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Market integration and prices of fruit crops such as apple play an important role in determining the production decisions of apple farmers. In this context, the present study examines the degree of spatial market integration and price transmission across five major apple markets of the country, viz. Shimla, Chandigarh, Delhi, Bengaluru and Mumbai by adopting Johansen’s Cointegration Test, Grangers Causality and Impulse Response Function. The outcomes of the study strongly buttress the cointegration and interdependence of the apple markets in India. To get additional information on whether and in which direction price transmission is occurring between market pairs, Ganger’s Causality Test has been used, which has confirmed Shimla to be the price determining market as it has causal relations with all the selected markets. The Impulse Response Function supported that all the selected markets responded well to standard deviation shock given to any other market. The major implication of the study is further improvement in market integration situation through dissemination of price and arrival data efficiently and developing communication means with in the markets by the government.

The wholesale apple markets in India are well integrated and have long-run associations across them. The impulse response function revealed that the standard deviation shock given to any market is transmitted quickly to all the other markets.

The study has culled monthly wholesale price (₹/Quintal) data from five major apple markets, namely, Shimla, Chandigarh, Delhi, Bengaluru, and Mumbai, for a period of thirteen years (January 2005 to December 2017). All the relevant secondary data have been collected from the official website of the National Horticulture Board. The analytical tools used in the study are described below.

The regression analysis of non-stationary time series produces spurious results, which can be misleading (

Where, _{n}_{–1)} – _{n}_{–2}); α_{0} = constant or drift; _{t} = pure white error term.

The maximum likelihood (ML) method of cointegration is applied to check long-run wholesale prices relation between the selected markets of India (

Where π denotes the (

Where, Γ

Both Γ_{t} is the _{t}_{t}_{t}_{t}_{t}

(

Where ^{th} largest squared eigenvalue obtained from the matrix П and the

The notion of the Granger causality is that if the two variables are integrated of order one, i.e., I(1), then the most accepted way to know the causal relationship between them is the Granger Causality proposed by (

Where _{t}_{A}and µ_{B} are the error terms of both the model.

The above-mentioned two equations with respect to market

_{11}, δ_{12} …δ_{n}] ≠ 0 and [∂_{21}, ∂_{22},….. ∂_{n}] = 0

Expression 1 indicates the unidirectional causality from

_{11}, δ_{12} …δ_{n}] = 0 and [∂_{21} ,∂_{22},….. ∂_{n}] ≠ 0

Expression 2 indicates the unidirectional causality from

_{11}, δ_{12} …δ_{n}] ≠ 0 and [∂_{21} ,∂_{22},…..δ_{n}] ≠ 0

Expression 3 indicates the bidirectional causality between

When the sets of market A and market B coefficients are statistically significant, it is said to be Feedback, or bilateral causality (

Granger causality test provides only the direction of causality for the selected time span. However, it fails to demonstrate the effect of shock on future values. The impulse response function shows a specific point of time _{0}, that a shock originates from one equation and proceeds through the system (

Where, _{t+k}_{t–}_{1} is the history.

The descriptive statistics of monthly wholesale prices of apples for selected markets from January 2005 to December 2017 are presented in

The perusal of the table reveals that the minimum average prices varied from ₹ 1010.69 to ₹ 2183.29 per quintal in Shimla and Delhi markets, respectively, whereas the maximum average prices ranged between ₹ 11849.95 to ₹ 15191.52 per quintal in Chandigarh and Shimla market during the period of study. The highest average wholesale price was found in the Delhi market (₹ 6570.50/qtl) and the lowest in Chandigarh (₹ 3841.60/qtl) market. The analysis of the coefficient of variations showed that the highest variation was observed in the Shimla market (58.38%) followed by the Mumbai market (55.60%). The lowest variation in monthly wholesale price was found in Delhi (37.32%) and Bengaluru markets (38.66%).

The market integration among the selected apple markets was analyzed using Johansen’s cointegration method, which necessitates that the time series should be integrated at order one, i.e., I (1). Therefore, the standard Augmented Dickey-Fuller unit root test (ADF) was applied to determine the order of integration, and results have been presented in

The results of Johansen’s maximum likelihood approach (maximum eigen value and trace test) are given in

Descriptive statistics of monthly wholesale prices for selected markets

ADF unit root test results for wholesale prices of apple (including intercept and no trend as exogenous)

Joint cointegration in selected apple markets of India.

The causal relation between the selected price series of apple markets was examined through Granger causality technique. Granger’s causality shows the direction of price transmission between two markets and related spatial arbitrage, i.e., physical movement of a commodity to adjust the price differences (Gafoor

The results of Granger’s causality revealed that unidirectional causality was found between market pairs; Shimla-Chandigarh, Delhi-Chandigarh wholesale markets, meaning that a price change in the former market in each pair Granger cause price change in the latter market and same is not feedbacked by the price change in the former market in each pair. There exists bidirectional causality between Mumbai-Shimla, Delhi-Shimla, Bengaluru-Shimla, and Bengaluru-Chandigarh. In these cases, the former market in each pair Granger causes the wholesale price formation in the latter market, which in turn provides the feedback to the former market as well. Further, four market pairs, Delhi-Mumbai, Chandigarh- Mumbai, Bengaluru-Mumbai, and Bengaluru- Delhi, were found to have no direct causality between them.

Therefore, it was further concluded from the table that since all the four F-statistics for the causality tests of wholesale prices in the Shimla market are statistically significant. Therefore, the Shimla market is holding a key position in price determination in other markets.

Pair-wise granger causality in major apple markets

Impulse response function was used to determine the relative strength of causality effect beyond the selected time span, as causality tests are inappropriate because these tests are unable to show how much feedback exists from one variable to the other beyond the selected sample period (Rehman and Shahbaz, 2013). The best way to interpret the implications of the models for the patterns of price transmission, causality, and adjustments is to consider the time paths of prices after exogenous shock i.e., impulse response. The impulse response function explicates the responsiveness of one of the endogenous variables due to the shock on the current and future values of all the other endogenous variables in the VAR system. The shock affects the variable itself and is transmitted to the rest of the explanatory variables (Bhanumurthy

The results of impulse response function in Chandigarh market were almost similar to those of Shimla market, i.e., the prices dropped immediately during the past 4 months but stabilized thereafter. In Delhi, market price shock of one-unit standard deviation resulted in an immediate decline in prices which then stabilized after 4 to 5 months. The wholesale prices in the Delhi market were observed to be inversely related to those of the Mumbai market for the initial 5 months. In the Bengaluru market, the results of the impulse response function revealed a sharp decline in prices. Similarly, the wholesale prices in the Mumbai market were observed to be inversely related to Bengaluru up to the first 3 months. In the Mumbai market, prices reacted immediately by going down and then stabilized after 4 to 5 months. The overall results of the impulse response function explicate that the responses exhibit large magnitudes over 2600 unit standard deviations. Moreover, the price information process is quick for all the selected apple markets as they respond immediately to a shock that seems to fade away in 4 to 5 months.

Franger causality direction between market pairs impuse response function

Response of Shimla to cholesky one standard deviation shock

Response of Chandigarh to cholesky one standard deviation shock

Response of Delhi to cholesky one standard deviation shock

Response of Bengaluru to cholesky one standard deviation shock

Response of Mumbai to cholesky one standard deviation shock

The results of the overall cointegration test indicate that different wholesale apple markets in the country are well integrated and have long-run association across them. Granger causality test has indicated that, unlike the other market pairs, four market pairs, namely, Delhi-Mumbai, Chandigarh-Mumbai, Bengaluru-Mumbai, and Bengaluru-Delhi, have no causality direction on price formation between them. The impulse response function, revealed that the standard deviation shock given to any market is transmitted quickly to all the other markets. Therefore, the overall results of the study suggest that wholesale markets for apple are strongly integrated, although geographically isolated. The major implication of the study is further improvement in the market integration situation through the dissemination of price and arrival data efficiently and developing communication means within the markets by the government.