The Spatial Integration of Potato Wholesale Markets of Uttarakhand in India
The Spatial Integration of Potato Wholesale Markets of Uttarakhand in India
The Spatial Integration of Potato Wholesale Markets of Uttarakhand in India
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ABSTRACT
This paper analyzes the spatial integration of potato markets in
Uttarakhand using monthly wholesale price for ten years. The maximum
likelihood method of cointegration developed by Johansen (1988) was
used in the study. The dynamics of short-run price responses were
examined using vector error correction model (VECM). The results
indicated that five potato markets reacted on the long-run cointegrating
equations while the speed of price adjustment in the short-run was
almost absent. Moreover, it was found that the longer the distance
between the markets, the weaker the integration was. To increase the
efficiency of potato markets in Uttarakhand, there is need to focus on
building an improved market information system. This system should be
able to disseminate timely market information about price, demand and
supply of produce to enable producers, traders and consumers to make
proper production and marketing decisions.
Keywords: Potato, wholesale, spatial, integration
INTRODUCTION
The nature of markets and their role in price determination are central to
economies. Spatial price behaviour in regional markets is an important indicator of
overall market performance. Typically, agricultural products are bulky and/or
perishable and area of production and consumption are separated; hence,
transportation is costly. To measure demand and supply, price discovery, and
structure of competition, geographical boundaries of a market are important. The
geographical integration of markets determines the extent to which weather risk is
shared over space by smoothing idiosyncratic price variations. Integrated markets
have limited price differences in time, form, and space when it comes to marketing
costs. Markets that are not integrated may convey inaccurate price signals that might
distort producers’ marketing decisions and contribute to inefficient product
*
Corresponding author email: rozni.mishra13@gmail.com
Received: 08.05.2016
SPATIAL INTEGRATION OF POTATO WHOLESALE MARKETS 21
A cointegration test does not require the examination of the univariate time-
series properties of the data. It confirms that all price series are non-stationary and
integrated in the same order. This is performed using the Augmented Dickey-Fuller
(ADF) test developed by Dickey and Fuller (1979, 1981). The test is based on the
statistics obtained from applying the Ordinary Least Squares (OLS) method to the
following regression equation:
……..(1)
Where: k = number of lagged difference terms required so that the error term
is serially independent. To determine whether Pt is non-stationary, the tau-statistic
( ), is used to test the unit-root null hypothesis H0: = 0. Since does not
have the usual properties of student-t distribution, there is need to use critical values
tabulated by Fuller (1979) for testing the level of significance. The lagged first
difference terms are included in the equations to take care of possible correlation in
the residuals. If the unit-root null is rejected for the first-difference of the series but
cannot be rejected for the level, then the series contains one unit root and is
integrated of order one, I (1). The lag length, at which the prices are mostly
integrated, was defined using VAR on the differenced series. In VAR analysis,
Akaike Information Criterion (AIC) and Schwartz Criterion (SC) were used to select
a suitable lag length. This is important because the inclusion of excessive lagged
terms will introduce the problem of multicollinearity. Meanwhile, very few lags will
lead to specification error. The lower the values of AIC and SC statistic, the better the
model is.
Cointegration test
If Pt denotes an (n×1) vector of I (1) prices, then the k-th order VAR representation
of Pt may be written as:
(t = 1, 2,…….., T) ……..(2)
The procedure for testing cointegration is based on the error correction (ECM)
representation of Pt given by:
....…..(3)
Where: = − (I− …..,− ); i = 1, 2, ….., k-1; = − (I− …..,− );
Each of is n×n matrix of parameters; is an independently distributed n-
dimensional vector of residuals with zero mean and variance matrix. Since Pt-k is I
(1), but ∆Pt and ∆Pt-i variables are I (0), equation (2) will be balanced if is I
(0). So, it is the matrix that conveys information about long-run relationship
among the variables in Pt. The rank of , r, determines the number of cointegrating
24 A. Kumar and R. Mishra
It can be seen that the null hypothesis of non-stationarity cannot be rejected for
wholesale prices in levels, but can be rejected in first differences. Therefore,
wholesale prices are non-stationary in their levels but stationary in first differences.
This implies that all wholesale price series contained a single unit root and were
integrated of order one. As such, taking first differences as variables in the model
eliminates the stochastic trend in the nominal series.
The cointegration tests were then conducted since the entire wholesale price
series were integrated of the same order. The integration of potato markets of
Uttarakhand was evaluated by investigating the long-run relationship between the
wholesale price series of potato in spatially separated locations of Uttarakhand.
The results of the multivariate cointegration tests for wholesale price series of
potato crop in Uttarakhand are reported in table 2. The main task was to examine the
rank or the number of cointegrating vectors for wholesale price series of potato.
Using the cointegration test available in EVIEWS, the rank of was determined.
The λ –max test, also known as ML ratio test, was more powerful than the trace test.
The λ–max test indicated the presence of 5 cointegrating vector for wholesale
markets of potato at 5 percent level of significance and the test defined the rank of
= 5. The above empirical evidence suggests that the wholesale price series of all the
markets of potato in Uttarakhand were cointegrated to a long-run equilibrium. The
farmers transfer their produce from one market to the other according to the price
changes. Meanwhile, arbitrage through trade ties their prices together.
26 A. Kumar and R. Mishra
+ 1.328 + 2.300
GAD - 2.719 JAS + 7.011 KIC - 0.730 RAM
KAS KHA
= (- 2.785)* (5.468)* (- 1.013)
(1.586) (3.091)*
CointEq (3)
- 2.624
+ 1.029 RIS - 4.955 SIT - 2.327 TAN + 5.534 VIK
RUD
(1.281) (- 6.095)* (- 4.039)* (2.867)*
(- 3.204)*
+ 1.292 +7.010
HAL + 1.081 JAS + 6.170 KIC - 5.379 RAM
KAS KHA
= (0.487) (2.842)* (- 3.368)*
(0.696) (4.248)*
CointEq (4)
- 6.680
+ 1.769 RIS - 7.818 SIT - 2.166 TAN + 5.504 VIK
RUD
(0.993) (- 4.338)* (- 1.694) (2.051)
(- 3.678)*
+ 0.133 + 0.692
HAR + 1.752 JAS - 3.114 KAS - 0.134 KIC
KHA RAM
= (3.319)* (- 7.452)* (- 0.197)
(0.338) (1.821)
CointEq (5)
+ 0.968
- 1.485 RIS + 0.535 SIT +0.890 TAN - 1.389 VIK
RUD
(- 3.502)* (1.247) (2.027) (- 3.019)*
(2.241)
Note: All the values in parentheses are t-values
*Significant at 1 percent level of significance and critical t-value= 2.32
0.153 0.120 0.069 0.092 0.094 - 0.009 0.005 0.041 - 0.026 0.005 0.025 -0.027 -0.016 0.006 0.027
Coint
(0.081) (0.047) (0.041) (0.024) (0.057) (0.005) (0.016) (0.018) (0.012) (0.023) (0.025) (0.016) (0.015) (0.013) (0.017)
Eq (1)
[1.896] [2.544] [1.677] [3.834] [1.631] [- 1.800] [0.361] [2.317] [-2.16] [0.237] [1.020] [-1.65] [- 1.06] [0.442] [1.608]
0.034 -0.709 -0.452 -0.589 - 0.442 -0.001 0.049 -0.262 0.099 0.005 0.025 - 0.027 - 0.016 - 0.136 - 0.314
Coint
(0.434) (0.253) (0.222) (0.129) (0.311) (0.029) (0.087) (0.096) (0.066 (0.023) (0.025) (0.016) (0.015) (0.075) (0.092)
Eq (2)
[0.078] [-2.79] [-2.04] [-4.55] [-1.42] [-0.04] [0.564] [-2.71] [1.484] [0.237] [1.020] [-1.65] [-1.06] [1.813] [-3.41]
0.052 - 0.236 0.048 0.142 0.227 0.033 0.012 - 0.309 - 0.177 -0.033 - 0.005 0.122 - 0.016 -0.136 -0.314
Coint
(0.331) (0.194) (0.176) (0.099)[1. (0.238) (0.022) (0.067) (0.074) (0.051) (0.124) (0.134) (0.089) (0.015) (0.075) (0.092)[-
Eq (3)
[0.157] [- 1.21] [0.282] 439] [0.953] [1.462] [0.184] [- 4.169] [- 3.45] [-0.26] [-0.03] [1.368] [-1.06] [1.812] 3.41]
0.183 -0.272 - 0.160 - 0.383 - 0.269 - 0.011 0.068 - 0.065 0.117 0.047 - 0.192 0.020 -0.009 -0.901 -0.236
Coint
(0.264) (0.154) (0.135) (0.078) (0.189) (0.018) (0.053) (0.059) (0.041) (0.075) (0.081) (0.054) (0.062) (0.045) (0.056)
Eq (4)
[0.693] [-1.76] [- 1.18] [- 4.85] [- 1.42] [- 0.637] [1.276] [-1.115] [2.882] [0.629] [-2.35] [-0.36] [-0.15] [-19.7] [-4.21]
-0.337 - 0.072 0.284 0.383 0.451 - 0.126 - 0.126 0.296 - 0.310 0.171 0.029 -0.015 -0.010 0.046 -0.011
Coint
(0.473) (0.276) (0.242) (0.141) (0.338) (0.032) (0.032) (0.095) (0.105) (0.135) (0.146) (0.097) (0.049) (0.081) (0.100)
Eq (5)
[- 7.119] [-0.26] [1.176] [2.713] [1.332] [1.332] [-3.86] [3.092] [-2.93] [1.258] [0.203] [-0.16] [-0.21] [0.562] [-0.11]
Note: All the figures in parentheses (….) are standard error and figures in [….] are t-values
29 A. Kumar and R. Mishra
ACKNOWLEDGEMENT
The financial assistant from National Institute of Agricultural Economics and
Policy Research (NIAP) and ICAR for its project “Network Project on Market
Intelligence” on which paper is based is duly acknowledged. The authors are grateful
to Dr. Ramesh Chand, Director, NIAP, Dr. P. S. Brithal, Principal Scientist and Dr.
Raka Saxena, Senior Scientist & PI, for their valuable suggestions.
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