Market integration occurs when prices among different locations or related goods follow similar pattern over a long period of time. Groups of prices often move proportionally to each other and when this relation is very clear among different markets it is said that the markets are integrated. In order to establish the long run equilibrium relation among the price series, it is very necessary to co-integrate them. Advanced econometric techniques like Johansen Co-integration Test, Granger Causality Test and Vector Error Correction Model has been used to study the integration of markets at state and national level. Before starting any statistical test there is a need to check whether the variables are stationary or not as it will help to avoid spurious results. Therefore, it is necessary to examine the time series properties of the variables. Further to establish the long-run equilibrium relation among the price series, it is necessary to co integrate them. The Johansen and Juselius (1990) co-integration test for the long run relationship among the price series were employed. Maximum likelihood ratio test statistics are proposed to test number of co-integrating vectors. The Granger causality test conducted within the framework of a vector auto regressive (VAR) model was used to test the existence and direction of long-run causal price relationship between the markets.
Market integration is one of the ways to focus on the issues of price fluctuation for the analysis of the market performance (Mukhtar and Javed 2007).
How to cite this article: Mohapatra, S. and Singh, J. (2021). Cointegration of Major Vegetable Markets in Punjab, India. Economic Affairs,66(2): 181-187.
Source of Support: None; Conflict of Interest: None
When there is a change in prices in one market, it will be transmitted to another market on a “one-toone” basis either instantly or over a number of lags (Sanogo and Amadou 2010). The degree, to which consumers and producers would get benefited, is mainly dependent on integration of domestic markets with world markets and that of regional markets with each other (Varela et al. 2012).
The integration between the markets can be easily calculated by the accurate estimation of strength and the speed of price transmission in between the markets across several regions of the nation (Ghafoor et al. 2009). The regional disparities in the extent of integration of markets are mainly caused by the regional variations found in different institutional structure of agricultural markets (Ghosh 2010; Ghosh 2013). Despite the geographical dispersion of regional markets, the long-run spatial linkages may be exhibited in prices across different market centers within and across the states. Therefore the evaluation of performances of spatially separated markets can be done by stating the relationships between the prevailing prices in those markets. Co-integration suggests that all the exchange locations are integrated and that prices provide relevant market signals. Thus, it is very important to examine the spatial integration among major vegetable markets as the results will have important implications for agricultural price policy.
LITERATURE REVIEW
Methodology
Here in this study the major vegetables being produced in Punjab were selected based on their share in total vegetable production of the state i.e. Potato, Tomato, cauliflower and peas accounted for 55.4, 4.5, 5.5, and 5.4 per cent respectively of total vegetable production during TE 2019-20 contributing around 70.8 per cent of total production of vegetables in the state. The selected markets in Punjab for the study were Ludhiana, Amritsar and Jalandhar based on annual market arrivals and data availability. In addition, one national market viz. Delhi was also taken as sample market. Time series data regarding monthly prices and arrivals in the major markets were collected from various secondary sources like agricultural produce market committees of Amritsar, Jalandhar, Ludhiana and New Delhi, www.nhrdf.org, www.indiastat.com, www.fao.org, www.agmarknet.nic.in and www. usda.gov etc.
Co-integration Test
![]()
The degree of deviation from the long run equilibrium relationship is explicated by co integration test by the non-stationary series. It is the yoke between integrated processes and steady state equilibrium and hence furnishes the relevant theoretical framework for examining kinetics of instantaneous changes in a pair of series along with their valuable long run information. Once it was confirmed that all of the price-series were stationary at same order of differences, the co-integration of markets were tested by Johansen maximum-likelihood techniques. The Johansen and Juselius (1990) co-integration test for the long run relationship among the price series were employed. Maximum likelihood ratio test statistics are proposed to test number of cointegrating vectors. The null hypothesis of at most r co-integrating vectors against a general alternative hypothesis of more than r co-integrating vectors is tested by trace statistics. The null hypothesis of r co integrating vector against the alternative hypothesis of r + 1 is tested by Maximum-eigen-value-statistic. The number of so integrating vectors indicated by the tests is an important indicator of the extent of co-movement of prices. An increase in the number of co-integrating vectors implies an increase in the strength and stability of price linkages.
Granger Causality Test
Granger’s causality technique was used to reveal the causal relationship between the prices series in selected vegetable markets. The Granger causality test conducted within the framework of a vector auto regressive (VAR) model was used to test the existence and direction of long-run causal price relationship between the markets. An autoregressive distributed lag (ADL) model for the Granger- causality test was specified as below:
Xt=∑i=1naIYt−i+∑j=1nβjXt−j+u1tYt=∑i=1nλIYt−i+∑j=1nδjXt−j+u2t
Where, t is the time period; u1t and u2t are the error terms and X and Y are the prices series of different markets.
CONCLUSION
The above study has concluded that among the selected four vegetables, the co-integration tests indicated that all of the four selected markets were integrated and had long run equilibrium with each other with an exception in tomato where there were found only 3 co integrating vectors. The Granger Causality Test was conducted for all of the four vegetables. In case of potato the Jalandhar market was well integrated with other selected markets of Punjab. However, it was the national market (New Delhi) which influenced the prices of potato in all of the selected markets of the state unidirectionally. In case of tomato, the Amritsar market was well integrated with other selected markets of Punjab and it was the New Delhi market which influenced the prices of tomato in all of the selected state markets unidirectionally except for Amritsar market where it had a two way price relationship. In case of cauliflower, the price in Jalandhar and Ludhiana market showed unidirectional causality in price transmission with price of Amritsar market. The cauliflower price in Ludhiana market showed bidirectional causality in price transmission with cauliflower price of Jalandhar. The New Delhi market showed unidirectional causality in price transmission with cauliflower price of Ludhiana and Jalandhar markets whereas bidirectional causality with cauliflower price of Amritsar market. For peas the national market was well integrated with other selected markets of Punjab. Overall it can be concluded that all of the selected vegetable markets of Punjab were well integrated among each other but most of them were influenced by New Delhi market in unidirectional way i.e. the price change in selected state markets were only being influenced by the price change in New Delhi market but not influencing the price change in New Delhi market.
Policy Implications
The efficiency of vegetable marketing has not yet reached an optimal level as they are not spatially well integrated with one another in all the cases. The reasons for this might be poor market intelligence, slow transition of the information between the markets and poor physical infrastructure. The policy intervention calls for strengthening market intelligence wing in all markets along with the establishing of online marketing system through computerization and networking. The market price information should be made available to the stakeholders through electronic and print media. Development/strengthening of market infrastructure including transportation and communication facilities are the need of time in order to fully integrate the market prices.