The Integration of the Indian Wheat
Sector into the Global Market
May 2008
Ashish Shenoy†
Department of Economics
Stanford University
Stanford,CA 94305
Under the direction of Professor Peter Hansen
Abstract
World food prices have risen rapidly in recent months. The trend has
brought up concerns about how markets in developing nations respond to international conditions. In this paper, I try to determine whether the price of
wheat in India converges to the world level. Using monthly prices from the
United States of America, Canada, Australia, Argentina, and India over a period of thirteen years, I look for evidence of cointegration among the series.
Cointegrated series follow a common stochastic process, and thus can be said
to move together. I first test for cointegration without restrictions to identify
the number of cointegrating vectors and common trends, and then impose restrictions to see how quickly markets adjust to disequilibria. I find evidence
that the world wheat trading centers are integrated, with Australia being the
most dominant. The Indian wheat price does not converge with the other four.
I next use the Granger Representation Theorem to model the adjustment of
the markets to shocks. I find that the Indian market adjusts more slowly to
a new equilibrium, but the total magnitude of adjustment is greater. Possible
explanations include poor infrastructure, regional segmentation within India,
and high levels of government intervention.
Keywords: India, wheat, food price, cointegration
†
I would like to thank Professor Hansen for his patient mentoring and guidance, without which
this project would not have been possible. I am also grateful to Professor Wally Falcon, Professor
Roz Naylor, Professor Nick Hope, Dr. Charan Singh, Smt. Asha Kannan, Dr. Ramesh Golait,
Pankaj Kumar, and Professor Geoffrey Rothwell for their help in various stages of my research. Any
errors that remain are solely my own.
Contents
1 Introduction
2
2 Literature Review
2.1 Price Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 World Wheat Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3 Developing Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3 Indian Wheat
3.1 Production . . . .
3.2 Market Structure .
3.3 Government Policy
3.4 Recent History . .
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4 Data
4.1 Data Selection and Treatment . . . . . . . . . . . . . . . . . . . . . .
4.2 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . .
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5 Methodology
5.1 Maximum Likelihood Model
5.2 Unrestricted Estimation . .
5.3 Restricted Estimation . . . .
5.4 Adjustment . . . . . . . . .
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6 Results
6.1 Unrestricted Estimate . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 Restricted Estimate . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.3 Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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7 Discussion
7.1 Interpretation of Results . . . . . . . . . . . . . . . . . . . . . . . . .
7.2 Possible Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . .
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8 Conclusion
46
References
49
CONTENTS
Shenoy 1
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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 off 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. Officials 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.
Some of the factors leading to the current crisis seem temporary. A drought in
Australia coupled with sub-par wheat harvests in the United States and Europe drove
down global supply, causing prices to rise. Poor policy has compounded the problem
as many countries that had in the past held large buffer stocks in case of shortages
let stocks dwindle, expecting to rely on imports to make up shortfalls in production
(Krugman, 2008). Without a buffer crop to combat rising prices, many large grain
producers, including India and Thailand, have severely limited exports in order to
protect domestic consumers. Such export restrictions contract supply even further,
putting more pressure on prices.
High energy costs have exacerbated the current situation. Farmers in countries
that export large amounts of food tend to use machinery for production, meaning
that gas prices are directly represented in farm production costs (Krugman, 2008).
Mechanization of agriculture, use of synthetic fertilizers, and transportation from farm
Introduction
Shenoy 2
to market all feed into price transmission from energy to food. As the cost of energy
increases, demand for foodgrains rises as well (Naylor et al., 2007). High gas prices
make alternatives such as biofuels more attractive, meaning consumers must compete
with biofuel producers in the market. Although only a few crops are currently used
for energy alternatives, food supply in other sectors decreases as farmers substitute
into production of the more lucrative fuel crops. Energy costs generate both supply
and demand effects that raise the price of food.
Financial capital invested in an attractive food market may be amplifying the
problem. Some investors coming out of shrinking housing and derivatives markets
turned to food as a new financial instrument. These traders added to the competition
for goods normally sought by corporations for resale and distribution to households.
It is difficult to identify exactly how much of this investment is simply response to
economic incentives, but some experts believe that speculative trading has driven
prices to some extent (see Timmer, 2008). The financial pressures on food prices are
somewhat nominal because commodities are generally traded in dollars. The declining
value of the currency has caused inflated the apparent affect of other pressures. Dollar
devaluation is only a small part of the story, however, and food costs have seen
significant real appreciation.
Although many of these determinants seem temporary, some structural factors
indicate that higher prices may be more permanent. Even taking into account the
current weather patterns, growth in annual food production has consistently outpaced
population growth; the problem lies in food demand and not food supply (Timmer,
2008). While financial pressures may inflate food demand, alternative energy and
changing diets compose the factors underlying rising food demand. Scientists seeking
new sources for fuel have turned to foods as a way to generate energy. The price of
oil drives this process to some extent, but environmental concerns surrounding fossil
fuels would inspire the preference for renewable energy even in the presence of cheap
Introduction
Shenoy 3
oil. Subsidies in developed nations promoting the use of ethanol and other food-based
fuels support the industry, guaranteeing that producers of biofuel will compete with
consumers for grains and crop land.
Global poverty reduction has a feedback effect on food prices as well. This effect is
most pronounced in East and South Asia, where incomes are rising rapidly in nations
with very high populations. As incomes grow, consumption preferences among the
population shift. More people are able to afford more food, which directly augments
food demand. People also shift away from cheaper grains and starchy roots into
dairy, poultry, and meat (Bennett, 1941). Calories from these luxury goods require
a higher amount of grain used as feed for animals than calories from grains directly,
so substitution into these goods increases the effective amount of food people seek to
consume. While poverty reduction on the whole increases access to food for the lowest
income households, it drives up prices in world commodity markets. As a result of
these structural factors, even if next year brings good harvests, there is still reason
to believe that food prices will remain high in the future.
This climate of high food prices has left many concerned about international stability. It has already sparked violence in some regions, and prices are expected to rise
further in the near future. Medium-term forecasts estimate that the cost of food will
remain much higher than 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 effective wage will be lower, and many will not be able to cover the added costs.
Officials 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.
Introduction
Shenoy 4
Given these concerns, it is important to understand how nations respond to international 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
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 producers 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 profitability 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
Shenoy 5
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 effets across markets. Analysis of commodity prices is complicated 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 differencing 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 coefficients 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
Shenoy 6
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 = ρzt−1 + a + bt + εt
(1)
where (a + bt) represents a linear deterministic trend and εt is an independent and
identically distributed stochastic process. In (1), ρ determines the persistence of past
shocks in the series. A value of ρ such that |ρ| < 1 guarantees that any stochastic
shock to the process will dissipate over time, meaning that the process centers on the
deterministic trend. In contrast, |ρ| = 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, |ρ| = 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 coefficients 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 differences.
Literature Review
Shenoy 7
When more than two nonstationary series are present, multiple cointegrating vectors may exist in the data. For a group of p series, the presence of r unique cointegrating 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 affects 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 definition of market integration comes from Ravallion (1986), who notes the distinction
between integration and efficiency. Integration simply refers to the comovement of
prices; it is still possible that single actors hold monopoly power or are able to manipulate prices through other means. Cointegration provides appropriate information 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 efficiency of resource
allocation within the market.
Literature Review
Shenoy 8
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
lim E(Pi,t+k − Pj,t+k |It ) = 0
k→∞
(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, ξ] where
ξ 6= 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 different 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 commodity 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. Estimation also reveals cointegrating coefficients very close to 1, supporting the LOP.
Ardeni finds rejects the cointegration hypothesis for any other commodity, and concludes 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
Shenoy 9
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
Shenoy 10
Argentina. Like Goodwin, the authors find one cointegrating vector among world
markets, which they use to define a long-run relationship. They then estimate adjustment coefficients 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 responding to changes in the Australian price. The American price affects 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 distinct 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 American, Canadian, Australian, Argentine, and Indian markets. They find evidence of
two cointegrating vectors among the five markets, meaning that the markets are connected, 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
Shenoy 11
protein content, and different types of wheat have different 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 different types of wheat
traded in the United States, Canada, the European Union, Australia, and Argentina
and categorize them into three different 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
Shenoy 12
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 different 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
different world prices do not exist. The estimated adjustment coefficients 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 follow from broader economic reforms. Baffes 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 identifiable political decisions followed by implementation to some extent at major trading
centers. They look at 31 different 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
Shenoy 13
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
Shenoy 14
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 off. 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
Shenoy 15
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 different 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 accurately 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 different parts of the country facing different
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
Shenoy 16
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 different markets may diverge due to local pressures, and one price may not adequately 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
different markets from April 1997 to June 2003. His results confirm those of the earlier study: There exists evidence of cointegration among markets, but with multiple
common trends. The presence of multiple trends indicates that prices do not necessarily 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 different goals. According to the
Ministry of Agriculture, the government seeks to establish environmentally, economically, and socially sustainable food security through its actions in the agricultural
sector (Roy, 2007). This overarching philosophy generates a patchwork of often contradictory policies with varying objectives. In an effort to promote production and
limit reliance on food imports, most state governments offer water, fertilizer, and
Indian Wheat
Shenoy 17
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 marketing 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 responsible for procuring foodgrains and distributing them publicly under various schemes.
The largest distribution scheme is the Public Distribution System(PDS), revised to
be more effective 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 buffer 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 effort 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
Shenoy 18
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 government 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 effort to control prices and meet demand,
the Ministry of Commerce banned wheat exports and the State Trading Corporation(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
Shenoy 19
of buffer 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 efforts, so the FCI has turned to further imports to meet
its public distribution and buffer 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 Office 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, although 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 effect 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
Shenoy 20
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 different markets
in 8 major wheat-trading states for normalization.
I use monthly prices in the selected markets taken from April, 1994 to September, 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 inconsistent 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
Shenoy 21
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 Staff,
2007). Seasonal adjustment removes the effects 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 differences. Figure 2 plots the five series in logarithmic terms over the
thirteen year span. The American, Canadian, Australian, and Argentine prices appear to follow the same general trend. The Indian price occasionally displays similar
movement, but with less consistency.
Data
Shenoy 22
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
differences that are stationary. To confirm that this is the case with my data, I apply
two different unit root tests to the value and first difference 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
Shenoy 23
Descriptive Statistics
Max
Min
Median
Mean
Standard Deviation
U.S.A.
(PU S )
5.80
4.62
5.02
5.03
0.221
Canada
(PCAN )
5.61
4.93
5.21
5.20
0.156
Australia
(PAU S )
5.85
4.88
5.16
5.19
0.179
Argentina
(PARG )
5.57
4.54
4.93
4.99
0.233
India
(PIN D )
5.38
4.78
5.00
5.02
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 differences 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% confidence 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 difference 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 differences because of the
information lost by differencing. Cointegration can address simultaneous causality,
accounts for the influence of lags, and provides information regarding both the long-
Methodology
Shenoy 24
Unit Root Tests
Series
USA
level
difference
Canada
level
difference
Australia
level
difference
Argentina
level
difference
India
level
difference
Critical Values:
Level:10%
5%
1%
Difference:10%
5%
1%
ADF
KPSS
-1.61
-10.21***
0.262***
0.125
-1.18
-11.42***
0.271***
0.077
-1.68
-10.06***
0.230***
0.135
-2.82
-8.27***
0.182**
0.075
-2.54
-9.79***
0.141*
0.108
-3.14
-3.44
-4.02
0.119
0.146
0.216
-2.58
-2.88
-3.47
0.347
0.463
0.739
Table 2: Lag length selected using the Akaike Information Criterion (Akaike, 1974).
Critical Values derived from MacKinnon (1996). *, **, *** denote rejection of H0 at
the 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 coefficients 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
Shenoy 25
t = 1, . . . , T , the VAR representation of the system using k lags can be written as
Pt =
k
X
Πi Pt−i + µ + δt + εt
(3)
i=1
where µ is a constant, δ describes a linear trend, and ε is a stochastic error term.
In (3), each Πi is a p × p matrix of coefficients for its respective vector of lags.
The VAR can be rewritten in its error-correction form (ECM) by differencing the
price terms:
∆Pt = ΠPt−1 +
k
X
Γi ∆Pt−i + µ + δt + εt
(4)
i=1
In the ECM form, the left-hand side is stationary because it is simply the first difference 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, ΠPt−1 must also be I(0). The matrix
Π 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 Π. For a
value of r where 0 < r < p, Π can be broken down into p × r matrices α and β such
that Π = αβ ′ . Each column of β represents a cointegrating vector, meaning each
column describes a long-run relationship exhibited in the data. Formally, a column
vector in β of < β1,1 , . . . , βp,1 > indicates a long run equilibrium of
β1,1 P1,t + · · · + βp,1 Pp,t = c
(5)
where c is a stationary process. Each column of α represents a vector of adjustment
coefficients; the values describe the rates at which the variables adjust when the
system is not in the equilibrium described by the corresponding column of β.
Methodology
Shenoy 26
5.2
Unrestricted Estimation
I start by estimating a VAR without any restrictions on α or β. Johansen and Juselius
consider multiple possible specifications for the estimation problem. In my analysis,
I include a constant to account for possible differences 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 = αβ ′ (Pt−1 ) +
k
X
Γi ∆Pt−i + µ0 + µ1 t + εt
(6)
i=1
where µ1 t can can be broken down into the matrix α and the vector ϕ such that
µ1 = αϕ′ .
The model describe in (6) can be expressed in the form
Yt = ΠXt + ΨZt + εt
(7)
where Xt represents the nonstationary component of the ECM equation with the
linear trend and Zt represents the stationary component and the constant. Π can still
be broken down into α and β, where Π = αβ ′ . A procedure to estimate coefficients
in (7) is reported by Hansen (2008).
Given the unrestricted estimation, the rank of Π is determined by solving the
eigenvector problem from Johansen and Juselius (1990). For p eigenvalues, ordered
such that λ1 > · · · > λp , the authors present a likelihood ratio statistic, or trace
statistic, calculated by
Trace Statistic (λ-trace) = −T
p
X
log (1 − λi )
(8)
i=r+1
Methodology
Shenoy 27
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. β is composed of the
eigenvectors corresponding to the r largest eigenvalues λ1 , . . . , λr 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 β 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 difference 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 requires that the difference between prices approach zero over time, described in (2).
Although the authors consider the possibility of a cointegrating vector of [1, ξ] where
ξ 6= 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
Shenoy 28
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 differences. 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 β.
For instance, if r = 4, then the conditions on β are such that

1
1
1
1
−1 0
0
0



0
−1
0
0
β=


0
0 −1 0 
0
0
0 −1

(9)
The restricted value of β and free parameters in the cointegrating space from ϕ can
be expressed as vec(β, ϕ) = Hϕ + h where ϕ 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 β, I can predict how the three markets respond to differences in price across trading centers. The estimated values of α describe the rates at
which markets adjust to disequilibrium conditions. Specifically, a [1, −1] cointegrating
vector yields an adjustment in period t of


 α̂i (Pi − Pj )t−1 
∆Pt = 

α̂j (Pi − Pj )t−1
Methodology
(10)
Shenoy 29
Figure 3: The vector α represents the adjustment made by the two markets i and j, and β⊥
characterizes their long-run relationship. The projection of α onto β gives the magnitude
of 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 α
and β. The values of α 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 coefficients in the
data, I try to predict how a shock to the variables affects 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⊥ )| =
6 0. With this notation, the long-run
impact on prices predicted by the Granger Representation Theorem is
′
′
Γβ⊥ )−1 α⊥
εt
P = Cεt = β⊥ (α⊥
where Γ = (I −
Methodology
Pk
i=1
(11)
Γi ) from (6). The matrix C describes the impact of a shock
Shenoy 30
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 Pt∗ (Hansen and Lunde, 2006). The size of the
change is given by
∂Pt+h
′
′
= (C + Ch ) × Ωα⊥ (α⊥
Ωα⊥ )−1 α⊥
Γβ⊥
∂Pt∗
I estimate Ω using the average of the residuals, formally Ω =
(12)
1
T
PT
i=1
εi ε′i . Ch is
defined recursively starting with C1 as
∆Ct = αβ ′ Ct−1 +
k
X
Γi ∆Ct−i
(13)
i=1
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 = ∂Pt∗ β⊥′ . 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 β⊥ , 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 coefficient corresponds to PU S , the second corresponds to
PCAN , the third to PAU S , the fourth to PARG , and the fifth to PIN D . The AIC selects
Results
Shenoy 31
Trace Test Results
Model:
r=0
r≤1
r≤2
r≤3
r≤4
µ0 = 0, µ1 = 0
Value
Concl.
85.25
Reject
(59.46)
56.50
Reject
(39.89)
32.39
Reject
(24.31)
9.23
Accept
(12.53)
1.78
Accept
(3.84)
µ0 = αρ′ , µ1 = 0
Value
Concl.
81.95
Reject
(34.40)
52.98
Reject
(28.14)
28.92
Reject
(22.00)
8.42
Accept
(15.67)
2.70
Accept
(9.24)
µ0 6= 0, µ1 = 0
Value
Concl.
78.54
Reject
(33.46)
49.92
Reject
(27.07)
25.88
Reject
(20.79)
6.35
Accept
(14.07)
1.77
Accept
(3.76)
µ0 6= 0, µ1 = αρ′
Value
Concl.
85.25
Reject
(37.52)
56.56
Reject
(31.46)
32.39
Reject
(25.54)
9.29
Accept
(18.96)
2.75
Accept
(12.25)
Table 3: Trace Test from (8) to determine the rank of Π. Critical values are taken
from Osterwald-Lenum (1992). All specifications point to the existence of three cointegrating 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 relationships present in the data. The number of cointegrating relationships, r, is computed
as the rank of Π 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 unrestricted 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
Shenoy 32
Unrestricted VAR Estimators
Model
µ0 6= 0, µ1 = αρ′
(log L = 2771.79)
µ0 6= 0, µ1 = 0
(log L = 2769.88)
0.007
0.24
0.038
0.13
-0.014
0.006
0.23
0.038
0.13
-0.023
α̂
0.024
0.079
0.28
-0.23
-0.08
0.013
0.049
0.28
-0.20
-0.064
-0.039
-0.059
-0.048
0.20
-0.006
-0.038
-0.051
-0.048
0.20
0.004
0.77
-1
0
0
0.009
0.76
-1
0
0
0.18
β̂
0.77
0
-1
0
0.19
0.77
0
-1
0
0.10
0.89
0
0
-1
0.015
0.91
0
0
-1
-0.39
µ̂0
-0.005
0.29
0.13
0.19
-0.05
-0.075
0.041
0.13
0.38
-0.05
µ̂1
4.5e-5
1.7e-4
8.8e-7
-1.2e-4
2.3e-5
–
–
–
–
–
Table 4: Unrestricted estimation of the VAR. I first include an unrestricted constant
and a restricted trend (top table), then drop the trend and estimate again (bottom
table).
results in the first part of Table 4. Estimation with an unrestricted constant and a
restricted trend yields coefficients 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 appropriate 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 different restrictions on β 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
Shenoy 33
Test of Possible Market Structures
Market
Excluded
logL
P1
P2
log L
P1
P2
log L
U.S.A
Canada
Australia
Argentina
India
2758.45
PU S
PCAN
2758.15
PCAN
PARG
2757.22
2757.46
PU S
PAU S
2756.49
PCAN
PIN
2757.63
2755.61
PU S
PARG
2756.26
PAU S
PARG
2757.42
2757.19
PU S
PIN
2757.76
PAU S
PIN
2755.59
2758.57*
PCAN
PAU S
2756.19
PARG
PIN
2756.44
Table 5: Likelihood values with different restrictions on β. The top row tests cases in
which a single market is excluded, and the bottom two rows consider a dual economy
with two markets in one relationship and three in the other. * indicates the maximum
value.
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 include differentiation 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
Shenoy 34
Restricted VAR Estimators
Coefficients
Variable
PU S
PCAN
PAU S
PARG
PIN D
PU S
PCAN
PAU S
PARG
PIN D
−0.004
(0.005)
0.19∗∗∗
(0.005)
−0.013∗∗∗
(0.004)
0.10∗∗∗
(0.006)
0.001
(0.002)
0
0.19
(0.003)
-0.013
(0.002)
0.10
(0.005)
0
α̂
−0.07∗∗∗
(0.007)
−0.18∗∗∗
(0.007)
0.024∗∗∗
(0.005)
−0.23∗∗∗
(0.009)
0.037∗∗∗
(0.003)
-0.07
(0.004)
-0.18
(0.004)
0.024
(0.003)
-0.23
(0.007)
0.037
(0.001)
β
-0.004
(0.003)
-0.002
(0.003)
-0.001
(0.003)
0.19∗∗∗
(0.004)
0.002
(0.002)
1
1
1
-1
0
0
0
-1
0
0
0
-1
0
0
0
0
1
1
1
0
-1
0
0
0
0
-1
0
0.19
(0.004)
0
0
-1
0
0
0
0
µ̂0
−0.005∗∗∗
(0.001)
0.29∗∗∗
(0.001)
0.13∗∗∗
(0.001)
0.1∗∗∗
(0.001)
−0.05∗∗∗
(0.0005)
-0.01
(0.007)
0.01
(0.001)
0.004
(0.0004)
-0.02
(0.001)
0.01
(0.0001)
Table 6: Restricted estimation with the Indian market excluded. Estimates of all
variables are presented in the top table and estimates after dropping insignificant
variables are presented in the bottom table. All remaining terms are significant at
the 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 χ2 in VAR estimation, giving a χ2 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
Shenoy 35
Figure 4: Price spreads between the PAU S and other prices. PIN D shows the least evidence
of 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 difference between PAU S 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 meanreverting, the difference between the Australian and Indian price varies the most with
Results
Shenoy 36
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 α corresponding to the
column of β that describes their long-run conditions. The total rate of adjustment
in two markets is calculated by projecting the sum of the coefficients in α onto β.
Because the long-run equilibrium lies on the 45◦ line, the projection problem resolves
cleanly into α2 − α1 .
The first column in α corresponds to the relationship between PU S 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 α corresponds to the relationship between PU S and PAU S .
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
Shenoy 37
Figure 5: Adjustment rates between pairs of markets out of equilibrium. The graphs
only indicate the speed of adjustment relative to the size of the price difference, and the
magnitude of the units have no meaning.
Results
Shenoy 38
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 α corresponds to the relationship between PU S 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 affect 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 coefficients paint a picture of a world in which the Australian price
leads the market. All markets respond to both PU S and PAU S , 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 coefficient on
PIN D in the second relationship is significant, which means that world prices have an
effect 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 calculate the long-run impacts of a stochastic shock. With β fixed, it is easy to define
its orthogonal complement as
Results
Shenoy 39

1
1

β⊥ = 
1
1
0

0
0

0

0
1
(14)
This matrix places the four integrated markets together in one relationship and the
Indian market apart in a separate relationship. The matrix α⊥ is generated using
α̂ 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 affect 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 Ĉ 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 C̃ to describe projected longrun responses as the product of Ĉ and a matrix of expected errors. The expectation
matrix has 1 along its diagonal and δi,j at every other value, where δi,j = E[εi |εj = 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. Ĉ assumes no effect on εi,t from εj,t over the course of a month and C̃
assumes that εi,t can be explained entirely by εj,t. The actual long-run impact of a
stochastic shock lies somewhere between Ĉ and C̃.
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 α̂. The
Argentine market has the smallest predicted impact, and the coefficients from α̂
Results
Shenoy 40
Long-Run Impacts
Effect
of:
εU S
εCAN
εAU S
εARG
εIN D
PU S
0.48
0.059
0.93
0
–
Ĉ: Minimum impact on
PCAN PAU S PARG PIN D
0.48
0.48
0.48
–
0.059 0.059 0.059
–
0.93
0.93
0.93
–
0
0
0
–
–
–
–
1.27
PU S
1.12
0.82
1.50
0.57
–
C̃: Maximum impact on
PCAN PAU S PARG PIN D
1.12
1.12
1.12
–
0.82
0.82
0.82
–
1.50
1.50
1.50
–
0.57
0.57
0.57
–
–
–
–
1.28
Table 7: Long-run equilibrium response to shocks in period t. The left table presents
lower bounds and the right table presents upper bounds.
suggest that the actual value is at the lower bound of 0. Ĉ and C̃ also predict that
shocks in the Indian market have a feedback effect 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 Ĉ and C̃ are presented in Table 7.
When markets face a shock that drives them toward a new equilibrium, they respond at different 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 actually 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 equilibrium, 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
Shenoy 41
Figure 6: Adjustment rates in response to a new equilibrium. The Indian market responds
much 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 differences between the way prices behave in the Indian market and
the way they behave in other world markets. These differences might be a result of
Indian government policies or of inefficiencies 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 international 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
Shenoy 42
other prices adjust to match the Australian price when the system is out of equilibrium. 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 differences, 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
Shenoy 43
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 effect. 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
differences, meaning that price signals may not be effectively 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 government 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 difficult
as higher prices raise the opportunity cost of not exporting and make state imports
more expensive. Thus far, however, the government has demonstrated its willingness 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
Shenoy 44
officials 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 buffer 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 effect 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 signals 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 difficult 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 effectively 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
Shenoy 45
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 intervention and regional market segmentation. As a result, there is reason to expect different
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 information 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
Shenoy 46
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 inefficient
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 effect 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 differently. Knowledge of the differences 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
Shenoy 47
This study has implications for Indian policymakers, outside agencies, and Indian 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 effects under current market structure. It is up to officials to
determine whether these achievements justify the costs of Indian wheat policy. Outside 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 differently 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
Shenoy 48
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