Paper Price Cointegration

Price Co-integration between Producers and Retailers/ Producers and Wholesalers
of Agricultural Commodities in India
Tanpreet Singh, M.Sc. Agribusiness Economics student in Gokhale Institute of Politics & Economics
Abstract
The objective of this paper is to test how the producers and retailers/ producers and wholesalers prices
are co-integrated and at the same time to show the causality that exists between the producers and
retailers/wholesalers prices in the Indian market. The paper focuses on Mustard, Groundnut, Potato,
Wheat and Rice. Farm harvest prices of the mentioned commodities are taken as a proxy for producers
prices. The data used is monthly recorded price series of farm harvest prices, retail prices and wholesale
prices, for the period from January 2009 to December 2014 taken from the Ministry of Agriculture and
Farmers Welfare. For each individual crop, four major producing states and four metropolitan cities are
taken in order to study the co-integration between the price series. The study shows that there is long
run co-integration between the farm harvest prices and the retail/wholesale prices in some of the cases.
However, it is observed that the retailers/wholesalers are dominant over price discrimination. This
shows that the market structure is in favor of retailers and wholesalers, which adversely affect the
welfare of producers in the country. The paper also focuses on the way through which the co-
integration between the farm harvest prices and retail/wholesale prices can be achieved.
1. Introduction
Prices are the most important determinants of income level of farmers of agricultural commodities and
the economic welfare of consumers. Information on market efficiency is achieved only by knowing the
relationship between the prices received by farmers and paid by consumers. The study of co-integration
between the price series is important to understand the economic theory as well as to show the
existence of market failure.
India has been implementing liberal economic policies since the early 1990s but in case of agriculture it
took time to implement those policies. It has been argued that market reforms are required for
achieving efficient agricultural markets and hence an efficient agricultural production system. Until,
agricultural markets are integrated, producers and consumers will not realize the potential gains from
liberalization.
The government’s motive is to balance the twin objective of self-sufficiency through the provision of
remunerative prices to producers and protection to consumers by providing them subsidized food
through the Public Distribution System. To achieve these objectives, there’s a need to keep a close
watch on the prices of a few essential commodities.
Some of the previous research study findings are highlighted in this research paper. These prominent
research papers are further classified as per their focus area:
Sharma (2001) examined the price behavior of a few selected agricultural commodities with the
objectives to analyze: the behavior of the procurement prices of wheat, rice and groundnut; the
relationship between their procurement prices and cost of production, farm harvest prices and
wholesale prices; the variability in the prices of the selected commodities; and, the structure of markets.
The study was done with the help of Error Correction Model.
Reddy and Reddy (2011) examined the co-integration of wholesale prices of groundnut pod, oil and cake
(groundnut complex) in major markets of India. It was noticed that only in few markets, prices of

groundnut pods and groundnut oil were vertically integrated in the long run, while in most markets
wholesale prices of cake were not integrated either with groundnut pod or oil wholesale prices in the
long run.
Rahman (2015) examined whether the price transmission is asymmetrical in the Indian pulses market.
The results suggested that the wholesale and retail prices behave independent of each other in the
short-run and the agricultural market is imperfect in nature.
2. Objective
The general objective of the study is to investigate price co-integration between farm harvest and
retail/wholesale prices of various agricultural commodities namely Mustard oil, Groundnut oil, Potato,
Rice and Wheat. More specifically, the study was undertaken;
 To assess the short-term and long-term association between farm harvest price and
retail/wholesale price, and
 To show the direction of causality that exists between farm harvest and retail/wholesale price.
3. Methodology
Before starting with the bivariate model, a unit root test for the variables is mandatory. The unit root
test was undertaken, using Augmented Dickey Fuller test. The data which is non stationary is not
favorable for estimating relationship between variables because of spurious regression and invalid
regression estimates. In this paper, farm harvest, retail and wholesale price series are non-stationary
having unit root. In other words both price series are integrated of degree 1 (denoted as I(1)).
However, data showing such properties can be made stationary by first differencing. If a series is such
that its first difference is stationary (and has positive spectrum at zero frequency) then the series has a
unit root.
3.1 Testing for Unit Root
Before conducting co-integrating tests, there is a need to examine the univariate time-series properties
of the data and confirm that all the price series are non-stationary and integrated in the same order.
This can be established by using the Augmented Dickey Fuller (ADF) Test developed by Dickey and Fuller
(1981). The ADF test involves regressing the first-difference of a variable on a constant, it's lagged
level, and k lagged first differences:
∆𝑃𝑡 = 𝛽0 + 𝛽1 𝑃𝑡−1 + ∑ 𝛾1
𝑘
𝑖=1
∆𝑃𝑡−𝑖 + 𝑒𝑡
Where, Pt is the price series. Equation (1) tests for a unit root in the price series by testing the null
hypothesis Ho: β1=0, by using the ADF test statistics. The null hypothesis of a unit root is rejected in
favor of the alternative of level stationarity if β1 is significantly different from zero. Unit root tests were
done with the assumption of an intercept and no trend in all the series.
3.2 Selecting optimal number of lags
Since the values in the past affects the present values of a variable thus there is a need for the lags. In
other words the variables are persistent in nature. There are five methods to determine how many lags
to use. The methods used in this study to determine optimal lag length are the Akaike Information
Criterion (AIC), the Schwarz’ Bayesian Information Criterion (SBIC), the Final Prediction Error (FPE), the
Hannan Quinn Information Criterion (HQIC) and the Likelihood ration test (LR). These rules choose lag
length p to minimize:
Log(SSR (p)/n) + (p + 1)C(n)/n,

Where, SSR (p) is the sum or squared residuals for the VAR with p lags and n is the number of
observations.
3.3 Testing Co-integration between the price series using Johansen and Juselius’ Approach
After performing the stationarity test, there is a need to examine the existence of co-integration
between the two variables. In this test, search for the existence of the number of co-integrated vectors,
r is done within the framework of Johansen and Juselius. Using their technique, a k-dimensional VAR of
the following form is implemented.
𝑃𝑡 = 𝜇 + ∑ ⨅ 𝑡
𝑘
𝑗=1
𝑃𝑡−1 + 𝑒𝑡
Where Pt = (2 X 1) vector matrix of the farm harvest and retail prices,
𝑒𝑡 = Gaussian residual and
j =number of lags in the observations
Following Johansen’s procedure, the trace and maximum eigenvalue statistics are used to determine the
rank of Π and to reach a conclusion on the number of co-integrating equations, r.
3.4 Vector Error Correction Model
After establishing existence of long run relationships and rank of the co-integrating vectors, the ECM
was applied to investigate further on the short-run interaction causality among variables and also to
know the speed of adjustment from short-run dis-equilibrium to the long-run equilibrium. The error
correction model can be expressed as follows:
∆𝑃𝑡 = 𝑐 + ⨅𝑃𝑡−1 + ∑ 𝐵𝑗
𝑘−1
𝑡=1
∆𝑃𝑡−𝑗 + 𝑒𝑡
Where Π = (2 × 2) matrix of long-run and adjustment parameters,
Bj = (2 × 2) matrix of the short-run parameters,
𝑒𝑡 = vector of residuals and
j = number of lags.in the observations
The error correction terms (load factors or speed of adjustment parameters) are the residuals obtained
from the co-integrating equation of co-integrating price series. The number of error correction terms in
each equation, depends on the number of co-integrated vectors in the price series. Note that the term
in equations represents the extent of the disequilibrium levels in prices in the previous period. Thus, the
VECM representation states that changes in prices in one locality not only depends on changes of the
price of other locality and its own past changes, but also on the extent of the disequilibrium between
the levels of prices. Hence, the past values of error term in the equation have an impact on the changes
of variable price. Note also that the larger is the speed of adjustment parameters (with the right signs),
the greater is the convergence rate toward equilibrium. The appeal of the VECM formulation is that it
combines flexibility in dynamic specification with desirable long-run properties: it could be seen as
capturing the transitional dynamics of the system to the long run equilibrium suggested by economic
theory.

4. Results
This section will involve the steps of analysis for test for stationary, optimal length of the lags, test of co-
integration and test of causality. The analysis crop wise done is shown below
4.1 Mustard
Bihar, Punjab, Rajasthan and Uttar Pradesh are the four largest producing states of Mustard in India. All
the four major producing states are analyzed along with the four metropolitan cities.
In case of mustard, only the following price series are found to be co-integrated (rest shows no co-
integration)
1. Bihar farm harvest prices of mustard and Delhi retail prices of mustard oil
2. Bihar farm harvest prices of mustard and Mumbai retail prices of mustard oil
3. Punjab farm harvest prices of mustard and Kolkata retail prices of mustard oil
4. Bihar farm harvest prices of mustard and Delhi wholesale prices of mustard oil
5. Bihar farm harvest prices of mustard and Kolkata wholesale prices of mustard oil
And the process for the co-integration and causality adjustments is described below.
For static analysis, Augmented Dickey Fuller (ADF) test were used. Both the farm harvest and
retail/wholesale price series were found to be non-stationary in the level (table 1), but stationary upon
first differencing (table 2). It implies that the series are integrated of the order of 1 i.e. I(1).
Table 1. Results of Augmented Dickey Fuller Test
Variable Test Statistics
Value
Critical Value
at 1%
Critical Value
at 5%
Critical Value at
10%
Bihar Farm Harvest Price -1.277 -3.552 -2.914 -2.592
Punjab Farm Harvest Price -1.631 -3.569 -2.924 -2.597
Delhi Retail Price -0.808 -3.552 -2.914 -2.592
Mumbai Retail Price -0.869 -3.552 -2.914 -2.592
Kolkata Retail Price -0.559 -3.552 -2.914 -2.592
Delhi Wholesale Price -0.938 -3.553 -2.915 -2.592
Kolkata Wholesale Price -1.181 -3.552 -2.914 -2.592
Table 2. First Differential Variable- Dickey Fuller Test
Value
Critical Value
at 1%
Critical Value
at 5%
Critical Value at
10%
Δ Bihar Farm Harvest Price -10.744 -3.552 -2.914 -2.592
Δ Punjab Farm Harvest Price -7.487 -3.569 -2.924 -2.597
Δ Delhi Retail Price -3.788 -3.552 -2.914 -2.592
Δ Mumbai Retail Price -8.481 -3.552 -2.914 -2.592
Δ Kolkata Retail Price -6.266 -3.552 -2.914 -2.592
Δ Delhi Wholesale Price -7.214 -3.553 -2.915 -2.592
Δ Kolkata Wholesale Price -7.170 -3.552 -2.914 -2.592
To employ Johansen technique, it is necessary to calculate numbers of lags of endogenous variables
in the model. According to the lag-order selection statistics (LR, FPE, AIC, HQIC and SBIC), the following
are the optimal number of lags-

Table 3. Optimal Lag length of the endogenous variables
Variables Number of lags
Bihar FHP and Delhi RP 1
Bihar FHP and Mumbai RP 1
Punjab FHP and Kolkata RP 1
Bihar FHP and Delhi WP 1
Bihar FHP and Kolkata WP 1
Once the number of lags was determined, the Johansen and Juselius’ framework was implemented to
determine the number of co-integration equations. The estimated result is presented in Table 4. The
estimation was carried out to determine the rank of the co-integration matrix. As indicated in the table
4, we reject the hypothesis that there is no integration between farm harvest prices and
retail/wholesale prices i.e. r = 0. Because both the trace and the max statistics are greater than their
respective 5% critical values when r = 0, and are lesser when r=1 therefore, there is only one relationship
between respective farm harvest and retail/wholesale price series.
Table 4. Johansen’s tests for co-integration of the price series
Co-integrating
Relationships
Rank Eigen
Values
Trace
Statistics
5% Critical
Value(trace)
Max
Statistics
5% Critical
Value(max)
Bihar FHP and
Delhi RP
r=0 29.7043 15.41 28.8834 14.07
r≤1 0.33423 0.8209* 3.76 0.8209 3.76
r≤2 0.01150
Bihar FHP and
Mumbai RP
r=0 24.6068 15.41 22.4474 14.07
r≤1 0.27106 2.1594* 3.76 2.1594 3.76
r≤2 0.02996
Punjab FHP and
Kolkata RP
r=0 19.0714 15.41 15.9652 14.07
r≤1 0.23708 3.1063* 3.76 3.1063 3.76
r≤2 0.05129
Bihar FHP and
Delhi WP
r=0 25.6081 15.41 24.8448 14.07
r≤1 0.29526 0.7634* 3.76 0.7634 3.76
r≤2 0.01069
Bihar FHP and
Kolkata WP
r=0 16.7548 15.41 14.7341 14.07
r≤1 0.18740 2.0208* 3.76 2.0208 3.76
r≤2 0.02806
Thus, Johansen technique confirms the existence of a long-run equilibrium relationship between the
price series in the mustard market and so the VECM causality can be studied.
Now there’s a need to test which price causes the other. This is analyzed using Engel Granger-Vector
Error Correction Model. The estimated results are presented in Table 5.
Bihar Farm Harvest Prices and Delhi Retail Prices are co-integrated, coefficient of the adjustment
parameter when farm harvest price becomes dependent variable, i.e. -.245 shows the speed of
adjustment of farm harvest price when there is change in retail price. It shows that when there is
disequilibrium caused by the retail price, the farm harvest price changes by about 24% in a month. This
implies that it takes more than four months for the farm harvest price to fully adjust if there is no

additional shock in retail price. But when the dependent variable is retail prices in Delhi, the coefficient
sign is positive implying divergence from the equilibrium.
Table 5. Vector Error Correction Model for Mustard (Long run Causalities)
Co-integrating
Relationships
Dependent
Variable
Independent
Variable
Coefficient Standard Error P-value
Bihar FHP and
Delhi RP
D_bihar FHP Adjustment
(Delhi RP)
-0.2459098 0.0508224 0.000
Constant 0.2951094 0.0798364 0.000
D_delhi RP Adjustment
(Bihar FHP)
0.6338311 0.2140394 0.003
Constant 0.1144947 0.3362322 0.773
Bihar FHP and
Mumbai RP
(Mumbai RP)
-0.1386031 0.038204 0.000
Constant 0.2160463 0.0815547 0.008
D_mumbai
RP
Adjustment
(Bihar FHP)
0.6508069 0.2100447 0.002
Constant 0.0460116 0.4483857 0.918
Punjab FHP
and Kolkata RP
D_punjab
FHP
Adjustment
(Kolkata RP)
-0.0690135 0.0496093 0.164
Constant 0.0379955 0.0658073 0.564
D_kolkata RP Adjustment
(Punjab FHP)
0.859342 0.2446387 0.000
Constant 0.0030514 0.324516 0.992
Bihar FHP and
Delhi WP
(Delhi WP)
-0.1755768 0.0396764 0.000
Constant 0.2316055 0.0788202 0.003
D_delhi WP Adjustment
(Bihar FHP)
0.4997874 0.1785278 0.005
Constant 0.0813637 0.3546591 0.819
Bihar FHP and
Kolkata WP
(Kolkata WP)
-0.1742466 0.0444604 0.000
Constant 0.3408036 0.0894997 0.000
D_kolkata
WP
Adjustment
(Bihar FHP)
0.2403626 0.1768314 0.174
Constant 0.2470596 0.3559652 0.488
Note: D_variable means variable after first differencing.
Also, Bihar Farm Harvest Prices and Mumbai Retail Prices are found to be co-integrated, coefficient of
the adjustment parameter when farm harvest price becomes dependent variable, i.e. -.138 shows the
speed of adjustment of farm harvest price when there is change in retail price. It shows when the
disequilibrium is caused by retail price, the farm harvest price changes by about 13% in a month. This
implies it takes more than eight months for the farm harvest price to fully adjust if there is no additional

shock in retail price. When the dependent variable is retail prices in Mumbai, the coefficient sign is
positive implying divergence from the equilibrium.
Punjab Farm Harvest Prices are found to be co-integrated only with the retail prices of mustard oil in
Kolkata. The speed of adjustment in farm harvest price is 6.5% per month, which is very insignificant.
Whereas, when the dependent variable is retail prices in Kolkata, the coefficient sign is positive implying
divergence from the equilibrium.
Taking wholesale prices into consideration, Bihar farm harvest price and Delhi wholesale price are found
to be co-integrated. When the dependent variable is farm harvest prices in Bihar, the speed of
adjustment in farm harvest price is 17% per month due to disequilibrium caused by the wholesale price
i.e. it takes more than 5 months for farm harvest prices to adjust if there is no additional shock in the
wholesale prices. But when the dependent variable is wholesale prices in Delhi, the coefficient sign is
positive implying divergence from the equilibrium.
Bihar farm harvest price and Kolkata wholesale price are also found to be co-integrated. When the
dependent variable is farm harvest prices in Bihar, the speed of adjustment in farm harvest price is 17%
per month i.e. it takes more than 5 months for farm harvest prices to adjust if there is no additional
shock in the wholesale prices. Again, when the dependent variable is wholesale prices in Kolkata, the
coefficient sign is positive implying divergence from the equilibrium.
Since the number of lags in all the price series were found to be 1, so there were no short run
adjustments coefficients found in the vector error correction model.
It is found that not much of the price series are co-integrated. The problems faced by the mustard oil
industry are collectively presented along with groundnut oil in the next section.
4.2 Groundnut
Andhra Pradesh, Gujarat, Karnataka and Tamil Nadu are the four major producing states of groundnut in
India.
In case of groundnut, the following price series are found to be co-integrated
1. Andhra Pradesh farm harvest price of groundnut and Delhi retail price of groundnut oil
2. Andhra Pradesh farm harvest price of groundnut and Mumbai retail price of groundnut oil
3. Andhra Pradesh farm harvest price of groundnut and Kolkata retail price of groundnut oil
4. Andhra Pradesh farm harvest price of groundnut and Mumbai wholesale price of groundnut oil
5. Andhra Pradesh farm harvest price of groundnut and Kolkata wholesale price of groundnut oil
6. Karnataka farm harvest price of groundnut and Kolkata wholesale price of groundnut oil
And the process for the co-integration and causality adjustments is described below.
To make sure the price series are stationary, ADF test was used. The results are shown in table 6
implying that none of the price series is stationary as the real values of test statistics is less than the real
critical values.

Table 6. Results of Augmented Dickey Fuller test
Value
Critical Value
at 1%
Critical Value
at 5%
Critical Value at
10%
Andhra Pradesh Farm Harvest Price -2.465 -3.553 -2.915 -2.592
Karnataka Farm Harvest Price -2.068 -3.552 -2.914 -2.592
Mumbai Retail Price -1.135 -3.552 -2.914 -2.592
Kolkata Retail Price -1.533 -3.552 -2.914 -2.592
Mumbai Wholesale Price -1.877 -3.553 -2.915 -2.592
Kolkata Wholesale Price -1.661 -3.553 -2.915 -2.592
To overcome the problem of stationarity, first differencing was done. The result of ADF test post first
differencing is shown for all the price series in table 7. It is evident from table 7 that the price series are
integrated of order 1, the real values of test statistics are more than the real critical values.
Table 7. First Differential Variable- Augmented Dickey Fuller test
Value
Critical Value
at 1%
Critical Value
at 5%
Critical Value at
10%
Δ Andhra Pradesh Farm Harvest Price -4.159 -3.558 -2.917 -2.594
Δ Karnataka Farm Harvest Price -8.248 -3.552 -2.914 -2.592
Δ Mumbai Retail Price -7.540 -3.552 -2.914 -2.592
Δ Kolkata Retail Price -7.581 -3.552 -2.914 -2.592
Δ Mumbai Wholesale Price -4.234 -3.555 -2.916 -2.593
Δ Kolkata Wholesale Price -7.888 -3.553 -2.915 -2.592
Now, to employ Johansen technique, it is necessary to calculate numbers of lags of endogenous
variables in the model. According to the lag-order selection statistics (LR, FPE, AIC, HQIC and SBIC), the
following are the optimal number of lags-
Table 8. Optimal lag length of the endogenous variables
Andhra Pradesh FHP and Delhi RP 4
Andhra Pradesh FHP and Mumbai RP 2
Andhra Pradesh FHP and Kolkata RP 2
Andhra Pradesh FHP and Mumbai WP 2
Andhra Pradesh FHP and Kolkata WP
Karnataka FHP and Kolkata WP
2
1
determine the number of co-integration equations. The estimation was carried out to determine the
rank of the co-integration matrix. As indicated in the table 9, we reject the hypothesis that there is no
integration between farm harvest prices and retail/wholesale prices i.e. r = 0. Both the trace and the
max statistics are greater than their respective 5% critical values when r = 0, and are lesser when r=1,
therefore, there is only one relationship between respective farm harvest and retail/wholesale price
series.

Table 9. Johansen’s tests for co-integration of the price series
Co-integrating
Relationships
Rank Eigen
Values
Trace
Statistics
5% Critical
Value(trace)
Max
Statistics
5% Critical
Value(max)
Andhra Pradesh
FHP and Delhi
RP
r=0 18.1309 15.41 15.6723 14.07
r≤1 0.20584 2.4586* 3.76 2.4586 3.76
r≤2 0.03551
Andhra Pradesh
FHP and
Mumbai RP
r=0 15.7593 15.41 14.6499 14.07
r≤1 0.18883 1.1094* 3.76 1.1094 3.76
r≤2 0.01572
Andhra Pradesh
FHP and Kolkata
RP
r=0 16.7506 15.41 14.6885 14.07
r≤1 0.18928 2.0621* 3.76 2.0621 3.76
r≤2 0.02903
Andhra Pradesh
FHP and
Mumbai WP
r=0 18.5643 15.41 15.6716 14.07
r≤1 0.20059 2.8927* 3.76 2.8927 3.76
r≤2 0.04048
Andhra Pradesh
FHP and Kolkata
WP
r=0 16.2793 15.41 13.7161 14.07
r≤1 0.17794 2.5631* 3.76 2.5631 3.76
r≤2 0.03595
Karnataka FHP
and Kolkata WP
r=0 22.0668 15.41 18.8131 14.07
r≤1 0.23277 3.2537* 3.76 3.2537 3.76
r≤2 0.04479
The Johansen technique confirms the existence of a long-run equilibrium relationship between the price
series in the groundnut market and so the VECM causality can be studied.
Error Correction Model. The estimation results are presented in Table 10(a) and 10(b).
Table 10(a) shows the estimated results for groundnut in the long run.
Andhra Pradesh farm harvest prices are found to be co-integrated with the retail prices in Delhi, Kolkata
and Mumbai and with the wholesale prices of Mumbai and Kolkata. First Considering retail prices, when
Andhra Pradesh farm harvest prices are taken as a dependent variable, the speed of adjustment in the
farm harvest is found to be 32%, 17% and 14% due to the disequilibrium caused by the retail prices in
Delhi, Kolkata and Mumbai respectively. The signs are negative implying convergence to the equilibrium
and are significant too. It implies that it takes more than 3 months for the Andhra Pradesh farm harvest
prices to fully adjust if there is no additional in the Delhi retail prices. It takes more than 5 months for
the Andhra Pradesh farm harvest prices to fully adjust if there is no additional shock in the Kolkata and
Mumbai retail prices.
Now considering wholesale prices, when Andhra Pradesh farm harvest prices are taken as a dependent
variable, the speed of adjustment in the farm harvest is found to be 7% and 17% due to the
disequilibrium caused by the wholesale prices in Mumbai and Kolkata respectively. The signs are
negative implying convergence to the equilibrium and are significant at 10%. It implies that it takes more

than 5 months for the Andhra Pradesh farm harvest prices to fully adjust (to the disequilibrium caused
by the wholesale prices in Kolkata) if there is no additional shock in the Kolkata wholesale prices.
Table 10(a). Vector Error Correction Model for Groundnut (Long run causalities)
Co-integrating
Relationships
Dependent
Variable
Independent
Variable
Andhra
Pradesh FHP
and Delhi RP
D_Andhra
Pradesh FHP
Adjustment -0.3281541 0.083696 0.000
Constant -0.0427327 0.3350694 0.899
D_Delhi RP Adjustment -0.0357934 0.1315279 0.786
Constant 0.3917731 0.5265601 0.547
Andhra
Pradesh FHP
and Mumbai
RP
D_ Andhra
Pradesh FHP
Adjustment -0.1455196 0.0571184 0.011
Constant 0.3773328 0.329505 0.252
D_Mumbai
RP
Adjustment 0.2987935 0.1097494 0.006
Constant 0.1837701 0.6331226 0.772
Andhra
Pradesh FHP
and Kolkata RP
D_ Andhra
Pradesh FHP
Adjustment -0.1741381 0.0510139 0.001
Constant 0.2964466 0.306039 0.333
D_Kolkata RP Adjustment 0.2173626 0.1039445 0.037
Constant 0.2374956 0.6235764 0.703
Andhra
Pradesh FHP
and Mumbai
WP
D_ Andhra
Pradesh FHP
Adjustment -0.0768399 0.0462612 0.097
Constant 0.1652772 0.3378527 0.625
D_Mumbai
WP
Adjustment 0.4802391 0.129271 0.000
Constant 0.0264448 0.9440855 0.978
Andhra
Pradesh FHP
and Kolkata
WP
D_ Andhra
Pradesh FHP
Adjustment -0.1764988 0.0509681 0.001
Constant 0.3671365 0.3155174 0.245
D_Kolkata
WP
Adjustment 0.3922231 0.2067416 0.058
Constant 0.1652099 1.279832 0.897
Karnataka FHP
and Kolkata
WP
D_ Karnataka
FHP
Adjustment
Constant
-0.1966732
0.0912347
0.0753947
0.2441013
0.009
0.709
D_Kolkata
WP
Adjustment
Constant
1.271608
0.0141108
0.3889885
1.259408
0.001
0.991

Karnataka farm harvest price and Kolkata retail price are also co-integrated. When Karnataka farm
harvest prices are taken as a dependent variable, the speed of adjustment is 19% per month due to the
disequilibrium caused by the wholesale prices in Kolkata. The coefficient sign is negative implying
convergence to the equilibrium and is significant too. It implies that it takes more than 5 months for the
Karnataka farm harvest prices to fully adjust if there is no additional shock in the wholesale prices in
Kolkata.
The above interpretation is for the case when the dependent variable is farm harvest price (of different
states) but when the retail/wholesale prices (of different cities) are taken as dependent variable, the
coefficient signs are positive implying divergence from the equilibrium in the long run. Only in the case
of Delhi retail price, when it is taken as a dependent variable, the coefficient sign is negative implying
convergence to the equilibrium but it is highly insignificant. This implies that the retailers and
wholesalers are more dominant over price discrimination.
Table 10(b). Vector Error Correction Model for Groundnut (Short run Causalities)
Co-integrating
Relationships
Dependent
Variable
Independent
Variable
Andhra
Pradesh FHP
and Delhi RP
D_Andhra
Pradesh FHP
Delhi RP LD
Delhi RP L2D
Delhi RP L3D
-0.0131489
0.0812097
-0.0339508
0.0785677
0.0748013
0.0753218
0.867
0.278
0.652
D_Delhi RP Andhra FHP LD
Andhra FHP L2D
Andhra FHP L3D
0.1189497
-0.5749011
0.4612882
0.1910642
0.1957859
0.2069624
0.534
0.003
0.023
Andhra
Pradesh FHP
and Mumbai
RP
D_ Andhra
Pradesh FHP
Mumbai RP LD -0.1390456 0.0601809 0.021
D_Mumbai
RP
Andhra FHP LD -0.3778407 0.2226797 0.090
Andhra
Pradesh FHP
and Kolkata RP
D_ Andhra
Pradesh FHP
Kolkata RP LD -0.2063765 0.0586243 0.000
D_Kolkata RP Andhra FHP LD 0.4028148 0.2223257 0.070
Andhra
Pradesh FHP
and Mumbai
WP
D_ Andhra
Pradesh FHP
Mumbai WP LD -0.0550577 0.0360456 0.127
D_Mumbai
WP
Andhra FHP LD -0.4452415 0.3451678 0.197
Andhra
Pradesh FHP
and Kolkata
WP
D_ Andhra
Pradesh FHP
Kolkata WP LD -0.0915451 0.0282883 0.001
D_Kolkata
WP
Andhra FHP LD -0.5540027 0.4453089 0.213

Note: LD means one Lagged difference, L2D means two lagged difference and L3D means three lagged
difference. Also, D_variable means the variable after first differencing.
Table 10(b) shows the estimated results for groundnut in the short run.
Andhra Pradesh farm harvest price and Delhi retail price are co-integrated. When the dependent
variable is retail price in Delhi, short run causality is found running with two lags from the farm harvest
price in Andhra Pradesh to the retail prices in Delhi. It implies that 57% is the correction that happens in
2 weeks in the retail prices when disequilibrium is caused by the farm harvest prices. When the
dependent variable is farm harvest prices in Andhra Pradesh, no short run causality found running since
insignificant p-values.
Andhra Pradesh farm harvest price and Mumbai retail price are co-integrated. When the dependent
variable is Andhra Pradesh farm harvest prices, short run causality found with a lag implying 13%
correction in the farm harvest prices per week due to disequilibrium caused by retail prices in Mumbai.
Similarly, when the dependent variable is Mumbai retail prices, short run causality found with a lag
implying 37% correction per week in the retail prices due to disequilibrium caused by farm harvest
prices in the Andhra Pradesh.
Andhra Pradesh farm harvest price and Kolkata retail price are co-integrated. When the dependent
variable is Andhra Pradesh farm harvest prices, short run causality found with a lag implying 20%
correction per week in the farm harvest prices due to disequilibrium caused by retail prices in Kolkata.
But when the dependent variable is Kolkata retail prices, the sign of the lagged coefficient is positive
implying divergence from the equilibrium.
Now considering the co-integration between the wholesale and farm harvest prices, Andhra Pradesh
farm harvest prices and Mumbai wholesale prices are co-integrated. When the dependent variable is
Andhra farm harvest price, no short run causality found since insignificant p-values. Similar is the case
when the dependent variable is Mumbai wholesale price.
Andhra Pradesh farm harvest prices and Kolkata wholesale prices are also co-integrated. When the
dependent variable is farm harvest prices in Andhra Pradesh, short run causality is found running with a
lag implying 9% correction per week happening in the farm harvest prices when disequilibrium is caused
by the Kolkata wholesale prices. But when the dependent variable is Kolkata wholesale prices, no short
run causality found due to highly insignificant p-value.
Some of the reasons that explain no co-integration between the farm harvest prices and the
retail/wholesale prices in India’s oilseed and edible oil markets could be
 Supply shortages due to deceleration in yield and hence fluctuations in prices,
 Weak infrastructure,
 Price information gaps,
 Presence of monopolies and cartels between the traders may result in no reduction in the
margins when price falls,
 Lack of uniformity in octroi and tax rates across the states ,
 High processing and marketing costs,
 Inventory holding behavior of firms, especially when high international price expectation leads
to accumulation of stocks.

Also, it is seen that the palm oil is considered as a substitute for the groundnut oil and mustard oil due
to its cheap price. Majority of the demand of the palm oil is meet by the imports and hence it’s a matter
of study whether the wholesale and retail prices of groundnut oil and mustard oil are influenced by the
changes in the price (import) of Palm oil.
4.3 Potato
Uttar Pradesh, West Bengal, Bihar and Gujarat are the major producers of Potato in India. The farm
harvest prices in these states were analyzed along with the retail/wholesale prices of the metropolitan
cities. The following price series were only found to be co-integrated
1. Bihar farm harvest price and Delhi retail price,
2. West Bengal farm harvest price and Delhi retail price
3. Bihar farm harvest price and Delhi wholesale price
4. West Bengal farm harvest price and Delhi wholesale price
The process of co-integration and causality adjustment is described below.
Table 11. Result of Augmented Dickey Fuller Test
Value
Critical Value
at 1%
Critical Value
at 5%
Critical Value at
10%
Bihar Farm Harvest Price -0.940 -3.552 -2.914 -2.592
West Bengal Farm Harvest Price -0.202 -3.567 -2.923 -2.596
Delhi Wholesale Price -1.806 -3.553 -2.915 -2.592
Table 12. First Differential Variable- Augmented Dickey Fuller Test
Value
Critical Value
at 1%
Critical Value
at 5%
Critical Value at
10%
Δ Bihar Farm Harvest Price -8.429 -3.552 -2.914 -2.592
Δ West Bengal Farm Harvest Price -7.812 -3.567 -2.923 -2.596
Δ Delhi Wholesale Price -3.861 -3.553 -2.915 -2.592
Table 13. Optimal lag lengths of the endogenous variables
Bihar FHP and Delhi RP 3
West Bengal FHP and Delhi RP 2
Bihar FHP and Delhi WP 2
West Bengal FHP and Delhi WP 2

Table 14. Johansen test for co-integration of the price series
Co-integrating
Relationships
Rank Eigen
Values
Trace
Statistics
5% Critical
Value(trace)
Max
Statistics
5% Critical
Value(max)
Bihar FHP and
Delhi RP
r=0 15.6622 15.41 15.5816 14.07
r≤1 0.20214 0.0806* 3.76 0.0806 3.76
r≤2 0.00117
West Bengal
FHP and Delhi
RP
r=0 18.4144 15.41 18.4110 14.07
r≤1 0.26806 0.0034* 3.76 0.0034 3.76
r≤2 0.00006
Bihar FHP and
Delhi WP
r=0 26.1512 15.41 26.1252 14.07
r≤1 0.31520 0.0260* 3.76 0.0260 3.76
r≤2 0.00038
West Bengal
FHP and Delhi
WP
r=0 17.1350 15.41 17.1343 14.07
r≤1 0.25205 0.0006* 3.76 0.0006 3.76
r≤2 0.00001
The Johansen and Juselius’ framework was implemented to determine the number of co-integration
equations. The estimation was carried out to determine the rank of the co-integration matrix. As
indicated in the table 14, reject the hypothesis that there is no integration between farm harvest prices
and retail/wholesale prices i.e. r = 0. Both the trace and the max statistics are greater than their
respective 5% critical values when r = 0, and are lesser when r=1. Thus, the farm harvest price series and
retail/wholesale price series are co-integrated between the mentioned producing states and metro
cities.
Error Correction Model. The estimation results are presented in Table 15(a) and 15(b).
Table 15(a) shows the estimated results for potato in the long run.
In case of Potato, only the farm harvest prices in Bihar and West Bengal shows co-integration with the
retail and wholesale prices In Delhi. Considering Bihar farm harvest prices, when it is taken as a
dependent variable, the speed of adjustment in the farm harvest prices is found to be 0.6% and 3% due
to the disequilibrium caused by the Delhi retail price and Delhi wholesale price respectively. The sign in
case of retail price is negative showing convergence to the equilibrium but is highly insignificant
whereas, the coefficient sign in case of wholesale price is positive implying divergence from the
equilibrium.
Considering West Bengal farm harvest prices, when it is taken as a dependent variable, the speed of
adjustment in the farm harvest prices is found to be 0.2% and 0.7% due to the disequilibrium caused by
the Delhi retail price and Delhi wholesale price respectively. The sign in case of retail price is negative
showing convergence to the equilibrium but is highly insignificant whereas, the coefficient sign in case of
wholesale price is positive implying divergence from the equilibrium and is highly insignificant.

coefficient signs are positive implying divergence from the equilibrium in the long run. This implies that
the retailers and wholesalers are more dominant over price discrimination.
Table 15(a). Vector Error Correction Model for Potato (Long run Causalities)
Co-integrating
Relationships
Dependent
Variable
Independent
Variable
Bihar FHP and
Delhi RP
D_Bihar FHP Adjustment -0.0061653 0.0272155 0.821
Constant 0.0515401 0.0203649 0.011
D_Delhi RP Adjustment 1.742798 0.4366332 0.000
Constant .0001823 0.3267259 1.000
West Bengal
FHP and Delhi
RP
D_West
Bengal FHP
Adjustment -0.0029529 0.0201504 0.883
Constant 0.145179 0.0841211 0.084
Constant 0.0013272 0.3020653 0.996
Bihar FHP and
Delhi WP
D_Bihar FHP Adjustment 0.0369726 0.0208252 0.076
Constant 0.0317413 0.0233295 0.174
D_Delhi WP Adjustment 0.9920836 0.1955055 0.000
Constant -0.0011829 0.2190157 0.996
West Bengal
FHP and Delhi
WP
D_West
Bengal FHP
Adjustment 0.0071906 0.0165433 0.664
Constant 0.1386381 0.0812518 0.088
Constant -0.0052383 0.2174129 0.981
Table 15(b) shows the estimated results for potato in the short run.
In the short run, no causality is found running from Bihar farm harvest prices to Delhi retail prices when
the dependent variable is retail prices in Delhi since the p-values are insignificant. When the dependent
variable is Bihar farm harvest prices, short run causality is running from Delhi retail prices to the farm
harvest prices with two lags. It implies that 4% is the correction happening in 2 weeks in the Bihar
harvest prices when disequilibrium is caused by retail prices in Delhi. Also, Bihar farm harvest prices are
co-integrated with the wholesale prices in Delhi but there is no short run causality as the p-values are
highly insignificant.

Table 15(b). Vector Error Correction Model for Potato (Short run causalities)
Co-integrating
Relationships
Dependent
Variable
Independent
Variable
Bihar FHP and
Delhi RP
D_Bihar FHP Delhi RP LD
Delhi RP L2D
-0.0014927
-0.0429672
0.0078336
0.0086826
0.849
0.000
D_Delhi RP Bihar FHP LD
Bihar FHP L2D
-1.867371
-0.23444
1.819081
1.802673
0.305
0.897
West Bengal
FHP and Delhi
RP
D_West Ben
FHP
Delhi RP LD -0.0718718 0.0312554 0.021
D_Delhi RP West B. FHP LD -0.0590424 0.4910604 0.904
Bihar FHP and
Delhi WP
D_Bihar FHP Delhi WP LD 0.0022005 0.0115123 0.848
D_Delhi WP Bihar FHP LD -0.6756284 1.212226 0.577
West Bengal
FHP and Delhi
WP
D_West Ben
FHP
Delhi WP LD -0.119604 0.0420176 0.004
D_Delhi WP West B. FHP LD -0.4187372 0.3448217 0.225
Note: LD means one Lagged difference, L2D means two lagged difference. Also, D_variable means the
variable after first differencing.
West Bengal farm harvest and Delhi retail prices are also co-integrated. In the short run, causality is
found running from Bihar farm harvest prices to Delhi retail prices when the dependent variable is farm
harvest prices, 7% per week is the correction happening in the short run in the Bihar harvest prices
when disequilibrium is caused by retail prices in Delhi. When dependent variable is retail prices in Delhi,
no short run causality is found since p-value is insignificant. Also, West Bengal farm harvest prices are
co-integrated with the wholesale prices in Delhi and there is short run causality found with a lag running
from Delhi wholesale prices to the farm harvest prices. It implies that 11% per week is the correction
happening in the farm harvest prices in West Bengal when disequilibrium is caused by the wholesale
prices in Delhi.
The reason why the prices in Potato market are not co-integrated is due to the presence of large
number of intermediaries in the supply chain. A chain of market intermediaries such as field
procurement/assembling agents, forwarding agents, commission agents, wholesalers and retailers are
operating to complete a marketing process. Due to lesser quantity of produce flowing through a
particular marketing chain, the marketing efficiency of the process is generally low.

4.4 Rice
Andhra Pradesh, Tamil Nadu, West Bengal and Punjab are the major producers of Rice in India. The farm
harvest prices in these states were analyzed along with the retail/wholesale prices of the metropolitan
cities. The following price series were only found to be co-integrated
1. Andhra Pradesh farm harvest price and Bengaluru retail price,
2. Tamil Nadu farm harvest price and Mumbai retail price
3. Tamil Nadu farm harvest price and Mumbai wholesale price
4. Andhra Pradesh farm harvest price and Mumbai wholesale price
5. Andhra Pradesh farm harvest price Bengaluru wholesale price
6. Punjab farm harvest price and Delhi wholesale price
The process of co-integration and causality adjustment is described below.
Value
Critical Value
at 1%
Critical Value at
5%
Critical Value at
10%
Andhra Pradesh Farm Harvest Price -1.204 -3.552 -2.914 -2.592
Tamil Nadu Farm Harvest Price
Punjab Farm Harvest Price
-0.754
-2.033
-3.552
-3.569
-2.914
-2.924
-2.592
-2.597
Bengaluru Retail Price
Mumbai Retail Price
-2.389
-1.882
-3.552
-3.552
-2.914
-2.914
-2.592
-2.592
Delhi Wholesale Price
Bengaluru Wholesale Price
Mumbai Wholesale Price
-1.085
-2.392
-0.777
-3.569
-3.552
-3.552
-2.924
-2.914
-2.914
-2.597
-2.592
-2.592
Table 17. First differential variable- Augmented Dickey Fuller Test
Value
Critical Value
at 1%
Critical Value at
5%
Critical Value at
10%
Δ Andhra Pradesh Farm Harvest Price -8.286 -3.552 -2.914 -2.592
Δ Tamil Nadu Farm Harvest Price
Δ Punjab Farm Harvest Price
-8.551
-7.570
-3.552
-3.569
-2.914
-2.924
-2.592
-2.597
Δ Bengaluru Retail Price
Δ Mumbai Retail Price
-7.489
-6.830
-3.552
-3.552
-2.914
-2.914
-2.592
-2.592
Δ Delhi Wholesale Price
Δ Bengaluru Wholesale Price
Δ Mumbai Wholesale Price
-6.903
-8.107
-8.200
-3.569
-3.552
-3.552
-2.924
-2.914
-2.914
-2.597
-2.592
-2.592
For Johansen technique, it is necessary to calculate numbers of lags of endogenous variables in the
model. According to the lag-order selection statistics (LR, FPE, AIC, HQIC and SBIC), the following are
the optimal number of lags-

Andhra Pradesh FHP and Bengaluru RP 1
Tamil Nadu FHP and Mumbai RP 4
Tamil Nadu FHP and Mumbai WP 4
Andhra Pradesh FHP and Mumbai WP 4
Andhra Pradesh FHP and Bengaluru WP 2
Punjab FHP and Delhi WP 1
rank of the co-integration matrix. As indicated in the table 19, reject the hypothesis that there is no
max statistics are greater than their respective 5% critical values when r = 0, and are lesser when r=1.
Thus, the farm harvest price series and retail/wholesale price series are co-integrated between the
mentioned producing states and metro cities.
Table 19. Johansen test for co-integration of the price series
Co-integrating
Relationships
Rank Eigen
Values
Trace
Statistics
5% Critical
Value(trace)
Max
Statistics
5% Critical
Value(max)
Andhra Pradesh
FHP and
Bengaluru RP
r=0 16.5035 15.41 14.8659 14.07
r≤1 0.18891 1.6376* 3.76 1.6376 3.76
r≤2 0.02280
Tamil Nadu FHP
and Mumbai RP
r=0 30.8587 15.41 30.6600 14.07
r≤1 0.36293 0.1987* 3.76 0.1987 3.76
r≤2 0.00292
Tamil Nadu FHP
and Mumbai
WP
r=0 36.7414 15.41 36.1055 14.07
r≤1 0.41196 0.6359* 3.76 0.6359 3.76
r≤2 0.00931
Andhra Pradesh
FHP and
Mumbai WP
r=0 24.4817 15.41 23.8272 14.07
r≤1 0.29559 0.6545* 3.76 0.6545 3.76
r≤2 0.00958
Andhra Pradesh
FHP and
Bengaluru WP
r=0 21.5717 15.41 19.7414 14.07
r≤1 0.24574 1.8303* 3.76 1.8303 3.76
r≤2 0.02581
Punjab FHP and
Delhi WP
r=0 17.2715 15.41 14.5285 14.07
r≤1 0.25216 2.7429* 3.76 2.7429 3.76
r≤2 0.05338
series in the rice market and so the VECM causality can be studied.

Table 20(a). Vector Error Correction Model for Rice (Long run causalities)
Co-integrating
Relationships
Dependent
Variable
Independent
Variable
Andhra
Pradesh FHP
and Bengaluru
RP
D_Andhra
Pradesh FHP
Adjustment 0.0075837 0.0446454 0.865
Constant 0.0385454 0.0716356 0.591
D_Bengaluru
RP
Adjustment 0.7187291 0.1792856 0.000
Constant -0.0004067 0.2876721 0.999
Tamil Nadu
FHP and
Mumbai RP
D_Tamil
Nadu FHP
Adjustment -0.167082 0.0982814 0.089
Constant 0.1305089 0.0590164 0.027
D_Mumbai
RP
Adjustment 0.8378498 0.1494044 0.000
Constant 0.0260258 0.089715 0.772
Tamil Nadu
FHP and
Mumbai WP
D_Tamil
Nadu FHP
Adjustment -0.0502125 0.0657994 0.445
Constant 0.0837173 0.0531498 0.115
D_Mumbai
WP
Adjustment 0.893089 0.1387561 0.000
Constant 0.0047069 0.1120808 0.967
Andhra
Pradesh FHP
and Mumbai
WP
D_Andhra
Pradesh FHP
Adjustment -0.1113094 0.0824509 0.177
Constant 0.0760255 0.0780908 0.330
D_Mumbai
WP
Adjustment 0.5521871 0.1269567 0.000
Constant 0.0153252 0.120243 0.899
Andhra
Pradesh FHP
and Bengaluru
WP
D_Andhra
Pradesh FHP
Adjustment 0.0034324 0.0416835 0.934
Constant 0.0354641 0.0734675 0.629
D_Bengaluru
WP
Adjustment 0.7653416 0.1650796 0.000
Constant -0.0001591 0.2909537 1.000
Punjab FHP
and Delhi WP
D_Punjab
FHP
Adjustment -0.0908139 0.1679627 0.589
Constant 0.2464723 0.1168112 0.035
Constant 0.06444 0.0762782 0.398
Andhra Pradesh farm harvest prices are found to be co-integrated with most of the retail and wholesale
prices of the nearby metro cities. When farm harvest price in Andhra Pradesh is taken as a dependent
variable, the speed of adjustment in the farm harvest prices are found to be 0.7%. 11% and 0.3% due to
the disequilibrium caused by the Bengaluru retail prices, Mumbai wholesale prices and Bengaluru
wholesale prices respectively. The signs are positive implying divergence from the equilibrium and are
highly insignificant.

Tamil Nadu farm harvest prices are found to be co-integrated with the wholesale and retail prices of
Mumbai. When farm harvest price in Tamil Nadu is taken as a dependent variable, the speed of
adjustment in the farm harvest prices are found to be 16% and 5% per month due to the disequilibrium
caused by the retail and wholesale prices in Mumbai respectively. The coefficient signs are negative
showing convergence to the equilibrium but are significant only in the case of retail prices in Mumbai. It
takes more than 6 months for the farm harvest prices in Tamil Nadu to fully adjust if there is no
additional shock in the retail prices of Mumbai.
Punjab farm harvest prices are found to be co-integrated with the wholesale prices in Delhi. When
Punjab farm harvest prices are taken as a dependent variable, the speed of adjustment in the farm
harvest prices is 9% per month due to disequilibrium caused by the retail prices in Delhi. The coefficient
sign is negative showing convergence to the equilibrium but is highly insignificant.
coefficient signs are positive implying divergence from the equilibrium in the long run. This implies that
the retailers and wholesalers are more dominant over price discrimination.
For short run causalities, refer to Table 20(b). Tamil Nadu FHP and Mumbai RP are co-integrated. In the
short run, when dependent variable is Tamil Nadu farm harvest prices, the short run causalities are
insignificant. When Mumbai retail price is taken as the dependent variable, the short run causalities are
significant with lags. It can be said that 64% per week is the correction happening in the Mumbai retail
prices in the short run when there is disequilibrium caused by the farm harvest prices in Tamil Nadu.
Similarly, Tamil Nadu FHP and Mumbai WP are co-integrated. In the short run, when dependent variable
is Tamil Nadu farm harvest prices, the short run causalities are insignificant. When Mumbai wholesale
price is taken as the dependent variable, the short run causalities are significant with lags. It can be said
that 51% per week is the correction happening in the short run in the Mumbai wholesale prices when
there is disequilibrium caused by the farm harvest prices in Tamil Nadu.
Andhra Pradesh FHP and Mumbai WP are co-integrated. In the short run, when dependent variable is
Andhra Pradesh farm harvest prices, the short run causalities are insignificant. When Mumbai wholesale
price is taken as the dependent variable, the short run causalities are significant with lags. It can be said
that 64% per week is the correction happening in the Mumbai wholesale prices in the short run when
there is disequilibrium caused by the farm harvest prices.
In case of Andhra Pradesh FHP and Bengaluru WP, the short run causalities are insignificant when
dependent variable is Andhra Pradesh farm harvest price. Also, when the dependent variable is
Bengaluru wholesale price, though the coefficient sign is negative showing convergence but is greater
than 1 implying no economic sense.

Table 20(b). Vector Error Correction Model for Rice (Short run Causalities)
Co-integrating
Relationships
Dependent
Variable
Independent
Variable
Coefficient Standard
Error
P-value
Tamil Nadu
FHP and
Mumbai RP
D_Tamil
Nadu FHP
Mumbai RP LD
Mumbai RP L2D
Mumbai RP L3D
-0.003634
-0.020768
-0.087982
0.0666201
0.0687249
0.0671894
0.956
0.763
0.190
D_Mumbai
RP
Tamilnadu FHP LD
Tamilnadu FHP L2D
Tamilnadu FHP L3D
-0.6455079
-0.7324838
-0.6998723
0.2250861
0.2207838
0.2208968
0.004
0.002
0.001
Tamil Nadu
FHP and
Mumbai WP
D_Tamil
Nadu FHP
Mumbai WP LD
Mumbai WP L2D
Mumbai WP L3D
-0.0361825
0.0511688
-0.0241268
0.0553366
0.0523793
0.0513619
0.513
0.329
0.639
D_Mumbai
WP
Tamilnadu FHP LD
Tamilnadu FHP L2D
Tamilnadu FHP L3D
-0.852414
-0.7333763
-0.6913808
0.2961007
0.2841443
0.2852451
0.004
0.010
0.015
Andhra
Pradesh FHP
and Mumbai
WP
D_Andhra
Pradesh FHP
Mumbai WP LD
Mumbai WP L2D
Mumbai WP L3D
-0.0316941
-0.0528021
-0.0288236
0.0782996
0.0756752
0.0754227
0.686
0.485
D_Mumbai
WP
Andhra P. FHP LD
Andhra P. FHP L2D
Andhra P. FHP L3D
-0.4026851
-0.5380632
-0.3512609
0.2132255
0.2061008
0.211646
0.059
0.009
0.097
Andhra
Pradesh FHP
and Bengaluru
WP
D_Andhra
Pradesh FHP
Bengaluru WP LD 0.0285278 0.0272498 0.295
D_Bengaluru
WP
Andhra P. FHP LD -1.669551 0.5047441 0.001
Note: LD means one Lagged difference, L2D means two lagged difference and L3D means three lagged
difference. Also, D_variable means the variable after first differencing.
It was observed that in state like Punjab, which is one of the highest producers of Rice in India, the farm
harvest prices in the state are not found to be co-integrated with the retail/wholesale prices in metro
cities. The reason may be very less consumption of Rice in the state. Punjab being one of the largest
producer, is not the largest consumer of Rice in India. The farmers produces rice and delivers it to the
other parts of India. So, farmers are not very fond of knowing the wholesale and retail prices of the crop
and sells it near the cost of cultivation or the Minimum Support Price.
Also, one of the reason the prices are not co-integrated in case of Rice is due to the Minimum Support
Price issued by the government. What MSP does is that, it creates a focal point for the farmers and as a
result, they tend to sell their produce near that price only. The farmers without MSP would not be
aware of the focal point and would tend to expect different prices of their produce.

It was also observed that the more wholesale prices of metro cities are found to be co-integrated with
the farm harvest prices. This implies the role of intermediaries which takes the farmers share of rupee
when the produce comes in the retail markets.
4.5 Wheat
Haryana, Madhya Pradesh, Punjab and Uttar Pradesh are the major producing states of wheat in India.
The farm harvest prices in these states were analyzed along with the retail/wholesale prices of the
metropolitan cities. Only Uttar Pradesh farm harvest prices are found to be co-integrated with the Delhi
retail price. The process and causality adjustments are presented below-
For static analysis, Augmented Dickey Fuller (ADF) test were used. Both the Uttar Pradesh farm harvest
and Delhi retail price series were found to be non-stationary in the level (table 21), but stationary upon
Value
Critical Value
at 1%
Critical Value at
5%
Critical Value at
10%
Uttar Pradesh Farm Harvest Price -0.241 -3.569 -2.924 -2.597
Table 22. First Differential Variable- Augmented Dickey Fuller Test
Value
Critical Value
at 1%
Critical Value at
5%
Critical Value at
10%
Δ Uttar Pradesh Farm Harvest Price -8.027 -3.569 -2.924 -2.597
Uttar Pradesh FHP and Delhi RP 2
rank of the co-integration matrix. As indicated in the table 24, reject the hypothesis that there is no
max statistics are greater than their respective 5% critical values when r = 0, and are lesser when r=1.
Thus, the Uttar Pradesh farm harvest prices and Delhi retail prices are co-integrated.

Table 24. Johansen test for the co-integration of the price series
Co-integrating
Relationship
Rank Eigen
Values
Trace
Statistics
5% Critical
Value(trace)
Max
Statistics
5% Critical
Value(max)
Uttar Pradesh
FHP and Delhi
RP
r=0 15.8544 15.41 15.8440 14.07
r≤1 0.23904 0.0104* 3.76 0.0104 3.76
r≤2 0.00018
Table 25(a). Vector Error Correction Model for Wheat (Long run causality)
Co-integrating
Relationship
Dependent
Variable
Independent
Variable
Error
P-value
Uttar Pradesh
FHP and Delhi
RP
D_UttarPradesh
FHP
Adjustment -0.1170007 0.0463581 0.012
Constant 0.0988676 0.0340753 0.004
Constant 0.0323621 0.0820523 0.693
As indicated in the table 25(a), Uttar Pradesh farm harvest prices are found to be co-integrated with the
retail prices of Delhi. When farm harvest price in Uttar Pradesh is taken as a dependent variable, the
speed of adjustment in the farm harvest prices is found to be 11% per month due to the disequilibrium
caused by the retail prices in Delhi. The coefficient signs are negative showing convergence to the
equilibrium and are significant too. It implies it takes more than 8 months to fully adjust if there is no
additional shock in the Delhi retail price. But when the retail prices in Delhi is taken as a dependent
variable, the sign of the adjustment coefficient is positive implying divergence from the equilibrium.
Table 25(b) Vector Error Correction Model for Wheat (Short run Causalities)
Co-integrating
Relationship
Dependent
Variable
Independent
Variable
Error
P-value
Uttar Pradesh
FHP and Delhi
RP
D_UttarPradesh
FHP
Delhi RP LD -0.0682617 0.0526025 0.194
D_Delhi RP UP FHP LD -0.8455259 0.3109038 0.007
Note: LD means one Lagged difference. Also, D_variable means variable after first differencing.
Short run causality has been found running from the Uttar Pradesh farm harvest price to the Delhi retail
price (Table 25(b)). When, Delhi retail price is dependent variable, 84% is the correction that happens
per week in the Delhi retail price if there is disequilibrium caused by the farm harvest prices in Uttar
Pradesh in the short run.
Surprisingly, in case of wheat, there’s a single price series that are co-integrated. One must not forget
that the production of wheat in the country is more than its demand. This plays an important role in
setting up the prices. Also, for wheat, the government issues a Minimum Support Price which creates a
focal point and as a result, the farmers sell their produce near that price only.

5. Measure to improve price integration
As a measure to improve integration between the prices, introduction of e-mandis (e-markets) in
agricultural sector can be done. E-mandi is a real-time electronic auctioning platform offering online
trading which enables farmers, traders, processors, exporters and importers to buy and sell agricultural
commodities in a transparent manner. E-mandi scheme started in Karnataka state in year 2011, but
picked up in year 2012-13 with around thirteen APMCs. The author has studied the impact of e-mandis
on the prices and arrivals of Copra, Groundnut and Rice and there has been a positive impact of the
scheme. Establishment of e-mandis can result in the following manner:
 The farmers being able to choose from a wide range of traders (both offline and online) and sell
to the one with the right price for their produce.
 Any transaction made will be recorded. This will reduce the chances of middlemen adding any
extra cost or seeking double commission as a result, inducing transparency in the market
system.
 Competition can be increased due to large number of farmers selling the same product on the
portal leading to increase in business over time.
 Objectives like higher returns to farmers, lower transaction costs for buyers, and stable prices
and availability to consumers can be achieved.
6. Conclusion
The paper analyzed how the producers and retailers/wholesalers prices are co-integrated and at the
same time to show the direction of causality that exists between the producers and retailers prices in
the Indian market for Mustard, Groundnut, Potato, Wheat and Rice. The following observations are
found-
 Farm harvest prices are co-integrated with the nearby wholesale and retail prices i.e. geographic
co-integration is present but that too where the dependent variable was the farm harvest
prices.
 When the retail and wholesale prices are taken as dependent variable, the coefficient sign is
implying divergence from the equilibrium. This implies that the retailers and wholesalers are
dominant over price discrimination. The market structure is in favor of retailers and wholesalers,
which adversely affect the welfare of the farmers in the country.
 It is also observed that in case of Rice and Wheat, Minimum Support Price creates a focal point
of prices and farmers sell their produce around that price only.
 In case of Groundnut and Mustard, the market structure is not performing efficiently due to
bottlenecks like cartels, deceleration in yield, lack of uniformity in the tax rates, etc.
 Price co-integration in potato market is less due to the presence of large intermediaries in the
market.
References
1. Andaleeb Rahman (2015), Is Price Transmission in the Indian Pulses Market Asymmetric? Journal
of Quantitative Economics ISSN 0971-1554 Volume 13 Number 1- page 129-146.
2. Anil Sharma (2001), An analysis of the price behavior of the selected agricultural commodities, A
study for the Planning Commission.
3. Baulch, B. 1997. Transfer Costs, Spatial Arbitrage and Testing for Food Market Integration.
American Journal of Agricultural Economic, 79, 477-487.
4. Dickey, D. A. & Fuller, W. A.1981, Likelihood Ratio Statistics for Auto-Regressive Time
Series with a Unit Root, Econometrica, 49, 1057-1072.

5. Ioanna Reziti, Price transmission analysis in the Greek milk market, SPOUDAI Journal of
Economics and Business, Vol.64 (2014), Issue 4, pp. 75-86.
6. Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian
Vector Autoregressive Models. Econometrica 59: 1551-80.
7. Johansen,S. and Juselius. 1990, Maximum Likelihood Estimation and Inference on Cointegration
with application to Demand for Money, Oxford Bulletin of Economics and
Statistics,Vol.52,No.2,pp.169-210.
8. K.D. Chopde, M.M. Kadam AND V.O. Bondhare, 2015, Relationship between the farm harvest
price index and general price index of Amravati division, International Research Journal of
Agricultural Economics and Statistics -Volume 6 | Issue 1| page 106-112.
9. Lemma and Singh (2015), Testing for Price Co-Integration between Producers and Retailers:
Evidence from Ethiopian Milk Market, Scientific Research Publishing Inc.
10. Reddy and Reddy (2011), Integration of Wholesale Prices of Groundnut Complex, Research gate
publication.

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