Application: These results suggest for market integration and competition rather than collusion in Potato markets in Punjab, Pakistan, and provide little justification for government intervention designed to improve competitiveness or to enhance market efficiency. Findings: The empirical results show as major Potato markets are integrated, i.e., there exist the law of one price. These findings also supported the results of the Granger causality analysis. The results of pairwise granger casualty show the direction of price transmission between the selected Potato markets in Punjab, Pakistan. Methods/Statistical analysis: The study estimated the data by using the Johansen Co-integration (JJ) technique, vector error correction model, and Vector autoregressive (VAR) model. Background/Objectives: This study investigated market integration and asymmetric price transmission in the potato markets for the seven major Potato markets, i.e., Okara, Faisalabad, Sargodha, Lahore, Gujranwala, Multan, and Rawalpindi in the province of Punjab, Pakistan.

The concept of spatial market integration retained with the contribution of Jasdanwalla

Price signaling provides an analytical tool to empirically investigate the hypothesis of spatial market integration. Jasdanwalla

Potato is the most widely produced and consumed vegetable crop due to its nutrient capacity, potentials for diverse uses as well as easy availability to low-income consumers in the world

To estimate the market integration through price transmission study uses the monthly wholesale price data January 2007 to November 2018 of potato for seven major markets Okara (Okara), Sargodha (SAR), Lahore (LHR), Faisalabad (FSD), Gujranwala (GUJ), Multan (MUL), and Rawalpindi (RWP)) of Punjab Province. Okara was set as a base market for the analysis and the data is taken from the Agricultural Market Information System (AMIS), Government of Punjab Pakistan

The study used the augmented Dickey-Fuller (ADF) test to test the stationarity problem of individual series used in the study, with and without a deterministic trend

ADF equation with a deterministic trend in case of Okara market:

Market integration is the long-term phenomenon, markets said to be integrated if there exists co-integration in the long term. Johnson co-integration method presented by the Johansen

Trace statistics of the stochastic matrix

Eigenvalue statistics of the stochastic matrix

Trace and These statistics are to determine the number of co-integrating equations. Johansen co-integration is based on the examination of

Where

Where

The long Run co-integration equation of the study

Short Run co-integration Equation

Granger causality provides an important implication of co-integration. According to Granger

Where

Vector autoregressive (VAR) mechanism can also further be used to verify the dynamics of price transmission mechanism among selected potato markets. General structure of the vector autoregressive (VAR) model for Cotton Lint Production:

Where are the parameter matrices, and assume to be normally distributed with zero mean and constant variance.

The VAR with only lags

The order p is selected by minimizing the lag order via Akaike information criterion (AIC)

And

The concept of market integration was introduced by Jasdanwalla

Markets | OKARA | FSD | LHR | RWP | SAR | MUL | GUJ |
---|---|---|---|---|---|---|---|

Okara | 1.00 | ||||||

FSD | 0.97 | 1.00 | |||||

LHR | 0.96 | 0.98 | 1.00 | ||||

RWP | 0.95 | 0.96 | 0.97 | 1.00 | |||

SAR | 0.95 | 0.97 | 0.95 | 0.93 | 1.00 | ||

MUL | 0.97 | 0.97 | 0.96 | 0.95 | 0.97 | 1.00 | |

GUJ | 0.96 | 0.98 | 0.98 | 0.97 | 0.96 | 0.97 | 1.00 |

Source: Author self-estimation, using EViews 10

Harriss

Before estimating the co-integration, it is a prerequisite to investigate the order of stationarity of the time-series data. The examined the stationarity order, using the Augmented Dickey-Fuller test (ADF) with drift only and with drift and trend. Table 2 depicted the unit root results of the Augmented Dickey-Fuller test (ADF) at the level and first difference. The estimated results of

Variables | Log Level Form | Log Level Form | First Difference Form | |||||

Without Trend | Pro | With Trend | Pro | Without Trend | Pro | With Trend | Pro | |

Okara | -2.89 | 0.05 | -2.82 | 0.19 | -10.00 | 0.00 | -10.09 | 0.00 |

SAR | -6.70 | 0.00 | -7.58 | 0.00 | -10.00 | 0.00 | -10.09 | 0.00 |

LHR | -2.68 | 0.08 | -2.85 | 0.18 | -2.90 | 0.05 | -3.90 | 0.01 |

FSD | -2.55 | 0.11 | -2.41 | 0.38 | -3.70 | 0.01 | -3.84 | 0.02 |

RWP | -3.45 | 0.07 | -3.36 | 0.06 | -5.00 | 0.00 | -5.45 | 0.00 |

MUL | -2.72 | 0.07 | -2.53 | 0.31 | -2.98 | 0.04 | -3.14 | 0.02 |

GUJ | -3.02 | 0.04 | -2.80 | 0.20 | -11.26 | 0.00 | -11.32 | 0.00 |

Source: Author self-estimation, using EViews 10.

The estimated results of the ADF unit root test shows all the series are integrated order one. So, we can apply the Johnson co-integration technique to estimate the market integration through the long-run relationship between the price series of selected markets of Punjab. Before estimating the long-run relationship, it is preconditioned to select the lag length, using a vector autoregressive (VAR) model.

Lag | LR | FPE | AIC | SC | HQ |
---|---|---|---|---|---|

0 | NA | 1.42E-12 | -7.41544 | -7.262564 | -7.353318 |

1 | 416.7865 | 1.04E-13 | -10.0342 | -8.811190* | -9.537223* |

2 | 105.3884 | 8.89e-14* | -10.19253* | -7.89939 | -9.260702 |

3 | 56.83355 | 1.13E-13 | -9.966773 | -6.603504 | -8.600095 |

4 | 63.11216 | 1.33E-13 | -9.837088 | -5.403688 | -8.035558 |

5 | 65.36344 | 1.48E-13 | -9.775533 | -4.272002 | -7.53915 |

6 | 66.0448 | 1.59E-13 | -9.775185 | -3.201523 | -7.10395 |

7 | 80.01884* | 1.40E-13 | -10.0086 | -2.364807 | -6.902514 |

8 | 33.58276 | 2.18E-13 | -9.713946 | -1.000022 | -6.173007 |

* indicates lag order selected by the criterion

LR: sequentially modified LR test statistic (each test at 5% level); FPE: Final prediction error; AIC: Akaike information criterion; SC: Schwarz information criterion; HQ: Hannan-Quinn information criterion

Co-integration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
r = 0*
0.41
288.45
125.62
0.00
r ≤ 1*
0.35
214.49
95.75
0.00
r ≤ 2*
0.32
154.32
69.82
0.00
r ≤ 3*
0.25
101.21
47.86
0.00
r ≤ 4*
0.19
62.11
29.80
0.00
r ≤ 5*
0.16
32.88
15.49
0.00
r ≤ 6
0.06
8.82
3.84
0.00
Co-integration Rank Test (Maximum Eigenvalue))
Hypothesized
Max-eigenvalue
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
r = 0*
0.41
73.96
46.23
0.00
r ≤ 1*
0.35
60.18
40.08
0.00
r ≤ 2*
0.32
53.10
33.88
0.00
r ≤ 3*
0.25
39.10
27.58
0.00
r ≤ 4*
0.19
29.23
21.13
0.00
r ≤ 5*
0.16
24.06
14.26
0.00
r ≤ 6
0.06
8.82
3.84
0.00

Trace and Max-eigenvalue test indicates 6 co-integrating eqn(s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

The alternative hypothesis of trace statistics r=k, while Alternative hypothesis of Max-eigenvalue statistics r=r*+1

**MacKinnon-Haug-Michelis (1999) p-values

Markets Pair
Hypothesized
Trace
0.05
Hypothesized
Max-Eigen
0.05
No. of CE(s)
Trace Statistic
Critical Value
Prob.
Max-Eigen Statistic
Critical Value
Prob.
Okara_MUL
r = 0*
59.88
15.49
0.00
37.26
14.26
0.00
r ≤ 1*
22.62
3.84
0.00
22.62
3.84
0.00
Okara_FSD
r = 0*
67.35
15.49
0.00
36.77
14.26
0.00
r ≤ 1*
30.58
3.84
0.00
30.58
3.84
0.00
Okara _LHR
r = 0*
51.42
15.49
0.00
41.48
14.26
0.00
r ≤ 1*
9.94
3.84
0.00
9.94
3.84
0.00
Okara _GUJ
r = 0*
51.60
15.49
0.00
40.08
14.26
0.00
r ≤ 1*
11.51
3.84
0.00
11.51
3.84
0.00
Okara _SAR
r = 0*
64.59
15.49
0.00
39.39
14.26
0.00
r ≤ 1*
25.20
3.84
0.00
25.20
3.84
0.00
Okara _RWP
r = 0*
62.45
15.49
0.00
48.91
14.26
0.00
r ≤ 1*
13.54
3.84
0.00
13.54
3.84
0.00

* denotes rejection of the hypothesis at the 0.05level

The alternative hypothesis of trace statistics r=k,while Alternative hypothesis of Max-eigenvalue statistics r=r*+1

**MacKinnon-Haug-Michelis(1999) p-values

Relationship of Okara with other markets
FSD
GUJ
LHR
MUL
RWP
SAR
Coefficient
0.76
0.82
0.73
-0.29
0.98
3.34
Stander error
0.06
0.07
0.07
0.22
0.06
0.34
T-Statistics
13.32
12.39
10.28
-1.36
15.18
9.76

Relationship with Okara
Coefficient
T-value
P-value
FSD
-0.80
-6.33
0.00
LHR
-0.69
-6.63
0.00
GUJ
-0.77
-6.63
0.00
MUL
-0.21
-5.90
0.00
RWP
-0.86
-7.24
0.00
SAR
0.08
3.75
0.00

The study used the Granger causality to investigate the law of one price (LOP) and provide implication of co-integration among the selected potato markets of Punjab. Because if there exist co-integration between the prices of two markets, then there must exist the causality at least one direction. Granger causality examines the direction of price transmission between the markets.

Pairwise Granger Causality
F-statistics
P-value
Direction
Okara → FSD
9.57
0.00
Uni-Directional
FSD → Okara
1.42
0.24
Okara → LHR
7.52
0.00
Bi-Directional
LHR → Okara
6.52
0.00
Okara → MUL
5.28
0.00
Uni-Direction
MUL→ Okara
0.53
0.66
Okara → GUJ
8.98
0.00
Bi-Directional
GUJ → Okara
3.79
0.01
Okara → SAR
15.80
0.00
Uni-Directional
SAR→ Okara
1.62
0.19
Okara → RWP
7.43
0.00
Bi-Direction
RWP→ Okara
4.98
0.00

The paper investigated the hypothesis of market integration in the selected major potato markets in Punjab using the correlation matrix method and Johnson co-integration analysis. To estimate the phenomena of integration in the potato markets prices study used the monthly wholesale price data from January 2007 to November 2018. The estimated results of the study show that there exists a strong positive correlation between the seven selected potato markets (Okara, Faisalabad, Lahore, Multan, Gujranwala, Sargodha, and Rawalpindi) in Punjab. The empirical result of the Johnson co-integration test also confirms the existence of the law of one price in the six major potato markets out of seven potato markets in Punjab.

The pairwise elasticities also confirmed market price linkages are important in economic analysis through the speed of adjustment in the long run. The estimated results show a one percent increase in the price of potato in the Okara market increases the 0.76, 0.82, 0.73, 0.98, and 3.34 percent in Faisalabad, Gujranwala, Lahore, Rawalpindi, and Sargodha markets respectively in long run. The results of pairwise granger casualty show the direction of price transmission between the selected Potato markets in Punjab. The empirical results of the study reported the high degree of market integration in major Potato markets and are consistent with the view that Potato markets in Pakistan are quite competitive and provide little justification for government intervention designed to improve competitiveness or to enhance market efficiency.