The document analyzes the extent of market integration between rural and urban oil palm wine (OPW) and raphia palm wine (RPW) markets in Southeast Nigeria. Primary time series data on retail prices was collected from these markets over four months. Cointegration, error correction, and Granger causality tests were used to analyze the data. The results found that palm wine prices across all markets were integrated in the long run, though RPW prices showed better integration than OPW prices. For OPW, past rural prices did not cause current urban prices, while past urban prices did cause current rural prices. For RPW, past rural prices caused current urban prices, but not vice versa. The study concludes that

1. Market Integration and Price Transmission between Rural and Urban Oil and Raphia Palm Wine Markets in South East, Nigeria
Market Integration and Price Transmission between Rural
and Urban Oil and Raphia Palm Wine Markets in South East,
Nigeria
*1Nwankwo, T.N., 2Ozor M. U., 3Ugwumba C.O.A.
1Department of Agricultural Economics and Extension, Nnamdi Azikiwe University, P.M.B, 5025 Awka, Anambra State,
Nigeria
2,3Department of Agricultural Economics and Extension, Chukwuemeka Odumegwu Ojukwu University, Igbariam Campus,
P.M.B, 6059, Awka, Anambra State, Nigeria
Market integration analysis is important in explaining the performance of markets in response to
changes in the prices of commodities. A well-integrated market system is essential to household
food security especially in both food deficit regions of the country. Flexible prices are thought to
be responsible for efficient resource allocation and price transmission is useful in integrating
markets both vertically and spatially. The study examined the extent of market integration of oil
palm wine (OPW) and raphia palm wine (RPW) rural and urban markets’ prices in South East,
Nigeria. Multi-stage sampling method was used to select 240 respondents (120 wholesalers and
120 retailers). Primary time series data of retail prices of oil palm wine and raphia palm wine were
collected every four local market days. Data were analyzed using co-integration, error correction
and Granger causality tests. Results of the analyses indicated that palm wine prices in all markets
in the area were integrated but RPW prices indicated better integration than OPW prices.
Furthermore, the price causality test revealed that past rural prices of OPW did not Granger cause
its current urban prices, while the past urban prices of OPW Granger caused its current rural
prices. On the other hand, past rural prices of RPW Granger caused its current urban prices
whereas the past urban prices did not Granger cause its current rural prices. Government at
Federal, State and Local levels should construct new link roads and rehabilitate the existing ones
to ensure proximity of markets.
Key words: Market integration, oil and raphia palm wines, Southeast Nigeria.
INTRODUCTION
Markets can be defined with respect to locations, seasons
and products. The most common factor with which
markets can be integrated is price of the product. Thus, the
principle of market integration is hinged on the “Law of One
Price”. Market integration refers to the co-movement of
prices and/or flows between markets. More generally, it
explains the relationship between two markets that are
spatially or temporarily separated (Ddungu, Ekere,
Bisikwa, Kawooya, Okello Kalule and Biruma, 2015).
According to Bopape and Christy (2002), there are three
forms of market integration: (1) integration across space;
(2) integration across product; and (3) integration across
time. Markets are integrated across space if, when trade
takes place between them, price in the importing market
equals price in the exporting market plus transportation
and other costs of moving the product between the two
markets. When integrated across product form, markets
are vertically integrated and the price differential between
two related commodities should not exceed transportation
and processing costs. Markets are said to be integrated
across time (inter-temporally integrated) when the
*Corresponding Author: Temple Nwankwo. Department
of Agricultural Economics and Extension, Nnamdi Azikiwe
University, P.M.B. 5025, Awka, Anambra State, Nigeria.
Email: templenwankwor@gmail.com
Co-Authors Email: 2
ozormaurice@yahoo.com
3
profcele2014@gmail.com
Research Article
Vol. 6(1), pp. 200-205, May, 2019. © www.premierpublishers.org, ISSN: 2167-0470
International Journal of Agricultural Marketing

2. Market Integration and Price Transmission between Rural and Urban Oil and Raphia Palm Wine Markets in South East, Nigeria
Nwankwo et al. 201
expected price differential does not exceed the cost of
storage. The study of market integration can suggest to the
producer as to where, when and how much to sell, which
in turn will have a bearing on their production strategies
and hence resource allocation.
When two series are stationary of the same order and co-
integrated, one can proceed to investigate for causality.
This is because at least, one Granger-causal relationship
exists in a group of co-integrated series. The basic logic of
the Granger causality procedure is that current price levels
predict future price levels. Researchers have therefore
generally relied on the use of historical information for
making forecasts about future outcomes, with the term,
“Granger-cause” being employed to link past and present
events to future events. Therefore, Granger causality
provides additional evidence as to whether, and in which
direction, price integration and transmission are occurring
between two price series or market levels. Palm wine
markets have experienced high price volatility and require
urgent need to understand the relationship and key
characteristics of long-term commodity price movements
among the markets with respect to the conditions under
which the recent markets operate.
MATERIALS AND METHODS
The study area is the Southeast geopolitical zone of
Nigeria. The States in the Southeast geopolitical zone are
Abia, Anambra, Ebonyi, Enugu, and Imo, States.
Southeastern Nigeria lies between latitude 400 501N to 700
101N and longitudes 600 401E to 800 301E. It spreads over
a total area of 26,982.67km2, representing 8.5% of the
nation’s total land area with a total population of
16,395,555 million (National Population Commission
(NPC), 2006). The study population comprised all the oil
palm wine and raphia palm wine marketers in South East,
Nigeria. Multi-stage, purposive and random sampling
techniques were used to select 120 respondents for the
study. In stage I, three states (Anambra, Imo and Enugu)
were purposively selected from the five States in South
East, Nigeria. The selection was based on the degree of
concentration of palm wine sellers evidenced from pre-
survey study and the familiarity of the researcher with
terrains of the selected states. Stage II involved purposive
selection of two LGAs from each State (six LGAs), and two
palm wine markets from each of the selected LGAs (twelve
markets). Some of the markets are rural while others are
urban. Rural markets are those located within the area of
production while urban markets are considered as those
located outside the area of production. Finally, a random
selection of five wholesalers and five retailers was made
from each market respectively. Therefore, the total number
of respondents from each market was twenty while the
total sample size was 240 respondents. Time series data
were used for the study. In order to run the market
integration analysis, four days local market prices for OPW
and RPW in selected markets in the States were collected
for a period of four months and this was used to determine
the integration of palm wine markets’ prices in the area.
These four days local market prices were used because of
the unavailability of statistics from which secondary price
series information can be sourced.
Co-integration and error-correction model
Due to non-stationary nature of many economic time
series, the concept of co-integration has become widely
used in econometric analysis. The concept of co-
integration is related to the definition of a long-run
equilibrium. The fact that two series are co-integrated
implies that the integrated series move together in the long
run. Testing co-integration of two price series is sometimes
believed to be equivalent to detecting long-run market
integration. The co-integration-testing framework has been
well developed by Engle and Granger (1987); and
Johansen (1988). Co-integration analysis was used to
determine the extent of market integration of oil palm wine
and raphia palm wine. Co-integration analysis was carried
out in three steps. To use the co-integration procedure,
several steps needed to be carried out on the price series
under examination. Before proceeding to the different
steps, consider the following basic relationship between
two markets.
tjtoit epbaP ++=
Where:
Pit and Pjt, = price series in two markets i and j at time’t’;
b = the coefficient;
a and b = parameters to be estimated; and
et= residual term assumed to be distributed identically and
independently at time t.
The first step is to pre-test the integrating orders of the
series, that is, each price series is tested for the order of
econometric integration, that is, for the number of times the
series need to be differenced before transforming it into a
stationary series. A series is said to be integrated of order
‘d’, I (d), if it has to be differenced ‘d’ times to produce
stationary series.
The most commonly employed test for stationary and
order of integration is the Augmented Dickey Fuller (ADF)
test, given as:
tit
n
k
kitoit ekpbpbap +−+−+= =1
1
The test statistic (t-statistic) on the estimated coefficient of
Pit-1 is used to test the null and alternative hypotheses. The
null hypothesis is that the series Pit is integrated of order 1
and the alternative hypothesis is that the series is of order
0. In short, H0: Pit is I (1) Versus H1: Pit is I (0). If the t-
statistic value for the coefficient b0 is greater in absolute
value than a critical value given by the ADF critical value,
then the null hypothesis is rejected, and the alternative
hypothesis of stationary is accepted. If the null hypothesis
is not rejected, then one must test whether the series is of

3. Market Integration and Price Transmission between Rural and Urban Oil and Raphia Palm Wine Markets in South East, Nigeria
Int. J. Agric. Mark. 202
order of integration higher than just 1, possibly of order 2.
In this case the same regression equation is applied to the
second difference, that is, the test will be repeated by using
(RPit in place of Pit) that is, by applying the regression:
+−++= =
−
kPbPbaP it
n
k
kitit
2
1
11
2
Where:
∆2 Pit = denotes second difference.
The ADF statistic therefore, tests the following
hypotheses: H0: Pit is I (2) versus H1: Pit is I (1),
respectively. If the ADF statistic is not large and negative,
H0 is not rejected.
The second step is to estimate the long-run equilibrium
relationship of the two time series, which are of the same
order of integration (co-integrating regression), that is;
titoit ePbaP +==
Where:
et = the deviation from equilibrium and this equilibrium
error in the long-run tends to zero. This equilibrium error of
the co-integration equation has to be stationary for co-
integration between two integrated variables to hold good.
Hence, the third step is to recover the residual from the co-
integration regression and to test their stationarity. The
most commonly employed test for stationary is the ADF
unit root test. To perform the ADF test, the autoregression
equation must be estimated. The equation is stated as:


=

−+−

++=
n
k
tktktt eeaeae
1
111
Where:
𝑒𝑡
∧
= the first stage estimate of the residual for the co-
integrating regression; and
et = the error term.
The null hypothesis of the ADF test is a1 = 0. Rejection of
the null hypothesis is that the series are non-stationary in
favour of the negative one sided alternative hypothesis.
This means the two series are co- integrated of order (1)
provided both series are I (1), that is, the ADF test statistic
is the t-ratio of the coefficient of 𝑒𝑡−1
∧
The fourth step involves the dynamic error correction
representation of the co-integrated variables. If two
variables are integrated of the same order and thus can be
co-integrated, then there exists an error correction
representation of the variables where the error corrects the
long-run equilibrium. The dynamic model is obtained by
introducing the residuals into the system of variables in
levels.
Therefore, the Error Correction Model (ECM) is
represented by the formula:
=
−−−− ++++−+=
n
k
tkjtkkitkjtitit ePPPjtaPbPaaP
1
21110120 )()( 
It is evident from the above equation that the disequilibria
in the previous period (t-1) are an explanatory variable.
Here it can be said that if in period (t-1), Pj exceeds the
equilibrium price, the changes in pi will lead the variable to
approach the equilibrium value. The speed at which the
price approaches equilibrium depends on the magnitude
of a2. Hence, the expected sign of a2 is negative. This test
confirms that the errors correct to the equilibrium in the
long run. Therefore, the final test of market integration can
be performed by testing the restriction a1 = 1, a2 = -1, and
the coefficients of any lagged terms be zero using F-
statistic.
RESULTS AND DISCUSSION
Market Integration of OPW and RPW
Unit root test result
The unit root test results of logged four months (4-native-
market-day-price points) price series of oil palm wine and
raphia palm markets in South East at levels and at first
differences using the Augmented Dickey Fuller (ADF) Test
are presented in Tables 1 and 2. The result indicated that
all palm wine price series in the model were non-stationary
at level both at 1% and 5% levels of significance (Table 1).
This is because the absolute values of critical statistic were
greater than the absolute values of the t-statistic and
hence, contains unit root and are non-stationary, that is
I(0). This prompted the test of stationarity of the first
difference.
After the differencing, the price series attained stationarity
because the absolute values of the t-statistics were greater
than the critical values and hence, all the variables were
integrated of order one, I(1) (Table 2). Therefore, the null
hypothesis of unit root was accepted at levels but rejected
at first difference for all the price series both at 1% and 5%
levels of significance. The reason for this process
according Okoroafor, Echebiri and Nwachukwu (2010),
was to avoid the consequences of regressing non-
stationary time series with the attendant problem of
spurious results due to inflation and seasonality. This
finding concur with earlier findings and conclusion that
food commodity price series are mostly stationary of order
one, that is I(1) (Okoroafor et al. 2010).
Thus, co-integration test was applied to see whether there
were long-run relationships between the markets.
Table 1. ADF unit root test for palm wine markets @ level
Series ADF @
t-statistic
5% critical
value
p value
OPW rural price -0.332001 -1.94456 0.5630
OPW urban price 0.345996 -1.94476 0.7827
RPW rural price -0.426160 -1.94457 0.5267
RPW urban price -0.591813 -1.94466 0.4582
Source: Field survey, 2017.

4. Market Integration and Price Transmission between Rural and Urban Oil and Raphia Palm Wine Markets in South East, Nigeria
Nwankwo et al. 203
Table 2. ADF unit root test for palm wine market @ first
difference
Series ADF @
t-statistic
5% critical
value
p value
OPW rural price -11.73431 -1.94453 0.0000
OPW urban price -7.10869 -1.94476 0.0000
RPW rural price -11.51743 -1.94457 0.0000
RPW urban price -8.35985 -1.94466 0.0000
Source: Field survey, 2017.
Co-integration Result for Long-run relationships
between the markets
The presence of co-integration between two series is an
indication of their inter-dependence and its absence
reflects market segmentation. Co-integration was tested
with the aid of Johansen’s maximum likelihood procedure
using two test statistics, namely the trace (λ-trace) and
eigenvalue (λi-max.). The result of the co-integration
analysis for OPW and RPW is presented in Tables 3 and
4. The result revealed that the two test statistics - the
maximum Eigenvalue and trace tests, were absolutely
harmonized during the period as to the number of co-
integrating vectors at the conventional 0.05 probability
level. Both the λ-trace and Eigenvalue statistics exceeded
the critical value at 5% level for null hypotheses of r = 0
and r = 1, therefore we reject the null hypothesis of no co-
integrating vectors in favour of alternative hypothesis of r
= 2. This implies that there were two co-integrating
relationships at the 0.05 level. Therefore, palm wine
markets are integrated.
The overall analysis points to the fact that there is inter-
dependence between palm wine markets in the study
area. The markets operated as unified markets which is an
indication that most of the markets adjusted significantly to
price changes. This implies that OPW and RPW markets
were strongly linked together and therefore, the long-run
equilibrium is stable. Shocks (deficit/surplus) from either
State will quickly be transferred until equilibrium is
(re)established, hence, according to Mafimisebi (2012),
the arbitrage activities of marketers, who distribute
commodities (palm wine) between low and high price
locations, will raise price in some markets whilst lowering
them in others.
In other words, prices of palm wine in one market do not
significantly differ from that of the corresponding market
within the study area. There is a tendency for the prices in
both OPW and RPW rural and urban markets to converge
in the long run according to a linear relationship. However,
in the short run, the prices may drift apart, as shocks in one
market may not be instantaneously transmitted to other
markets due to delays in transport. This discovery,
according to Akande and Akpokodje (2003), may be
attributed to free flow of information on prices within and
across markets in the study area. This result is also in
tandem with Adakaren (2013) who reported that the prices
of raphia palm wine in all the markets in South-South
States of Nigeria showed evidence of integration in the
long run.
Table 3. Co-integration test result for OPW markets
Hypothecized
No of CE(s)
Trace
Test
Statistics
5%
critical
value
Maximum
Eigenvalue
5%
critical
Value
None 56.65** 15.49 51.90** 14.26
At most 1 17.17** 3.84 32.74** 3.84
Note: **Significant at 0.05 level.
Source: Field Survey, 2017.
Table 4. Co-integration test result for RPW markets
Hypothecized
No of CE(s)
Trace
Test
Statistics
5%
critical
value
Maximum
Eigenvalue
5%
critical
Value
None 86.64** 15.49 51.90** 14.26
At most 1 32.74** 3.84 32.74** 3.84
Note: **Significant at 0.05 level.
Source: Field Survey, 2017.
Vector Error Correction Model (VECM)
The Vector Error Correction Model (VECM) was applied to
measure the short-run dynamics among rural and urban
palm wine markets. Linear VECM results for OPW and
RPW are presented in Tables 5, 6, 7 and 8. The VECM
results indicated that a 1% increase in rural price of OPW
would in the long run increase its urban price by 3.70%
(Table 5).
The result also revealed that all the estimated short-run
coefficients for OPW rural and urban markets’ prices were
negative and statistically significant at the 5% level.
Adjustment towards the long-run equilibrium in the short-
run also revealed that the price changes in OPW rural and
urban markets were transmitted to other markets at a rate
of 15% and 27% respectively, within four days. In other
words, 15% of the distortion in the rural prices of OPW was
corrected within four days. This implies that it took
approximately 28 days for the rural price of OPW to return
to equilibrium. This invariably suggests that the
transmission of price changes from one market to another
during the same month was weak. Adjustment towards the
long-run equilibrium in the short-run was slow. Also, the
speed with which the system will adjust to shocks and
restore equilibrium for the urban price of OPW was 27%
which was, however faster than the OPW rural price.
Based on the results, it implied that OPW rural and urban
markets were not well integrated in the short run.
Table 5. Long –run estimates of rural and urban market
prices of OPW
Regressors Long-Run
estimate
Standard
Error
t-value
Rural 1.0000
Urban Price 3.700504 0.55626 6.65
Constant -6773.362 -156.053
Source: Field survey, 2017.

5. Market Integration and Price Transmission between Rural and Urban Oil and Raphia Palm Wine Markets in South East, Nigeria
Int. J. Agric. Mark. 204
Table 6. Short –run estimates of rural and urban market
prices of OPW
Error correction D(Rural price) D(urban price)
Cointeq 1 -0.152461 -0.274118
t-value -2.97 -6.02
D(rural price(-1)cf -0.615592 0.174838
t-value -6.28 2.01
D(rural price(-2)cf -0.384892 0.197572
t-value -3.98 2.31
D(urban price(-1)cf 0.242794 0.414518
t-value 1.69 3.27
D(urban price(-2)cf 0.241240 0.054654
t-value 0.18 0.47
Constant -2.109400 -0.407068
R-squared 0.435673 0.467691
Source: Field survey, 2017.
On the other hand, the VECM results indicated that 1%
increase in rural price of RPW would in the long run
decrease its urban price by 0.29% (Table 7).
The error correction coefficient for RPW is -1.659498 for
rural price and 0.664751 for the urban price. The result
showed that the short-run coefficient of RPW rural price
was statistically significant at the 5% level. Adjustment to
long-run equilibrium in the short-run revealed that price
changes transmitted to other markets at a rate of 66% in
four days which suggests that the adjustment process was
very fast. This finding is consistent with the work of
Mohammad and Verbeke (2010) and Odularu (2010). On
the contrary, the model came out with an unexpected
positive sign for the urban market equilibrium adjustment
coefficient of RPW. This implied that the distortions in the
market lingered and equilibrium was not restored. In other
words, the urban prices of RPW did not converge in the
long run.
Finally, when OPW is compared with RPW, it was
observed that increase in the rural price of OPW led to an
increase its urban price, while any increase in the rural
price of RPW decreased its urban price. The reason was
that OPW market prices followed the same trend while
RPW prices follow different trends. Also, the speed of
price adjustment of RPW in the short run was faster than
that of OPW. The presence of co-integration between
OPW and RPW market prices implied that the prices do
follow the same long-run trend (presence of integration).
As a result, the market price of either OPW or RPW would
not drift above or below each other in the long run. This
study agrees with Adakaren (2013) who reported that
raphia palm wine markets in South South states of Nigeria
are integrated.
Table 7. Long –run estimates of rural and urban market
prices of RPW
Regressors Long-Run
estimate
Standard Error t-value
Rural Price 1.0000
Urban Price -0.298357 0.11763 -2.53
Constant -156.053
Source: Field survey, 2017.
Table 8. Short –run estimates of rural and urban market
prices of RPW
Error correction D(Rural price) D(urban price)
Cointeq 1 -1.65948 0.664751
t-value -7.20 2.5
D(rural price(-1)cf 0.370090 -0.369434
t-value 2.08 1.82
D(rural price(-2)cf 0.187868 0.196244
t-value 1.71 -1.56
D(urban price(-1)cf -0.325482 -0.631891
t-value -3.38 -5.75
D(urban price(-2)cf -0.134373 -0.310102
t-value -1.54 -3.10
Constant 0.138252 -0.802734
Source: Field survey, 2017.
Price Causality and Transmission in Palm wine
Marketing
Table 9 presents the direction of causality between urban
and rural prices of OPW. The result showed that urban
prices of OPW manifested a two-way causation with its
rural price at 5% level. This implied that no OPW market
was exclusively given the leadership position in the study
area. The result showed that an increase in the past urban
price of OPW caused that of the current rural price to
increase whereas increase in the past rural price did not
Granger cause the current urban price.
The direction of causality between urban and rural prices
of RPW in the study area is presented in Table 10. The null
hypothesis of no causality was rejected. In the first market
pair, rural price of RPW Granger-caused its urban price at
1% significance level which is an indication of a strong uni-
directional causality, that is, the rural market dominated
price formation with urban market.
The result indicated that rural price of RPW Granger
caused the urban price, whereas the urban price of RPW
did not Granger cause the rural price. In other words, an
increase in rural price of RPW brought about an increase
in the urban price. This finding is in line with Adakaren
(2013) who revealed that increase in rural price of RPW
will brought about an increase in the urban price, and an
increase in the urban price also caused an increase in rural
price of palm wine in the short-run.
Table 9. Pairwise Granger causality test of OPW prices
Null Hypotheses Observation F-
statistics
Probability
Rural Price of
OPW does not
Granger cause
the urban price
88 3.869049 0.1445
Urban price of
OPW does not
Granger cause
the rural Price
88 6.68241** 0.03
Note: ** means significant at 5% level.
Source: Field survey, 2017.

6. Market Integration and Price Transmission between Rural and Urban Oil and Raphia Palm Wine Markets in South East, Nigeria
Nwankwo et al. 205
Table 10. Pairwise Granger causality test of RPW prices
Null
Hypotheses
Observation F-statistics Probability
Rural Price of
RPW does not
Granger cause
the urban price
88 11.65813
*** 0.0029
Urban price of
RPW does not
Granger cause
the rural Price
88 3.341354 0.1881
Note: *** means significant at 1% level.
Source: Field survey, 2017.
CONCLUSION
Palm wine prices in all the markets in Southeast, Nigeria
showed evidence of integration in the long run but RPW
markets were more integrated than OPW markets. Also,
past rural price did not Granger cause the current urban
price of OPW whereas the past urban price of OPW
Granger caused the current rural price. On the other hand,
past rural price of RPW Granger caused the current urban
price while the past urban price of RPW did not Granger
cause the current rural price.
Institutions and bodies responsible for data generation and
storage would do well if they include OPW and RPW
production, consumption, export and import data (if any)
as one of their interest commodities.
In addition, the government at State and Local
Government levels should address the issue of bad/poor
road problems by constructing new link roads and
rehabilitating existing ones to ensure proximity of markets
to each other.
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Accepted 10 April 2019
Citation: Nwankwo TN, Ozor MU, Ugwumba COA (2019).
Market Integration and Price Transmission between Rural
and Urban Oil and Raphia Palm Wine Markets in South
East, Nigeria. International Journal of Agricultural
Marketing, 6(1): 200-205.
Copyright: © 2019: Nwankwo et al. This is an open-
access article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium,
provided the original author and source are cited.