movement. These

policies can influence the rate at which the market approaches a new equilibrium,

but they have little influence over the new equilibrium itself. In the absence of other

stabilizing markets to dilute the impact of shocks, the Indian wheat price faces the

full e!ect of a short-term movement and any feedback it may have.

Lack of regional integration within the Indian market can contribute to the slow

movement of prices. Markets in the nation are not well integrated, so price sig-

nals from foreign trade may halt at the border without penetrating further into the

country. The price of wheat in India may be slower to respond to new equilibrium

conditions simply because market information takes more time to disseminate from

one region to another. A new national price would not prevail until the signal has had

time to trickle down through a significant portion of the country. This gradual pace

of adjustment would also limit evidence of integration with international markets. If

the Indian price is responding slowly to volatile export prices in other nations, then

evidence of that response and correction will be di"cult to uncover.

The inertia in the Indian wheat market due to segmentation has its roots in

the nature of production. As discussed in Section 3, the majority of the sector is

composed of farmers with few resources and little market power. These farmers

often lack both the means and the information to respond e!ectively to price signals.

Many Indian producers do not have the capacity to store their crop or transport it

over long distances, so their options for sale are limited. They are forced to sell at

the local mandi to whatever buyers appear on the day they bring their harvest to

market regardless of potential opportunities elsewhere. Limited availability of market

Discussion 43

information adds risk to the sale process. If farmers do not know the price prevailing

at various trade centers, then they face the potential of wasting transportation costs

on a market with lower prices. Factors limiting the ability of producers in India to

make economic decisions with ample information may explain the lack of integration

between Indian and world wheat markets.

8 Conclusion

In this paper, I identify three stylized facts that characterize the Indian wheat market.

First, the price of wheat in India does not converge to the world level. There is

evidence that Indian price still responds to world prices, however, so the market is not

completely insulated from international pressures. Second, the Indian price adjusts

to changes in equilibrium much more slowly than does the price in the primary export

centers of wheat. Third, despite slow price movement, the overall level of adjustment

in response to stochastic shocks is much higher in the Indian market than in other

world markets.

These characteristics are most likely consequences of high government interven-

tion and regional market segmentation. As a result, there is reason to expect di!erent

behavior in the future. As Indian income grows and food prices rise, the political and

economic costs of policies that separate the Indian market from the rest of the world

multiply. Although the state has shown a willingness to bear the costs of its policy

up to now, it may not be able to do so for long. Regional segmentation may decrease

in the future as well. New communication technologies facilitate the transfer of infor-

mation in rural areas, and their absorption into the Indian countryside will dictate

the decision-making capabilities of farmers. Investments in rural infrastructure that

accompany economic development will also give wheat producers more freedom to

follow market signals.

Conclusion 44

My findings indicate that situations like the current world food price spike are

not felt as sharply in India as they are in the rest of the world. Indian producers

and consumers enjoy a degree of protection both from the state and from ine"cient

markets, although both of these insulators carry costs as well. Government policies

place high financial burdens on the population in relation to the benefits they create

and poor market structure limits the basic ability of producers and consumers to

respond to their environment and make economic decisions.

Even though the immediate impacts of price spikes are lower in India, the final

correction may be greater if the conditions persist. There is reason for concern that

if the world enters a new regime of high prices, the e!ect on the Indian market may

be greater than initial movements suggest. It is important to note that government

policy is motivated by price stability, so the relationship between India and the world

market may change if this turns out to be the case.

Future research in this direction can focus on modeling the exact impact of world

prices on the Indian price, which I am unable to do without an equilibrium condition.

Such studies might include other domestic price determinants to develop a more

complete understanding of how the Indian wheat market behaves. It would also

be useful to incorporate the role of the government to identify what portion of my

findings can be attributed to decisions by policymakers and how much is inherent in

the structure of the market. My results can most likely be generalized to the Indian

rice market, which is treated similarly by the state under the purvey of the FCI, but

other crops are produced and handled di!erently. Knowledge of the di!erences in

behavior by crop is required for a more clear picture of the Indian food economy.

Further study can also focus on the price linkages between the world wheat market

and prices in other developing countries. With a basket of countries and varying

levels of integration and price response, it may be possible to determine what factors

are most significant with regard to the behavior of prices.

Conclusion 45

This study has implications for Indian policymakers, outside agencies, and In-

dian consumers. Politicians in India craft laws in the food sector with the intent of

preserving a robust domestic supply, limiting volatility, and preventing prices from

settling at too high or too low a level. I find that they are somewhat successful in

limiting short-term volatility and rapid change in response to external shocks, but

cannot address long-run e!ects under current market structure. It is up to o"cials to

determine whether these achievements justify the costs of Indian wheat policy. Out-

side actors may be interested in price adjustments to know where to focus attention

in the aftermath of a price spike or other shock. My result suggests that immediate

attention is best paid to regions more responsive to foreign markets. External actors

should not ignore India entirely, however, because the ultimate magnitude of a shock

is larger than the initial movement or rate of adjustment may lead one to believe.

Finally, actors within the Indian market may have reason to behave di!erently from

actors in other markets. With the current government attitude and market structure,

there is low risk of rapid fluctuation and price movements tend to persist for longer

periods of time. These facts about the behavior of the Indian wheat price should

factor into economic decisions.

Conclusion 46

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